1
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Gadd DA, Hillary RF, Kuncheva Z, Mangelis T, Cheng Y, Dissanayake M, Admanit R, Gagnon J, Lin T, Ferber KL, Runz H, Foley CN, Marioni RE, Sun BB. Blood protein assessment of leading incident diseases and mortality in the UK Biobank. NATURE AGING 2024:10.1038/s43587-024-00655-7. [PMID: 38987645 DOI: 10.1038/s43587-024-00655-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 05/22/2024] [Indexed: 07/12/2024]
Abstract
The circulating proteome offers insights into the biological pathways that underlie disease. Here, we test relationships between 1,468 Olink protein levels and the incidence of 23 age-related diseases and mortality in the UK Biobank (n = 47,600). We report 3,209 associations between 963 protein levels and 21 incident outcomes. Next, protein-based scores (ProteinScores) are developed using penalized Cox regression. When applied to test sets, six ProteinScores improve the area under the curve estimates for the 10-year onset of incident outcomes beyond age, sex and a comprehensive set of 24 lifestyle factors, clinically relevant biomarkers and physical measures. Furthermore, the ProteinScore for type 2 diabetes outperforms a polygenic risk score and HbA1c-a clinical marker used to monitor and diagnose type 2 diabetes. The performance of scores using metabolomic and proteomic features is also compared. These data characterize early proteomic contributions to major age-related diseases, demonstrating the value of the plasma proteome for risk stratification.
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Affiliation(s)
- Danni A Gadd
- Optima Partners, Edinburgh, UK
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Robert F Hillary
- Optima Partners, Edinburgh, UK
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Zhana Kuncheva
- Optima Partners, Edinburgh, UK
- Bayes Centre, University of Edinburgh, Edinburgh, UK
| | - Tasos Mangelis
- Optima Partners, Edinburgh, UK
- Bayes Centre, University of Edinburgh, Edinburgh, UK
| | - Yipeng Cheng
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Manju Dissanayake
- Optima Partners, Edinburgh, UK
- Bayes Centre, University of Edinburgh, Edinburgh, UK
| | - Romi Admanit
- Biostatistics, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Jake Gagnon
- Biostatistics, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Tinchi Lin
- Biostatistics, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Kyle L Ferber
- Biostatistics, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Heiko Runz
- Translational Sciences, Research and Development, Biogen Inc., Cambridge, MA, USA
| | - Christopher N Foley
- Optima Partners, Edinburgh, UK.
- Bayes Centre, University of Edinburgh, Edinburgh, UK.
| | - Riccardo E Marioni
- Optima Partners, Edinburgh, UK.
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK.
| | - Benjamin B Sun
- Translational Sciences, Research and Development, Biogen Inc., Cambridge, MA, USA.
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
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2
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Vabalas A, Hartonen T, Vartiainen P, Jukarainen S, Viippola E, Rodosthenous RS, Liu A, Hägg S, Perola M, Ganna A. Deep learning-based prediction of one-year mortality in Finland is an accurate but unfair aging marker. NATURE AGING 2024:10.1038/s43587-024-00657-5. [PMID: 38914859 DOI: 10.1038/s43587-024-00657-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 05/27/2024] [Indexed: 06/26/2024]
Abstract
Short-term mortality risk, which is indicative of individual frailty, serves as a marker for aging. Previous age clocks focused on predicting either chronological age or longer-term mortality. Aging clocks predicting short-term mortality are lacking and their algorithmic fairness remains unexamined. We developed a deep learning model to predict 1-year mortality using nationwide longitudinal data from the Finnish population (FinRegistry; n = 5.4 million), incorporating more than 8,000 features spanning up to 50 years. We achieved an area under the curve (AUC) of 0.944, outperforming a baseline model that included only age and sex (AUC = 0.897). The model generalized well to different causes of death (AUC > 0.800 for 45 of 50 causes), including coronavirus disease 2019, which was absent in the training data. Performance varied among demographics, with young females exhibiting the best and older males the worst results. Extensive prediction fairness analyses highlighted disparities among disadvantaged groups, posing challenges to equitable integration into public health interventions. Our model accurately identified short-term mortality risk, potentially serving as a population-wide aging marker.
