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Zhang Y, Sun Q, Yu C, Sun D, Pang Y, Pei P, Du H, Yang L, Chen Y, Yang X, Chen X, Chen J, Chen Z, Li L, Lv J. Associations of traditional cardiovascular risk factors with 15-year blood pressure change and trajectories in Chinese adults: a prospective cohort study. J Hypertens 2024; 42:1340-1349. [PMID: 38525868 PMCID: PMC7616121 DOI: 10.1097/hjh.0000000000003717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/26/2024]
Abstract
OBJECTIVE How traditional cardiovascular disease (CVD) risk factors are related to long-term blood pressure change (BPC) or trajectories remain unclear. We aimed to examine the independent associations of these factors with 15-year BPC and trajectories in Chinese adults. METHODS We included 15 985 participants who had attended three surveys, including 2004-2008 baseline survey, and 2013-2014 and 2020-2021 resurveys, over 15 years in the China Kadoorie Biobank (CKB). We measured systolic and diastolic blood pressure (SBP and DBP), height, weight, and waist circumference (WC). We asked about the sociodemographic characteristics and lifestyle factors, including smoking, alcohol drinking, intake of fresh vegetables, fruits, and red meat, and physical activity, using a structured questionnaire. We calculated standard deviation (SD), cumulative blood pressure (cumBP), coefficient of variation (CV), and average real variability (ARV) as long-term BPC proxies. We identified blood pressure trajectories using the latent class growth model. RESULTS Most baseline sociodemographic and lifestyle characteristics were associated with cumBP. After adjusting for other characteristics, the cumSBP (mmHg × year) increased by 116.9 [95% confidence interval (CI): 111.0, 122.7] for every 10 years of age. The differences of cumSBP in heavy drinkers of ≥60 g pure alcohol per day and former drinkers were 86.7 (60.7, 112.6) and 48.9 (23.1, 74.8) compared with less than weekly drinkers. The cumSBP in participants who ate red meat less than weekly was 29.4 (12.0, 46.8) higher than those who ate red meat daily. The corresponding differences of cumSBP were 127.8 (120.7, 134.9) and 70.2 (65.0, 75.3) for BMI per 5 kg/m 2 and WC per 10 cm. Most of the findings of other BPC measures by baseline characteristics were similar to the cumBP, but the differences between groups were somewhat weaker. Alcohol drinking was associated with several high-risk trajectories of SBP and DBP. Both BMI and WC were independently associated with all high-risk blood pressure trajectories. CONCLUSIONS Several traditional CVD risk factors were associated with unfavorable long-term BPC or blood pressure trajectories in Chinese adults.
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Affiliation(s)
- Yiqian Zhang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing 100191, China
| | - Qiufen Sun
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing 100191, China
| | - Canqing Yu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education
| | - Dianjianyi Sun
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education
| | - Yuanjie Pang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education
| | - Pei Pei
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China
| | - Huaidong Du
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, United Kingdom
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Ling Yang
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, United Kingdom
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Yiping Chen
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, United Kingdom
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Xiaoming Yang
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Xiaofang Chen
- Chengdu Medical College, Chengdu, Sichuan 610500, China
| | - Junshi Chen
- China National Center for Food Safety Risk Assessment, Beijing, China
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Liming Li
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education
| | - Jun Lv
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education
- State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University
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Verma A, Huffman JE, Rodriguez A, Conery M, Liu M, Ho YL, Kim Y, Heise DA, Guare L, Panickan VA, Garcon H, Linares F, Costa L, Goethert I, Tipton R, Honerlaw J, Davies L, Whitbourne S, Cohen J, Posner DC, Sangar R, Murray M, Wang X, Dochtermann DR, Devineni P, Shi Y, Nandi TN, Assimes TL, Brunette CA, Carroll RJ, Clifford R, Duvall S, Gelernter J, Hung A, Iyengar SK, Joseph J, Kember R, Kranzler H, Kripke CM, Levey D, Luoh SW, Merritt VC, Overstreet C, Deak JD, Grant SFA, Polimanti R, Roussos P, Shakt G, Sun YV, Tsao N, Venkatesh S, Voloudakis G, Justice A, Begoli E, Ramoni R, Tourassi G, Pyarajan S, Tsao P, O'Donnell CJ, Muralidhar S, Moser J, Casas JP, Bick AG, Zhou W, Cai T, Voight BF, Cho K, Gaziano JM, Madduri RK, Damrauer S, Liao KP. Diversity and scale: Genetic architecture of 2068 traits in the VA Million Veteran Program. Science 2024; 385:eadj1182. [PMID: 39024449 DOI: 10.1126/science.adj1182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 05/10/2024] [Indexed: 07/20/2024]
Abstract
One of the justifiable criticisms of human genetic studies is the underrepresentation of participants from diverse populations. Lack of inclusion must be addressed at-scale to identify causal disease factors and understand the genetic causes of health disparities. We present genome-wide associations for 2068 traits from 635,969 participants in the Department of Veterans Affairs Million Veteran Program, a longitudinal study of diverse United States Veterans. Systematic analysis revealed 13,672 genomic risk loci; 1608 were only significant after including non-European populations. Fine-mapping identified causal variants at 6318 signals across 613 traits. One-third (n = 2069) were identified in participants from non-European populations. This reveals a broadly similar genetic architecture across populations, highlights genetic insights gained from underrepresented groups, and presents an extensive atlas of genetic associations.
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Affiliation(s)
- Anurag Verma
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA 19104, USA
- Department of Medicine, Division of Translational Medicine and Human Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
- Institute for Biomedical Informatics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Jennifer E Huffman
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA 02130, USA
- Palo Alto Veterans Institute for Research (PAVIR), Palo Alto Health Care System, Palo Alto, CA 94304, USA
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - Alex Rodriguez
- Data Science and Learning, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Mitchell Conery
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Molei Liu
- Department of Biostatistics, Columbia University's Mailman School of Public Health, New York, NY 10032, USA
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA 02130, USA
| | - Youngdae Kim
- Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - David A Heise
- National Security Sciences Directorate, Cyber Resilience and Intelligence Division, Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN 37831, USA
| | - Lindsay Guare
- Department of Medicine, Division of Translational Medicine and Human Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | | | - Helene Garcon
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA 02130, USA
| | - Franciel Linares
- R&D Systems Engineering, Information Technology Services Directorate, Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN 37831, USA
| | - Lauren Costa
- MVP Boston Coordinating Center, VA Boston Healthcare System, Boston, MA 02111, USA
| | - Ian Goethert
- Data Management and Engineering, Information Technology Services Division, Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN 37831, USA
| | - Ryan Tipton
- Knowledge Discovery Infrastructure, Information Technology Services Division, Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN 37831, USA
| | - Jacqueline Honerlaw
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA 02130, USA
| | - Laura Davies
- Computing and Computational Sciences Dir PMO, PMO, Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN 37831, USA
| | - Stacey Whitbourne
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- MVP Boston Coordinating Center, VA Boston Healthcare System, Boston, MA 02111, USA
- Department of Medicine, Division of Aging, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Jeremy Cohen
- National Security Sciences Directorate, Cyber Resilience and Intelligence Division, Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN 37831, USA
| | - Daniel C Posner
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA 02130, USA
| | - Rahul Sangar
- MVP Boston Coordinating Center, VA Boston Healthcare System, Boston, MA 02111, USA
| | - Michael Murray
- MVP Boston Coordinating Center, VA Boston Healthcare System, Boston, MA 02111, USA
| | - Xuan Wang
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT 84112, USA
| | - Daniel R Dochtermann
- VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA 02130, USA
| | - Poornima Devineni
- VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA 02130, USA
| | - Yunling Shi
- VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA 02130, USA
| | - Tarak Nath Nandi
- Data Science and Learning, Argonne National Laboratory, Lemont, IL 60439, USA
| | | | - Charles A Brunette
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- Research Service, VA Boston Healthcare System, Boston, MA 02130, USA
| | - Robert J Carroll
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37211, USA
| | - Royce Clifford
- Research Department, VA San Diego Healthcare System, San Diego, CA 92161, USA
- Department of Otolaryngology, UCSD San Diego, La Jolla, CA 92093, USA
| | - Scott Duvall
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT 84148, USA
- Internal Medicine, Epidemiology, University of Utah School of Medicine, Salt Lake City, UT 84132, USA
| | - Joel Gelernter
- Psychiatry, Human Genetics, Yale University, New Haven, CT, 06520, USA
- VA Connecticut Healthcare System West Haven, West Haven, CT, 06516, USA
| | - Adriana Hung
- Medicine, Nephrology & Hypertension, VA Tennessee Valley Healthcare System & Vanderbilt University, Nashville, TN 37232, USA
| | - Sudha K Iyengar
- Departments of Population and Quantitative Health Sciences, Genetics and Genome Sciences, and Ophthalmology and Visual Sciences and the Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Jacob Joseph
- Medicine, Cardiology Section, VA Providence Healthcare System, Providence, RI 02908, USA
- Department of Medicine, Brown University, Providence, RI, 02908, USA
| | - Rachel Kember
- Mental Illness Research, Education and Clinical Center, Corporal Michael Crescenz VA Medical Center, Philadelphia, PA 19104, USA
- Department of Psychiatry, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Henry Kranzler
- Mental Illness Research, Education and Clinical Center, Corporal Michael Crescenz VA Medical Center, Philadelphia, PA 19104, USA
- Department of Psychiatry, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Colleen M Kripke
- Department of Medicine, Division of Translational Medicine and Human Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Daniel Levey
- Psychiatry, Human Genetics, Yale University, New Haven, CT, 06520, USA
- Medicine, VA Connecticut Healthcare System West Haven, West Haven, CT 06516, USA
| | - Shiuh-Wen Luoh
- VA Portland Health Care System, Portland, OR 97239, USA
- Division of Hematology and Medical Oncology, Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239, USA
| | - Victoria C Merritt
- Research Department, VA San Diego Healthcare System, San Diego, CA 92161, USA
| | - Cassie Overstreet
- Psychiatry, Human Genetics, Yale University, New Haven, CT, 06520, USA
| | - Joseph D Deak
- Psychiatry, Yale University, New Haven, CT 06520, USA
- Psychiatry, VA Connecticut Healthcare System West Haven, West Haven, CT 06516, USA
| | - Struan F A Grant
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Pediatrics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
- Divisions of Human Genetics and Endocrinology and Diabetes, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | | | - Panos Roussos
- Psychiatry, Mental Illness Research, Education and Clinical Center, James J. Peters VA Medical Center; Icahn School of Medicine at Mount Sinai, Bronx, NY 10468, USA
| | - Gabrielle Shakt
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA 19104, USA
- Department of Surgery, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Yan V Sun
- Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA 30322, USA
| | - Noah Tsao
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA 19104, USA
- Department of Surgery, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Sanan Venkatesh
- Psychiatry, Mental Illness Research, Education and Clinical Center, James J. Peters VA Medical Center; Icahn School of Medicine at Mount Sinai, Bronx, NY 10468, USA
| | - Georgios Voloudakis
- Psychiatry, Mental Illness Research, Education and Clinical Center, James J. Peters VA Medical Center; Icahn School of Medicine at Mount Sinai, Bronx, NY 10468, USA
| | - Amy Justice
- Medicine, VA Connecticut Healthcare System West Haven, West Haven, CT 06516, USA
- Internal Medicine, General Medicine, Yale University, New Haven, CT 06520, USA
- Health Policy, Yale School of Public Health, New Haven, CT 06520, USA
| | - Edmon Begoli
- Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN, 37831, USA
| | - Rachel Ramoni
- Office of Research and Development, Department of Veterans Affairs, Washington, DC, 20420, USA
| | - Georgia Tourassi
- National Center for Computational Sciences, Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN, 37831, USA
| | - Saiju Pyarajan
- VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA 02130, USA
| | - Philip Tsao
- Medicine, Cardiology, VA Palo Alto Healthcare System, Palo Alto, CA 94304, USA
- Department of Medicine, Stanford University, Palo Alto, CA, 94304, USA
| | | | - Sumitra Muralidhar
- Office of Research and Development, Department of Veterans Affairs, Washington, DC, 20420, USA
| | - Jennifer Moser
- Office of Research and Development, Department of Veterans Affairs, Washington, DC, 20420, USA
| | - Juan P Casas
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA 02130, USA
| | - Alexander G Bick
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University, Nashville, TN, 37325, USA
| | - Wei Zhou
- Department of Medicine, Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research, Cambridge, MA 02142, USA
- Program in Medical and Population Genetics, Cambridge, MA 02142, USA
| | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
| | - Benjamin F Voight
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA 19104, USA
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
- Department of Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
- Institute of Translational Medicine and Therapeutics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Kelly Cho
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- MVP Boston Coordinating Center, VA Boston Healthcare System, Boston, MA 02111, USA
- Department of Medicine, Division of Aging, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - J Michael Gaziano
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- MVP Boston Coordinating Center, VA Boston Healthcare System, Boston, MA 02111, USA
- Department of Medicine, Division of Aging, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Ravi K Madduri
- Data Science and Learning, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Scott Damrauer
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA 19104, USA
- Department of Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
- Department of Surgery, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
- Cardiovascular Institute, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Katherine P Liao
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA 02130, USA
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
- Medicine, Rheumatology, VA Boston Healthcare System, Boston, MA 02130, USA
- Department of Medicine, Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, Boston, MA 02115, USA
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Huang R, Kartsonaki C, Turnbull I, Pei P, Chen Y, Liu J, Du H, Sun D, Yang L, Barnard M, Lv J, Yu C, Chen J, Li L, Chen Z, Bragg F. Incidence and mortality rates of 14 site-specific infectious diseases in 10 diverse areas of China: findings from China Kadoorie Biobank, 2006-2018. Int J Infect Dis 2024:107169. [PMID: 39002770 DOI: 10.1016/j.ijid.2024.107169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2024] [Revised: 06/20/2024] [Accepted: 07/08/2024] [Indexed: 07/15/2024] Open
Abstract
BACKGROUND Infectious diseases remain a major global health concern, including in China, with an estimated >10 million cases of infectious disease in 2019. We describe the burden of site-specific infectious diseases among Chinese adults. METHODS From 2004 to 2008, the prospective China Kadoorie Biobank enrolled 512,726 adults aged 30-79 years from 10 diverse areas (5 rural, 5 urban) of China. During the 12 years of follow-up, 101,673 participants were hospitalised for any infectious disease. Descriptive analyses examined standardised incidence, mortality, and case fatality of infections. FINDINGS The incidence of any infectious disease was 1856 per 100,000 person-years; respiratory tract infections (1069) were most common. The infectious disease mortality rate was 31.8 per 100,000 person years (20.3 and 9.4 for respiratory and non-respiratory infections, respectively) and case fatality was 2.2% (2.6% and 1.6% for respiratory and non-respiratory infections, respectively). Infectious disease incidence and mortality rates were higher at older ages and in rural areas. There were no clear sex-differences in infectious disease incidence rates, but mortality and case fatality rates were twice as high in men as in women. INTERPRETATION Infectious diseases were common in Chinese adults. The observed burden of, and disparities in, site-specific infections can inform targeted prevention efforts. FUNDING Kadoorie Foundation, Wellcome Trust, MRC, BHF, CR-UK, MoST, NNSF.
