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Oikonomou EK, Holste G, Yuan N, Coppi A, McNamara RL, Haynes NA, Vora AN, Velazquez EJ, Li F, Menon V, Kapadia SR, Gill TM, Nadkarni GN, Krumholz HM, Wang Z, Ouyang D, Khera R. A Multimodal Video-Based AI Biomarker for Aortic Stenosis Development and Progression. JAMA Cardiol 2024:2817468. [PMID: 38581644 PMCID: PMC10999005 DOI: 10.1001/jamacardio.2024.0595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 02/27/2024] [Indexed: 04/08/2024]
Abstract
Importance Aortic stenosis (AS) is a major public health challenge with a growing therapeutic landscape, but current biomarkers do not inform personalized screening and follow-up. A video-based artificial intelligence (AI) biomarker (Digital AS Severity index [DASSi]) can detect severe AS using single-view long-axis echocardiography without Doppler characterization. Objective To deploy DASSi to patients with no AS or with mild or moderate AS at baseline to identify AS development and progression. Design, Setting, and Participants This is a cohort study that examined 2 cohorts of patients without severe AS undergoing echocardiography in the Yale New Haven Health System (YNHHS; 2015-2021) and Cedars-Sinai Medical Center (CSMC; 2018-2019). A novel computational pipeline for the cross-modal translation of DASSi into cardiac magnetic resonance (CMR) imaging was further developed in the UK Biobank. Analyses were performed between August 2023 and February 2024. Exposure DASSi (range, 0-1) derived from AI applied to echocardiography and CMR videos. Main Outcomes and Measures Annualized change in peak aortic valve velocity (AV-Vmax) and late (>6 months) aortic valve replacement (AVR). Results A total of 12 599 participants were included in the echocardiographic study (YNHHS: n = 8798; median [IQR] age, 71 [60-80] years; 4250 [48.3%] women; median [IQR] follow-up, 4.1 [2.4-5.4] years; and CSMC: n = 3801; median [IQR] age, 67 [54-78] years; 1685 [44.3%] women; median [IQR] follow-up, 3.4 [2.8-3.9] years). Higher baseline DASSi was associated with faster progression in AV-Vmax (per 0.1 DASSi increment: YNHHS, 0.033 m/s per year [95% CI, 0.028-0.038] among 5483 participants; CSMC, 0.082 m/s per year [95% CI, 0.053-0.111] among 1292 participants), with values of 0.2 or greater associated with a 4- to 5-fold higher AVR risk than values less than 0.2 (YNHHS: 715 events; adjusted hazard ratio [HR], 4.97 [95% CI, 2.71-5.82]; CSMC: 56 events; adjusted HR, 4.04 [95% CI, 0.92-17.70]), independent of age, sex, race, ethnicity, ejection fraction, and AV-Vmax. This was reproduced across 45 474 participants (median [IQR] age, 65 [59-71] years; 23 559 [51.8%] women; median [IQR] follow-up, 2.5 [1.6-3.9] years) undergoing CMR imaging in the UK Biobank (for participants with DASSi ≥0.2 vs those with DASSi <.02, adjusted HR, 11.38 [95% CI, 2.56-50.57]). Saliency maps and phenome-wide association studies supported associations with cardiac structure and function and traditional cardiovascular risk factors. Conclusions and Relevance In this cohort study of patients without severe AS undergoing echocardiography or CMR imaging, a new AI-based video biomarker was independently associated with AS development and progression, enabling opportunistic risk stratification across cardiovascular imaging modalities as well as potential application on handheld devices.
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Affiliation(s)
- Evangelos K. Oikonomou
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Gregory Holste
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin
| | - Neal Yuan
- Department of Medicine, University of California, San Francisco
- Division of Cardiology, San Francisco Veterans Affairs Medical Center, San Francisco, California
| | - Andreas Coppi
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut
| | - Robert L. McNamara
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Norrisa A. Haynes
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Amit N. Vora
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Eric J. Velazquez
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Fan Li
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut
- Center for Methods in Implementation and Prevention Science, Yale School of Public Health, New Haven, Connecticut
| | - Venu Menon
- Heart and Vascular Institute, Department of Cardiovascular Medicine, Cleveland Clinic Foundation, Cleveland, Ohio
| | - Samir R. Kapadia
- Heart and Vascular Institute, Department of Cardiovascular Medicine, Cleveland Clinic Foundation, Cleveland, Ohio
| | - Thomas M. Gill
- Section of Geriatrics, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Girish N. Nadkarni
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Harlan M. Krumholz
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut
| | - Zhangyang Wang
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin
| | - David Ouyang
- Smidt Heart Institute, Department of Cardiology, Cedars-Sinai Medical Center, Los Angeles, California
- Division of Artificial Intelligence in Medicine, Cedars-Sinai Medical Center, Los Angeles, California
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut
- Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, Connecticut
- Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut
- Associate Editor, JAMA
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Sorin V, Glicksberg BS, Artsi Y, Barash Y, Konen E, Nadkarni GN, Klang E. Utilizing large language models in breast cancer management: systematic review. J Cancer Res Clin Oncol 2024; 150:140. [PMID: 38504034 PMCID: PMC10950983 DOI: 10.1007/s00432-024-05678-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Accepted: 03/01/2024] [Indexed: 03/21/2024]
Abstract
PURPOSE Despite advanced technologies in breast cancer management, challenges remain in efficiently interpreting vast clinical data for patient-specific insights. We reviewed the literature on how large language models (LLMs) such as ChatGPT might offer solutions in this field. METHODS We searched MEDLINE for relevant studies published before December 22, 2023. Keywords included: "large language models", "LLM", "GPT", "ChatGPT", "OpenAI", and "breast". The risk bias was evaluated using the QUADAS-2 tool. RESULTS Six studies evaluating either ChatGPT-3.5 or GPT-4, met our inclusion criteria. They explored clinical notes analysis, guideline-based question-answering, and patient management recommendations. Accuracy varied between studies, ranging from 50 to 98%. Higher accuracy was seen in structured tasks like information retrieval. Half of the studies used real patient data, adding practical clinical value. Challenges included inconsistent accuracy, dependency on the way questions are posed (prompt-dependency), and in some cases, missing critical clinical information. CONCLUSION LLMs hold potential in breast cancer care, especially in textual information extraction and guideline-driven clinical question-answering. Yet, their inconsistent accuracy underscores the need for careful validation of these models, and the importance of ongoing supervision.
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Affiliation(s)
- Vera Sorin
- Department of Diagnostic Imaging, Chaim Sheba Medical Center, Affiliated to the Sackler School of Medicine, Tel-Aviv University, Emek Haela St. 1, 52621, Ramat Gan, Israel.
- DeepVision Lab, Chaim Sheba Medical Center, Tel Hashomer, Israel.
| | - Benjamin S Glicksberg
- Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yaara Artsi
- Azrieli Faculty of Medicine, Bar-Ilan University, Zefat, Israel
| | - Yiftach Barash
- Department of Diagnostic Imaging, Chaim Sheba Medical Center, Affiliated to the Sackler School of Medicine, Tel-Aviv University, Emek Haela St. 1, 52621, Ramat Gan, Israel
- DeepVision Lab, Chaim Sheba Medical Center, Tel Hashomer, Israel
| | - Eli Konen
- Department of Diagnostic Imaging, Chaim Sheba Medical Center, Affiliated to the Sackler School of Medicine, Tel-Aviv University, Emek Haela St. 1, 52621, Ramat Gan, Israel
| | - Girish N Nadkarni
- Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Eyal Klang
- Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Anandakrishnan N, Yi Z, Sun Z, Liu T, Haydak J, Eddy S, Jayaraman P, DeFronzo S, Saha A, Sun Q, Yang D, Mendoza A, Mosoyan G, Wen HH, Schaub JA, Fu J, Kehrer T, Menon R, Otto EA, Godfrey B, Suarez-Farinas M, Leffters S, Twumasi A, Meliambro K, Charney AW, García-Sastre A, Campbell KN, Gusella GL, He JC, Miorin L, Nadkarni GN, Wisnivesky J, Li H, Kretzler M, Coca SG, Chan L, Zhang W, Azeloglu EU. Integrated multiomics implicates dysregulation of ECM and cell adhesion pathways as drivers of severe COVID-associated kidney injury. medRxiv 2024:2024.03.18.24304401. [PMID: 38562892 PMCID: PMC10984064 DOI: 10.1101/2024.03.18.24304401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
COVID-19 has been a significant public health concern for the last four years; however, little is known about the mechanisms that lead to severe COVID-associated kidney injury. In this multicenter study, we combined quantitative deep urinary proteomics and machine learning to predict severe acute outcomes in hospitalized COVID-19 patients. Using a 10-fold cross-validated random forest algorithm, we identified a set of urinary proteins that demonstrated predictive power for both discovery and validation set with 87% and 79% accuracy, respectively. These predictive urinary biomarkers were recapitulated in non-COVID acute kidney injury revealing overlapping injury mechanisms. We further combined orthogonal multiomics datasets to understand the mechanisms that drive severe COVID-associated kidney injury. Functional overlap and network analysis of urinary proteomics, plasma proteomics and urine sediment single-cell RNA sequencing showed that extracellular matrix and autophagy-associated pathways were uniquely impacted in severe COVID-19. Differentially abundant proteins associated with these pathways exhibited high expression in cells in the juxtamedullary nephron, endothelial cells, and podocytes, indicating that these kidney cell types could be potential targets. Further, single-cell transcriptomic analysis of kidney organoids infected with SARS-CoV-2 revealed dysregulation of extracellular matrix organization in multiple nephron segments, recapitulating the clinically observed fibrotic response across multiomics datasets. Ligand-receptor interaction analysis of the podocyte and tubule organoid clusters showed significant reduction and loss of interaction between integrins and basement membrane receptors in the infected kidney organoids. Collectively, these data suggest that extracellular matrix degradation and adhesion-associated mechanisms could be a main driver of COVID-associated kidney injury and severe outcomes.
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Mayourian J, La Cava WG, Vaid A, Nadkarni GN, Ghelani SJ, Mannix R, Geva T, Dionne A, Alexander ME, Duong SQ, Triedman JK. Pediatric ECG-Based Deep Learning to Predict Left Ventricular Dysfunction and Remodeling. Circulation 2024; 149:917-931. [PMID: 38314583 PMCID: PMC10948312 DOI: 10.1161/circulationaha.123.067750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 12/20/2023] [Indexed: 02/06/2024]
Abstract
BACKGROUND Artificial intelligence-enhanced ECG analysis shows promise to detect ventricular dysfunction and remodeling in adult populations. However, its application to pediatric populations remains underexplored. METHODS A convolutional neural network was trained on paired ECG-echocardiograms (≤2 days apart) from patients ≤18 years of age without major congenital heart disease to detect human expert-classified greater than mild left ventricular (LV) dysfunction, hypertrophy, and dilation (individually and as a composite outcome). Model performance was evaluated on single ECG-echocardiogram pairs per patient at Boston Children's Hospital and externally at Mount Sinai Hospital using area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC). RESULTS The training cohort comprised 92 377 ECG-echocardiogram pairs (46 261 patients; median age, 8.2 years). Test groups included internal testing (12 631 patients; median age, 8.8 years; 4.6% composite outcomes), emergency department (2830 patients; median age, 7.7 years; 10.0% composite outcomes), and external validation (5088 patients; median age, 4.3 years; 6.1% composite outcomes) cohorts. Model performance was similar on internal test and emergency department cohorts, with model predictions of LV hypertrophy outperforming the pediatric cardiologist expert benchmark. Adding age and sex to the model added no benefit to model performance. When using quantitative outcome cutoffs, model performance was similar between internal testing (composite outcome: AUROC, 0.88, AUPRC, 0.43; LV dysfunction: AUROC, 0.92, AUPRC, 0.23; LV hypertrophy: AUROC, 0.88, AUPRC, 0.28; LV dilation: AUROC, 0.91, AUPRC, 0.47) and external validation (composite outcome: AUROC, 0.86, AUPRC, 0.39; LV dysfunction: AUROC, 0.94, AUPRC, 0.32; LV hypertrophy: AUROC, 0.84, AUPRC, 0.25; LV dilation: AUROC, 0.87, AUPRC, 0.33), with composite outcome negative predictive values of 99.0% and 99.2%, respectively. Saliency mapping highlighted ECG components that influenced model predictions (precordial QRS complexes for all outcomes; T waves for LV dysfunction). High-risk ECG features include lateral T-wave inversion (LV dysfunction), deep S waves in V1 and V2 and tall R waves in V6 (LV hypertrophy), and tall R waves in V4 through V6 (LV dilation). CONCLUSIONS This externally validated algorithm shows promise to inexpensively screen for LV dysfunction and remodeling in children, which may facilitate improved access to care by democratizing the expertise of pediatric cardiologists.
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Affiliation(s)
- Joshua Mayourian
- Department of Cardiology, Boston Children’s Hospital, Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - William G. La Cava
- Computational Health Informatics Program, Boston Children’s Hospital, Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Akhil Vaid
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Girish N. Nadkarni
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Sunil J. Ghelani
- Department of Cardiology, Boston Children’s Hospital, Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Rebekah Mannix
- Department of Medicine, Division of Emergency Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Tal Geva
- Department of Cardiology, Boston Children’s Hospital, Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Audrey Dionne
- Department of Cardiology, Boston Children’s Hospital, Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Mark E. Alexander
- Department of Cardiology, Boston Children’s Hospital, Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Son Q. Duong
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- Division of Pediatric Cardiology, Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY
| | - John K. Triedman
- Department of Cardiology, Boston Children’s Hospital, Department of Pediatrics, Harvard Medical School, Boston, MA, USA
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Oikonomou EK, Holste G, Coppi A, McNamara RL, Nadkarni GN, Baloescu C, Krumholz HM, Wang Z, Khera R. Artificial intelligence-guided detection of under-recognized cardiomyopathies on point-of-care cardiac ultrasound. medRxiv 2024:2024.03.10.24304044. [PMID: 38559021 PMCID: PMC10980112 DOI: 10.1101/2024.03.10.24304044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Background Point-of-care ultrasonography (POCUS) enables access to cardiac imaging directly at the bedside but is limited by brief acquisition, variation in acquisition quality, and lack of advanced protocols. Objective To develop and validate deep learning models for detecting underdiagnosed cardiomyopathies on cardiac POCUS, leveraging a novel acquisition quality-adapted modeling strategy. Methods To develop the models, we identified transthoracic echocardiograms (TTEs) of patients across five hospitals in a large U.S. health system with transthyretin amyloid cardiomyopathy (ATTR-CM, confirmed by Tc99m-pyrophosphate imaging), hypertrophic cardiomyopathy (HCM, confirmed by cardiac magnetic resonance), and controls enriched for the presence of severe AS. In a sample of 290,245 TTE videos, we used novel augmentation approaches and a customized loss function to weigh image and view quality to train a multi-label, view agnostic video-based convolutional neural network (CNN) to discriminate the presence of ATTR-CM, HCM, and/or AS. Models were tested across 3,758 real-world POCUS videos from 1,879 studies in 1,330 independent emergency department (ED) patients from 2011 through 2023. Results Our multi-label, view-agnostic classifier demonstrated state-of-the-art performance in discriminating ATTR-CM (AUROC 0.98 [95%CI: 0.96-0.99]) and HCM (AUROC 0.95 [95% CI: 0.94-0.96]) on standard TTE studies. Automated metrics of anatomical view correctness confirmed significantly lower quality in POCUS vs TTE videos (median view classifier confidence of 0.63 [IQR: 0.44-0.88] vs 0.93 [IQR: 0.69-1.00], p<0.001). When deployed to POCUS videos, our algorithm effectively discriminated ATTR-CM and HCM with AUROC of up to 0.94 (parasternal long-axis (PLAX)), and 0.85 (apical 4 chamber), corresponding to positive diagnostic odds ratios of 46.7 and 25.5, respectively. In total, 18/35 (51.4%) of ATTR-CM and 32/57 (41.1%) of HCM patients in the POCUS cohort had an AI-positive screen in the year before their eventual confirmatory imaging. Conclusions We define and validate an AI framework that enables scalable, opportunistic screening of under-diagnosed cardiomyopathies using POCUS.
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Affiliation(s)
- Evangelos K. Oikonomou
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Gregory Holste
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA
| | - Andreas Coppi
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA
| | - Robert L. McNamara
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Girish N. Nadkarni
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Cristiana Baloescu
- Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Harlan M. Krumholz
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA
| | - Zhangyang Wang
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
- Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT
- Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT
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Vy HMT, Coca SG, Sawant A, Sakhuja A, Gutierrez OM, Cooper R, Loos RJ, Horowitz CR, Do R, Nadkarni GN. Genome-Wide Polygenic Risk Score for CKD in Individuals with APOL1 High-Risk Genotypes. Clin J Am Soc Nephrol 2024; 19:374-376. [PMID: 37962879 PMCID: PMC10937008 DOI: 10.2215/cjn.0000000000000379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 11/07/2023] [Indexed: 11/15/2023]
Affiliation(s)
- Ha My T. Vy
- Icahn School of Medicine, New York City, New York
| | | | | | | | | | - Richard Cooper
- Loyola University School of Public Health, Chicago, Illinois
| | | | | | - Ron Do
- Icahn School of Medicine, New York City, New York
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Oikonomou EK, Holste G, Yuan N, Coppi A, McNamara RL, Haynes N, Vora AN, Velazquez EJ, Li F, Menon V, Kapadia SR, Gill TM, Nadkarni GN, Krumholz HM, Wang Z, Ouyang D, Khera R. A Multimodality Video-Based AI Biomarker For Aortic Stenosis Development And Progression. medRxiv 2024:2023.09.28.23296234. [PMID: 37808685 PMCID: PMC10557799 DOI: 10.1101/2023.09.28.23296234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Importance Aortic stenosis (AS) is a major public health challenge with a growing therapeutic landscape, but current biomarkers do not inform personalized screening and follow-up. Objective A video-based artificial intelligence (AI) biomarker (Digital AS Severity index [DASSi]) can detect severe AS using single-view long-axis echocardiography without Doppler. Here, we deploy DASSi to patients with no or mild/moderate AS at baseline to identify AS development and progression. Design Setting and Participants We defined two cohorts of patients without severe AS undergoing echocardiography in the Yale-New Haven Health System (YNHHS) (2015-2021, 4.1[IQR:2.4-5.4] follow-up years) and Cedars-Sinai Medical Center (CSMC) (2018-2019, 3.4[IQR:2.8-3.9] follow-up years). We further developed a novel computational pipeline for the cross-modality translation of DASSi into cardiac magnetic resonance (CMR) imaging in the UK Biobank (2.5[IQR:1.6-3.9] follow-up years). Analyses were performed between August 2023-February 2024. Exposure DASSi (range: 0-1) derived from AI applied to echocardiography and CMR videos. Main Outcomes and Measures Annualized change in peak aortic valve velocity (AV-Vmax) and late (>6 months) aortic valve replacement (AVR). Results A total of 12,599 participants were included in the echocardiographic study (YNHHS: n=8,798, median age of 71 [IQR (interquartile range):60-80] years, 4250 [48.3%] women, and CSMC: n=3,801, 67 [IQR:54-78] years, 1685 [44.3%] women). Higher baseline DASSi was associated with faster progression in AV-Vmax (per 0.1 DASSi increments: YNHHS: +0.033 m/s/year [95%CI:0.028-0.038], n=5,483, and CSMC: +0.082 m/s/year [0.053-0.111], n=1,292), with levels ≥ vs <0.2 linked to a 4-to-5-fold higher AVR risk (715 events in YNHHS; adj.HR 4.97 [95%CI: 2.71-5.82], 56 events in CSMC: 4.04 [0.92-17.7]), independent of age, sex, ethnicity/race, ejection fraction and AV-Vmax. This was reproduced across 45,474 participants (median age 65 [IQR:59-71] years, 23,559 [51.8%] women) undergoing CMR in the UK Biobank (adj.HR 11.4 [95%CI:2.56-50.60] for DASSi ≥vs<0.2). Saliency maps and phenome-wide association studies supported links with traditional cardiovascular risk factors and diastolic dysfunction. Conclusions and Relevance In this cohort study of patients without severe AS undergoing echocardiography or CMR imaging, a new AI-based video biomarker is independently associated with AS development and progression, enabling opportunistic risk stratification across cardiovascular imaging modalities as well as potential application on handheld devices.
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Affiliation(s)
- Evangelos K. Oikonomou
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Gregory Holste
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA
| | - Neal Yuan
- Department of Medicine, University of California San Francisco, San Francisco, CA, USA
- Division of Cardiology, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
| | - Andreas Coppi
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA
| | - Robert L. McNamara
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Norrisa Haynes
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Amit N. Vora
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Eric J. Velazquez
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Fan Li
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
- Center for Methods in Implementation and Prevention Science, Yale School of Public Health, New Haven, CT, USA
| | - Venu Menon
- Department of Cardiovascular Medicine, Heart and Vascular Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Samir R. Kapadia
- Department of Cardiovascular Medicine, Heart and Vascular Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Thomas M Gill
- Section of Geriatrics, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Girish N. Nadkarni
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Harlan M. Krumholz
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA
| | - Zhangyang Wang
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA
| | - David Ouyang
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Division of Artificial Intelligence in Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA
- Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT
- Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT
- Associate Editor, JAMA
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Khunte A, Sangha V, Oikonomou EK, Dhingra LS, Aminorroaya A, Coppi A, Shankar SV, Mortazavi BJ, Bhatt DL, Krumholz HM, Nadkarni GN, Vaid A, Khera R. Automated Diagnostic Reports from Images of Electrocardiograms at the Point-of-Care. medRxiv 2024:2024.02.17.24302976. [PMID: 38405776 PMCID: PMC10889032 DOI: 10.1101/2024.02.17.24302976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Timely and accurate assessment of electrocardiograms (ECGs) is crucial for diagnosing, triaging, and clinically managing patients. Current workflows rely on a computerized ECG interpretation using rule-based tools built into the ECG signal acquisition systems with limited accuracy and flexibility. In low-resource settings, specialists must review every single ECG for such decisions, as these computerized interpretations are not available. Additionally, high-quality interpretations are even more essential in such low-resource settings as there is a higher burden of accuracy for automated reads when access to experts is limited. Artificial Intelligence (AI)-based systems have the prospect of greater accuracy yet are frequently limited to a narrow range of conditions and do not replicate the full diagnostic range. Moreover, these models often require raw signal data, which are unavailable to physicians and necessitate costly technical integrations that are currently limited. To overcome these challenges, we developed and validated a format-independent vision encoder-decoder model - ECG-GPT - that can generate free-text, expert-level diagnosis statements directly from ECG images. The model shows robust performance, validated on 2.6 million ECGs across 6 geographically distinct health settings: (1) 2 large and diverse US health systems- Yale-New Haven and Mount Sinai Health Systems, (2) a consecutive ECG dataset from a central ECG repository from Minas Gerais, Brazil, (3) the prospective cohort study, UK Biobank, (4) a Germany-based, publicly available repository, PTB-XL, and (5) a community hospital in Missouri. The model demonstrated consistently high performance (AUROC≥0.81) across a wide range of rhythm and conduction disorders. This can be easily accessed via a web-based application capable of receiving ECG images and represents a scalable and accessible strategy for generating accurate, expert-level reports from images of ECGs, enabling accurate triage of patients globally, especially in low-resource settings.
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Affiliation(s)
- Akshay Khunte
- Department of Computer Science, Yale University, New Haven, CT
| | - Veer Sangha
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT
- Department of Engineering Science, Oxford University, Oxford, UK
| | - Evangelos K Oikonomou
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT
| | - Lovedeep S Dhingra
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT
| | - Arya Aminorroaya
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT
| | - Andreas Coppi
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT
- Department of Computer Science & Engineering, Texas A&M University, College Station, TX
| | - Sumukh Vasisht Shankar
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT
| | - Bobak J Mortazavi
- Department of Computer Science & Engineering, Texas A&M University, College Station, TX
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT
| | - Deepak L Bhatt
- Mount Sinai Fuster Heart Hospital, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Harlan M Krumholz
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT
- Department of Health Policy and Management, Yale School of Public Health, New Haven, CT
| | - Girish N Nadkarni
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Division of Data Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Akhil Vaid
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Division of Data Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT
- Department of Computer Science & Engineering, Texas A&M University, College Station, TX
- Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT
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9
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Takkavatakarn K, Dai Y, Hsun Wen H, Kauffman J, Charney A, Coca SG, Nadkarni GN, Chan L. Comparison of predicting cardiovascular disease hospitalization using individual, ZIP code-derived, and machine learning model-predicted educational attainment in New York City. PLoS One 2024; 19:e0297919. [PMID: 38329973 PMCID: PMC10852236 DOI: 10.1371/journal.pone.0297919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 01/15/2024] [Indexed: 02/10/2024] Open
Abstract
BACKGROUND Area-level social determinants of health (SDOH) based on patients' ZIP codes or census tracts have been commonly used in research instead of individual SDOHs. To our knowledge, whether machine learning (ML) could be used to derive individual SDOH measures, specifically individual educational attainment, is unknown. METHODS This is a retrospective study using data from the Mount Sinai BioMe Biobank. We included participants that completed a validated questionnaire on educational attainment and had home addresses in New York City. ZIP code-level education was derived from the American Community Survey matched for the participant's gender and race/ethnicity. We tested several algorithms to predict individual educational attainment from routinely collected clinical and demographic data. To evaluate how using different measures of educational attainment will impact model performance, we developed three distinct models for predicting cardiovascular (CVD) hospitalization. Educational attainment was imputed into models as either survey-derived, ZIP code-derived, or ML-predicted educational attainment. RESULTS A total of 20,805 participants met inclusion criteria. Concordance between survey and ZIP code-derived education was 47%, while the concordance between survey and ML model-predicted education was 67%. A total of 13,715 patients from the cohort were included into our CVD hospitalization prediction models, of which 1,538 (11.2%) had a history of CVD hospitalization. The AUROC of the model predicting CVD hospitalization using survey-derived education was significantly higher than the model using ZIP code-level education (0.77 versus 0.72; p < 0.001) and the model using ML model-predicted education (0.77 versus 0.75; p < 0.001). The AUROC for the model using ML model-predicted education was also significantly higher than that using ZIP code-level education (p = 0.003). CONCLUSION The concordance of survey and ZIP code-level educational attainment in NYC was low. As expected, the model utilizing survey-derived education achieved the highest performance. The model incorporating our ML model-predicted education outperformed the model relying on ZIP code-derived education. Implementing ML techniques can improve the accuracy of SDOH data and consequently increase the predictive performance of outcome models.
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Affiliation(s)
- Kullaya Takkavatakarn
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
- Division of Nephrology, Department of Medicine, King Chulalongkorn Memorial Hospital, Chulalongkorn University, Bangkok, Thailand
| | - Yang Dai
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - Huei Hsun Wen
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - Justin Kauffman
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - Alexander Charney
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - Steven G. Coca
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - Girish N. Nadkarni
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
- Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - Lili Chan
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
- Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
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10
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Oh W, Jayaraman P, Tandon P, Chaddha US, Kovatch P, Charney AW, Glicksberg BS, Nadkarni GN. A novel method leveraging time series data to improve subphenotyping and application in critically ill patients with COVID-19. Artif Intell Med 2024; 148:102750. [PMID: 38325922 PMCID: PMC10864255 DOI: 10.1016/j.artmed.2023.102750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 12/12/2023] [Accepted: 12/14/2023] [Indexed: 02/09/2024]
Abstract
Computational subphenotyping, a data-driven approach to understanding disease subtypes, is a prominent topic in medical research. Numerous ongoing studies are dedicated to developing advanced computational subphenotyping methods for cross-sectional data. However, the potential of time-series data has been underexplored until now. Here, we propose a Multivariate Levenshtein Distance (MLD) that can account for address correlation in multiple discrete features over time-series data. Our algorithm has two distinct components: it integrates an optimal threshold score to enhance the sensitivity in discriminating between pairs of instances, and the MLD itself. We have applied the proposed distance metrics on the k-means clustering algorithm to derive temporal subphenotypes from time-series data of biomarkers and treatment administrations from 1039 critically ill patients with COVID-19 and compare its effectiveness to standard methods. In conclusion, the Multivariate Levenshtein Distance metric is a novel method to quantify the distance from multiple discrete features over time-series data and demonstrates superior clustering performance among competing time-series distance metrics.
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Affiliation(s)
- Wonsuk Oh
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Division of Data-Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Pushkala Jayaraman
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Division of Data-Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Pranai Tandon
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Udit S Chaddha
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Patricia Kovatch
- Department of Scientific Computing, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alexander W Charney
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Benjamin S Glicksberg
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Character Biosciences, New York, NY, USA
| | - Girish N Nadkarni
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Division of Data-Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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11
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Gulamali F, Jayaraman P, Sawant AS, Desman J, Fox B, Chang A, Soong BY, Arivazaghan N, Reynolds AS, Duong SQ, Vaid A, Kovatch P, Freeman R, Hofer IS, Sakhuja A, Dangayach NS, Reich DS, Charney AW, Nadkarni GN. Derivation, External Validation and Clinical Implications of a deep learning approach for intracranial pressure estimation using non-cranial waveform measurements. medRxiv 2024:2024.01.30.24301974. [PMID: 38352556 PMCID: PMC10863000 DOI: 10.1101/2024.01.30.24301974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/19/2024]
Abstract
Importance Increased intracranial pressure (ICP) is associated with adverse neurological outcomes, but needs invasive monitoring. Objective Development and validation of an AI approach for detecting increased ICP (aICP) using only non-invasive extracranial physiological waveform data. Design Retrospective diagnostic study of AI-assisted detection of increased ICP. We developed an AI model using exclusively extracranial waveforms, externally validated it and assessed associations with clinical outcomes. Setting MIMIC-III Waveform Database (2000-2013), a database derived from patients admitted to an ICU in an academic Boston hospital, was used for development of the aICP model, and to report association with neurologic outcomes. Data from Mount Sinai Hospital (2020-2022) in New York City was used for external validation. Participants Patients were included if they were older than 18 years, and were monitored with electrocardiograms, arterial blood pressure, respiratory impedance plethysmography and pulse oximetry. Patients who additionally had intracranial pressure monitoring were used for development (N=157) and external validation (N=56). Patients without intracranial monitors were used for association with outcomes (N=1694). Exposures Extracranial waveforms including electrocardiogram, arterial blood pressure, plethysmography and SpO2. Main Outcomes and Measures Intracranial pressure > 15 mmHg. Measures were Area under receiver operating characteristic curves (AUROCs), sensitivity, specificity, and accuracy at threshold of 0.5. We calculated odds ratios and p-values for phenotype association. Results The AUROC was 0.91 (95% CI, 0.90-0.91) on testing and 0.80 (95% CI, 0.80-0.80) on external validation. aICP had accuracy, sensitivity, and specificity of 73.8% (95% CI, 72.0%-75.6%), 99.5% (95% CI 99.3%-99.6%), and 76.9% (95% CI, 74.0-79.8%) on external validation. A ten-percentile increment was associated with stroke (OR=2.12; 95% CI, 1.27-3.13), brain malignancy (OR=1.68; 95% CI, 1.09-2.60), subdural hemorrhage (OR=1.66; 95% CI, 1.07-2.57), intracerebral hemorrhage (OR=1.18; 95% CI, 1.07-1.32), and procedures like percutaneous brain biopsy (OR=1.58; 95% CI, 1.15-2.18) and craniotomy (OR = 1.43; 95% CI, 1.12-1.84; P < 0.05 for all). Conclusions and Relevance aICP provides accurate, non-invasive estimation of increased ICP, and is associated with neurological outcomes and neurosurgical procedures in patients without intracranial monitoring.
