1
|
Liu C, Chiang Y, Hui Q, Zhou JJ, Wilson PWF, Joseph J, Sun YV. High Variability of Body Mass Index Is Independently Associated With Incident Heart Failure. J Am Heart Assoc 2024:e031861. [PMID: 38686888 DOI: 10.1161/jaha.123.031861] [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: 07/20/2023] [Accepted: 03/27/2024] [Indexed: 05/02/2024]
Abstract
BACKGROUND Heart failure (HF) is a serious condition with increasing prevalence, high morbidity, and increased mortality. Obesity is an established risk factor for HF. Fluctuation in body mass index (BMI) has shown a higher risk of cardiovascular outcomes. We investigated the association between BMI variability and incident HF. METHODS AND RESULTS In the UK Biobank, we established a prospective cohort after excluding participants with prevalent HF or cancer at enrollment. A total of 99 368 White participants with ≥3 BMI measures during >2 years preceding enrollment were included, with a median follow-up of 12.5 years. The within-participant variability of BMI was evaluated using standardized SD and coefficient of variation. The association of BMI variability with incident HF was assessed using Fine and Gray's competing risk model, adjusting for confounding factors and participant-specific rate of BMI change. Higher BMI variability measured in both SD and coefficient of variation was significantly associated with higher risk in HF incidence (SD: hazard ratio [HR], 1.05 [95% CI, 1.03-1.08], P<0.0001; coefficient of variation: HR, 1.07 [95% CI, 1.04-1.10], P<0.0001). CONCLUSIONS Longitudinal health records capture BMI fluctuation, which independently predicts HF incidence.
Collapse
Affiliation(s)
- Chang Liu
- Department of Epidemiology Emory University Rollins School of Public Health Atlanta GA
| | - Yiyun Chiang
- Department of Epidemiology Emory University Rollins School of Public Health Atlanta GA
| | - Qin Hui
- Department of Epidemiology Emory University Rollins School of Public Health Atlanta GA
| | - Jin J Zhou
- Department of Medicine and Biostatistics University of California Los Angeles CA
| | - Peter W F Wilson
- Atlanta VA Health Care System Decatur GA
- Department of Medicine Emory University School of Medicine Atlanta GA
| | - Jacob Joseph
- VA Providence Healthcare System Providence RI
- Warren Alpert Medical School Brown University Providence RI
| | - Yan V Sun
- Department of Epidemiology Emory University Rollins School of Public Health Atlanta GA
- Atlanta VA Health Care System Decatur GA
| |
Collapse
|
2
|
Li YR, Zhou Y, Yu J, Zhu Y, Lee D, Zhu E, Li Z, Kim YJ, Zhou K, Fang Y, Lyu Z, Chen Y, Tian Y, Huang J, Cen X, Husman T, Cho JM, Hsiai T, Zhou JJ, Wang P, Puliafito BR, Larson SM, Yang L. Engineering allorejection-resistant CAR-NKT cells from hematopoietic stem cells for off-the-shelf cancer immunotherapy. Mol Ther 2024:S1525-0016(24)00221-1. [PMID: 38584391 DOI: 10.1016/j.ymthe.2024.04.005] [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: 12/31/2023] [Revised: 02/21/2024] [Accepted: 04/03/2024] [Indexed: 04/09/2024] Open
Abstract
The clinical potential of current FDA-approved chimeric antigen receptor (CAR)-engineered T (CAR-T) cell therapy is encumbered by its autologous nature, which presents notable challenges related to manufacturing complexities, heightened costs, and limitations in patient selection. Therefore, there is a growing demand for off-the-shelf universal cell therapies. In this study, we have generated universal CAR-engineered NKT (UCAR-NKT) cells by integrating iNKT TCR engineering and HLA gene editing on hematopoietic stem cells (HSCs), along with an ex vivo, feeder-free HSC differentiation culture. The UCAR-NKT cells are produced with high yield, purity, and robustness, and they display a stable HLA-ablated phenotype that enables resistance to host cell-mediated allorejection. These UCAR-NKT cells exhibit potent antitumor efficacy to blood cancers and solid tumors, both in vitro and in vivo, employing a multifaceted array of tumor-targeting mechanisms. These cells are further capable of altering the tumor microenvironment by selectively depleting immunosuppressive tumor-associated macrophages and myeloid-derived suppressor cells. In addition, UCAR-NKT cells demonstrate a favorable safety profile with low risks of graft-versus-host disease and cytokine release syndrome. Collectively, these preclinical studies underscore the feasibility and significant therapeutic potential of UCAR-NKT cell products and lay a foundation for their translational and clinical development.
Collapse
Affiliation(s)
- Yan-Ruide Li
- Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Yang Zhou
- Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Jiaji Yu
- Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Yichen Zhu
- Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Derek Lee
- Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Enbo Zhu
- Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA; Division of Cardiology, Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Zhe Li
- Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Yu Jeong Kim
- Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Kuangyi Zhou
- Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Ying Fang
- Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Zibai Lyu
- Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Yuning Chen
- Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Yanxin Tian
- Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Jie Huang
- Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Xinjian Cen
- Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Tiffany Husman
- Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Jae Min Cho
- Division of Cardiology, Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Tzung Hsiai
- Division of Cardiology, Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Jin J Zhou
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Pin Wang
- Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA 90089, USA
| | - Benjamin R Puliafito
- Department of Hematology and Oncology, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Sarah M Larson
- Department of Internal Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Jonsson Comprehensive Cancer Centre, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Lili Yang
- Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA; Jonsson Comprehensive Cancer Centre, University of California, Los Angeles, Los Angeles, CA 90095, USA; Eli and Edythe Broad Centre of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, Los Angeles, CA 90095, USA; Molecular Biology Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA.
| |
Collapse
|
3
|
Almuwaqqat Z, Hui Q, Liu C, Zhou JJ, Voight BF, Ho YL, Posner DC, Vassy JL, Gaziano JM, Cho K, Wilson PWF, Sun YV. Long-Term Body Mass Index Variability and Adverse Cardiovascular Outcomes. JAMA Netw Open 2024; 7:e243062. [PMID: 38512255 PMCID: PMC10958234 DOI: 10.1001/jamanetworkopen.2024.3062] [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] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 01/23/2024] [Indexed: 03/22/2024] Open
Abstract
Importance Body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) is a commonly used estimate of obesity, which is a complex trait affected by genetic and lifestyle factors. Marked weight gain and loss could be associated with adverse biological processes. Objective To evaluate the association between BMI variability and incident cardiovascular disease (CVD) events in 2 distinct cohorts. Design, Setting, and Participants This cohort study used data from the Million Veteran Program (MVP) between 2011 and 2018 and participants in the UK Biobank (UKB) enrolled between 2006 and 2010. Participants were followed up for a median of 3.8 (5th-95th percentile, 3.5) years. Participants with baseline CVD or cancer were excluded. Data were analyzed from September 2022 and September 2023. Exposure BMI variability was calculated by the retrospective SD and coefficient of variation (CV) using multiple clinical BMI measurements up to the baseline. Main Outcomes and Measures The main outcome was incident composite CVD events (incident nonfatal myocardial infarction, acute ischemic stroke, and cardiovascular death), assessed using Cox proportional hazards modeling after adjustment for CVD risk factors, including age, sex, mean BMI, systolic blood pressure, total cholesterol, high-density lipoprotein cholesterol, smoking status, diabetes status, and statin use. Secondary analysis assessed whether associations were dependent on the polygenic score of BMI. Results Among 92 363 US veterans in the MVP cohort (81 675 [88%] male; mean [SD] age, 56.7 [14.1] years), there were 9695 Hispanic participants, 22 488 non-Hispanic Black participants, and 60 180 non-Hispanic White participants. A total of 4811 composite CVD events were observed from 2011 to 2018. The CV of BMI was associated with 16% higher risk for composite CVD across all groups (hazard ratio [HR], 1.16; 95% CI, 1.13-1.19). These associations were unchanged among subgroups and after adjustment for the polygenic score of BMI. The UKB cohort included 65 047 individuals (mean [SD] age, 57.30 (7.77) years; 38 065 [59%] female) and had 6934 composite CVD events. Each 1-SD increase in BMI variability in the UKB cohort was associated with 8% increased risk of cardiovascular death (HR, 1.08; 95% CI, 1.04-1.11). Conclusions and Relevance This cohort study found that among US veterans, higher BMI variability was a significant risk marker associated with adverse cardiovascular events independent of mean BMI across major racial and ethnic groups. Results were consistent in the UKB for the cardiovascular death end point. Further studies should investigate the phenotype of high BMI variability.
Collapse
Affiliation(s)
- Zakaria Almuwaqqat
- Veterans Affairs Atlanta Healthcare System, Decatur, Georgia
- Division of Cardiology, Department of Medicine, Emory University School of Medicine, Atlanta, Georgia
| | - Qin Hui
- Veterans Affairs Atlanta Healthcare System, Decatur, Georgia
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, Georgia
| | - Chang Liu
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, Georgia
| | - Jin J. Zhou
- Department of Medicine and Biostatistics, University of California, Los Angeles
- Veterans Affairs Phoenix Healthcare System, Phoenix, Arizona
| | - Benjamin F. Voight
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania
- Department of Systems Pharmacology and Translational Therapeutics, Department of Genetics, University of Pennsylvania, Philadelphia\
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston
| | - Daniel C. Posner
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston
| | - Jason L. Vassy
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston
- Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - J. Michael Gaziano
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston
- Division of Aging, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston
- Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Peter W. F. Wilson
- Veterans Affairs Atlanta Healthcare System, Decatur, Georgia
- Division of Cardiology, Department of Medicine, Emory University School of Medicine, Atlanta, Georgia
| | - Yan V. Sun
- Veterans Affairs Atlanta Healthcare System, Decatur, Georgia
- Division of Cardiology, Department of Medicine, Emory University School of Medicine, Atlanta, Georgia
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, Georgia
| |
Collapse
|
4
|
Khera R, Aminorroaya A, Dhingra LS, Thangaraj PM, Camargos AP, Bu F, Ding X, Nishimura A, Anand TV, Arshad F, Blacketer C, Chai Y, Chattopadhyay S, Cook M, Dorr DA, Duarte-Salles T, DuVall SL, Falconer T, French TE, Hanchrow EE, Kaur G, Lau WC, Li J, Li K, Liu Y, Lu Y, Man KK, Matheny ME, Mathioudakis N, McLeggon JA, McLemore MF, Minty E, Morales DR, Nagy P, Ostropolets A, Pistillo A, Phan TP, Pratt N, Reyes C, Richter L, Ross J, Ruan E, Seager SL, Simon KR, Viernes B, Yang J, Yin C, You SC, Zhou JJ, Ryan PB, Schuemie MJ, Krumholz HM, Hripcsak G, Suchard MA. Comparative Effectiveness of Second-line Antihyperglycemic Agents for Cardiovascular Outcomes: A Large-scale, Multinational, Federated Analysis of the LEGEND-T2DM Study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.02.05.24302354. [PMID: 38370787 PMCID: PMC10871374 DOI: 10.1101/2024.02.05.24302354] [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: 02/20/2024]
Abstract
Background SGLT2 inhibitors (SGLT2is) and GLP-1 receptor agonists (GLP1-RAs) reduce major adverse cardiovascular events (MACE) in patients with type 2 diabetes mellitus (T2DM). However, their effectiveness relative to each other and other second-line antihyperglycemic agents is unknown, without any major ongoing head-to-head trials. Methods Across the LEGEND-T2DM network, we included ten federated international data sources, spanning 1992-2021. We identified 1,492,855 patients with T2DM and established cardiovascular disease (CVD) on metformin monotherapy who initiated one of four second-line agents (SGLT2is, GLP1-RAs, dipeptidyl peptidase 4 inhibitor [DPP4is], sulfonylureas [SUs]). We used large-scale propensity score models to conduct an active comparator, target trial emulation for pairwise comparisons. After evaluating empirical equipoise and population generalizability, we fit on-treatment Cox proportional hazard models for 3-point MACE (myocardial infarction, stroke, death) and 4-point MACE (3-point MACE + heart failure hospitalization) risk, and combined hazard ratio (HR) estimates in a random-effects meta-analysis. Findings Across cohorts, 16·4%, 8·3%, 27·7%, and 47·6% of individuals with T2DM initiated SGLT2is, GLP1-RAs, DPP4is, and SUs, respectively. Over 5·2 million patient-years of follow-up and 489 million patient-days of time at-risk, there were 25,982 3-point MACE and 41,447 4-point MACE events. SGLT2is and GLP1-RAs were associated with a lower risk for 3-point MACE compared with DPP4is (HR 0·89 [95% CI, 0·79-1·00] and 0·83 [0·70-0·98]), and SUs (HR 0·76 [0·65-0·89] and 0·71 [0·59-0·86]). DPP4is were associated with a lower 3-point MACE risk versus SUs (HR 0·87 [0·79-0·95]). The pattern was consistent for 4-point MACE for the comparisons above. There were no significant differences between SGLT2is and GLP1-RAs for 3-point or 4-point MACE (HR 1·06 [0·96-1·17] and 1·05 [0·97-1·13]). Interpretation In patients with T2DM and established CVD, we found comparable cardiovascular risk reduction with SGLT2is and GLP1-RAs, with both agents more effective than DPP4is, which in turn were more effective than SUs. These findings suggest that the use of GLP1-RAs and SGLT2is should be prioritized as second-line agents in those with established CVD. Funding National Institutes of Health, United States Department of Veterans Affairs.
Collapse
Affiliation(s)
- Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, 06510, USA
- Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, CT, 06510, USA
- Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT, 06520, USA
| | - Arya Aminorroaya
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, 06510, USA
| | - Lovedeep Singh Dhingra
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, 06510, USA
| | - Phyllis M Thangaraj
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, 06510, USA
| | - Aline Pedroso Camargos
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, 06510, USA
| | - Fan Bu
- Department of Biostatistics, University of Michigan - Ann Arbor, Ann Arbor, MI, 48105, USA
| | - Xiyu Ding
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Akihiko Nishimura
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Tara V Anand
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10027, USA
| | - Faaizah Arshad
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Clair Blacketer
- Observational Health Data Analytics, Janssen Research and Development, LLC, Titusville, NJ, 8560, USA
| | - Yi Chai
- Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, The University of Hong Kong
| | - Shounak Chattopadhyay
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Michael Cook
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - David A Dorr
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA
| | - Talita Duarte-Salles
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, 8007, Spain
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Scott L DuVall
- Veterans Affairs Informatics and Computing Infrastructure, United States Department of Veterans Affairs, Salt Lake City, UT, USA
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Thomas Falconer
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10027, USA
| | - Tina E French
- Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Elizabeth E Hanchrow
- Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Guneet Kaur
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, DD1 9SY, United Kingdom
| | - Wallis Cy Lau
- Research Department of Practice and Policy, School of Pharmacy, University College London, London, WC1H 9JP, United Kingdom
- Centre for Medicines Optimisation Research and Education, University College London Hospitals NHS Foundation Trust, London, United Kingdom
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong, Hong Kong
| | - Jing Li
- Data Transformation, Analytics, and Artificial Intelligence, Real World Solutions, IQVIA, Durham, NC, USA
| | - Kelly Li
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Yuntian Liu
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, 06510, USA
- Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, CT, 06510, USA
| | - Yuan Lu
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, 06510, USA
| | - Kenneth Kc Man
- Research Department of Practice and Policy, School of Pharmacy, University College London, London, WC1H 9JP, United Kingdom
- Centre for Medicines Optimisation Research and Education, University College London Hospitals NHS Foundation Trust, London, United Kingdom
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong, Hong Kong
| | - Michael E Matheny
- Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nestoras Mathioudakis
- Division of Endocrinology, Diabetes, and Metabolism, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jody-Ann McLeggon
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10027, USA
| | - Michael F McLemore
- Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Evan Minty
- Faculty of Medicine, O'Brien Institute for Public Health, University of Calgary, Calgary, AB, T2N4N1, Canada
| | - Daniel R Morales
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, DD1 9SY, United Kingdom
| | - Paul Nagy
- Division of Endocrinology, Diabetes, and Metabolism, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Anna Ostropolets
- Observational Health Data Analytics, Janssen Research and Development, LLC, Titusville, NJ, 8560, USA
| | - Andrea Pistillo
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, 8007, Spain
| | | | - Nicole Pratt
- Quality Use of Medicines and Pharmacy Research Centre, UniSA Clinical and Health Sciences, University of South Australia, Adelaide, Australia
| | - Carlen Reyes
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, 8007, Spain
| | - Lauren Richter
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10027, USA
| | - Joseph Ross
- Section of General Medicine and National Clinician Scholars Program, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, CT, 06510, USA
| | - Elise Ruan
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10027, USA
| | - Sarah L Seager
- Data Transformation, Analytics, and Artificial Intelligence, Real World Solutions, IQVIA, London, UK
| | - Katherine R Simon
- Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Benjamin Viernes
- Veterans Affairs Informatics and Computing Infrastructure, United States Department of Veterans Affairs, Salt Lake City, UT, USA
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Jianxiao Yang
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Can Yin
- Data Transformation, Analytics, and Artificial Intelligence, Real World Solutions, IQVIA, Shanghai, China
| | - Seng Chan You
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, South Korea
- Institute for Innovation in Digital Healthcare, Yonsei University College of Medicine, Seoul, South Korea
| | - Jin J Zhou
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90024, USA
| | - Patrick B Ryan
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10027, USA
| | - Martijn J Schuemie
- Epidemiology, Office of the Chief Medical Officer, Johnson & Johnson, Titusville, NJ, 8560, USA
| | - Harlan M Krumholz
- Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, CT, 06510, USA
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, 06510, USA
- Section of Cardiovascular Medicine, Department of Health Policy and Management, Yale School of Public Health, New Haven, CT, 06510, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10027, USA
| | - Marc A Suchard
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Biomathematics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Veterans Affairs Informatics and Computing Infrastructure, United States Department of Veterans Affairs, Salt Lake City, UT, USA
| |
Collapse
|
5
|
Okuno T, Vansomphone A, Zhang E, Zhou H, Koska J, Reaven P, Zhou JJ. Association of Both Short-term and Long-term Glycemic Variability With the Development of Microalbuminuria in the ACCORD Trial. Diabetes 2023; 72:1864-1869. [PMID: 37725902 PMCID: PMC10658063 DOI: 10.2337/db23-0374] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 09/07/2023] [Indexed: 09/21/2023]
Abstract
Both long- and short-term glycemic variability have been associated with incident diabetes complications. We evaluated their relative and potential additive effects on incident renal complications in the Action to Control Cardiovascular Risk in Diabetes trial. A marker of short-term glycemic variability, 1,5-anhydroglucitol (1,5-AG), was measured in 4,000 random 12-month postrandomization plasma samples (when hemoglobin A1c [HbA1c] was stable). Visit-to-visit fasting plasma glucose coefficient of variation (CV-FPG) was determined from 4 months postrandomization until the end point of microalbuminuria or macroalbuminuria. Using Cox proportional hazards models, high CV-FPG and low 1,5-AG were independently associated with microalbuminuria after adjusting for clinical risk factors. However, only the CV-FPG association remained after additional adjustment for average HbA1c. Only CV-FPG was a significant risk factor for macroalbuminuria. This post hoc analysis indicates that long-term rather than short-term glycemic variability better predicts the risk of renal disease in type 2 diabetes. ARTICLE HIGHLIGHTS The relative and potential additive effects of long- and short-term glycemic variability on the development of diabetic complications are unknown. We aimed to assess the individual and combined relationships of long-term visit-to-visit glycemic variability, measured as the coefficient of variation of fasting plasma glucose, and short-term glucose fluctuation, estimated by the biomarker 1,5-anhydroglucitol, with the development of proteinuria. Both estimates of glycemic variability were independently associated with microalbuminuria, but only long-term glycemic variability remained significant after adjusting for average hemoglobin A1c. Our findings suggest that longer-term visit-to-visit glucose variability improves renal disease prediction in type 2 diabetes.