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Affiliation(s)
- Rui Huang
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, UK
| | - Christiana Kartsonaki
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, UK.
| | - Iain Turnbull
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, UK
| | - Pei Pei
- Peking University Centre for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China
| | - Yiping Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, UK
| | - Jingchao Liu
- Suzhou Centre of Disease Prevention and Control, 269 Taihu West Road, Suzhou 215128, China
| | - Huaidong Du
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, UK
| | - Dianjianyi Sun
- Peking University Centre for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China; Department of Epidemiology and Biostatistics, School of Public Health, Peking University, 38 Xueyuan Road, Beijing 100191, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education
| | - Ling Yang
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, UK
| | - Maxim Barnard
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, UK
| | - Jun Lv
- Peking University Centre for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China; Department of Epidemiology and Biostatistics, School of Public Health, Peking University, 38 Xueyuan Road, Beijing 100191, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education
| | - Canqing Yu
- Peking University Centre for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China; Department of Epidemiology and Biostatistics, School of Public Health, Peking University, 38 Xueyuan Road, Beijing 100191, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education
| | - Junshi Chen
- National Centre for Food Safety Risk Assessment, 37 Guangqu Road, Beijing 100021, China
| | - Liming Li
- Peking University Centre for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China; Department of Epidemiology and Biostatistics, School of Public Health, Peking University, 38 Xueyuan Road, Beijing 100191, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, UK
| | - Fiona Bragg
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, UK; Health Data Research UK Oxford, University of Oxford, Oxford, UK
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4
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Sun D, Ding Y, Yu C, Sun D, Pang Y, Pei P, Yang L, Millwood IY, Walters RG, Du H, Chen X, Schmidt D, Stevens R, Chen J, Chen Z, Li L, Lv J. Joint impact of polygenic risk score and lifestyles on early- and late-onset cardiovascular diseases. Nat Hum Behav 2024:10.1038/s41562-024-01923-7. [PMID: 38987358 DOI: 10.1038/s41562-024-01923-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 06/10/2024] [Indexed: 07/12/2024]
Abstract
Understanding the interactions between genetic risk and lifestyles on different types and age onsets of cardiovascular disease (CVD) risk can help identify individuals for whom lifestyle changes would be beneficial. Here we developed three polygenic risk scores, called MetaPRSs, for coronary artery disease, ischaemic stroke and intracerebral haemorrhage by combining PRSs for CVD and CVD-related risk factors in 96,400 participants from the prospective China Kadoorie Biobank. Genetic and lifestyle risks were categorized by the disease-specific MetaPRSs and the number of unfavourable lifestyles. High genetic risk and unfavourable lifestyles were found to be more strongly associated with early than late onset of CVD outcomes in men and women. Change from unfavourable to favourable lifestyles resulted in 14.7-, 2.5- and 2.6-fold greater reductions in incidence rates of early-onset coronary artery disease and ischaemic stroke and late-onset coronary artery disease in high than low genetic risk group. Young adults at high genetic risk may have larger benefits in preventing CVD from lifestyle improvements.
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5
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Yang S, Xiao Y, Jing D, Liu H, Su J, Shen M, Chen X. Socioeconomic disparity in the natural history of cutaneous melanoma: evidence from two large prospective cohorts. J Epidemiol Community Health 2024:jech-2024-222158. [PMID: 38977296 DOI: 10.1136/jech-2024-222158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 06/25/2024] [Indexed: 07/10/2024]
Abstract
BACKGROUND Previous studies on the associations between socioeconomic status (SES) and cutaneous malignant melanoma (CMM) failed to distinguish the effects of different SES factors under an individual-data-based prospective study design. METHODS Based on UK Biobank (UKB) and China Kadoorie Biobank (CKB), we estimated the effects of four SES factors on transitions from baseline to CMM in situ, subsequently to invasive CMM and further CMM mortality by applying multistate models. We further explored to which extent the associations between SES and CMM incidence could be explained by potential mediators including sun exposure, lifestyle and ageing in UKB. RESULTS In multistate analyses, good household income was independently associated with an increased risk of CMM in situ (HR=1.38, 95% CI: 1.21 to 1.58) and invasive CMM (HR=1.34, 95% CI: 1.22 to 1.48) in UKB. These findings were partly validated in CKB. Especially in UKB, we observed an increased risk of CMM in situ and invasive CMM among participants with good type of house; only good education was independently associated with lower risk of evolving to invasive CMM among patients with CMM in situ (HR=0.69, 95% CI: 0.52 to 0.92); only good household income was independently associated with lower risk of CMM mortality among patients with CMM (HR=0.65, 95% CI: 0.45 to 0.95). In mediation analysis, the proportions attributable to the mediating effect were <6% for all selected variables, including self-reported sun exposure-related factors. CONCLUSION SES factors have different effects on the incidence and progression of CMM. The association between SES and incident CMM is neither causal nor well explained by selected mediators.
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Affiliation(s)
- Songchun Yang
- Department of Dermatology | Hunan Engineering Research Center of Skin Health and Disease | Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Furong Laboratory, Changsha, Hunan, China
| | - Yi Xiao
- Department of Dermatology | Hunan Engineering Research Center of Skin Health and Disease | Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Furong Laboratory, Changsha, Hunan, China
| | - Danrong Jing
- Department of Dermatology | Hunan Engineering Research Center of Skin Health and Disease | Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Furong Laboratory, Changsha, Hunan, China
| | - Hong Liu
- Department of Dermatology | Hunan Engineering Research Center of Skin Health and Disease | Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Furong Laboratory, Changsha, Hunan, China
| | - Juan Su
- Department of Dermatology | Hunan Engineering Research Center of Skin Health and Disease | Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Furong Laboratory, Changsha, Hunan, China
| | - Minxue Shen
- Department of Dermatology | Hunan Engineering Research Center of Skin Health and Disease | Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Furong Laboratory, Changsha, Hunan, China
- Department of Social Medicine and Health Management, Xiangya School of Public Health, Central South University, Changsha, Hunan, China
| | - Xiang Chen
- Department of Dermatology | Hunan Engineering Research Center of Skin Health and Disease | Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Furong Laboratory, Changsha, Hunan, China
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6
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Rivera NV. Big data in sarcoidosis. Curr Opin Pulm Med 2024:00063198-990000000-00180. [PMID: 38967053 DOI: 10.1097/mcp.0000000000001102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/06/2024]
Abstract
PURPOSE OF REVIEW This review provides an overview of recent advancements in sarcoidosis research, focusing on collaborative networks, phenotype characterization, and molecular studies. It highlights the importance of collaborative efforts, phenotype characterization, and the integration of multilevel molecular data for advancing sarcoidosis research and paving the way toward personalized medicine. RECENT FINDINGS Sarcoidosis exhibits heterogeneous clinical manifestations influenced by various factors. Efforts to define sarcoidosis endophenotypes show promise, while technological advancements enable extensive molecular data generation. Collaborative networks and biobanks facilitate large-scale studies, enhancing biomarker discovery and therapeutic protocols. SUMMARY Sarcoidosis presents a complex challenge due to its unknown cause and heterogeneous clinical manifestations. Collaborative networks, comprehensive phenotype delineation, and the utilization of cutting-edge technologies are essential for advancing our understanding of sarcoidosis biology and developing personalized medicine approaches. Leveraging large-scale epidemiological resources and biobanks and integrating multilevel molecular data offer promising avenues for unraveling the disease's heterogeneity and improving patient outcomes.
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Affiliation(s)
- Natalia V Rivera
- Division of Respiratory Medicine, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
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7
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Su J, Fan X, Li M, Yu H, Geng H, Qin Y, Lu Y, Pei P, Sun D, Yu C, Lv J, Tao R, Zhou J, Ma H, Wu M. Association of lifestyle with reduced stroke risk in 41 314 individuals with diabetes: Two prospective cohort studies in China. Diabetes Obes Metab 2024; 26:2869-2880. [PMID: 38685601 DOI: 10.1111/dom.15606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 04/03/2024] [Accepted: 04/04/2024] [Indexed: 05/02/2024]
Abstract
AIM To investigate the associations of individual and combined healthy lifestyle factors (HLS) with the risk of stroke in individuals with diabetes in China. METHODS This prospective analysis included 41 314 individuals with diabetes [15 191 from the Comprehensive Research on the Prevention and Control of the Diabetes (CRPCD) project and 26 123 from the China Kadoorie Biobank (CKB) study]. Associations of lifestyle factors, including cigarette smoking, alcohol consumption, physical activity, diet, body shape and sleep duration, with the risk of stroke, intracerebral haemorrhage (ICH) and ischaemic stroke (IS) were assessed using Cox proportional hazard models. RESULTS During median follow-up periods of 8.02 and 9.05 years, 2499 and 4578 cases of stroke, 2147 and 4024 of IS, and 160 and 728 of ICH were documented in individuals with diabetes in the CRPCD and CKB cohorts, respectively. In the CRPCD cohort, patients with ≥5 HLS had a 14% lower risk of stroke (hazard ratio (HR): 0.86, 95% confidence interval (CI): 0.75-0.98) than those with ≤2 HLS. In the CKB cohort, the adjusted HR (95% CI) for patients with ≥5 HLS were 0.74 (0.66-0.83) for stroke, 0.74 (0.66-0.83) for IS, and 0.57 (0.42-0.78) for ICH compared with those with ≤2 HLS. The pooled adjusted HR (95% CI) comparing patients with ≥5 HLS versus ≤2 HLS was 0.79 (0.69-0.92) for stroke, 0.80 (0.68-0.93) for IS, and 0.60 (0.46-0.78) for ICH. CONCLUSIONS Maintaining a healthy lifestyle was associated with a lower risk of stroke, IS and ICH among individuals with diabetes.
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Affiliation(s)
- Jian Su
- Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, China
- Department of Noncommunicable Chronic Disease Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
| | - Xikang Fan
- Department of Noncommunicable Chronic Disease Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
| | - Mengyao Li
- Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Hao Yu
- Department of Noncommunicable Chronic Disease Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
| | - Houyue Geng
- Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Yu Qin
- Department of Noncommunicable Chronic Disease Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
| | - Yan Lu
- Department of Non-communicable Chronic Disease Control and Prevention, Suzhou Center for Disease Control and Prevention, Suzhou, China
| | - Pei Pei
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
| | - Dianjianyi Sun
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Ministry of Education (Peking University), Beijing, China
| | - Canqing Yu
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Ministry of Education (Peking University), Beijing, China
| | - Jun Lv
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Ministry of Education (Peking University), Beijing, China
| | - Ran Tao
- Department of Noncommunicable Chronic Disease Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
| | - Jinyi Zhou
- Department of Noncommunicable Chronic Disease Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
| | - Hongxia Ma
- Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Ming Wu
- Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, China
- Department of Noncommunicable Chronic Disease Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
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8
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Gao Z, Zhao Q, Hastie T. PathGPS: discover shared genetic architecture using GWAS summary data. Biometrics 2024; 80:ujae060. [PMID: 39005072 PMCID: PMC11247175 DOI: 10.1093/biomtc/ujae060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 12/28/2023] [Accepted: 07/05/2024] [Indexed: 07/16/2024]
Abstract
The increasing availability and scale of biobanks and "omic" datasets bring new horizons for understanding biological mechanisms. PathGPS is an exploratory data analysis tool to discover genetic architectures using Genome Wide Association Studies (GWAS) summary data. PathGPS is based on a linear structural equation model where traits are regulated by both genetic and environmental pathways. PathGPS decouples the genetic and environmental components by contrasting the GWAS associations of "signal" genes with those of "noise" genes. From the estimated genetic component, PathGPS then extracts genetic pathways via principal component and factor analysis, leveraging the low-rank and sparse properties. In addition, we provide a bootstrap aggregating ("bagging") algorithm to improve stability under data perturbation and hyperparameter tuning. When applied to a metabolomics dataset and the UK Biobank, PathGPS confirms several known gene-trait clusters and suggests multiple new hypotheses for future investigations.
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Affiliation(s)
- Zijun Gao
- Marshall Business School, University of Southern California, Los Angeles CA, 90089, United States
| | - Qingyuan Zhao
- Department of Pure Mathematics and Mathematical Statistics, University of Cambridge, Cambridge, CB3 0WB, United Kingdom
| | - Trevor Hastie
- Department of Statistics and Department of Biomedical Data Science, Stanford University, Stanford, CA, 94305, United States
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9
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Chen Y, Yu W, Lv J, Sun D, Pei P, Du H, Yang L, Chen Y, Zhang H, Chen J, Chen Z, Li L, Yu C. Early adulthood BMI and cardiovascular disease: a prospective cohort study from the China Kadoorie Biobank. Lancet Public Health 2024:S2468-2667(24)00043-4. [PMID: 38885669 DOI: 10.1016/s2468-2667(24)00043-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 02/21/2024] [Accepted: 02/23/2024] [Indexed: 06/20/2024]
Abstract
BACKGROUND The associations of early adulthood BMI with cardiovascular diseases have yet to be completely delineated. There is little reliable evidence about these associations among east Asian populations, that differ in fat distribution, disease patterns, and lifestyle factors from other populations. We aimed to study the associations between early adulthood BMI and cardiovascular diseases in a Chinese population, and the effect of midlife lifestyle factors on outcomes. METHODS In this prospective analysis, we used data from the China Kadoorie Biobank, a large and long-term cohort from five urban areas and five rural areas, using participants aged 35-70 years. The primary outcome was the incidence of cardiovascular diseases as a group, ischaemic heart disease, haemorrhagic stroke, and ischaemic stroke, which were obtained mainly through linkage to disease registries and the national database for health insurance claims. Early adulthood BMI was assessed through self-report at baseline survey. We used Cox proportional hazards regression models to examine the prospective associations. We also undertook multiplicative and additive interaction analyses to investigate the potential modification effect of midlife healthy lifestyle factors (a combined score covering smoking, drinking, physical activity, and diet). FINDINGS Participants were recruited for baseline survey between June, 2004, and July, 2008. During a median follow-up of 12·0 years (IQR 11·3-13·1), we documented 57 203 (15·9%) of incident cardiovascular diseases in 360 855 participants. After adjustment for potential confounders, monotonic dose-response associations were observed between higher early adulthood BMI and increased risks of incident cardiovascular diseases. Compared with an early adulthood BMI of 20·5-22·4 kg/m2 (the reference group), the hazard ratios for a BMI of less than 18·5 kg/m2 was 0·97 (95% CI 0·94-1·00), 18·5-20·4 kg/m2 was 0·97 (0·95-0·99), 22·5-23·9 kg/m2 was 1·04 (1·02-1·07), 24·0-25·9 kg/m2 was 1·12 (1·09-1·15), 26·0-27·9 kg/m2 was 1·19 (1·14-1·24), 28·0-29·9 kg/m2 was 1·34 (1·25-1·44), and ≥30·0 kg/m2 was 1·58 (1·42-1·75). Except for haemorrhagic stroke, lower early adulthood BMI (<20·5 kg/m2) was associated with decreased incident cardiovascular disease risks. No significant interaction was found between midlife healthy lifestyle factors and early adulthood BMI on cardiovascular disease risks. INTERPRETATION Increased risks of cardiovascular disease incidence were found among participants with high early adulthood adiposity, including ischaemic heart disease, haemorrhagic stroke, and ischaemic stroke. Our findings suggest early adulthood as an important time to focus on weight management and obesity prevention for cardiovascular health later in life. FUNDING National Natural Science Foundation of China, National Key Research and Development Program of China, Chinese Ministry of Science and Technology, Kadoorie Charitable Foundation, and the Wellcome Trust.