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Affiliation(s)
- Andrius Vabalas
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Tuomo Hartonen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Pekka Vartiainen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Pediatric Research Center, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Sakari Jukarainen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Essi Viippola
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | | | - Aoxing Liu
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Sara Hägg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Markus Perola
- The Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Andrea Ganna
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
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3
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Kuo CL, Chen Z, Liu P, Pilling LC, Atkins JL, Fortinsky RH, Kuchel GA, Diniz BS. Proteomic aging clock (PAC) predicts age-related outcomes in middle-aged and older adults. Aging Cell 2024:e14195. [PMID: 38747160 DOI: 10.1111/acel.14195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Revised: 04/19/2024] [Accepted: 04/23/2024] [Indexed: 05/28/2024] Open
Abstract
Beyond mere prognostication, optimal biomarkers of aging provide insights into qualitative and quantitative features of biological aging and might, therefore, offer useful information for the testing and, ultimately, clinical use of gerotherapeutics. We aimed to develop a proteomic aging clock (PAC) for all-cause mortality risk as a proxy of biological age. Data were from the UK Biobank Pharma Proteomics Project, including 53,021 participants aged between 39 and 70 years and 2923 plasma proteins assessed using the Olink Explore 3072 assay®. 10.9% of the participants died during a mean follow-up of 13.3 years, with the mean age at death of 70.1 years. The Spearman correlation between PAC proteomic age and chronological age was 0.77. PAC showed robust age-adjusted associations and predictions for all-cause mortality and the onset of various diseases in general and disease-free participants. The proteins associated with PAC proteomic age deviation were enriched in several processes related to the hallmarks of biological aging. Our results expand previous findings by showing that biological age acceleration, based on PAC, strongly predicts all-cause mortality and several incident disease outcomes. Particularly, it facilitates the evaluation of risk for multiple conditions in a disease-free population, thereby, contributing to the prevention of initial diseases, which vary among individuals and may subsequently lead to additional comorbidities.
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Affiliation(s)
- Chia-Ling Kuo
- Department of Public Health Sciences, University of Connecticut Health Center, Farmington, Connecticut, USA
- The Cato T. Laurencin Institute for Regenerative Engineering, University of Connecticut Health Center, Farmington, Connecticut, USA
- UConn Center on Aging, University of Connecticut Health Center, Farmington, Connecticut, USA
| | - Zhiduo Chen
- UConn Center on Aging, University of Connecticut Health Center, Farmington, Connecticut, USA
| | - Peiran Liu
- The Cato T. Laurencin Institute for Regenerative Engineering, University of Connecticut Health Center, Farmington, Connecticut, USA
| | - Luke C Pilling
- Epidemiology and Public Health Group, Department of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK
| | - Janice L Atkins
- Epidemiology and Public Health Group, Department of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK
| | - Richard H Fortinsky
- UConn Center on Aging, University of Connecticut Health Center, Farmington, Connecticut, USA
| | - George A Kuchel
- UConn Center on Aging, University of Connecticut Health Center, Farmington, Connecticut, USA
| | - Breno S Diniz
- Department of Public Health Sciences, University of Connecticut Health Center, Farmington, Connecticut, USA
- UConn Center on Aging, University of Connecticut Health Center, Farmington, Connecticut, USA
- Department of Psychiatry, University of Connecticut Health Center, Farmington, Connecticut, USA
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4
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Moqri M, Herzog C, Poganik JR, Ying K, Justice JN, Belsky DW, Higgins-Chen AT, Chen BH, Cohen AA, Fuellen G, Hägg S, Marioni RE, Widschwendter M, Fortney K, Fedichev PO, Zhavoronkov A, Barzilai N, Lasky-Su J, Kiel DP, Kennedy BK, Cummings S, Slagboom PE, Verdin E, Maier AB, Sebastiano V, Snyder MP, Gladyshev VN, Horvath S, Ferrucci L. Validation of biomarkers of aging. Nat Med 2024; 30:360-372. [PMID: 38355974 PMCID: PMC11090477 DOI: 10.1038/s41591-023-02784-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 12/19/2023] [Indexed: 02/16/2024]
Abstract
The search for biomarkers that quantify biological aging (particularly 'omic'-based biomarkers) has intensified in recent years. Such biomarkers could predict aging-related outcomes and could serve as surrogate endpoints for the evaluation of interventions promoting healthy aging and longevity. However, no consensus exists on how biomarkers of aging should be validated before their translation to the clinic. Here, we review current efforts to evaluate the predictive validity of omic biomarkers of aging in population studies, discuss challenges in comparability and generalizability and provide recommendations to facilitate future validation of biomarkers of aging. Finally, we discuss how systematic validation can accelerate clinical translation of biomarkers of aging and their use in gerotherapeutic clinical trials.