Collapse
Affiliation(s)
- Tomoki Okuno
- Department of Biostatistics, University of California Los Angeles, Los Angeles, CA
- Phoenix Veterans Affairs Health Care System, Phoenix, AZ
| | | | | | - Hua Zhou
- Department of Biostatistics, University of California Los Angeles, Los Angeles, CA
- Department of Computational Medicine, University of California Los Angeles, Los Angeles, CA
| | - Juraj Koska
- Phoenix Veterans Affairs Health Care System, Phoenix, AZ
| | - Peter Reaven
- Phoenix Veterans Affairs Health Care System, Phoenix, AZ
| | - Jin J. Zhou
- Department of Biostatistics, University of California Los Angeles, Los Angeles, CA
- Phoenix Veterans Affairs Health Care System, Phoenix, AZ
- Department of Medicine, University of California Los Angeles, Los Angeles, CA
| |
Collapse
|
6
|
Lee D, Dunn ZS, Guo W, Rosenthal CJ, Penn NE, Yu Y, Zhou K, Li Z, Ma F, Li M, Song TC, Cen X, Li YR, Zhou JJ, Pellegrini M, Wang P, Yang L. Unlocking the potential of allogeneic Vδ2 T cells for ovarian cancer therapy through CD16 biomarker selection and CAR/IL-15 engineering. Nat Commun 2023; 14:6942. [PMID: 37938576 PMCID: PMC10632431 DOI: 10.1038/s41467-023-42619-2] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 10/17/2023] [Indexed: 11/09/2023] Open
Abstract
Allogeneic Vγ9Vδ2 (Vδ2) T cells have emerged as attractive candidates for developing cancer therapy due to their established safety in allogeneic contexts and inherent tumor-fighting capabilities. Nonetheless, the limited clinical success of Vδ2 T cell-based treatments may be attributed to donor variability, short-lived persistence, and tumor immune evasion. To address these constraints, we engineer Vδ2 T cells with enhanced attributes. By employing CD16 as a donor selection biomarker, we harness Vδ2 T cells characterized by heightened cytotoxicity and potent antibody-dependent cell-mediated cytotoxicity (ADCC) functionality. RNA sequencing analysis supports the augmented effector potential of Vδ2 T cells derived from CD16 high (CD16Hi) donors. Substantial enhancements are further achieved through CAR and IL-15 engineering methodologies. Preclinical investigations in two ovarian cancer models substantiate the effectiveness and safety of engineered CD16Hi Vδ2 T cells. These cells target tumors through multiple mechanisms, exhibit sustained in vivo persistence, and do not elicit graft-versus-host disease. These findings underscore the promise of engineered CD16Hi Vδ2 T cells as a viable therapeutic option for cancer treatment.
Collapse
Affiliation(s)
- Derek Lee
- Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, CA, USA
| | - Zachary Spencer Dunn
- Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, CA, USA
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA, USA
| | - Wenbin Guo
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, CA, USA
| | - Carl J Rosenthal
- Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, CA, USA
| | - Natalie E Penn
- Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, CA, USA
| | - Yanqi Yu
- Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, CA, USA
| | - Kuangyi Zhou
- Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, CA, USA
| | - Zhe Li
- Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, CA, USA
| | - Feiyang Ma
- Department of Molecular, Cell, and Developmental Biology, University of California, Los Angeles, CA, USA
| | - Miao Li
- Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, CA, USA
| | - Tsun-Ching Song
- Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, CA, USA
| | - Xinjian Cen
- Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, CA, USA
| | - Yan-Ruide Li
- Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, CA, USA
| | - Jin J Zhou
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, CA, USA
| | - Matteo Pellegrini
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, CA, USA
- Department of Molecular, Cell, and Developmental Biology, University of California, Los Angeles, CA, USA
- Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, CA, USA
| | - Pin Wang
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA, USA
| | - Lili Yang
- Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, CA, USA.
- Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, CA, USA.
- Jonsson Comprehensive Cancer Center, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
- Molecular Biology Institute, University of California, Los Angeles, CA, USA.
| |
Collapse
|
7
|
Li YR, Ochoa CJ, Zhu Y, Kramer A, Wilson M, Fang Y, Chen Y, Singh T, Di Bernardo G, Zhu E, Lee D, Moatamed NA, Bando J, Zhou JJ, Memarzadeh S, Yang L. Profiling ovarian cancer tumor and microenvironment during disease progression for cell-based immunotherapy design. iScience 2023; 26:107952. [PMID: 37810241 PMCID: PMC10558812 DOI: 10.1016/j.isci.2023.107952] [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] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 08/28/2023] [Accepted: 09/14/2023] [Indexed: 10/10/2023] Open
Abstract
Ovarian cancer (OC) is highly lethal due to late detection and frequent recurrence. Initial treatments, comprising surgery and chemotherapy, lead to disease remission but are invariably associated with subsequent relapse. The identification of novel therapies and an improved understanding of the molecular and cellular characteristics of OC are urgently needed. Here, we conducted a comprehensive analysis of primary tumor cells and their microenvironment from 16 chemonaive and 10 recurrent OC patient samples. Profiling OC tumor biomarkers allowed for the identification of potential molecular targets for developing immunotherapies, while profiling the microenvironment yielded insights into its cellular composition and property changes between chemonaive and recurrent samples. Notably, we identified CD1d as a biomarker of the OC microenvironment and demonstrated its targeting by invariant natural killer T (iNKT) cells. Overall, our study presents a comprehensive immuno-profiling of OC tumor and microenvironment during disease progression, guiding the development of immunotherapies for OC treatment, especially for recurrent disease.
Collapse
Affiliation(s)
- Yan-Ruide Li
- Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Christopher J Ochoa
- Department of Obstetrics and Gynecology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
- Molecular Biology Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Yichen Zhu
- Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Adam Kramer
- Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Matthew Wilson
- Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Ying Fang
- Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Yuning Chen
- Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Tanya Singh
- Department of Obstetrics and Gynecology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
- Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Jonsson Comprehensive Cancer Center, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Gabriella Di Bernardo
- Department of Obstetrics and Gynecology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
- Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Jonsson Comprehensive Cancer Center, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Enbo Zhu
- Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Derek Lee
- Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Neda A Moatamed
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Joanne Bando
- Department of Medicine, Division of Pulmonary and Critical Care, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Jin J Zhou
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Sanaz Memarzadeh
- Department of Obstetrics and Gynecology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
- Molecular Biology Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Jonsson Comprehensive Cancer Center, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- The VA Greater Los Angeles Healthcare System, Los Angeles, CA 90073, USA
| | - Lili Yang
- Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Molecular Biology Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Jonsson Comprehensive Cancer Center, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| |
Collapse
|
8
|
Khera R, Dhingra LS, Aminorroaya A, Li K, Zhou JJ, Arshad F, Blacketer C, Bowring MG, Bu F, Cook M, Dorr DA, Duarte-Salles T, DuVall SL, Falconer T, French TE, Hanchrow EE, Horban S, Lau WCY, Li J, Liu Y, Lu Y, Man KKC, Matheny ME, Mathioudakis N, McLemore MF, Minty E, Morales DR, Nagy P, Nishimura A, Ostropolets A, Pistillo A, Posada JD, Pratt N, Reyes C, Ross JS, Seager S, Shah N, Simon K, Wan EYF, Yang J, Yin C, You SC, Schuemie MJ, Ryan PB, Hripcsak G, Krumholz H, Suchard MA. Multinational patterns of second line antihyperglycaemic drug initiation across cardiovascular risk groups: federated pharmacoepidemiological evaluation in LEGEND-T2DM. BMJ Med 2023; 2:e000651. [PMID: 37829182 PMCID: PMC10565313 DOI: 10.1136/bmjmed-2023-000651] [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] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 07/07/2023] [Indexed: 10/14/2023]
Abstract
Objective To assess the uptake of second line antihyperglycaemic drugs among patients with type 2 diabetes mellitus who are receiving metformin. Design Federated pharmacoepidemiological evaluation in LEGEND-T2DM. Setting 10 US and seven non-US electronic health record and administrative claims databases in the Observational Health Data Sciences and Informatics network in eight countries from 2011 to the end of 2021. Participants 4.8 million patients (≥18 years) across US and non-US based databases with type 2 diabetes mellitus who had received metformin monotherapy and had initiated second line treatments. Exposure The exposure used to evaluate each database was calendar year trends, with the years in the study that were specific to each cohort. Main outcomes measures The outcome was the incidence of second line antihyperglycaemic drug use (ie, glucagon-like peptide-1 receptor agonists, sodium-glucose cotransporter-2 inhibitors, dipeptidyl peptidase-4 inhibitors, and sulfonylureas) among individuals who were already receiving treatment with metformin. The relative drug class level uptake across cardiovascular risk groups was also evaluated. Results 4.6 million patients were identified in US databases, 61 382 from Spain, 32 442 from Germany, 25 173 from the UK, 13 270 from France, 5580 from Scotland, 4614 from Hong Kong, and 2322 from Australia. During 2011-21, the combined proportional initiation of the cardioprotective antihyperglycaemic drugs (glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors) increased across all data sources, with the combined initiation of these drugs as second line drugs in 2021 ranging from 35.2% to 68.2% in the US databases, 15.4% in France, 34.7% in Spain, 50.1% in Germany, and 54.8% in Scotland. From 2016 to 2021, in some US and non-US databases, uptake of glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors increased more significantly among populations with no cardiovascular disease compared with patients with established cardiovascular disease. No data source provided evidence of a greater increase in the uptake of these two drug classes in populations with cardiovascular disease compared with no cardiovascular disease. Conclusions Despite the increase in overall uptake of cardioprotective antihyperglycaemic drugs as second line treatments for type 2 diabetes mellitus, their uptake was lower in patients with cardiovascular disease than in people with no cardiovascular disease over the past decade. A strategy is needed to ensure that medication use is concordant with guideline recommendations to improve outcomes of patients with type 2 diabetes mellitus.
Collapse
Affiliation(s)
- Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, USA
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT, USA
- Section of Health Informatics, Department of Biostatistics, Yale University School of Public Health, New Haven, CT, USA
| | - Lovedeep Singh Dhingra
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, USA
| | - Arya Aminorroaya
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, USA
| | - Kelly Li
- Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, USA
| | - Jin J Zhou
- Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, USA
- Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Faaizah Arshad
- Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, USA
| | - Clair Blacketer
- Observational Health Data Analytics, Janssen Research and Development, Titusville, NJ, USA
| | - Mary G Bowring
- Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Fan Bu
- Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, USA
| | - Michael Cook
- Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - David A Dorr
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University School of Medicine, Portland, OR, USA
| | - Talita Duarte-Salles
- Real-World Epidemiology Research Group, Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Scott L DuVall
- Veterans Affairs Informatics and Computing Infrastructure, United States Department of Veterans Affairs, Salt Lake City, UT, USA
- The University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Thomas Falconer
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
| | - Tina E French
- Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Elizabeth E Hanchrow
- Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Scott Horban
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Wallis CY Lau
- Research Department of Practice and Policy, School of Pharmacy, University College London, London, UK
- Centre for Medicines Optimisation Research and Education, University College London Hospitals NHS Foundation Trust, London, UK
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, China
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong, China
| | - Jing Li
- Data Transformation, Analytics, and Artificial Intelligence, Real World Solutions, IQVIA Inc, Durham, NC, USA
| | - Yuntian Liu
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, USA
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT, USA
| | - Yuan Lu
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, USA
| | - Kenneth KC Man
- Research Department of Practice and Policy, School of Pharmacy, University College London, London, UK
- Centre for Medicines Optimisation Research and Education, University College London Hospitals NHS Foundation Trust, London, UK
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, China
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong, China
| | - Michael E Matheny
- Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nestoras Mathioudakis
- Division of Endocrinology, Diabetes, and Metabolism, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Michael F McLemore
- Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Evan Minty
- Faculty of Medicine, O'Brien Institute for Public Health, University of Calgary, Calgary, AB, Canada
| | - Daniel R Morales
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Paul Nagy
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Division of Health Science Informatics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA
| | - Akihiko Nishimura
- Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA
| | - Anna Ostropolets
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
| | - Andrea Pistillo
- Real-World Epidemiology Research Group, Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Jose D Posada
- Systems Engineering and Computing, School of Engineering, Universidad del Norte, Barranquilla, Colombia
| | - Nicole Pratt
- Quality Use of Medicines and Pharmacy Research Centre, UniSA Clinical and Health Sciences, University of South Australia, Adelaide, SA, Australia
| | - Carlen Reyes
- Real-World Epidemiology Research Group, Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Joseph S Ross
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT, USA
- Section of General Medicine and National Clinician Scholars Program, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Department of Health Policy and Management, Yale University School of Public Health, New Haven, CT, USA
| | - Sarah Seager
- Data Transformation, Analytics, and Artificial Intelligence, Real World Solutions, IQVIA Inc, Durham, NC, USA
| | - Nigam Shah
- Center for Biomedical Informatics Research, Stanford University School of Medicine, Stanford, CA, USA
- Clinical Excellence Research Center, Stanford University School of Medicine, Stanford, CA, USA
- Technology and Digital Solutions, Stanford Health Care, Stanford, CA, USA
| | - Katherine Simon
- Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Eric YF Wan
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, China
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong, China
- Department of Family Medicine and Primary Care, School of Clinical Medicine, University of Hong Kong, Hong Kong, China
| | - Jianxiao Yang
- Department of Computational Medicine, University of California Los Angeles David Geffen School of Medicine, Los Angeles, CA, USA
| | - Can Yin
- Data Transformation, Analytics, and Artificial Intelligence, Real World Solutions, IQVIA Inc, Durham, NC, USA
| | - Seng Chan You
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea (aka South Korea)
- Institute for Innovation in Digital Healthcare, Yonsei University, Seoul, Republic of Korea (aka South Korea)
| | - Martijn J Schuemie
- Epidemiology, Office of the Chief Medical Officer, Johnson & Johnson, Titusville, NJ, USA
| | - Patrick B Ryan
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
| | - Harlan Krumholz
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, USA
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT, USA
- Department of Health Policy and Management, Yale University School of Public Health, New Haven, CT, USA
| | - Marc A Suchard
- Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, USA
- Veterans Affairs Informatics and Computing Infrastructure, United States Department of Veterans Affairs, Salt Lake City, UT, USA
- Department of Biomathematics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| |
Collapse
|
9
|
Sun YV, Liu C, Hui Q, Zhou JJ, Gaziano JM, Wilson PW, Joseph J, Phillips LS. Correction for Collider Bias in the Genome-wide Association Study of Diabetes-Related Heart Failure due to Bidirectional Relationship between Heart Failure and Type 2 Diabetes. medRxiv 2023:2023.09.22.23295915. [PMID: 37808641 PMCID: PMC10557768 DOI: 10.1101/2023.09.22.23295915] [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: 10/10/2023]
Abstract
Aims Type 2 diabetes (T2D) is a major risk factor for heart failure (HF) across demographic groups. On the other hand, metabolic impairment, including elevated T2D incidence is a hallmark of HF pathophysiology. We investigated the bidirectional relationship between T2D and HF, and identified genetic associations with diabetes-related HF after correction for potential collider bias. Methods We performed a genome-wide association study (GWAS) of HF to identify genetic instrumental variables (GIVs) for HF, and to enable bidirectional Mendelian Randomization (MR) analysis between T2D and HF. Since genetics and HF can independently influence T2D, collider bias may occur when T2D (i.e., collider) is controlled for by design or analysis. Thus, we conducted GWAS of diabetes-related HF with correction for collider bias. Results We first identified 61 genomic loci, including 24 novel loci, significantly associated with all-cause HF in 114,275 HF cases and over 1.5 million controls of European ancestry. Combined with the summary statistics of a T2D GWAS, we obtained 59 and 82 GIVs for HF and T2D, respectively. Using a two-sample bidirectional MR approach, we estimated that T2D increased HF risk (OR 1.07, 95% CI 1.04-1.10), while HF also increased T2D risk (OR 1.60, 95% CI 1.36-1.88). Then we performed a GWAS of diabetes-related HF corrected for collider bias due to prevalent HF affecting incidence of T2D. After removing the spurious association of TCF7L2 locus due to collider bias, we identified two genome-wide significant loci close to PITX2 (chromosome 4) and CDKN2B-AS1 (chromosome 9) associated with diabetes-related HF in the Million Veteran Program, and replicated the associations in the UK Biobank study. Conclusion We identified novel HF-associated loci to enable bidirectional MR study of T2D and HF. Our MR findings support T2D as a HF risk factor and provide strong evidence that HF increases T2D risk. As a result, collider bias leads to spurious genetic associations of diabetes-related HF, which can be effectively corrected to identify true positive loci. Evaluation of collider bias should be a critical component when conducting GWAS of complex disease phenotypes such as diabetes-related cardiovascular complications.
Collapse
|
10
|
Zhou JJ, Wang W, Fu YY, Zhang Q, Li RQ, Zhao S, Sun QN, Wang DR. [Feasibility study of R method of gastrojejunostomy applied to Billroth II digestive tract reconstruction after laparoscopic radical distal gastrectomy]. Zhonghua Wei Chang Wai Ke Za Zhi 2023; 26:790-793. [PMID: 37574297 DOI: 10.3760/cma.j.cn441530-20221205-00507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
|
11
|
Chu BB, Ko S, Zhou JJ, Jensen A, Zhou H, Sinsheimer JS, Lange K. Multivariate Genome-wide Association Analysis by Iterative Hard Thresholding. Bioinformatics 2023; 39:7126408. [PMID: 37067496 PMCID: PMC10133532 DOI: 10.1093/bioinformatics/btad193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 04/07/2023] [Accepted: 04/13/2023] [Indexed: 04/18/2023]
Abstract
MOTIVATION In a genome-wide association study (GWAS), analyzing multiple correlated traits simultaneously is potentially superior to analyzing the traits one by one. Standard methods for multivariate GWAS operate marker-by-marker and are computationally intensive. RESULTS We present a sparsity constrained regression algorithm for multivariate GWAS based on iterative hard thresholding (IHT) and implement it in a convenient Julia package MendelIHT.jl. In simulation studies with up to 100 quantitative traits, IHT exhibits similar true positive rates, smaller false positive rates, and faster execution times than GEMMA's linear mixed models and mv-PLINK's canonical correlation analysis. On UK Biobank data with 470, 228 variants, MendelIHT completed a 3-trait joint analysis (n = 185, 656) in 20 hours and an 18-trait joint analysis (n = 104, 264) in 53 hours with a 80GB memory footprint. In short, MendelIHT enables geneticists to fit a single regression model that simultaneously considers the effect of all SNPs and dozens of traits. AVAILABILITY Software, documentation, and scripts to reproduce our results are available from https://github.com/OpenMendel/MendelIHT.jl. SUPPLEMENTARY INFORMATION Supplementary data are available from Bioinformatics online.