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Affiliation(s)
- Yuanyuan Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Wei Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Bejing, China
| | - Dianjianyi Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China; Center for Public Health and Epidemic Preparedness & Response, Peking University, Beijing, China
| | - Pei Pei
- Center for Public Health and Epidemic Preparedness & Response, Peking University, Beijing, China
| | - Huaidong Du
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Ling Yang
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Yiping Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Huanxu Zhang
- Tongxiang Center for Disease Control and Prevention, Zhejiang, China
| | - Junshi Chen
- China National Center for Food Safety Risk Assessment, Beijing, China
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China; Center for Public Health and Epidemic Preparedness & Response, Peking University, Beijing, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Bejing, China.
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10
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Yang L, Kartsonaki C, Simon J, Yao P, Guo Y, Lv J, Walters RG, Chen Y, Fry H, Avery D, Yu C, Jin J, Mentzer AJ, Allen N, Butt J, Hill M, Li L, Millwood IY, Waterboer T, Chen Z. Prospective evaluation of the relevance of Epstein-Barr virus antibodies for early detection of nasopharyngeal carcinoma in Chinese adults. Int J Epidemiol 2024; 53:dyae098. [PMID: 39008896 PMCID: PMC11249388 DOI: 10.1093/ije/dyae098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 07/10/2024] [Indexed: 07/17/2024] Open
Abstract
BACKGROUND Epstein-Barr virus (EBV) is a major cause of nasopharyngeal carcinoma (NPC) and measurement of different EBV antibodies in blood may improve early detection of NPC. Prospective studies can help assess the roles of different EBV antibodies in predicting NPC risk over time. METHODS A case-cohort study within the prospective China Kadoorie Biobank of 512 715 adults from 10 (including two NPC endemic) areas included 295 incident NPC cases and 745 subcohort participants. A multiplex serology assay was used to quantify IgA and IgG antibodies against 16 EBV antigens in stored baseline plasma samples. Cox regression was used to estimate adjusted hazard ratios (HRs) for NPC and C-statistics to assess the discriminatory ability of EBV-markers, including two previously identified EBV-marker combinations, for predicting NPC. RESULTS Sero-positivity for 15 out of 16 EBV-markers was significantly associated with higher NPC risk. Both IgA and IgG antibodies against the same three EBV-markers showed the most extreme HRs, i.e. BGLF2 (IgA: 124.2 (95% CI: 63.3-243.9); IgG: 8.6 (5.5-13.5); LF2: [67.8 (30.0-153.1), 10.9 (7.2-16.4)]); and BFRF1: 26.1 (10.1-67.5), 6.1 (2.7-13.6). Use of a two-marker (i.e. LF2/BGLF2 IgG) and a four-marker (i.e. LF2/BGLF2 IgG and LF2/EA-D IgA) combinations yielded C-statistics of 0.85 and 0.84, respectively, which persisted for at least 5 years after sample collection in both endemic and non-endemic areas. CONCLUSIONS In Chinese adults, plasma EBV markers strongly predict NPC occurrence many years before clinical diagnosis. LF2 and BGLF2 IgG could identify NPC high-risk individuals to improve NPC early detection in community and clinical settings.
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Affiliation(s)
- Ling Yang
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Christiana Kartsonaki
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Julia Simon
- Infections and Cancer Epidemiology Division, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Pang Yao
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Yu Guo
- National Center for Cardiovascular Diseases, Fuwai Hospital Chinese Academy of Medical Sciences, Beijing, China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Centre, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Peking University, Beijing, China
| | - Robin G Walters
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Yiping Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Hannah Fry
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Daniel Avery
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Centre, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Peking University, Beijing, China
| | | | | | - Naomi Allen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Julia Butt
- Infections and Cancer Epidemiology Division, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Michael Hill
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Centre, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Peking University, Beijing, China
| | - Iona Y Millwood
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Tim Waterboer
- Infections and Cancer Epidemiology Division, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
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11
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Hang D, Sun D, Du L, Huang J, Li J, Zhu C, Wang L, He J, Zhu X, Zhu M, Song C, Dai J, Yu C, Xu Z, Li N, Ma H, Jin G, Yang L, Chen Y, Du H, Cheng X, Chen Z, Lv J, Hu Z, Li L, Shen H. Development and evaluation of a risk prediction tool for risk-adapted screening of colorectal cancer in China. Cancer Lett 2024; 597:217057. [PMID: 38876387 DOI: 10.1016/j.canlet.2024.217057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 06/03/2024] [Accepted: 06/10/2024] [Indexed: 06/16/2024]
Abstract
Risk prediction tools for colorectal cancer (CRC) have potential to improve the efficiency of population-based screening by facilitating risk-adapted strategies. However, such an applicable tool has yet to be established in the Chinese population. In this study, a risk score was created using data from the China Kadoorie Biobank (CKB), a nationwide cohort study of 409,854 eligible participants. Diagnostic performance of the risk score was evaluated in an independent CRC screening programme, which included 91,575 participants who accepted colonoscopy at designed hospitals in Zhejiang Province, China. Over a median follow-up of 11.1 years, 3,136 CRC cases were documented in the CKB. A risk score was created based on nine questionnaire-derived variables, showing moderate discrimination for 10-year CRC risk (C-statistic =0.68, 95% CI: 0.67-0.69). In the CRC screening programme, the detection rates of CRC were 0.25%, 0.82%, and 1.93% in low-risk (score <6), intermediate-risk (score: 6-19), and high-risk (score >19) groups, respectively. The newly developed score exhibited a C-statistic of 0.65 (95% CI: 0.63-0.66), surpassing the widely adopted tools such as the Asia-Pacific Colorectal Screening (APCS), modified APCS, and Korean Colorectal Screening scores (all C-statistics =0.60). In conclusion, we developed a novel risk prediction tool that is useful to identify individuals at high risk of CRC. A user-friendly online calculator was also constructed to encourage broader adoption of the tool.
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Affiliation(s)
- Dong Hang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China
| | - Dianjianyi Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China; Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Lingbin Du
- Department of Cancer Prevention, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China; Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Jianv Huang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Jiacong Li
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Chen Zhu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China; Department of Cancer Prevention, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China; Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Le Wang
- Department of Cancer Prevention, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China; Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Jingjing He
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Xia Zhu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Meng Zhu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China
| | - Ci Song
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China
| | - Juncheng Dai
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China; Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Zekuan Xu
- Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Ni Li
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Hongxia Ma
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China
| | - Guangfu Jin
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China
| | - Ling Yang
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, United Kingdom; Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Yiping Chen
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, United Kingdom; Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Huaidong Du
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, United Kingdom; Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Xiangdong Cheng
- Department of Cancer Prevention, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China; Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China; Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Zhibin Hu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China.
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China; Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China.
| | - Hongbing Shen
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China; Research Units of Cohort Study on Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing, China.
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Holmes MV, Kartsonaki C, Boxall R, Lin K, Reeve N, Yu C, Lv J, Bennett DA, Hill MR, Yang L, Chen Y, Du H, Turnbull I, Collins R, Clarke RJ, Tobin MD, Li L, Millwood IY, Chen Z, Walters RG. PCSK9 genetic variants and risk of vascular and non-vascular diseases in Chinese and UK populations. Eur J Prev Cardiol 2024; 31:1015-1025. [PMID: 38198221 PMCID: PMC11144468 DOI: 10.1093/eurjpc/zwae009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 12/15/2023] [Accepted: 12/20/2023] [Indexed: 01/12/2024]
Abstract
AIMS Lowering low-density lipoprotein cholesterol (LDL-C) through PCSK9 inhibition represents a new therapeutic approach to preventing and treating cardiovascular disease (CVD). Phenome-wide analyses of PCSK9 genetic variants in large biobanks can help to identify unexpected effects of PCSK9 inhibition. METHODS AND RESULTS In the prospective China Kadoorie Biobank, we constructed a genetic score using three variants at the PCSK9 locus associated with directly measured LDL-C [PCSK9 genetic score (PCSK9-GS)]. Logistic regression gave estimated odds ratios (ORs) for PCSK9-GS associations with CVD and non-CVD outcomes, scaled to 1 SD lower LDL-C. PCSK9-GS was associated with lower risks of carotid plaque [n = 8340 cases; OR = 0.61 (95% confidence interval: 0.45-0.83); P = 0.0015], major occlusive vascular events [n = 15 752; 0.80 (0.67-0.95); P = 0.011], and ischaemic stroke [n = 11 467; 0.80 (0.66-0.98); P = 0.029]. However, PCSK9-GS was also associated with higher risk of hospitalization with chronic obstructive pulmonary disease [COPD: n = 6836; 1.38 (1.08-1.76); P = 0.0089] and with even higher risk of fatal exacerbations amongst individuals with pre-existing COPD [n = 730; 3.61 (1.71-7.60); P = 7.3 × 10-4]. We also replicated associations for a PCSK9 variant, reported in UK Biobank, with increased risks of acute upper respiratory tract infection (URTI) [pooled OR after meta-analysis of 1.87 (1.38-2.54); P = 5.4 × 10-5] and self-reported asthma [pooled OR of 1.17 (1.04-1.30); P = 0.0071]. There was no association of a polygenic LDL-C score with COPD hospitalization, COPD exacerbation, or URTI. CONCLUSION The LDL-C-lowering PCSK9 genetic variants are associated with lower risk of subclinical and clinical atherosclerotic vascular disease but higher risks of respiratory diseases. Pharmacovigilance studies may be required to monitor patients treated with therapeutic PCSK9 inhibitors for exacerbations of respiratory diseases or respiratory tract infections. LAY SUMMARY Genetic analyses of over 100 000 participants of the China Kadoorie Biobank, mimicking the effect of new drugs intended to reduce cholesterol by targeting the PCSK9 protein, have identified potential severe effects of lower PCSK9 activity in patients with existing respiratory disease.PCSK9 genetic variants that are associated with lower cholesterol and reduced rates of cardiovascular disease are also associated with increased risk of a range of respiratory diseases, including asthma, upper respiratory tract infections, and hospitalization with chronic obstructive pulmonary disease (COPD).These genetic variants are not associated with whether or not individuals have COPD; instead, they are specifically associated with an increase in the chance of those who already have COPD being hospitalized and even dying, suggesting that careful monitoring of such patients should be considered during development of and treatment with anti-PCSK9 medication.
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Affiliation(s)
- Michael V Holmes
- Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford OX3 7LF, UK
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford OX3 7LF, UK
| | - Christiana Kartsonaki
- Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford OX3 7LF, UK
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford OX3 7LF, UK
| | - Ruth Boxall
- Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford OX3 7LF, UK
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford OX3 7LF, UK
| | - Kuang Lin
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford OX3 7LF, UK
| | - Nicola Reeve
- Department of Population Health Sciences, University of Leicester, Leicester, UK
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Derrick A Bennett
- Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford OX3 7LF, UK
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford OX3 7LF, UK
| | - Michael R Hill
- Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford OX3 7LF, UK
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford OX3 7LF, UK
| | - Ling Yang
- Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford OX3 7LF, UK
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford OX3 7LF, UK
| | - Yiping Chen
- Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford OX3 7LF, UK
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford OX3 7LF, UK
| | - Huaidong Du
- Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford OX3 7LF, UK
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford OX3 7LF, UK
| | - Iain Turnbull
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford OX3 7LF, UK
| | - Rory Collins
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford OX3 7LF, UK
| | - Robert J Clarke
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford OX3 7LF, UK
| | - Martin D Tobin
- Department of Population Health Sciences, University of Leicester, Leicester, UK
- National Institute for Health and Care Research, Leicester Biomedical Research Centre, University of Leicester, Leicester, UK
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Iona Y Millwood
- Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford OX3 7LF, UK
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford OX3 7LF, UK
| | - Zhengming Chen
- Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford OX3 7LF, UK
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford OX3 7LF, UK
| | - Robin G Walters
- Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford OX3 7LF, UK
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford OX3 7LF, UK
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Neale N, Lona-Durazo F, Ryten M, Gagliano Taliun SA. Leveraging sex-genetic interactions to understand brain disorders: recent advances and current gaps. Brain Commun 2024; 6:fcae192. [PMID: 38894947 PMCID: PMC11184352 DOI: 10.1093/braincomms/fcae192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Revised: 04/11/2024] [Accepted: 05/30/2024] [Indexed: 06/21/2024] Open
Abstract
It is established that there are sex differences in terms of prevalence, age of onset, clinical manifestations, and response to treatment for a variety of brain disorders, including neurodevelopmental, psychiatric, and neurodegenerative disorders. Cohorts of increasing sample sizes with diverse data types collected, including genetic, transcriptomic and/or phenotypic data, are providing the building blocks to permit analytical designs to test for sex-biased genetic variant-trait associations, and for sex-biased transcriptional regulation. Such molecular assessments can contribute to our understanding of the manifested phenotypic differences between the sexes for brain disorders, offering the future possibility of delivering personalized therapy for females and males. With the intention of raising the profile of this field as a research priority, this review aims to shed light on the importance of investigating sex-genetic interactions for brain disorders, focusing on two areas: (i) variant-trait associations and (ii) transcriptomics (i.e. gene expression, transcript usage and regulation). We specifically discuss recent advances in the field, current gaps and provide considerations for future studies.