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Affiliation(s)
- Mahdi Moqri
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Genetics, School of Medicine, Stanford University, Stanford, CA, USA
- Department of Obstetrics and Gynecology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Chiara Herzog
- European Translational Oncology Prevention and Screening Institute, Universität Innsbruck, Innsbruck, Austria
| | - Jesse R Poganik
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Kejun Ying
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Jamie N Justice
- Department of Internal Medicine, Section on Gerontology and Geriatric Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Daniel W Belsky
- Department of Epidemiology, Butler Columbia Aging Center, Mailman School of Public Health, Columbia University, New York, NY, USA
| | | | - Brian H Chen
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, San Diego, CA, USA
| | - Alan A Cohen
- Department of Environmental Health Sciences, Butler Columbia Aging Center, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Georg Fuellen
- Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, Rostock, Germany
- School of Medicine, University College Dublin, Dublin, Ireland
| | - Sara Hägg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Martin Widschwendter
- European Translational Oncology Prevention and Screening Institute, Universität Innsbruck, Innsbruck, Austria
- Department of Women's Cancer, EGA Institute for Women's Health, University College London, London, UK
- Department of Women's and Children's Health, Division of Obstetrics and Gynaecology, Karolinska Institutet, Stockholm, Sweden
| | | | | | | | - Nir Barzilai
- Institute for Aging Research, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Jessica Lasky-Su
- Department of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Douglas P Kiel
- Musculoskeletal Research Center, Hinda and Arthur Marcus Institute for Aging Research and Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Brian K Kennedy
- Healthy Longevity Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Healthy Longevity Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Centre for Healthy Longevity, @AgeSingapore, National University Health System, Singapore, Singapore
| | - Steven Cummings
- San Francisco Coordinating Center, California Pacific Medical Center Research Institute, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - P Eline Slagboom
- Section of Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | - Eric Verdin
- Buck Institute for Research on Aging, Novato, CA, USA
| | - Andrea B Maier
- Healthy Longevity Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Centre for Healthy Longevity, @AgeSingapore, National University Health System, Singapore, Singapore
- Department of Human Movement Sciences, @AgeAmsterdam, Amsterdam Movement Sciences, Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Vittorio Sebastiano
- Department of Obstetrics and Gynecology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Michael P Snyder
- Department of Genetics, School of Medicine, Stanford University, Stanford, CA, USA
| | - Vadim N Gladyshev
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
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5
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Rattan SIS. Seven knowledge gaps in modern biogerontology. Biogerontology 2024; 25:1-8. [PMID: 38206540 DOI: 10.1007/s10522-023-10089-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2024]
Abstract
About a year ago, members of the editorial board of Biogerontology were requested to respond to a query by the editor-in-chief of the journal as to what one question within their field of ageing research still needs to be asked and answered. This editorial is inspired by the wide range and variety of questions, ideas, comments and suggestions received in response to that query. The seven knowledge gaps identified in this article are arranged into three main categories: evolutionary aspects of longevity, biological survival and death aspects, and heterogeneity in the progression and phenotype of ageing. This is not an exhaustive and exclusive list, and may be modified and expanded. Implications of these knowledge gaps, especially in the context of ongoing attempts to develop effective interventions in ageing and longevity are also discussed.
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Affiliation(s)
- Suresh I S Rattan
- Department of Molecular Biology and Genetics, Aarhus University, 8000, Aarhus C, Denmark.
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6
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Haldar SM. Keeping translational research grounded in human biology. J Clin Invest 2024; 134:e178332. [PMID: 38226617 PMCID: PMC10763720 DOI: 10.1172/jci178332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2024] Open
Affiliation(s)
- Saptarsi M. Haldar
- Amgen Research, South San Francisco, California, USA
- UCSF, San Francisco, California, USA
- Gladstone Institutes, San Francisco, California, USA
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7
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Srialluri N, Surapaneni A, Schlosser P, Chen TK, Schmidt IM, Rhee EP, Coresh J, Grams ME. Circulating Proteins and Mortality in CKD: A Proteomics Study of the AASK and ARIC Cohorts. Kidney Med 2023; 5:100714. [PMID: 37711886 PMCID: PMC10498294 DOI: 10.1016/j.xkme.2023.100714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/16/2023] Open
Abstract
Rationale & Objective Proteomics could provide pathophysiologic insight into the increased risk of mortality in patients with chronic kidney disease (CKD). This study aimed to investigate associations between the circulating proteome and all-cause mortality among patients with CKD. Study Design Observational cohort study. Setting & Participants Primary analysis in 703 participants in the African American Study of Kidney Disease and Hypertension (AASK) and validation in 1,628 participants with CKD in the Atherosclerosis Risk in Communities (ARIC) study who attended visit 5. Exposure Circulating proteins. Outcome All-cause mortality. Analytical Approach Among AASK participants, we evaluated the associations of 6,790 circulating proteins with all-cause mortality using multivariable Cox proportional hazards models. Proteins with significant associations were further studied in ARIC Visit 5 participants with CKD. Results In the AASK cohort, the mean age was 54.5 years, 271 (38.5%) were women, and the mean measured glomerular filtration rate (GFR) was 46 mL/min/1.73 m2. The median follow-up was 9.6 years, and 7 distinct proteins were associated with all-cause mortality at the Bonferroni-level threshold (P < 0.05 of the 6,790) after adjustment for demographics and clinical factors, including baseline measured estimated GFR and proteinuria. In the ARIC visit 5 cohort, the mean age was 77.2 years, 903 (55.5%) were women, the mean estimated GFR was 54 mL/min/1.73 m2 and median follow-up was 6.9 years. Of the 7 proteins found in AASK, 3 (β2-microglobulin, spondin-1, and N-terminal pro-brain natriuretic peptide) were available in the ARIC data, with all 3 significantly associated with death in ARIC. Limitations Possibility of unmeasured confounding. Cause of death was not known. Conclusions Using large-scale proteomic analysis, proteins were reproducibly associated with mortality in 2 cohorts of participants with CKD. Plain-Language Summary Patients with chronic kidney disease (CKD) have a high risk of premature death, with various pathophysiological processes contributing to this increased risk of mortality. This observational cohort study aimed to investigate the associations between circulating proteins and all-cause mortality in patients with CKD using large-scale proteomic analysis. The study analyzed data from the African American Study of Kidney Disease and Hypertension (AASK) study and validated the findings in the Atherosclerosis Risk in Communities (ARIC) Study. A total of 6,790 circulating proteins were evaluated in AASK, and 7 proteins were significantly associated with all-cause mortality. Three of these proteins (β2-microglobulin, spondin-1, and N-terminal pro-brain natriuretic peptide (BNP)) were also measured in ARIC and were significantly associated with death. Additional studies assessing biomarkers associated with mortality among patients with CKD are needed to evaluate their use in clinical practice.