Collapse
Affiliation(s)
- Benjamin B Chu
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, USA
| | - Seyoon Ko
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, USA
- Department of Biostatistics, Fielding School of Public Health at UCLA, Los Angeles, USA
| | - Jin J Zhou
- Department of Biostatistics, Fielding School of Public Health at UCLA, Los Angeles, USA
- Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, USA
| | - Aubrey Jensen
- Department of Biostatistics, Fielding School of Public Health at UCLA, Los Angeles, USA
| | - Hua Zhou
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, USA
- Department of Biostatistics, Fielding School of Public Health at UCLA, Los Angeles, USA
| | - Janet S Sinsheimer
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, USA
- Department of Biostatistics, Fielding School of Public Health at UCLA, Los Angeles, USA
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, USA
| | - Kenneth Lange
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, USA
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, USA
- Department of Statistics at UCLA, Los Angeles, USA
| |
Collapse
|
12
|
Reaven PD, Newell M, Rivas S, Zhou X, Norman GJ, Zhou JJ. Initiation of Continuous Glucose Monitoring Is Linked to Improved Glycemic Control and Fewer Clinical Events in Type 1 and Type 2 Diabetes in the Veterans Health Administration. Diabetes Care 2023; 46:854-863. [PMID: 36807492 PMCID: PMC10260873 DOI: 10.2337/dc22-2189] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 01/23/2023] [Indexed: 02/19/2023]
Abstract
OBJECTIVE To determine the benefit of starting continuous glucose monitoring (CGM) in adult-onset type 1 diabetes (T1D) and type 2 diabetes (T2D) with regard to longer-term glucose control and serious clinical events. RESEARCH DESIGN AND METHODS A retrospective observational cohort study within the Veterans Affairs Health Care System was used to compare glucose control and hypoglycemia- or hyperglycemia-related admission to an emergency room or hospital and all-cause hospitalization between propensity score overlap weighted initiators of CGM and nonusers over 12 months. RESULTS CGM users receiving insulin (n = 5,015 with T1D and n = 15,706 with T2D) and similar numbers of nonusers were identified from 1 January 2015 to 31 December 2020. Declines in HbA1c were significantly greater in CGM users with T1D (-0.26%; 95% CI -0.33, -0.19%) and T2D (-0.35%; 95% CI -0.40, -0.31%) than in nonusers at 12 months. Percentages of patients achieving HbA1c <8 and <9% after 12 months were greater in CGM users. In T1D, CGM initiation was associated with significantly reduced risk of hypoglycemia (hazard ratio [HR] 0.69; 95% CI 0.48, 0.98) and all-cause hospitalization (HR 0.75; 95% CI 0.63, 0.90). In patients with T2D, there was a reduction in risk of hyperglycemia in CGM users (HR 0.87; 95% CI 0.77, 0.99) and all-cause hospitalization (HR 0.89; 95% CI 0.83, 0.97). Several subgroups (based on baseline age, HbA1c, hypoglycemic risk, or follow-up CGM use) had even greater responses. CONCLUSIONS In a large national cohort, initiation of CGM was associated with sustained improvement in HbA1c in patients with later-onset T1D and patients with T2D using insulin. This was accompanied by a clear pattern of reduced risk of admission to an emergency room or hospital for hypoglycemia or hyperglycemia and of all-cause hospitalization.
Collapse
Affiliation(s)
| | | | - Salvador Rivas
- Phoenix Veterans Affairs Health Care System, Phoenix, AZ
| | - Xinkai Zhou
- Medicine and Biostatistics, University of California Los Angeles, Los Angeles, CA
| | | | - Jin J. Zhou
- Phoenix Veterans Affairs Health Care System, Phoenix, AZ
- Medicine and Biostatistics, University of California Los Angeles, Los Angeles, CA
| |
Collapse
|
13
|
Kim DH, Jensen A, Jones K, Raghavan S, Phillips LS, Hung A, Sun YV, Li G, Reaven P, Zhou H, Zhou JJ. A platform for phenotyping disease progression and associated longitudinal risk factors in large-scale EHRs, with application to incident diabetes complications in the UK Biobank. JAMIA Open 2023; 6:ooad006. [PMID: 36789288 PMCID: PMC9912368 DOI: 10.1093/jamiaopen/ooad006] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 01/19/2023] [Accepted: 01/31/2023] [Indexed: 02/12/2023] Open
Abstract
Objective Modern healthcare data reflect massive multi-level and multi-scale information collected over many years. The majority of the existing phenotyping algorithms use case-control definitions of disease. This paper aims to study the time to disease onset and progression and identify the time-varying risk factors that drive them. Materials and Methods We developed an algorithmic approach to phenotyping the incidence of diseases by consolidating data sources from the UK Biobank (UKB), including primary care electronic health records (EHRs). We focused on defining events, event dates, and their censoring time, including relevant terms and existing phenotypes, excluding generic, rare, or semantically distant terms, forward-mapping terminology terms, and expert review. We applied our approach to phenotyping diabetes complications, including a composite cardiovascular disease (CVD) outcome, diabetic kidney disease (DKD), and diabetic retinopathy (DR), in the UKB study. Results We identified 49 049 participants with diabetes. Among them, 1023 had type 1 diabetes (T1D), and 40 193 had type 2 diabetes (T2D). A total of 23 833 diabetes subjects had linked primary care records. There were 3237, 3113, and 4922 patients with CVD, DKD, and DR events, respectively. The risk prediction performance for each outcome was assessed, and our results are consistent with the prediction area under the ROC (receiver operating characteristic) curve (AUC) of standard risk prediction models using cohort studies. Discussion and Conclusion Our publicly available pipeline and platform enable streamlined curation of incidence events, identification of time-varying risk factors underlying disease progression, and the definition of a relevant cohort for time-to-event analyses. These important steps need to be considered simultaneously to study disease progression.
Collapse
Affiliation(s)
- Do Hyun Kim
- Department of Biostatistics, University of California, Los Angeles, California, USA
| | - Aubrey Jensen
- Department of Biostatistics, University of California, Los Angeles, California, USA
| | - Kelly Jones
- Department of Computer Science, Columbia University, New York, New York, USA
| | - Sridharan Raghavan
- Division of Hospital Medicine, University of Colorado School of Medicine, Aurora, Colorado, USA
- Rocky Mountain Regional VA Medical Center, Aurora, Colorado, USA
| | - Lawrence S Phillips
- Division of Endocrinology, Emory University School of Medicine, Atlanta, Georgia, USA
- Atlanta VA Medical Center, Decatur, Georgia, USA
| | - Adriana Hung
- VA Tennessee Valley Healthcare System, Nashville, Tennessee, USA
- Vanderbilt University, Nashville, Tennessee, USA
| | - Yan V Sun
- Department of Epidemiology, Emory University, Atlanta, Georgia, USA
| | - Gang Li
- Department of Biostatistics, University of California, Los Angeles, California, USA
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, California, USA
| | - Peter Reaven
- Phoenix VA Health Care System, Phoenix, Arizona, USA
| | - Hua Zhou
- Department of Biostatistics, University of California, Los Angeles, California, USA
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, California, USA
| | - Jin J Zhou
- Department of Biostatistics, University of California, Los Angeles, California, USA
- Phoenix VA Health Care System, Phoenix, Arizona, USA
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, California, USA
| |
Collapse
|
14
|
Liu C, Chiang Y, Hui Q, Zhou JJ, Wilson PW, Joseph J, Sun YV. High Variability of Body Mass Index Independently Associated with Incident Heart Failure. medRxiv 2023:2023.03.30.23287990. [PMID: 37034580 PMCID: PMC10081412 DOI: 10.1101/2023.03.30.23287990] [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] [Subscribe] [Scholar Register] [Indexed: 04/30/2023]
Abstract
Background Heart failure (HF) is a serious condition with increasing prevalence, high morbidity, and increased mortality. Obesity is an established risk factor for cardiovascular diseases, including HF. Fluctuation in body mass index (BMI) has shown a higher risk of cardiovascular outcomes. We investigated the association between BMI variability and incident HF. Methods In the UK Biobank, we established a prospective cohort after excluding participants with prevalent HF or cancer at enrollment. A total of 99,368 White (British, Irish, and any other white background) participants with ≥ 3 BMI measures during > 2 years preceding enrollment were included, with a median follow-up of 12.5 years. The within-participant variability of BMI was evaluated using standardized standard deviation (SD) and coefficient of variation (CV). The association of BMI variability with incident HF was assessed using Fine and Gray's competing risk model, and adjusted for age, sex, smoking history, alcohol consumption, diabetes, hypertension, history of heart attack, stroke, atrial fibrillation, lipids, estimated glomerular filtration rate and mean BMI per individual. Results In the fully adjusted model, higher BMI variability measured in both SD and CV were significantly associated with higher risk in HF incidence (SD: Hazard Ratio [HR] 1.05, 95% Confidence Interval [CI] 1.02 - 1.07, p = 0.0002; CV: HR 1.06, 95% CI 1.04 - 1.09, p < 0.0001). Conclusions Longitudinal health records capture BMI fluctuation, which independently predicts HF incidence. Integration of long-term BMI and other routinely measured health factors may improve risk prediction of HF and other cardiovascular outcomes.
Collapse
Affiliation(s)
- Chang Liu
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, Georgia, USA
| | - Yiyun Chiang
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, Georgia, USA
| | - Qin Hui
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, Georgia, USA
| | - Jin J. Zhou
- Department of Medicine and Biostatistics, University of California, Los Angeles, California, USA
| | - Peter W.F. Wilson
- Atlanta VA Health Care System, Decatur, GA, USA
- Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA
| | - Jacob Joseph
- VA Providence Healthcare System, Providence, Rhode Island, USA
- Warren Alpert Medical School, Brown University, Providence, Rhode Island, USA
| | - Yan V. Sun
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, Georgia, USA
- Atlanta VA Health Care System, Decatur, GA, USA
| |
Collapse
|
15
|
Verma A, Minnier J, Wan ES, Huffman JE, Gao L, Joseph J, Ho YL, Wu WC, Cho K, Gorman BR, Rajeevan N, Pyarajan S, Garcon H, Meigs JB, Sun YV, Reaven PD, McGeary JE, Suzuki A, Gelernter J, Lynch JA, Petersen JM, Zekavat SM, Natarajan P, Dalal S, Jhala DN, Arjomandi M, Gatsby E, Lynch KE, Bonomo RA, Freiberg M, Pathak GA, Zhou JJ, Donskey CJ, Madduri RK, Wells QS, Huang RDL, Polimanti R, Chang KM, Liao KP, Tsao PS, Wilson PWF, Hung AM, O’Donnell CJ, Gaziano JM, Hauger RL, Iyengar SK, Luoh SW. A MUC5B Gene Polymorphism, rs35705950-T, Confers Protective Effects Against COVID-19 Hospitalization but Not Severe Disease or Mortality. Am J Respir Crit Care Med 2022; 206:1220-1229. [PMID: 35771531 PMCID: PMC9746845 DOI: 10.1164/rccm.202109-2166oc] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
Rationale: A common MUC5B gene polymorphism, rs35705950-T, is associated with idiopathic pulmonary fibrosis (IPF), but its role in severe acute respiratory syndrome coronavirus 2 infection and disease severity is unclear. Objectives: To assess whether rs35705950-T confers differential risk for clinical outcomes associated with coronavirus disease (COVID-19) infection among participants in the Million Veteran Program (MVP). Methods: The MUC5B rs35705950-T allele was directly genotyped among MVP participants; clinical events and comorbidities were extracted from the electronic health records. Associations between the incidence or severity of COVID-19 and rs35705950-T were analyzed within each ancestry group in the MVP followed by transancestry meta-analysis. Replication and joint meta-analysis were conducted using summary statistics from the COVID-19 Host Genetics Initiative (HGI). Sensitivity analyses with adjustment for additional covariates (body mass index, Charlson comorbidity index, smoking, asbestosis, rheumatoid arthritis with interstitial lung disease, and IPF) and associations with post-COVID-19 pneumonia were performed in MVP subjects. Measurements and Main Results: The rs35705950-T allele was associated with fewer COVID-19 hospitalizations in transancestry meta-analyses within the MVP (Ncases = 4,325; Ncontrols = 507,640; OR = 0.89 [0.82-0.97]; P = 6.86 × 10-3) and joint meta-analyses with the HGI (Ncases = 13,320; Ncontrols = 1,508,841; OR, 0.90 [0.86-0.95]; P = 8.99 × 10-5). The rs35705950-T allele was not associated with reduced COVID-19 positivity in transancestry meta-analysis within the MVP (Ncases = 19,168/Ncontrols = 492,854; OR, 0.98 [0.95-1.01]; P = 0.06) but was nominally significant (P < 0.05) in the joint meta-analysis with the HGI (Ncases = 44,820; Ncontrols = 1,775,827; OR, 0.97 [0.95-1.00]; P = 0.03). Associations were not observed with severe outcomes or mortality. Among individuals of European ancestry in the MVP, rs35705950-T was associated with fewer post-COVID-19 pneumonia events (OR, 0.82 [0.72-0.93]; P = 0.001). Conclusions: The MUC5B variant rs35705950-T may confer protection in COVID-19 hospitalizations.
Collapse
Affiliation(s)
- Anurag Verma
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania;,Department of Medicine, Perelman School of Medicine, and
| | - Jessica Minnier
- OHSU-PSU School of Public Health and,Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon;,VA Portland Health Care System, Portland, Oregon
| | - Emily S. Wan
- Department of Medicine, Pulmonary, Critical Care, Sleep, and Allergy Section,,Channing Division of Network Medicine and
| | | | - Lina Gao
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon;,VA Portland Health Care System, Portland, Oregon
| | - Jacob Joseph
- Department of Medicine,,Medicine, Cardiovascular, Brigham & Women’s Hospital, Boston, Massachusetts
| | | | - Wen-Chih Wu
- Department of Medicine, Cardiology, Providence VA Healthcare System, Providence, Rhode Island;,Alpert Medical School & School of Public Health, Brown University, Providence, Rhode Island
| | - Kelly Cho
- MAVERIC,,Medicine, Aging, Brigham & Women’s Hospital and
| | | | - Nallakkandi Rajeevan
- Yale Center for Medical Informatics,,Clinical Epidemiology Research Center (CERC)
| | - Saiju Pyarajan
- MAVERIC,,Department of Medicine, Harvard Medical School, Boston, Massachusetts
| | | | | | - Yan V. Sun
- Epidemiology, School of Public Health and,Atlanta VA Healthcare System, Decatur, Georgia
| | - Peter D. Reaven
- Department of Medicine, Phoenix VA Healthcare System, Phoenix, Arizona;,College of Medicine, University of Arizona, Phoenix, Arizona
| | - John E. McGeary
- Department of Psychiatry and Human Behavior, Providence VA Medical Center, Providence, Rhode Island;,Department of Psychiatry and Human Behavior, Brown University Medical School, Providence, Rhode Island
| | - Ayako Suzuki
- Department of Medicine, Gastroenterology, Durham VA Medical Center, Durham, North Carolina;,Department of Medicine, Gastroenterology, Duke University, Durham, North Carolina
| | - Joel Gelernter
- Division of Human Genetics, Department of Psychiatry, and,VA Connecticut Healthcare System, West Haven, Connecticut
| | - Julie A. Lynch
- VA Informatics & Computing Infrastructure (VINCI), VA Salt Lake City Healthcare System, Salt Lake City, Utah;,Department of Medicine and
| | - Jeffrey M. Petersen
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania;,Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Seyedeh Maryam Zekavat
- Computational Biology & Bioinformatics, Yale University School of Medicine, New Haven, Connecticut;,Program in Medical and Population Genetics, Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, Massachusetts
| | - Pradeep Natarajan
- Department of Medicine, Harvard Medical School, Boston, Massachusetts;,Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts;,Program in Medical and Population Genetics, Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, Massachusetts
| | - Sharvari Dalal
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania;,Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Darshana N. Jhala
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania;,Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Mehrdad Arjomandi
- Medicine, Pulmonary and Critical Care, San Francisco VA Healthcare System, University of California, San Francisco, San Francisco, California
| | - Elise Gatsby
- VA Informatics & Computing Infrastructure (VINCI), VA Salt Lake City Healthcare System, Salt Lake City, Utah
| | - Kristine E. Lynch
- VA Informatics & Computing Infrastructure (VINCI), VA Salt Lake City Healthcare System, Salt Lake City, Utah;,Internal Medicine, Epidemiology, University of Utah School of Medicine, Salt Lake City, Utah
| | | | | | - Gita A. Pathak
- Division of Human Genetics, Department of Psychiatry, and,VA Connecticut Healthcare System, West Haven, Connecticut
| | - Jin J. Zhou
- Department of Medicine, University of California, Los Angeles, Los Angeles, California;,Epidemiology and Biostatistics, University of Arizona, Tucson, Arizona
| | | | - Ravi K. Madduri
- Data Science and Learning, Argonne National Laboratory, Lemont, Illinois
| | - Quinn S. Wells
- Department of Medicine,,Department of Biomedical Informatics, and,Department of Pharmacology, Vanderbilt University Medical Center, Nashville, Tennessee
| | | | - Renato Polimanti
- Division of Human Genetics, Department of Psychiatry, and,VA Connecticut Healthcare System, West Haven, Connecticut
| | - Kyong-Mi Chang
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania
| | | | - Philip S. Tsao
- Precision Medicine, VA Palo Alto Health Care System, Palo Alto, California
| | - Peter W. F. Wilson
- Emory University, Atlanta, Georgia;,Atlanta VA Healthcare System, Decatur, Georgia
| | - Adriana M. Hung
- Department of Veteran’s Affairs, Tennessee Valley Healthcare System, Vanderbilt University Medical Center, Division of Nephrology & Hypertension, Nashville, Tennessee
| | | | | | - Richard L. Hauger
- Center of Excellence for Stress & Mental Health, VA San Diego Healthcare System, San Diego, California; and,Center for Behavioral Genetics of Aging, University of California, San Diego, La Jolla, California
| | - Sudha K. Iyengar
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio;,Louis Stokes Cleveland VA Medical Center, Cleveland, Ohio
| | - Shiuh-Wen Luoh
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon;,VA Portland Health Care System, Portland, Oregon
| | | |
Collapse
|
16
|
Kim W, Hecker J, Barr RG, Boerwinkle E, Cade B, Correa A, Dupuis J, Gharib SA, Lange L, London SJ, Morrison AC, O'Connor GT, Oelsner EC, Psaty BM, Vasan RS, Redline S, Rich SS, Rotter JI, Yu B, Lange C, Manichaikul A, Zhou JJ, Sofer T, Silverman EK, Qiao D, Cho MH. Assessing the contribution of rare genetic variants to phenotypes of chronic obstructive pulmonary disease using whole-genome sequence data. Hum Mol Genet 2022; 31:3873-3885. [PMID: 35766891 PMCID: PMC9652112 DOI: 10.1093/hmg/ddac117] [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: 01/21/2022] [Revised: 04/13/2022] [Accepted: 05/16/2021] [Indexed: 01/04/2023] Open
Abstract
RATIONALE Genetic variation has a substantial contribution to chronic obstructive pulmonary disease (COPD) and lung function measurements. Heritability estimates using genome-wide genotyping data can be biased if analyses do not appropriately account for the nonuniform distribution of genetic effects across the allele frequency and linkage disequilibrium (LD) spectrum. In addition, the contribution of rare variants has been unclear. OBJECTIVES We sought to assess the heritability of COPD and lung function using whole-genome sequence data from the Trans-Omics for Precision Medicine program. METHODS Using the genome-based restricted maximum likelihood method, we partitioned the genome into bins based on minor allele frequency and LD scores and estimated heritability of COPD, FEV1% predicted and FEV1/FVC ratio in 11 051 European ancestry and 5853 African-American participants. MEASUREMENTS AND MAIN RESULTS In European ancestry participants, the estimated heritability of COPD, FEV1% predicted and FEV1/FVC ratio were 35.5%, 55.6% and 32.5%, of which 18.8%, 19.7%, 17.8% were from common variants, and 16.6%, 35.8%, and 14.6% were from rare variants. These estimates had wide confidence intervals, with common variants and some sets of rare variants showing a statistically significant contribution (P-value < 0.05). In African-Americans, common variant heritability was similar to European ancestry participants, but lower sample size precluded calculation of rare variant heritability. CONCLUSIONS Our study provides updated and unbiased estimates of heritability for COPD and lung function, and suggests an important contribution of rare variants. Larger studies of more diverse ancestry will improve accuracy of these estimates.