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Affiliation(s)
- Faris Gulamali
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Pushkala Jayaraman
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Ashwin S. Sawant
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Jacob Desman
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Benjamin Fox
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Annie Chang
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Brian Y. Soong
- Department of Medical Education, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Naveen Arivazaghan
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Alexandra S. Reynolds
- Department of Neurosurgery and Neurology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Son Q Duong
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Akhil Vaid
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Patricia Kovatch
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Robert Freeman
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Ira S. Hofer
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Ankit Sakhuja
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Neha S. Dangayach
- Department of Neurosurgery and Neurology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - David S. Reich
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Alexander W Charney
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Girish N. Nadkarni
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
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12
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Duong SQ, Vaid A, My VTH, Butler LR, Lampert J, Pass RH, Charney AW, Narula J, Khera R, Sakhuja A, Greenspan H, Gelb BD, Do R, Nadkarni GN. Quantitative Prediction of Right Ventricular Size and Function From the ECG. J Am Heart Assoc 2024; 13:e031671. [PMID: 38156471 PMCID: PMC10863807 DOI: 10.1161/jaha.123.031671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 11/20/2023] [Indexed: 12/30/2023]
Abstract
BACKGROUND Right ventricular ejection fraction (RVEF) and end-diastolic volume (RVEDV) are not readily assessed through traditional modalities. Deep learning-enabled ECG analysis for estimation of right ventricular (RV) size or function is unexplored. METHODS AND RESULTS We trained a deep learning-ECG model to predict RV dilation (RVEDV >120 mL/m2), RV dysfunction (RVEF ≤40%), and numerical RVEDV and RVEF from a 12-lead ECG paired with reference-standard cardiac magnetic resonance imaging volumetric measurements in UK Biobank (UKBB; n=42 938). We fine-tuned in a multicenter health system (MSHoriginal [Mount Sinai Hospital]; n=3019) with prospective validation over 4 months (MSHvalidation; n=115). We evaluated performance with area under the receiver operating characteristic curve for categorical and mean absolute error for continuous measures overall and in key subgroups. We assessed the association of RVEF prediction with transplant-free survival with Cox proportional hazards models. The prevalence of RV dysfunction for UKBB/MSHoriginal/MSHvalidation cohorts was 1.0%/18.0%/15.7%, respectively. RV dysfunction model area under the receiver operating characteristic curve for UKBB/MSHoriginal/MSHvalidation cohorts was 0.86/0.81/0.77, respectively. The prevalence of RV dilation for UKBB/MSHoriginal/MSHvalidation cohorts was 1.6%/10.6%/4.3%. RV dilation model area under the receiver operating characteristic curve for UKBB/MSHoriginal/MSHvalidation cohorts was 0.91/0.81/0.92, respectively. MSHoriginal mean absolute error was RVEF=7.8% and RVEDV=17.6 mL/m2. The performance of the RVEF model was similar in key subgroups including with and without left ventricular dysfunction. Over a median follow-up of 2.3 years, predicted RVEF was associated with adjusted transplant-free survival (hazard ratio, 1.40 for each 10% decrease; P=0.031). CONCLUSIONS Deep learning-ECG analysis can identify significant cardiac magnetic resonance imaging RV dysfunction and dilation with good performance. Predicted RVEF is associated with clinical outcome.
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Affiliation(s)
- Son Q. Duong
- Division of Pediatric Cardiology, Department of PediatricsIcahn School of Medicine at Mount SinaiNew YorkNY
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount SinaiNew YorkNY
- Mindich Child Health and Development Institute, Icahn School of Medicine at Mount SinaiNew YorkNY
| | - Akhil Vaid
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount SinaiNew YorkNY
| | - Vy Thi Ha My
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount SinaiNew YorkNY
| | - Liam R. Butler
- Division of Pediatric Cardiology, Department of PediatricsIcahn School of Medicine at Mount SinaiNew YorkNY
| | - Joshua Lampert
- Helmsley Center for Electrophysiology at The Mount Sinai HospitalNew YorkNY
| | - Robert H. Pass
- Division of Pediatric Cardiology, Department of PediatricsIcahn School of Medicine at Mount SinaiNew YorkNY
| | - Alexander W. Charney
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount SinaiNew YorkNY
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNY
| | - Jagat Narula
- Mount Sinai Heart, Icahn School of Medicine at Mount SinaiNew YorkNY
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal MedicineYale School of MedicineNew HavenCT
- Section of Health Informatics, Department of BiostatisticsYale School of Public HealthNew HavenCT
- Biomedical Informatics and Data Science, Yale School of MedicineNew HavenCT
- Center for Outcomes Research and Evaluation, Yale‐New Haven HospitalNew HavenCT
| | - Ankit Sakhuja
- Division of Cardiovascular Critical Care, Department of Cardiac and Thoracic SurgeryWest Virginia UniversityMorgantownWV
| | - Hayit Greenspan
- Biomedical Engineering and Imaging Institute, Icahn School of Medicine at Mount SinaiNew YorkNY
| | - Bruce D. Gelb
- Division of Pediatric Cardiology, Department of PediatricsIcahn School of Medicine at Mount SinaiNew YorkNY
- Mindich Child Health and Development Institute, Icahn School of Medicine at Mount SinaiNew YorkNY
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNY
| | - Ron Do
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount SinaiNew YorkNY
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNY
| | - Girish N. Nadkarni
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount SinaiNew YorkNY
- The Division of Data Driven and Digital Medicine (D3M), Department of MedicineIcahn School of Medicine at Mount SinaiNew YorkNY
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13
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Tokita J, Lam D, Vega A, Wang S, Amoruso L, Muller T, Naik N, Rathi S, Martin S, Zabetian A, Liu C, Sinfield C, McNicholas T, Fleming F, Coca SG, Nadkarni GN, Tun R, Kattan M, Donovan MJ, Rahim AK. A Real-World Precision Medicine Program Including the KidneyIntelX Test Effectively Changes Management Decisions and Outcomes for Patients With Early-Stage Diabetic Kidney Disease. J Prim Care Community Health 2024; 15:21501319231223437. [PMID: 38185870 PMCID: PMC10773280 DOI: 10.1177/21501319231223437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Revised: 12/07/2023] [Accepted: 12/12/2023] [Indexed: 01/09/2024] Open
Abstract
INTRODUCTION/OBJECTIVE The KidneyIntelX is a multiplex, bioprognostic, immunoassay consisting of 3 plasma biomarkers and clinical variables that uses machine learning to predict a patient's risk for a progressive decline in kidney function over 5 years. We report the 1-year pre- and post-test clinical impact on care management, eGFR slope, and A1C along with engagement of population health clinical pharmacists and patient coordinators to promote a program of sustainable kidney, metabolic, and cardiac health. METHODS The KidneyIntelX in vitro prognostic test was previously validated for patients with type 2 diabetes and diabetic kidney disease (DKD) to predict kidney function decline within 5 years was introduced into the RWE study (NCT04802395) across the Health System as part of a population health chronic disease management program from [November 2020 to April 2023]. Pre- and post-test patients with a minimum of 12 months of follow-up post KidneyIntelX were assessed across all aspects of the program. RESULTS A total of 5348 patients with DKD had a KidneyIntelX assay. The median age was 68 years old, 52% were female, 27% self-identified as Black, and 89% had hypertension. The median baseline eGFR was 62 ml/min/1.73 m2, urine albumin-creatinine ratio was 54 mg/g, and A1C was 7.3%. The KidneyIntelX risk level was low in 49%, intermediate in 40%, and high in 11% of cases. New prescriptions for SGLT2i, GLP-1 RA, or referral to a specialist were noted in 19%, 33%, and 43% among low-, intermediate-, and high-risk patients, respectively. The median A1C decreased from 8.2% pre-test to 7.5% post-test in the high-risk group (P < .001). UACR levels in the intermediate-risk patients with albuminuria were reduced by 20%, and in a subgroup treated with new scripts for SGLT2i, UACR levels were lowered by approximately 50%. The median eGFR slope improved from -7.08 ml/min/1.73 m2/year to -4.27 ml/min/1.73 m2/year in high-risk patients (P = .0003), -2.65 to -1.04 in intermediate risk, and -3.26 ml/min/1.73 m2/year to +0.45 ml/min/1.73 m2/year in patients with low-risk (P < .001). CONCLUSIONS Deployment and risk stratification by KidneyIntelX was associated with an escalation in action taken to optimize cardio-kidney-metabolic health including medications and specialist referrals. Glycemic control and kidney function trajectories improved post-KidneyIntelX testing, with the greatest improvements observed in those scored as high-risk.
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Affiliation(s)
- Joji Tokita
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - David Lam
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Aida Vega
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Stephanie Wang
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Tamara Muller
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Nidhi Naik
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Shivani Rathi
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | | | - Catherine Liu
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | | | | | - Steven G. Coca
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Roger Tun
- Renalytix AI, Inc., New York, NY, USA
| | | | - Michael J. Donovan
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Renalytix AI, Inc., New York, NY, USA
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Klang E, Sourosh A, Nadkarni GN, Sharif K, Lahat A. Evaluating the role of ChatGPT in gastroenterology: a comprehensive systematic review of applications, benefits, and limitations. Therap Adv Gastroenterol 2023; 16:17562848231218618. [PMID: 38149123 PMCID: PMC10750546 DOI: 10.1177/17562848231218618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Accepted: 11/16/2023] [Indexed: 12/28/2023] Open
Abstract
Background The integration of artificial intelligence (AI) into healthcare has opened new avenues for enhancing patient care and clinical research. In gastroenterology, the potential of AI tools, specifically large language models like ChatGPT, is being explored to understand their utility and effectiveness. Objectives The primary goal of this systematic review is to assess the various applications, ascertain the benefits, and identify the limitations of utilizing ChatGPT within the realm of gastroenterology. Design Through a systematic approach, this review aggregates findings from multiple studies to evaluate the impact of ChatGPT on the field. Data sources and methods The review was based on a detailed literature search of the PubMed database, targeting research that delves into the use of ChatGPT for gastroenterological purposes. It incorporated six selected studies, which were meticulously evaluated for quality using the Joanna Briggs Institute critical appraisal instruments. The data were then synthesized narratively to encapsulate the roles, advantages, and drawbacks of ChatGPT in gastroenterology. Results The investigation unearthed various roles of ChatGPT, including its use in patient education, diagnostic self-assessment, disease management, and the formulation of research queries. Notable benefits were its capability to provide pertinent recommendations, enhance communication between patients and physicians, and prompt valuable research inquiries. Nonetheless, it encountered obstacles in decoding intricate medical questions, yielded inconsistent responses at times, and exhibited limitations in generating novel content. The review also considered ethical implications. Conclusion ChatGPT has demonstrated significant potential in the field of gastroenterology, especially in facilitating patient-physician interactions and managing diseases. Despite these advancements, the review underscores the necessity for ongoing refinement, customization, and ethical regulation of AI tools. These findings serve to enrich the dialog concerning AI's role in healthcare, with a specific focus on ChatGPT's application in gastroenterology.
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Affiliation(s)
- Eyal Klang
- Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- ARC Innovation Center, Sheba Medical Center at Tel Hashomer Affiliated with Tel Aviv Medical School, Tel Aviv University, Tel Aviv, Israel
| | - Ali Sourosh
- Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Girish N. Nadkarni
- Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kassem Sharif
- Department of Gastroenterology, Sheba Medical Center at Tel Hashomer Affiliated with Tel Aviv Medical School, Tel Aviv University, Tel Aviv, Israel
| | - Adi Lahat
- Department of Gastroenterology, Sheba Medical Center at Tel Hashomer, Ramat Gan, 52621 Affiliated with Tel Aviv Medical School, Tel Aviv University, Tel Aviv, Israel
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15
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Takkavatakarn K, Oh W, Cheng E, Nadkarni GN, Chan L. Machine learning models to predict end-stage kidney disease in chronic kidney disease stage 4. BMC Nephrol 2023; 24:376. [PMID: 38114923 PMCID: PMC10731874 DOI: 10.1186/s12882-023-03424-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 12/05/2023] [Indexed: 12/21/2023] Open
Abstract
INTRODUCTION End-stage kidney disease (ESKD) is associated with increased morbidity and mortality. Identifying patients with stage 4 CKD (CKD4) at risk of rapid progression to ESKD remains challenging. Accurate prediction of CKD4 progression can improve patient outcomes by improving advanced care planning and optimizing healthcare resource allocation. METHODS We obtained electronic health record data from patients with CKD4 in a large health system between January 1, 2006, and December 31, 2016. We developed and validated four models, including Least Absolute Shrinkage and Selection Operator (LASSO) regression, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network (ANN), to predict ESKD at 3 years. We utilized area under the receiver operating characteristic curve (AUROC) to evaluate model performances and utilized Shapley additive explanation (SHAP) values and plots to define feature dependence of the best performance model. RESULTS We included 3,160 patients with CKD4. ESKD was observed in 538 patients (21%). All approaches had similar AUROCs; ANN yielded the highest AUROC (0.77; 95%CI 0.75 to 0.79) and LASSO regression (0.77; 95%CI 0.75 to 0.79), followed by random forest (0.76; 95% CI 0.74 to 0.79), and XGBoost (0.76; 95% CI 0.74 to 0.78). CONCLUSIONS We developed and validated several models for near-term prediction of kidney failure in CKD4. ANN, random forest, and XGBoost demonstrated similar predictive performances. Using this suite of models, interventions can be customized based on risk, and population health and resources appropriately allocated.
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Affiliation(s)
- Kullaya Takkavatakarn
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Division of Nephrology, Department of Medicine, King Chulalongkorn Memorial Hospital, Chulalongkorn University, Bangkok, Thailand
| | - Wonsuk Oh
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ella Cheng
- The Cooper Union for the Advancement of Science and Art, New York, NY, USA
| | - Girish N Nadkarni
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Lili Chan
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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16
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Abbott EE, Oh W, Dai Y, Feuer C, Chan L, Carr BG, Nadkarni GN. Joint Modeling of Social Determinants and Clinical Factors to Define Subphenotypes in Out-of-Hospital Cardiac Arrest Survival: Cluster Analysis. JMIR Aging 2023; 6:e51844. [PMID: 38059569 PMCID: PMC10721134 DOI: 10.2196/51844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 10/28/2023] [Accepted: 10/29/2023] [Indexed: 12/08/2023] Open
Abstract
Background Machine learning clustering offers an unbiased approach to better understand the interactions of complex social and clinical variables via integrative subphenotypes, an approach not studied in out-of-hospital cardiac arrest (OHCA). Objective We conducted a cluster analysis for a cohort of OHCA survivors to examine the association of clinical and social factors for mortality at 1 year. Methods We used a retrospective observational OHCA cohort identified from Medicare claims data, including area-level social determinants of health (SDOH) features and hospital-level data sets. We applied k-means clustering algorithms to identify subphenotypes of beneficiaries who had survived an OHCA and examined associations of outcomes by subphenotype. Results We identified 27,028 unique beneficiaries who survived to discharge after OHCA. We derived 4 distinct subphenotypes. Subphenotype 1 included a distribution of more urban, female, and Black beneficiaries with the least robust area-level SDOH measures and the highest 1-year mortality (2375/4417, 53.8%). Subphenotype 2 was characterized by a greater distribution of male, White beneficiaries and had the strongest zip code-level SDOH measures, with 1-year mortality at 49.9% (4577/9165). Subphenotype 3 had the highest rates of cardiac catheterization at 34.7% (1342/3866) and the greatest distribution with a driving distance to the index OHCA hospital from their primary residence >16.1 km at 85.4% (8179/9580); more were also discharged to a skilled nursing facility after index hospitalization. Subphenotype 4 had moderate median household income at US $51,659.50 (IQR US $41,295 to $67,081) and moderate to high median unemployment at 5.5% (IQR 4.2%-7.1%), with the lowest 1-year mortality (1207/3866, 31.2%). Joint modeling of these features demonstrated an increased hazard of death for subphenotypes 1 to 3 but not for subphenotype 4 when compared to reference. Conclusions We identified 4 distinct subphenotypes with differences in outcomes by clinical and area-level SDOH features for OHCA. Further work is needed to determine if individual or other SDOH domains are specifically tied to long-term survival after OHCA.
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Affiliation(s)
- Ethan E Abbott
- Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
- Institute for Health Equity Research, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
- Division of Data-Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
| | - Wonsuk Oh
- Division of Data-Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
| | - Yang Dai
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
| | - Cole Feuer
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
| | - Lili Chan
- Division of Data-Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
| | - Brendan G Carr
- Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
| | - Girish N Nadkarni
- Institute for Health Equity Research, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
- Division of Data-Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New YorkNY, United States
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17
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Klang E, Sourosh A, Nadkarni GN, Sharif K, Lahat A. Deep Learning and Gastric Cancer: Systematic Review of AI-Assisted Endoscopy. Diagnostics (Basel) 2023; 13:3613. [PMID: 38132197 PMCID: PMC10742887 DOI: 10.3390/diagnostics13243613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Revised: 11/23/2023] [Accepted: 12/02/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND Gastric cancer (GC), a significant health burden worldwide, is typically diagnosed in the advanced stages due to its non-specific symptoms and complex morphological features. Deep learning (DL) has shown potential for improving and standardizing early GC detection. This systematic review aims to evaluate the current status of DL in pre-malignant, early-stage, and gastric neoplasia analysis. METHODS A comprehensive literature search was conducted in PubMed/MEDLINE for original studies implementing DL algorithms for gastric neoplasia detection using endoscopic images. We adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The focus was on studies providing quantitative diagnostic performance measures and those comparing AI performance with human endoscopists. RESULTS Our review encompasses 42 studies that utilize a variety of DL techniques. The findings demonstrate the utility of DL in GC classification, detection, tumor invasion depth assessment, cancer margin delineation, lesion segmentation, and detection of early-stage and pre-malignant lesions. Notably, DL models frequently matched or outperformed human endoscopists in diagnostic accuracy. However, heterogeneity in DL algorithms, imaging techniques, and study designs precluded a definitive conclusion about the best algorithmic approach. CONCLUSIONS The promise of artificial intelligence in improving and standardizing gastric neoplasia detection, diagnosis, and segmentation is significant. This review is limited by predominantly single-center studies and undisclosed datasets used in AI training, impacting generalizability and demographic representation. Further, retrospective algorithm training may not reflect actual clinical performance, and a lack of model details hinders replication efforts. More research is needed to substantiate these findings, including larger-scale multi-center studies, prospective clinical trials, and comprehensive technical reporting of DL algorithms and datasets, particularly regarding the heterogeneity in DL algorithms and study designs.
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Affiliation(s)
- Eyal Klang
- Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA (A.S.); (G.N.N.)
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- ARC Innovation Center, Sheba Medical Center, Affiliated with Tel Aviv University Medical School, Tel Hashomer, Ramat Gan 52621, Tel Aviv, Israel
| | - Ali Sourosh
- Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA (A.S.); (G.N.N.)
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Girish N. Nadkarni
- Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA (A.S.); (G.N.N.)
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Kassem Sharif
- Department of Gastroenterology, Sheba Medical Center, Affiliated with Tel Aviv University Medical School, Tel Hashomer, Ramat Gan 52621, Tel Aviv, Israel;
| | - Adi Lahat
- Department of Gastroenterology, Sheba Medical Center, Affiliated with Tel Aviv University Medical School, Tel Hashomer, Ramat Gan 52621, Tel Aviv, Israel;
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Nadkarni GN, Stapleton S, Takale D, Edwards K, Moran K, Mosoyan G, Hansen MK, Donovan MJ, Heerspink HJL, Fleming F, Coca SG. Derivation and independent validation of kidneyintelX.dkd: A prognostic test for the assessment of diabetic kidney disease progression. Diabetes Obes Metab 2023; 25:3779-3787. [PMID: 37722962 DOI: 10.1111/dom.15273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 08/28/2023] [Accepted: 08/29/2023] [Indexed: 09/20/2023]
Abstract
AIMS To develop and validate an updated version of KidneyIntelX (kidneyintelX.dkd) to stratify patients for risk of progression of diabetic kidney disease (DKD) stages 1 to 3, to simplify the test for clinical adoption and support an application to the US Food and Drug Administration regulatory pathway. METHODS We used plasma biomarkers and clinical data from the Penn Medicine Biobank (PMBB) for training, and independent cohorts (BioMe and CANVAS) for validation. The primary outcome was progressive decline in kidney function (PDKF), defined by a ≥40% sustained decline in estimated glomerular filtration rate or end-stage kidney disease within 5 years of follow-up. RESULTS In 573 PMBB participants with DKD, 15.4% experienced PDKF over a median of 3.7 years. We trained a random forest model using biomarkers and clinical variables. Among 657 BioMe participants and 1197 CANVAS participants, 11.7% and 7.5%, respectively, experienced PDKF. Based on training cut-offs, 57%, 35% and 8% of BioMe participants, and 56%, 38% and 6% of CANVAS participants were classified as having low-, moderate- and high-risk levels, respectively. The cumulative incidence at these risk levels was 5.9%, 21.2% and 66.9% in BioMe and 6.7%, 13.1% and 59.6% in CANVAS. After clinical risk factor adjustment, the adjusted hazard ratios were 7.7 (95% confidence interval [CI] 3.0-19.6) and 3.7 (95% CI 2.0-6.8) in BioMe, and 5.4 (95% CI 2.5-11.9) and 2.3 (95% CI 1.4-3.9) in CANVAS, for high- versus low-risk and moderate- versus low-risk levels, respectively. CONCLUSIONS Using two independent cohorts and a clinical trial population, we validated an updated KidneyIntelX test (named kidneyintelX.dkd), which significantly enhanced risk stratification in patients with DKD for PDKF, independently from known risk factors for progression.
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Affiliation(s)
- Girish N Nadkarni
- Barbara T Murphy Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Division of Digital and Data Driven Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | | | | | - Kara Moran
- Renalytix AI, PLC, New York, New York, USA
| | - Gohar Mosoyan
- Barbara T Murphy Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Michael K Hansen
- Janssen Research & Development, LLC, Spring House, Pennsylvania, USA
| | | | - Hiddo J L Heerspink
- Department of Clinical Pharmacy and Pharmacology, University of Groningen, Groningen, The Netherlands
| | | | - Steven G Coca
- Barbara T Murphy Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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Kashani KB, Awdishu L, Bagshaw SM, Barreto EF, Claure-Del Granado R, Evans BJ, Forni LG, Ghosh E, Goldstein SL, Kane-Gill SL, Koola J, Koyner JL, Liu M, Murugan R, Nadkarni GN, Neyra JA, Ninan J, Ostermann M, Pannu N, Rashidi P, Ronco C, Rosner MH, Selby NM, Shickel B, Singh K, Soranno DE, Sutherland SM, Bihorac A, Mehta RL. Digital health and acute kidney injury: consensus report of the 27th Acute Disease Quality Initiative workgroup. Nat Rev Nephrol 2023; 19:807-818. [PMID: 37580570 DOI: 10.1038/s41581-023-00744-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/06/2023] [Indexed: 08/16/2023]
Abstract
Acute kidney injury (AKI), which is a common complication of acute illnesses, affects the health of individuals in community, acute care and post-acute care settings. Although the recognition, prevention and management of AKI has advanced over the past decades, its incidence and related morbidity, mortality and health care burden remain overwhelming. The rapid growth of digital technologies has provided a new platform to improve patient care, and reports show demonstrable benefits in care processes and, in some instances, in patient outcomes. However, despite great progress, the potential benefits of using digital technology to manage AKI has not yet been fully explored or implemented in clinical practice. Digital health studies in AKI have shown variable evidence of benefits, and the digital divide means that access to digital technologies is not equitable. Upstream research and development costs, limited stakeholder participation and acceptance, and poor scalability of digital health solutions have hindered their widespread implementation and use. Here, we provide recommendations from the Acute Disease Quality Initiative consensus meeting, which involved experts in adult and paediatric nephrology, critical care, pharmacy and data science, at which the use of digital health for risk prediction, prevention, identification and management of AKI and its consequences was discussed.
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Affiliation(s)
- Kianoush B Kashani
- Division of Nephrology and Hypertension, Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN, USA.
| | - Linda Awdishu
- Clinical Pharmacy, San Diego Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Sean M Bagshaw
- Department of Critical Care Medicine, Faculty of Medicine and Dentistry, University of Alberta and Alberta Health Services, Edmonton, Canada
| | | | - Rolando Claure-Del Granado
- Division of Nephrology, Hospital Obrero No 2 - CNS, Cochabamba, Bolivia
- Universidad Mayor de San Simon, School of Medicine, Cochabamba, Bolivia
| | - Barbara J Evans
- Intelligent Critical Care Center, University of Florida, Gainesville, FL, USA
| | - Lui G Forni
- Department of Critical Care, Royal Surrey Hospital NHS Foundation Trust & Department of Clinical & Experimental Medicine, University of Surrey, Guildford, UK
| | - Erina Ghosh
- Philips Research North America, Cambridge, MA, USA
| | - Stuart L Goldstein
- Center for Acute Care Nephrology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Sandra L Kane-Gill
- Biomedical Informatics and Clinical Translational Sciences, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jejo Koola
- UC San Diego Health Department of Biomedical Informatics, Department of Medicine, La Jolla, CA, USA
| | - Jay L Koyner
- Section of Nephrology, Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Mei Liu
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Raghavan Murugan
- The Program for Critical Care Nephrology, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- The Clinical Research, Investigation, and Systems Modelling of Acute Illness Center, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Girish N Nadkarni
- Division of Data-Driven and Digital Medicine (D3M), Department of Medicine, Icahn School of Medicine at Mount Sinai; Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Javier A Neyra
- Division of Nephrology, Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Jacob Ninan
- Division of Pulmonary, Critical Care and Sleep Medicine, Mayo Clinic, Rochester, MN, USA
| | - Marlies Ostermann
- Department of Critical Care, King's College London, Guy's & St Thomas' Hospital, London, UK
| | - Neesh Pannu
- Division of Nephrology, University of Alberta, Edmonton, Canada
| | - Parisa Rashidi
- Intelligent Critical Care Center, University of Florida, Gainesville, FL, USA
| | - Claudio Ronco
- Università di Padova; Scientific Director Foundation IRRIV; International Renal Research Institute; San Bortolo Hospital, Vicenza, Italy
| | - Mitchell H Rosner
- Department of Medicine, University of Virginia Health, Charlottesville, VA, USA
| | - Nicholas M Selby
- Centre for Kidney Research and Innovation, Academic Unit of Translational Medical Sciences, University of Nottingham, Nottingham, UK
- Department of Renal Medicine, Royal Derby Hospital, Derby, UK
| | - Benjamin Shickel
- Intelligent Critical Care Center, University of Florida, Gainesville, FL, USA
| | - Karandeep Singh
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Danielle E Soranno
- Section of Nephrology, Department of Pediatrics, Indiana University, Riley Hospital for Children, Indianapolis, IN, USA
| | - Scott M Sutherland
- Division of Nephrology, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Azra Bihorac
- Intelligent Critical Care Center, University of Florida, Gainesville, FL, USA.
| | - Ravindra L Mehta
- Division of Nephrology-Hypertension, Department of Medicine, University of California San Diego, La Jolla, CA, USA.
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20
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Hirten RP, Danieletto M, Landell K, Zweig M, Golden E, Orlov G, Rodrigues J, Alleva E, Ensari I, Bottinger E, Nadkarni GN, Fuchs TJ, Fayad ZA. Development of the ehive Digital Health App: Protocol for a Centralized Research Platform. JMIR Res Protoc 2023; 12:e49204. [PMID: 37971801 PMCID: PMC10690532 DOI: 10.2196/49204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 09/14/2023] [Accepted: 09/22/2023] [Indexed: 11/19/2023] Open
Abstract
BACKGROUND The increasing use of smartphones, wearables, and connected devices has enabled the increasing application of digital technologies for research. Remote digital study platforms comprise a patient-interfacing digital application that enables multimodal data collection from a mobile app and connected sources. They offer an opportunity to recruit at scale, acquire data longitudinally at a high frequency, and engage study participants at any time of the day in any place. Few published descriptions of centralized digital research platforms provide a framework for their development. OBJECTIVE This study aims to serve as a road map for those seeking to develop a centralized digital research platform. We describe the technical and functional aspects of the ehive app, the centralized digital research platform of the Hasso Plattner Institute for Digital Health at Mount Sinai Hospital, New York, New York. We then provide information about ongoing studies hosted on ehive, including usership statistics and data infrastructure. Finally, we discuss our experience with ehive in the broader context of the current landscape of digital health research platforms. METHODS The ehive app is a multifaceted and patient-facing central digital research platform that permits the collection of e-consent for digital health studies. An overview of its development, its e-consent process, and the tools it uses for participant recruitment and retention are provided. Data integration with the platform and the infrastructure supporting its operations are discussed; furthermore, a description of its participant- and researcher-facing dashboard interfaces and the e-consent architecture is provided. RESULTS The ehive platform was launched in 2020 and has successfully hosted 8 studies, namely 6 observational studies and 2 clinical trials. Approximately 1484 participants downloaded the app across 36 states in the United States. The use of recruitment methods such as bulk messaging through the EPIC electronic health records and standard email portals enables broad recruitment. Light-touch engagement methods, used in an automated fashion through the platform, maintain high degrees of engagement and retention. The ehive platform demonstrates the successful deployment of a central digital research platform that can be modified across study designs. CONCLUSIONS Centralized digital research platforms such as ehive provide a novel tool that allows investigators to expand their research beyond their institution, engage in large-scale longitudinal studies, and combine multimodal data streams. The ehive platform serves as a model for groups seeking to develop similar digital health research programs. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/49204.