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Affiliation(s)
- Nityasree Srialluri
- Department of Medicine, Johns Hopkins University, Baltimore, Maryland
- Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins University, Baltimore, Maryland
| | - Aditya Surapaneni
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland
- Division of Precision Medicine, Department of Medicine, New York University, New York, New York
| | - Pascal Schlosser
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland
| | - Teresa K. Chen
- Department of Medicine, Johns Hopkins University, Baltimore, Maryland
- Kidney Health Research Collaborative; Division of Nephrology, Department of Medicine, University of California San Francisco and San Francisco VA Health Care System, San Francisco, California
| | - Insa M. Schmidt
- Department of Medicine, Boston University School of Medicine, Boston Medical Center, Boston, Massachusetts
| | - Eugene P. Rhee
- Nephrology Division and Endocrine Unit, Massachusetts General Hospital, Boston, Massachusetts
| | - Josef Coresh
- Department of Medicine, Johns Hopkins University, Baltimore, Maryland
- Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins University, Baltimore, Maryland
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland
| | - Morgan E. Grams
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland
- Division of Precision Medicine, Department of Medicine, New York University, New York, New York
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8
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Møller PL, Rohde PD, Dahl JN, Rasmussen LD, Schmidt SE, Nissen L, McGilligan V, Bentzon JF, Gudbjartsson DF, Stefansson K, Holm H, Winther S, Bøttcher M, Nyegaard M. Combining Polygenic and Proteomic Risk Scores With Clinical Risk Factors to Improve Performance for Diagnosing Absence of Coronary Artery Disease in Patients With de novo Chest Pain. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2023; 16:442-451. [PMID: 37753640 DOI: 10.1161/circgen.123.004053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 08/11/2023] [Indexed: 09/28/2023]
Abstract
BACKGROUND Patients with de novo chest pain, referred for evaluation of possible coronary artery disease (CAD), frequently have an absence of CAD resulting in millions of tests not having any clinical impact. The objective of this study was to investigate whether polygenic risk scores and targeted proteomics improve the prediction of absence of CAD in patients with suspected CAD, when added to the PROMISE (Prospective Multicenter Imaging Study for Evaluation of Chest Pain) minimal risk score (PMRS). METHODS Genotyping and targeted plasma proteomics (N=368 proteins) were performed in 1440 patients with symptoms suspected to be caused by CAD undergoing coronary computed tomography angiography. Based on individual genotypes, a polygenic risk score for CAD (PRSCAD) was calculated. The prediction was performed using combinations of PRSCAD, proteins, and PMRS as features in models using stability selection and machine learning. RESULTS Prediction of absence of CAD yielded an area under the curve of PRSCAD-model, 0.64±0.03; proteomic-model, 0.58±0.03; and PMRS model, 0.76±0.02. No significant correlation was found between the genetic and proteomic risk scores (Pearson correlation coefficient, -0.04; P=0.13). Optimal predictive ability was achieved by the full model (PRSCAD+protein+PMRS) yielding an area under the curve of 0.80±0.02 for absence of CAD, significantly better than the PMRS model alone (P<0.001). For reclassification purpose, the full model enabled down-classification of 49% (324 of 661) of the 5% to 15% pretest probability patients and 18% (113 of 611) of >15% pretest probability patients. CONCLUSIONS For patients with chest pain and low-intermediate CAD risk, incorporating targeted proteomics and polygenic risk scores into the risk assessment substantially improved the ability to predict the absence of CAD. Genetics and proteomics seem to add complementary information to the clinical risk factors and improve risk stratification in this large patient group. REGISTRATION URL: https://www. CLINICALTRIALS gov; Unique identifier: NCT02264717.