Collapse
Affiliation(s)
- Wonji Kim
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Julian Hecker
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - R Graham Barr
- Departments of Medicine and Epidemiology, Columbia University Medical Center, New York, NY 10032, USA
| | - Eric Boerwinkle
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Brian Cade
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Adolfo Correa
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA
| | - Josée Dupuis
- Department of Biostatistics, Boston University of Public Health, Boston, MA 02118, USA
| | - Sina A Gharib
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA 98101, USA
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Washington, Seattle, WA 98109, USA
| | - Leslie Lange
- Department of Medicine, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Stephanie J London
- Epidemiology Branch, National Institute of Environmental Health Sciences, Department of Health and Human Services, National Institutes of Health, Research Triangle Park, NC 27709, USA
| | - Alanna C Morrison
- Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - George T O'Connor
- Pulmonary Center, Boston University School of Medicine, Boston, MA 02118, USA
| | - Elizabeth C Oelsner
- Departments of Medicine and Epidemiology, Columbia University Medical Center, New York, NY 10032, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA 98101, USA
- Departments of Epidemiology and Health Services, University of Washington, Seattle, WA 98101, USA
| | - Ramachandran S Vasan
- Lung and Blood Institute Framingham Heart Study, Boston University and National Heart, Framingham, MA 01702, USA
- Department of Preventive Medicine and Epidemiology, School of Medicine and Public Health, Boston University, Boston, MA 02118, USA
| | - Susan Redline
- Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA 22908, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Bing Yu
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Christoph Lange
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Ani Manichaikul
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
| | - Jin J Zhou
- Department of Epidemiology and Biostatistics, University of Arizona, Tucson, AZ 85721, USA
| | - Tamar Sofer
- Division of Sleep and Circadian Disorder, Brigham and Women’s Hospital, Boston, MA 02115, USA
| | - Edwin K Silverman
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Dandi Qiao
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Michael H Cho
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | | |
Collapse
|
17
|
Ko S, Zhou H, Zhou JJ, Won JH. High-Performance Statistical Computing in the Computing Environments of the 2020s. Stat Sci 2022; 37:494-518. [PMID: 37168541 PMCID: PMC10168006 DOI: 10.1214/21-sts835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Technological advances in the past decade, hardware and software alike, have made access to high-performance computing (HPC) easier than ever. We review these advances from a statistical computing perspective. Cloud computing makes access to supercomputers affordable. Deep learning software libraries make programming statistical algorithms easy and enable users to write code once and run it anywhere-from a laptop to a workstation with multiple graphics processing units (GPUs) or a supercomputer in a cloud. Highlighting how these developments benefit statisticians, we review recent optimization algorithms that are useful for high-dimensional models and can harness the power of HPC. Code snippets are provided to demonstrate the ease of programming. We also provide an easy-to-use distributed matrix data structure suitable for HPC. Employing this data structure, we illustrate various statistical applications including large-scale positron emission tomography and ℓ1-regularized Cox regression. Our examples easily scale up to an 8-GPU workstation and a 720-CPU-core cluster in a cloud. As a case in point, we analyze the onset of type-2 diabetes from the UK Biobank with 200,000 subjects and about 500,000 single nucleotide polymorphisms using the HPC ℓ1-regularized Cox regression. Fitting this half-million-variate model takes less than 45 minutes and reconfirms known associations. To our knowledge, this is the first demonstration of the feasibility of penalized regression of survival outcomes at this scale.
Collapse
Affiliation(s)
- Seyoon Ko
- Seyoon Ko is Postdoctoral Scholar, Department of Biostatistics, UCLA Fielding School of Public Health, Los Angeles, California 90095, USA
| | - Hua Zhou
- Hua Zhou is Professor, Department of Biostatistics, UCLA Fielding School of Public Health, Los Angeles, California 90095, USA
| | - Jin J. Zhou
- Jin J. Zhou is Associate Professor, Department of Medicine, UCLA David Geffen School of Medicine, Los Angeles, California 90095, USA, and Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Ari
| | - Joong-Ho Won
- Joong-Ho Won is Associate Professor, Department of Statistics, Seoul National University, Seoul, Korea
| |
Collapse
|
18
|
Tang X, Brinton RD, Chen Z, Farland LV, Klimentidis Y, Migrino R, Reaven P, Rodgers K, Zhou JJ. Use of oral diabetes medications and the risk of incident dementia in US veterans aged ≥60 years with type 2 diabetes. BMJ Open Diabetes Res Care 2022; 10:10/5/e002894. [PMID: 36220195 PMCID: PMC9472121 DOI: 10.1136/bmjdrc-2022-002894] [Citation(s) in RCA: 8] [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] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 08/16/2022] [Indexed: 11/07/2022] Open
Abstract
INTRODUCTION Studies have reported that antidiabetic medications (ADMs) were associated with lower risk of dementia, but current findings are inconsistent. This study compared the risk of dementia onset in patients with type 2 diabetes (T2D) treated with sulfonylurea (SU) or thiazolidinedione (TZD) to patients with T2D treated with metformin (MET). RESEARCH DESIGN AND METHODS This is a prospective observational study within a T2D population using electronic medical records from all sites of the Veterans Affairs Healthcare System. Patients with T2D who initiated ADM from January 1, 2001, to December 31, 2017, were aged ≥60 years at the initiation, and were dementia-free were identified. A SU monotherapy group, a TZD monotherapy group, and a control group (MET monotherapy) were assembled based on prescription records. Participants were required to take the assigned treatment for at least 1 year. The primary outcome was all-cause dementia, and the two secondary outcomes were Alzheimer's disease and vascular dementia, defined by International Classification of Diseases (ICD), 9th Revision, or ICD, 10th Revision, codes. The risks of developing outcomes were compared using propensity score weighted Cox proportional hazard models. RESULTS Among 559 106 eligible veterans (mean age 65.7 (SD 8.7) years), the all-cause dementia rate was 8.2 cases per 1000 person-years (95% CI 6.0 to 13.7). After at least 1 year of treatment, TZD monotherapy was associated with a 22% lower risk of all-cause dementia onset (HR 0.78, 95% CI 0.75 to 0.81), compared with MET monotherapy, and 11% lower for MET and TZD dual therapy (HR 0.89, 95% CI 0.86 to 0.93), whereas the risk was 12% higher for SU monotherapy (HR 1.12 95% CI 1.09 to 1.15). CONCLUSIONS Among patients with T2D, TZD use was associated with a lower risk of dementia, and SU use was associated with a higher risk compared with MET use. Supplementing SU with either MET or TZD may partially offset its prodementia effects. These findings may help inform medication selection for elderly patients with T2D at high risk of dementia.
Collapse
Affiliation(s)
- Xin Tang
- Epidemiology and Biostatistics, The University of Arizona Mel and Enid Zuckerman College of Public Health, Tucson, Arizona, USA
| | - Roberta Diaz Brinton
- UA Center for Innovation in Brain Science, The University of Arizona Health Sciences, Tucson, Arizona, USA
| | - Zhao Chen
- Epidemiology and Biostatistics, The University of Arizona Mel and Enid Zuckerman College of Public Health, Tucson, Arizona, USA
| | - Leslie V Farland
- Epidemiology and Biostatistics, The University of Arizona Mel and Enid Zuckerman College of Public Health, Tucson, Arizona, USA
| | - Yann Klimentidis
- Department of Epidemiology and Biostatistics, The University of Arizona Mel and Enid Zuckerman College of Public Health, Tucson, Arizona, USA
| | - Raymond Migrino
- Division of Cardiology, The University of Arizona College of Medicine Phoenix, Phoenix, Arizona, USA
- Division of Endocrinology, Phoenix VA Health Care System, Phoenix, Arizona, USA
| | - Peter Reaven
- Division of Endocrinology, Phoenix VA Health Care System, Phoenix, Arizona, USA
- Division of Endocrinology, The University of Arizona College of Medicine Phoenix, Phoenix, Arizona, USA
| | - Kathleen Rodgers
- Department of Pharmacology and Toxicology, The University of Arizona, Tucson, Arizona, USA
| | - Jin J Zhou
- Department of Medicine, University of California, Los Angeles, California, USA
- Department of Biostatistics, University of California, Los Angeles, California, USA
| |
Collapse
|
19
|
Weng JW, Yu J, Jin F, Peng YG, Zhou JJ, Chen Y, Zhang J, Hei MY. [Clinical characteristics of 14 cases of neonatal tracheotomy in neonatal intensive care unit]. Zhonghua Er Ke Za Zhi 2022; 60:815-819. [PMID: 35922194 DOI: 10.3760/cma.j.cn112140-20220226-00152] [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] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Objective: To analyze the clinical characteristics of neonatal tracheotomy in neonatal intensive care unit (NICU). Methods: This single-center retrospective study included 14 neonates admitted to NICU of Beijing Children's Hospital, Capital Medical University from January 2016 to August 2021, and were<28 days of age on admission, who met the criteria of tracheotomy and finally completed the procedure. The clinical characteristics including age, weight, duration of ventilation, etiology of tracheotomy, length of hospital stay and prognosis were summarized and analyzed. Wilcoxon signed-rank test was used to compare the weight gain velocity and the duration of ventilation before and after tracheotomy. Paired t-test was used to compare the hospitalization length before and after tracheotomy. Spearman correlation was used to analyze the correlation between the clinical characteristics and outcomes. Results: For the 14 neonates, the gestational age was (38±4) weeks and birth weight was (2 824±949) g. Nine of them were male. The age on transportation was 16 (6, 25) d. A total of 10 neonates were on invasive ventilation on admission, the other 4 were on nasal continuous positive airway pressure support. Bilateral vocal cord paralysis (7 cases) was the commonest cause of tracheotomy. The age on operation was 33 (22, 44) d. There were statistically significant differences in duration of ventilation and weight gain velocity before and after operation (19.00 (10.50, 34.00) vs. 0.86 (0.06, 3.25) d, 1.66 (-0.16, 5.54) vs. 4.69 (2.30, 9.32) g/(kg·d), Z=3.01 and -1.98, both P<0.05). The total hospital stay in NICU was (37±12) d. One neonate died during hospitalization. The existence of pneumonia on admission was positively correlated to NICU stay length (r=0.57, P=0.027), the pre-operational weight gain velocity was negatively correlated to the post-operational NICU stay length (r=-0.73, P=0.020). There were 4 neonates de-cannulated during 7-38 months after the tracheotomy, and 5 neonates still wearing the tracheal cannulation during 15-66 months after the tracheotomy. Two neonates died and 2 neonates lost follow-up after discharge. All neonates could not vocalize normally before de-cannulation, and the language development obviously lagged behind the normal age group after de-cannulation. Conclusions: Bilateral vocal cord paralysis is the commonest cause of neonatal tracheotomy. The benefit of tracheotomy for NICU neonates with surgical indications is obvious, especially in facilitating extubation and improving weight gain.
Collapse
Affiliation(s)
- J W Weng
- Department of Neonatology, Neonatal Center, National Center for Children's Health, Beijing Children's Hospital, Capital Medical University, Beijing 100045, China
| | - J Yu
- Department of Neonatology, Neonatal Center, National Center for Children's Health, Beijing Children's Hospital, Capital Medical University, Beijing 100045, China
| | - F Jin
- Department of Neonatology, Neonatal Center, National Center for Children's Health, Beijing Children's Hospital, Capital Medical University, Beijing 100045, China
| | - Y G Peng
- Center for Clinical Epidemiology and Evidence-based Medicine, National Center for Children's Health,Beijing Children's Hospital, Capital Medical University, Beijing 100045, China
| | - J J Zhou
- Department of Neonatology, Neonatal Center, National Center for Children's Health, Beijing Children's Hospital, Capital Medical University, Beijing 100045, China
| | - Y Chen
- Department of Neonatology, Neonatal Center, National Center for Children's Health, Beijing Children's Hospital, Capital Medical University, Beijing 100045, China
| | - J Zhang
- Department of Otorhinolaryngology, National Center for Children's Health, Beijing Children's Hospital, Capital Medical University, Beijing 100045, China
| | - M Y Hei
- Department of Neonatology, Neonatal Center, National Center for Children's Health, Beijing Children's Hospital, Capital Medical University, Beijing 100045, China
| |
Collapse
|
20
|
Verma A, Huffman JE, Gao L, Minnier J, Wu WC, Cho K, Ho YL, Gorman BR, Pyarajan S, Rajeevan N, Garcon H, Joseph J, McGeary JE, Suzuki A, Reaven PD, Wan ES, Lynch JA, Petersen JM, Meigs JB, Freiberg MS, Gatsby E, Lynch KE, Zekavat SM, Natarajan P, Dalal S, Jhala DN, Arjomandi M, Bonomo RA, Thompson TK, Pathak GA, Zhou JJ, Donskey CJ, Madduri RK, Wells QS, Gelernter J, Huang RDL, Polimanti R, Chang KM, Liao KP, Tsao PS, Sun YV, Wilson PWF, O’Donnell CJ, Hung AM, Gaziano JM, Hauger RL, Iyengar SK, Luoh SW. Association of Kidney Comorbidities and Acute Kidney Failure With Unfavorable Outcomes After COVID-19 in Individuals With the Sickle Cell Trait. JAMA Intern Med 2022; 182:796-804. [PMID: 35759254 PMCID: PMC9237798 DOI: 10.1001/jamainternmed.2022.2141] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [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: 12/15/2022]
Abstract
Importance Sickle cell trait (SCT), defined as the presence of 1 hemoglobin beta sickle allele (rs334-T) and 1 normal beta allele, is prevalent in millions of people in the US, particularly in individuals of African and Hispanic ancestry. However, the association of SCT with COVID-19 is unclear. Objective To assess the association of SCT with the prepandemic health conditions in participants of the Million Veteran Program (MVP) and to assess the severity and sequelae of COVID-19. Design, Setting, and Participants COVID-19 clinical data include 2729 persons with SCT, of whom 353 had COVID-19, and 129 848 SCT-negative individuals, of whom 13 488 had COVID-19. Associations between SCT and COVID-19 outcomes were examined using firth regression. Analyses were performed by ancestry and adjusted for sex, age, age squared, and ancestral principal components to account for population stratification. Data for the study were collected between March 2020 and February 2021. Exposures The hemoglobin beta S (HbS) allele (rs334-T). Main Outcomes and Measures This study evaluated 4 COVID-19 outcomes derived from the World Health Organization severity scale and phenotypes derived from International Classification of Diseases codes in the electronic health records. Results Of the 132 577 MVP participants with COVID-19 data, mean (SD) age at the index date was 64.8 (13.1) years. Sickle cell trait was present in 7.8% of individuals of African ancestry and associated with a history of chronic kidney disease, diabetic kidney disease, hypertensive kidney disease, pulmonary embolism, and cerebrovascular disease. Among the 4 clinical outcomes of COVID-19, SCT was associated with an increased COVID-19 mortality in individuals of African ancestry (n = 3749; odds ratio, 1.77; 95% CI, 1.13 to 2.77; P = .01). In the 60 days following COVID-19, SCT was associated with an increased incidence of acute kidney failure. A counterfactual mediation framework estimated that on average, 20.7% (95% CI, -3.8% to 56.0%) of the total effect of SCT on COVID-19 fatalities was due to acute kidney failure. Conclusions and Relevance In this genetic association study, SCT was associated with preexisting kidney comorbidities, increased COVID-19 mortality, and kidney morbidity.