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Affiliation(s)
- Robert P Hirten
- Henry D Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Hasso Plattner Institute for Digital Health at Mount Sinai, New York, NY, United States
- Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Matteo Danieletto
- Hasso Plattner Institute for Digital Health at Mount Sinai, New York, NY, United States
- Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Kyle Landell
- Hasso Plattner Institute for Digital Health at Mount Sinai, New York, NY, United States
- Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Micol Zweig
- Hasso Plattner Institute for Digital Health at Mount Sinai, New York, NY, United States
- Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Eddye Golden
- Hasso Plattner Institute for Digital Health at Mount Sinai, New York, NY, United States
- Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Georgy Orlov
- Hasso Plattner Institute for Digital Health at Mount Sinai, New York, NY, United States
- Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Jovita Rodrigues
- Hasso Plattner Institute for Digital Health at Mount Sinai, New York, NY, United States
- Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Eugenia Alleva
- Hasso Plattner Institute for Digital Health at Mount Sinai, New York, NY, United States
| | - Ipek Ensari
- Hasso Plattner Institute for Digital Health at Mount Sinai, New York, NY, United States
- Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Erwin Bottinger
- Hasso Plattner Institute for Digital Health at Mount Sinai, New York, NY, United States
- Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Girish N Nadkarni
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Thomas J Fuchs
- Hasso Plattner Institute for Digital Health at Mount Sinai, New York, NY, United States
- Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Zahi A Fayad
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
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21
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van Gerwen M, Colicino E, Guan H, Dolios G, Nadkarni GN, Vermeulen RCH, Wolff MS, Arora M, Genden EM, Petrick LM. Per- and polyfluoroalkyl substances (PFAS) exposure and thyroid cancer risk. EBioMedicine 2023; 97:104831. [PMID: 37884429 PMCID: PMC10667111 DOI: 10.1016/j.ebiom.2023.104831] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 09/19/2023] [Accepted: 09/27/2023] [Indexed: 10/28/2023] Open
Abstract
BACKGROUND Although per- and polyfluoroalkyl substances (PFAS) exposure is a potential contributor to the increasing thyroid cancer trend, limited studies have investigated the association between PFAS exposure and thyroid cancer in human populations. We therefore investigated associations between plasma PFAS levels and thyroid cancer diagnosis using a nested case-control study of patients with thyroid cancer with plasma samples collected at/before cancer diagnosis. METHODS 88 patients with thyroid cancer using diagnosis codes and 88 healthy (non-cancer) controls pair-matched on sex, age (±5 years), race/ethnicity, body mass index, smoking status, and year of sample collection were identified in the BioMe population (a medical record-linked biobank at the Icahn School of Medicine at Mount Sinai in New York); 74 patients had papillary thyroid cancer. Eight plasma PFAS were measured using untargeted analysis with liquid chromatography-high resolution mass spectrometry and suspect screening. Associations between individual PFAS levels and thyroid cancer were evaluated using unconditional logistic regression models to estimate adjusted odds ratios (ORadj) and 95% confidence intervals (CI). FINDINGS There was a 56% increased rate of thyroid cancer diagnosis per doubling of linear perfluorooctanesulfonic acid (n-PFOS) intensity (ORadj, 1.56, 95% CI: 1.17-2.15, P = 0.004); results were similar when including patients with papillary thyroid cancer only (ORadj, 1.56, 95% CI: 1.13-2.21, P = 0.009). This positive association remained in subset analysis investigating exposure timing including 31 thyroid cancer cases diagnosed ≥1 year after plasma sample collection (ORadj, 2.67, 95% CI: 1.59-4.88, P < 0.001). INTERPRETATION This study reports associations between exposure to PFAS and increased rate of (papillary) thyroid cancer. Thyroid cancer risk from PFAS exposure is a global concern given the prevalence of PFAS exposure. Individual PFAS studied here are a small proportion of the total number of PFAS supporting additional large-scale prospective studies investigating thyroid cancer risk associated with exposure to PFAS chemicals. FUNDING National Institutes of Health grants and The Andrea and Charles Bronfman Philanthropies.
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Affiliation(s)
- Maaike van Gerwen
- Department of Otolaryngology - Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Institute for Translational Epidemiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
| | - Elena Colicino
- Institute for Translational Epidemiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Institute for Exposomic Research, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Haibin Guan
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Georgia Dolios
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Girish N Nadkarni
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; The Division of Data Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Roel C H Vermeulen
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, the Netherlands; Department of Population Health Sciences, Institute for Risk Assessment Sciences (IRAS), Utrecht University, Utrecht, the Netherlands
| | - Mary S Wolff
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Institute for Exposomic Research, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Manish Arora
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Institute for Exposomic Research, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Eric M Genden
- Department of Otolaryngology - Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Lauren M Petrick
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Institute for Exposomic Research, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; The Bert Strassburger Metabolic Center, Sheba Medical Center, Tel-Hashomer, Israel.
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22
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Kittrell HD, DiMenna FJ, Arad AD, Oh W, Hofer I, Walker RW, Loos RJF, Albu JB, Nadkarni GN. Discrepancy between predicted and measured exercise intensity for eliciting the maximal rate of lipid oxidation. Nutr Metab Cardiovasc Dis 2023; 33:2189-2198. [PMID: 37567789 DOI: 10.1016/j.numecd.2023.07.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 07/10/2023] [Accepted: 07/10/2023] [Indexed: 08/13/2023]
Abstract
BACKGROUND AND AIMS Ectopic lipid storage is implicated in type 2 diabetes pathogenesis; hence, exercise to deplete stores (i.e., at the intensity that allows for maximal rate of lipid oxidation; MLO) might be optimal for restoring metabolic health. This intensity ("Fatmax") is estimated during incremental exercise ("Fatmax test"). However, in "the field" general recommendations exist regarding a range of percentages of maximal heart rate (HR) to elicit MLO. The degree to which this range is aligned with measured Fatmax has not been investigated. We compared measured HR at Fatmax, with maximal HR percentages within the typically recommended range in a sample of 26 individuals (Female: n = 11, European ancestry: n = 17). METHODS AND RESULTS Subjects completed a modified Fatmax test with a 5-min warmup, followed by incremental stages starting at 15 W with work rate increased by 15 W every 5 min until termination criteria were reached. Pulmonary gas exchange was recorded and average values for V˙ o2 and V˙ co2 for the final minute of each stage were used to estimate substrate-oxidation rates. We modeled lipid-oxidation kinetics using a sinusoidal model and expressed MLO relative to peak V˙ o2 and HR. Bland-Altman analysis demonstrated lack of concordance between HR at Fatmax and at 50%, 70%, and 80% of age-predicted maximum with a mean difference of 23 b·min-1. CONCLUSION Our results indicate that estimated "fat-burning" heart rate zones are inappropriate for prescribing exercise to elicit MLO and we recommend direct individual exercise lipid oxidation measurements to elicit these values.
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Affiliation(s)
- Hannah D Kittrell
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA; The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Fred J DiMenna
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Avigdor D Arad
- The Integrated Center for Preventive Medicine, Tel Aviv Medical Center, Tel Aviv, Israel
| | - Wonsuk Oh
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA; The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ira Hofer
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Anesthesiology, Pain, and Perioperative Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ryan W Walker
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ruth J F Loos
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jeanine B Albu
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Girish N Nadkarni
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA; The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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23
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Jayaraman P, Rajagopal M, Paranjpe I, Liharska L, Suarez-Farinas M, Thompson R, Del Valle DM, Beckmann N, Oh W, Gulamali FF, Kauffman J, Gonzalez-Kozlova E, Dellepiane S, Vasquez-Rios G, Vaid A, Jiang J, Chen A, Sakhuja A, Chen S, Kenigsberg E, He JC, Coca SG, Chan L, Schadt E, Merad M, Kim-Schulze S, Gnjatic S, Tsalik E, Langley R, Charney AW, Nadkarni GN. Peripheral Transcriptomics in Acute and Long-Term Kidney Dysfunction in SARS-CoV2 Infection. medRxiv 2023:2023.10.25.23297469. [PMID: 37961671 PMCID: PMC10635190 DOI: 10.1101/2023.10.25.23297469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Background Acute kidney injury (AKI) is common in hospitalized patients with SARS-CoV2 infection despite vaccination and leads to long-term kidney dysfunction. However, peripheral blood molecular signatures in AKI from COVID-19 and their association with long-term kidney dysfunction are yet unexplored. Methods In patients hospitalized with SARS-CoV2, we performed bulk RNA sequencing using peripheral blood mononuclear cells(PBMCs). We applied linear models accounting for technical and biological variability on RNA-Seq data accounting for false discovery rate (FDR) and compared functional enrichment and pathway results to a historical sepsis-AKI cohort. Finally, we evaluated the association of these signatures with long-term trends in kidney function. Results Of 283 patients, 106 had AKI. After adjustment for sex, age, mechanical ventilation, and chronic kidney disease (CKD), we identified 2635 significant differential gene expressions at FDR<0.05. Top canonical pathways were EIF2 signaling, oxidative phosphorylation, mTOR signaling, and Th17 signaling, indicating mitochondrial dysfunction and endoplasmic reticulum (ER) stress. Comparison with sepsis associated AKI showed considerable overlap of key pathways (48.14%). Using follow-up estimated glomerular filtration rate (eGFR) measurements from 115 patients, we identified 164/2635 (6.2%) of the significantly differentiated genes associated with overall decrease in long-term kidney function. The strongest associations were 'autophagy', 'renal impairment via fibrosis', and 'cardiac structure and function'. Conclusions We show that AKI in SARS-CoV2 is a multifactorial process with mitochondrial dysfunction driven by ER stress whereas long-term kidney function decline is associated with cardiac structure and function and immune dysregulation. Functional overlap with sepsis-AKI also highlights common signatures, indicating generalizability in therapeutic approaches. SIGNIFICANCE STATEMENT Peripheral transcriptomic findings in acute and long-term kidney dysfunction after hospitalization for SARS-CoV2 infection are unclear. We evaluated peripheral blood molecular signatures in AKI from COVID-19 (COVID-AKI) and their association with long-term kidney dysfunction using the largest hospitalized cohort with transcriptomic data. Analysis of 283 hospitalized patients of whom 37% had AKI, highlighted the contribution of mitochondrial dysfunction driven by endoplasmic reticulum stress in the acute stages. Subsequently, long-term kidney function decline exhibits significant associations with markers of cardiac structure and function and immune mediated dysregulation. There were similar biomolecular signatures in other inflammatory states, such as sepsis. This enhances the potential for repurposing and generalizability in therapeutic approaches.
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24
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Eoli A, Ibing S, Schurmann C, Nadkarni GN, Heyne H, Böttinger E. A clustering approach to improve our understanding of the genetic and phenotypic complexity of chronic kidney disease. Res Sq 2023:rs.3.rs-3424565. [PMID: 37886494 PMCID: PMC10602158 DOI: 10.21203/rs.3.rs-3424565/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
Chronic kidney disease (CKD) is a complex disorder that causes a gradual loss of kidney function, affecting approximately 9.1% of the world's population. Here, we use a soft-clustering algorithm to deconstruct its genetic heterogeneity. First, we selected 322 CKD-associated independent genetic variants from published genome-wide association studies (GWAS) and added association results for 229 traits from the GWAS catalog. We then applied nonnegative matrix factorization (NMF) to discover overlapping clusters of related traits and variants. We computed cluster-specific polygenic scores and validated each cluster with a phenome-wide association study (PheWAS) on the BioMe biobank (n=31,701). NMF identified nine clusters that reflect different aspects of CKD, with the top-weighted traits signifying areas such as kidney function, type 2 diabetes (T2D), and body weight. For most clusters, the top-weighted traits were confirmed in the PheWAS analysis. Results were found to be more significant in the cross-ancestry analysis, although significant ancestry-specific associations were also identified. While all alleles were associated with a decreased kidney function, associations with CKD-related diseases (e.g., T2D) were found only for a smaller subset of variants and differed across genetic ancestry groups. Our findings leverage genetics to gain insights into the underlying biology of CKD and investigate population-specific associations.
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Affiliation(s)
- Andrea Eoli
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai
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25
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Vaid A, Sawant A, Suarez-Farinas M, Lee J, Kaul S, Kovatch P, Freeman R, Jiang J, Jayaraman P, Fayad Z, Argulian E, Lerakis S, Charney AW, Wang F, Levin M, Glicksberg B, Narula J, Hofer I, Singh K, Nadkarni GN. Implications of the Use of Artificial Intelligence Predictive Models in Health Care Settings : A Simulation Study. Ann Intern Med 2023; 176:1358-1369. [PMID: 37812781 DOI: 10.7326/m23-0949] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/11/2023] Open
Abstract
BACKGROUND Substantial effort has been directed toward demonstrating uses of predictive models in health care. However, implementation of these models into clinical practice may influence patient outcomes, which in turn are captured in electronic health record data. As a result, deployed models may affect the predictive ability of current and future models. OBJECTIVE To estimate changes in predictive model performance with use through 3 common scenarios: model retraining, sequentially implementing 1 model after another, and intervening in response to a model when 2 are simultaneously implemented. DESIGN Simulation of model implementation and use in critical care settings at various levels of intervention effectiveness and clinician adherence. Models were either trained or retrained after simulated implementation. SETTING Admissions to the intensive care unit (ICU) at Mount Sinai Health System (New York, New York) and Beth Israel Deaconess Medical Center (Boston, Massachusetts). PATIENTS 130 000 critical care admissions across both health systems. INTERVENTION Across 3 scenarios, interventions were simulated at varying levels of clinician adherence and effectiveness. MEASUREMENTS Statistical measures of performance, including threshold-independent (area under the curve) and threshold-dependent measures. RESULTS At fixed 90% sensitivity, in scenario 1 a mortality prediction model lost 9% to 39% specificity after retraining once and in scenario 2 a mortality prediction model lost 8% to 15% specificity when created after the implementation of an acute kidney injury (AKI) prediction model; in scenario 3, models for AKI and mortality prediction implemented simultaneously, each led to reduced effective accuracy of the other by 1% to 28%. LIMITATIONS In real-world practice, the effectiveness of and adherence to model-based recommendations are rarely known in advance. Only binary classifiers for tabular ICU admissions data were simulated. CONCLUSION In simulated ICU settings, a universally effective model-updating approach for maintaining model performance does not seem to exist. Model use may have to be recorded to maintain viability of predictive modeling. PRIMARY FUNDING SOURCE National Center for Advancing Translational Sciences.
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Affiliation(s)
- Akhil Vaid
- Division of Data-Driven and Digital Medicine, Department of Medicine, and The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York (A.V., P.J.)
| | - Ashwin Sawant
- Division of Data-Driven and Digital Medicine, Department of Medicine; The Charles Bronfman Institute of Personalized Medicine; and Division of Hospital Medicine, Icahn School of Medicine at Mount Sinai, New York, New York (A.S.)
| | - Mayte Suarez-Farinas
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York (M.S., J.L.)
| | - Juhee Lee
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York (M.S., J.L.)
| | - Sanjeev Kaul
- Department of Surgery, Hackensack Meridian School of Medicine, Nutley, New Jersey (S.K.)
| | - Patricia Kovatch
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York (P.K., B.G.)
| | - Robert Freeman
- Division of Data-Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York (R.F.)
| | - Joy Jiang
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York (J.J.)
| | - Pushkala Jayaraman
- Division of Data-Driven and Digital Medicine, Department of Medicine, and The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York (A.V., P.J.)
| | - Zahi Fayad
- BioMedical Engineering and Imaging Institute and Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, New York (Z.F.)
| | - Edgar Argulian
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, New York (E.A., S.L., J.N.)
| | - Stamatios Lerakis
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, New York (E.A., S.L., J.N.)
| | - Alexander W Charney
- The Charles Bronfman Institute of Personalized Medicine and Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, and Department of Surgery, Hackensack Meridian School of Medicine, Nutley, New Jersey (A.W.C.)
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York (F.W.)
| | - Matthew Levin
- Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, New York (M.L.)
| | - Benjamin Glicksberg
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York (P.K., B.G.)
| | - Jagat Narula
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, New York (E.A., S.L., J.N.)
| | - Ira Hofer
- Division of Data-Driven and Digital Medicine, Department of Medicine; The Charles Bronfman Institute of Personalized Medicine; and Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York (I.H.)
| | - Karandeep Singh
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, Michigan (K.S.)
| | - Girish N Nadkarni
- Division of Data-Driven and Digital Medicine, Department of Medicine; The Charles Bronfman Institute of Personalized Medicine; and Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York (G.N.N.)
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26
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Nargesi AA, Adejumo P, Dhingra L, Rosand B, Hengartner A, Coppi A, Benigeri S, Sen S, Ahmad T, Nadkarni GN, Lin Z, Ahmad FS, Krumholz HM, Khera R. Automated Identification of Heart Failure with Reduced Ejection Fraction using Deep Learning-based Natural Language Processing. medRxiv 2023:2023.09.10.23295315. [PMID: 37745445 PMCID: PMC10516088 DOI: 10.1101/2023.09.10.23295315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Background The lack of automated tools for measuring care quality has limited the implementation of a national program to assess and improve guideline-directed care in heart failure with reduced ejection fraction (HFrEF). A key challenge for constructing such a tool has been an accurate, accessible approach for identifying patients with HFrEF at hospital discharge, an opportunity to evaluate and improve the quality of care. Methods We developed a novel deep learning-based language model for identifying patients with HFrEF from discharge summaries using a semi-supervised learning framework. For this purpose, hospitalizations with heart failure at Yale New Haven Hospital (YNHH) between 2015 to 2019 were labeled as HFrEF if the left ventricular ejection fraction was under 40% on antecedent echocardiography. The model was internally validated with model-based net reclassification improvement (NRI) assessed against chart-based diagnosis codes. We externally validated the model on discharge summaries from hospitalizations with heart failure at Northwestern Medicine, community hospitals of Yale New Haven Health in Connecticut and Rhode Island, and the publicly accessible MIMIC-III database, confirmed with chart abstraction. Results A total of 13,251 notes from 5,392 unique individuals (mean age 73 ± 14 years, 48% female), including 2,487 patients with HFrEF (46.1%), were used for model development (train/held-out test: 70/30%). The deep learning model achieved an area under receiving operating characteristic (AUROC) of 0.97 and an area under precision-recall curve (AUPRC) of 0.97 in detecting HFrEF on the held-out set. In external validation, the model had high performance in identifying HFrEF from discharge summaries with AUROC 0.94 and AUPRC 0.91 on 19,242 notes from Northwestern Medicine, AUROC 0.95 and AUPRC 0.96 on 139 manually abstracted notes from Yale community hospitals, and AUROC 0.91 and AUPRC 0.92 on 146 manually reviewed notes at MIMIC-III. Model-based prediction of HFrEF corresponded to an overall NRI of 60.2 ± 1.9% compared with the chart diagnosis codes (p-value < 0.001) and an increase in AUROC from 0.61 [95% CI: 060-0.63] to 0.91 [95% CI 0.90-0.92]. Conclusions We developed and externally validated a deep learning language model that automatically identifies HFrEF from clinical notes with high precision and accuracy, representing a key element in automating quality assessment and improvement for individuals with HFrEF.
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Affiliation(s)
- Arash A. Nargesi
- Heart and Vascular Center, Brigham and Women’s Hospital, Harvard School of Medicine, Boston, MA
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT
| | - Philip Adejumo
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT
| | - Lovedeep Dhingra
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT
| | - Benjamin Rosand
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT
| | - Astrid Hengartner
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT
| | - Andreas Coppi
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT
- Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, CT
| | - Simon Benigeri
- Division of Cardiology, Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL
| | - Sounok Sen
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT
| | - Tariq Ahmad
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT
| | - Girish N Nadkarni
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Zhenqiu Lin
- Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, CT
| | - Faraz S. Ahmad
- Division of Cardiology, Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL
| | - Harlan M Krumholz
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT
- Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, CT
- Department of Health Policy and Management, Yale School of Public Health, New Haven, CT
- Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT
- Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, CT
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27
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Paranjpe I, Wang X, Anandakrishnan N, Haydak JC, Van Vleck T, DeFronzo S, Li Z, Mendoza A, Liu R, Fu J, Forrest I, Zhou W, Lee K, O'Hagan R, Dellepiane S, Menon KM, Gulamali F, Kamat S, Gusella GL, Charney AW, Hofer I, Cho JH, Do R, Glicksberg BS, He JC, Nadkarni GN, Azeloglu EU. Deep learning on electronic medical records identifies distinct subphenotypes of diabetic kidney disease driven by genetic variations in the Rho pathway. medRxiv 2023:2023.09.06.23295120. [PMID: 37732187 PMCID: PMC10508814 DOI: 10.1101/2023.09.06.23295120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
Kidney disease affects 50% of all diabetic patients; however, prediction of disease progression has been challenging due to inherent disease heterogeneity. We use deep learning to identify novel genetic signatures prognostically associated with outcomes. Using autoencoders and unsupervised clustering of electronic health record data on 1,372 diabetic kidney disease patients, we establish two clusters with differential prevalence of end-stage kidney disease. Exome-wide associations identify a novel variant in ARHGEF18, a Rho guanine exchange factor specifically expressed in glomeruli. Overexpression of ARHGEF18 in human podocytes leads to impairments in focal adhesion architecture, cytoskeletal dynamics, cellular motility, and RhoA/Rac1 activation. Mutant GEF18 is resistant to ubiquitin mediated degradation leading to pathologically increased protein levels. Our findings uncover the first known disease-causing genetic variant that affects protein stability of a cytoskeletal regulator through impaired degradation, a potentially novel class of expression quantitative trait loci that can be therapeutically targeted.
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Vaid A, Takkavatakarn K, Divers J, Charytan DM, Chan L, Nadkarni GN. Deep Learning on Electrocardiograms for Prediction of In-hospital Intradialytic Hypotension in Patients with ESKD. Kidney360 2023; 4:e1293-e1296. [PMID: 37418626 PMCID: PMC10547223 DOI: 10.34067/kid.0000000000000208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 06/29/2023] [Indexed: 07/09/2023]
Abstract
Intradialytic hypotension is common in patients who are on hemodialysis. We applied deep learning techniques to ECGs to predict patients at risk of IDH. The performance of the model was good with an AUC of 0.763 and AUPRC of 0.35.
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Affiliation(s)
- Akhil Vaid
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Kullaya Takkavatakarn
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Jasmin Divers
- Division of Health Services, Department of Medicine, NYU (New York University) Long Island School of Medicine, Mineola, New York
| | - David M. Charytan
- Division of Nephrology, Department of Medicine, NYU (New York University) Grossman School of Medicine and NYU Langone Medical Center, New York, New York
| | - Lili Chan
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Girish N. Nadkarni
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
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29
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Nadkarni GN, Sakhuja A. Clinical Informatics in Critical Care Medicine. Yale J Biol Med 2023; 96:397-405. [PMID: 37780994 PMCID: PMC10524812 DOI: 10.59249/wttu3055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 10/03/2023]
Abstract
Continuous monitoring and treatment of patients in intensive care units generates vast amounts of data. Critical Care Medicine clinicians incorporate this continuously evolving data to make split-second, life or death decisions for management of these patients. Despite the abundance of data, it can be challenging to consider every accessible data point when making the quick decisions necessary at the point of care. Consequently, Clinical Informatics offers a natural partnership to improve the care for critically ill patients. The last two decades have seen a significant evolution in the role of Clinical Informatics in Critical Care Medicine. In this review, we will discuss how Clinical Informatics improves the care of critically ill patients by enhancing not only data collection and visualization but also bedside medical decision making. We will further discuss the evolving role of machine learning algorithms in Clinical Informatics as it pertains to Critical Care Medicine.
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Affiliation(s)
- Girish N. Nadkarni
- Division of Nephrology, Department of Medicine, Icahn
School of Medicine at Mount Sinai, New York, NY, USA
- The Charles Bronfman Institute for Personalized
Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ankit Sakhuja
- Division of Cardiovascular Critical Care, Department of
Cardiovascular and Thoracic Surgery, West Virginia University, Morgantown, WV,
USA
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30
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Schilling J, Shokouhi S, Montgomery A, Nadkarni GN, Charney AW, Shanker A, Singh R, Jhaveri K, Singh KS, Khadke P, Jain P. Development of a decentralized cohort for studying post-acute sequelae of COVID-19 in India in the Data4life Study. Commun Med (Lond) 2023; 3:117. [PMID: 37626117 PMCID: PMC10457339 DOI: 10.1038/s43856-023-00349-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 08/15/2023] [Indexed: 08/27/2023] Open
Abstract
BACKGROUND Decentralized, digital health studies can provide real-world evidence of the lasting effects of COVID-19 on physical, socioeconomic, psychological, and social determinant factors of health in India. Existing research cohorts, however, are small and were not designed for longitudinal collection of comprehensive data from India's diverse population. Data4Life is a nationwide, digitally enabled, health research initiative to examine the post-acute sequelae of COVID-19 across individuals, communities, and regions. Data4Life seeks to build an ethnically and geographically diverse population of at least 100,000 participants in India. METHODS Here we discuss the feasibility of developing a completely decentralized COVID-19 cohort in India through qualitative analysis of data collection procedures, participant characteristics, participant perspectives on recruitment and reported study motivation. RESULTS As of June 13th, 2022, more than 6,000 participants from 17 Indian states completed baseline surveys. Friend and family referral were identified as the most common recruitment method (64.8%) across all demographic groups. Helping family and friends was the primary reason reported for joining the study (61.5%). CONCLUSIONS Preliminary findings support the use of digital technology for rapid enrollment and data collection to develop large health research cohorts in India. This demonstrates the potential for expansion of digitally enabled health research in India. These findings also outline the value of person-to-person recruitment strategies when conducting digital health research in modern-day India. Qualitative analysis reveals opportunities to increase diversity and retention in real time. It also informs strategies for improving participant experiences in the current Data4Life initiative and future studies.
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Affiliation(s)
- Josh Schilling
- Vibrent Health, 4114 Legato Rd #900, Fairfax, VA, 22033, USA
| | | | | | - Girish N Nadkarni
- Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY, 10029, USA
| | - Alexander W Charney
- Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY, 10029, USA
| | - Anil Shanker
- Meharry Medical College, 1005 Dr DB Todd Jr Blvd, Nashville, TN, 37208, USA
| | - Rajbir Singh
- Meharry Medical College, 1005 Dr DB Todd Jr Blvd, Nashville, TN, 37208, USA
| | - Kenar Jhaveri
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, 500 Hofstra Blvd, Hempstead, NY, 11549, USA
| | - Karandeep S Singh
- Learning Health Sciences, University of Michigan, 1111 E Catherine St, Ann Arbor, MI, 48109, USA
| | - Prashant Khadke
- Pensieve Health, 3A, 3rd Floor, Vascon Wekfield Chambers, Satpal Malhotra Marg, Nagar Road, Pune, Maharashtra, 411014, India
| | - Praduman Jain
- Vibrent Health, 4114 Legato Rd #900, Fairfax, VA, 22033, USA
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31
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Lampert J, Vaid A, Whang W, Koruth J, Miller MA, Langan MN, Musikantow D, Turagam M, Maan A, Kawamura I, Dukkipati S, Nadkarni GN, Reddy VY. A Novel ECG-Based Deep Learning Algorithm to Predict Cardiomyopathy in Patients With Premature Ventricular Complexes. JACC Clin Electrophysiol 2023; 9:1437-1451. [PMID: 37480862 DOI: 10.1016/j.jacep.2023.05.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 05/17/2023] [Accepted: 05/24/2023] [Indexed: 07/24/2023]
Abstract
BACKGROUND Premature ventricular complexes (PVCs) are prevalent and, although often benign, they may lead to PVC-induced cardiomyopathy. We created a deep-learning algorithm to predict left ventricular ejection fraction (LVEF) reduction in patients with PVCs from a 12-lead electrocardiogram (ECG). OBJECTIVES This study aims to assess a deep-learning model to predict cardiomyopathy among patients with PVCs. METHODS We used electronic medical records from 5 hospitals and identified ECGs from adults with documented PVCs. Internal training and testing were performed at one hospital. External validation was performed with the others. The primary outcome was first diagnosis of LVEF ≤40% within 6 months. The dataset included 383,514 ECGs, of which 14,241 remained for analysis. We analyzed area under the receiver operating curves and explainability plots for representative patients, algorithm prediction, PVC burden, and demographics in a multivariable Cox model to assess independent predictors for cardiomyopathy. RESULTS Among the 14,241-patient cohort (age 67.6 ± 14.8 years; female 43.8%; White 29.5%, Black 8.6%, Hispanic 6.5%, Asian 2.2%), 22.9% experienced reductions in LVEF to ≤40% within 6 months. The model predicted reductions in LVEF to ≤40% with area under the receiver operating curve of 0.79 (95% CI: 0.77-0.81). The gradient weighted class activation map explainability framework highlighted the sinus rhythm QRS complex-ST segment. In patients who underwent successful PVC ablation there was a post-ablation improvement in LVEF with resolution of cardiomyopathy in most (89%) patients. CONCLUSIONS Deep-learning on the 12-lead ECG alone can accurately predict new-onset cardiomyopathy in patients with PVCs independent of PVC burden. Model prediction performed well across sex and race, relying on the QRS complex/ST-segment in sinus rhythm, not PVC morphology.