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Affiliation(s)
- Peter Loof Møller
- Department of Biomedicine (P.L.M., M.N.), Aarhus University
- Department of Health Science and Technology, Aalborg University (P.L.M., P.D.R., S.E.S., M.N.)
| | - Palle Duun Rohde
- Department of Health Science and Technology, Aalborg University (P.L.M., P.D.R., S.E.S., M.N.)
| | - Jonathan Nørtoft Dahl
- Department of Clinical Medicine (J.N.D., L.D.R., L.N., J.F.B., S.W., M.B.), Aarhus University
- Department of Cardiology, Gødstrup Hospital, Herning, Denmark (J.N.D., L.D.R., L.N., S.W., M.B.)
| | - Laust Dupont Rasmussen
- Department of Clinical Medicine (J.N.D., L.D.R., L.N., J.F.B., S.W., M.B.), Aarhus University
- Department of Cardiology, Gødstrup Hospital, Herning, Denmark (J.N.D., L.D.R., L.N., S.W., M.B.)
| | - Samuel Emil Schmidt
- Department of Health Science and Technology, Aalborg University (P.L.M., P.D.R., S.E.S., M.N.)
| | - Louise Nissen
- Department of Clinical Medicine (J.N.D., L.D.R., L.N., J.F.B., S.W., M.B.), Aarhus University
- Department of Cardiology, Gødstrup Hospital, Herning, Denmark (J.N.D., L.D.R., L.N., S.W., M.B.)
| | - Victoria McGilligan
- Personalized Medicine Centre, Ulster University, Derry, Northern Ireland (V.M.)
| | - Jacob F Bentzon
- Department of Clinical Medicine (J.N.D., L.D.R., L.N., J.F.B., S.W., M.B.), Aarhus University
- Centro Nacional de Investigaciones Cardiovasculares, Madrid, Spain (J.F.B.)
| | - Daniel F Gudbjartsson
- deCODE genetics/Amgen, Inc. (D.F.G., K.S., H.H.)
- School of Engineering and Natural Sciences (D.F.G.)
| | - Kari Stefansson
- deCODE genetics/Amgen, Inc. (D.F.G., K.S., H.H.)
- Faculty of Medicine, University of Iceland, Reykjavik (K.S.)
| | - Hilma Holm
- deCODE genetics/Amgen, Inc. (D.F.G., K.S., H.H.)
| | - Simon Winther
- Department of Clinical Medicine (J.N.D., L.D.R., L.N., J.F.B., S.W., M.B.), Aarhus University
- Department of Cardiology, Gødstrup Hospital, Herning, Denmark (J.N.D., L.D.R., L.N., S.W., M.B.)
| | - Morten Bøttcher
- Department of Clinical Medicine (J.N.D., L.D.R., L.N., J.F.B., S.W., M.B.), Aarhus University
- Department of Cardiology, Gødstrup Hospital, Herning, Denmark (J.N.D., L.D.R., L.N., S.W., M.B.)
| | - Mette Nyegaard
- Department of Biomedicine (P.L.M., M.N.), Aarhus University
- Department of Health Science and Technology, Aalborg University (P.L.M., P.D.R., S.E.S., M.N.)
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9
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Helgason H, Eiriksdottir T, Ulfarsson MO, Choudhary A, Lund SH, Ivarsdottir EV, Hjorleifsson Eldjarn G, Einarsson G, Ferkingstad E, Moore KHS, Honarpour N, Liu T, Wang H, Hucko T, Sabatine MS, Morrow DA, Giugliano RP, Ostrowski SR, Pedersen OB, Bundgaard H, Erikstrup C, Arnar DO, Thorgeirsson G, Masson G, Magnusson OT, Saemundsdottir J, Gretarsdottir S, Steinthorsdottir V, Thorleifsson G, Helgadottir A, Sulem P, Thorsteinsdottir U, Holm H, Gudbjartsson D, Stefansson K. Evaluation of Large-Scale Proteomics for Prediction of Cardiovascular Events. JAMA 2023; 330:725-735. [PMID: 37606673 PMCID: PMC10445198 DOI: 10.1001/jama.2023.13258] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 06/29/2023] [Indexed: 08/23/2023]
Abstract
Importance Whether protein risk scores derived from a single plasma sample could be useful for risk assessment for atherosclerotic cardiovascular disease (ASCVD), in conjunction with clinical risk factors and polygenic risk scores, is uncertain. Objective To develop protein risk scores for ASCVD risk prediction and compare them to clinical risk factors and polygenic risk scores in primary and secondary event populations. Design, Setting, and Participants The primary analysis was a retrospective study of primary events among 13 540 individuals in Iceland (aged 40-75 years) with proteomics data and no history of major ASCVD events at recruitment (study duration, August 23, 2000 until October 26, 2006; follow-up through 2018). We also analyzed a secondary event population from a randomized, double-blind lipid-lowering clinical trial (2013-2016), consisting of individuals with stable ASCVD receiving statin therapy and for whom proteomic data were available for 6791 individuals. Exposures Protein risk scores (based on 4963 plasma protein levels and developed in a training set in the primary event population); polygenic risk scores for coronary artery disease and stroke; and clinical risk factors that included age, sex, statin use, hypertension treatment, type 2 diabetes, body mass index, and smoking status at the time of plasma sampling. Main Outcomes and Measures Outcomes were composites of myocardial infarction, stroke, and coronary heart disease death or cardiovascular death. Performance was evaluated using Cox survival models and measures of discrimination and reclassification that accounted for the competing risk of non-ASCVD death. Results In the primary event population test set (4018 individuals [59.0% women]; 465 events; median follow-up, 15.8 years), the protein risk score had a hazard ratio (HR) of 1.93 per SD (95% CI, 1.75 to 2.13). Addition of protein risk score and polygenic risk scores significantly increased the C index when added to a clinical risk factor model (C index change, 0.022 [95% CI, 0.007 to 0.038]). Addition of the protein risk score alone to a clinical risk factor model also led to a significantly increased C index (difference, 0.014 [95% CI, 0.002 to 0.028]). Among White individuals in the secondary event population (6307 participants; 432 events; median follow-up, 2.2 years), the protein risk score had an HR of 1.62 per SD (95% CI, 1.48 to 1.79) and significantly increased C index when added to a clinical risk factor model (C index change, 0.026 [95% CI, 0.011 to 0.042]). The protein risk score was significantly associated with major adverse cardiovascular events among individuals of African and Asian ancestries in the secondary event population. Conclusions and Relevance A protein risk score was significantly associated with ASCVD events in primary and secondary event populations. When added to clinical risk factors, the protein risk score and polygenic risk score both provided statistically significant but modest improvement in discrimination.
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Affiliation(s)
- Hannes Helgason
- deCODE genetics/Amgen, Inc, Reykjavik, Iceland
- University of Iceland, Reykjavik, Iceland
| | | | - Magnus O. Ulfarsson
- deCODE genetics/Amgen, Inc, Reykjavik, Iceland
- University of Iceland, Reykjavik, Iceland
| | | | | | | | | | | | | | | | | | | | - Huei Wang
- Amgen, Inc, Thousand Oaks, California
| | | | - Marc S. Sabatine
- TIMI Study Group, Division of Cardiovascular Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts
| | - David A. Morrow
- TIMI Study Group, Division of Cardiovascular Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts
| | - Robert P. Giugliano
- TIMI Study Group, Division of Cardiovascular Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts
| | - Sisse Rye Ostrowski
- Department of Clinical Immunology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ole Birger Pedersen
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Immunology, Zealand University Hospital, Køge, Denmark
| | - Henning Bundgaard
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Cardiology, The Heart Center, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Christian Erikstrup
- Department of Clinical Immunology, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - David O. Arnar
- deCODE genetics/Amgen, Inc, Reykjavik, Iceland
- University of Iceland, Reykjavik, Iceland
- Landspitali—The National University Hospital of Iceland, Reykjavik, Iceland
| | - Gudmundur Thorgeirsson
- deCODE genetics/Amgen, Inc, Reykjavik, Iceland
- University of Iceland, Reykjavik, Iceland
- Landspitali—The National University Hospital of Iceland, Reykjavik, Iceland
| | | | | | | | | | | | | | | | | | | | - Hilma Holm
- deCODE genetics/Amgen, Inc, Reykjavik, Iceland
| | - Daniel Gudbjartsson
- deCODE genetics/Amgen, Inc, Reykjavik, Iceland
- University of Iceland, Reykjavik, Iceland
| | - Kari Stefansson
- deCODE genetics/Amgen, Inc, Reykjavik, Iceland
- University of Iceland, Reykjavik, Iceland
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Donlon TA, Morris BJ, Chen R, Lim E, Morgen EK, Fortney K, Shah N, Masaki KH, Willcox BJ. Proteomic basis of mortality resilience mediated by FOXO3 longevity genotype. GeroScience 2023; 45:2303-2324. [PMID: 36881352 PMCID: PMC10651822 DOI: 10.1007/s11357-023-00740-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 01/23/2023] [Indexed: 03/08/2023] Open
Abstract
FOXO3 is a ubiquitous transcription factor expressed in response to cellular stress caused by nutrient deprivation, inflammatory cytokines, reactive oxygen species, radiation, hypoxia, and other factors. We showed previously that the association of inherited FOXO3 variants with longevity was the result of partial protection against mortality risk posed by aging-related life-long stressors, particularly cardiometabolic disease. We then referred to the longevity-associated genotypes as conferring "mortality resilience." Serum proteins whose levels change with aging and are associated with mortality risk may be considered as "stress proteins." They may serve as indirect measures of life-long stress. Our aims were to (1) identify stress proteins that increase with aging and are associated with an increased risk of mortality, and (2) to determine if FOXO3 longevity/resilience genotype dampens the expected increase in mortality risk they pose. A total of 4500 serum protein aptamers were quantified using the Somalogic SomaScan proteomics platform in the current study of 975 men aged 71-83 years. Stress proteins associated with mortality were identified. We then used age-adjusted multivariable Cox models to investigate the interaction of stress protein with FOXO3 longevity-associated rs12212067 genotypes. For all the analyses, the p values were corrected for multiple comparisons by false discovery rate. This led to the identification of 44 stress proteins influencing the association of FOXO3 genotype with reduced mortality. Biological pathways were identified for these proteins. Our results suggest that the FOXO3 resilience genotype functions by reducing mortality in pathways related to innate immunity, bone morphogenetic protein signaling, leukocyte migration, and growth factor response.