Collapse
Affiliation(s)
- Anurag Verma
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania
- Perelman School of Medicine, Department of Medicine, University of Pennsylvania, Philadelphia
| | | | - Lina Gao
- Knight Cancer Institute, Biostatistics Shared Resource, Oregon Health & Science University, Portland
- VA Portland Health Care System, Portland, Oregon
| | - Jessica Minnier
- VA Portland Health Care System, Portland, Oregon
- OHSU-PSU School of Public Health, Oregon Health & Science University, Portland
- Knight Cancer Institute, Biostatistics Shared Resource, Oregon Health & Science University, Portland
| | - Wen-Chih Wu
- Department of Medicine, Cardiology, Providence VA Healthcare System, Providence, Rhode Island
- Alpert Medical School & School of Public Health, Brown University, Providence, Rhode Island
| | - Kelly Cho
- MAVERIC, VA Boston Healthcare System, Boston, Massachusetts
- Medicine, Aging, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Yuk-Lam Ho
- MAVERIC, VA Boston Healthcare System, Boston, Massachusetts
| | | | - Saiju Pyarajan
- VA Boston Healthcare System, Boston, Massachusetts
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
| | - Nallakkandi Rajeevan
- Yale Center for Medical Informatics, Yale School of Medicine, New Haven, Connecticut
- Clinical Epidemiology Research Center (CERC), VA Connecticut Healthcare System, West Haven
| | - Helene Garcon
- MAVERIC, VA Boston Healthcare System, Boston, Massachusetts
| | - Jacob Joseph
- Department of Medicine, VA Boston Healthcare System, Boston, Massachusetts
- Brigham & Women’s Hospital, Boston, Massachusetts
| | - John E. McGeary
- Department of Psychiatry and Human Behavior, Providence VA Medical Center, Providence, Rhode Island
- Brown University Medical School, Providence, Rhode Island
| | - Ayako Suzuki
- Department of Medicine, Gastroenterology, Durham VA Medical Center, Durham, North Carolina
- Department of Medicine, Gastroenterology, Duke University, Durham, North Carolina
| | - Peter D. Reaven
- Department of Medicine, Phoenix VA Healthcare System, Phoenix, Arizona
- University of Arizona, Phoenix
| | - Emily S. Wan
- Department of Medicine, Pulmonary, Critical Care, Sleep, and Allergy Section, VA Boston Healthcare System, Boston, Massachusetts
- Channing Division of Network Medicine, Brigham & Women’s Hospital, Boston, Massachusetts
| | - Julie A. Lynch
- VA Informatics & Computing Infrastructure, VA Salt Lake City Utah & University of Utah, School of Medicine, Salt Lake City
| | - Jeffrey M. Petersen
- Pathology and Laboratory Medicine, Corporal Michael Crescenz VA Medical Center, Philadelphia, Pennsylvania
- Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - James B. Meigs
- Medicine, General Internal Medicine, Massachusetts General Hospital, Boston
| | | | - Elise Gatsby
- VA Informatics and Computing Infrastructure (VINCI), VA Salt Lake City Healthcare System, Salt Lake City, Utah
| | - Kristine E. Lynch
- VA Informatics and Computing Infrastructure (VINCI), VA Salt Lake City Healthcare System, Salt Lake City, Utah
- Internal Medicine, Epidemiology, University of Utah School of Medicine, Salt Lake City
| | - Seyedeh Maryam Zekavat
- Computational Biology & Bioinformatics, Yale School of Medicine, New Haven, Connecticut
- Program in Medical and Population Genetics, Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, Massachusetts
| | - Pradeep Natarajan
- Program in Medical and Population Genetics, Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, Massachusetts
- Cardiovascular Research Center, Massachusetts General Hospital, Boston
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
- Clinical Data Science Research Group, ORD, Portland VA Medical Center, Portland, Oregon
| | - Sharvari Dalal
- Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Pathology and Laboratory Medicine, Corporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania
| | - Darshana N. Jhala
- Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Pathology and Laboratory Medicine, Corporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania
| | - Mehrdad Arjomandi
- Medicine, Pulmonary and Critical Care, San Francisco VA Healthcare System, San Francisco, California
- University of California San Francisco
| | - Robert A. Bonomo
- Cleveland VA Medical Center, Cleveland, Ohio
- Medicine, Case Western Reserve University School of Medicine, Cleveland, Ohio
| | | | - Gita A. Pathak
- Department of Psychiatry, Division of Human Genetics, Yale School of Medicine, New Haven, Connecticut
- VA Connecticut Healthcare System, West Haven
| | - Jin J. Zhou
- Medicine, University of California, Los Angeles
- Epidemiology and Biostatistics, University of Arizona, Phoenix
| | - Curtis J. Donskey
- Infectious Disease Section, Louis Stokes Cleveland VA, Cleveland, Ohio
- Case Western Reserve University, Cleveland, Ohio
| | - Ravi K. Madduri
- Data Science and Learning, Argonne National Laboratory, Lemont, Illinois
| | - Quinn S. Wells
- Departments of Medicine, Biomedical Informatics, and Pharmacology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Joel Gelernter
- VA Connecticut Healthcare System, West Haven
- Psychiatry, Human Genetics, Yale University School of Medicine, West Haven, Connecticut
| | | | - Renato Polimanti
- Departments of Medicine, Biomedical Informatics, and Pharmacology, Vanderbilt University Medical Center, Nashville, Tennessee
- Psychiatry, Human Genetics, Yale University School of Medicine, West Haven, Connecticut
| | - Kyong-Mi Chang
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania
- Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Katherine P. Liao
- Medicine, Rheumatology, VA Boston Healthcare System, Boston, Massachusetts
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women’s Hospital, Boston, Massachusetts
- Department of Medicine & Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - Philip S. Tsao
- Precision Medicine, VA Palo Alto Health Care System, Palo Alto, California
| | - Yan V. Sun
- Epidemiology, Emory University School of Public Health, Atlanta, Georgia
- Atlanta VA Health Care System, Decatur, Georgia
| | - Peter W. F. Wilson
- Atlanta VA Health Care System, Decatur, Georgia
- Emory University School of Medicine, Atlanta, Georgia
| | | | - Adriana M. Hung
- Vanderbilt University Medical Center, Nashville, Tennessee
- Nashville VA Medical Center, Nashville, Tennessee
| | - J. Michael Gaziano
- VA Boston Health Care System, Boston, Massachusetts
- Medicine, Harvard Medical School, Boston, Massachusetts
| | - Richard L. Hauger
- Center of Excellence for Stress & Mental Health, VA San Diego Healthcare System, San Diego, California
- Center for Behavioral Genetics of Aging, University of California, San Diego, La Jolla
| | - Sudha K. Iyengar
- Departments of Population and Quantitative Health Sciences, Ophthalmology and Visual Sciences and Genetics and Genome Sciences, Case Western Reserve University, Cleveland, Ohio
- Louis Stokes Cleveland VA Medical Center, Cleveland, Ohio
| | - Shiuh-Wen Luoh
- VA Portland Health Care System, Portland, Oregon
- Division of Hematology and Medical Oncology, Knight Cancer Institute, Oregon Health and Science University, Portland
| |
Collapse
|
21
|
Zhou X, Zhou JJ, Zhou H. Bag of little bootstraps for massive and distributed longitudinal data. Stat Anal Data Min 2022; 15:314-321. [DOI: 10.1002/sam.11563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- Xinkai Zhou
- Department of Biostatistics University of California Los Angeles California USA
| | - Jin J. Zhou
- Department of Medicine University of California Los Angeles California USA
| | - Hua Zhou
- Department of Biostatistics University of California Los Angeles California USA
- Department of Computational Medicine University of California Los Angeles California USA
| |
Collapse
|
22
|
Kim J, Jensen A, Ko S, Raghavan S, Phillips LS, Hung A, Sun Y, Zhou H, Reaven P, Zhou JJ. Systematic Heritability and Heritability Enrichment Analysis for Diabetes Complications in UK Biobank and ACCORD Studies. Diabetes 2022; 71:1137-1148. [PMID: 35133398 PMCID: PMC9044130 DOI: 10.2337/db21-0839] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [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: 09/16/2021] [Accepted: 02/02/2022] [Indexed: 11/13/2022]
Abstract
Diabetes-related complications reflect longstanding damage to small and large vessels throughout the body. In addition to the duration of diabetes and poor glycemic control, genetic factors are important contributors to the variability in the development of vascular complications. Early heritability studies found strong familial clustering of both macrovascular and microvascular complications. However, they were limited by small sample sizes and large phenotypic heterogeneity, leading to less accurate estimates. We take advantage of two independent studies-UK Biobank and the Action to Control Cardiovascular Risk in Diabetes trial-to survey the single nucleotide polymorphism heritability for diabetes microvascular (diabetic kidney disease and diabetic retinopathy) and macrovascular (cardiovascular events) complications. Heritability for diabetic kidney disease was estimated at 29%. The heritability estimate for microalbuminuria ranged from 24 to 60% and was 41% for macroalbuminuria. Heritability estimates of diabetic retinopathy ranged from 6 to 33%, depending on the phenotype definition. More severe diabetes retinopathy possessed higher genetic contributions. We show, for the first time, that rare variants account for much of the heritability of diabetic retinopathy. This study suggests that a large portion of the genetic risk of diabetes complications is yet to be discovered and emphasizes the need for additional genetic studies of diabetes complications.
Collapse
Affiliation(s)
- Juhyun Kim
- Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA
- Department of Biostatistics, University of Michigan, Ann Arbor, MI
| | - Aubrey Jensen
- Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA
| | - Seyoon Ko
- Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA
| | - Sridharan Raghavan
- University of Colorado School of Medicine, Aurora, CO
- Rocky Mountain Regional Veterans Affairs Medical Center, Aurora, CO
| | - Lawrence S. Phillips
- Division of Endocrinology, Emory University School of Medicine, Atlanta, GA
- Atlanta Veterans Affairs Medical Center, Decatur, GA
| | - Adriana Hung
- Tennessee Valley Healthcare System and Vanderbilt University, Nashville, TN
| | - Yan Sun
- Department of Epidemiology, Emory University, Atlanta, GA
| | - Hua Zhou
- Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA
| | - Peter Reaven
- Phoenix Veterans Affairs Health Care System, Phoenix, AZ
| | - Jin J. Zhou
- Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA
- Phoenix Veterans Affairs Health Care System, Phoenix, AZ
- Department of Epidemiology and Biostatistics, University of Arizona, Tucson, AZ
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA
| |
Collapse
|
23
|
Nuyujukian DS, Newell MS, Zhou JJ, Koska J, Reaven PD. Baseline blood pressure modifies the role of blood pressure variability in mortality: Results from the ACCORD trial. Diabetes Obes Metab 2022; 24:951-955. [PMID: 35014154 PMCID: PMC8986598 DOI: 10.1111/dom.14649] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 12/22/2021] [Accepted: 01/06/2022] [Indexed: 11/30/2022]
Affiliation(s)
- Daniel S Nuyujukian
- Research Service, Phoenix VA Health Care System, Phoenix, Arizona, USA
- Epidemiology and Biostatistics, University of Arizona, Tucson, Arizona, USA
| | - Michelle S Newell
- Research Service, Phoenix VA Health Care System, Phoenix, Arizona, USA
| | - Jin J Zhou
- Epidemiology and Biostatistics, University of Arizona, Tucson, Arizona, USA
- Medicine, University of California, Los Angeles, California, USA
| | - Juraj Koska
- Research Service, Phoenix VA Health Care System, Phoenix, Arizona, USA
| | - Peter D Reaven
- Research Service, Phoenix VA Health Care System, Phoenix, Arizona, USA
- College of Medicine-Phoenix, University of Arizona, Phoenix, Arizona, USA
| |
Collapse
|
24
|
Ko S, German CA, Jensen A, Shen J, Wang A, Mehrotra DV, Sun YV, Sinsheimer JS, Zhou H, Zhou JJ. GWAS of longitudinal trajectories at biobank scale. Am J Hum Genet 2022; 109:433-445. [PMID: 35196515 PMCID: PMC8948167 DOI: 10.1016/j.ajhg.2022.01.018] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [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: 08/10/2021] [Accepted: 01/25/2022] [Indexed: 12/12/2022] Open
Abstract
Biobanks linked to massive, longitudinal electronic health record (EHR) data make numerous new genetic research questions feasible. One among these is the study of biomarker trajectories. For example, high blood pressure measurements over visits strongly predict stroke onset, and consistently high fasting glucose and Hb1Ac levels define diabetes. Recent research reveals that not only the mean level of biomarker trajectories but also their fluctuations, or within-subject (WS) variability, are risk factors for many diseases. Glycemic variation, for instance, is recently considered an important clinical metric in diabetes management. It is crucial to identify the genetic factors that shift the mean or alter the WS variability of a biomarker trajectory. Compared to traditional cross-sectional studies, trajectory analysis utilizes more data points and captures a complete picture of the impact of time-varying factors, including medication history and lifestyle. Currently, there are no efficient tools for genome-wide association studies (GWASs) of biomarker trajectories at the biobank scale, even for just mean effects. We propose TrajGWAS, a linear mixed effect model-based method for testing genetic effects that shift the mean or alter the WS variability of a biomarker trajectory. It is scalable to biobank data with 100,000 to 1,000,000 individuals and many longitudinal measurements and robust to distributional assumptions. Simulation studies corroborate that TrajGWAS controls the type I error rate and is powerful. Analysis of eleven biomarkers measured longitudinally and extracted from UK Biobank primary care data for more than 150,000 participants with 1,800,000 observations reveals loci that significantly alter the mean or WS variability.
Collapse
Affiliation(s)
- Seyoon Ko
- Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA,Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Christopher A. German
- Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Aubrey Jensen
- Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Judong Shen
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Kenilworth, NJ 07033, USA
| | - Anran Wang
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Kenilworth, NJ 07033, USA
| | - Devan V. Mehrotra
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Kenilworth, NJ 07033, USA
| | - Yan V. Sun
- Department of Epidemiology, Emory University, Atlanta, GA 30322, USA
| | - Janet S. Sinsheimer
- Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA,Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA 90095, USA,Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Hua Zhou
- Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA 90095, USA.
| | - Jin J. Zhou
- Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA 90095, USA,Department of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA,Department of Epidemiology and Biostatistics, University of Arizona, Tucson, AZ 85721, USA,Corresponding author
| |
Collapse
|
25
|
Peloso GM, Tcheandjieu C, McGeary JE, Posner DC, Ho YL, Zhou JJ, Hilliard AT, Joseph J, O’Donnell CJ, Efird JT, Crawford DC, Wu WC, Arjomandi M, Sun YV, Assimes TL, Huffman JE. Genetic Loci Associated With COVID-19 Positivity and Hospitalization in White, Black, and Hispanic Veterans of the VA Million Veteran Program. Front Genet 2022; 12:777076. [PMID: 35222515 PMCID: PMC8864634 DOI: 10.3389/fgene.2021.777076] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 12/27/2021] [Indexed: 12/13/2022] Open
Abstract
SARS-CoV-2 has caused symptomatic COVID-19 and widespread death across the globe. We sought to determine genetic variants contributing to COVID-19 susceptibility and hospitalization in a large biobank linked to a national United States health system. We identified 19,168 (3.7%) lab-confirmed COVID-19 cases among Million Veteran Program participants between March 1, 2020, and February 2, 2021, including 11,778 Whites, 4,893 Blacks, and 2,497 Hispanics. A multi-population genome-wide association study (GWAS) for COVID-19 outcomes identified four independent genetic variants (rs8176719, rs73062389, rs60870724, and rs73910904) contributing to COVID-19 positivity, including one novel locus found exclusively among Hispanics. We replicated eight of nine previously reported genetic associations at an alpha of 0.05 in at least one population-specific or the multi-population meta-analysis for one of the four MVP COVID-19 outcomes. We used rs8176719 and three additional variants to accurately infer ABO blood types. We found that A, AB, and B blood types were associated with testing positive for COVID-19 compared with O blood type with the highest risk for the A blood group. We did not observe any genome-wide significant associations for COVID-19 severity outcomes among those testing positive. Our study replicates prior GWAS findings associated with testing positive for COVID-19 among mostly White samples and extends findings at three loci to Black and Hispanic individuals. We also report a new locus among Hispanics requiring further investigation. These findings may aid in the identification of novel therapeutic agents to decrease the morbidity and mortality of COVID-19 across all major ancestral populations.
Collapse
Affiliation(s)
- Gina M. Peloso
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA, United States
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, United States
| | - Catherine Tcheandjieu
- VA Palo Alto Healthcare System, Palo Alto, CA, United States
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, United States
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, United States
| | - John E. McGeary
- Providence VA Healthcare System, Providence, RI, United States
- Department of Psychiatry and Human Behavior, Brown University, Providence, RI, United States
| | - Daniel C. Posner
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA, United States
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA, United States
| | - Jin J. Zhou
- Phoenix VA Health Care System, Phoenix, AZ, United States
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, United States
| | | | - Jacob Joseph
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA, United States
- Cardiology Section, VA Boston Healthcare System, Boston, MA, United States
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA, United States
| | - Christopher J. O’Donnell
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA, United States
- Cardiology Section, VA Boston Healthcare System, Boston, MA, United States
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA, United States
| | - Jimmy T. Efird
- Cooperative Studies Program Epidemiology Center-Durham, Durham VA Health Care System, Durham, NC, United States
| | - Dana C. Crawford
- Louis Stokes Cleveland VA Medical Center, Cleveland, OH, United States
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, United States
- Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, OH, United States
- Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, United States
| | - Wen-Chih Wu
- Providence VA Healthcare System, Providence, RI, United States
- Department of Medicine, Alpert Medical School, Brown University, Providence, RI, United States
| | - Mehrdad Arjomandi
- Medical Service, San Francisco VA Medical Center, San Francisco, CA, United States
- Department of Medicine, University of California, San Francisco, San Francisco, CA, United States
| | | | - Yan V. Sun
- Atlanta VA Health Care System, Decatur, GA, United States
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, United States
| | - Themistocles L Assimes
- VA Palo Alto Healthcare System, Palo Alto, CA, United States
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, United States
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, United States
| | - Jennifer E. Huffman
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA, United States
- *Correspondence: Jennifer E. Huffman,
| |
Collapse
|
26
|
Jin F, Weng JW, Zhou JJ, Chen Y, Zhang J, Hei MY. [Clinical characteristics and outcomes of 111 neonates with upper airway obstruction admitted via transportation]. Zhonghua Er Ke Za Zhi 2022; 60:88-93. [PMID: 35090223 DOI: 10.3760/cma.j.cn112140-20210701-00547] [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] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Objectives: To analyze the clinical characteristics and outcomes of neonates with upper airway obstruction (UAO) who were admitted via transportation, hence to provide more evidence-based information for the clinical management of UAO. Methods: This was a single center retrospective study. Patients were hospitalized in Beijing Children's Hospital from January 1, 2016 to May 31, 2021 with age <28 days or postmenstrual age (PMA) ≤44 weeks, and UAO as the first diagnosis. The general information of patients, obstructed sites in the upper airway, treatment, complications and prognosis were analyzed. The outcomes of surgical UAO vs. non-surgical UAO were analyzed by 2 by 2 χ2 test. Results: A total of 111 cases were analyzed (2.3% of the total NICU hospitalized 4 826 infants in the same period), in which 62 (55.9%) were boys and 101 (91.0%) were term infants, and their gestational age was (38.7±2.0) weeks, birth weight (3 207±585) g, PMA on admission (40.8±2.5) weeks and weight on admission was (3 221±478) g. There were 92 cases (82.9%) with symptoms of UAO presenting on postnatal day 1, and 35 cases (31.5%) had extra-uterine growth retardation on admission. The diagnosis of UAO and the obstructive site was confirmed in 25 cases (22.5%) before transportation. There were 24 cases (21.6%), 71 cases (64.0%), and 16 cases (14.4%) who had UAO due to nasal, throat, and neck problems, respectively. The top 5 diagnosis of UAO were vocal cord paralysis (28 cases), bilateral choanal atresia (20 cases), laryngomalacia (15 cases), pharynx and larynx cysts (7 cases), and subglottic hemangioma (6 cases). The diagnosis and treatment of all the patients followed a multidisciplinary approach consisted of neonatal intensive care unit, ear-nose-throat department and medical image departments. A total of 102 cases (91.9%) underwent both bronchofiberscope and fiber nasopharyngoscope investigation. Seventy cases (63.1%) required ventilation. Among the 58 cases (52.3%) who required surgical intervention, 16 had tracheotomy. For cases with vs. without surgical intervention, the rate of cure and (or) improvement were 94.8% (55/58) vs. 54.7% (29/53), and the rate of being discharged against medical arrangement were 1.7% (1/58) vs. 45.3% (24/53) (χ²=24.21 and 30.11, both P<0.01). Conclusions: Neonatal UAO may locate at various sites of the upper airway. The overall prognosis of neonatal UAO is favorable. A multidisciplinary approach is necessary for efficient evaluation and appropriate surgical intervention.
Collapse
Affiliation(s)
- F Jin
- Department of Neonatology, Neonatal Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China
| | - J W Weng
- Department of Neonatology, Neonatal Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China
| | - J J Zhou
- Department of Neonatology, Neonatal Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China
| | - Y Chen
- Department of Neonatology, Neonatal Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China
| | - J Zhang
- Department of Otorhinolaryngology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China
| | - M Y Hei
- Department of Neonatology, Neonatal Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China
| |
Collapse
|
27
|
Zhou JJ, Wang SF. [Introduction of landmarking approach and its application in dynamic prediction]. Zhonghua Liu Xing Bing Xue Za Zhi 2022; 43:112-117. [PMID: 35130661 DOI: 10.3760/cma.j.cn112338-20210122-00051] [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] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Conventional prediction model, as a static prediction model, can be only used to predict the probability of the occurrence of an event during the observation period using the information available at baseline survey. However, based on current clinical demands, dynamic prediction, which obtains prediction probabilities for both baseline survey and later time points given the history of the events and covariates up to that time, is gaining a growing attention. As a dynamic prediction model, the landmarking approach is simple, easy to use, computationally efficient and has a comparable performance of joint modeling, which makes it to be widely used in recent researches. Because of its limited application in China, this paper makes a brief introduction of its ideas and basic application to further promote its applications in clinical dynamic prediction.