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Affiliation(s)
- Joshua Lampert
- Helmsley Electrophysiology Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
| | - Akhil Vaid
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - William Whang
- Helmsley Electrophysiology Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Jacob Koruth
- Helmsley Electrophysiology Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Marc A Miller
- Helmsley Electrophysiology Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Marie-Noelle Langan
- Helmsley Electrophysiology Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Daniel Musikantow
- Helmsley Electrophysiology Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Mohit Turagam
- Helmsley Electrophysiology Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Abhishek Maan
- Helmsley Electrophysiology Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Iwanari Kawamura
- Helmsley Electrophysiology Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Srinivas Dukkipati
- Helmsley Electrophysiology Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Girish N Nadkarni
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Division of Data Driven and Digital Medicine (D3M), The Charles Bronfman Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Vivek Y Reddy
- Helmsley Electrophysiology Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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32
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Liharska LE, Park YJ, Ziafat K, Wilkins L, Silk H, Linares LM, Vornholt E, Sullivan B, Cohen V, Kota P, Feng C, Cheng E, Moya E, Thompson RC, Johnson JS, Rieder MK, Huang J, Scarpa J, Hashemi A, Polanco J, Levin MA, Nadkarni GN, Sebra R, Crary J, Schadt EE, Beckmann ND, Kopell BH, Charney AW. A study of gene expression in the living human brain. medRxiv 2023:2023.04.21.23288916. [PMID: 37163086 PMCID: PMC10168405 DOI: 10.1101/2023.04.21.23288916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
A goal of medical research is to determine the molecular basis of human brain health and illness. One way to achieve this goal is through observational studies of gene expression in human brain tissue. Due to the unavailability of brain tissue from living people, most such studies are performed using tissue from postmortem brain donors. An assumption underlying this practice is that gene expression in the postmortem human brain is an accurate representation of gene expression in the living human brain. Here, this assumption - which, until now, had not been adequately tested - is tested by comparing human prefrontal cortex gene expression between 275 living samples and 243 postmortem samples. Expression levels differed significantly for nearly 80% of genes, and a systematic examination of alternative explanations for this observation determined that these differences are not a consequence of cell type composition, RNA quality, postmortem interval, age, medication, morbidity, symptom severity, tissue pathology, sample handling, batch effects, or computational methods utilized. Analyses integrating the data generated for this study with data from earlier landmark studies that used tissue from postmortem brain donors showed that postmortem brain gene expression signatures of neurological and mental illnesses, as well as of normal traits such as aging, may not be accurate representations of these gene expression signatures in the living brain. By using tissue from large cohorts living people, future observational studies of human brain biology have the potential to (1) determine the medical research questions that can be addressed using postmortem tissue as a proxy for living tissue and (2) expand the scope of medical research to include questions about the molecular basis of human brain health and illness that can only be addressed in living people (e.g., "What happens at the molecular level in the brain as a person experiences an emotion?").
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33
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Kwak SH, Hernandez-Cancela RB, DiCorpo DA, Condon DE, Merino J, Wu P, Brody JA, Yao J, Guo X, Ahmadizar F, Meyer M, Sincan M, Mercader JM, Lee S, Haessler J, Vy HMT, Lin Z, Armstrong ND, Gu S, Tsao NL, Lange LA, Wang N, Wiggins KL, Trompet S, Liu S, Loos RJ, Judy R, Schroeder PH, Hasbani NR, Bos MM, Morrison AC, Jackson RD, Reiner AP, Manson JE, Chaudhary NS, Carmichael LK, Chen YDI, Taylor KD, Ghanbari M, van Meurs J, Pitsillides AN, Psaty BM, Noordam R, Do R, Park KS, Jukema JW, Kavousi M, Correa A, Rich SS, Damrauer SM, Hajek C, Cho NH, Irvin MR, Pankow JS, Nadkarni GN, Sladek R, Goodarzi MO, Florez JC, Chasman DI, Heckbert SR, Kooperberg C, Dupuis J, Malhotra R, de Vries PS, Liu CT, Rotter JI, Meigs JB. Time-to-Event Genome-Wide Association Study for Incident Cardiovascular Disease in People with Type 2 Diabetes Mellitus. medRxiv 2023:2023.07.25.23293180. [PMID: 37546893 PMCID: PMC10402212 DOI: 10.1101/2023.07.25.23293180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
BACKGROUND Type 2 diabetes mellitus (T2D) confers a two- to three-fold increased risk of cardiovascular disease (CVD). However, the mechanisms underlying increased CVD risk among people with T2D are only partially understood. We hypothesized that a genetic association study among people with T2D at risk for developing incident cardiovascular complications could provide insights into molecular genetic aspects underlying CVD. METHODS From 16 studies of the Cohorts for Heart & Aging Research in Genomic Epidemiology (CHARGE) Consortium, we conducted a multi-ancestry time-to-event genome-wide association study (GWAS) for incident CVD among people with T2D using Cox proportional hazards models. Incident CVD was defined based on a composite of coronary artery disease (CAD), stroke, and cardiovascular death that occurred at least one year after the diagnosis of T2D. Cohort-level estimated effect sizes were combined using inverse variance weighted fixed effects meta-analysis. We also tested 204 known CAD variants for association with incident CVD among patients with T2D. RESULTS A total of 49,230 participants with T2D were included in the analyses (31,118 European ancestries and 18,112 non-European ancestries) which consisted of 8,956 incident CVD cases over a range of mean follow-up duration between 3.2 and 33.7 years (event rate 18.2%). We identified three novel, distinct genetic loci for incident CVD among individuals with T2D that reached the threshold for genome-wide significance (P<5.0×10-8): rs147138607 (intergenic variant between CACNA1E and ZNF648) with a hazard ratio (HR) 1.23, 95% confidence interval (CI) 1.15 - 1.32, P=3.6×10-9, rs11444867 (intergenic variant near HS3ST1) with HR 1.89, 95% CI 1.52 - 2.35, P=9.9×10-9, and rs335407 (intergenic variant between TFB1M and NOX3) HR 1.25, 95% CI 1.16 - 1.35, P=1.5×10-8. Among 204 known CAD loci, 32 were associated with incident CVD in people with T2D with P<0.05, and 5 were significant after Bonferroni correction (P<0.00024, 0.05/204). A polygenic score of these 204 variants was significantly associated with incident CVD with HR 1.14 (95% CI 1.12 - 1.16) per 1 standard deviation increase (P=1.0×10-16). CONCLUSIONS The data point to novel and known genomic regions associated with incident CVD among individuals with T2D.
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Affiliation(s)
- Soo Heon Kwak
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
| | | | - Daniel A DiCorpo
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
| | | | - Jordi Merino
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
| | - Peitao Wu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
| | - Jennifer A Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA
| | - Jie Yao
- Department of Pediatrics, Institute for Translational Genomics and Population Science, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA
| | - Xiuqing Guo
- Department of Pediatrics, Institute for Translational Genomics and Population Science, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA
| | - Fariba Ahmadizar
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
- Department of Data Science and Biostatistics, Julius Global Health, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Mariah Meyer
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - Murat Sincan
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
| | - Josep M. Mercader
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - Sujin Lee
- Division of Vascular Surgery and Endovascular Therapy, Massachusetts General Hospital, Boston, MA
| | - Jeffrey Haessler
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA
| | - Ha My T. Vy
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Zhaotong Lin
- Department of Biostatistics, University of Minnesota, Minneapolis, MN
| | - Nicole D. Armstrong
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL
| | - Shaopeng Gu
- Department of Internal Medicine, Sanford Health, Sioux Falls, SD
| | - Noah L. Tsao
- Corporal Michael Crescenz VA Medical Center, and Department of Surgery, Perelman School of Medicine, Philadelphia, PA
| | - Leslie A. Lange
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - Ningyuan Wang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
| | - Kerri L. Wiggins
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA
| | - Stella Trompet
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands
- Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Simin Liu
- Department of Epidemiology, Brown University, Providence, RI
| | - Ruth J.F. Loos
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Renae Judy
- Corporal Michael Crescenz VA Medical Center, and Department of Surgery, Perelman School of Medicine, Philadelphia, PA
| | - Philip H. Schroeder
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - Natalie R. Hasbani
- Human Genetics Center, Department of Epidemiology Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX
| | - Maxime M. Bos
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Alanna C. Morrison
- Human Genetics Center, Department of Epidemiology Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX
| | - Rebecca D. Jackson
- Division of Endocrinology, Diabetes and Metabolism, Department of Internal Medicine, Ohio State University, Columbus, OH
| | - Alexander P. Reiner
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA
- Department of Epidemiology, University of Washington, Seattle, WA
| | - JoAnn E. Manson
- Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
| | - Ninad S. Chaudhary
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL
| | | | - Yii-Der Ida Chen
- Department of Pediatrics, Institute for Translational Genomics and Population Science, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA
| | - Kent D. Taylor
- Department of Pediatrics, Institute for Translational Genomics and Population Science, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Joyce van Meurs
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | | | - Bruce M. Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA
- Department of Epidemiology, University of Washington, Seattle, WA
- Department of Health Systems and Population Health, University of Washington, Seattle, WA
| | - Raymond Noordam
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands
| | - Ron Do
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Kyong Soo Park
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
| | - J Wouter Jukema
- Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands
- The Netherlands Heart Institute, Utrecht, the Netherlands
| | - Maryam Kavousi
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Adolfo Correa
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA
| | - Scott M. Damrauer
- Corporal Michael Crescenz VA Medical Center, and Department of Surgery, Perelman School of Medicine, Philadelphia, PA
- Department of Genetics, Perelman School of Medicine, Philadelphia, PA
| | - Catherine Hajek
- Department of Internal Medicine, Sanford Health, Sioux Falls, SD
| | - Nam H. Cho
- Department of Preventive Medicine, Ajou University School of Medicine, Suwon, Korea
| | - Marguerite R. Irvin
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL
| | - James S. Pankow
- Department of Biostatistics, University of Minnesota, Minneapolis, MN
| | - Girish N. Nadkarni
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Robert Sladek
- Department of Medicine and Department of Human Genetics, McGill University, Montréal, QC, Canada
| | - Mark O. Goodarzi
- Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, Cedars-Sinai Medical Center
| | - Jose C. Florez
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - Daniel I. Chasman
- Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
| | - Susan R. Heckbert
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA
- Department of Epidemiology, University of Washington, Seattle, WA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA
| | - Josée Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada
| | - Rajeev Malhotra
- Cardiovascular Research Center, Cardiology Division of the Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | - Paul S. de Vries
- Human Genetics Center, Department of Epidemiology Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX
| | - Ching-Ti Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
| | - Jerome I. Rotter
- Department of Pediatrics, Institute for Translational Genomics and Population Science, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA
| | - James B. Meigs
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Department of Medicine, Harvard Medical School, Boston, MA
- Department of General Internal Medicine, Harvard Medical School and Massachusetts General Hospital, Boston, MA
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Hirten RP, Suprun M, Danieletto M, Zweig M, Golden E, Pyzik R, Kaur S, Helmus D, Biello A, Landell K, Rodrigues J, Bottinger EP, Keefer L, Charney D, Nadkarni GN, Suarez-Farinas M, Fayad ZA. A machine learning approach to determine resilience utilizing wearable device data: analysis of an observational cohort. JAMIA Open 2023; 6:ooad029. [PMID: 37143859 PMCID: PMC10152991 DOI: 10.1093/jamiaopen/ooad029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 03/22/2023] [Accepted: 04/06/2023] [Indexed: 05/06/2023] Open
Abstract
Objective To assess whether an individual's degree of psychological resilience can be determined from physiological metrics passively collected from a wearable device. Materials and Methods Data were analyzed in this secondary analysis of the Warrior Watch Study dataset, a prospective cohort of healthcare workers enrolled across 7 hospitals in New York City. Subjects wore an Apple Watch for the duration of their participation. Surveys were collected measuring resilience, optimism, and emotional support at baseline. Results We evaluated data from 329 subjects (mean age 37.4 years, 37.1% male). Across all testing sets, gradient-boosting machines (GBM) and extreme gradient-boosting models performed best for high- versus low-resilience prediction, stratified on a median Connor-Davidson Resilience Scale-2 score of 6 (interquartile range = 5-7), with an AUC of 0.60. When predicting resilience as a continuous variable, multivariate linear models had a correlation of 0.24 (P = .029) and RMSE of 1.37 in the testing data. A positive psychological construct, comprised of resilience, optimism, and emotional support was also evaluated. The oblique random forest method performed best in estimating high- versus low-composite scores stratified on a median of 32.5, with an AUC of 0.65, a sensitivity of 0.60, and a specificity of 0.70. Discussion In a post hoc analysis, machine learning models applied to physiological metrics collected from wearable devices had some predictive ability in identifying resilience states and a positive psychological construct. Conclusions These findings support the further assessment of psychological characteristics from passively collected wearable data in dedicated studies.
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Affiliation(s)
- Robert P Hirten
- Corresponding Author: Robert P. Hirten, MD, The Dr. Henry D. Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, Annenberg Building RM 5-12, New York, NY 10029, USA;
| | - Maria Suprun
- Department of Population Health Science and Policy, Center for Biostatistics, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Matteo Danieletto
- The Hasso Plattner Institute for Digital Health at the Mount Sinai, New York, New York, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Micol Zweig
- The Hasso Plattner Institute for Digital Health at the Mount Sinai, New York, New York, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Eddye Golden
- The Hasso Plattner Institute for Digital Health at the Mount Sinai, New York, New York, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Renata Pyzik
- The BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Sparshdeep Kaur
- The Hasso Plattner Institute for Digital Health at the Mount Sinai, New York, New York, USA
| | - Drew Helmus
- The Dr. Henry D. Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Anthony Biello
- The Dr. Henry D. Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Kyle Landell
- The Hasso Plattner Institute for Digital Health at the Mount Sinai, New York, New York, USA
| | - Jovita Rodrigues
- The Hasso Plattner Institute for Digital Health at the Mount Sinai, New York, New York, USA
| | - Erwin P Bottinger
- The Hasso Plattner Institute for Digital Health at the Mount Sinai, New York, New York, USA
| | - Laurie Keefer
- The Dr. Henry D. Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Dennis Charney
- Office of the Dean, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Girish N Nadkarni
- The Hasso Plattner Institute for Digital Health at the Mount Sinai, New York, New York, USA
- The Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Mayte Suarez-Farinas
- Department of Population Health Science and Policy, Center for Biostatistics, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Zahi A Fayad
- The BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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35
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Klarin D, Devineni P, Sendamarai AK, Angueira AR, Graham SE, Shen YH, Levin MG, Pirruccello JP, Surakka I, Karnam PR, Roychowdhury T, Li Y, Wang M, Aragam KG, Paruchuri K, Zuber V, Shakt GE, Tsao NL, Judy RL, Vy HMT, Verma SS, Rader DJ, Do R, Bavaria JE, Nadkarni GN, Ritchie MD, Burgess S, Guo DC, Ellinor PT, LeMaire SA, Milewicz DM, Willer CJ, Natarajan P, Tsao PS, Pyarajan S, Damrauer SM. Genome-wide association study of thoracic aortic aneurysm and dissection in the Million Veteran Program. Nat Genet 2023; 55:1106-1115. [PMID: 37308786 PMCID: PMC10335930 DOI: 10.1038/s41588-023-01420-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 05/05/2023] [Indexed: 06/14/2023]
Abstract
The current understanding of the genetic determinants of thoracic aortic aneurysms and dissections (TAAD) has largely been informed through studies of rare, Mendelian forms of disease. Here, we conducted a genome-wide association study (GWAS) of TAAD, testing ~25 million DNA sequence variants in 8,626 participants with and 453,043 participants without TAAD in the Million Veteran Program, with replication in an independent sample of 4,459 individuals with and 512,463 without TAAD from six cohorts. We identified 21 TAAD risk loci, 17 of which have not been previously reported. We leverage multiple downstream analytic methods to identify causal TAAD risk genes and cell types and provide human genetic evidence that TAAD is a non-atherosclerotic aortic disorder distinct from other forms of vascular disease. Our results demonstrate that the genetic architecture of TAAD mirrors that of other complex traits and that it is not solely inherited through protein-altering variants of large effect size.
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Affiliation(s)
- Derek Klarin
- Veterans Affairs (VA) Palo Alto Healthcare System, Palo Alto, CA, USA.
- Department of Surgery, Stanford University School of Medicine, Palo Alto, CA, USA.
| | - Poornima Devineni
- Center for Data and Computational Sciences, VA Boston Healthcare System, Boston, MA, USA
| | - Anoop K Sendamarai
- Center for Data and Computational Sciences, VA Boston Healthcare System, Boston, MA, USA
- Carbone Cancer Center, University of Wisconsin-Madison, Madison, WI, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Anthony R Angueira
- Institute for Diabetes, Obesity and Metabolism, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Sarah E Graham
- Department of Internal Medicine, Division of Cardiology, University of Michigan, Ann Arbor, MI, USA
| | - Ying H Shen
- Division of Cardiothoracic Surgery, Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX, USA
- Department of Cardiovascular Surgery, Texas Heart Institute, Houston, TX, USA
| | - Michael G Levin
- Division of Cardiovascular Medicine, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Medicine, Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
| | - James P Pirruccello
- Division of Cardiology, University of California San Francisco, San Francisco, CA, USA
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA
| | - Ida Surakka
- Department of Internal Medicine, Division of Cardiology, University of Michigan, Ann Arbor, MI, USA
| | - Purushotham R Karnam
- Center for Data and Computational Sciences, VA Boston Healthcare System, Boston, MA, USA
| | - Tanmoy Roychowdhury
- Department of Internal Medicine, Division of Cardiology, University of Michigan, Ann Arbor, MI, USA
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA
| | - Yanming Li
- Division of Cardiothoracic Surgery, Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX, USA
| | - Minxian Wang
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Krishna G Aragam
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kaavya Paruchuri
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Verena Zuber
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- UK Dementia Research Institute at Imperial College, Imperial College London, London, UK
| | - Gabrielle E Shakt
- Division of Cardiovascular Medicine, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Noah L Tsao
- Division of Cardiovascular Medicine, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Renae L Judy
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Ha My T Vy
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Shefali S Verma
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Daniel J Rader
- Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
- Department of Genetics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Ron Do
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Joseph E Bavaria
- Division of Cardiovascular Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Marylyn D Ritchie
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Institute for Biomedical Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Stephen Burgess
- Medical Research Council Biostatistics Unit, University of Cambridge, Cambridge, UK
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Dong-Chuan Guo
- Division of Medical Genetics, Department of Internal Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Patrick T Ellinor
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiology Division, Massachusetts General Hospital, Boston, MA, USA
| | - Scott A LeMaire
- Division of Cardiothoracic Surgery, Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX, USA
- Department of Cardiovascular Surgery, Texas Heart Institute, Houston, TX, USA
- Cardiovascular Research Institute, Baylor College of Medicine, Houston, TX, USA
| | - Dianna M Milewicz
- Division of Medical Genetics, Department of Internal Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Cristen J Willer
- Department of Internal Medicine, Division of Cardiology, University of Michigan, Ann Arbor, MI, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Pradeep Natarajan
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Philip S Tsao
- Veterans Affairs (VA) Palo Alto Healthcare System, Palo Alto, CA, USA
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cardiovascular Institute, Stanford, CA, USA
| | - Saiju Pyarajan
- Center for Data and Computational Sciences, VA Boston Healthcare System, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Scott M Damrauer
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA.
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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36
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Horwitz LI, Thaweethai T, Brosnahan SB, Cicek MS, Fitzgerald ML, Goldman JD, Hess R, Hodder SL, Jacoby VL, Jordan MR, Krishnan JA, Laiyemo AO, Metz TD, Nichols L, Patzer RE, Sekar A, Singer NG, Stiles LE, Taylor BS, Ahmed S, Algren HA, Anglin K, Aponte-Soto L, Ashktorab H, Bassett IV, Bedi B, Bhadelia N, Bime C, Bind MAC, Black LJ, Blomkalns AL, Brim H, Castro M, Chan J, Charney AW, Chen BK, Chen LQ, Chen P, Chestek D, Chibnik LB, Chow DC, Chu HY, Clifton RG, Collins S, Costantine MM, Cribbs SK, Deeks SG, Dickinson JD, Donohue SE, Durstenfeld MS, Emery IF, Erlandson KM, Facelli JC, Farah-Abraham R, Finn AV, Fischer MS, Flaherman VJ, Fleurimont J, Fonseca V, Gallagher EJ, Gander JC, Gennaro ML, Gibson KS, Go M, Goodman SN, Granger JP, Greenway FL, Hafner JW, Han JE, Harkins MS, Hauser KSP, Heath JR, Hernandez CR, Ho O, Hoffman MK, Hoover SE, Horowitz CR, Hsu H, Hsue PY, Hughes BL, Jagannathan P, James JA, John J, Jolley S, Judd SE, Juskowich JJ, Kanjilal DG, Karlson EW, Katz SD, Kelly JD, Kelly SW, Kim AY, Kirwan JP, Knox KS, Kumar A, Lamendola-Essel MF, Lanca M, Lee-lannotti JK, Lefebvre RC, Levy BD, Lin JY, Logarbo BP, Logue JK, Longo MT, Luciano CA, Lutrick K, Malakooti SK, Mallett G, Maranga G, Marathe JG, Marconi VC, Marshall GD, Martin CF, Martin JN, May HT, McComsey GA, McDonald D, Mendez-Figueroa H, Miele L, Mittleman MA, Mohandas S, Mouchati C, Mullington JM, Nadkarni GN, Nahin ER, Neuman RB, Newman LT, Nguyen A, Nikolich JZ, Ofotokun I, Ogbogu PU, Palatnik A, Palomares KTS, Parimon T, Parry S, Parthasarathy S, Patterson TF, Pearman A, Peluso MJ, Pemu P, Pettker CM, Plunkett BA, Pogreba-Brown K, Poppas A, Porterfield JZ, Quigley JG, Quinn DK, Raissy H, Rebello CJ, Reddy UM, Reece R, Reeder HT, Rischard FP, Rosas JM, Rosen CJ, Rouphael NG, Rouse DJ, Ruff AM, Saint Jean C, Sandoval GJ, Santana JL, Schlater SM, Sciurba FC, Selvaggi C, Seshadri S, Sesso HD, Shah DP, Shemesh E, Sherif ZA, Shinnick DJ, Simhan HN, Singh U, Sowles A, Subbian V, Sun J, Suthar MS, Teunis LJ, Thorp JM, Ticotsky A, Tita ATN, Tragus R, Tuttle KR, Urdaneta AE, Utz PJ, VanWagoner TM, Vasey A, Vernon SD, Vidal C, Walker T, Ward HD, Warren DE, Weeks RM, Weiner SJ, Weyer JC, Wheeler JL, Whiteheart SW, Wiley Z, Williams NJ, Wisnivesky JP, Wood JC, Yee LM, Young NM, Zisis SN, Foulkes AS. Researching COVID to Enhance Recovery (RECOVER) adult study protocol: Rationale, objectives, and design. PLoS One 2023; 18:e0286297. [PMID: 37352211 PMCID: PMC10289397 DOI: 10.1371/journal.pone.0286297] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 05/12/2023] [Indexed: 06/25/2023] Open
Abstract
IMPORTANCE SARS-CoV-2 infection can result in ongoing, relapsing, or new symptoms or other health effects after the acute phase of infection; termed post-acute sequelae of SARS-CoV-2 infection (PASC), or long COVID. The characteristics, prevalence, trajectory and mechanisms of PASC are ill-defined. The objectives of the Researching COVID to Enhance Recovery (RECOVER) Multi-site Observational Study of PASC in Adults (RECOVER-Adult) are to: (1) characterize PASC prevalence; (2) characterize the symptoms, organ dysfunction, natural history, and distinct phenotypes of PASC; (3) identify demographic, social and clinical risk factors for PASC onset and recovery; and (4) define the biological mechanisms underlying PASC pathogenesis. METHODS RECOVER-Adult is a combined prospective/retrospective cohort currently planned to enroll 14,880 adults aged ≥18 years. Eligible participants either must meet WHO criteria for suspected, probable, or confirmed infection; or must have evidence of no prior infection. Recruitment occurs at 86 sites in 33 U.S. states, Washington, DC and Puerto Rico, via facility- and community-based outreach. Participants complete quarterly questionnaires about symptoms, social determinants, vaccination status, and interim SARS-CoV-2 infections. In addition, participants contribute biospecimens and undergo physical and laboratory examinations at approximately 0, 90 and 180 days from infection or negative test date, and yearly thereafter. Some participants undergo additional testing based on specific criteria or random sampling. Patient representatives provide input on all study processes. The primary study outcome is onset of PASC, measured by signs and symptoms. A paradigm for identifying PASC cases will be defined and updated using supervised and unsupervised learning approaches with cross-validation. Logistic regression and proportional hazards regression will be conducted to investigate associations between risk factors, onset, and resolution of PASC symptoms. DISCUSSION RECOVER-Adult is the first national, prospective, longitudinal cohort of PASC among US adults. Results of this study are intended to inform public health, spur clinical trials, and expand treatment options. REGISTRATION NCT05172024.