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Affiliation(s)
- Nikita Neale
- Faculty of Medicine, Université de Montréal, Québec, H3C 3J7 Canada
| | - Frida Lona-Durazo
- Faculty of Medicine, Université de Montréal, Québec, H3C 3J7 Canada
- Research Centre, Montreal Heart Institute, Québec, H1T 1C8 Canada
| | - Mina Ryten
- Department of Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, WC1N 1EH London, UK
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, 20815 MD, USA
- NIHR Great Ormond Street Hospital Biomedical Research Centre, Great Ormond Street Institute of Child Health, Bloomsbury, WC1N 1EH London, UK
| | - Sarah A Gagliano Taliun
- Research Centre, Montreal Heart Institute, Québec, H1T 1C8 Canada
- Department of Medicine & Department of Neurosciences, Faculty of Medicine, Université de Montréal, Québec, H3C 3J7 Canada
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14
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Song S, Lv J, Li L, Pang Y. Editorial: Exploring the influence of diet on later-onset ulcerative colitis-Are eggs and spicy foods the key factors in Asia? Authors' reply. Aliment Pharmacol Ther 2024; 59:1453-1454. [PMID: 38711361 DOI: 10.1111/apt.17999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
LINKED CONTENTThis article is linked to Song et al papers. To view these articles, visit https://doi.org/10.1111/apt.17963 and https://doi.org/10.1111/apt.17983
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Affiliation(s)
- Shuyao Song
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Jun Lv
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Liming Li
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Yuanjie Pang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
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15
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Pang Y, Åberg F, Chen Z, Li L, Kartsonaki C. Predicting risk of chronic liver disease in Chinese adults: External validation of the CLivD score. J Hepatol 2024; 80:e264-e266. [PMID: 38181826 DOI: 10.1016/j.jhep.2023.12.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 12/21/2023] [Indexed: 01/07/2024]
Affiliation(s)
- Yuanjie Pang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing 100191, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, China.
| | - Fredrik Åberg
- Transplantation and Liver Surgery, HUCH Meilahti Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Liming Li
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing 100191, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, China; Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China
| | - Christiana Kartsonaki
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, United Kingdom; Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, United Kingdom.
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16
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Yao P, Iona A, Pozarickij A, Said S, Wright N, Lin K, Millwood I, Fry H, Kartsonaki C, Mazidi M, Chen Y, Bragg F, Liu B, Yang L, Liu J, Avery D, Schmidt D, Sun D, Pei P, Lv J, Yu C, Hill M, Bennett D, Walters R, Li L, Clarke R, Du H, Chen Z. Proteomic Analyses in Diverse Populations Improved Risk Prediction and Identified New Drug Targets for Type 2 Diabetes. Diabetes Care 2024; 47:1012-1019. [PMID: 38623619 PMCID: PMC7615965 DOI: 10.2337/dc23-2145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 03/09/2024] [Indexed: 04/17/2024]
Abstract
OBJECTIVE Integrated analyses of plasma proteomics and genetic data in prospective studies can help assess the causal relevance of proteins, improve risk prediction, and discover novel protein drug targets for type 2 diabetes (T2D). RESEARCH DESIGN AND METHODS We measured plasma levels of 2,923 proteins using Olink Explore among ∼2,000 randomly selected participants from China Kadoorie Biobank (CKB) without prior diabetes at baseline. Cox regression assessed associations of individual protein with incident T2D (n = 92 cases). Proteomic-based risk models were developed with discrimination, calibration, reclassification assessed using area under the curve (AUC), calibration plots, and net reclassification index (NRI), respectively. Two-sample Mendelian randomization (MR) analyses using cis-protein quantitative trait loci identified in a genome-wide association study of CKB and UK Biobank for specific proteins were conducted to assess their causal relevance for T2D, along with colocalization analyses to examine shared causal variants between proteins and T2D. RESULTS Overall, 33 proteins were significantly associated (false discovery rate <0.05) with risk of incident T2D, including IGFBP1, GHR, and amylase. The addition of these 33 proteins to a conventional risk prediction model improved AUC from 0.77 (0.73-0.82) to 0.88 (0.85-0.91) and NRI by 38%, with predicted risks well calibrated with observed risks. MR analyses provided support for the causal relevance for T2D of ENTR1, LPL, and PON3, with replication of ENTR1 and LPL in Europeans using different genetic instruments. Moreover, colocalization analyses showed strong evidence (pH4 > 0.6) of shared genetic variants of LPL and PON3 with T2D. CONCLUSIONS Proteomic analyses in Chinese adults identified novel associations of multiple proteins with T2D with strong genetic evidence supporting their causal relevance and potential as novel drug targets for prevention and treatment of T2D.
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Affiliation(s)
- Pang Yao
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Andri Iona
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Alfred Pozarickij
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Saredo Said
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Neil Wright
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Kuang Lin
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Iona Millwood
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Hannah Fry
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Christiana Kartsonaki
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Mohsen Mazidi
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Yiping Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Fiona Bragg
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Bowen Liu
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Ling Yang
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Junxi Liu
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Daniel Avery
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Dan Schmidt
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Dianjianyi Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Pei Pei
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Michael Hill
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Derrick Bennett
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Robin Walters
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Robert Clarke
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Huaidong Du
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
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Song S, Wu Z, Lv J, Yu C, Sun D, Pei P, Pan L, Yang L, Chen Y, Du H, Chen L, Schmidt D, Avery D, Duan L, Chen J, Chen Z, Li L, Pang Y. Dietary factors and patterns in relation to risk of later-onset ulcerative colitis in Chinese: A prospective study of 0.5 million people. Aliment Pharmacol Ther 2024; 59:1425-1434. [PMID: 38654428 DOI: 10.1111/apt.17963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Revised: 02/27/2024] [Accepted: 03/10/2024] [Indexed: 04/26/2024]
Abstract
BACKGROUND There is limited evidence on the associations of dietary factors and patterns with risk of later-onset ulcerative colitis (UC) in Chinese adults. AIMS To investigate the associations of dietary factors and patterns with risk of later-onset UC in Chinese. METHODS The prospective China Kadoorie Biobank cohort study recruited 512,726 participants aged 30-79. Dietary habits were assessed using food frequency questionnaires. Dietary patterns were derived by factor analysis with a principal component method. Cox regression analysis was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). RESULTS During a median follow-up of 12.1 years, 312 cases of newly diagnosed UC were documented (median age of diagnosis 60.1 years). Egg consumption was associated with higher risk of UC (HR for daily vs. never or rarely: 2.29 [95% CI: 1.26-4.16]), while spicy food consumption was inversely associated with risk of UC (HR: 0.63 [0.45-0.88]). The traditional northern dietary pattern, characterised by high intake of wheat and low intake of rice, was associated with higher risk of UC (HR for highest vs. lowest quartile of score: 2.79 [1.93-4.05]). The modern dietary pattern, characterised by high intake of animal-origin foods and fruits, was associated with higher risk of UC (HR: 2.48 [1.63-3.78]). Population attributable fraction was 13.04% (7.71%-19.11%) for daily/almost daily consumption of eggs and 9.87% (1.94%-18.22%) for never/rarely consumption of spicy food. CONCLUSIONS The findings highlight the importance of evaluating dietary factors and patterns in the primary prevention of later-onset UC in Chinese adults.
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Affiliation(s)
- Shuyao Song
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Zhiyu Wu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Jun Lv
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Canqing Yu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Dianjianyi Sun
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Pei Pei
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Lang Pan
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Ling Yang
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Yiping Chen
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Huaidong Du
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Lingli Chen
- Tongxiang Center for Disease Control and Prevention, Tongxiang, China
| | - Dan Schmidt
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Daniel Avery
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Liping Duan
- Department of Gastroenterology, Peking University Third Hospital, Beijing, China
| | - Junshi Chen
- China National Center for Food Safety Risk Assessment, Beijing, China
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Liming Li
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Yuanjie Pang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
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18
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Li J, Xie C, Lan J, Tan J, Tan X, Chen N, Wei L, Liang J, Pan R, Zhu T, Pei P, Sun D, Su L, Zhou L. Spicy food consumption reduces the risk of ischaemic stroke: a prospective study. Br J Nutr 2024; 131:1777-1785. [PMID: 38287709 DOI: 10.1017/s0007114524000229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2024]
Abstract
Previous studies revealed that consuming spicy food reduced mortality from CVD and lowered stroke risk. However, no studies reported the relationship between spicy food consumption, stroke types and dose–response. This study aimed to further explore the association between the frequency of spicy food intake and the risk of stroke in a large prospective cohort study. In this study, 50 174 participants aged 30–79 years were recruited. Spicy food consumption data were collected via a baseline survey questionnaire. Outcomes were incidence of any stroke, ischaemic stroke (IS) and haemorrhagic stroke (HS). Multivariable-adjusted Cox proportional hazard models estimated the association between the consumption of spicy food and incident stroke. Restricted cubic spline analysis was used to examine the dose–response relationship. During the median 10·7-year follow-up, 3967 strokes were recorded, including 3494 IS and 516 HS. Compared with those who never/rarely consumed spicy food, those who consumed spicy food monthly, 1–2 d/week and 3–5 d/week had hazard ratio (HR) of 0·914 (95 % CI 0·841, 0·995), 0·869 (95 % CI 0·758, 0·995) and 0·826 (95 % CI 0·714, 0·956) for overall stroke, respectively. For IS, the corresponding HR) were 0·909 (95 % CI 0·832, 0·994), 0·831 (95 % CI 0·718, 0·962) and 0·813 (95 % CI 0·696, 0·951), respectively. This protective effect showed a U-shaped dose–response relationship. For obese participants, consuming spicy food ≥ 3 d/week was negatively associated with the risk of IS. We found the consumption of spicy food was negatively associated with the risk of IS and had a U-shaped dose–response relationship with risk of IS. Individuals who consumed spicy food 3–5 d/week had a significantly lowest risk of IS.
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Affiliation(s)
- Jiale Li
- School of Public Health of Guangxi Medical University, 22 Shuangyong Road, Nanning, Guangxi, 530021, People's Republic of China
| | - Changping Xie
- Liuzhou Center for Disease Control and Prevention, Liuzhou, Guangxi, 545005, People's Republic of China
| | - Jian Lan
- Liuzhou Center for Disease Control and Prevention, Liuzhou, Guangxi, 545005, People's Republic of China
| | - Jinxue Tan
- Liuzhou Center for Disease Control and Prevention, Liuzhou, Guangxi, 545005, People's Republic of China
| | - Xiaoping Tan
- Liuzhou Center for Disease Control and Prevention, Liuzhou, Guangxi, 545005, People's Republic of China
| | - Ningyu Chen
- Liuzhou Center for Disease Control and Prevention, Liuzhou, Guangxi, 545005, People's Republic of China
| | - Liuping Wei
- Liuzhou Center for Disease Control and Prevention, Liuzhou, Guangxi, 545005, People's Republic of China
| | - Jiajia Liang
- Liuzhou Center for Disease Control and Prevention, Liuzhou, Guangxi, 545005, People's Republic of China
| | - Rong Pan
- Liuzhou Center for Disease Control and Prevention, Liuzhou, Guangxi, 545005, People's Republic of China
| | - Tingping Zhu
- Liuzhou Center for Disease Control and Prevention, Liuzhou, Guangxi, 545005, People's Republic of China
| | - Pei Pei
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing100191, People's Republic of China
| | - Dianjianyi Sun
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing100191, People's Republic of China
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Xueyuan Road, Haidian District, Beijing100191, People's Republic of China
| | - Li Su
- School of Public Health of Guangxi Medical University, 22 Shuangyong Road, Nanning, Guangxi, 530021, People's Republic of China
| | - Lifang Zhou
- Liuzhou Center for Disease Control and Prevention, Liuzhou, Guangxi, 545005, People's Republic of China
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Step K, Ndong Sima CAA, Mata I, Bardien S. Exploring the role of underrepresented populations in polygenic risk scores for neurodegenerative disease risk prediction. Front Neurosci 2024; 18:1380860. [PMID: 38859922 PMCID: PMC11163124 DOI: 10.3389/fnins.2024.1380860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Accepted: 05/14/2024] [Indexed: 06/12/2024] Open
Affiliation(s)
- Kathryn Step
- Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Carene Anne Alene Ndong Sima
- Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Ignacio Mata
- Genomic Medicine, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, United States
| | - Soraya Bardien
- Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
- South African Medical Research Council/Stellenbosch University Genomics of Brain Disorders Research Unit, Stellenbosch University, Cape Town, South Africa
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20
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Chen GB, Liu S, Zhang L, Huang T, Tang X, Li Y, Zeng C. Building and sharing medical cohorts for research. Innovation (N Y) 2024; 5:100623. [PMID: 38665391 PMCID: PMC11043840 DOI: 10.1016/j.xinn.2024.100623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 04/01/2024] [Indexed: 04/28/2024] Open
Affiliation(s)
- Guo-Bo Chen
- Department of Genetic and Genomic Medicine, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital of Hangzhou Medical College, Hangzhou 310014, China
- Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo 315000, China
| | - Siyang Liu
- School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen 518017, China
| | - Lei Zhang
- China National GeneBank, Shenzhen 518116, China
| | - Tao Huang
- Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai 200031, China
| | - Xiaohua Tang
- Department of Genetic and Genomic Medicine, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital of Hangzhou Medical College, Hangzhou 310014, China
- Wenzhou Central Hospital, Dingli Clinical Medical School of Wenzhou Medical University, Wenzhou 325000, China
| | - Yixue Li
- Guangzhou Laboratory, Guangzhou 510320, China
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21
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Fang X, Zhang X, Yang Z, Yu L, Lin K, Chen T, Zhong W. Healthy lifestyles and rapid progression of carotid plaque in population with atherosclerosis: A prospective cohort study in China. Prev Med Rep 2024; 41:102697. [PMID: 38560595 PMCID: PMC10979119 DOI: 10.1016/j.pmedr.2024.102697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 03/19/2024] [Accepted: 03/19/2024] [Indexed: 04/04/2024] Open
Abstract
Background Healthy lifestyles are effective means to reduce major cardiovascular events. However, little is known about the association of healthy lifestyles with development of carotid atherosclerosis at the early stage of cardiovascular diseases (CVDs). Methods We enrolled participants from Fujian province in the China PEACE MPP project. We calculated a healthy lifestyle score by adherence to non-smoking, sufficient physical activity, healthy diet and healthy body mass index. Cox proportional hazards regression models and restricted cubic splines (RCS) were used to explore the association between the healthy lifestyles and rapid progression of carotid plaque. Results 8379 participants were included (mean age: 60.6 ± 8.3 years, 54.6 % female), with a median follow-up of 1.2 years (inter quartile range: 1.0-1.6). RCS showed a significant inverse association between the healthy lifestyle score and progression of carotid plaque. Participants with "intermediate" (HR: 0.72 [95 % confidence interval (CI): 0.65-0.80]) or "ideal" (HR: 0.68 [0.59-0.78]) adherence to healthy lifestyles had a lower risk of progression of carotid plaque compared to those with "poor" adherence. Age, sex, occupation, income, residence type and metabolic status were significant factors influencing the relationship. Farmers benefited more in non-smoking and sufficient physical activity compared to non-farmers, and participants with lower income or without dyslipidaemia benefited more in sufficient physical activity and healthy diet compared to their counterparts (p-for-interaction < 0.05). Conclusions Healthy lifestyles were associated with lower risk of progression of carotid plaque in populations with atherosclerosis. Promotion of healthy lifestyles from the early stage of carotid atherosclerosis could reduce the burden of CVDs in China.