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Affiliation(s)
- Timothy A Donlon
- Department of Research, NIH Center of Biomedical Research Excellence for Clinical and Translational Research on Aging, Kuakini Medical Center, Honolulu, Hawaii, 96817, USA
- Department of Cell and Molecular Biology, John A. Burns School of Medicine, University of Hawaii, Honolulu, Hawaii, USA
| | - Brian J Morris
- Department of Research, NIH Center of Biomedical Research Excellence for Clinical and Translational Research on Aging, Kuakini Medical Center, Honolulu, Hawaii, 96817, USA.
- Department of Geriatric Medicine, John A. Burns School of Medicine, University of Hawaii, Honolulu, Hawaii, USA.
- School of Medical Sciences, University of Sydney, Sydney, New South Wales, Australia.
| | - Randi Chen
- Department of Research, NIH Center of Biomedical Research Excellence for Clinical and Translational Research on Aging, Kuakini Medical Center, Honolulu, Hawaii, 96817, USA
| | - Eunjung Lim
- Department of Quantitative Health Sciences, John A. Burns School of Medicine, University of Hawaii, Honolulu, Hawaii, USA
| | - Eric K Morgen
- BioAge Labs Inc., 1445A S 50th St, Richmond, California, USA
| | - Kristen Fortney
- BioAge Labs Inc., 1445A S 50th St, Richmond, California, USA
| | - Naisha Shah
- BioAge Labs Inc., 1445A S 50th St, Richmond, California, USA
| | - Kamal H Masaki
- Department of Research, NIH Center of Biomedical Research Excellence for Clinical and Translational Research on Aging, Kuakini Medical Center, Honolulu, Hawaii, 96817, USA
- Department of Geriatric Medicine, John A. Burns School of Medicine, University of Hawaii, Honolulu, Hawaii, USA
| | - Bradley J Willcox
- Department of Research, NIH Center of Biomedical Research Excellence for Clinical and Translational Research on Aging, Kuakini Medical Center, Honolulu, Hawaii, 96817, USA
- Department of Geriatric Medicine, John A. Burns School of Medicine, University of Hawaii, Honolulu, Hawaii, USA
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11
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Ros AC, Bacci S, Luna A, Legaz I. Forensic Impact of the Omics Science Involved in the Wound: A Systematic Review. Front Med (Lausanne) 2022; 8:786798. [PMID: 35071269 PMCID: PMC8770859 DOI: 10.3389/fmed.2021.786798] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 12/14/2021] [Indexed: 12/28/2022] Open
Abstract
Background: In forensic autopsies, examining the wounds is one of the most critical aspects to clarify the causal relationship between the cause of death and the wounds observed on the corpse. However, on many occasions, it is difficult to differentiate antemortem injuries from post-mortem injuries, mainly when they occur very close to the moment of death. At present, various studies try to find biomarkers and clarify the molecular mechanisms involved in a wound due to the high variability of conditions in which they occur, thus being one of the most challenging problems in forensic pathology. This review aimed to study the omics data to determine the main lines of investigation emerging in the diagnosis of vital injuries, time of appearance, estimation of the age and vitality of the wound, and its possible contributions to the forensic field. Methods: A systematic review of the human wound concerning forensic science was carried out by following PRISMA guidelines. Results: This study sheds light on the role of omics research during the process of wounding, identifying different cytokines and other inflammatory mediators, as well as cells involved in the specific stage of the wound healing process, show great use in estimating the age of a wound. On the other hand, the expression levels of skin enzymes, proteins, metal ions, and other biomarkers play an essential role in differentiating vital and post-mortem wounds. More recent studies have begun to analyze and quantify mRNA from different genes that encode proteins that participate in the inflammation phase of a wound and miRNAs related to various cellular processes. Conclusions: This study sheds light on the role of research in the molecular characterization of vital wounds, heralding a promising future for molecular characterization of wounds in the field of forensic pathology, opening up an important new area of research. Systematic Review Registration: URL: https://www.crd.york.ac.uk/prospero/#myprospero, Identifier: CRD42021286623.