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Affiliation(s)
- Leora I. Horwitz
- Department of Population Health, NYU Grossman School of Medicine, New York, New York, United States of America
| | - Tanayott Thaweethai
- Department of Biostatistics, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Shari B. Brosnahan
- Division of Pulmonary Critical Care and Sleep Medicine, NYU Langone Health, New York, New York, United States of America
| | - Mine S. Cicek
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Megan L. Fitzgerald
- Patient Led Research Collaboration on COVID-19, Washington, DC, United States of America
| | - Jason D. Goldman
- Division of Infectious Diseases, Providence Swedish Medical Center, Seattle, Washington, United States of America
| | - Rachel Hess
- Department of Population Health Sciences and Internal Medicine, University of Utah School of Medicine, Salt Lake City, Utah, United States of America
| | - S. L. Hodder
- Department of Medicine, West Virginia University, Morgantown, West Virginia, United States of America
| | - Vanessa L. Jacoby
- Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Francisco, San Francisco, California, United States of America
| | - Michael R. Jordan
- Division of Geographic Medicine and Infectious Diseases, Tufts Medical Center, Medford, Massachusetts, United States of America
| | - Jerry A. Krishnan
- Department of Medicine, University of Illinois Chicago, Chicago, Illinois, United States of America
| | - Adeyinka O. Laiyemo
- Department of Medicine, Howard University, Washington, DC, United States of America
| | - Torri D. Metz
- Department of Obstetrics and Gynecology, University of Utah Health, Salt Lake City, Utah, United States of America
| | - Lauren Nichols
- Body Politic COVID-19 Support Group, Boston, Massachusetts, United States of America
| | - Rachel E. Patzer
- Department of Medicine and Surgery, Health Services Research Center, Emory University School of Medicine, Atlanta, Georgia, United States of America
| | - Anisha Sekar
- Patient Led Research Collaboration on COVID-19, Washington, DC, United States of America
| | - Nora G. Singer
- Department of Medicine and Rheumatology, The MetroHealth Medical Center, Cleveland, Ohio, United States of America
| | - Lauren E. Stiles
- Department of Neurology, Stony Brook University Renaissance School of Medicine, Stony Brook, New York, United States of America
| | - Barbara S. Taylor
- Department of Medicine, Division of Infectious Diseases and Infectious Diseases, Long School of Medicine, University of Texas Health Science Center San Antonio, San Antonio, Texas, United States of America
| | - Shifa Ahmed
- Department of Biostatistics, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Heather A. Algren
- Swedish Center for Research and Innovation, Providence Swedish Medical Center, Seattle, Washington, United States of America
| | - Khamal Anglin
- Department of Epidemiology and Biostatistics, University of California at San Francisco Institute of Global Health Sciences, San Francisco, San Francisco, California, United States of America
| | - Lisa Aponte-Soto
- College of Science and Health, Department of Health Sciences, DePaul University, Chicago, Illinois, United States of America
| | - Hassan Ashktorab
- Department of Medicine, Howard University, Washington, DC, United States of America
| | - Ingrid V. Bassett
- Medical Practice Evaluation Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Brahmchetna Bedi
- Department of Medicine, Emory University, Atlanta, Georgia, United States of America
| | - Nahid Bhadelia
- Center for Emerging Infectious Diseases Policy and Research, Boston University School of Medicine, Boston, Massachusetts, United States of America
| | - Christian Bime
- Department of Medicine, University of Arizona, Tucson, Arizona, United States of America
| | - Marie-Abele C. Bind
- Department of Biostatistics, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Lora J. Black
- Department of Clinical Research, Sanford Research, Sioux Falls, South Dakota, United States of America
| | - Andra L. Blomkalns
- Department of Emergency Medicine, Stanford University, Stanford, California, United States of America
| | - Hassan Brim
- Department of Pathology, Howard University, Washington, DC, United States of America
| | - Mario Castro
- Division of Pulmonary and Critical Care, University of Kansas Medical Center, Kansas City, Kansas City, United States of America
| | - James Chan
- Department of Biostatistics, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Alexander W. Charney
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Benjamin K. Chen
- Division of Infectious Disease, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Li Qing Chen
- Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
| | - Peter Chen
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, United States of America
| | - David Chestek
- Department of Emergency Medicine, University of Illinois Chicago, Chicago, Illinois, United States of America
| | - Lori B. Chibnik
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Dominic C. Chow
- Department of Medicine, University of Hawaii at Manoa John A. Burns School of Medicine, Honolulu, Hawaii, United States of America
| | - Helen Y. Chu
- Department of Allergy & Infectious Diseases, University of Washington, Seattle, Washington, United States of America
| | - Rebecca G. Clifton
- Department of Biostatistics, George Washington University, Washington, DC, United States of America
| | - Shelby Collins
- Department of Medicine, Emory University, Atlanta, Georgia, United States of America
| | - Maged M. Costantine
- Department of Obstetrics and Gynecology, The Ohio State University Hospital, Columbus, Ohio, United States of America
| | - Sushma K. Cribbs
- Department of Medicine, Emory University, Atlanta, Georgia, United States of America
| | - Steven G. Deeks
- Department of Medicine, University of California, San Francisco, San Francisco, California, United States of America
| | - John D. Dickinson
- Department of Internal Medicine, University of Nebraska Medical Center, Omaha, Nebraska, United States of America
| | - Sarah E. Donohue
- Department of Research Services, University of Illinois College of Medicine, Peoria, Illinois, United States of America
| | - Matthew S. Durstenfeld
- Department of Medicine, Division of Cardiology at Zuckerberg San Francisco General, University of California San Francisco, San Francisco, California, United States of America
| | - Ivette F. Emery
- MaineHealth Institute for Research, MaineHealth, Scarborough, Maine, United States of America
| | - Kristine M. Erlandson
- Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
| | - Julio C. Facelli
- Department of Biomedical Informatics and Clinical and Translational Science Institute, University of Utah, Salt Lake City, Utah, United States of America
| | - Rachael Farah-Abraham
- Department of Medicine, Division of Infectious Diseases, Emory University School of Medicine, Atlanta, Georgia, United States of America
| | - Aloke V. Finn
- Department of Pathology, CVPath Institute, Gaithersburg, Maryland, United States of America
| | - Melinda S. Fischer
- Department of Medicine, Division of Infectious Diseases and Infectious Diseases, Long School of Medicine, University of Texas Health Science Center San Antonio, San Antonio, Texas, United States of America
| | - Valerie J. Flaherman
- Department of Pediatrics, University of California, San Francisco, San Francisco, California, United States of America
| | - Judes Fleurimont
- Mile Square Health Center, University of Illinois Chicago, University of Illinois Chicago, Chicago, Illinois, United States of America
| | - Vivian Fonseca
- Department of Medicine, Tulane University School of Medicine, New Orleans, Louisiana, United States of America
| | - Emily J. Gallagher
- Department of Endocrinology, Diabetes and Bone Disease, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Jennifer C. Gander
- Center for Research and Evaluation, Kaiser Permanente of Georgia, Atlanta, Georgia, United States of America
| | - Maria Laura Gennaro
- Public Health Research Institute and Department of Medicine, Rutgers New Jersey Medical School, Newark, New Jersey, United States of America
| | - Kelly S. Gibson
- Department of Obstetrics and Gynecology, MetroHealth System, Cleveland, Ohio, United States of America
| | - Minjoung Go
- Department of Medicine, Stanford University, Stanford, California, United States of America
| | - Steven N. Goodman
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, California, United States of America
| | - Joey P. Granger
- Department of Physiology & Biophysics, University of Mississippi Medical Center, Jackson, Mississippi, United States of America
| | - Frank L. Greenway
- Clinical Trials, Pennington Biomedical Research Center, Baton Rouge, Louisiana, United States of America
| | - John W. Hafner
- Department of Emergency Medicine, OSF Saint Francis Medical Center, Peoria, Illinois, United States of America
| | - Jenny E. Han
- Department of Pulmonary and Critical Care, Emory University School of Medicine, Atlanta, Georgia, United States of America
| | - Michelle S. Harkins
- Department of Internal Medicine University of New Mexico, Health Science Center, Albuquerque, New Mexico, United States of America
| | - Kristine S. P. Hauser
- Clinical Research Center, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America
| | - James R. Heath
- Department of Bioengineering, Institute for Systems Biology, Seattle, Washington, United States of America
| | - Carla R. Hernandez
- Clinical Research Center, University Hospitals Cleveland Medical Center, Cleveland, Ohio, United States of America
| | - On Ho
- Seattle Children’s Therapeutics, Seattle, Washington, United States of America
| | - Matthew K. Hoffman
- Department of Obstetrics and Gynecology, Christiana Care Health Services, Newark, Delaware, United States of America
| | - Susan E. Hoover
- Department of Clinical Research, Sanford Research, Sioux Falls, South Dakota, United States of America
| | - Carol R. Horowitz
- Institute for Health Equity Research, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Harvey Hsu
- Department of Internal Medicine, University of Arizona, Phoenix, Arizona, United States of America
| | - Priscilla Y. Hsue
- Department of Medicine, University of California, San Francisco, San Francisco, California, United States of America
| | - Brenna L. Hughes
- Department of Obstetrics and Gynecology, Duke University, Durham, North Carolina, United States of America
| | - Prasanna Jagannathan
- Department of Medicine, Stanford University, Stanford, California, United States of America
| | - Judith A. James
- Department of Arthritis & Clinical Immunology, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, United States of America
| | - Janice John
- Department of Family Medicine, Cambridge Health Alliance, Cambridge, Massachusetts, United States of America
| | - Sarah Jolley
- Department of Pulmonary and Critical Care Medicine, University of Colorado, Aurora, Colorado, United States of America
| | - S. E. Judd
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Joy J. Juskowich
- Department of Medicine, Division of Infectious Diseases, West Virginia School of Medicine, Morgantown, West Virginia, United States of America
| | - Diane G. Kanjilal
- Department of Infectious Diseases, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Elizabeth W. Karlson
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Stuart D. Katz
- Department of Medicine, NYU Langone Health, New York, New York, United States of America
| | - J. Daniel Kelly
- Department of Medicine, University of California, San Francisco, San Francisco, California, United States of America
| | - Sara W. Kelly
- Department of Pediatrics & Department of Research Services, University of Illinois College of Medicine, Peoria, Illinois, United States of America
| | - Arthur Y. Kim
- Department of Infectious Diseases, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - John P. Kirwan
- Department Integrated Physiology and Molecular Medicine, Pennington Biomedical Research Center, Baton Rouge, Louisiana, United States of America
| | - Kenneth S. Knox
- Department of Internal Medicine, University of Arizona, Phoenix, Arizona, United States of America
| | - Andre Kumar
- Department of Medicine, Stanford University, Stanford, California, United States of America
| | | | - Margaret Lanca
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Joyce K. Lee-lannotti
- Department of Internal Medicine and Neurology, University of Arizona College of Medicine Phoenix, Phoenix, Arizona, United States of America
| | - R. Craig Lefebvre
- Communications Practice Area, RTI International, Research Triangle Park, North Carolina, United States of America
| | - Bruce D. Levy
- Department of Internal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
| | - Janet Y. Lin
- Department of Emergency Medicine, University of Illinois Chicago, Chicago, Illinois, United States of America
| | - Brian P. Logarbo
- Tulane Center for Clinical Research, Tulane University School of Medicine, New Orleans, Louisiana, United States of America
| | - Jennifer K. Logue
- Department of Medicine, University of Washington, Seattle, Washington, United States of America
| | - Michele T. Longo
- Tulane Center for Clinical Neurosciences, Tulane School of Medicine, New Orleans, Louisiana, United States of America
| | - Carlos A. Luciano
- Department of Neurology, University of Puerto Rico School of Medicine, San Juan, Puerto Rico, United States of America
| | - Karen Lutrick
- Department of Family & Community Medicine, University of Arizona, College of Medicine – Tucson, Tucson, Arizona, United States of America
| | - Shahdi K. Malakooti
- Department of Medicine, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America
| | - Gail Mallett
- Department of Obstetrics and Gynecology, Northwestern University, Chicago, Illinois, United States of America
| | - Gabrielle Maranga
- Department of Population Health, NYU Grossman School of Medicine, New York, New York, United States of America
| | - Jai G. Marathe
- Department of Medicine, Section of Infectious Diseases, Boston University Medical Center, Boston, Massachusetts, United States of America
| | - Vincent C. Marconi
- Department of Medicine, Infectious Diseases and Department of Global Health, Emory University School of Medicine, Atlanta, Georgia, United States of America
| | - Gailen D. Marshall
- Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, United States of America
| | - Christopher F. Martin
- Department of Medicine, Division of Infectious Diseases, Emory University School of Medicine, Atlanta, Georgia, United States of America
| | - Jeffrey N. Martin
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California, United States of America
| | - Heidi T. May
- Department of Cardiology, Intermountain Medical Center, Salt Lake City, Utah, United States of America
| | - Grace A. McComsey
- Department of Medicine, Department of Pediatrics, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America
| | - Dylan McDonald
- Department of Allergy & Infectious Diseases, University of Washington, Seattle, Washington, United States of America
| | - Hector Mendez-Figueroa
- Department of Obstetrics, Gynecology and Reproductive Sciences, University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Lucio Miele
- Department of Genetics, Louisiana State University Health Sciences Center, New Orleans, Louisiana, United States of America
| | - Murray A. Mittleman
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Sindhu Mohandas
- Department of Infectious Diseases, Children’s Hospital Los Angeles, University of Southern California, Los Angeles, California, United States of America
| | - Christian Mouchati
- Department of Medicine, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America
| | - Janet M. Mullington
- Department of Neurology and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America
| | - Girish N. Nadkarni
- Division of General Internal Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Erica R. Nahin
- Department of Medicine, NYU Langone Health, New York, New York, United States of America
| | - Robert B. Neuman
- Division of Cardiology, Kaiser Permanente of Georgia, Atlanta, Georgia, United States of America
| | - Lisa T. Newman
- Department of Social, Statistical and Environmental Sciences, RTI International, Research Triangle Park, North Carolina, United States of America
| | - Amber Nguyen
- Department of Biostatistics, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Janko Z. Nikolich
- Department of Immunobiology, University of Arizona College of Medicine, Tucson, Arizona, United States of America
| | - Igho Ofotokun
- Department of Medicine, Division of Infectious Diseases, Emory University School of Medicine, Atlanta, Georgia, United States of America
| | - Princess U. Ogbogu
- Division of Pediatric Allergy, Immunology, and Rheumatology, University Hospitals Rainbow Babies and Children’s Hospital, Cleveland, Ohio, United States of America
| | - Anna Palatnik
- Department of Obstetrics and Gynecology, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America
| | - Kristy T. S. Palomares
- Department of Obstetrics and Gynecology, Division of Maternal Fetal Medicine, Saint Peter’s University Hospital, New Brunswick, New Jersey, United States of America
| | - Tanyalak Parimon
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, United States of America
| | - Samuel Parry
- Department of Obstetrics and Gynecology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Sairam Parthasarathy
- Department of Medicine, University of Arizona, Tucson, Arizona, United States of America
| | - Thomas F. Patterson
- Department of Medicine, Department of Infectious Disease, University of Texas Health, San Antonio, Texas, United States of America
| | - Ann Pearman
- Department of Medicine, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America
| | - Michael J. Peluso
- Division of HIV, Infectious Disease, and Global Medicine, University of California, San Francisco, California, United States of America
| | - Priscilla Pemu
- Department of Medicine, Morehouse School of Medicine, Atlanta, Georgia, United States of America
| | - Christian M. Pettker
- Department of Obstetrics, Gynecology & Reproductive Sciences, Yale School of Medicine, New Haven, Connecticut, United States of America
| | - Beth A. Plunkett
- Department of Obstetrics and Gynecology, NorthShore University Health System, Evanston, Illinois, United States of America
| | - Kristen Pogreba-Brown
- Department of Epidemiology and Biostatistics, University of Arizona, Tucson, Arizona, United States of America
| | - Athena Poppas
- Division of Cardiology, Warren Alpert Medical School of Brown University, Providence, Rhode Island, United States of America
| | - J. Zachary Porterfield
- Department of Internal Medicine, Division of Infectious Diseases, University of Kentucky, Lexington, Kentucky, United States of America
| | - John G. Quigley
- Department of Medicine, Division of Hematology/Oncology, University of Illinois Chicago, Chicago, Illinois, United States of America
| | - Davin K. Quinn
- Department of Psychiatry and Behavioral Sciences, University of New Mexico School of Medicine, Albuquerque, New Mexico, United States of America
| | - Hengameh Raissy
- Department of Pediatrics, University of New Mexico, Health Sciences Center, Albuquerque, New Mexico, United States of America
| | - Candida J. Rebello
- Department of Nutrition and Chronic Disease, Pennington Biomedical Research Center, Baton Rouge, Louisiana, United States of America
| | - Uma M. Reddy
- Department of Obstetrics and Gynecology, Columbia University, New York, New York, United States of America
| | - Rebecca Reece
- Department of Medicine, Division of Infectious Diseases, West Virginia School of Medicine, Morgantown, West Virginia, United States of America
| | - Harrison T. Reeder
- Department of Biostatistics, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Franz P. Rischard
- Department of Pulmonary and Critical Care, University of Arizona, Tucson, Arizona, United States of America
| | - Johana M. Rosas
- Department of Medicine, NYU Langone Health, New York, New York, United States of America
| | - Clifford J. Rosen
- MaineHealth Institute for Research, MaineHealth, Scarborough, Maine, United States of America
| | - Nadine G. Rouphael
- Department of Medicine, Emory University, Atlanta, Georgia, United States of America
| | - Dwight J. Rouse
- Department of Obstetrics and Gynecology, Brown University, Providence, Rhode Island, United States of America
| | - Adam M. Ruff
- Division of Pulmonary and Critical Care, University of Kansas Medical Center, Kansas City, Kansas City, United States of America
| | - Christina Saint Jean
- Department of Population Health, NYU Grossman School of Medicine, New York, New York, United States of America
| | - Grecio J. Sandoval
- Department of Biostatistics, George Washington University, Washington, DC, United States of America
| | - Jorge L. Santana
- Department of Medicine, University of Puerto Rico, San Juan, Puerto Rico, United States of America
| | - Shannon M. Schlater
- Department of Internal Medicine, University of Utah, Salt Lake City, Utah, United States of America
| | - Frank C. Sciurba
- Department of Medicine, Division of Pulmonary Allergy and Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Caitlin Selvaggi
- Department of Biostatistics, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Sudha Seshadri
- Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Sciences Center San Antonio, San Antonio, Texas, United States of America
| | - Howard D. Sesso
- Division of Preventive Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
| | - Dimpy P. Shah
- Department of Population Health Sciences, Mays Cancer Center, University of Texas Health, San Antonio, Texas, United States of America
| | - Eyal Shemesh
- Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Zaki A. Sherif
- Department of Biochemistry & Molecular Biology, Howard University College of Medicine, Washington, DC, United States of America
| | - Daniel J. Shinnick
- Department of Biostatistics, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Hyagriv N. Simhan
- Department of Obstetrics, Gynecology, and Reproductive Sciences, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America
| | - Upinder Singh
- Department of Internal Medicine, Stanford University, Stanford, California, United States of America
| | - Amber Sowles
- Department of Obstetrics and Gynecology, University of Utah Health, Salt Lake City, Utah, United States of America
| | - Vignesh Subbian
- Department of Biomedical Engineering, Department of Systems and Industrial Engineering, University of Arizona College of Engineering, Tucson, Arizona, United States of America
| | - Jun Sun
- Department of Medicine, University of Illinois Chicago, Chicago, Illinois, United States of America
| | - Mehul S. Suthar
- Department of Pediatrics, Emory Vaccine Center, Emory University, Atlanta, Georgia, United States of America
| | - Larissa J. Teunis
- Health Services Research Center, Emory University, Atlanta, Georgia, United States of America
| | - John M. Thorp
- Department of Obstetrics and Gynecology, University of North Carolina Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Amberly Ticotsky
- Department of Family Medicine, Cambridge Health Alliance, Cambridge, Massachusetts, United States of America
| | - Alan T. N. Tita
- Department of Obstetrics and Gynecology and Center for Women’s Reproductive Health, University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Robin Tragus
- Department of Medicine, University of Texas Health Science Center, San Antonio, Texas, United States of America
| | - Katherine R. Tuttle
- Department of Medicine, Division of Nephrology, University of Washington School of Medicine, Spokane, Washington, United States of America
| | - Alfredo E. Urdaneta
- Department of Emergency Medicine, Stanford University, Stanford, California, United States of America
| | - P. J. Utz
- Department of Internal Medicine, Stanford University, Stanford, California, United States of America
| | - Timothy M. VanWagoner
- Department of Pediatrics, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, United States of America
| | - Andrew Vasey
- Department of Internal Medicine, University of Nebraska Medical Center, Omaha, Nebraska, United States of America
| | - Suzanne D. Vernon
- Department of Research, Bateman Horne Center, Salt Lake City, Utah, United States of America
| | - Crystal Vidal
- Department of Population Health, NYU Grossman School of Medicine, New York, New York, United States of America
| | - Tiffany Walker
- Department of Medicine, Emory University, Atlanta, Georgia, United States of America
| | - Honorine D. Ward
- Department of Medicine, Tufts Medical Center, Boston, Massachusetts, United States of America
| | - David E. Warren
- Department of Neurological Sciences, University of Nebraska Medical Center, Omaha, Nebraska, United States of America
| | - Ryan M. Weeks
- Department of Microbiology, Immunology, and Molecular Genetics, University of Kentucky, Lexington, Kentucky, United States of America
| | - Steven J. Weiner
- Department of Biostatistics, George Washington University, Washington, DC, United States of America
| | - Jordan C. Weyer
- Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Jennifer L. Wheeler
- Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Sidney W. Whiteheart
- Department of Molecular and Cellular Biochemistry, University of Kentucky, Lexington, Kentucky, United States of America
| | - Zanthia Wiley
- Department of Medicine, Division of Infectious Diseases, Emory University School of Medicine, Atlanta, Georgia, United States of America
| | - Natasha J. Williams
- Institute for Excellence in Health Equity, NYU Grossman School of Medicine, New York, New York, United States of America
| | - Juan P. Wisnivesky
- Division of General Internal Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - John C. Wood
- Department of Pediatrics and Radiology, Children’s Hospital of Los Angeles, Los Angeles, California, United States of America
| | - Lynn M. Yee
- Department of Obstetrics and Gynecology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
| | - Natalie M. Young
- Swedish Center for Research and Innovation, Providence Swedish Medical Center, Seattle, Washington, United States of America
| | - Sokratis N. Zisis
- Department of Medicine, Department of Pediatrics, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America
| | - Andrea S. Foulkes
- Department of Biostatistics, Massachusetts General Hospital, Boston, Massachusetts, United States of America
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Thaweethai T, Jolley SE, Karlson EW, Levitan EB, Levy B, McComsey GA, McCorkell L, Nadkarni GN, Parthasarathy S, Singh U, Walker TA, Selvaggi CA, Shinnick DJ, Schulte CCM, Atchley-Challenner R, Horwitz LI, Foulkes AS. Development of a Definition of Postacute Sequelae of SARS-CoV-2 Infection. JAMA 2023; 329:1934-1946. [PMID: 37278994 PMCID: PMC10214179 DOI: 10.1001/jama.2023.8823] [Citation(s) in RCA: 131] [Impact Index Per Article: 131.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 05/01/2023] [Indexed: 06/07/2023]
Abstract
Importance SARS-CoV-2 infection is associated with persistent, relapsing, or new symptoms or other health effects occurring after acute infection, termed postacute sequelae of SARS-CoV-2 infection (PASC), also known as long COVID. Characterizing PASC requires analysis of prospectively and uniformly collected data from diverse uninfected and infected individuals. Objective To develop a definition of PASC using self-reported symptoms and describe PASC frequencies across cohorts, vaccination status, and number of infections. Design, Setting, and Participants Prospective observational cohort study of adults with and without SARS-CoV-2 infection at 85 enrolling sites (hospitals, health centers, community organizations) located in 33 states plus Washington, DC, and Puerto Rico. Participants who were enrolled in the RECOVER adult cohort before April 10, 2023, completed a symptom survey 6 months or more after acute symptom onset or test date. Selection included population-based, volunteer, and convenience sampling. Exposure SARS-CoV-2 infection. Main Outcomes and Measures PASC and 44 participant-reported symptoms (with severity thresholds). Results A total of 9764 participants (89% SARS-CoV-2 infected; 71% female; 16% Hispanic/Latino; 15% non-Hispanic Black; median age, 47 years [IQR, 35-60]) met selection criteria. Adjusted odds ratios were 1.5 or greater (infected vs uninfected participants) for 37 symptoms. Symptoms contributing to PASC score included postexertional malaise, fatigue, brain fog, dizziness, gastrointestinal symptoms, palpitations, changes in sexual desire or capacity, loss of or change in smell or taste, thirst, chronic cough, chest pain, and abnormal movements. Among 2231 participants first infected on or after December 1, 2021, and enrolled within 30 days of infection, 224 (10% [95% CI, 8.8%-11%]) were PASC positive at 6 months. Conclusions and Relevance A definition of PASC was developed based on symptoms in a prospective cohort study. As a first step to providing a framework for other investigations, iterative refinement that further incorporates other clinical features is needed to support actionable definitions of PASC.
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Affiliation(s)
- Tanayott Thaweethai
- Massachusetts General Hospital, Boston
- Harvard Medical School, Boston, Massachusetts
| | | | | | | | - Bruce Levy
- Harvard Medical School, Boston, Massachusetts
- Brigham and Women’s Hospital, Boston, Massachusetts
| | | | - Lisa McCorkell
- Patient-Led Research Collaborative, Calabasas, California
| | | | | | - Upinder Singh
- Stanford University School of Medicine, Stanford, California
| | | | | | | | | | | | | | | | - Andrea S. Foulkes
- Massachusetts General Hospital, Boston
- Harvard Medical School, Boston, Massachusetts
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38
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Paranjpe I, Jayaraman P, Su CY, Zhou S, Chen S, Thompson R, Del Valle DM, Kenigsberg E, Zhao S, Jaladanki S, Chaudhary K, Ascolillo S, Vaid A, Gonzalez-Kozlova E, Kauffman J, Kumar A, Paranjpe M, Hagan RO, Kamat S, Gulamali FF, Xie H, Harris J, Patel M, Argueta K, Batchelor C, Nie K, Dellepiane S, Scott L, Levin MA, He JC, Suarez-Farinas M, Coca SG, Chan L, Azeloglu EU, Schadt E, Beckmann N, Gnjatic S, Merad M, Kim-Schulze S, Richards B, Glicksberg BS, Charney AW, Nadkarni GN. Proteomic characterization of acute kidney injury in patients hospitalized with SARS-CoV2 infection. Commun Med (Lond) 2023; 3:81. [PMID: 37308534 PMCID: PMC10258469 DOI: 10.1038/s43856-023-00307-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 05/18/2023] [Indexed: 06/14/2023] Open
Abstract
BACKGROUND Acute kidney injury (AKI) is a known complication of COVID-19 and is associated with an increased risk of in-hospital mortality. Unbiased proteomics using biological specimens can lead to improved risk stratification and discover pathophysiological mechanisms. METHODS Using measurements of ~4000 plasma proteins in two cohorts of patients hospitalized with COVID-19, we discovered and validated markers of COVID-associated AKI (stage 2 or 3) and long-term kidney dysfunction. In the discovery cohort (N = 437), we identified 413 higher plasma abundances of protein targets and 30 lower plasma abundances of protein targets associated with COVID-AKI (adjusted p < 0.05). Of these, 62 proteins were validated in an external cohort (p < 0.05, N = 261). RESULTS We demonstrate that COVID-AKI is associated with increased markers of tubular injury (NGAL) and myocardial injury. Using estimated glomerular filtration (eGFR) measurements taken after discharge, we also find that 25 of the 62 AKI-associated proteins are significantly associated with decreased post-discharge eGFR (adjusted p < 0.05). Proteins most strongly associated with decreased post-discharge eGFR included desmocollin-2, trefoil factor 3, transmembrane emp24 domain-containing protein 10, and cystatin-C indicating tubular dysfunction and injury. CONCLUSIONS Using clinical and proteomic data, our results suggest that while both acute and long-term COVID-associated kidney dysfunction are associated with markers of tubular dysfunction, AKI is driven by a largely multifactorial process involving hemodynamic instability and myocardial damage.
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Affiliation(s)
- Ishan Paranjpe
- Department of Medicine, Stanford University, Stanford, CA, USA
| | - Pushkala Jayaraman
- The Charles Bronfman Institute for Personalized Medicine (CBIPM), Division of Data Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Chen-Yang Su
- Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, QC, Canada
- Department of Computer Science, Quantitative Life Sciences, McGill University, Montreal, QC, Canada
| | - Sirui Zhou
- Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, QC, Canada
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada
| | - Steven Chen
- The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ryan Thompson
- The Charles Bronfman Institute for Personalized Medicine (CBIPM), Division of Data Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Diane Marie Del Valle
- The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ephraim Kenigsberg
- The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Shan Zhao
- Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Suraj Jaladanki
- The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kumardeep Chaudhary
- Clinical Informatics, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), New Delhi, India
| | - Steven Ascolillo
- The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Akhil Vaid
- The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Edgar Gonzalez-Kozlova
- The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Justin Kauffman
- The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Arvind Kumar
- The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Manish Paranjpe
- Division of Health Sciences and Technology, Harvard Medical School, Boston, MA, USA
| | - Ross O Hagan
- The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Samir Kamat
- The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Faris F Gulamali
- The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Hui Xie
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Joceyln Harris
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Manishkumar Patel
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kimberly Argueta
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Craig Batchelor
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kai Nie
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sergio Dellepiane
- Department of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Leisha Scott
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Matthew A Levin
- The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - John Cijiang He
- Department of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Mayte Suarez-Farinas
- Department of Biostatistics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Steven G Coca
- Department of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Lili Chan
- Department of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Evren U Azeloglu
- Department of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Eric Schadt
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Noam Beckmann
- The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sacha Gnjatic
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Miram Merad
- The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Seunghee Kim-Schulze
- The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Brent Richards
- Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, QC, Canada
- Department of Computer Science, McGill University, Montreal, QC, Canada
- Department of Human Genetics, McGill University, Montreal, QC, Canada
- Department of Twin Research, King's College London, London, GB, UK
| | | | - Alexander W Charney
- Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, QC, Canada
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Girish N Nadkarni
- The Charles Bronfman Institute for Personalized Medicine (CBIPM), Division of Data Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada.
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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39
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Vaid A, Jiang J, Sawant A, Lerakis S, Argulian E, Ahuja Y, Lampert J, Charney A, Greenspan H, Narula J, Glicksberg B, Nadkarni GN. A foundational vision transformer improves diagnostic performance for electrocardiograms. NPJ Digit Med 2023; 6:108. [PMID: 37280346 PMCID: PMC10242218 DOI: 10.1038/s41746-023-00840-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 05/05/2023] [Indexed: 06/08/2023] Open
Abstract
The electrocardiogram (ECG) is a ubiquitous diagnostic modality. Convolutional neural networks (CNNs) applied towards ECG analysis require large sample sizes, and transfer learning approaches for biomedical problems may result in suboptimal performance when pre-training is done on natural images. We leveraged masked image modeling to create a vision-based transformer model, HeartBEiT, for electrocardiogram waveform analysis. We pre-trained this model on 8.5 million ECGs and then compared performance vs. standard CNN architectures for diagnosis of hypertrophic cardiomyopathy, low left ventricular ejection fraction and ST elevation myocardial infarction using differing training sample sizes and independent validation datasets. We find that HeartBEiT has significantly higher performance at lower sample sizes compared to other models. We also find that HeartBEiT improves explainability of diagnosis by highlighting biologically relevant regions of the EKG vs. standard CNNs. Domain specific pre-trained transformer models may exceed the classification performance of models trained on natural images especially in very low data regimes. The combination of the architecture and such pre-training allows for more accurate, granular explainability of model predictions.
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Affiliation(s)
- Akhil Vaid
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, NY, USA.
| | - Joy Jiang
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ashwin Sawant
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Stamatios Lerakis
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Cardiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Edgar Argulian
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Cardiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yuri Ahuja
- Department of Medicine, NYU Langone Health, New York, NY, USA
| | - Joshua Lampert
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Cardiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alexander Charney
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Hayit Greenspan
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, 6997801, Israel
| | - Jagat Narula
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Cardiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Benjamin Glicksberg
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, NY, USA
| | - Girish N Nadkarni
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, NY, USA
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Vasquez-Rios G, Oh W, Lee S, Bhatraju P, Mansour SG, Moledina DG, Gulamali FF, Siew ED, Garg AX, Sarder P, Chinchilli VM, Kaufman JS, Hsu CY, Liu KD, Kimmel PL, Go AS, Wurfel MM, Himmelfarb J, Parikh CR, Coca SG, Nadkarni GN. Joint Modeling of Clinical and Biomarker Data in Acute Kidney Injury Defines Unique Subphenotypes with Differing Outcomes. Clin J Am Soc Nephrol 2023; 18:716-726. [PMID: 36975209 PMCID: PMC10278836 DOI: 10.2215/cjn.0000000000000156] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 03/13/2023] [Indexed: 03/29/2023]
Abstract
BACKGROUND AKI is a heterogeneous syndrome. Current subphenotyping approaches have only used limited laboratory data to understand a much more complex condition. METHODS We focused on patients with AKI from the Assessment, Serial Evaluation, and Subsequent Sequelae in AKI (ASSESS-AKI). We used hierarchical clustering with Ward linkage on biomarkers of inflammation, injury, and repair/health. We then evaluated clinical differences between subphenotypes and examined their associations with cardiorenal events and death using Cox proportional hazard models. RESULTS We included 748 patients with AKI: 543 (73%) of them had AKI stage 1, 112 (15%) had AKI stage 2, and 93 (12%) had AKI stage 3. The mean age (±SD) was 64 (13) years; 508 (68%) were men; and the median follow-up was 4.7 (Q1: 2.9, Q3: 5.7) years. Patients with AKI subphenotype 1 ( N =181) had the highest kidney injury molecule (KIM-1) and troponin T levels. Subphenotype 2 ( N =250) had the highest levels of uromodulin. AKI subphenotype 3 ( N =159) comprised patients with markedly high pro-brain natriuretic peptide and plasma tumor necrosis factor receptor-1 and -2 and low concentrations of KIM-1 and neutrophil gelatinase-associated lipocalin. Finally, patients with subphenotype 4 ( N =158) predominantly had sepsis-AKI and the highest levels of vascular/kidney inflammation (YKL-40, MCP-1) and injury (neutrophil gelatinase-associated lipocalin, KIM-1). AKI subphenotypes 3 and 4 were independently associated with a higher risk of death compared with subphenotype 2 and had adjusted hazard ratios of 2.9 (95% confidence interval, 1.8 to 4.6) and 1.6 (95% confidence interval, 1.01 to 2.6, P = 0.04), respectively. Subphenotype 3 was also independently associated with a three-fold risk of CKD and cardiovascular events. CONCLUSIONS We discovered four AKI subphenotypes with differing clinical features and biomarker profiles that are associated with longitudinal clinical outcomes.