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Affiliation(s)
- Xin Fang
- Laboratory of Non-communicable Chronic Disease Control, Fujian Provincial Center for Disease Control and Prevention, Fuzhou, People's Republic of China
| | - Xingyi Zhang
- National Clinical Research Center of Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China
| | - Ze Yang
- Laboratory of Non-communicable Chronic Disease Control, Fujian Provincial Center for Disease Control and Prevention, Fuzhou, People's Republic of China
| | - Ling Yu
- Department of Cardiovascular Medicine, Fujian Provincial Hospital, Dongjie 134, Fuzhou, People's Republic of China
| | - Kaiyang Lin
- Department of Cardiovascular Medicine, Fujian Provincial Hospital, Dongjie 134, Fuzhou, People's Republic of China
| | - Tiehui Chen
- Laboratory of Non-communicable Chronic Disease Control, Fujian Provincial Center for Disease Control and Prevention, Fuzhou, People's Republic of China
| | - Wenling Zhong
- Laboratory of Non-communicable Chronic Disease Control, Fujian Provincial Center for Disease Control and Prevention, Fuzhou, People's Republic of China
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22
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Xu HM, Han Y, Liu ZC, Yin ZY, Wang MY, Yu C, Ma JL, Sun D, Liu WD, Zhang Y, Zhou T, Zhang JY, Pei P, Yang L, Millwood IY, Walters RG, Chen Y, Du H, Chen Z, You WC, Li L, Pan KF, Lv J, Li WQ. Helicobacter pylori Treatment and Gastric Cancer Risk Among Individuals With High Genetic Risk for Gastric Cancer. JAMA Netw Open 2024; 7:e2413708. [PMID: 38809553 PMCID: PMC11137637 DOI: 10.1001/jamanetworkopen.2024.13708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 03/25/2024] [Indexed: 05/30/2024] Open
Abstract
Importance Helicobacter pylori treatment and nutrition supplementation may protect against gastric cancer (GC), but whether the beneficial effects only apply to potential genetic subgroups and whether high genetic risk may be counteracted by these chemoprevention strategies remains unknown. Objective To examine genetic variants associated with the progression of gastric lesions and GC risk and to assess the benefits of H pylori treatment and nutrition supplementation by levels of genetic risk. Design, Setting, and Participants This cohort study used follow-up data of the Shandong Intervention Trial (SIT, 1989-2022) and China Kadoorie Biobank (CKB, 2004-2018) in China. Based on the SIT, a longitudinal genome-wide association study was conducted to identify genetic variants for gastric lesion progression. Significant variants were examined for incident GC in a randomly sampled set of CKB participants (set 1). Polygenic risk scores (PRSs) combining independent variants were assessed for GC risk in the remaining CKB participants (set 2) and in an independent case-control study in Linqu. Exposures H pylori treatment and nutrition supplementation. Main Outcomes and Measures Primary outcomes were the progression of gastric lesions (in SIT only) and the risk of GC. The associations of H pylori treatment and nutrition supplementation with GC were evaluated among SIT participants with different levels of genetic risk. Results Our analyses included 2816 participants (mean [SD] age, 46.95 [9.12] years; 1429 [50.75%] women) in SIT and 100 228 participants (mean [SD] age, 53.69 [11.00] years; 57 357 [57.23%] women) in CKB, with 147 GC cases in SIT and 825 GC cases in CKB identified during follow-up. A PRS integrating 12 genomic loci associated with gastric lesion progression and incident GC risk was derived, which was associated with GC risk in CKB (highest vs lowest decile of PRS: hazard ratio [HR], 2.54; 95% CI, 1.80-3.57) and further validated in the analysis of 702 case participants and 692 control participants (mean [SD] age, 54.54 [7.66] years; 527 [37.80%] women; odds ratio, 1.83; 95% CI, 1.11-3.05). H pylori treatment was associated with reduced GC risk only for individuals with high genetic risk (top 25% of PRS: HR, 0.45; 95% CI, 0.25-0.82) but not for those with low genetic risk (HR, 0.81; 95% CI, 0.50-1.34; P for interaction = .03). Such effect modification was not found for vitamin (P for interaction = .93) or garlic (P for interaction = .41) supplementation. Conclusions and Relevance The findings of this cohort study indicate that a high genetic risk of GC may be counteracted by H pylori treatment, suggesting primary prevention could be tailored to genetic risk for more effective prevention.
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Affiliation(s)
- Heng-Min Xu
- State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers, Beijing Key Laboratory of Carcinogenesis and Translational Research, Department of Cancer Epidemiology, Peking University Cancer Hospital & Institute, Beijing, China
| | - Yuting Han
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Zong-Chao Liu
- State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers, Beijing Key Laboratory of Carcinogenesis and Translational Research, Department of Cancer Epidemiology, Peking University Cancer Hospital & Institute, Beijing, China
| | - Zhou-Yi Yin
- State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers, Beijing Key Laboratory of Carcinogenesis and Translational Research, Department of Cancer Epidemiology, Peking University Cancer Hospital & Institute, Beijing, China
| | - Meng-Yuan Wang
- State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers, Beijing Key Laboratory of Carcinogenesis and Translational Research, Department of Cancer Epidemiology, Peking University Cancer Hospital & Institute, Beijing, China
| | - Canqing Yu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Jun-Ling Ma
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Beijing, China
| | - Dianjianyi Sun
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Wei-Dong Liu
- Linqu Public Health Bureau, Linqu, Shandong, China
| | - Yang Zhang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Beijing, China
| | - Tong Zhou
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Beijing, China
| | - Jing-Ying Zhang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Beijing, China
| | - Pei Pei
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Ling Yang
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, United Kingdom
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Iona Y. Millwood
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, United Kingdom
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Robin G. Walters
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, United Kingdom
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Yiping Chen
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, United Kingdom
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Huaidong Du
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, United Kingdom
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Wei-Cheng You
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Beijing, China
| | - Liming Li
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Kai-Feng Pan
- State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers, Beijing Key Laboratory of Carcinogenesis and Translational Research, Department of Cancer Epidemiology, Peking University Cancer Hospital & Institute, Beijing, China
| | - Jun Lv
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
- State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, Beijing, China
| | - Wen-Qing Li
- State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers, Beijing Key Laboratory of Carcinogenesis and Translational Research, Department of Cancer Epidemiology, Peking University Cancer Hospital & Institute, Beijing, China
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Wen X, Zhang X, Qiu Y, Wang Y, Zhu L, Liu T, Ruan Z. The Minhang Pediatric Biobank cohort study: protocol overview and baseline characteristics. BMC Pediatr 2024; 24:282. [PMID: 38678186 PMCID: PMC11055290 DOI: 10.1186/s12887-024-04763-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 04/15/2024] [Indexed: 04/29/2024] Open
Abstract
BACKGROUND Little has been done to establish biobanks for studying the environment and lifestyle risk factors for diseases among the school-age children. The Minhang Pediatric Biobank (MPB) cohort study aims to identify factors associated with health and diseases of school-aged children living in the urban or suburban area of Shanghai. METHODS This population-based cohort study was started in all sub-districts/towns of Minhang district of Shanghai in 2014. First-grade students in elementary school were enrolled during the time of their routine physical examinations, with self-administered questionnaires completed by their primary caregivers. Additional information was extracted from multiple health information systems. Urine and saliva samples were collected during the baseline survey and follow-up visits. RESULTS At the end of 2014 academic year, a total number of 8412 children and their parents were recruited, including 4339 boys and 4073 girls. All the participants completed the baseline survey and physical examination, and 7128 urine and 2767 saliva samples were collected. The five most prevalent childhood diseases in this population were dental caries, bronchitis, pneumonia, asthma and overweight/obese. CONCLUSIONS The MPB cohort has been successfully established, serving as a useful platform for future research relating to the genetic, environmental and lifestyle risk factors for childhood diseases.
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Affiliation(s)
- Xiaosa Wen
- Minhang District Center for Disease Control and Prevention, Shanghai, China
| | - Xinyue Zhang
- Key Laboratory of Environmental Medicine and Engineering of Ministry of Education, and Department of Epidemiology & Health Statistics, School of Public Health, Southeast University, 87 Dingjiaqiao, Gulou District, Jiangsu, Nanjing, 210096, China
| | - Yun Qiu
- Department of Public Health, School of Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Yaqin Wang
- School of Health and Life Sciences, University of Health and Rehabilitation Sciences, Qingdao, China
| | - Liujie Zhu
- Key Laboratory of Environmental Medicine and Engineering of Ministry of Education, and Department of Epidemiology & Health Statistics, School of Public Health, Southeast University, 87 Dingjiaqiao, Gulou District, Jiangsu, Nanjing, 210096, China
| | - Tao Liu
- Department of Biochemistry and Molecular Biology, School of Medicine, Southeast University, Jiangsu, Nanjing, 210096, China.
- Jiangsu Provincial Key Laboratory of Critical Care Medicine and Department of Critical Care Medicine, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, China.
| | - Zengliang Ruan
- Key Laboratory of Environmental Medicine and Engineering of Ministry of Education, and Department of Epidemiology & Health Statistics, School of Public Health, Southeast University, 87 Dingjiaqiao, Gulou District, Jiangsu, Nanjing, 210096, China.
- Department of Epidemiology, School of Public Health, Sun Yat-Sen University, Guangzhou, China.
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
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24
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Weaver C, Nam A, Settle C, Overton M, Giddens M, Richardson KP, Piver R, Mysona DP, Rungruang B, Ghamande S, McIndoe R, Purohit S. Serum Proteomic Signatures in Cervical Cancer: Current Status and Future Directions. Cancers (Basel) 2024; 16:1629. [PMID: 38730581 PMCID: PMC11083044 DOI: 10.3390/cancers16091629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 04/18/2024] [Accepted: 04/19/2024] [Indexed: 05/13/2024] Open
Abstract
In 2020, the World Health Organization (WHO) reported 604,000 new diagnoses of cervical cancer (CC) worldwide, and over 300,000 CC-related fatalities. The vast majority of CC cases are caused by persistent human papillomavirus (HPV) infections. HPV-related CC incidence and mortality rates have declined worldwide because of increased HPV vaccination and CC screening with the Papanicolaou test (PAP test). Despite these significant improvements, developing countries face difficulty implementing these programs, while developed nations are challenged with identifying HPV-independent cases. Molecular and proteomic information obtained from blood or tumor samples have a strong potential to provide information on malignancy progression and response to therapy in CC. There is a large amount of published biomarker data related to CC available but the extensive validation required by the FDA approval for clinical use is lacking. The ability of researchers to use the big data obtained from clinical studies and to draw meaningful relationships from these data are two obstacles that must be overcome for implementation into clinical practice. We report on identified multimarker panels of serum proteomic studies in CC for the past 5 years, the potential for modern computational biology efforts, and the utilization of nationwide biobanks to bridge the gap between multivariate protein signature development and the prediction of clinically relevant CC patient outcomes.
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Affiliation(s)
- Chaston Weaver
- Center for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta University, Augusta, GA 30912, USA; (C.W.); (K.P.R.); (R.P.); (D.P.M.); (R.M.)
| | - Alisha Nam
- Department of Undergraduate Health Professions, College of Allied Health Sciences, Augusta University, Augusta, GA 30912, USA; (A.N.); (C.S.); (M.O.); (M.G.)
| | - Caitlin Settle
- Department of Undergraduate Health Professions, College of Allied Health Sciences, Augusta University, Augusta, GA 30912, USA; (A.N.); (C.S.); (M.O.); (M.G.)
| | - Madelyn Overton
- Department of Undergraduate Health Professions, College of Allied Health Sciences, Augusta University, Augusta, GA 30912, USA; (A.N.); (C.S.); (M.O.); (M.G.)
| | - Maya Giddens
- Department of Undergraduate Health Professions, College of Allied Health Sciences, Augusta University, Augusta, GA 30912, USA; (A.N.); (C.S.); (M.O.); (M.G.)
| | - Katherine P. Richardson
- Center for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta University, Augusta, GA 30912, USA; (C.W.); (K.P.R.); (R.P.); (D.P.M.); (R.M.)
| | - Rachael Piver
- Center for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta University, Augusta, GA 30912, USA; (C.W.); (K.P.R.); (R.P.); (D.P.M.); (R.M.)
- Department of Obstetrics and Gynecology, Medical College of Georgia, Augusta University, Augusta, GA 30912, USA; (B.R.); (S.G.)
| | - David P. Mysona
- Center for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta University, Augusta, GA 30912, USA; (C.W.); (K.P.R.); (R.P.); (D.P.M.); (R.M.)
- Department of Obstetrics and Gynecology, Medical College of Georgia, Augusta University, Augusta, GA 30912, USA; (B.R.); (S.G.)
| | - Bunja Rungruang
- Department of Obstetrics and Gynecology, Medical College of Georgia, Augusta University, Augusta, GA 30912, USA; (B.R.); (S.G.)
| | - Sharad Ghamande
- Department of Obstetrics and Gynecology, Medical College of Georgia, Augusta University, Augusta, GA 30912, USA; (B.R.); (S.G.)
| | - Richard McIndoe
- Center for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta University, Augusta, GA 30912, USA; (C.W.); (K.P.R.); (R.P.); (D.P.M.); (R.M.)
- Department of Obstetrics and Gynecology, Medical College of Georgia, Augusta University, Augusta, GA 30912, USA; (B.R.); (S.G.)
| | - Sharad Purohit
- Center for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta University, Augusta, GA 30912, USA; (C.W.); (K.P.R.); (R.P.); (D.P.M.); (R.M.)
- Department of Undergraduate Health Professions, College of Allied Health Sciences, Augusta University, Augusta, GA 30912, USA; (A.N.); (C.S.); (M.O.); (M.G.)
- Department of Obstetrics and Gynecology, Medical College of Georgia, Augusta University, Augusta, GA 30912, USA; (B.R.); (S.G.)
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Yu H, Tao R, Zhou J, Su J, Lu Y, Hua Y, Jin J, Pei P, Yu C, Sun D, Chen Z, Li L, Lv J. Temporal change in multimorbidity prevalence, clustering patterns, and the association with mortality: findings from the China Kadoorie Biobank study in Jiangsu Province. Front Public Health 2024; 12:1389635. [PMID: 38699413 PMCID: PMC11064014 DOI: 10.3389/fpubh.2024.1389635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Accepted: 03/18/2024] [Indexed: 05/05/2024] Open
Abstract
Objectives The characteristics of multimorbidity in the Chinese population are currently unclear. We aimed to determine the temporal change in multimorbidity prevalence, clustering patterns, and the association of multimorbidity with mortality from all causes and four major chronic diseases. Methods This study analyzed data from the China Kadoorie Biobank study performed in Wuzhong District, Jiangsu Province. A total of 53,269 participants aged 30-79 years were recruited between 2004 and 2008. New diagnoses of 15 chronic diseases and death events were collected during the mean follow-up of 10.9 years. Yule's Q cluster analysis method was used to determine the clustering patterns of multimorbidity. A Cox proportional hazards model was used to estimate the associations of multimorbidity with mortalities. Results The overall multimorbidity prevalence rate was 21.1% at baseline and 27.7% at the end of follow-up. Multimorbidity increased more rapidly during the follow-up in individuals who had a higher risk at baseline. Three main multimorbidity patterns were identified: (i) cardiometabolic multimorbidity (diabetes, coronary heart disease, stroke, and hypertension), (ii) respiratory multimorbidity (tuberculosis, asthma, and chronic obstructive pulmonary disease), and (iii) mental, kidney and arthritis multimorbidity (neurasthenia, psychiatric disorders, chronic kidney disease, and rheumatoid arthritis). There were 3,433 deaths during the follow-up. The mortality risk increased by 24% with each additional disease [hazard ratio (HR) = 1.24, 95% confidence interval (CI) = 1.20-1.29]. Compared with those without multimorbidity at baseline, both cardiometabolic multimorbidity and respiratory multimorbidity were associated with increased mortality from all causes and four major chronic diseases. Cardiometabolic multimorbidity was additionally associated with mortality from cardiovascular diseases and diabetes, with HRs of 2.64 (95% CI = 2.19-3.19) and 28.19 (95% CI = 14.85-53.51), respectively. Respiratory multimorbidity was associated with respiratory disease mortality, with an HR of 9.76 (95% CI = 6.22-15.31). Conclusion The prevalence of multimorbidity has increased substantially over the past decade. This study has revealed that cardiometabolic multimorbidity and respiratory multimorbidity have significantly increased mortality rates. These findings indicate the need to consider high-risk populations and to provide local evidence for intervention strategies and health management in economically developed regions.