Collapse
Affiliation(s)
- J J Zhou
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
| | - S F Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
| |
Collapse
|
28
|
Kim J, Shen J, Wang A, Mehrotra DV, Ko S, Zhou JJ, Zhou H. VCSEL: Prioritizing SNP-set by penalized variance component selection. Ann Appl Stat 2021; 15:1652-1672. [DOI: 10.1214/21-aoas1491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Juhyun Kim
- Department of Biostatistics, University of California, Los Angeles
| | - Judong Shen
- Biostatistics and Research Decision Sciences, Merck & Co., Inc
| | - Anran Wang
- Biostatistics and Research Decision Sciences, Merck & Co., Inc
| | | | - Seyoon Ko
- Department of Biostatistics, University of California, Los Angeles
| | - Jin J. Zhou
- Department of Medicine, University of California, Los Angeles
| | - Hua Zhou
- Department of Biostatistics, University of California, Los Angeles
| |
Collapse
|
29
|
Nuyujukian DS, Zhou JJ, Koska J, Reaven PD. Refining determinants of associations of visit-to-visit blood pressure variability with cardiovascular risk: results from the Action to Control Cardiovascular Risk in Diabetes Trial. J Hypertens 2021; 39:2173-2182. [PMID: 34232160 PMCID: PMC8500916 DOI: 10.1097/hjh.0000000000002931] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [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] [Indexed: 11/27/2022]
Abstract
OBJECTIVES As there is uncertainty about the extent to which baseline blood pressure level or cardiovascular risk modifies the relationship between blood pressure variability (BPv) and cardiovascular disease, we comprehensively examined the role of BPv in cardiovascular disease risk in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) Trial. METHODS Using data from ACCORD, we examined the relationship of BPv with development of the primary CVD outcome, major coronary heart disease (CHD), and total stroke using time-dependent Cox proportional hazards models. RESULTS BPv was associated with the primary CVD outcome and major CHD but not stroke. The positive association with the primary CVD outcome and major CHD was more pronounced in low and high strata of baseline SBP (<120 and >140 mmHg) and DBP (<70 and >80 mmHg). The effect of BPv on CVD and CHD was more pronounced in those with both prior CVD history and low blood pressure. Dips, not elevations, in blood pressure appeared to drive these associations. The relationships were generally not attenuated by adjustment for mean blood pressure, medication adherence, or baseline comorbidities. A sensitivity analysis using CVD events from the long-term posttrial follow-up (ACCORDION) was consistent with the results from ACCORD. CONCLUSION In ACCORD, the effect of BPv on adverse cardiovascular (but not cerebrovascular) outcomes is modified by baseline blood pressure and prior CVD. Recognizing these more nuanced relationships may help improve risk stratification and blood pressure management decisions as well as provide insight into potential underlying mechanisms.
Collapse
Affiliation(s)
| | - Jin J Zhou
- Phoenix VA Healthcare System, Phoenix
- Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson
| | | | - Peter D Reaven
- Phoenix VA Healthcare System, Phoenix
- College of Medicine-Phoenix, University of Arizona, Phoenix, Arizona, USA
| |
Collapse
|
30
|
Hoppe-Jones C, Griffin SC, Gulotta JJ, Wallentine DD, Moore PK, Beitel SC, Flahr LM, Zhai J, Zhou JJ, Littau SR, Dearmon-Moore D, Jung AM, Garavito F, Snyder SA, Burgess JL. Evaluation of fireground exposures using urinary PAH metabolites. J Expo Sci Environ Epidemiol 2021; 31:913-922. [PMID: 33654270 PMCID: PMC8445814 DOI: 10.1038/s41370-021-00311-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [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: 07/06/2020] [Revised: 02/10/2021] [Accepted: 02/12/2021] [Indexed: 06/02/2023]
Abstract
BACKGROUND Firefighters have increased cancer incidence and mortality rates compared to the general population, and are exposed to multiple products of combustion including known and suspected carcinogens. OBJECTIVE The study objective was to quantify fire response exposures by role and self-reported exposure risks. METHODS Urinary hydroxylated metabolites of polycyclic aromatic hydrocarbons (PAH-OHs) were measured at baseline and 2-4 h after structural fires and post-fire surveys were collected. RESULTS Baseline urine samples were collected from 242 firefighters. Of these, 141 responded to at least one of 15 structural fires and provided a post-fire urine. Compared with baseline measurements, the mean fold change of post-fire urinary PAH-OHs increased similarly across roles, including captains (2.05 (95% CI 1.59-2.65)), engineers (2.10 (95% CI 1.47-3.05)), firefighters (2.83 (95% CI 2.14-3.71)), and paramedics (1.84 (95% CI 1.33-2.60)). Interior responses, smoke odor on skin, and lack of recent laundering or changing of hoods were significantly associated with increased post-fire urinary PAH-OHs. SIGNIFICANCE Ambient smoke from the fire represents an exposure hazard for all individuals on the fireground; engineers and paramedics in particular may not be aware of the extent of their exposure. Post-fire surveys identified specific risks associated with increased exposure.
Collapse
Affiliation(s)
- Christiane Hoppe-Jones
- Department of Chemical and Environmental Engineering, College of Engineering, University of Arizona, Tucson, AZ, USA
| | - Stephanie C Griffin
- Department of Community, Environment and Policy, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA
| | | | | | | | - Shawn C Beitel
- Department of Community, Environment and Policy, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA
| | - Leanne M Flahr
- Department of Chemical and Environmental Engineering, College of Engineering, University of Arizona, Tucson, AZ, USA
| | - Jing Zhai
- Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA
| | - Jin J Zhou
- Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA
| | - Sally R Littau
- Department of Community, Environment and Policy, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA
| | - Devi Dearmon-Moore
- Department of Community, Environment and Policy, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA
| | - Alesia M Jung
- Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA
| | - Fernanda Garavito
- Department of Community, Environment and Policy, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA
| | - Shane A Snyder
- Department of Chemical and Environmental Engineering, College of Engineering, University of Arizona, Tucson, AZ, USA
| | - Jefferey L Burgess
- Department of Community, Environment and Policy, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA.
| |
Collapse
|
31
|
Gaziano L, Giambartolomei C, Pereira AC, Gaulton A, Posner DC, Swanson SA, Ho YL, Iyengar SK, Kosik NM, Vujkovic M, Gagnon DR, Bento AP, Barrio-Hernandez I, Rönnblom L, Hagberg N, Lundtoft C, Langenberg C, Pietzner M, Valentine D, Gustincich S, Tartaglia GG, Allara E, Surendran P, Burgess S, Zhao JH, Peters JE, Prins BP, Angelantonio ED, Devineni P, Shi Y, Lynch KE, DuVall SL, Garcon H, Thomann LO, Zhou JJ, Gorman BR, Huffman JE, O'Donnell CJ, Tsao PS, Beckham JC, Pyarajan S, Muralidhar S, Huang GD, Ramoni R, Beltrao P, Danesh J, Hung AM, Chang KM, Sun YV, Joseph J, Leach AR, Edwards TL, Cho K, Gaziano JM, Butterworth AS, Casas JP. Actionable druggable genome-wide Mendelian randomization identifies repurposing opportunities for COVID-19. Nat Med 2021; 27:668-676. [PMID: 33837377 PMCID: PMC7612986 DOI: 10.1038/s41591-021-01310-z] [Citation(s) in RCA: 86] [Impact Index Per Article: 28.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Accepted: 03/05/2021] [Indexed: 12/31/2022]
Abstract
Drug repurposing provides a rapid approach to meet the urgent need for therapeutics to address COVID-19. To identify therapeutic targets relevant to COVID-19, we conducted Mendelian randomization analyses, deriving genetic instruments based on transcriptomic and proteomic data for 1,263 actionable proteins that are targeted by approved drugs or in clinical phase of drug development. Using summary statistics from the Host Genetics Initiative and the Million Veteran Program, we studied 7,554 patients hospitalized with COVID-19 and >1 million controls. We found significant Mendelian randomization results for three proteins (ACE2, P = 1.6 × 10-6; IFNAR2, P = 9.8 × 10-11 and IL-10RB, P = 2.3 × 10-14) using cis-expression quantitative trait loci genetic instruments that also had strong evidence for colocalization with COVID-19 hospitalization. To disentangle the shared expression quantitative trait loci signal for IL10RB and IFNAR2, we conducted phenome-wide association scans and pathway enrichment analysis, which suggested that IFNAR2 is more likely to play a role in COVID-19 hospitalization. Our findings prioritize trials of drugs targeting IFNAR2 and ACE2 for early management of COVID-19.
Collapse
Affiliation(s)
- Liam Gaziano
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Claudia Giambartolomei
- Central RNA Lab, Istituto Italiano di Tecnologia, Genoa, Italy
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Alexandre C Pereira
- Laboratory of Genetics and Molecular Cardiology, Heart Institute, University of São Paulo, São Paulo, Brazil
- Genetics Department, Harvard Medical School, Harvard University, Boston, MA, USA
| | - Anna Gaulton
- Chemical Biology, European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Daniel C Posner
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - Sonja A Swanson
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - Sudha K Iyengar
- Louis Stokes Cleveland VA Medical Center, Cleveland, OH, USA
- Department of Population and Quantitative Health Sciences, Case Western Reserve University and Louis Stoke, Cleveland VA, Cleveland, OH, USA
| | - Nicole M Kosik
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - Marijana Vujkovic
- The Corporal Michael J. Crescenz VA Medical Center, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
- Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - David R Gagnon
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
- Biostatistics, School of Public Health, Boston University, Boston, MA, USA
| | - A Patrícia Bento
- Chemical Biology, European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | | | - Lars Rönnblom
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Niklas Hagberg
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | | | - Claudia Langenberg
- Berlin Institute of Health, Charité University Medicine Berlin, Berlin, Germany
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Maik Pietzner
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Dennis Valentine
- Institute of Health Informatics, University College London, London, UK
- Health Data Research, University College London, London, UK
| | | | | | - Elias Allara
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Praveen Surendran
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Stephen Burgess
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Jing Hua Zhao
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - James E Peters
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- Centre for Inflammatory Disease, Dept of Immunology and Inflammation, Imperial College, London, UK
| | - Bram P Prins
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | - Emanuele Di Angelantonio
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
- National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, Cambridge, UK
| | - Poornima Devineni
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - Yunling Shi
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - Kristine E Lynch
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT, USA
- Department of Internal Medicine, Epidemiology, University of Utah, Salt Lake City, UT, USA
| | - Scott L DuVall
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT, USA
- Department of Internal Medicine, Epidemiology, University of Utah, Salt Lake City, UT, USA
| | - Helene Garcon
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - Lauren O Thomann
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - Jin J Zhou
- Department of Epidemiology and Biostatistics, University of Arizona, Tucson, AZ, USA
- Phoenix VA Health Care System, Phoenix, AZ, USA
| | - Bryan R Gorman
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - Jennifer E Huffman
- Center for Population Genomics, Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - Christopher J O'Donnell
- Cardiology, VA Boston Healthcare System, Boston, MA, USA
- Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Philip S Tsao
- Epidemiology Research and Information Center (ERIC), VA Palo Alto Health Care System, Palo Alto, CA, USA
- Department of Medicine, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Jean C Beckham
- MIRECC, Durham VA Medical Center, Durham, NC, USA
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Saiju Pyarajan
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - Sumitra Muralidhar
- Office of Research and Development, Department of Veterans Affairs, Washington, DC, USA
| | - Grant D Huang
- Office of Research and Development, Department of Veterans Affairs, Washington, DC, USA
| | - Rachel Ramoni
- Office of Research and Development, Department of Veterans Affairs, Washington, DC, USA
| | - Pedro Beltrao
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - John Danesh
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
- National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, Cambridge, UK
| | - Adriana M Hung
- VA Tennessee Valley Healthcare System, Nashville, TN, USA
- Nephrology & Hypertension, Vanderbilt University, Nashville, TN, USA
| | - Kyong-Mi Chang
- Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
- The Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
| | - Yan V Sun
- Atlanta VA Health Care System, Decatur, GA, USA
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA
| | - Jacob Joseph
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
- Medicine, Cardiovascular, VA Boston Healthcare System and Brigham & Women's Hospital, Boston, MA, USA
| | - Andrew R Leach
- Chemical Biology, European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Todd L Edwards
- Department of Veterans Affairs, Tennessee Valley Healthcare System, Vanderbilt University, Nashville, TN, USA
- Medicine, Epidemiology, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
- Division of Aging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - J Michael Gaziano
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
- Division of Aging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Adam S Butterworth
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK.
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK.
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK.
- National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, Cambridge, UK.
| | - Juan P Casas
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA.
- Division of Aging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| |
Collapse
|
32
|
Abstract
AIMS The association of glycemic variability with microvascular disease complications in type 2 diabetes (T2D) has been under-studied and remains unclear. We investigated this relationship using both Action to Control Cardiovascular Risk in Diabetes (ACCORD) and the Veteran Affairs Diabetes Trial (VADT). METHODS In ACCORD, fasting plasma glucose (FPG) was measured 1 to 3 times/year for up to 84 months in 10 251 individuals. In the VADT, FPG was measured every 3 months for up to 87 months in 1791 individuals. Variability measures included coefficient of variation (CV) and average real variability (ARV) for fasting glucose. The primary composite outcome was time to either severe nephropathy or retinopathy event and secondary outcomes included each outcome individually. To assess the association, we considered variability measures as time-dependent covariates in Cox proportional hazard models. We conducted a meta-analysis across the 2 trials to estimate the risk of fasting glucose variability as well as to assess the heterogenous effects of FPG variability across treatment arms. RESULTS In both ACCORD and the VADT, the CV and ARV of FPG were associated with development of future microvascular outcomes even after adjusting for other risk factors, including measures of average glycemic control (ie, cumulative average of HbA1c). Meta-analyses of these 2 trials confirmed these findings and indicated FPG variation may be more harmful in those with less intensive glucose control. CONCLUSIONS This post hoc analysis indicates that variability of FPG plays a role in, and/or is an independent and readily available marker of, development of microvascular complications in T2D.
Collapse
Affiliation(s)
- Jin J Zhou
- Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA
- Carl T. Hayden Phoenix VA Health Care System (111E), Phoenix, AZ, USA
- Correspondence: Jin J. Zhou, PhD, Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, 1295 N. Martin Ave., Tucson, AZ 85724, USA.
| | - Juraj Koska
- Carl T. Hayden Phoenix VA Health Care System (111E), Phoenix, AZ, USA
| | - Gideon Bahn
- Edward Hines, Jr. VA Hospital, Hines, IL, USA
| | - Peter Reaven
- Carl T. Hayden Phoenix VA Health Care System (111E), Phoenix, AZ, USA
| |
Collapse
|
33
|
Zhang W, Zhou JJ, Liu GZ, Wang SF, Li LM. [A study on the online medical consulting websites based on the personal computer side]. Zhonghua Liu Xing Bing Xue Za Zhi 2021; 42:303-308. [PMID: 33626620 DOI: 10.3760/cma.j.cn112338-20200120-00050] [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] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Online medical consulting, acting as the primary type of Internet medical market, has been developing dramatically in the past ten years and begins to take shape. This study collected available information to describe service content status and service provider for online medical consulting websites. The current online medical consulting sites are mainly comprehensive medical consultation websites. The most common consulting provision from is combining graphics and text, which might not meet users' primary demand. The registered physicians are mostly the ones with junior position and work in the eastern and south-central parts of China. Activities of the registered physicians vary across the departments, but with extremely low initiatives.
Collapse
Affiliation(s)
- W Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
| | - J J Zhou
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
| | - G Z Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
| | - S F Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
| | - L M Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
| |
Collapse
|
34
|
Zhou JJ, Zhai J, Zhou H, Chen Y, Guerra S, Robey I, Weinstock GM, Weinstock E, Dong Q, Knox KS, Twigg HL. Supraglottic Lung Microbiome Taxa Are Associated with Pulmonary Abnormalities in an HIV Longitudinal Cohort. Am J Respir Crit Care Med 2021; 202:1727-1731. [PMID: 32783620 DOI: 10.1164/rccm.202004-1086le] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Affiliation(s)
| | - Jing Zhai
- University of Arizona Tucson, Arizona
| | - Hua Zhou
- University of California, Los Angeles Los Angeles, California
| | - Yin Chen
- University of Arizona Tucson, Arizona
| | | | - Ian Robey
- University of Arizona Tucson, Arizona
| | | | - Erica Weinstock
- Jackson Laboratory for Genomic Medicine Farmington, Connecticut
| | | | - Kenneth S Knox
- University of Arizona College of Medicine-Phoenix Phoenix, Arizona and
| | - Homer L Twigg
- Indiana University Medical Center Indianapolis, Indiana
| |
Collapse
|
35
|
Li S, Lu BP, Feng J, Zhou JJ, Xie ZZ, Liang C, Li XR, Huang Y, Yu XB. Clone, expression and plasminogen binding property of three fructose-1,6-bisphosphate aldolases from Clonorchis sinensis. Trop Biomed 2020; 37:852-863. [PMID: 33612738 DOI: 10.47665/tb.37.4.852] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Fructose-1,6-bisphosphate aldolase (FbA), a well characterized glycometabolism enzyme, has been found to participate in other important processes besides the classic catalysis. To understand the important functions of three fructose-1,6-bisphosphate aldolases from Clonorchis sinensis (CsFbAs, CsFbA-1/2/3) in host-parasite interplay, the open reading frames of CsFbAs were cloned into pET30a (+) vector and the resulting recombinant plasmids were transformed into Escherichia coli BL21 (DE3) for expression of the proteins. Purified recombinant CsFbAs proteins (rCsFbAs) were approximately 45.0 kDa on 12% SDS-PAGE and could be probed with each rat anti-rCsFbAs sera by western blotting analysis. ELISA and ligand blot overlay indicated that rCsFbAs of 45.0 kDa as well as native CsFbAs of 39.5 kDa from total worm extracts and excretory-secretory products of Clonorchis sinensis (CsESPs) could bind to human plasminogen, and the binding could be efficiently inhibited by lysine analog ε-aminocaproic acid. Our results suggested that as both the components of CsESPs and the plasminogen binding proteins, three CsFbAs might be involved in preventing the formation of the blood clot so that Clonorchis sinensis could acquire enough nutrients from host tissue for their successful survival and colonization in the host. Our work will provide us with new information about the biological function of three CsFbAs and their roles in hostparasite interplay.