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Affiliation(s)
- George Vasquez-Rios
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Wonsuk Oh
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York
- Division of Data-Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Samuel Lee
- Icahn School of Medicine at Mount Sinai, New York, New York
| | - Pavan Bhatraju
- Division of Nephrology, Department of Medicine, Kidney Research Institute, University of Washington, Seattle, Washington
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Washington, Seattle, Washington
| | - Sherry G. Mansour
- Section of Nephrology, Yale University School of Medicine, New Haven, Connecticut
| | - Dennis G. Moledina
- Section of Nephrology, Yale University School of Medicine, New Haven, Connecticut
| | - Faris F. Gulamali
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York
- Division of Data-Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Edward D. Siew
- Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Amit X. Garg
- Division of Nephrology, Department of Medicine, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Pinaki Sarder
- Department of Biomedical Engineering, SUNY Buffalo, Buffalo, New York
| | - Vernon M. Chinchilli
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, Pennsylvania
| | - James S. Kaufman
- Division of Nephrology, Veterans Affairs New York Harbor Healthcare System and New York University School of Medicine, New York, New York
| | - Chi-yuan Hsu
- Division of Nephrology, Department of Medicine, University of California, San Francisco, San Francisco, California
| | - Kathleen D. Liu
- Division of Nephrology, Department of Medicine, University of California, San Francisco, San Francisco, California
| | - Paul L. Kimmel
- Division of Kidney, Urologic, and Hematologic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland
| | - Alan S. Go
- Kaiser Permanente Northern California, Oakland, California
| | - Mark M. Wurfel
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Washington, Seattle, Washington
| | - Jonathan Himmelfarb
- Division of Nephrology, Department of Medicine, Kidney Research Institute, University of Washington, Seattle, Washington
| | - Chirag R. Parikh
- Division of Nephrology, Department of Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Steven G. Coca
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Girish N. Nadkarni
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York
- Division of Data-Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
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Honigberg MC, Truong B, Khan RR, Xiao B, Bhatta L, Vy HMT, Guerrero RF, Schuermans A, Selvaraj MS, Patel AP, Koyama S, Cho SMJ, Vellarikkal SK, Trinder M, Urbut SM, Gray KJ, Brumpton BM, Patil S, Zöllner S, Antopia MC, Saxena R, Nadkarni GN, Do R, Yan Q, Pe'er I, Verma SS, Gupta RM, Haas DM, Martin HC, van Heel DA, Laisk T, Natarajan P. Polygenic prediction of preeclampsia and gestational hypertension. Nat Med 2023; 29:1540-1549. [PMID: 37248299 PMCID: PMC10330886 DOI: 10.1038/s41591-023-02374-9] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 04/26/2023] [Indexed: 05/31/2023]
Abstract
Preeclampsia and gestational hypertension are common pregnancy complications associated with adverse maternal and child outcomes. Current tools for prediction, prevention and treatment are limited. Here we tested the association of maternal DNA sequence variants with preeclampsia in 20,064 cases and 703,117 control individuals and with gestational hypertension in 11,027 cases and 412,788 control individuals across discovery and follow-up cohorts using multi-ancestry meta-analysis. Altogether, we identified 18 independent loci associated with preeclampsia/eclampsia and/or gestational hypertension, 12 of which are new (for example, MTHFR-CLCN6, WNT3A, NPR3, PGR and RGL3), including two loci (PLCE1 and FURIN) identified in the multitrait analysis. Identified loci highlight the role of natriuretic peptide signaling, angiogenesis, renal glomerular function, trophoblast development and immune dysregulation. We derived genome-wide polygenic risk scores that predicted preeclampsia/eclampsia and gestational hypertension in external cohorts, independent of clinical risk factors, and reclassified eligibility for low-dose aspirin to prevent preeclampsia. Collectively, these findings provide mechanistic insights into the hypertensive disorders of pregnancy and have the potential to advance pregnancy risk stratification.
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Affiliation(s)
- Michael C Honigberg
- Cardiology Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
| | - Buu Truong
- Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Raiyan R Khan
- Department of Computer Science, Columbia University, New York, NY, USA
| | - Brenda Xiao
- University of Pennsylvania, Philadelphia, PA, USA
| | - Laxmi Bhatta
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- HUNT Research Centre, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger, Norway
| | - Ha My T Vy
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Rafael F Guerrero
- Department of Biological Sciences, North Carolina State University, Raleigh, NC, USA
| | - Art Schuermans
- Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Faculty of Medicine, KU Leuven, Leuven, Belgium
| | - Margaret Sunitha Selvaraj
- Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Aniruddh P Patel
- Cardiology Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Satoshi Koyama
- Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - So Mi Jemma Cho
- Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Integrative Research Center for Cerebrovascular and Cardiovascular Diseases, Seoul, Republic of Korea
| | - Shamsudheen Karuthedath Vellarikkal
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Cardiovascular Division, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Mark Trinder
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Centre for Heart Lung Innovation, University of British Columbia, Vancouver, British Columbia, Canada
| | - Sarah M Urbut
- Cardiology Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Kathryn J Gray
- Department of Obstetrics and Gynecology, Brigham and Women's Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Ben M Brumpton
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- HUNT Research Centre, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger, Norway
| | - Snehal Patil
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Sebastian Zöllner
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Mariah C Antopia
- Department of Integrative Biology, University of Texas at San Antonio, San Antonio, TX, USA
| | - Richa Saxena
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Girish N Nadkarni
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ron Do
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Qi Yan
- Department of Obstetrics and Gynecology, Columbia University, New York, NY, USA
| | - Itsik Pe'er
- Department of Computer Science, Columbia University, New York, NY, USA
| | | | - Rajat M Gupta
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Cardiovascular Division, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - David M Haas
- Indiana University School of Medicine, Indianapolis, IN, USA
| | - Hilary C Martin
- Department of Human Genetics, Wellcome Sanger Institute, Cambridge, UK
| | - David A van Heel
- Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Triin Laisk
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Pradeep Natarajan
- Cardiology Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
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Graham SE, Clarke SL, Wu KHH, Kanoni S, Zajac GJM, Ramdas S, Surakka I, Ntalla I, Vedantam S, Winkler TW, Locke AE, Marouli E, Hwang MY, Han S, Narita A, Choudhury A, Bentley AR, Ekoru K, Verma A, Trivedi B, Martin HC, Hunt KA, Hui Q, Klarin D, Zhu X, Thorleifsson G, Helgadottir A, Gudbjartsson DF, Holm H, Olafsson I, Akiyama M, Sakaue S, Terao C, Kanai M, Zhou W, Brumpton BM, Rasheed H, Ruotsalainen SE, Havulinna AS, Veturi Y, Feng Q, Rosenthal EA, Lingren T, Pacheco JA, Pendergrass SA, Haessler J, Giulianini F, Bradford Y, Miller JE, Campbell A, Lin K, Millwood IY, Hindy G, Rasheed A, Faul JD, Zhao W, Weir DR, Turman C, Huang H, Graff M, Mahajan A, Brown MR, Zhang W, Yu K, Schmidt EM, Pandit A, Gustafsson S, Yin X, Luan J, Zhao JH, Matsuda F, Jang HM, Yoon K, Medina-Gomez C, Pitsillides A, Hottenga JJ, Willemsen G, Wood AR, Ji Y, Gao Z, Haworth S, Mitchell RE, Chai JF, Aadahl M, Yao J, Manichaikul A, Warren HR, Ramirez J, Bork-Jensen J, Kårhus LL, Goel A, Sabater-Lleal M, Noordam R, Sidore C, Fiorillo E, McDaid AF, Marques-Vidal P, Wielscher M, Trompet S, Sattar N, Møllehave LT, Thuesen BH, Munz M, Zeng L, Huang J, Yang B, Poveda A, Kurbasic A, Lamina C, Forer L, Scholz M, Galesloot TE, Bradfield JP, Daw EW, Zmuda JM, Mitchell JS, Fuchsberger C, Christensen H, Brody JA, Feitosa MF, Wojczynski MK, Preuss M, Mangino M, Christofidou P, Verweij N, Benjamins JW, Engmann J, Kember RL, Slieker RC, Lo KS, Zilhao NR, Le P, Kleber ME, Delgado GE, Huo S, Ikeda DD, Iha H, Yang J, Liu J, Leonard HL, Marten J, Schmidt B, Arendt M, Smyth LJ, Cañadas-Garre M, Wang C, Nakatochi M, Wong A, Hutri-Kähönen N, Sim X, Xia R, Huerta-Chagoya A, Fernandez-Lopez JC, Lyssenko V, Ahmed M, Jackson AU, Yousri NA, Irvin MR, Oldmeadow C, Kim HN, Ryu S, Timmers PRHJ, Arbeeva L, Dorajoo R, Lange LA, Chai X, Prasad G, Lorés-Motta L, Pauper M, Long J, Li X, Theusch E, Takeuchi F, Spracklen CN, Loukola A, Bollepalli S, Warner SC, Wang YX, Wei WB, Nutile T, Ruggiero D, Sung YJ, Hung YJ, Chen S, Liu F, Yang J, Kentistou KA, Gorski M, Brumat M, Meidtner K, Bielak LF, Smith JA, Hebbar P, Farmaki AE, Hofer E, Lin M, Xue C, Zhang J, Concas MP, Vaccargiu S, van der Most PJ, Pitkänen N, Cade BE, Lee J, van der Laan SW, Chitrala KN, Weiss S, Zimmermann ME, Lee JY, Choi HS, Nethander M, Freitag-Wolf S, Southam L, Rayner NW, Wang CA, Lin SY, Wang JS, Couture C, Lyytikäinen LP, Nikus K, Cuellar-Partida G, Vestergaard H, Hildalgo B, Giannakopoulou O, Cai Q, Obura MO, van Setten J, Li X, Schwander K, Terzikhan N, Shin JH, Jackson RD, Reiner AP, Martin LW, Chen Z, Li L, Highland HM, Young KL, Kawaguchi T, Thiery J, Bis JC, Nadkarni GN, Launer LJ, Li H, Nalls MA, Raitakari OT, Ichihara S, Wild SH, Nelson CP, Campbell H, Jäger S, Nabika T, Al-Mulla F, Niinikoski H, Braund PS, Kolcic I, Kovacs P, Giardoglou T, Katsuya T, Bhatti KF, de Kleijn D, de Borst GJ, Kim EK, Adams HHH, Ikram MA, Zhu X, Asselbergs FW, Kraaijeveld AO, Beulens JWJ, Shu XO, Rallidis LS, Pedersen O, Hansen T, Mitchell P, Hewitt AW, Kähönen M, Pérusse L, Bouchard C, Tönjes A, Chen YDI, Pennell CE, Mori TA, Lieb W, Franke A, Ohlsson C, Mellström D, Cho YS, Lee H, Yuan JM, Koh WP, Rhee SY, Woo JT, Heid IM, Stark KJ, Völzke H, Homuth G, Evans MK, Zonderman AB, Polasek O, Pasterkamp G, Hoefer IE, Redline S, Pahkala K, Oldehinkel AJ, Snieder H, Biino G, Schmidt R, Schmidt H, Chen YE, Bandinelli S, Dedoussis G, Thanaraj TA, Kardia SLR, Kato N, Schulze MB, Girotto G, Jung B, Böger CA, Joshi PK, Bennett DA, De Jager PL, Lu X, Mamakou V, Brown M, Caulfield MJ, Munroe PB, Guo X, Ciullo M, Jonas JB, Samani NJ, Kaprio J, Pajukanta P, Adair LS, Bechayda SA, de Silva HJ, Wickremasinghe AR, Krauss RM, Wu JY, Zheng W, den Hollander AI, Bharadwaj D, Correa A, Wilson JG, Lind L, Heng CK, Nelson AE, Golightly YM, Wilson JF, Penninx B, Kim HL, Attia J, Scott RJ, Rao DC, Arnett DK, Hunt SC, Walker M, Koistinen HA, Chandak GR, Yajnik CS, Mercader JM, Tusié-Luna T, Aguilar-Salinas CA, Villalpando CG, Orozco L, Fornage M, Tai ES, van Dam RM, Lehtimäki T, Chaturvedi N, Yokota M, Liu J, Reilly DF, McKnight AJ, Kee F, Jöckel KH, McCarthy MI, Palmer CNA, Vitart V, Hayward C, Simonsick E, van Duijn CM, Lu F, Qu J, Hishigaki H, Lin X, März W, Parra EJ, Cruz M, Gudnason V, Tardif JC, Lettre G, 't Hart LM, Elders PJM, Damrauer SM, Kumari M, Kivimaki M, van der Harst P, Spector TD, Loos RJF, Province MA, Psaty BM, Brandslund I, Pramstaller PP, Christensen K, Ripatti S, Widén E, Hakonarson H, Grant SFA, Kiemeney LALM, de Graaf J, Loeffler M, Kronenberg F, Gu D, Erdmann J, Schunkert H, Franks PW, Linneberg A, Jukema JW, Khera AV, Männikkö M, Jarvelin MR, Kutalik Z, Cucca F, Mook-Kanamori DO, van Dijk KW, Watkins H, Strachan DP, Grarup N, Sever P, Poulter N, Rotter JI, Dantoft TM, Karpe F, Neville MJ, Timpson NJ, Cheng CY, Wong TY, Khor CC, Sabanayagam C, Peters A, Gieger C, Hattersley AT, Pedersen NL, Magnusson PKE, Boomsma DI, de Geus EJC, Cupples LA, van Meurs JBJ, Ghanbari M, Gordon-Larsen P, Huang W, Kim YJ, Tabara Y, Wareham NJ, Langenberg C, Zeggini E, Kuusisto J, Laakso M, Ingelsson E, Abecasis G, Chambers JC, Kooner JS, de Vries PS, Morrison AC, North KE, Daviglus M, Kraft P, Martin NG, Whitfield JB, Abbas S, Saleheen D, Walters RG, Holmes MV, Black C, Smith BH, Justice AE, Baras A, Buring JE, Ridker PM, Chasman DI, Kooperberg C, Wei WQ, Jarvik GP, Namjou B, Hayes MG, Ritchie MD, Jousilahti P, Salomaa V, Hveem K, Åsvold BO, Kubo M, Kamatani Y, Okada Y, Murakami Y, Thorsteinsdottir U, Stefansson K, Ho YL, Lynch JA, Rader DJ, Tsao PS, Chang KM, Cho K, O'Donnell CJ, Gaziano JM, Wilson P, Rotimi CN, Hazelhurst S, Ramsay M, Trembath RC, van Heel DA, Tamiya G, Yamamoto M, Kim BJ, Mohlke KL, Frayling TM, Hirschhorn JN, Kathiresan S, Boehnke M, Natarajan P, Peloso GM, Brown CD, Morris AP, Assimes TL, Deloukas P, Sun YV, Willer CJ. Author Correction: The power of genetic diversity in genome-wide association studies of lipids. Nature 2023; 618:E19-E20. [PMID: 37237109 PMCID: PMC10355188 DOI: 10.1038/s41586-023-06194-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Affiliation(s)
- Sarah E Graham
- Department of Internal Medicine, Division of Cardiology, University of Michigan, Ann Arbor, MI, USA
| | - Shoa L Clarke
- VA Palo Alto Health Care System, Palo Alto, CA, USA
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Kuan-Han H Wu
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Stavroula Kanoni
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Greg J M Zajac
- Department of Biostatistics and Center for Statistics Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Shweta Ramdas
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ida Surakka
- Department of Internal Medicine, Division of Cardiology, University of Michigan, Ann Arbor, MI, USA
| | - Ioanna Ntalla
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Sailaja Vedantam
- Endocrinology, Boston Children's Hospital, Boston, MA, USA
- Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Thomas W Winkler
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
| | - Adam E Locke
- McDonnell Genome Institute and Department of Medicine, Washington University, St Louis, MO, USA
| | - Eirini Marouli
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Mi Yeong Hwang
- Department of Precision Medicine, Division of Genome Science, National Institute of Health, Cheongju-si, South Korea
| | - Sohee Han
- Department of Precision Medicine, Division of Genome Science, National Institute of Health, Cheongju-si, South Korea
| | - Akira Narita
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Ananyo Choudhury
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Amy R Bentley
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Kenneth Ekoru
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Anurag Verma
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Bhavi Trivedi
- Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | | | - Karen A Hunt
- Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Qin Hui
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA
- Atlanta VA Health Care System, Decatur, GA, USA
| | - Derek Klarin
- Malcolm Randall VA Medical Center, Gainesville, FL, USA
- Division of Vascular Surgery and Endovascular Therapy, University of Florida College of Medicine, Gainesville, FL, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Xiang Zhu
- VA Palo Alto Health Care System, Palo Alto, CA, USA
- Department of Statistics, The Pennsylvania State University, University Park, PA, USA
- Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, USA
- Department of Statistics, Stanford University, Stanford, CA, USA
| | | | | | - Daniel F Gudbjartsson
- deCODE genetics/Amgen, Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | - Hilma Holm
- deCODE genetics/Amgen, Reykjavik, Iceland
| | - Isleifur Olafsson
- Department of Clinical Biochemistry, Landspitali-National University Hospital of Iceland, Reykjavik, Iceland
| | - Masato Akiyama
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Department of Ophthalmology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Saori Sakaue
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Chikashi Terao
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Masahiro Kanai
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Wei Zhou
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Ben M Brumpton
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Department of Thoracic Medicine, St Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Humaira Rasheed
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Sanni E Ruotsalainen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Aki S Havulinna
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Yogasudha Veturi
- Department of Genetics, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - QiPing Feng
- Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Elisabeth A Rosenthal
- Department of Medicine (Medical Genetics), University of Washington, Seattle, WA, USA
| | - Todd Lingren
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | | | | | - Jeffrey Haessler
- Fred Hutchinson Cancer Research Center, Division of Public Health Sciences, Seattle, WA, USA
| | - Franco Giulianini
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Yuki Bradford
- Department of Genetics, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jason E Miller
- Department of Genetics, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Archie Campbell
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK
- Usher Institute for Population Health Sciences and Informatics, The University of Edinburgh, Edinburgh, UK
| | - Kuang Lin
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Iona Y Millwood
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - George Hindy
- Department of Population Medicine, Qatar University College of Medicine, QU Health, Doha, Qatar
| | - Asif Rasheed
- Center for Non-Communicable Diseases, Karachi, Pakistan
| | - Jessica D Faul
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Wei Zhao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - David R Weir
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Constance Turman
- Program in Genetic Epidemiology and Statistical Genetics, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Hongyan Huang
- Program in Genetic Epidemiology and Statistical Genetics, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Mariaelisa Graff
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
| | - Anubha Mahajan
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Michael R Brown
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Weihua Zhang
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Department of Cardiology, Ealing Hospital, London North West University Healthcare NHS Trust, Southall, UK
- Imperial College Healthcare NHS Trust, London, UK
| | - Ketian Yu
- Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Ellen M Schmidt
- Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Anita Pandit
- Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Stefan Gustafsson
- Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Xianyong Yin
- Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Jian'an Luan
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Jing-Hua Zhao
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge Strangeways Research Laboratory, Cambridge, UK
| | - Fumihiko Matsuda
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Hye-Mi Jang
- Department of Precision Medicine, Division of Genome Science, National Institute of Health, Cheongju-si, South Korea
| | - Kyungheon Yoon
- Department of Precision Medicine, Division of Genome Science, National Institute of Health, Cheongju-si, South Korea
| | - Carolina Medina-Gomez
- Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Achilleas Pitsillides
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Jouke Jan Hottenga
- Department of Biological Psychology, Behavioral and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health, VU Medical Center Amsterdam, Amsterdam, The Netherlands
| | - Gonneke Willemsen
- Department of Biological Psychology, Behavioral and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, VU Medical Center Amsterdam, Amsterdam, The Netherlands
| | - Andrew R Wood
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Yingji Ji
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Zishan Gao
- Department of Clinical Acupuncture and Moxibustion, Nanjing University of Chinese Medicine, Nanjing, China
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Simon Haworth
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Bristol Dental School, University of Bristol, Bristol, UK
| | - Ruth E Mitchell
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Jin Fang Chai
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Mette Aadahl
- Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark
| | - Jie Yao
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute for Biomedical Innovations (Formerly LABioMed) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Ani Manichaikul
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Helen R Warren
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, John Vane Science Centre, Queen Mary University of London, London, UK
- NIHR Barts Cardiovascular Biomedical Research Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Julia Ramirez
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, John Vane Science Centre, Queen Mary University of London, London, UK
| | - Jette Bork-Jensen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Line L Kårhus
- Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark
| | - Anuj Goel
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Maria Sabater-Lleal
- Group of Genomics of Complex Diseases, Research Institute of Hospital de la Santa Creu i Sant Pau (IIB Sant Pau), Barcelona, Spain
- Cardiovascular Medicine Unit, Department of Medicine, Karolinska Institutet, Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Raymond Noordam
- Department of Internal Medicine, Section Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands
| | - Carlo Sidore
- Institute for Genetic and Biomedical Research, Italian National Council of Research (IRGB CNR), Cagliari,, Italy
| | - Edoardo Fiorillo
- Institute for Genetic and Biomedical Research, Italian National Council of Research (IRGB CNR), Lanusei, Italy
| | - Aaron F McDaid
- University Center for Primary Care and Public Health, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Pedro Marques-Vidal
- Department of Medicine, Internal Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Matthias Wielscher
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Stella Trompet
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands
| | - Naveed Sattar
- BHF Glasgow Cardiovascular Research Centre, Faculty of Medicine, Glasgow, UK
| | - Line T Møllehave
- Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark
| | - Betina H Thuesen
- Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark
| | - Matthias Munz
- Institute for Cardiogenetics, University of Lübeck, DZHK (German Research Centre for Cardiovascular Research), Partner site Hamburg/Lübeck/Kiel, University Heart Center Lübeck, Lübeck, Germany
- Charité-University Medicine Berlin, Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Department of Periodontology and Synoptic Dentistry, Berlin Institute of Health, Institute for Dental and Craniofacial Sciences, Berlin, Germany
| | - Lingyao Zeng
- Deutsches Herzzentrum München, Klinik für Herz- und Kreislauferkrankungen, Technische Universität München, Munich, Germany
- Deutsches Zentrum für Herz-Kreislauf-Forschung (DZHK), Partner site Munich Heart Alliance, Munich, Germany
| | - Jianfeng Huang
- Key Laboratory of Cardiovascular Epidemiology and Department of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Bin Yang
- Key Laboratory of Cardiovascular Epidemiology and Department of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | | | | | - Claudia Lamina
- Department of Genetics and Pharmacology, Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria
- German Chronic Kidney Disease Study, Innsbruck, Austria
| | - Lukas Forer
- Department of Genetics and Pharmacology, Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria
- German Chronic Kidney Disease Study, Innsbruck, Austria
| | - Markus Scholz
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Centre for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Tessel E Galesloot
- Radboud University Medical Center, Radboud Institute for Health Sciences, Nijmegen, The Netherlands
| | | | - E Warwick Daw
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St Louis, MO, USA
| | - Joseph M Zmuda
- Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jonathan S Mitchell
- Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, Bolzano, Italy
| | - Christian Fuchsberger
- Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, Bolzano, Italy
| | - Henry Christensen
- Department of Clinical Biochemistry, Lillebaelt Hospital, Vejle, Denmark
| | - Jennifer A Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Mary F Feitosa
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St Louis, MO, USA
| | - Mary K Wojczynski
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St Louis, MO, USA
| | - Michael Preuss
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Massimo Mangino
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
- NIHR Biomedical Research Centre at Guy's and St Thomas' Foundation Trust, London, UK
| | | | - Niek Verweij
- Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Jan W Benjamins
- Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Jorgen Engmann
- Institute of Cardiovascular Sciences, University College London, London, UK
- Department of Epidemiology and Public Health, University College London, London, UK
| | - Rachel L Kember
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Roderick C Slieker
- Amsterdam UMC, Department of Epidemiology and Biostatistics, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, The Netherlands
| | - Ken Sin Lo
- Montreal Heart Institute, Montreal, Quebec, Canada
| | | | - Phuong Le
- Department of Anthropology, University of Toronto at Mississauga, Mississauga, Ontario, Canada
| | - Marcus E Kleber
- Vth Department of Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Synlab MVZ Humangenetik Mannheim, Mannheim, Germany
| | - Graciela E Delgado
- Vth Department of Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Shaofeng Huo
- Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Daisuke D Ikeda
- Biomedical Technology Research Center, Tokushima Research Institute, Otsuka Pharmaceutical, Tokushima, Japan
| | - Hiroyuki Iha
- Biomedical Technology Research Center, Tokushima Research Institute, Otsuka Pharmaceutical, Tokushima, Japan
| | - Jian Yang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
- Institute for Advanced Research, Wenzhou Medical University, Wenzhou, China
| | - Jun Liu
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Hampton L Leonard
- Laboratory of Neurogenetics, National Institute on Aging, NIH, Bethesda, MD, USA
- Data Tecnica International, Glen Echo, MD, USA
| | - Jonathan Marten
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Börge Schmidt
- Institute for Medical Informatics, Biometrie and Epidemiology, University of Duisburg-Essen, Essen, Germany
| | - Marina Arendt
- Institute for Medical Informatics, Biometrie and Epidemiology, University of Duisburg-Essen, Essen, Germany
- Department of Computer Science, University of Applied Sciences and Arts Dortmund, Dortmund, Germany
| | - Laura J Smyth
- Centre for Public Health, Queen's University of Belfast, Belfast, UK
| | | | - Chaolong Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
| | - Masahiro Nakatochi
- Public Health Informatics Unit, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at UCL, London, UK
| | - Nina Hutri-Kähönen
- Department of Pediatrics, Tampere University Hospital, Tampere, Finland
- Department of Pediatrics, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Xueling Sim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Rui Xia
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Alicia Huerta-Chagoya
- Departamento de Medicina Genómica y Toxicología Ambiental, Instituto de Investigaciones Biomédicas, UNAM, Ciudad de Mexico, Mexico, Mexico
| | - Juan Carlos Fernandez-Lopez
- Departamento de Genómica Computacional, Instituto Nacional de Medicina Genómica, Ciudad de Mexico, Mexico, Mexico
| | - Valeriya Lyssenko
- Center for Diabetes Research, University of Bergen, Bergen, Norway
- Lund University Diabetes Center, Lund University, Malmo, Sweden
| | - Meraj Ahmed
- Genomic Research on Complex Diseases (GRC Group), CSIR-Centre for Cellular and Molecular Biology, Hyderabad, India
| | - Anne U Jackson
- Department of Biostatistics and Center for Statistics Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Noha A Yousri
- Department of Genetic Medicine, Weill Cornell Medicine-Qatar, Doha, Qatar
- Department of Computer and Systems Engineering, Alexandria University, Alexandria, Egypt
| | - Marguerite R Irvin
- Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA
| | | | - Han-Na Kim
- Medical Research Institute, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea
- Department of Clinical Research Design and Evaluation, SAIHST, Sungkyunkwan University, Seoul, Korea
| | - Seungho Ryu
- Center for Cohort Studies, Total Healthcare Center, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea
- Department of Occupational and Environmental Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Paul R H J Timmers
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Liubov Arbeeva
- Thurston Arthritis Research Center, University of North Carolina, Chapel Hill, NC, USA
| | - Rajkumar Dorajoo
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
| | - Leslie A Lange
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, Anschutz Medical Campus, University of Colorado, Denver, Aurora, CO, USA
| | - Xiaoran Chai
- Ocular Epidemiology, Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Department of Ophthalmology, National University of Singapore and National University Health System, Singapore, Singapore
| | - Gauri Prasad
- Genomics and Molecular Medicine Unit, CSIR-Institute of Genomics and Integrative Biology, New Delhi, India
- Academy of Scientific and Innovative Research, CSIR-Institute of Genomics and Integrative Biology Campus, New Delhi, India
| | - Laura Lorés-Motta
- Departments of Ophthalmology and Human Genetics, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands
| | - Marc Pauper
- Departments of Ophthalmology and Human Genetics, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands
| | - Jirong Long
- Vanderbilt Epidemiology Center, Division of Epidemiology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Xiaohui Li
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute for Biomedical Innovations (Formerly LABioMed) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Elizabeth Theusch
- Department of Pediatrics, University of California, San Francisco, Oakland, CA,, USA
| | | | - Cassandra N Spracklen
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
- Department of Biostatistics and Epidemiology, University of Massachusetts-Amherst, Amherst, MA,, USA
| | - Anu Loukola
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Sailalitha Bollepalli
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Sophie C Warner
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Ya Xing Wang
- Beijing Institute of Ophthalmology, Beijing Key Laboratory of Ophthalmology and Visual Sciences, Beijing, China
- Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Wen B Wei
- Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Teresa Nutile
- Institute of Genetics and Biophysics "Adriano Buzzati-Traverso"-CNR, Naples, Italy
| | - Daniela Ruggiero
- Institute of Genetics and Biophysics "Adriano Buzzati-Traverso"-CNR, Naples, Italy
- IRCCS Neuromed, Pozzilli, Isernia, Italy
| | - Yun Ju Sung
- Department of Psychiatry, Washington University, St Louis, MO, USA
| | - Yi-Jen Hung
- Division of Endocrinology and Metabolism, Tri-Service General Hospital Songshan Branch, Taipei, Taiwan
| | - Shufeng Chen
- Key Laboratory of Cardiovascular Epidemiology and Department of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Fangchao Liu
- Key Laboratory of Cardiovascular Epidemiology and Department of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jingyun Yang
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
| | - Katherine A Kentistou
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Mathias Gorski
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
- Department of Nephrology, University Hospital Regensburg, Regensburg, Germany
| | - Marco Brumat
- Department of Medicine, Surgery and Health Sciences, University of Trieste, Trieste, Italy
| | - Karina Meidtner
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Lawrence F Bielak
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Jennifer A Smith
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Prashantha Hebbar
- Department of Genetics and Bioinformatics, Dasman Diabetes Institute, Kuwait, Kuwait
| | - Aliki-Eleni Farmaki
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University of Athens, Athens, Greece
- Department of Population Science and Experimental Medicine, University College London, London, UK
| | - Edith Hofer
- Department of Neurology, Clinical Division of Neurogeriatrics, Medical University of Graz, Graz, Austria
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria
| | - Maoxuan Lin
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC, USA
| | - Chao Xue
- Department of Internal Medicine, Division of Cardiology, University of Michigan, Ann Arbor, MI, USA
| | - Jifeng Zhang
- Department of Internal Medicine, Division of Cardiology, University of Michigan, Ann Arbor, MI, USA
| | - Maria Pina Concas
- Institute for Maternal and Child Health, IRCCS Burlo Garofolo, Trieste, Italy
| | - Simona Vaccargiu
- Institute of Genetic and Biomedical Research, National Research Council of Italy, UOS of Sassari, Sassari, Italy
| | - Peter J van der Most
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Niina Pitkänen
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
| | - Brian E Cade
- Sleep Medicine and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA
| | - Jiwon Lee
- Sleep Medicine and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
| | - Sander W van der Laan
- Central Diagnostics Laboratory, Division Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Kumaraswamy Naidu Chitrala
- Laboratory of Epidemiology and Population Science, National Institute on Aging Intramural Research Program, NIH Biomedical Research Center, NIA, Baltimore, MD, USA
| | - Stefan Weiss
- Department of Functional Genomics, Interfaculty Institute for Genetics and Functional Genomics, University of Greifswald and University Medicine Greifswald, Greifswald, Germany
| | - Martina E Zimmermann
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
| | | | - Hyeok Sun Choi
- Department of Biomedical Science, Hallym University, Chuncheon, Korea
| | - Maria Nethander
- Centre for Bone and Arthritis Research, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Bioinformatics Core Facility, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Sandra Freitag-Wolf
- Institute of Medical Informatics and Statistics, Kiel University, Kiel, Germany
| | - Lorraine Southam
- Institute of Translational Genomics, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- Wellcome Trust Sanger Institute, Hinxton, UK
| | - Nigel W Rayner
- Wellcome Sanger Institute, Hinxton, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Institute of Translational Genomics, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- Oxford Centre for Diabetes Endocrinology and Metabolism, Oxford, UK
| | - Carol A Wang
- School of Medicine and Public Health, College of Health, Medicine and Wellbeing, University of Newcastle, Newcastle, New South Wales, Australia
| | - Shih-Yi Lin