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Affiliation(s)
- Hao Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Department of Noncommunicable Chronic Disease and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
| | - Ran Tao
- Department of Noncommunicable Chronic Disease and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
| | - Jinyi Zhou
- Department of Noncommunicable Chronic Disease and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
| | - Jian Su
- Department of Noncommunicable Chronic Disease and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
| | - Yan Lu
- Department of Noncommunicable Chronic Disease Control and Prevention, Suzhou City Center for Disease Control and Prevention, Suzhou, China
| | - Yujie Hua
- Department of Noncommunicable Chronic Disease Control and Prevention, Suzhou City Center for Disease Control and Prevention, Suzhou, China
| | - Jianrong Jin
- Department of Noncommunicable Chronic Disease Control and Prevention, Wuzhong District Center for Disease Control and Prevention, Suzhou, China
| | - Pei Pei
- Peking University Center for Public Health, Epidemic Preparedness and Response, Beijing, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health, Epidemic Preparedness and Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Peking University, Ministry of Education, Beijing, China
| | - Dianjianyi Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health, Epidemic Preparedness and Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Peking University, Ministry of Education, Beijing, China
| | - Zhengming Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health, Epidemic Preparedness and Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Peking University, Ministry of Education, Beijing, China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health, Epidemic Preparedness and Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Peking University, Ministry of Education, Beijing, China
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Zhang Y, Ding Y, Yu C, Sun D, Pei P, Du H, Yang L, Chen Y, Schmidt D, Avery D, Chen J, Chen J, Chen Z, Li L, Lv J. Predictive value of 8-year blood pressure measures in intracerebral hemorrhage risk over 5 years. Eur J Prev Cardiol 2024:zwae147. [PMID: 38629743 DOI: 10.1093/eurjpc/zwae147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/18/2024] [Revised: 03/21/2024] [Accepted: 04/16/2024] [Indexed: 05/07/2024]
Abstract
AIMS The relationships between long-term blood pressure (BP) measures and intracerebral hemorrhage (ICH), as well as their predictive ability on ICH, were unclear. We aimed to investigate the independent associations of multiple BP measures with subsequent 5-year ICH risk, as well as the incremental value of these measures over a single-point BP measurement in ICH risk prediction. METHODS We included 12,398 participants from the China Kadoorie Biobank (CKB) who completed three surveys every four to five years. The following long-term BP measures were calculated: mean, minimum, maximum, standard deviation, coefficient of variation, average real variability, and cumulative BP exposure (cumBP). Cox proportional hazard models were used to examine the associations between these measures and ICH. The potential incremental value of these measures in ICH risk prediction was assessed using Harrell's C statistics, continuous net reclassification improvement (cNRI), and relative integrated discrimination improvement (rIDI). RESULTS The hazard ratios (95% confidence intervals) of incident ICH associated with per SD increase in cumSBP and cumDBP were 1.62 (1.25, 2.10) and 1.59 (1.23, 2.07), respectively. When cumBP was added to the conventional 5-year ICH risk prediction model, the C-statistic change was 0.009 (-0.001, 0.019), the cNRI was 0.267 (0.070, 0.464), and the rIDI was 18.2% (5.8%, 30.7%). Further subgroup analyses revealed a consistent increase in cNRI and rIDI in men, rural residents, and participants without diabetes. Other long-term BP measures showed no statistically significant associations with incident ICH and generally did not improve model performance. CONCLUSION The nearly 10-year cumBP was positively associated with an increased 5-year risk of ICH and could significantly improve risk reclassification for the ICH risk prediction model that included single-point BP measurement.
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Affiliation(s)
- Yiqian Zhang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing 100191, China
| | - Yinqi Ding
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing 100191, China
| | - Canqing Yu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education
| | - Dianjianyi Sun
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education
| | - Pei Pei
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China
| | - Huaidong Du
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, United Kingdom
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Ling Yang
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, United Kingdom
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Yiping Chen
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, United Kingdom
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Dan Schmidt
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Daniel Avery
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Jianwei Chen
- Liuyang Centers for Disease Control and Prevention, Liuyang, Changsha, Hunan, China
| | - Junshi Chen
- China National Center for Food Safety Risk Assessment, Beijing, China
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, United Kingdom
| | - Liming Li
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education
| | - Jun Lv
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education
- State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University
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Kartsonaki C, Yao P, Butt J, Jeske R, de Martel C, Plummer M, Sun D, Clark S, Walters RG, Chen Y, Lv J, Yu C, Hill M, Peto R, Li L, Waterboer T, Chen Z, Millwood IY, Yang L. Infectious pathogens and risk of esophageal, gastric and duodenal cancers and ulcers in China: A case-cohort study. Int J Cancer 2024; 154:1423-1432. [PMID: 38108203 PMCID: PMC7615747 DOI: 10.1002/ijc.34814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 10/16/2023] [Accepted: 10/24/2023] [Indexed: 12/19/2023]
Abstract
Infection by certain pathogens is associated with cancer development. We conducted a case-cohort study of ~2500 incident cases of esophageal, gastric and duodenal cancer, and gastric and duodenal ulcer and a randomly selected subcohort of ~2000 individuals within the China Kadoorie Biobank study of >0.5 million adults. We used a bead-based multiplex serology assay to measure antibodies against 19 pathogens (total 43 antigens) in baseline plasma samples. Associations between pathogens and antigen-specific antibodies with risks of site-specific cancers and ulcers were assessed using Cox regression fitted using the Prentice pseudo-partial likelihood. Seroprevalence varied for different pathogens, from 0.7% for Hepatitis C virus (HCV) to 99.8% for Epstein-Barr virus (EBV) in the subcohort. Compared to participants seronegative for the corresponding pathogen, Helicobacter pylori seropositivity was associated with a higher risk of non-cardia (adjusted hazard ratio [HR] 2.73 [95% CI: 2.09-3.58]) and cardia (1.67 [1.18-2.38]) gastric cancer and duodenal ulcer (2.71 [1.79-4.08]). HCV was associated with a higher risk of duodenal cancer (6.23 [1.52-25.62]) and Hepatitis B virus was associated with higher risk of duodenal ulcer (1.46 [1.04-2.05]). There were some associations of antibodies again some herpesviruses and human papillomaviruses with risks of gastrointestinal cancers and ulcers but these should be interpreted with caution. This first study of multiple pathogens with risk of gastrointestinal cancers and ulcers demonstrated that several pathogens are associated with risks of gastrointestinal cancers and ulcers. This will inform future investigations into the role of infection in the etiology of these diseases.
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Affiliation(s)
- Christiana Kartsonaki
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Pang Yao
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Julia Butt
- Infections and Cancer Epidemiology Division, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Rima Jeske
- Infections and Cancer Epidemiology Division, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Catherine de Martel
- Early Detection, Prevention and Infections Branch, International Agency for Research on Cancer, Lyon, France
| | - Martyn Plummer
- Department of Statistics, University of Warwick, Coventry, UK
| | - Dianjianyi Sun
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Xueyuan Road, Haidian District, Beijing 100191, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing 100191, China
| | - Sarah Clark
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Robin G. Walters
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Yiping Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Jun Lv
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Xueyuan Road, Haidian District, Beijing 100191, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing 100191, China
| | - Canqing Yu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Xueyuan Road, Haidian District, Beijing 100191, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing 100191, China
| | - Michael Hill
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Richard Peto
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Liming Li
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Xueyuan Road, Haidian District, Beijing 100191, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing 100191, China
| | - Tim Waterboer
- Infections and Cancer Epidemiology Division, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Iona Y. Millwood
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Ling Yang
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
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28
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Yang K, Chen R. Association between cooking fuel exposure and respiratory health: Longitudinal evidence from the China Health and Retirement Longitudinal Study (CHARLS). ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2024; 275:116247. [PMID: 38520808 DOI: 10.1016/j.ecoenv.2024.116247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 03/19/2024] [Accepted: 03/20/2024] [Indexed: 03/25/2024]
Abstract
The epidemiological evidences for the association between cooking fuel exposure and respiratory health were inconsistent, and repeated-measures prospective evaluation of cooking fuel exposure was still lacking. We assessed the longitudinal association of chronic lung disease (CLD) and lung function with cooking fuel types among Chinese adults aged ≥ 40 years. In this prospective, nationwide representative cohort of the China Health and Retirement Longitudinal Study from 2011 to 2018, 9004 participants from 28 provinces in China were included. CLD was identified based on self-reported physician diagnosis in 2018. Lung function was assessed by peak expiratory flow (PEF) in 2011, 2013 and 2015. Multivariable logistic and linear mixed-effects repeated-measures models were conducted to measure the associations of CLD and PEF with cooking fuel types. Three-level mixed-effects model was performed as sensitivity analysis. Among the participants, 3508 and 3548 participants used persistent solid and clean cooking fuels throughout the survey, and 1948 participants who used solid cooking fuels at baseline switched to clean cooking fuels. Use of persistent clean cooking fuels (adjusted odds ratio [aOR] = 0.73, 95 % confidence interval [CI]: 0.61, 0.88) and switch of solid fuels to clean fuels (aOR = 0.81, 95 % CI: 0.67, 0.98) were associated with lower risk of CLD. The use of clean cooking fuels throughout the survey and switch of solid fuels to clean fuels in 2013 were also significantly associated with higher PEF level. Similar results were observed in stratified analyses and different statistical models. The evidence from CHARLS cohort suggested that reducing solid cooking fuel exposure was associated with lower risk of CLD and better lung function. Given the recent evidence, improving household air quality will reduce the burden of chronic lung diseases.
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Affiliation(s)
- Kai Yang
- Department of Pulmonary and Critical Care Medicine, Shenzhen Institute of Respiratory Diseases, First Affiliated Hospital of Southern University of Science and Technology (Shenzhen People's Hospital, Second Clinical Medical College of Jinan University), Shenzhen 518001, China
| | - Rongchang Chen
- Department of Pulmonary and Critical Care Medicine, Shenzhen Institute of Respiratory Diseases, First Affiliated Hospital of Southern University of Science and Technology (Shenzhen People's Hospital, Second Clinical Medical College of Jinan University), Shenzhen 518001, China.
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29
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Sun W, Shan S, Hou L, Li S, Cao J, Wu J, Yi Q, Luo Z, Song P. Socioeconomic disparities in the association of age at first live birth with incident stroke among Chinese parous women: A prospective cohort study. J Glob Health 2024; 14:04091. [PMID: 38587297 PMCID: PMC11000532 DOI: 10.7189/jogh.14.04091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/09/2024] Open
Abstract
Background Stroke has become a significant public health issue in China. Although studies have shown that women's age at first live birth (AFLB) might be associated with incident stroke, there is limited evidence on this relationship among Chinese parous women. Likewise, the nature of this association across urban-rural socioeconomic status (SES) has yet to be explored. In this prospective study, we sought to investigate the associations of women's AFLB with the risk of incident stroke and its subtypes (ischaemic stroke, intracerebral haemorrhage, and subarachnoid haemorrhage) and to explore the differences of these associations as well as the population-level impacts across SES classes. Methods We used data on 290 932 Chinese parous women from the China Kadoorie Biobank who were recruited in the baseline survey between 2004 and 2008 and followed up until 2015. We used latent class analysis to identify urban-rural SES classes and Cox proportional hazard regression to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for AFLB's association with incident stroke. We then calculated population attributable fraction (PAF) to demonstrate the population-level impact of later AFLB on stroke. Results Around 8.9% of parous women developed stroke after AFLB. Compared with women with AFLB <22 years, those with older AFLB had a higher risk of total stroke, with fully adjusted HRs (95% CI) of 1.71 (95% CI = 1.65-1.77) for 22-24 years and 3.37 (95% CI = 3.24-3.51) for ≥25 years. The associations of AFLB with ischaemic stroke were stronger among rural-low-SES participants. We found the highest PAFs of ischaemic stroke (60.1%; 95% CI = 46.2-70.3) associated with later AFLB for urban-high-SES individuals. Conclusions Older AFLB was associated with higher risks of incident stroke and its subtypes among Chinese parous women, with stronger associations between AFLB and ischaemic stroke among rural-low-SES participants. Targeted medical advice for pregnant women of different ages could have long-term benefits for stroke prevention.