Collapse
Affiliation(s)
- S Li
- School of Basic Medicine, Henan University of Chinese Medicine, Zhengzhou 450046, China.,Department of Parasitology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China.,Key Laboratory for Tropical Diseases Control of Ministry of Education, Sun Yat-sen University, Guangzhou 510080, China
| | - B P Lu
- School of Basic Medicine, Henan University of Chinese Medicine, Zhengzhou 450046, China
| | - J Feng
- Zhengzhou YIHE Hospital Affiliated to Henan University, Zhengzhou 450047, China
| | - J J Zhou
- Zhengzhou Key Laboratory for Children's Infection and Immunity, Children's Hospital Affiliated to Zhengzhou University, Zhengzhou 450047, China
| | - Z Z Xie
- Department of Clinical Laboratory, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China
| | - C Liang
- Department of Parasitology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China.,Key Laboratory for Tropical Diseases Control of Ministry of Education, Sun Yat-sen University, Guangzhou 510080, China
| | - X R Li
- Department of Parasitology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China.,Key Laboratory for Tropical Diseases Control of Ministry of Education, Sun Yat-sen University, Guangzhou 510080, China
| | - Y Huang
- Department of Parasitology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China.,Key Laboratory for Tropical Diseases Control of Ministry of Education, Sun Yat-sen University, Guangzhou 510080, China
| | - X B Yu
- Department of Parasitology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China.,Key Laboratory for Tropical Diseases Control of Ministry of Education, Sun Yat-sen University, Guangzhou 510080, China
| |
Collapse
|
36
|
Gu W, Shi J, Liu H, Zhang X, Zhou JJ, Li M, Zhou D, Li R, Lv J, Wen G, Zhu S, Qi T, Li W, Wang X, Wang Z, Zhu H, Zhou C, Knox KS, Wang T, Chen Q, Qian Z, Zhou T. Peripheral blood non-canonical small non-coding RNAs as novel biomarkers in lung cancer. Mol Cancer 2020; 19:159. [PMID: 33176804 PMCID: PMC7659116 DOI: 10.1186/s12943-020-01280-9] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Accepted: 11/03/2020] [Indexed: 01/09/2023] Open
Abstract
One unmet challenge in lung cancer diagnosis is to accurately differentiate lung cancer from other lung diseases with similar clinical symptoms and radiological features, such as pulmonary tuberculosis (TB). To identify reliable biomarkers for lung cancer screening, we leverage the recently discovered non-canonical small non-coding RNAs (i.e., tRNA-derived small RNAs [tsRNAs], rRNA-derived small RNAs [rsRNAs], and YRNA-derived small RNAs [ysRNAs]) in human peripheral blood mononuclear cells and develop a molecular signature composed of distinct ts/rs/ysRNAs (TRY-RNA). Our TRY-RNA signature precisely discriminates between control, lung cancer, and pulmonary TB subjects in both the discovery and validation cohorts and outperforms microRNA-based biomarkers, which bears the diagnostic potential for lung cancer screening.
Collapse
Affiliation(s)
- Wanjun Gu
- State Key Laboratory of Bioelectronics, School of Biological Sciences and Medical Engineering, Southeast University, 2 Sipailou, Nanjing, 210096, Jiangsu, China.
| | - Junchao Shi
- Division of Biomedical Sciences, School of Medicine, University of California, Riverside, 900 University Avenue, Riverside, CA, 92521, USA
| | - Hui Liu
- Anhui Province Key Laboratory of Immunology in Chronic Diseases, Anhui Key Laboratory of Infection and Immunity, Department of Laboratory Medicine, Bengbu Medical College, 2600 Donghaidadao, Bengbu, 233003, Anhui, China
| | - Xudong Zhang
- Division of Biomedical Sciences, School of Medicine, University of California, Riverside, 900 University Avenue, Riverside, CA, 92521, USA
| | - Jin J Zhou
- Department of Epidemiology and Biostatistics, University of Arizona, Tucson, AZ, 85721, USA
| | - Musheng Li
- Department of Physiology and Cell Biology, University of Nevada, Reno School of Medicine, 1664 North Virginia Street, Reno, Nevada, 89557, USA
| | - Dandan Zhou
- Department of Physiology and Cell Biology, University of Nevada, Reno School of Medicine, 1664 North Virginia Street, Reno, Nevada, 89557, USA
| | - Rui Li
- Anhui Province Key Laboratory of Immunology in Chronic Diseases, Anhui Key Laboratory of Infection and Immunity, Department of Laboratory Medicine, Bengbu Medical College, 2600 Donghaidadao, Bengbu, 233003, Anhui, China
| | - Jingzhu Lv
- Anhui Province Key Laboratory of Immunology in Chronic Diseases, Anhui Key Laboratory of Infection and Immunity, Department of Laboratory Medicine, Bengbu Medical College, 2600 Donghaidadao, Bengbu, 233003, Anhui, China
| | - Guoxia Wen
- State Key Laboratory of Bioelectronics, School of Biological Sciences and Medical Engineering, Southeast University, 2 Sipailou, Nanjing, 210096, Jiangsu, China
| | - Shanshan Zhu
- State Key Laboratory of Bioelectronics, School of Biological Sciences and Medical Engineering, Southeast University, 2 Sipailou, Nanjing, 210096, Jiangsu, China
| | - Ting Qi
- State Key Laboratory of Bioelectronics, School of Biological Sciences and Medical Engineering, Southeast University, 2 Sipailou, Nanjing, 210096, Jiangsu, China
| | - Wei Li
- Anhui Clinical and Preclinical Key Laboratory of Respiratory Disease, Department of Respiration, First Affiliated Hospital, Bengbu Medical College, Bengbu, 233000, Anhui, China
| | - Xiaojing Wang
- Anhui Clinical and Preclinical Key Laboratory of Respiratory Disease, Department of Respiration, First Affiliated Hospital, Bengbu Medical College, Bengbu, 233000, Anhui, China
| | - Zhaohua Wang
- The Infectious Disease Hospital of Bengbu City, Bengbu, 233000, Anhui, China
| | - Hua Zhu
- Department of Surgery, The Ohio State University, Columbus, OH, 43210, USA
| | - Changcheng Zhou
- Division of Biomedical Sciences, School of Medicine, University of California, Riverside, 900 University Avenue, Riverside, CA, 92521, USA
| | - Kenneth S Knox
- Department of Internal Medicine, College of Medicine Phoenix, University of Arizona, Phoenix, AZ, 85004, USA
| | - Ting Wang
- Department of Internal Medicine, College of Medicine Phoenix, University of Arizona, Phoenix, AZ, 85004, USA
| | - Qi Chen
- Division of Biomedical Sciences, School of Medicine, University of California, Riverside, 900 University Avenue, Riverside, CA, 92521, USA.
| | - Zhongqing Qian
- Anhui Province Key Laboratory of Immunology in Chronic Diseases, Anhui Key Laboratory of Infection and Immunity, Department of Laboratory Medicine, Bengbu Medical College, 2600 Donghaidadao, Bengbu, 233003, Anhui, China.
| | - Tong Zhou
- Department of Physiology and Cell Biology, University of Nevada, Reno School of Medicine, 1664 North Virginia Street, Reno, Nevada, 89557, USA.
| |
Collapse
|
37
|
Zhou JJ, Coleman R, Holman RR, Reaven P. Long-term glucose variability and risk of nephropathy complication in UKPDS, ACCORD and VADT trials. Diabetologia 2020; 63:2482-2485. [PMID: 32954444 PMCID: PMC7573923 DOI: 10.1007/s00125-020-05273-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [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: 05/27/2020] [Accepted: 07/29/2020] [Indexed: 01/06/2023]
Affiliation(s)
- Jin J Zhou
- Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA.
- Carl T. Hayden Phoenix VA Health Care System (111E), Phoenix, AZ, USA.
| | - Ruth Coleman
- Diabetes Trials Unit, Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
| | - Rury R Holman
- Diabetes Trials Unit, Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
| | - Peter Reaven
- Carl T. Hayden Phoenix VA Health Care System (111E), Phoenix, AZ, USA
| |
Collapse
|
38
|
Zhang LN, Zhou JJ, Zhang J, Wang ZY, Zheng HH, Gan MF. [Multiple primary lung adenocarcinoma with different mutations of EGFR gene: report of a case]. Zhonghua Bing Li Xue Za Zhi 2020; 49:855-857. [PMID: 32746560 DOI: 10.3760/cma.j.cn112151-20191209-00786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- L N Zhang
- Department of Pathology, Taizhou Hospital of Zhejiang Province, Affiliated to Wenzhou Medical University, Linhai 317000, China
| | - J J Zhou
- Department of Pathology, Taizhou Hospital of Zhejiang Province, Affiliated to Wenzhou Medical University, Linhai 317000, China
| | - J Zhang
- Department of Thoracic Surgery, Taizhou Hospital of Zhejiang Province, Affiliated to Wenzhou Medical University, Linhai 317000, China
| | - Z Y Wang
- Department of Pathology, Taizhou Hospital of Zhejiang Province, Affiliated to Wenzhou Medical University, Linhai 317000, China
| | - H H Zheng
- Department of Pathology, Taizhou Hospital of Zhejiang Province, Affiliated to Wenzhou Medical University, Linhai 317000, China
| | - M F Gan
- Department of Pathology, Taizhou Hospital of Zhejiang Province, Affiliated to Wenzhou Medical University, Linhai 317000, China
| |
Collapse
|
39
|
Chuan A, Zhou JJ, Hou RM, Stevens CJ, Bogdanovych A. Virtual reality for acute and chronic pain management in adult patients: a narrative review. Anaesthesia 2020; 76:695-704. [PMID: 32720308 DOI: 10.1111/anae.15202] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/19/2020] [Indexed: 12/01/2022]
Abstract
Virtual reality is a computer-generated environment that immerses the user in an interactive artificial world. This ability to distract from reality has been utilised for the purposes of providing pain relief from noxious stimuli. As technology rapidly matures, there is potential for anaesthetists and pain physicians to incorporate virtual reality devices as non-pharmacological therapy in a multimodal pain management strategy. This systematic narrative review evaluates clinical studies that used virtual reality in adult patients for management of acute and chronic pain. A literature search found 690 citations, out of which 18 studies satisfied the inclusion criteria. Studies were assessed for quality using the Jadad and Nottingham-Ottawa Scales. Agreement on scores between independent assessors was 0.87 (95%CI 0.73-0.94). Studies investigated virtual reality use: intra-operatively; for labour analgesia; for wound dressing changes; and in multiple chronic pain conditions. Twelve studies showed reduced pain scores in acute or chronic pain with virtual reality therapy, five studies showed no superiority to control treatment arms and in one study, the virtual reality exposure group had a worsening of acute pain scores. Studies were heterogeneous in: methods; patient population; and type of virtual reality used. These limitations suggest the evidence-base in adult patients is currently immature and more rigorous studies are required to validate the use of virtual reality as a non-pharmacological adjunct in multimodal pain management.
Collapse
Affiliation(s)
- A Chuan
- Faculty of Medicine, University of New South Wales Sydney and Ingham Institute of Applied Medical Research, Sydney, NSW, Australia.,Department of Anaesthesia, Liverpool Hospital, Sydney, NSW, Australia
| | - J J Zhou
- Department of Anaesthesia, Liverpool Hospital, Sydney, NSW, Australia
| | - R M Hou
- Faculty of Medicine, University of New South Wales Sydney and Ingham Institute of Applied Medical Research, Sydney, NSW, Australia.,Department of Pain Medicine, Liverpool Hospital, Sydney, NSW, Australia
| | - C J Stevens
- MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Sydney, NSW, Australia
| | - A Bogdanovych
- MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Sydney, Australia
| |
Collapse
|
40
|
Nuyujukian DS, Koska J, Bahn G, Reaven PD, Zhou JJ. Blood Pressure Variability and Risk of Heart Failure in ACCORD and the VADT. Diabetes Care 2020; 43:1471-1478. [PMID: 32327422 PMCID: PMC7305004 DOI: 10.2337/dc19-2540] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Accepted: 03/31/2020] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Although blood pressure variability is increasingly appreciated as a risk factor for cardiovascular disease, its relationship with heart failure (HF) is less clear. We examined the relationship between blood pressure variability and risk of HF in two cohorts of type 2 diabetes participating in trials of glucose and/or other risk factor management. RESEARCH DESIGN AND METHODS Data were drawn from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial and the Veterans Affairs Diabetes Trial (VADT). Coefficient of variation (CV) and average real variability (ARV) were calculated for systolic (SBP) and diastolic blood pressure (DBP) along with maximum and cumulative mean SBP and DBP during both trials. RESULTS In ACCORD, CV and ARV of SBP and DBP were associated with increased risk of HF, even after adjusting for other risk factors and mean blood pressure (e.g., CV-SBP: hazard ratio [HR] 1.15, P = 0.01; CV-DBP: HR 1.18, P = 0.003). In the VADT, DBP variability was associated with increased risk of HF (ARV-DBP: HR 1.16, P = 0.001; CV-DBP: HR 1.09, P = 0.04). Further, in ACCORD, those with progressively lower baseline blood pressure demonstrated a stepwise increase in risk of HF with higher CV-SBP, ARV-SBP, and CV-DBP. Effects of blood pressure variability were related to dips, not elevations, in blood pressure. CONCLUSIONS Blood pressure variability is associated with HF risk in individuals with type 2 diabetes, possibly a consequence of periods of ischemia during diastole. These results may have implications for optimizing blood pressure treatment strategies in those with type 2 diabetes.
Collapse
Affiliation(s)
- Daniel S Nuyujukian
- Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ .,Carl T. Hayden Veterans Affairs Medical Center, Phoenix, AZ
| | - Juraj Koska
- Carl T. Hayden Veterans Affairs Medical Center, Phoenix, AZ
| | - Gideon Bahn
- Hines Veterans Affairs Cooperative Studies Program Coordinating Center, Edward Hines, Jr. Veterans Affairs Hospital, Hines, IL
| | - Peter D Reaven
- Carl T. Hayden Veterans Affairs Medical Center, Phoenix, AZ
| | | | | |
Collapse
|
41
|
Chu BB, Keys KL, German CA, Zhou H, Zhou JJ, Sobel EM, Sinsheimer JS, Lange K. Iterative hard thresholding in genome-wide association studies: Generalized linear models, prior weights, and double sparsity. Gigascience 2020; 9:giaa044. [PMID: 32491161 PMCID: PMC7268817 DOI: 10.1093/gigascience/giaa044] [Citation(s) in RCA: 8] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 02/27/2020] [Accepted: 04/14/2020] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Consecutive testing of single nucleotide polymorphisms (SNPs) is usually employed to identify genetic variants associated with complex traits. Ideally one should model all covariates in unison, but most existing analysis methods for genome-wide association studies (GWAS) perform only univariate regression. RESULTS We extend and efficiently implement iterative hard thresholding (IHT) for multiple regression, treating all SNPs simultaneously. Our extensions accommodate generalized linear models, prior information on genetic variants, and grouping of variants. In our simulations, IHT recovers up to 30% more true predictors than SNP-by-SNP association testing and exhibits a 2-3 orders of magnitude decrease in false-positive rates compared with lasso regression. We also test IHT on the UK Biobank hypertension phenotypes and the Northern Finland Birth Cohort of 1966 cardiovascular phenotypes. We find that IHT scales to the large datasets of contemporary human genetics and recovers the plausible genetic variants identified by previous studies. CONCLUSIONS Our real data analysis and simulation studies suggest that IHT can (i) recover highly correlated predictors, (ii) avoid over-fitting, (iii) deliver better true-positive and false-positive rates than either marginal testing or lasso regression, (iv) recover unbiased regression coefficients, (v) exploit prior information and group-sparsity, and (vi) be used with biobank-sized datasets. Although these advances are studied for genome-wide association studies inference, our extensions are pertinent to other regression problems with large numbers of predictors.
Collapse
Affiliation(s)
- Benjamin B Chu
- Department of Computational Medicine, University of California, Los Angeles, 621 Charles E Young Dr S, Los Angeles, CA, 90095, USA
| | - Kevin L Keys
- Department of Medicine, University of California, San Francisco, 1701 Divisadero St, San Francisco, CA, 94115, USA
- Berkeley Institute of Data Science, University of California, Berkeley, 190 Doe Library, Berkeley, CA 94720, USA
| | - Christopher A German
- Department of Biostatistics, University of California, Los Angeles, 650 Charles E Young Dr S, Los Angeles, CA, 90095, USA
| | - Hua Zhou
- Department of Biostatistics, University of California, Los Angeles, 650 Charles E Young Dr S, Los Angeles, CA, 90095, USA
| | - Jin J Zhou
- Division of Epidemiology and Biostatistics, University of Arizona, 1295 N. Martin Ave. Tucson, AZ, 85724, USA
| | - Eric M Sobel
- Department of Computational Medicine, University of California, Los Angeles, 621 Charles E Young Dr S, Los Angeles, CA, 90095, USA
- Department of Human Genetics, University of California, Los Angeles, 695 Charles E Young Dr S, Los Angeles, CA, 90095 USA
| | - Janet S Sinsheimer
- Department of Computational Medicine, University of California, Los Angeles, 621 Charles E Young Dr S, Los Angeles, CA, 90095, USA
- Department of Biostatistics, University of California, Los Angeles, 650 Charles E Young Dr S, Los Angeles, CA, 90095, USA
- Department of Human Genetics, University of California, Los Angeles, 695 Charles E Young Dr S, Los Angeles, CA, 90095 USA
| | - Kenneth Lange
- Department of Computational Medicine, University of California, Los Angeles, 621 Charles E Young Dr S, Los Angeles, CA, 90095, USA
- Department of Human Genetics, University of California, Los Angeles, 695 Charles E Young Dr S, Los Angeles, CA, 90095 USA
| |
Collapse
|
42
|
Li R, Huang D, Zhu H, Sun QG, Wang Y, Zhang XH, Zhao XY, He J, Liu L, Zhou JJ, Liu H. [The performance of visual photoscreening for Chinese preschool children aged 4 to 5 years]. Zhonghua Yan Ke Za Zhi 2020; 56:189-196. [PMID: 32187947 DOI: 10.3760/cma.j.issn.0412-4081.2020.03.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Objective: To assess the accuracy of photoscreening for detecting refractive amblyopia risk factors (ARFs) in Chinese preschool children aged 4 to 5 years. Methods: A cross-sectional study. Comprehensive ocular examinations were conducted for preschool children in Nanjing, China from September to December, 2016. Photoscreening (Plusoptix A12C) was applied for refractive screening without cycloplegia. Voluntary children and children suspected of eyes abnormalities received cycloplegic retinoscopy (CR). Results of photoscreening and CR were compared using Wilcoxon signed rank test, and Bland-Altman plot were used to assess the agreement between the photoscreener and CR. According to the updated preschool vision screening guidelines from American Association for Pediatric Ophthalmology and Strabismus (AAPOS) in 2013, CR was adopted for identifying children with ARFs, which was considered as a golden standard. Based on the golden standard, the accuracy of 5 sets of referral criteria (including sensitivity standard, Matta/Silbert standard, AAPOS2013 standard, Alaska Blind Child Discovery standard, specificity standard) for photoscreener were tested. Receiver operating characteristics curves were constructed applied to evaluate the quality of the photoscreener in refractive ARFs detection and to find probably the best cut-off points. Results: In total, 1 986 children [mean age, (4.57±0.29) years] received comprehensive examinations, including 1 084 boys and 902 girls. The test ability of photoscreening was 99.04% (1 967/1 986) in the preschool children, and 96.56%(1 827/1 892) of the children got a reliable result within three screening attempts. In 538 children who had data of CR, refractive error of one child exceeded the upper limit of the photoscreener value setting, which was directly categorized as hyperopia, so in the end, 537 children were included to analyze the comparison between the two tests. The measurement values of photoscreening were lower than those of CR in sphere, cylinder and spherical equivalent [(0.75 (0.50, 1.25) D vs. 1.25 (1.00, 1.75) D, Z=-10.36, P<0.01; -0.50 (-0.75, -0.25) D vs. -0.25 (-0.75, 0.00) D, Z=-11.10, P<0.01; 0.63 (0.38, 0.88) D vs. 1.00 (0.75, 1.50) D, Z=-13.33, P<0.01]. The 95% limit of agreement cover rates between the photoscreening and CR in sphere, cylinder and spherical equivalent was 96.28% (517/537), 95.34% (512/537) and 96.65% (519/537), respectively. Based on the golden standard, 47 (8.74%) children had refractive ARFs, and the range of sensitivity, specificity, Youden index, positive predictive values and negative predictive values for detecting refractive ARFs of the 5 common used referral criteria was from 63.83% to 97.87%, from 53.36% to 97.56%, from 0.51 to 0.80, from 16.73% to 74.51% and from 96.57% to 99.62%, respectively. Considering particular refractive ARFs on the basis of the receiver operating characteristic curves, the optimal cut-off point for astigmatism was set at 1.38 D. Conclusion: Photoscreening could be an applicable tool to detect refractive ARFs in preschool children. (Chin J Ophthalmol, 2020, 56: 189-196).