- Center for Geriatrics and Gerontology, Division of Endocrinology and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
- School of Medicine, National Yang-Ming University, Taipei, Taiwan
- School of Medicine, National Defense Medical Center, Taipei, Taiwan
| | - Jun-Sing Wang
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
- Department of Medicine, School of Medicine, National Yang-Ming University, Taipei, Taiwan
| | | | - Leo-Pekka Lyytikäinen
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
- Department of Clinical Chemistry, Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Kjell Nikus
- Department of Cardiology, Heart Center, Tampere University Hospital, Tampere, Finland
- Department of Cardiology, Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Gabriel Cuellar-Partida
- University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, Queensland, Australia
| | - Henrik Vestergaard
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Medicine, Bornholms Hospital, Ronne, Denmark
| | - Bertha Hildalgo
- School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Olga Giannakopoulou
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Qiuyin Cai
- Vanderbilt Epidemiology Center, Division of Epidemiology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Morgan O Obura
- Amsterdam UMC, Department of Epidemiology and Biostatistics, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Jessica van Setten
- Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Xiaoyin Li
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Karen Schwander
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St Louis, MO, USA
| | - Natalie Terzikhan
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Jae Hun Shin
- Department of Biomedical Science, Hallym University, Chuncheon, Korea
| | | | | | - Lisa Warsinger Martin
- School of Medicine and Health Sciences, George Washington University, Washington, DC, USA
| | - Zhengming Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Liming Li
- Department of Epidemiology, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Heather M Highland
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
| | - Kristin L Young
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
| | - Takahisa Kawaguchi
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Joachim Thiery
- LIFE Research Centre for Civilization Diseases, University of Leipzig, Leipzig, Germany
- Institute for Laboratory Medicine, University Hospital Leipzig, Leipzig, Germany
| | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Girish N Nadkarni
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Lenore J Launer
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIH, Baltimore, MD, USA
| | - Huaixing Li
- Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Mike A Nalls
- Laboratory of Neurogenetics, National Institute on Aging, NIH, Bethesda, MD, USA
- Data Tecnica International, Glen Echo, MD, USA
| | - Olli T Raitakari
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
| | - Sahoko Ichihara
- Department of Environmental and Preventive Medicine, Jichi Medical University School of Medicine, Shimotsuke, Japan
| | - Sarah H Wild
- Centre for Population Health Sciences, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Christopher P Nelson
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Harry Campbell
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Susanne Jäger
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Toru Nabika
- Department of Functional Pathology, Shimane University School of Medicine, Izumo, Japan
| | - Fahd Al-Mulla
- Department of Genetics and Bioinformatics, Dasman Diabetes Institute, Kuwait, Kuwait
| | - Harri Niinikoski
- Department of Pediatrics and Adolescent Medicine, Turku University Hospital and University of Turku, Turku, Finland
- Department of Physiology, University of Turku, Turku, Finland
| | - Peter S Braund
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Ivana Kolcic
- Faculty of Medicine, University of Split, Split, Croatia
| | - Peter Kovacs
- Medical Department III-Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany
| | - Tota Giardoglou
- Department of Nutrition-Dietetics, Harokopio University, Eleftheriou Venizelou, Athens, Greece
| | - Tomohiro Katsuya
- Department of Clinical Gene Therapy, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Geriatric and General Medicine, Osaka University Graduate School of Medicine, Suita, Japan
| | - Konain Fatima Bhatti
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Dominique de Kleijn
- Department of Vascular Surgery, Division of Surgical Specialties, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Gert J de Borst
- Department of Vascular Surgery, Division of Surgical Specialties, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Eung Kweon Kim
- Corneal Dystrophy Research Institute, Department of Ophthalmology, Yonsei University College of Medicine, Seoul, Korea
| | - Hieab H H Adams
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Clinical Genetics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Xiaofeng Zhu
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Folkert W Asselbergs
- Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Adriaan O Kraaijeveld
- Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Joline W J Beulens
- Amsterdam UMC, Department of Epidemiology and Biostatistics, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Xiao-Ou Shu
- Vanderbilt Epidemiology Center, Division of Epidemiology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Loukianos S Rallidis
- Second Department of Cardiology, Medical School, National and Kapodistrian University of Athens, Attikon University Hospital, Athens, Greece
| | - Oluf Pedersen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Paul Mitchell
- Center for Vision Research, Department of Ophthalmology and The Westmead Institute, University of Sydney, Sydney, New South Wales, Australia
| | - Alex W Hewitt
- Menzies Institute for Medical Research, School of Medicine, University of Tasmania, Hobart, Tasmania, Australia
- Centre for Eye Research Australia, University of Melbourne, Melbourne, Victoria, Australia
| | - Mika Kähönen
- Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland
- Department of Clinical Physiology, Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Louis Pérusse
- Department of Kinesiology, Université Laval, Quebec, Quebec, Canada
- Institute of Nutrition and Functional Foods (INAF), Université Laval, Quebec, Quebec, Canada
| | | | - Anke Tönjes
- Medical Department III-Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany
| | - Yii-Der Ida Chen
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute for Biomedical Innovations (Formerly LABioMed) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Craig E Pennell
- School of Medicine and Public Health, College of Health, Medicine and Wellbeing, University of Newcastle, Newcastle, New South Wales, Australia
| | - Trevor A Mori
- Medical School, Faculty of Health and Medical Sciences, University of Western Australia, Perth, Western Australia, Australia
| | - Wolfgang Lieb
- Institute of Epidemiology, Kiel University, Kiel, Germany
| | - Andre Franke
- Institute of Clinical Molecular Biology, Kiel University, Kiel, Germany
| | - Claes Ohlsson
- Centre for Bone and Arthritis Research, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Drug Treatment, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Dan Mellström
- Centre for Bone and Arthritis Research, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Geriatric Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Yoon Shin Cho
- Department of Biomedical Science, Hallym University, Chuncheon, Korea
| | - Hyejin Lee
- Department of Internal Medicine, Ewha Womans University School of Medicine, Seoul, Korea
| | - Jian-Min Yuan
- Division of Cancer Control and Population Sciences, UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Woon-Puay Koh
- Healthy Longevity Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Singapore Institute for Clinical Sciences, Agency for Science Technology and Research (A*STAR), Singapore, Singapore
| | - Sang Youl Rhee
- Department of Endocrinology and Metabolism, Kyung Hee University School of Medicine, Seoul, Korea
| | - Jeong-Taek Woo
- Department of Endocrinology and Metabolism, Kyung Hee University School of Medicine, Seoul, Korea
| | - Iris M Heid
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
| | - Klaus J Stark
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
| | - Henry Völzke
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Georg Homuth
- Department of Functional Genomics, Interfaculty Institute for Genetics and Functional Genomics, University of Greifswald and University Medicine Greifswald, Greifswald, Germany
| | - Michele K Evans
- Laboratory of Epidemiology and Population Science, National Institute on Aging Intramural Research Program, NIH Biomedical Research Center, Baltimore, MD, USA
| | - Alan B Zonderman
- Laboratory of Epidemiology and Population Science, National Institute on Aging Intramural Research Program, NIH Biomedical Research Center, Baltimore, MD, USA
| | - Ozren Polasek
- Faculty of Medicine, University of Split, Split, Croatia
| | - Gerard Pasterkamp
- Central Diagnostics Laboratory, Division Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Imo E Hoefer
- Central Diagnostics Laboratory, Division Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Susan Redline
- Sleep Medicine and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA
| | - Katja Pahkala
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
- Paavo Nurmi Centre, Sports and Exercise Medicine Unit, Department of Physical Activity and Health, University of Turku, Turku, Finland
| | - Albertine J Oldehinkel
- Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Harold Snieder
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Ginevra Biino
- Institute of Molecular Genetics, National Research Council of Italy, Pavia, Italy
| | - Reinhold Schmidt
- Department of Neurology, Clinical Division of Neurogeriatrics, Medical University of Graz, Graz, Austria
| | - Helena Schmidt
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging, Medical University of Graz, Graz, Austria
| | - Y Eugene Chen
- Department of Internal Medicine, Division of Cardiology, University of Michigan, Ann Arbor, MI, USA
| | | | - George Dedoussis
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University of Athens, Athens, Greece
| | | | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Norihiro Kato
- National Center for Global Health and Medicine, Tokyo, Japan
| | - Matthias B Schulze
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute of Nutritional Science, University of Potsdam, Nuthetal, Germany
| | - Giorgia Girotto
- Department of Medicine, Surgery and Health Sciences, University of Trieste, Trieste, Italy
- Institute for Maternal and Child Health IRCCS "Burlo Garofolo", Trieste, Italy
| | - Bettina Jung
- Department of Nephrology, University Hospital Regensburg, Regensburg, Germany
| | - Carsten A Böger
- Department of Nephrology, University Hospital Regensburg, Regensburg, Germany
- Department of Nephrology, Diabetology, Rheumatology, Traunstein Hospital, Traunstein, Germany
- KfH Kidney Center Traunstein, Traunstein, Germany
| | - Peter K Joshi
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
| | - Philip L De Jager
- Center for Translational and Systems Neuroimmunology, Department of Neurology, Columbia University Medical Center, New York, NY, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Xiangfeng Lu
- Key Laboratory of Cardiovascular Epidemiology and Department of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Vasiliki Mamakou
- Medical School, National and Kapodistrian University Athens, Athens, Greece
- Dromokaiteio Psychiatric Hospital, Athens, Greece
| | - Morris Brown
- NIHR Barts Cardiovascular Biomedical Research Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- Clinical Pharmacology, William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Mark J Caulfield
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, John Vane Science Centre, Queen Mary University of London, London, UK
- NIHR Barts Cardiovascular Biomedical Research Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Patricia B Munroe
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, John Vane Science Centre, Queen Mary University of London, London, UK
- NIHR Barts Cardiovascular Biomedical Research Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute for Biomedical Innovations (Formerly LABioMed) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Marina Ciullo
- Institute of Genetics and Biophysics "Adriano Buzzati-Traverso"-CNR, Naples, Italy
- IRCCS Neuromed, Pozzilli, Isernia, Italy
| | - Jost B Jonas
- Department of Ophthalmology, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Beijing Institute of Ophthalmology, Beijing Key Laboratory of Ophthalmology and Visual Sciences, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
- Institute of Molecular and Clinical Ophthalmology Basel, Basel, Switzerland
| | - Nilesh J Samani
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Jaakko Kaprio
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Päivi Pajukanta
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, Los Angeles, CA, USA
| | - Linda S Adair
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
- Carolina Population Center, University of North Carolina, Chapel Hill, NC, USA
| | - Sonny Augustin Bechayda
- USC-Office of Population Studies Foundation, University of San Carlos, Cebu City, Philippines
- Department of Anthropology, Sociology, and History, University of San Carlos, Cebu City, Philippines
| | - H Janaka de Silva
- Department of Medicine, Faculty of Medicine, University of Kelaniya, Ragama, Sri Lanka
| | | | - Ronald M Krauss
- Departments of Pediatrics and Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Jer-Yuarn Wu
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Wei Zheng
- Vanderbilt Epidemiology Center, Division of Epidemiology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Anneke I den Hollander
- Departments of Ophthalmology and Human Genetics, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands
| | - Dwaipayan Bharadwaj
- Academy of Scientific and Innovative Research, CSIR-Institute of Genomics and Integrative Biology Campus, New Delhi, India
- Systems Genomics Laboratory, School of Biotechnology, Jawaharlal Nehru University, New Delhi, India
| | - Adolfo Correa
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - James G Wilson
- Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS, USA
| | - Lars Lind
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Chew-Kiat Heng
- Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Khoo Teck Puat-National University Children's Medical Institute, National University Health System, Singapore, Singapore
| | - Amanda E Nelson
- Thurston Arthritis Research Center, University of North Carolina, Chapel Hill, NC, USA
- Department of Medicine, University of North Carolina, Chapel Hill, NC, USA
| | - Yvonne M Golightly
- Thurston Arthritis Research Center, University of North Carolina, Chapel Hill, NC, USA
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
- Injury Prevention Research Center, University of North Carolina, Chapel Hill, NC, USA
- Division of Physical Therapy, University of North Carolina, Chapel Hill, NC, USA
| | - James F Wilson
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Brenda Penninx
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, VU Medical Center Amsterdam, Amsterdam, The Netherlands
| | - Hyung-Lae Kim
- Department of Biochemistry, College of Medicine, Ewha Womans University, Seoul, Korea
| | - John Attia
- Hunter Medical Research Institute, Newcastle, New South Wales, Australia
- Faculty of Health and Medicine, University of Newcastle, Newcastle, New South Wales, Australia
| | - Rodney J Scott
- Hunter Medical Research Institute, Newcastle, New South Wales, Australia
- Faculty of Health and Medicine, University of Newcastle, Newcastle, New South Wales, Australia
| | - D C Rao
- Division of Biostatistics, Washington University School of Medicine, St Louis, MO, USA
| | - Donna K Arnett
- University of Kentucky, College of Public Health, Lexington, KY, USA
| | - Steven C Hunt
- Department of Genetic Medicine, Weill Cornell Medicine-Qatar, Doha, Qatar
- Department of Internal Medicine, University of Utah, Salt Lake City, UT, USA
| | - Mark Walker
- Institute of Cellular Medicine (Diabetes), The Medical School, Newcastle University, Newcastle upon Tyne, UK
| | - Heikki A Koistinen
- Department of Population Health, Finnish Institute for Health and Welfare, Helsinki, Finland
- University of Helsinki and Department of Medicine, Helsinki University Hospital, Helsinki, Finland
- Minerva Foundation Institute for Medical Research, Helsinki, Finland
| | - Giriraj R Chandak
- Genomic Research on Complex Diseases (GRC Group), CSIR-Centre for Cellular and Molecular Biology, Hyderabad, India
- Academy of Scientific and Innovative Research (AcSIR), New Delhi, India
| | | | - Josep M Mercader
- Harvard Medical School, Boston, MA, USA
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Teresa Tusié-Luna
- Unidad de Biología Molecular y Medicina Genómica, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico, Mexico
- Instituto de Investigaciones Biomédicas, UNAM, Ciudad de México, Mexico, Mexico
- Unidad de Biología Molecular y Medicina Genómica, Instituto de Investigaciones Bimédicas UNAM/ Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico, Mexico
| | - Carlos A Aguilar-Salinas
- Departamento de Endocrinología y Metabolismo, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico, Mexico
- Dirección de Nutrición and Unidad de Estudios de Enfermedades Metabólicas, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico, Mexico
| | | | - Lorena Orozco
- Instituto Nacional de Medicina Genómica, Mexico, Mexico
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
- Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - E Shyong Tai
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Rob M van Dam
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
- Department of Clinical Chemistry, Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | | | | | - Jianjun Liu
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
| | | | | | - Frank Kee
- Centre for Public Health, Queen's University of Belfast, Belfast, UK
| | - Karl-Heinz Jöckel
- Institute for Medical Informatics, Biometrie and Epidemiology, University of Duisburg-Essen, Essen, Germany
| | - Mark I McCarthy
- Genentech, South San Francisco, CA, USA
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
| | - Colin N A Palmer
- Population Health and Genomics, University of Dundee, Ninwells Hospital and Medical School, Dundee, UK
| | - Veronique Vitart
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Caroline Hayward
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Eleanor Simonsick
- Intramural Research Program, National Institute on Aging, Baltimore, MD, USA
| | | | - Fan Lu
- The Eye Hospital, School of Ophthalmology and Optometry, Wenzhou Medical University, Wenzhou, China
| | - Jia Qu
- The Eye Hospital, School of Ophthalmology and Optometry, Wenzhou Medical University, Wenzhou, China
| | - Haretsugu Hishigaki
- Biomedical Technology Research Center, Tokushima Research Institute, Otsuka Pharmaceutical, Tokushima, Japan
| | - Xu Lin
- Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Winfried März
- Vth Department of Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Synlab Academy, Synlab, Mannheim, Germany
- Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz, Austria
| | - Esteban J Parra
- Department of Anthropology, University of Toronto at Mississauga, Mississauga, Ontario, Canada
| | - Miguel Cruz
- Unidad de Investigacion Medica en Bioquimica, Hospital de Especialidades, Centro Medico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico, Mexico
| | - Vilmundur Gudnason
- Icelandic Heart Association, Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Jean-Claude Tardif
- Montreal Heart Institute, Montreal, Quebec, Canada
- Department of Medicine, Faculty of Medecine, Université de Montréal, Quebec, Quebec, Canada
| | - Guillaume Lettre
- Montreal Heart Institute, Montreal, Quebec, Canada
- Department of Medicine, Faculty of Medicine, Université de Montréal, Montreal, Quebec, Canada
| | - Leen M 't Hart
- Amsterdam UMC, Department of Epidemiology and Biostatistics, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, The Netherlands
- Department of Biomedical Data Sciences, Section Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Petra J M Elders
- Amsterdam UMC, Department of General Practice and Elderly Care, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Scott M Damrauer
- Department of Surgery, University of Pennsylvania, Philadelphia, PA, USA
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA, USA
| | - Meena Kumari
- Institute of Social and Economic Research, University of Essex, Essex, UK
| | - Mika Kivimaki
- Department of Epidemiology and Public Health, University College London, London, UK
| | - Pim van der Harst
- Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Tim D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Ruth J F Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Michael A Province
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St Louis, MO, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology and Health Services, University of Washington, Seattle, WA, USA
- Kaiser Permanent Washington Health Research Institute, Seattle, WA, USA
| | - Ivan Brandslund
- Department of Clinical Biochemistry, Lillebaelt Hospital, Vejle, Denmark
- Institute of Regional Health Research, University of Southern Denmark, Odense, Denmark
| | - Peter P Pramstaller
- Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, Bolzano, Italy
| | - Kaare Christensen
- Danish Aging Research Center, University of Southern Denmark, Odense, Denmark
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Elisabeth Widén
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Hakon Hakonarson
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pediatrics, Perelman School of Medicine, The University of Pennsylvania, Philadelphia, PA, USA
| | - Struan F A Grant
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pediatrics, Perelman School of Medicine, The University of Pennsylvania, Philadelphia, PA, USA
- Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Lambertus A L M Kiemeney
- Radboud University Medical Center, Radboud Institute for Health Sciences, Nijmegen, The Netherlands
| | - Jacqueline de Graaf
- Radboud University Medical Center, Radboud Institute for Health Sciences, Nijmegen, The Netherlands
| | - Markus Loeffler
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Centre for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Florian Kronenberg
- German Chronic Kidney Disease Study, Innsbruck, Austria
- Institute of Genetic Epidemiology, Department of Genetics and Pharmacology, Medical University of Innsbruck, Innsbruck, Austria
| | - Dongfeng Gu
- Key Laboratory of Cardiovascular Epidemiology and Department of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- School of Medicine, Southern University of Science and Technology, Shenzhen, China
| | - Jeanette Erdmann
- Institute for Cardiogenetics, University of Lübeck, DZHK (German Research Centre for Cardiovascular Research), Partner site Hamburg/Lübeck/Kiel, and University Heart Center Lübeck, Lübeck, Germany
| | - Heribert Schunkert
- Deutsches Herzzentrum München, Klinik für Herz- und Kreislauferkrankungen, Technische Universität München, Munich, Germany
- Deutsches Zentrum für Herz-Kreislauf-Forschung (DZHK) e.V., Partner site Munich Heart Alliance, Munich, Germany
| | | | - Allan Linneberg
- Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - J Wouter Jukema
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
- Netherlands Heart Institute, Utrecht, The Netherlands
| | - Amit V Khera
- Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Program of Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Minna Männikkö
- Northern Finland Birth Cohorts, Infrastructure for Population Studies, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Marjo-Riitta Jarvelin
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter of Oulu, University of Oulu, Oulu, Finland
| | - Zoltan Kutalik
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- University Center for Primary Care and Public Health, Lausanne, Switzerland
| | - Francesco Cucca
- Institute for Genetic and Biomedical Research, Italian National Council of Research (IRGB CNR), Cagliari, Italy
- University of Sassari, Sassari, Italy
| | - Dennis O Mook-Kanamori
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
- Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, The Netherlands
| | - Ko Willems van Dijk
- Department of Internal Medicine, Division of Endocrinology, Leiden University Medical Center, Leiden, the Netherlands
- Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, the Netherlands
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Hugh Watkins
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - David P Strachan
- Population Health Research Institute, St George's University of London, London, UK
| | - Niels Grarup
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Peter Sever
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Neil Poulter
- School of Public Health, Imperial College London, London, UK
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute for Biomedical Innovations (Formerly LABioMed) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Thomas M Dantoft
- Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark
| | - Fredrik Karpe
- OCDEM, University of Oxford, Churchill Hospital, Oxford, UK
- NIHR Oxford Biomedical Research Centre, Churchill Hospital, Oxford, UK
| | - Matt J Neville
- OCDEM, University of Oxford, Churchill Hospital, Oxford, UK
- NIHR Oxford Biomedical Research Centre, Churchill Hospital, Oxford, UK
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Ching-Yu Cheng
- Ocular Epidemiology, Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
| | - Tien-Yin Wong
- Ocular Epidemiology, Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
| | - Chiea Chuen Khor
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
| | - Charumathi Sabanayagam
- Ocular Epidemiology, Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- DZHK (German Centre for Cardiovascular Research), Munich Heart Alliance Partner Site, Munich, Germany
| | - Christian Gieger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | | | - Nancy L Pedersen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Patrik K E Magnusson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Dorret I Boomsma
- Department of Biological Psychology, Behavioral and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health, VU Medical Center Amsterdam, Amsterdam, The Netherlands
- Amsterdam Reproduction and Development Research Institute, VU Medical Center Amsterdam, Amsterdam, the Netherlands
| | - Eco J C de Geus
- Department of Biological Psychology, Behavioral and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, VU Medical Center Amsterdam, Amsterdam, The Netherlands
| | - L Adrienne Cupples
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Framingham Heart Study, National Heart, Lung, and Blood Institute, US National Institutes of Health, Bethesda, MD, USA
| | - Joyce B J van Meurs
- Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Genetics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Penny Gordon-Larsen
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
- Carolina Population Center, University of North Carolina, Chapel Hill, NC, USA
| | - Wei Huang
- Department of Genetics, Shanghai-MOST Key Laboratory of Health and Disease Genomics, Chinese National Human Genome Center at Shanghai, Shanghai, China
| | - Young Jin Kim
- Department of Precision Medicine, Division of Genome Science, National Institute of Health, Cheongju-si, South Korea
| | - Yasuharu Tabara
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Nicholas J Wareham
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Eleftheria Zeggini
- Institute of Translational Genomics, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- Wellcome Trust Sanger Institute, Hinxton, UK
- TUM School of Medicine, Technical University of Munich (TUM) and Klinikum Rechts der Isar, Munich, Germany
| | - Johanna Kuusisto
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Erik Ingelsson
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA
| | - Goncalo Abecasis
- Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
- Regeneron Pharmaceuticals, Tarrytown, NY, USA
| | - John C Chambers
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Department of Cardiology, Ealing Hospital, London North West University Healthcare NHS Trust, Southall, UK
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- Imperial College Healthcare NHS Trust, Imperial College London, London, UK
| | - Jaspal S Kooner
- Department of Cardiology, Ealing Hospital, London North West University Healthcare NHS Trust, Southall, UK
- Imperial College Healthcare NHS Trust, London, UK
- MRC-PHE Centre for Environment and Health, Imperial College London, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Paul S de Vries
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Kari E North
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
| | - Martha Daviglus
- Institute for Minority Health Research, University of Illinois College of Medicine, Chicago, IL, USA
| | - Peter Kraft
- Program in Genetic Epidemiology and Statistical Genetics, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Nicholas G Martin
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - John B Whitfield
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Shahid Abbas
- Center for Non-Communicable Diseases, Karachi, Pakistan
- Faisalabad Institute of Cardiology, Faislabad, Pakistan
| | - Danish Saleheen
- Center for Non-Communicable Diseases, Karachi, Pakistan
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
- Department of Cardiology, Columbia University Irving Medical Center, New York, NY, USA
| | - Robin G Walters
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Big Data Institute, University of Oxford, Oxford, UK
| | - Michael V Holmes
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospitals, Oxford, UK
| | - Corri Black
- Aberdeen Centre for Health Data Science, School of Medicine, Medical Science and Nutrition, University of Aberdeen, Aberdeen, UK
| | - Blair H Smith
- Division of Population Health and Genomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK
| | - Anne E Justice
- Biomedical and Translational Informatics, Geisinger Health, Danville, PA, USA
| | - Aris Baras
- Regeneron Pharmaceuticals, Tarrytown, NY, USA
| | - Julie E Buring
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Paul M Ridker
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Daniel I Chasman
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Charles Kooperberg
- Fred Hutchinson Cancer Research Center, Division of Public Health Sciences, Seattle, WA, USA
| | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Gail P Jarvik
- Departments of Medicine (Medical Genetics) and Genome Sciences, University of Washington Medical Center, Seattle, WA, USA
| | - Bahram Namjou
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center (CCHMC), Cincinnati, OH, USA
| | - M Geoffrey Hayes
- Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University, Feinberg School of Medicine, Chicago, IL, USA
- Department of Anthropology, Northwestern University, Evanston, IL, USA
- Center for Genetic Medicine, Northwestern University, Feinberg School of Medicine, Chicago, IL, USA
| | - Marylyn D Ritchie
- Department of Genetics, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Pekka Jousilahti
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Veikko Salomaa
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Kristian Hveem
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- HUNT Research Centre, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger, Norway
- Department of Medicine, Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
| | - Bjørn Olav Åsvold
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- HUNT Research Centre, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger, Norway
- Department of Endocrinology, St Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Michiaki Kubo
- RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Yoichiro Kamatani
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Yukinori Okada
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan
- Laboratory of Statistical Immunology, WPI Immunology Frontier Research Center, Osaka University, Osaka, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Osaka, Japan
| | - Yoshinori Murakami
- Division of Molecular Pathology, Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Unnur Thorsteinsdottir
- deCODE genetics/Amgen, Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Kari Stefansson
- deCODE genetics/Amgen, Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Yuk-Lam Ho
- VA Boston Healthcare System, Boston, MA, USA
| | - Julie A Lynch
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT, USA
- University of Massachusetts, Boston, MA, USA
| | - Daniel J Rader
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Philip S Tsao
- VA Palo Alto Health Care System, Palo Alto, CA, USA
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Kyong-Mi Chang
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
| | - Kelly Cho
- VA Boston Healthcare System, Boston, MA, USA
- Department of Medicine, Brigham Women's Hospital, Boston, MA, USA
| | - Christopher J O'Donnell
- VA Boston Healthcare System, Boston, MA, USA
- Department of Medicine, Brigham Women's Hospital, Boston, MA, USA
| | - John M Gaziano
- VA Boston Healthcare System, Boston, MA, USA
- Department of Medicine, Brigham Women's Hospital, Boston, MA, USA
| | - Peter Wilson
- Atlanta VA Medical Center, Atlanta, GA, USA
- Division of Cardiology, Emory University School of Medicine, Atlanta, GA, USA
| | - Charles N Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Scott Hazelhurst
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, South Africa
| | - Michèle Ramsay
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Division of Human Genetics, National Health Laboratory Service and School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Richard C Trembath
- School of Basic and Medical Biosciences, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - David A van Heel
- Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Gen Tamiya
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Masayuki Yamamoto
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Bong-Jo Kim
- Department of Precision Medicine, Division of Genome Science, National Institute of Health, Cheongju-si, South Korea
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Timothy M Frayling
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Joel N Hirschhorn
- Endocrinology, Boston Children's Hospital, Boston, MA, USA
- Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
- Departments of Pediatrics and Genetics, Harvard Medical School, Boston, MA, USA
| | - Sekar Kathiresan
- Program of Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistics Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Pradeep Natarajan
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Cardiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Gina M Peloso
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Christopher D Brown
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Andrew P Morris
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, The University of Manchester, Manchester, UK
| | - Themistocles L Assimes
- VA Palo Alto Health Care System, Palo Alto, CA, USA.
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA.
- Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, USA.
| | - Panos Deloukas
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- NIHR Barts Cardiovascular Biomedical Research Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- Princess Al-Jawhara Al-Brahim Centre of Excellence in Research of Hereditary Disorders (PACER-HD), King Abdulaziz University, Jeddah, Saudi Arabia
| | - Yan V Sun
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA
- Atlanta VA Health Care System, Decatur, GA, USA
| | - Cristen J Willer
- Department of Internal Medicine, Division of Cardiology, University of Michigan, Ann Arbor, MI, USA.
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA.