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30
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Dai S, Qiu G, Li Y, Yang S, Yang S, Jia P. State of the Art of Lifecourse Cohort Establishment. China CDC Wkly 2024; 6:300-304. [PMID: 38634101 PMCID: PMC11018708 DOI: 10.46234/ccdcw2024.058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Accepted: 03/08/2024] [Indexed: 04/19/2024] Open
Affiliation(s)
- Shaoqing Dai
- School of Resource and Environmental Sciences, Wuhan University, Wuhan City, Hubei Province, China
- International Institute of Spatial Lifecourse Health (ISLE), Wuhan University, Wuhan City, Hubei Province, China
| | - Ge Qiu
- School of Resource and Environmental Sciences, Wuhan University, Wuhan City, Hubei Province, China
- International Institute of Spatial Lifecourse Health (ISLE), Wuhan University, Wuhan City, Hubei Province, China
| | - Yuchen Li
- International Institute of Spatial Lifecourse Health (ISLE), Wuhan University, Wuhan City, Hubei Province, China
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
- Department of Geography, The Ohio State University, Columbus, OH, USA
| | - Shuhan Yang
- School of Resource and Environmental Sciences, Wuhan University, Wuhan City, Hubei Province, China
- International Institute of Spatial Lifecourse Health (ISLE), Wuhan University, Wuhan City, Hubei Province, China
| | - Shujuan Yang
- International Institute of Spatial Lifecourse Health (ISLE), Wuhan University, Wuhan City, Hubei Province, China
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu City, Sichuan Province, China
| | - Peng Jia
- School of Resource and Environmental Sciences, Wuhan University, Wuhan City, Hubei Province, China
- International Institute of Spatial Lifecourse Health (ISLE), Wuhan University, Wuhan City, Hubei Province, China
- Hubei Luojia Laboratory, Wuhan City, Hubei Province, China
- School of Public Health, Wuhan University, Wuhan City, Hubei Province, China
- Renmin Hospital, Wuhan University, Wuhan City, Hubei Province, China
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31
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Lu R, Qin Y, Xie C, Tan X, Zhu T, Tan J, Wang S, Liang J, Qin Z, Pan R, Pei P, Sun D, Su L, Lan J. Secondhand smoke exposure can increase the risk of first ischemic stroke: A 10.7-year prospective cohort study in China. Ann Epidemiol 2024; 92:25-34. [PMID: 38367798 DOI: 10.1016/j.annepidem.2024.02.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Revised: 02/13/2024] [Accepted: 02/13/2024] [Indexed: 02/19/2024]
Abstract
INTRODUCTION Passive smoking is considered a major public health issue in China. Prospective evidence regarding the link between secondhand smoke (SHS) and ischemic stroke in China is scarce. METHODS The China Kadoorie Biobank (CKB) study in Liuzhou City recruited 50,174 participants during 2004-2008. Of these 30,456 never-smokers were included in our study. The median follow-up period was 10.7 years. The incidence of ischemic stroke was obtained through the China Disease Surveillance Points (DSP) system and the Health Insurance (HI) database. Cox proportional risk models were used to evaluate the association between SHS exposure and ischemic stroke. RESULTS During 320,678 person-years of follow-up, there were 2059 patients with ischemic stroke observed and the incidence of ischemic stroke was 6.42 per thousand person-years. Participants exposed to SHS daily faced a 21 % higher risk of ischemic stroke (HR = 1.21, 95 %CI: 1.09-1.34) compared to those exposed to SHS less than once a week. Subgroup analyses revealed that daily SHS exposure was linked to heightened risk of ischemic stroke among women, non-employed, and non-weekly tea drinkers. CONCLUSIONS Daily SHS exposure was associated with higher risks of ischemic stroke. Proactive tobacco control strategies are necessary to decrease the risk of ischemic stroke in never smokers.
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Affiliation(s)
- Rumei Lu
- School of Public Health, Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, China
| | - Yulu Qin
- Liuzhou Center for Disease Prevention and Control, Liuzhou, Guangxi Zhuang Autonomous Region 545007, China
| | - Changping Xie
- Liuzhou Center for Disease Prevention and Control, Liuzhou, Guangxi Zhuang Autonomous Region 545007, China
| | - Xiaoping Tan
- Liuzhou Center for Disease Prevention and Control, Liuzhou, Guangxi Zhuang Autonomous Region 545007, China
| | - Tingping Zhu
- Liuzhou Center for Disease Prevention and Control, Liuzhou, Guangxi Zhuang Autonomous Region 545007, China
| | - Jinxue Tan
- Liuzhou Center for Disease Prevention and Control, Liuzhou, Guangxi Zhuang Autonomous Region 545007, China
| | - Sisi Wang
- Liuzhou Center for Disease Prevention and Control, Liuzhou, Guangxi Zhuang Autonomous Region 545007, China
| | - Jiajia Liang
- Liuzhou Center for Disease Prevention and Control, Liuzhou, Guangxi Zhuang Autonomous Region 545007, China
| | - Zhongshu Qin
- Liuzhou Center for Disease Prevention and Control, Liuzhou, Guangxi Zhuang Autonomous Region 545007, China
| | - Rong Pan
- Liuzhou Center for Disease Prevention and Control, Liuzhou, Guangxi Zhuang Autonomous Region 545007, China
| | - Pei Pei
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China
| | - Dianjianyi Sun
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China; Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Xueyuan Road, Haidian District, Beijing 100191, China
| | - Li Su
- School of Public Health, Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, China.
| | - Jian Lan
- Liuzhou Center for Disease Prevention and Control, Liuzhou, Guangxi Zhuang Autonomous Region 545007, China.
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Levy M, Buckell J, Clarke R, Wu N, Pei P, Sun D, Avery D, Zhang H, Lv J, Yu C, Li L, Chen Z, Yip W, Chen Y, Mihaylova B. Association between health insurance cost-sharing and choice of hospital tier for cardiovascular diseases in China: a prospective cohort study. THE LANCET REGIONAL HEALTH. WESTERN PACIFIC 2024; 45:101020. [PMID: 38380231 PMCID: PMC10876671 DOI: 10.1016/j.lanwpc.2024.101020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 12/13/2023] [Accepted: 01/16/2024] [Indexed: 02/22/2024]
Abstract
Background Hospitals in China are classified into tiers (1, 2 or 3), with the largest (tier 3) having more equipment and specialist staff. Differential health insurance cost-sharing by hospital tier (lower deductibles and higher reimbursement rates in lower tiers) was introduced to reduce overcrowding in higher tier hospitals, promote use of lower tier hospitals, and limit escalating healthcare costs. However, little is known about the effects of differential cost-sharing in health insurance schemes on choice of hospital tiers. Methods In a 9-year follow-up of a prospective study of 0.5 M adults from 10 areas in China, we examined the associations between differential health insurance cost-sharing and choice of hospital tiers for patients with a first hospitalisation for stroke or ischaemic heart disease (IHD) in 2009-2017. Analyses were performed separately in urban areas (stroke: n = 20,302; IHD: n = 19,283) and rural areas (stroke: n = 21,130; IHD: n = 17,890), using conditional logit models and adjusting for individual socioeconomic and health characteristics. Findings About 64-68% of stroke and IHD cases in urban areas and 27-29% in rural areas chose tier 3 hospitals. In urban areas, higher reimbursement rates in each tier and lower tier 3 deductibles were associated with a greater likelihood of choosing their respective hospital tiers. In rural areas, the effects of cost-sharing were modest, suggesting a greater contribution of other factors. Higher socioeconomic status and greater disease severity were associated with a greater likelihood of seeking care in higher tier hospitals in urban and rural areas. Interpretation Patient choice of hospital tiers for treatment of stroke and IHD in China was influenced by differential cost-sharing in urban areas, but not in rural areas. Further strategies are required to incentivise appropriate health seeking behaviour and promote more efficient hospital use. Funding Wellcome Trust, Medical Research Council, British Heart Foundation, Cancer Research UK, Kadoorie Charitable Foundation, China Ministry of Science and Technology, and National Natural Science Foundation of China.
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Affiliation(s)
- Muriel Levy
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, UK
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, UK
| | - John Buckell
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, UK
| | - Robert Clarke
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, UK
| | - Nina Wu
- School of Public Health, Capital Medical University, Beijing, China
| | - Pei Pei
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Dianjianyi Sun
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Centre, Beijing, China
| | - Daniel Avery
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, UK
| | - Hua Zhang
- NCDs Prevention and Control Department, Qingdao Centre for Disease Control and Prevention, Qingdao, China
| | - Jun Lv
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Centre, Beijing, China
| | - Canqing Yu
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Centre, Beijing, China
| | - Liming Li
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Centre, Beijing, China
| | - Zhengming Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, UK
| | - Winnie Yip
- Harvard T H Chan School of Public Health, Boston, MA, USA
| | - Yiping Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, UK
| | - Borislava Mihaylova
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, UK
- Health Economics and Policy Research Unit, Wolfson Institute of Population Health, Queen Mary University of London, London, UK
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Elbasheer MMA, Bohrmann B, Chen Y, Lv J, Sun D, Wu X, Yang X, Avery D, Li L, Chen Z, Kartsonaki C, Chan KH, Yang L. Reproductive factors and risk of lung cancer among 300,000 Chinese female never-smokers: evidence from the China Kadoorie Biobank study. BMC Cancer 2024; 24:384. [PMID: 38532314 PMCID: PMC10964706 DOI: 10.1186/s12885-024-12133-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Accepted: 03/17/2024] [Indexed: 03/28/2024] Open
Abstract
BACKGROUND Lung cancer is the leading cause of cancer mortality among Chinese females despite the low smoking prevalence among this population. This study assessed the roles of reproductive factors in lung cancer development among Chinese female never-smokers. METHODS The prospective China Kadoorie Biobank (CKB) recruited over 0.5 million Chinese adults (0.3 million females) from 10 geographical areas in China in 2004-2008 when information on socio-demographic/lifestyle/environmental factors, physical measurements, medical history, and reproductive history collected through interviewer-administered questionnaires. Cox proportional hazard regression was used to estimate adjusted hazard ratios (HRs) of lung cancer by reproductive factors. Subgroup analyses by menopausal status, birth year, and geographical region were performed. RESULTS During a median follow-up of 11 years, 2,284 incident lung cancers occurred among 282,558 female never-smokers. Ever oral contraceptive use was associated with a higher risk of lung cancer (HR = 1.16, 95% CI: 1.02-1.33) with a significant increasing trend associated with longer duration of use (p-trend = 0.03). Longer average breastfeeding duration per child was associated with a decreased risk (0.86, 0.78-0.95) for > 12 months compared with those who breastfed for 7-12 months. No statistically significant association was detected between other reproductive factors and lung cancer risk. CONCLUSION Oral contraceptive use was associated with an increased risk of lung cancer in Chinese female never-smokers. Further studies are needed to assess lung cancer risk related to different types of oral contraceptives in similar populations.
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Affiliation(s)
- Marwa M A Elbasheer
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Bastian Bohrmann
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Yiping Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council (MRC) Population Health Research Unit (MRC PHRU), University of Oxford, Oxford, UK
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Center for Public Health and Epidemic Preparedness & Response, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Dianjianyi Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Center for Public Health and Epidemic Preparedness & Response, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Xia Wu
- Pengzhou Centre for Disease Control and Prevention (CDC), Pengzhou, China
| | - Xiaoming Yang
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Daniel Avery
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Center for Public Health and Epidemic Preparedness & Response, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Zhengming Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council (MRC) Population Health Research Unit (MRC PHRU), University of Oxford, Oxford, UK
| | - Christiana Kartsonaki
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council (MRC) Population Health Research Unit (MRC PHRU), University of Oxford, Oxford, UK
| | - Ka Hung Chan
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Oxford BHF Centre of Research Excellence, University of Oxford, Oxford, UK
| | - Ling Yang
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK.
- Medical Research Council (MRC) Population Health Research Unit (MRC PHRU), University of Oxford, Oxford, UK.
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Espinosa Dice AL, Lawn RB, Ratanatharathorn A, Roberts AL, Denckla CA, Kim AH, de la Rosa PA, Zhu Y, VanderWeele TJ, Koenen KC. Childhood maltreatment and health in the UK Biobank: triangulation of outcome-wide and polygenic risk score analyses. BMC Med 2024; 22:135. [PMID: 38523269 PMCID: PMC10962116 DOI: 10.1186/s12916-024-03360-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 03/15/2024] [Indexed: 03/26/2024] Open
Abstract
BACKGROUND Childhood maltreatment is common globally and impacts morbidity, mortality, and well-being. Our understanding of its impact is constrained by key substantive and methodological limitations of extant research, including understudied physical health outcomes and bias due to unmeasured confounding. We address these limitations through a large-scale outcome-wide triangulation study. METHODS We performed two outcome-wide analyses (OWAs) in the UK Biobank. First, we examined the relationship between self-reported maltreatment exposure (number of maltreatment types, via Childhood Trauma Screener) and 414 outcomes in a sub-sample of 157,316 individuals using generalized linear models ("observational OWA"). Outcomes covered a broad range of health themes including health behaviors, cardiovascular disease, digestive health, socioeconomic status, and pain. Second, we examined the relationship between a polygenic risk score for maltreatment and 298 outcomes in a non-overlapping sample of 243,006 individuals ("genetic OWA"). We triangulated results across OWAs based on differing sources of bias. RESULTS Overall, 23.8% of the analytic sample for the observational OWA reported at least one maltreatment type. Of 298 outcomes examined in both OWAs, 25% were significant in both OWAs and concordant in the direction of association. Most of these were considered robust in the observational OWA according to sensitivity analyses and included outcomes such as marital separation (OR from observational OWA, ORo = 1.25 (95% CI: 1.21, 1.29); OR from genetic OWA, ORg = 1.06 (1.03, 1.08)), major diet changes due to illness (ORo = 1.27 (1.24, 1.29); ORg = 1.01 (1.00, 1.03)), certain intestinal diseases (ORo = 1.14 (1.10, 1.18); ORg = 1.03 (1.01, 1.06)), hearing difficulty with background noise (ORo = 1.11 (1.11, 1.12); ORg = 1.01 (1.00, 1.01)), knee arthrosis (ORo = 1.13 (1.09, 1.18); ORg = 1.03 (1.01, 1.05)), frequent sleeplessness (ORo = 1.21 (1.20, 1.23); ORg = 1.02 (1.01, 1.03)), and low household income (ORo = 1.28 (1.26, 1.31); ORg = 1.02 (1.01, 1.03)). Approximately 62% of results were significant in the observational OWA but not the genetic OWA, including numerous cardiovascular outcomes. Only 6 outcomes were significant in the genetic OWA and null in the observational OWA; these included diastolic blood pressure and glaucoma. No outcomes were statistically significant in opposite directions in the two analyses, and 11% were not significant in either OWA. CONCLUSIONS Our findings underscore the far-reaching negative effects of childhood maltreatment in later life and the utility of an outcome-wide triangulation design with sensitivity analyses for improving causal inference.
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Affiliation(s)
- Ana Lucia Espinosa Dice
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Rebecca B Lawn
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Andrew Ratanatharathorn
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York City, NY, USA
| | - Andrea L Roberts
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Christy A Denckla
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Ariel H Kim
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
| | - Pedro A de la Rosa
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Institute for Culture and Society, University of Navarra, Pamplona, Spain
| | - Yiwen Zhu
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Tyler J VanderWeele
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Karestan C Koenen
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Psychiatric Neurodevelopmental Genetics Unit, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
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Zhu Y, Zhuang Z, Lv J, Sun D, Pei P, Yang L, Millwood IY, Walters RG, Chen Y, Du H, Liu F, Stevens R, Chen J, Chen Z, Li L, Yu C. A genome-wide association study based on the China Kadoorie Biobank identifies genetic associations between snoring and cardiometabolic traits. Commun Biol 2024; 7:305. [PMID: 38461358 PMCID: PMC10924953 DOI: 10.1038/s42003-024-05978-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 02/27/2024] [Indexed: 03/11/2024] Open
Abstract
Despite the high prevalence of snoring in Asia, little is known about the genetic etiology of snoring and its causal relationships with cardiometabolic traits. Based on 100,626 Chinese individuals, a genome-wide association study on snoring was conducted. Four novel loci were identified for snoring traits mapped on SLC25A21, the intergenic region of WDR11 and FGFR, NAA25, ALDH2, and VTI1A, respectively. The novel loci highlighted the roles of structural abnormality of the upper airway and craniofacial region and dysfunction of metabolic and transport systems in the development of snoring. In the two-sample bi-directional Mendelian randomization analysis, higher body mass index, weight, and elevated blood pressure were causal for snoring, and a reverse causal effect was observed between snoring and diastolic blood pressure. Altogether, our results revealed the possible etiology of snoring in China and indicated that managing cardiometabolic health was essential to snoring prevention, and hypertension should be considered among snorers.