Collapse
Affiliation(s)
- R Li
- Department of Ophthalmology, the First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
| | - D Huang
- Department of Child Healthcare, the First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
| | - H Zhu
- Department of Ophthalmology, the First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Q G Sun
- Department of Ophthalmology, the First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China, is now working at the Department of Ophthalmology, Maternal and Child Healthcare Hospital of Yuhuatai District, Nanjing 210012, China
| | - Y Wang
- Department of Ophthalmology, the First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
| | - X H Zhang
- Department of Ophthalmology, the First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
| | - X Y Zhao
- Department of Ophthalmology, the First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
| | - J He
- the Fourth School of Clinical Medicine of Nanjing Medical University, Nanjing 210029, China, is now working at the Department of Ophthalmology, Subei People's Hospital of Jiangsu Province, Yangzhou 225001, China
| | - L Liu
- Department of Ophthalmology, the First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
| | - J J Zhou
- the Fourth School of Clinical Medicine of Nanjing Medical University, Nanjing 210029, China
| | - H Liu
- Department of Ophthalmology, the First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
| |
Collapse
|
43
|
Hu JJ, Nie SM, Gao Y, Yan XS, Huang JX, Li TL, Liu SS, Mao CX, Zhou JJ, Xu YJ, Wang W, Meng FJ, Feng XQ. [The correlations and prognostic value of neutrophil to lymphocyte ratio, immunophenotype and cytogenetic abnormalities in patients with newly diagnosed multiple myeloma]. Zhonghua Xue Ye Xue Za Zhi 2020; 40:1044-1046. [PMID: 32023739 PMCID: PMC7342691 DOI: 10.3760/cma.j.issn.0253-2727.2019.12.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Affiliation(s)
- J J Hu
- Department of Hematology, the Affiliated Hospital of Qingdao University, Qingdao 266000, China
| | - S M Nie
- Department of Hematology, the Affiliated Hospital of Qingdao University, Qingdao 266000, China
| | - Y Gao
- Department of Hematology, the Affiliated Hospital of Qingdao University, Qingdao 266000, China
| | - X S Yan
- Department of Hematology, the Affiliated Hospital of Qingdao University, Qingdao 266000, China
| | - J X Huang
- Department of Hematology, the Affiliated Hospital of Qingdao University, Qingdao 266000, China
| | - T L Li
- Department of Hematology, the Affiliated Hospital of Qingdao University, Qingdao 266000, China
| | - S S Liu
- Department of Hematology, the Affiliated Hospital of Qingdao University, Qingdao 266000, China
| | - C X Mao
- Department of Hematology, the Affiliated Hospital of Qingdao University, Qingdao 266000, China
| | - J J Zhou
- Department of Hematology, the Affiliated Hospital of Qingdao University, Qingdao 266000, China
| | - Y J Xu
- Department of Hematology, the Affiliated Hospital of Qingdao University, Qingdao 266000, China
| | - W Wang
- Department of Hematology, the Affiliated Hospital of Qingdao University, Qingdao 266000, China
| | - F J Meng
- Department of Hematology, the Affiliated Hospital of Qingdao University, Qingdao 266000, China
| | - X Q Feng
- Department of Hematology, the Affiliated Hospital of Qingdao University, Qingdao 266000, China
| |
Collapse
|
44
|
German CA, Sinsheimer JS, Klimentidis YC, Zhou H, Zhou JJ. Ordered multinomial regression for genetic association analysis of ordinal phenotypes at Biobank scale. Genet Epidemiol 2019; 44:248-260. [PMID: 31879980 DOI: 10.1002/gepi.22276] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [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: 08/03/2019] [Revised: 10/23/2019] [Accepted: 11/25/2019] [Indexed: 12/23/2022]
Abstract
Logistic regression is the primary analysis tool for binary traits in genome-wide association studies (GWAS). Multinomial regression extends logistic regression to multiple categories. However, many phenotypes more naturally take ordered, discrete values. Examples include (a) subtypes defined from multiple sources of clinical information and (b) derived phenotypes generated by specific phenotyping algorithms for electronic health records (EHR). GWAS of ordinal traits have been problematic. Dichotomizing can lead to a range of arbitrary cutoff values, generating inconsistent, hard to interpret results. Using multinomial regression ignores trait value hierarchy and potentially loses power. Treating ordinal data as quantitative can lead to misleading inference. To address these issues, we analyze ordinal traits with an ordered, multinomial model. This approach increases power and leads to more interpretable results. We derive efficient algorithms for computing test statistics, making ordinal trait GWAS computationally practical for Biobank scale data. Our method is available as a Julia package OrdinalGWAS.jl. Application to a COPDGene study confirms previously found signals based on binary case-control status, but with more significance. Additionally, we demonstrate the capability of our package to run on UK Biobank data by analyzing hypertension as an ordinal trait.
Collapse
Affiliation(s)
- Christopher A German
- Department of Biostatistics, UCLA Fielding School of Public Health, Los Angeles, California
| | - Janet S Sinsheimer
- Department of Biostatistics, UCLA Fielding School of Public Health, Los Angeles, California.,Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, California.,Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, California
| | - Yann C Klimentidis
- Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, Arizona
| | - Hua Zhou
- Department of Biostatistics, UCLA Fielding School of Public Health, Los Angeles, California
| | - Jin J Zhou
- Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, Arizona
| |
Collapse
|
45
|
Chen J, Xing N, Zhou JJ, Huang WX, Xue DJ. [Effects of different fluid resuscitation methods on hemorheology in pigs during burn shock stage]. Zhonghua Yi Xue Za Zhi 2019; 99:1421-1426. [PMID: 31137132 DOI: 10.3760/cma.j.issn.0376-2491.2019.18.013] [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] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Objective: To investigate the impact of different methods of fluid resuscitation on hemorheology during burn shock stage. Methods: Twenty four miniature swines were randomly divided into four groups with 6 in each group (succinylated gelatin group, hydroxyethyl starch group, Parkland group and allogeneic plasma group). Severe burn shock model was established by burning miniature swine with napalm. Two hours after injury, succinylated gelatin, hydroxyethyl starch (130/0.4) and swine allogenic plasma were used as colloid (alternative colloid) in fluid resuscitation according to the burn shock fluid resuscitation formula which is commonly accepted in the field of Burns Surgery. In Parkland group, miniature swines received liquid recovery according to Parkland Formula. The vital signs before and within 48 h after burn were observed by Solar 8000i electrocardiomonitor during the process of transfusion. The infusion speed was adjusted based on the heart rate, blood pressure, urine volume and central venous pressure. The level of hematocrit (HCT), viscosity of plasma (ηp), index of rigidity (IR), red cell assembling index (RCA) and erythrocyte electrophoresis time (EFT) were measured at the time of pre-injury as well as 4, 8, 24 and 48 h post-injury and statistical analysis was performed. Results: HCT in hydroxyethyl starch group and Parkland group at 8 h post-injury were significantly higher than pre-injury [(0.395±0.047) vs (0.333±0.042), (0.379±0.026) vs (0.352±0.019)] (both P<0.05). And compared with pre-injury, HCT in hydroxyethyl starch (130/0.4) group at 48 h decreased significantly (0.232±0.021) vs (0.333±0.042) (P<0.05). HCT in Parkland group at 24, 48 h post-injury were lower than pre-injury [(0.277±0.021), (0.241±0.029) vs (0.352±0.019)] (both P<0.05). Compared with pre-injury, the levels of ηp in Parkland group decreased substantially at 4, 8 and 24 h post-injury [(1.61±0.07), (1.55±0.07) and (1.63±0.07) vs (1.73±0.04) mPa·s] (all P<0.05). Compared with allogeneic plasma group, IR decreased in succinylated gelatin group at 24, 48 h post-injury [(1.10±0.05 vs 1.26±0.07), (1.11±0.05 vs 1.32±0.05)](both P<0.05). RCA in succinylated gelatin group was significantly higher (both P<0.05) at 4 h (6.80±0.87) than pre-injury (5.92±0.43). RCA in hydroxyethyl starch group at 8 h post-injury (6.73±0.56) was significantly higher (both P<0.05) than pre-injury (6.03±0.53). Compared with pre-injury (17.3±1.3 s, 16.4±1.5 s), the levels of EFT in hydroxyethyl starch group (15.5±1.4 s) and Parkland group (13.4±1.2 s) decreased substantially at 48 h post-injury (both P<0.05). Compared with allogeneic plasma group, the level of EFT in succinylated gelatin group at 4 h post-injury (19.5±2.3 s) increased and decreased at 24 h post-injury (12.0±5.7 s) (both P<0.05). Conclusion: During swine burn shock stage, the hemorheological parameters of shock resuscitation with artificial colloid are more stable than those with Parkland formula resuscitation.
Collapse
Affiliation(s)
- J Chen
- Ruian Burns Research Institute, the Third Affiliated Hospital of Wenzhou Medical University, Ruian 325200, China
| | - N Xing
- Burn Department of Weihai Municipal Hospital, Weihai 250021, China
| | - J J Zhou
- Ruian Burns Research Institute, the Third Affiliated Hospital of Wenzhou Medical University, Ruian 325200, China
| | - W X Huang
- Ruian Burns Research Institute, the Third Affiliated Hospital of Wenzhou Medical University, Ruian 325200, China
| | - D J Xue
- Ruian Burns Research Institute, the Third Affiliated Hospital of Wenzhou Medical University, Ruian 325200, China
| |
Collapse
|
46
|
Zhou JJ, Luo L. [Hypersomnia and syncope as initial manifestations of neuromyelitis optica spectrum disorder: a case report]. Zhonghua Nei Ke Za Zhi 2019; 58:309-311. [PMID: 30917426 DOI: 10.3760/cma.j.issn.0578-1426.2019.04.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Affiliation(s)
- J J Zhou
- Department of Neurology, The Hangzhou Xixi Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou 310023, China
| | | |
Collapse
|
47
|
Zhou H, Sinsheimer JS, Bates DM, Chu BB, German CA, Ji SS, Keys KL, Kim J, Ko S, Mosher GD, Papp JC, Sobel EM, Zhai J, Zhou JJ, Lange K. OPENMENDEL: a cooperative programming project for statistical genetics. Hum Genet 2019; 139:61-71. [PMID: 30915546 DOI: 10.1007/s00439-019-02001-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [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: 10/26/2018] [Accepted: 03/15/2019] [Indexed: 01/06/2023]
Abstract
Statistical methods for genome-wide association studies (GWAS) continue to improve. However, the increasing volume and variety of genetic and genomic data make computational speed and ease of data manipulation mandatory in future software. In our view, a collaborative effort of statistical geneticists is required to develop open source software targeted to genetic epidemiology. Our attempt to meet this need is called the OPENMENDEL project (https://openmendel.github.io). It aims to (1) enable interactive and reproducible analyses with informative intermediate results, (2) scale to big data analytics, (3) embrace parallel and distributed computing, (4) adapt to rapid hardware evolution, (5) allow cloud computing, (6) allow integration of varied genetic data types, and (7) foster easy communication between clinicians, geneticists, statisticians, and computer scientists. This article reviews and makes recommendations to the genetic epidemiology community in the context of the OPENMENDEL project.
Collapse
Affiliation(s)
- Hua Zhou
- Department of Biostatistics, UCLA Fielding School of Public Health, Los Angeles, USA.
| | - Janet S Sinsheimer
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, USA.
| | - Douglas M Bates
- Department of Statistics, University of Wisconsin, Madison, USA
| | - Benjamin B Chu
- Department of Biomathematics, David Geffen School of Medicine at UCLA, Los Angeles, USA
| | - Christopher A German
- Department of Biostatistics, UCLA Fielding School of Public Health, Los Angeles, USA
| | - Sarah S Ji
- Department of Biostatistics, UCLA Fielding School of Public Health, Los Angeles, USA
| | - Kevin L Keys
- Department of Medicine, University of California, San Francisco, USA
| | - Juhyun Kim
- Department of Biostatistics, UCLA Fielding School of Public Health, Los Angeles, USA
| | - Seyoon Ko
- Department of Statistics, Seoul National University, Seoul, South Korea
| | - Gordon D Mosher
- Departments of Statistics and Computer Science, University of California, Riverside, USA
| | - Jeanette C Papp
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, USA
| | - Eric M Sobel
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, USA
| | - Jing Zhai
- Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, USA
| | - Jin J Zhou
- Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, USA
| | - Kenneth Lange
- Department of Biomathematics, David Geffen School of Medicine at UCLA, Los Angeles, USA.
| |
Collapse
|
48
|
Abstract
Diabetes is associated with substantially increased mortality. Classic risk factors explain a portion of the excess of mortality in type 2 diabetes. The aim of this study was to examine whether visit-to-visit variation in fasting glucose and haemoglobin A1c values in the Veteran Affairs Diabetes Trial were associated with all-cause mortality in patients with type 2 diabetes in addition to other comorbidity conditions, hypoglycaemic events and adverse lifestyle behaviours. The Veteran Affairs Diabetes Trial was a randomized trial that enrolled 1791 military veterans who had a suboptimal response to therapy for type 2 diabetes to receive either intensive or standard glucose control. During the Veteran Affairs Diabetes Trial, fasting glucose and haemoglobin A1c were measured quarterly for up to 84 months. Variability measures included coefficient of variation and average real variability. We found that variability measures (coefficient of variation and average real variability) of fasting glucose were predictors of all-cause mortality, even after adjusting for comorbidity index, mean fasting glucose and adverse lifestyle behaviour during the study. Accounting for severe hypoglycaemia did not weaken this association. Our analysis indicates that in the Veteran Affairs Diabetes Trial, longitudinal variation in fasting glucose was associated with all-cause mortality, even when accounting for standard measures of glucose control as well as comorbidity and lifestyle factors.
Collapse
Affiliation(s)
- Jin J Zhou
- Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, The University of Arizona, Tucson, AZ, USA
- Carl T. Hayden Phoenix VA Health Care System, Phoenix, AZ, USA
| | - Juraj Koska
- Carl T. Hayden Phoenix VA Health Care System, Phoenix, AZ, USA
| | - Gideon Bahn
- Edward Hines, Jr. VA Hospital, Hines, IL, USA
| | - Peter Reaven
- Carl T. Hayden Phoenix VA Health Care System, Phoenix, AZ, USA
| | | |
Collapse
|
49
|
Zhai J, Knox K, Twigg HL, Zhou H, Zhou JJ. Exact variance component tests for longitudinal microbiome studies. Genet Epidemiol 2019; 43:250-262. [PMID: 30623484 DOI: 10.1002/gepi.22185] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [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: 08/24/2018] [Revised: 10/28/2018] [Accepted: 11/26/2018] [Indexed: 01/12/2023]
Abstract
In metagenomic studies, testing the association between microbiome composition and clinical outcomes translates to testing the nullity of variance components. Motivated by a lung human immunodeficiency virus (HIV) microbiome project, we study longitudinal microbiome data by using variance component models with more than two variance components. Current testing strategies only apply to models with exactly two variance components and when sample sizes are large. Therefore, they are not applicable to longitudinal microbiome studies. In this paper, we propose exact tests (score test, likelihood ratio test, and restricted likelihood ratio test) to (a) test the association of the overall microbiome composition in a longitudinal design and (b) detect the association of one specific microbiome cluster while adjusting for the effects from related clusters. Our approach combines the exact tests for null hypothesis with a single variance component with a strategy of reducing multiple variance components to a single one. Simulation studies demonstrate that our method has a correct type I error rate and superior power compared to existing methods at small sample sizes and weak signals. Finally, we apply our method to a longitudinal pulmonary microbiome study of HIV-infected patients and reveal two interesting genera Prevotella and Veillonella associated with forced vital capacity. Our findings shed light on the impact of the lung microbiome on HIV complexities. The method is implemented in the open-source, high-performance computing language Julia and is freely available at https://github.com/JingZhai63/VCmicrobiome.
Collapse
Affiliation(s)
- Jing Zhai
- Department of Epidemiology and Biostatistics, University of Arizona, Tucson, Arizona
| | - Kenneth Knox
- Division of Pulmonary, Allergy, Critical Care, Sleep Medicine, Department of Medicine, University of Arizona, Tucson, Arizona
| | - Homer L Twigg
- Division of Pulmonary, Critical Care, Sleep, and Occupational Medicine, Indiana University Medical Center, Indianapolis, Indiana
| | - Hua Zhou
- Department of Biostatistics, University of California, Los Angeles, California
| | - Jin J Zhou
- Department of Epidemiology and Biostatistics, University of Arizona, Tucson, Arizona
| |
Collapse
|
50
|
Zhou T, Xie X, Li M, Shi J, Zhou JJ, Knox KS, Wang T, Chen Q, Gu W. Rat BodyMap transcriptomes reveal unique circular RNA features across tissue types and developmental stages. RNA 2018; 24:1443-1456. [PMID: 30093490 PMCID: PMC6191709 DOI: 10.1261/rna.067132.118] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/01/2018] [Accepted: 08/03/2018] [Indexed: 06/08/2023]
Abstract
Circular RNAs (circRNAs) are a novel class of regulatory RNAs. Here, we present a comprehensive investigation of circRNA expression profiles across 11 tissues and four developmental stages in rats, along with cross-species analyses in humans and mice. Although the expression of circRNAs is positively correlated with that of cognate mRNAs, highly expressed genes tend to splice a larger fraction of circular transcripts. Moreover, circRNAs exhibit higher tissue specificity than cognate mRNAs. Intriguingly, while we observed a monotonic increase of circRNA abundance with age in the rat brain, we further discovered a dynamic, age-dependent pattern of circRNA expression in the testes that is characterized by a dramatic increase with advancing stages of sexual maturity and a decrease with aging. The age-sensitive testicular circRNAs are highly associated with spermatogenesis, independent of cognate mRNA expression. The tissue/age implications of circRNAs suggest that they present unique physiological functions rather than simply occurring as occasional by-products of gene transcription.
Collapse
Affiliation(s)
- Tong Zhou
- Department of Physiology and Cell Biology, The University of Nevada, Reno School of Medicine, Reno, Nevada 89557, USA
| | - Xueying Xie
- State Key Laboratory of Bioelectronics, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, Jiangsu 210096, China
| | - Musheng Li
- State Key Laboratory of Bioelectronics, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, Jiangsu 210096, China
| | - Junchao Shi
- Department of Physiology and Cell Biology, The University of Nevada, Reno School of Medicine, Reno, Nevada 89557, USA
| | - Jin J Zhou
- Department of Epidemiology and Biostatistics, The University of Arizona, Tucson, Arizona 85721, USA
| | - Kenneth S Knox
- Department of Internal Medicine, College of Medicine Phoenix, The University of Arizona, Phoenix, Arizona 85004, USA
| | - Ting Wang
- Department of Internal Medicine, College of Medicine Phoenix, The University of Arizona, Phoenix, Arizona 85004, USA
| | - Qi Chen
- Department of Physiology and Cell Biology, The University of Nevada, Reno School of Medicine, Reno, Nevada 89557, USA
| | - Wanjun Gu
- State Key Laboratory of Bioelectronics, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, Jiangsu 210096, China
| |
Collapse
|