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Affiliation(s)
- Aurelia Collados Ros
- Department of Legal and Forensic Medicine, Biomedical Research Institute (IMIB), Regional Campus of International Excellence "Campus Mare Nostrum", Faculty of Medicine, University of Murcia, Murcia, Spain
| | - Stefano Bacci
- Department of Biology, Research Unit of Histology and Embriology, University of Florence, Florence, Italy
| | - Aurelio Luna
- Department of Legal and Forensic Medicine, Biomedical Research Institute (IMIB), Regional Campus of International Excellence "Campus Mare Nostrum", Faculty of Medicine, University of Murcia, Murcia, Spain
| | - Isabel Legaz
- Department of Legal and Forensic Medicine, Biomedical Research Institute (IMIB), Regional Campus of International Excellence "Campus Mare Nostrum", Faculty of Medicine, University of Murcia, Murcia, Spain
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12
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Belsky DW, Caspi A, Corcoran DL, Sugden K, Poulton R, Arseneault L, Baccarelli A, Chamarti K, Gao X, Hannon E, Harrington HL, Houts R, Kothari M, Kwon D, Mill J, Schwartz J, Vokonas P, Wang C, Williams BS, Moffitt TE. DunedinPACE, a DNA methylation biomarker of the pace of aging. eLife 2022; 11:e73420. [PMID: 35029144 PMCID: PMC8853656 DOI: 10.7554/elife.73420] [Citation(s) in RCA: 213] [Impact Index Per Article: 106.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 12/13/2021] [Indexed: 01/09/2023] Open
Abstract
Background Measures to quantify changes in the pace of biological aging in response to intervention are needed to evaluate geroprotective interventions for humans. Previously, we showed that quantification of the pace of biological aging from a DNA-methylation blood test was possible (Belsky et al., 2020). Here, we report a next-generation DNA-methylation biomarker of Pace of Aging, DunedinPACE (for Pace of Aging Calculated from the Epigenome). Methods We used data from the Dunedin Study 1972-1973 birth cohort tracking within-individual decline in 19 indicators of organ-system integrity across four time points spanning two decades to model Pace of Aging. We distilled this two-decade Pace of Aging into a single-time-point DNA-methylation blood-test using elastic-net regression and a DNA-methylation dataset restricted to exclude probes with low test-retest reliability. We evaluated the resulting measure, named DunedinPACE, in five additional datasets. Results DunedinPACE showed high test-retest reliability, was associated with morbidity, disability, and mortality, and indicated faster aging in young adults with childhood adversity. DunedinPACE effect-sizes were similar to GrimAge Clock effect-sizes. In analysis of incident morbidity, disability, and mortality, DunedinPACE and added incremental prediction beyond GrimAge. Conclusions DunedinPACE is a novel blood biomarker of the pace of aging for gerontology and geroscience. Funding This research was supported by US-National Institute on Aging grants AG032282, AG061378, AG066887, and UK Medical Research Council grant MR/P005918/1.
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Affiliation(s)
- Daniel W Belsky
- Department of Epidemiology & Butler Columbia Aging Center, Columbia UniversityNew YorkUnited States
| | - Avshalom Caspi
- Center for Genomic and Computational Biology, Duke UniversityDurhamUnited States
| | - David L Corcoran
- Center for Genomic and Computational Biology, Duke UniversityDurhamUnited States
| | - Karen Sugden
- Department of Psychology and Neuroscience, Duke UniversityDurhamUnited States
| | - Richie Poulton
- Department of Psychology, University of OtagoOtagoNew Zealand
| | - Louise Arseneault
- Social, Genetic, and Developmental Psychiatry Centre, King's College LondonLondonUnited Kingdom
| | - Andrea Baccarelli
- Department of Environmental Health Sciences, Columbia UniversityNew YorkUnited States
| | - Kartik Chamarti
- Department of Psychology and Neuroscience, Duke UniversityDurhamUnited States
| | - Xu Gao
- Department of Occupational and Environmental Health, Peking UniversityBeijingChina
| | - Eilis Hannon
- Complex Disease Epigenetics Group, University of ExeterExeterUnited Kingdom
| | - Hona Lee Harrington
- Department of Psychology and Neuroscience, Duke UniversityDurhamUnited States
| | - Renate Houts
- Department of Psychology and Neuroscience, Duke UniversityDurhamUnited States
| | - Meeraj Kothari
- Robert N Butler Columbia Aging Center, Columbia UniversityBrooklynUnited States
| | - Dayoon Kwon
- Robert N Butler Columbia Aging Center, Columbia UniversityNew YorkUnited States
| | - Jonathan Mill
- Complex Disease Epigenetics Group, University of ExeterExeterUnited Kingdom
| | - Joel Schwartz
- Department of Environmental Health Sciences, Harvard TH Chan School of Public Health, Harvard UniversityBostonUnited States
| | - Pantel Vokonas
- Department of Medicine, VA Boston Healthcare SystemBostonUnited States
| | - Cuicui Wang
- Department of Environmental Health Sciences, Harvard TH Chan School of Public Health, Harvard UniversityBostonUnited States
| | - Benjamin S Williams
- Department of Psychology and Neuroscience, Duke UniversityDurhamUnited States
| | - Terrie E Moffitt
- Department of Psychology and Neuroscience, Duke UniversityDurhamUnited States
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