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Stone GW, Farkouh ME, Lala A, Tinuoye E, Dressler O, Moreno PR, Palacios IF, Goodman SG, Esper RB, Abizaid A, Varade D, Betancur JF, Ricalde A, Payro G, Castellano JM, Hung IFN, Nadkarni GN, Giustino G, Godoy LC, Feinman J, Camaj A, Bienstock SW, Furtado RHM, Granada C, Bustamante J, Peyra C, Contreras J, Owen R, Bhatt DL, Pocock SJ, Fuster V. Randomized Trial of Anticoagulation Strategies for Noncritically Ill Patients Hospitalized With COVID-19. J Am Coll Cardiol 2023; 81:1747-1762. [PMID: 36889611 PMCID: PMC9987252 DOI: 10.1016/j.jacc.2023.02.041] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 02/27/2023] [Indexed: 03/08/2023]
Abstract
BACKGROUND Prior studies of therapeutic-dose anticoagulation in patients with COVID-19 have reported conflicting results. OBJECTIVES We sought to determine the safety and effectiveness of therapeutic-dose anticoagulation in noncritically ill patients with COVID-19. METHODS Patients hospitalized with COVID-19 not requiring intensive care unit treatment were randomized to prophylactic-dose enoxaparin, therapeutic-dose enoxaparin, or therapeutic-dose apixaban. The primary outcome was the 30-day composite of all-cause mortality, requirement for intensive care unit-level of care, systemic thromboembolism, or ischemic stroke assessed in the combined therapeutic-dose groups compared with the prophylactic-dose group. RESULTS Between August 26, 2020, and September 19, 2022, 3,398 noncritically ill patients hospitalized with COVID-19 were randomized to prophylactic-dose enoxaparin (n = 1,141), therapeutic-dose enoxaparin (n = 1,136), or therapeutic-dose apixaban (n = 1,121) at 76 centers in 10 countries. The 30-day primary outcome occurred in 13.2% of patients in the prophylactic-dose group and 11.3% of patients in the combined therapeutic-dose groups (HR: 0.85; 95% CI: 0.69-1.04; P = 0.11). All-cause mortality occurred in 7.0% of patients treated with prophylactic-dose enoxaparin and 4.9% of patients treated with therapeutic-dose anticoagulation (HR: 0.70; 95% CI: 0.52-0.93; P = 0.01), and intubation was required in 8.4% vs 6.4% of patients, respectively (HR: 0.75; 95% CI: 0.58-0.98; P = 0.03). Results were similar in the 2 therapeutic-dose groups, and major bleeding in all 3 groups was infrequent. CONCLUSIONS Among noncritically ill patients hospitalized with COVID-19, the 30-day primary composite outcome was not significantly reduced with therapeutic-dose anticoagulation compared with prophylactic-dose anticoagulation. However, fewer patients who were treated with therapeutic-dose anticoagulation required intubation and fewer died (FREEDOM COVID [FREEDOM COVID Anticoagulation Strategy]; NCT04512079).
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Affiliation(s)
- Gregg W Stone
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Michael E Farkouh
- Peter Munk Cardiac Centre, University of Toronto, Toronto, Ontario, Canada
| | - Anuradha Lala
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Elizabeth Tinuoye
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | - Pedro R Moreno
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | - Shaun G Goodman
- St Michael's Hospital, Unity Heath, University of Toronto, Toronto, Ontario, Canada; Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada
| | | | - Alexandre Abizaid
- Heart Institute (InCor), University of São Paulo Medical School, São Paulo, Brazil
| | | | | | | | - Gerardo Payro
- Instituto Nacional de Ciencias Medicas y Nutrición Salvador Zubiran, Mexico City, Mexico
| | - José María Castellano
- Centro Integral de Enfermedades Cardiovasculares (CIEC), Hospital Universitario Monterpincipe, Grupo HM Hospitales, Madrid, Spain
| | | | - Girish N Nadkarni
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Gennaro Giustino
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Lucas C Godoy
- Peter Munk Cardiac Centre, University of Toronto, Toronto, Ontario, Canada
| | - Jason Feinman
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Anton Camaj
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Solomon W Bienstock
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | - Carlos Granada
- CogenTech Medical and Digital Innovation, Mahwah, New Jersey, USA
| | - Jessica Bustamante
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Carlos Peyra
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Johanna Contreras
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Ruth Owen
- London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Deepak L Bhatt
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Stuart J Pocock
- London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Valentin Fuster
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain.
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Forrest IS, Petrazzini BO, Duffy Á, Park JK, O'Neal AJ, Jordan DM, Rocheleau G, Nadkarni GN, Cho JH, Blazer AD, Do R. A machine learning model identifies patients in need of autoimmune disease testing using electronic health records. Nat Commun 2023; 14:2385. [PMID: 37169741 PMCID: PMC10130143 DOI: 10.1038/s41467-023-37996-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 04/05/2023] [Indexed: 05/13/2023] Open
Abstract
Systemic autoimmune rheumatic diseases (SARDs) can lead to irreversible damage if left untreated, yet these patients often endure long diagnostic journeys before being diagnosed and treated. Machine learning may help overcome the challenges of diagnosing SARDs and inform clinical decision-making. Here, we developed and tested a machine learning model to identify patients who should receive rheumatological evaluation for SARDs using longitudinal electronic health records of 161,584 individuals from two institutions. The model demonstrated high performance for predicting cases of autoantibody-tested individuals in a validation set, an external test set, and an independent cohort with a broader case definition. This approach identified more individuals for autoantibody testing compared with current clinical standards and a greater proportion of autoantibody carriers among those tested. Diagnoses of SARDs and other autoimmune conditions increased with higher model probabilities. The model detected a need for autoantibody testing and rheumatology encounters up to five years before the test date and assessment date, respectively. Altogether, these findings illustrate that the clinical manifestations of a diverse array of autoimmune conditions are detectable in electronic health records using machine learning, which may help systematize and accelerate autoimmune testing.
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Affiliation(s)
- Iain S Forrest
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Medical Scientist Training Program, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ben O Petrazzini
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Áine Duffy
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Joshua K Park
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Medical Scientist Training Program, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Anya J O'Neal
- Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Daniel M Jordan
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ghislain Rocheleau
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Girish N Nadkarni
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Judy H Cho
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ashira D Blazer
- Division of Rheumatology, Hospital for Special Surgery, New York, NY, USA
| | - Ron Do
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- The BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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Park JK, Petrazzini BO, Saha A, Vaid A, Vy HMT, Márquez‐Luna C, Chan L, Nadkarni GN, Do R. Machine Learning Identifies Plasma Metabolites Associated With Heart Failure in Underrepresented Populations With the TTR V122I Variant. J Am Heart Assoc 2023; 12:e027736. [PMID: 37042260 PMCID: PMC10227245 DOI: 10.1161/jaha.122.027736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 02/22/2023] [Indexed: 04/13/2023]
Affiliation(s)
- Joshua K. Park
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount SinaiNew YorkNYUSA
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNYUSA
- Medical Scientist Training ProgramIcahn School of Medicine at Mount SinaiNew YorkNYUSA
| | - Ben O. Petrazzini
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount SinaiNew YorkNYUSA
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNYUSA
| | - Aparna Saha
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNYUSA
- Department of MedicineIcahn School of Medicine at Mount SinaiNew YorkNYUSA
- The BioMe Phenomics Center, Icahn School of Medicine at Mount SinaiNew YorkNYUSA
| | - Akhil Vaid
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount SinaiNew YorkNYUSA
- Division of Data‐Driven and Digital Medicine (D3M)Icahn School of Medicine at Mount SinaiNew YorkNYUSA
| | - Ha My T. Vy
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount SinaiNew YorkNYUSA
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNYUSA
| | - Carla Márquez‐Luna
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount SinaiNew YorkNYUSA
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNYUSA
| | - Lili Chan
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount SinaiNew YorkNYUSA
- Department of MedicineIcahn School of Medicine at Mount SinaiNew YorkNYUSA
- The BioMe Phenomics Center, Icahn School of Medicine at Mount SinaiNew YorkNYUSA
- The Mount Sinai Clinical Intelligence CenterIcahn School of Medicine at Mount SinaiNew YorkNYUSA
| | - Girish N. Nadkarni
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount SinaiNew YorkNYUSA
- Department of MedicineIcahn School of Medicine at Mount SinaiNew YorkNYUSA
- The BioMe Phenomics Center, Icahn School of Medicine at Mount SinaiNew YorkNYUSA
- Division of Data‐Driven and Digital Medicine (D3M)Icahn School of Medicine at Mount SinaiNew YorkNYUSA
- The Mount Sinai Clinical Intelligence CenterIcahn School of Medicine at Mount SinaiNew YorkNYUSA
| | - Ron Do
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount SinaiNew YorkNYUSA
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNYUSA
- The BioMe Phenomics Center, Icahn School of Medicine at Mount SinaiNew YorkNYUSA
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Mark PB, Carrero JJ, Matsushita K, Sang Y, Ballew SH, Grams ME, Coresh J, Surapaneni A, Brunskill NJ, Chalmers J, Chan L, Chang AR, Chinnadurai R, Chodick G, Cirillo M, de Zeeuw D, Evans M, Garg AX, Gutierrez OM, Heerspink HJL, Heine GH, Herrington WG, Ishigami J, Kronenberg F, Lee JY, Levin A, Major RW, Marks A, Nadkarni GN, Naimark DMJ, Nowak C, Rahman M, Sabanayagam C, Sarnak M, Sawhney S, Schneider MP, Shalev V, Shin JI, Siddiqui MK, Stempniewicz N, Sumida K, Valdivielso JM, van den Brand J, Yee-Moon Wang A, Wheeler DC, Zhang L, Visseren FLJ, Stengel B. Major cardiovascular events and subsequent risk of kidney failure with replacement therapy: a CKD Prognosis Consortium study. Eur Heart J 2023; 44:1157-1166. [PMID: 36691956 PMCID: PMC10319959 DOI: 10.1093/eurheartj/ehac825] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 12/14/2022] [Accepted: 12/23/2022] [Indexed: 01/25/2023] Open
Abstract
AIMS Chronic kidney disease (CKD) increases risk of cardiovascular disease (CVD). Less is known about how CVD associates with future risk of kidney failure with replacement therapy (KFRT). METHODS AND RESULTS The study included 25 903 761 individuals from the CKD Prognosis Consortium with known baseline estimated glomerular filtration rate (eGFR) and evaluated the impact of prevalent and incident coronary heart disease (CHD), stroke, heart failure (HF), and atrial fibrillation (AF) events as time-varying exposures on KFRT outcomes. Mean age was 53 (standard deviation 17) years and mean eGFR was 89 mL/min/1.73 m2, 15% had diabetes and 8.4% had urinary albumin-to-creatinine ratio (ACR) available (median 13 mg/g); 9.5% had prevalent CHD, 3.2% prior stroke, 3.3% HF, and 4.4% prior AF. During follow-up, there were 269 142 CHD, 311 021 stroke, 712 556 HF, and 605 596 AF incident events and 101 044 (0.4%) patients experienced KFRT. Both prevalent and incident CVD were associated with subsequent KFRT with adjusted hazard ratios (HRs) of 3.1 [95% confidence interval (CI): 2.9-3.3], 2.0 (1.9-2.1), 4.5 (4.2-4.9), 2.8 (2.7-3.1) after incident CHD, stroke, HF and AF, respectively. HRs were highest in first 3 months post-CVD incidence declining to baseline after 3 years. Incident HF hospitalizations showed the strongest association with KFRT [HR 46 (95% CI: 43-50) within 3 months] after adjustment for other CVD subtype incidence. CONCLUSION Incident CVD events strongly and independently associate with future KFRT risk, most notably after HF, then CHD, stroke, and AF. Optimal strategies for addressing the dramatic risk of KFRT following CVD events are needed.
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Affiliation(s)
- Patrick B Mark
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, United Kingdom
| | - Juan J Carrero
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Huddinge, Sweden
- Division of Nephrology, Department of Clinical Sciences, Karolinska Institutet, Danderyd Hospital, Stockholm, Sweden
| | - Kunihiro Matsushita
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 2024 E. Monument Street, Baltimore, MD 21205, USA
| | - Yingying Sang
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 2024 E. Monument Street, Baltimore, MD 21205, USA
| | - Shoshana H Ballew
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 2024 E. Monument Street, Baltimore, MD 21205, USA
| | - Morgan E Grams
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 2024 E. Monument Street, Baltimore, MD 21205, USA
- Department of Medicine, New York University Grossman School of Medicine, 227 East 30th Street, #825 New York, NY 10016, USA
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 2024 E. Monument Street, Baltimore, MD 21205, USA
| | - Aditya Surapaneni
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 2024 E. Monument Street, Baltimore, MD 21205, USA
| | - Nigel J Brunskill
- John Walls Renal Unit, Leicester General Hospital, University Hospitals of Leicester NHS Trust, Leicester, United Kingdom
- Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom
| | - John Chalmers
- The George Institute for Global Health, University of New South Wales, Sydney, NSW, Australia
| | - Lili Chan
- Department of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alex R Chang
- Departments of Nephrology and Population Health Sciences, Geisinger Health, 100 N Academy Ave, Danville, PA 17822, USA
| | - Rajkumar Chinnadurai
- Department of Renal Medicine, Salford Care Organisation, Northern Care Alliance NHS Foundation Trust, Salford, United Kingdom
| | - Gabriel Chodick
- Medical Division, Maccabi Healthcare Services, and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Massimo Cirillo
- Dept. "Scuola Medica Salernitana" University of Salerno Fisciano (SA), Italy
| | - Dick de Zeeuw
- Department of Clinical Pharmacy and Pharmacology, University of Groningen, University Medical Center, Hanzeplein 1, 9713 GZ, Groningen, Netherlands
| | - Marie Evans
- Department of Clinical Intervention, and Technology (CLINTEC), Karolinska University Hospital and Karolinska Institutet, Stockholm, Sweden
| | - Amit X Garg
- ICES, London, Ontario, Canada
- Division of Nephrology, Western University, London, Ontario, Canada
| | - Orlando M Gutierrez
- Departments of Epidemiology and Medicine, University of Alabama at Birmingham, Birmingham, AL
| | - Hiddo J L Heerspink
- Department of Clinical Pharmacy and Pharmacology, University of Groningen, University Medical Center, Hanzeplein 1, 9713 GZ, Groningen, Netherlands
| | - Gunnar H Heine
- Saarland University Medical Center, Internal Medicine IV, Nephrology and Hypertension, Medizinische Klinik IIWilhelm-Epstein-Straße 4 60431 Frankfurt am Main, Germany
| | - William G Herrington
- Medical Research Council Population Health Research Unit, Nuffield Department of Population Health (NDPH), and Clinical Trial Service Unit and Epidemiological Studies Unit, NDPH, University of Oxford, Richard Doll Building Old Road Campus Oxford, Oxfordshire, OX3 7LF, United Kingdom
| | - Junichi Ishigami
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 2024 E. Monument Street, Baltimore, MD 21205, USA
| | - Florian Kronenberg
- Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Jun Young Lee
- Transplantation Center, Department of Nephrology, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju 26426, Korea
| | - Adeera Levin
- Division of Nephrology, University of British Columbia, Vancouver, Canada
| | - Rupert W Major
- John Walls Renal Unit, Leicester General Hospital, University Hospitals of Leicester NHS Trust, Leicester, United Kingdom
- Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom
| | - Angharad Marks
- Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, United Kingdom
| | - Girish N Nadkarni
- Department of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - David M J Naimark
- Sunnybrook Hospital, University of Toronto, Rm 3861929 Bayview Ave. Toronto, Ontario M4G 3E8, Canada
| | - Christoph Nowak
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Mahboob Rahman
- Division of Nephrology, Department of Medicine, Case Western Reserve University, Cleveland, OH
| | - Charumathi Sabanayagam
- Ocular Epidemiology Research Group, Singapore Eye Research Institute, Singapore National Eye Centre, The Academia, 20 College Road, Discovery Tower Level 6, Singapore (169856), Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, NUHS Tower Block, 1E Kent Ridge Road Level 11, Singapore (119228), Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (EYE-ACP), Duke-NUS Medical School, 8 College Road, Singapore (169857), Singapore
| | - Mark Sarnak
- Division of Nephrology, Tufts Medical Center, Boston, MA
| | | | - Markus P Schneider
- Department of Nephrology and Hypertension, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Varda Shalev
- Institute for Health and Research and Innovation, Maccabi Healthcare Services and Tel Aviv University, Tel Aviv, Israel
| | - Jung-Im Shin
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 2024 E. Monument Street, Baltimore, MD 21205, USA
| | - Moneeza K Siddiqui
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, United Kingdom
| | | | - Keiichi Sumida
- Division of Nephrology, Department of Medicine, University of Tennessee Health Science Center, Memphis, TN
| | - José M Valdivielso
- Vascular & Renal Translational Research Group, IRBLleida, Spain and Spanish Research Network for Renal Diseases (RedInRen. ISCIII), Lleida, Spain
| | - Jan van den Brand
- Department of Nephrology, Radboud Institute for Health Sciences, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, The Netherlands
| | - Angela Yee-Moon Wang
- Department of Medicine, Queen Mary Hospital, The University of Hong Kong, 102 Pok Fu Lam Road, Pok Fu Lam, Hong Kong SAR, Hong Kong
| | - David C Wheeler
- Centre for Nephrology, University College London, London, United Kingdom
| | - Lihua Zhang
- National Clinical Research Center of Kidney Disease, Jinling Hospital, Nanjing University School of Medicine, Nanjing, Jiangsu, P.R. China
| | - Frank L J Visseren
- Department of Vascular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Benedicte Stengel
- Clinical Epidemiology team, Centre for Research in Epidemiology and Population Health (CESP), University Paris-Saclay, UVSQ, Inserm, Villejuif, France
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Nadkarni GN. Introduction to Artificial Intelligence and Machine Learning in Nephrology. Clin J Am Soc Nephrol 2023; 18:392-393. [PMID: 36747314 PMCID: PMC10103234 DOI: 10.2215/cjn.0000000000000068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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48
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Grams ME, Brunskill NJ, Ballew SH, Sang Y, Coresh J, Matsushita K, Surapaneni A, Bell S, Carrero JJ, Chodick G, Evans M, Heerspink HJ, Inker LA, Iseki K, Kalra PA, Kirchner HL, Lee BJ, Levin A, Major RW, Medcalf J, Nadkarni GN, Naimark DM, Ricardo AC, Sawhney S, Sood MM, Staplin N, Stempniewicz N, Stengel B, Sumida K, Traynor JP, van den Brand J, Wen CP, Woodward M, Yang JW, Wang AYM, Tangri N. The Kidney Failure Risk Equation: Evaluation of Novel Input Variables including eGFR Estimated Using the CKD-EPI 2021 Equation in 59 Cohorts. J Am Soc Nephrol 2023; 34:482-494. [PMID: 36857500 PMCID: PMC10103205 DOI: 10.1681/asn.0000000000000050] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 10/23/2022] [Indexed: 01/28/2023] Open
Abstract
SIGNIFICANCE STATEMENT The kidney failure risk equation (KFRE) uses age, sex, GFR, and urine albumin-to-creatinine ratio (ACR) to predict 2- and 5-year risk of kidney failure in populations with eGFR <60 ml/min per 1.73 m 2 . However, the CKD-EPI 2021 creatinine equation for eGFR is now recommended for use but has not been fully tested in the context of KFRE. In 59 cohorts comprising 312,424 patients with CKD, the authors assessed the predictive performance and calibration associated with the use of the CKD-EPI 2021 equation and whether additional variables and accounting for the competing risk of death improves the KFRE's performance. The KFRE generally performed well using the CKD-EPI 2021 eGFR in populations with eGFR <45 ml/min per 1.73 m 2 and was not improved by adding the 2-year prior eGFR slope and cardiovascular comorbidities. BACKGROUND The kidney failure risk equation (KFRE) uses age, sex, GFR, and urine albumin-to-creatinine ratio (ACR) to predict kidney failure risk in people with GFR <60 ml/min per 1.73 m 2 . METHODS Using 59 cohorts with 312,424 patients with CKD, we tested several modifications to the KFRE for their potential to improve the KFRE: using the CKD-EPI 2021 creatinine equation for eGFR, substituting 1-year average ACR for single-measure ACR and 1-year average eGFR in participants with high eGFR variability, and adding 2-year prior eGFR slope and cardiovascular comorbidities. We also assessed calibration of the KFRE in subgroups of eGFR and age before and after accounting for the competing risk of death. RESULTS The KFRE remained accurate and well calibrated overall using the CKD-EPI 2021 eGFR equation. The other modifications did not improve KFRE performance. In subgroups of eGFR 45-59 ml/min per 1.73 m 2 and in older adults using the 5-year time horizon, the KFRE demonstrated systematic underprediction and overprediction, respectively. We developed and tested a new model with a spline term in eGFR and incorporating the competing risk of mortality, resulting in more accurate calibration in those specific subgroups but not overall. CONCLUSIONS The original KFRE is generally accurate for eGFR <45 ml/min per 1.73 m 2 when using the CKD-EPI 2021 equation. Incorporating competing risk methodology and splines for eGFR may improve calibration in low-risk settings with longer time horizons. Including historical averages, eGFR slopes, or a competing risk design did not meaningfully alter KFRE performance in most circumstances.
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Affiliation(s)
- Morgan E. Grams
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
- Department of Medicine, New York University Grossman School of Medicine, New York, New York
| | - Nigel J. Brunskill
- Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom
- John Walls Renal Unit, Leicester General Hospital, University Hospitals of Leicester NHS Trust, Leicester, United Kingdom
| | - Shoshana H. Ballew
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Yingying Sang
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Kunihiro Matsushita
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Aditya Surapaneni
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
- Department of Medicine, New York University Grossman School of Medicine, New York, New York
| | - Samira Bell
- Renal Unit, Ninewells Hospital, Dundee, United Kingdom and Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, United Kingdom
| | - Juan J. Carrero
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Huddinge, Sweden
| | - Gabriel Chodick
- Medical Division, Maccabi Healthcare Services, and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Marie Evans
- Department of Clinical Intervention, and Technology (CLINTEC), Karolinska University Hospital and Karolinska Institutet, Stockholm, Sweden
| | - Hiddo J.L. Heerspink
- Department of Clinical Pharmacy and Pharmacology, University of Groningen, University Medical Center, Groningen, Netherlands
| | | | | | - Philip A. Kalra
- Department of Renal Medicine, Salford Royal Hospital, Northern Care Alliance NHS Foundation Trust, Salford, United Kingdom
| | - H. Lester Kirchner
- Department of Population Health Sciences, Geisinger, Danville, Pennsylvania
| | - Brian J. Lee
- Kaiser Permanente, Hawaii Region, and Moanalua Medical Center, Honolulu, Hawaii
| | - Adeera Levin
- Division of Nephrology, University of British Columbia, Vancouver, Canada
| | - Rupert W. Major
- Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom
- John Walls Renal Unit, Leicester General Hospital, University Hospitals of Leicester NHS Trust, Leicester, United Kingdom
| | - James Medcalf
- Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom
- John Walls Renal Unit, Leicester General Hospital, University Hospitals of Leicester NHS Trust, Leicester, United Kingdom
| | - Girish N. Nadkarni
- Department of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York
| | | | - Ana C. Ricardo
- Department of Medicine, University of Illinois, Chicago, Illinois
| | - Simon Sawhney
- University of Aberdeen, Aberdeen, Scotland, United Kingdom
| | - Manish M. Sood
- Division of Nephrology, Department of Medicine, University of Ottawa, Ottawa, Canada
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
- Division of Nephrology, Department of Medicine, The Ottawa Hospital, Ottawa, Ontario, Canada
| | - Natalie Staplin
- MRC Population Health Research Unit, Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Oxford, United Kingdom
| | - Nikita Stempniewicz
- AMGA (American Medical Group Association), Alexandria, Virginia and OptumLabs Visiting Fellow
| | - Benedicte Stengel
- Clinical Epidemiology Team, Centre for Research in Epidemiology and Population Health (CESP), University Paris-Saclay, UVSQ, Inserm, Villejuif, France
| | - Keiichi Sumida
- Division of Nephrology, Department of Medicine, University of Tennessee Health Science Center, Memphis, Tennessee
| | - Jamie P. Traynor
- Glasgow Renal and Transplant Unit, Queen Elizabeth University Hospital Glasgow Scotland, United Kingdom
| | - Jan van den Brand
- Department of Nephrology, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Chi-Pang Wen
- National Health Research Institutes, Miaoli, Taiwan and China Medical University Hospital, Taichung, Taiwan
| | - Mark Woodward
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
- George Institute for Global Health, University of New South Wales, Sydney, Australia
- George Institute for Global Health, Imperial College London, London, United Kingdom
| | - Jae Won Yang
- Department of Internal Medicine, Yonsei University Wonju College of Medicine, Wonju, Republic of Korea
| | - Angela Yee-Moon Wang
- Department of Medicine, Queen Mary Hospital, The University of Hong Kong, Hong Kong SAR
| | - Navdeep Tangri
- Division of Nephrology, Department of Medicine, University of Manitoba, Winnipeg, Canada
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Vaid A, Argulian E, Lerakis S, Beaulieu-Jones BK, Krittanawong C, Klang E, Lampert J, Reddy VY, Narula J, Nadkarni GN, Glicksberg BS. Multi-center retrospective cohort study applying deep learning to electrocardiograms to identify left heart valvular dysfunction. Commun Med (Lond) 2023; 3:24. [PMID: 36788316 PMCID: PMC9929085 DOI: 10.1038/s43856-023-00240-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 01/09/2023] [Indexed: 02/16/2023] Open
Abstract
BACKGROUND Aortic Stenosis and Mitral Regurgitation are common valvular conditions representing a hidden burden of disease within the population. The aim of this study was to develop and validate deep learning-based screening and diagnostic tools that can help guide clinical decision making. METHODS In this multi-center retrospective cohort study, we acquired Transthoracic Echocardiogram reports from five Mount Sinai hospitals within New York City representing a demographically diverse cohort of patients. We developed a Natural Language Processing pipeline to extract ground-truth labels about valvular status and paired these to Electrocardiograms (ECGs). We developed and externally validated deep learning models capable of detecting valvular disease, in addition to considering scenarios of clinical deployment. RESULTS We use 617,338 ECGs paired to transthoracic echocardiograms from 123,096 patients to develop a deep learning model for detection of Mitral Regurgitation. Area Under Receiver Operating Characteristic curve (AUROC) is 0.88 (95% CI:0.88-0.89) in internal testing, and 0.81 (95% CI:0.80-0.82) in external validation. To develop a model for detection of Aortic Stenosis, we use 617,338 Echo-ECG pairs for 128,628 patients. AUROC is 0.89 (95% CI: 0.88-0.89) in internal testing, going to 0.86 (95% CI: 0.85-0.87) in external validation. The model's performance increases leading up to the time of the diagnostic echo, and it performs well in validation against requirement of Transcatheter Aortic Valve Replacement procedures. CONCLUSIONS Deep learning based tools can increase the amount of information extracted from ubiquitous investigations such as the ECG. Such tools are inexpensive, can help in earlier disease detection, and potentially improve prognosis.
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Affiliation(s)
- Akhil Vaid
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Edgar Argulian
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Cardiology, Mount Sinai Morningside Hospital, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Stamatios Lerakis
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Cardiology, Mount Sinai Morningside Hospital, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Brett K Beaulieu-Jones
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Section of Biomedical Data Science, Department of Medicine, University of Chicago, Chicago, IL, USA
| | | | - Eyal Klang
- Sheba Medical Center, Department of Diagnostic Imaging, Tel Hashomer, Israel
- Sackler Medical School, Tel Aviv University, Tel Aviv, 52621, Israel
| | - Joshua Lampert
- Helmsley Electrophysiology Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Vivek Y Reddy
- Helmsley Electrophysiology Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jagat Narula
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Cardiology, Mount Sinai Morningside Hospital, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Girish N Nadkarni
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Division of Data Driven and Digital Medicine (D3M), The Department of Medicine, Icahn School of Medicine at Mount Siniai, New York, NY, USA
| | - Benjamin S Glicksberg
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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Pacheco JA, Rasmussen LV, Wiley K, Person TN, Cronkite DJ, Sohn S, Murphy S, Gundelach JH, Gainer V, Castro VM, Liu C, Mentch F, Lingren T, Sundaresan AS, Eickelberg G, Willis V, Furmanchuk A, Patel R, Carrell DS, Deng Y, Walton N, Satterfield BA, Kullo IJ, Dikilitas O, Smith JC, Peterson JF, Shang N, Kiryluk K, Ni Y, Li Y, Nadkarni GN, Rosenthal EA, Walunas TL, Williams MS, Karlson EW, Linder JE, Luo Y, Weng C, Wei W. Evaluation of the portability of computable phenotypes with natural language processing in the eMERGE network. Sci Rep 2023; 13:1971. [PMID: 36737471 PMCID: PMC9898520 DOI: 10.1038/s41598-023-27481-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 01/03/2023] [Indexed: 02/05/2023] Open
Abstract
The electronic Medical Records and Genomics (eMERGE) Network assessed the feasibility of deploying portable phenotype rule-based algorithms with natural language processing (NLP) components added to improve performance of existing algorithms using electronic health records (EHRs). Based on scientific merit and predicted difficulty, eMERGE selected six existing phenotypes to enhance with NLP. We assessed performance, portability, and ease of use. We summarized lessons learned by: (1) challenges; (2) best practices to address challenges based on existing evidence and/or eMERGE experience; and (3) opportunities for future research. Adding NLP resulted in improved, or the same, precision and/or recall for all but one algorithm. Portability, phenotyping workflow/process, and technology were major themes. With NLP, development and validation took longer. Besides portability of NLP technology and algorithm replicability, factors to ensure success include privacy protection, technical infrastructure setup, intellectual property agreement, and efficient communication. Workflow improvements can improve communication and reduce implementation time. NLP performance varied mainly due to clinical document heterogeneity; therefore, we suggest using semi-structured notes, comprehensive documentation, and customization options. NLP portability is possible with improved phenotype algorithm performance, but careful planning and architecture of the algorithms is essential to support local customizations.
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Affiliation(s)
| | | | - Ken Wiley
- National Human Genome Research Institute, Bethesda, USA
| | | | - David J Cronkite
- Kaiser Permanente Washington Health Research Institute, Seattle, USA
| | | | | | | | | | | | - Cong Liu
- Columbia University, New York, USA
| | - Frank Mentch
- Children's Hospital of Philadelphia, Philadelphia, USA
| | - Todd Lingren
- Cincinnati Children's Hospital Medical Center, Cincinnati, USA
| | | | | | | | | | | | - David S Carrell
- Kaiser Permanente Washington Health Research Institute, Seattle, USA
| | - Yu Deng
- Northwestern University, Evanston, USA
| | | | | | | | | | | | | | | | | | - Yizhao Ni
- Cincinnati Children's Hospital Medical Center, Cincinnati, USA
| | - Yikuan Li
- Northwestern University, Evanston, USA
| | | | | | | | | | | | | | - Yuan Luo
- Northwestern University, Evanston, USA
| | | | - WeiQi Wei
- Vanderbilt University Medical Center, Nashville, USA
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