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Affiliation(s)
- Yunqing Zhu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Zhenhuang Zhuang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Jun Lv
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, 100191, China
| | - Dianjianyi Sun
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, 100191, China
| | - Pei Pei
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China
| | - Ling Yang
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, OX3 7LF, United Kingdom
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, United Kingdom
| | - Iona Y Millwood
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, OX3 7LF, United Kingdom
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, United Kingdom
| | - Robin G Walters
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, OX3 7LF, United Kingdom
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, United Kingdom
| | - Yiping Chen
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, OX3 7LF, United Kingdom
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, United Kingdom
| | - Huaidong Du
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, OX3 7LF, United Kingdom
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, United Kingdom
| | - Fang Liu
- Suzhou Centers for Disease Control, NO.72 Sanxiang Road, Gusu District, Suzhou, 215004, Jiangsu, China
| | - Rebecca Stevens
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, United Kingdom
| | - Junshi Chen
- China National Center for Food Safety Risk Assessment, Beijing, 100022, China
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, United Kingdom
| | - Liming Li
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, 100191, China
| | - Canqing Yu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China.
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China.
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, 100191, China.
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Yeo NKW, Lim CK, Yaung KN, Khoo NKH, Arkachaisri T, Albani S, Yeo JG. Genetic interrogation for sequence and copy number variants in systemic lupus erythematosus. Front Genet 2024; 15:1341272. [PMID: 38501057 PMCID: PMC10944961 DOI: 10.3389/fgene.2024.1341272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 02/20/2024] [Indexed: 03/20/2024] Open
Abstract
Early-onset systemic lupus erythematosus presents with a more severe disease and is associated with a greater genetic burden, especially in patients from Black, Asian or Hispanic ancestries. Next-generation sequencing techniques, notably whole exome sequencing, have been extensively used in genomic interrogation studies to identify causal disease variants that are increasingly implicated in the development of autoimmunity. This Review discusses the known casual variants of polygenic and monogenic systemic lupus erythematosus and its implications under certain genetic disparities while suggesting an age-based sequencing strategy to aid in clinical diagnostics and patient management for improved patient care.
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Affiliation(s)
- Nicholas Kim-Wah Yeo
- Translational Immunology Institute, SingHealth Duke-NUS Academic Medical Centre, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Che Kang Lim
- Duke-NUS Medical School, Singapore, Singapore
- Department of Clinical Translation Research, Singapore General Hospital, Singapore, Singapore
| | - Katherine Nay Yaung
- Translational Immunology Institute, SingHealth Duke-NUS Academic Medical Centre, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Nicholas Kim Huat Khoo
- Translational Immunology Institute, SingHealth Duke-NUS Academic Medical Centre, Singapore, Singapore
| | - Thaschawee Arkachaisri
- Translational Immunology Institute, SingHealth Duke-NUS Academic Medical Centre, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
- Rheumatology and Immunology Service, KK Women's and Children's Hospital, Singapore, Singapore
| | - Salvatore Albani
- Translational Immunology Institute, SingHealth Duke-NUS Academic Medical Centre, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
- Rheumatology and Immunology Service, KK Women's and Children's Hospital, Singapore, Singapore
| | - Joo Guan Yeo
- Translational Immunology Institute, SingHealth Duke-NUS Academic Medical Centre, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
- Rheumatology and Immunology Service, KK Women's and Children's Hospital, Singapore, Singapore
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Zhu Y, Zhuang Z, Lv J, Sun D, Pei P, Yang L, Millwood IY, Walters RG, Chen Y, Du H, Wu X, Schmidt D, Avery D, Chen J, Chen Z, Li L, Yu C. Causal association between snoring and stroke: a Mendelian randomization study in a Chinese population. THE LANCET REGIONAL HEALTH. WESTERN PACIFIC 2024; 44:101001. [PMID: 38304719 PMCID: PMC10832459 DOI: 10.1016/j.lanwpc.2023.101001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 11/24/2023] [Accepted: 12/19/2023] [Indexed: 02/03/2024]
Abstract
Background Previous observational studies established a positive relationship between snoring and stroke. We aimed to investigate the causal effect of snoring on stroke. Methods Based on 82,339 unrelated individuals with qualified genotyping data of Asian descent from the China Kadoorie Biobank (CKB), we conducted a Mendelian randomization (MR) analysis of snoring and stroke. Genetic variants identified in the genome-wide association analysis (GWAS) of snoring in CKB and UK Biobank (UKB) were selected for constructing genetic risk scores (GRS). A two-stage method was applied to estimate the associations of the genetically predicted snoring with stroke and its subtypes. Besides, MR analysis among the non-obese group (body mass index, BMI <24.0 kg/m2), as well as multivariable MR (MVMR), were performed to control for potential pleiotropy from BMI. In addition, the inverse-variance weighted (IVW) method was applied to estimate the causal association with genetic variants identified in CKB GWAS. Findings Positive associations were found between snoring and total stroke, hemorrhagic stroke (HS), and ischemic stroke (IS). With GRS of CKB, the corresponding HRs (95% CIs) were 1.56 (1.15, 2.12), 1.50 (0.84, 2.69), 2.02 (1.36, 3.01), and the corresponding HRs (95% CIs) using GRS of UKB were 1.78 (1.30, 2.43), 1.94 (1.07, 3.52), and 1.74 (1.16, 2.61). The associations remained stable in the MR among the non-obese group, MVMR analysis, and MR analysis using the IVW method. Interpretation This study suggests that, among Chinese adults, genetically predicted snoring could increase the risk of total stroke, IS, and HS, and the causal effect was independent of BMI. Funding National Natural Science Foundation of China, Kadoorie Charitable Foundation Hong Kong, UK Wellcome Trust, National Key R&D Program of China, Chinese Ministry of Science and Technology.
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Affiliation(s)
- Yunqing Zhu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Zhenhuang Zhuang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Jun Lv
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, 100191, China
| | - Dianjianyi Sun
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, 100191, China
| | - Pei Pei
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China
| | - Ling Yang
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, OX3 7LF, United Kingdom
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, United Kingdom
| | - Iona Y. Millwood
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, OX3 7LF, United Kingdom
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, United Kingdom
| | - Robin G. Walters
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, OX3 7LF, United Kingdom
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, United Kingdom
| | - Yiping Chen
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, OX3 7LF, United Kingdom
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, United Kingdom
| | - Huaidong Du
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, OX3 7LF, United Kingdom
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, United Kingdom
| | - Xianping Wu
- Suzhou Centers for Disease Control, NO.72 Sanxiang Road, Gusu District, Suzhou, 215004, Jiangsu, China
| | - Dan Schmidt
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, United Kingdom
| | - Daniel Avery
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, United Kingdom
| | - Junshi Chen
- China National Center for Food Safety Risk Assessment, Beijing, 100022, China
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, United Kingdom
| | - Liming Li
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, 100191, China
| | - Canqing Yu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, 100191, China
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Tarp J, Luo M, Sanchez-Lastra MA, Dalene KE, Cruz BDP, Ried-Larsen M, Thomsen RW, Ekelund U, Ding D. Leisure-time physical activity and all-cause mortality and cardiovascular disease in adults with type 2 diabetes: Cross-country comparison of cohort studies. JOURNAL OF SPORT AND HEALTH SCIENCE 2024; 13:212-221. [PMID: 37839525 PMCID: PMC10980889 DOI: 10.1016/j.jshs.2023.10.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 08/24/2023] [Accepted: 09/12/2023] [Indexed: 10/17/2023]
Abstract
PURPOSE This study aimed to quantify the dose-response association and the minimal effective dose of leisure-time physical activity (PA) to prevent mortality and cardiovascular disease in adults with type 2 diabetes. METHODS Cross-country comparison of 2 prospective cohort studies including 14,913 and 17,457 population-based adults with type 2 diabetes from the UK and China. Baseline leisure-time PA was self-reported and categorized by metabolic equivalent hours per week (MET-h/week) according to World Health Organization recommendations: none, below recommendation (>0-7.49 MET-h/week); at recommended level (7.5-14.9 MET-h/week); above recommendation (≥15 MET-h/week). Mortality and cardiovascular disease data were obtained from national registries. RESULTS During a median follow-up of 12.4 and 9.7 years, in the UK and China cohorts, repectively, higher levels of leisure-time PA were inversely associated with all-cause (1571 and 2351 events) and cardiovascular mortality (392 and 1060 events), mostly consistent with a linear dose-response relationship. PA below, at, and above recommendations, compared with no activity, yielded all-cause mortality hazard ratios of 0.94 (95% confidence interval (95%CI): 0.79-1.12), 0.90 (95%CI: 0.74-1.10), and 0.85 (95%CI: 0.70-1.02) in British adults and 0.87 (95%CI: 0.68-1.10), 0.88 (95%CI: 0.74-1.03), and 0.77 (95%CI: 0.70-0.85) in Chinese adults. Associations with cardiovascular mortality were more pronounced in British adults (0.80 (95%CI: 0.58-1.11), 0.75 (95%CI: 0.52-1.09), and 0.69 (95%CI: 0.48-0.97)) but less pronounced in Chinese adults (1.06 (95%CI: 0.76-1.47), 1.01 (95%CI: 0.80-1.28), and 0.79 (95%CI: 0.69-0.92)). PA at recommended levels was not associated with lower rates of major adverse cardiovascular events (2345 and 4458 events). CONCLUSION Leisure-time PA at the recommended levels was not convincingly associated with lower mortality and had no association with risk of major adverse cardiovascular events in British or Chinese adults with type 2 diabetes. Leisure-time PA above current recommendations may be needed to prevent cardiovascular disease and premature mortality in adults with type 2 diabetes.
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Affiliation(s)
- Jakob Tarp
- Department of Clinical Epidemiology, Aarhus University & Aarhus University Hospital, Aarhus 8200, Denmark.
| | - Mengyun Luo
- Prevention Research Collaboration, Sydney School of Public Health, The University of Sydney, Camperdown, NSW 2006, Australia; Charles Perkins Centre, the University of Sydney, Camperdown, NSW 2050, Australia
| | - Miguel Adriano Sanchez-Lastra
- Department of Sports Medicine, Norwegian School of Sports Sciences, Oslo 0806, Norway; Department of Special Didactics, Faculty of Education and Sports Sciences, University of Vigo, Pontevedra 36005, Spain; Well-Move Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo 36213, Spain
| | - Knut Eirik Dalene
- Department of Chronic Diseases, Norwegian Institute of Public Health, Oslo 0473, Norway
| | - Borja Del Pozo Cruz
- Centre for Active and Healthy Ageing, Department of Sports Science and Clinical Biomechanics, University of Southern Denmark, Odense 5230, Denmark; Faculty of Education, University of Cádiz, Cádiz 11519, Spain; Biomedical Research and Innovation Institute of Cádiz (INiBICA) Research Unit, Puerta del Mar University Hospital, University of Cádiz, Cádiz 11009, Spain
| | - Mathias Ried-Larsen
- The Centre of Inflammation and Metabolism & the Centre for Physical Activity Research, Rigshospitalet, University of Copenhagen, Copenhagen 2100, Denmark; Department of Sports Science and Clinical Biomechanics, University of Southern Denmark, Odense 5230, Denmark
| | - Reimar Wernich Thomsen
- Department of Clinical Epidemiology, Aarhus University & Aarhus University Hospital, Aarhus 8200, Denmark
| | - Ulf Ekelund
- Department of Sports Medicine, Norwegian School of Sports Sciences, Oslo 0806, Norway; Department of Chronic Diseases, Norwegian Institute of Public Health, Oslo 0473, Norway
| | - Ding Ding
- Prevention Research Collaboration, Sydney School of Public Health, The University of Sydney, Camperdown, NSW 2006, Australia; Charles Perkins Centre, the University of Sydney, Camperdown, NSW 2050, Australia
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Shi K, Zhu Y, Lv J, Sun D, Pei P, Du H, Chen Y, Yang L, Han B, Stevens R, Chen J, Chen Z, Li L, Yu C. Association of physical activity with risk of chronic kidney disease in China: A population-based cohort study. JOURNAL OF SPORT AND HEALTH SCIENCE 2024; 13:204-211. [PMID: 37532222 PMCID: PMC10980896 DOI: 10.1016/j.jshs.2023.07.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 05/29/2023] [Accepted: 07/06/2023] [Indexed: 08/04/2023]
Abstract
BACKGROUND Information on the association between physical activity (PA) and the risk of chronic kidney disease (CKD) is limited. We aimed to explore the associations of total, domain-specific, and intensity-specific PA with CKD and its subtypes in China. METHODS The study included 475,376 adults from the China Kadoorie Biobank aged 30-79 years during 2004-2008 at baseline. An interviewer-administered questionnaire was used to collect the information about PA, which was quantified as metabolic equivalent of task hours per day (MET-h/day) and categorized into 4 groups based on quartiles. Cox regression was used to analyze the association between PA and CKD risk. RESULTS During a median follow-up of 12.1 years, 5415 incident CKD cases were documented, including 1159 incident diabetic kidney disease (DKD) cases and 362 incident hypertensive nephropathy (HTN) cases. Total PA was inversely associated with CKD risk, with an adjusted hazard ratio (HR, 95% confidence interval (95%CI)) of 0.83 (0.75-0.92) for incident CKD in the highest quartile of total PA as compared with participants in the lowest quartile. Similar results were observed for risk of DKD and HTN, and the corresponding HRs (95%CIs) were 0.75 (0.58-0.97) for DKD risk and 0.56 (0.37-0.85) for HTN risk. Increased nonoccupational PA, low-intensity PA, and moderate-to-vigorous-intensity PA were significantly associated with a decreased risk of CKD, with HRs (95%CIs) of 0.80 (0.73-0.88), 0.85 (0.77-0.94), and 0.85 (0.76-0.95) in the highest quartile, respectively. CONCLUSION PA, including nonoccupational PA, low-intensity PA, and moderate-to-vigorous-intensity PA, was inversely associated with the risk of CKD, including DKD, HTN, and other CKD, and such associations were dose dependent.
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Affiliation(s)
- Kexiang Shi
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University Health Science Center, Beijing 100191, China
| | - Yunqing Zhu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University Health Science Center, Beijing 100191, China
| | - Jun Lv
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University Health Science Center, Beijing 100191, China; Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
| | - Dianjianyi Sun
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University Health Science Center, Beijing 100191, China; Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
| | - Pei Pei
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China
| | - Huaidong Du
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK; Medical Research Council Population Health Research Unit at the University of Oxford, Oxford OX3 7LF, UK
| | - Yiping Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK; Medical Research Council Population Health Research Unit at the University of Oxford, Oxford OX3 7LF, UK
| | - Ling Yang
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK; Medical Research Council Population Health Research Unit at the University of Oxford, Oxford OX3 7LF, UK
| | - Bing Han
- NCDs Prevention and Control Department, Henan Center for Disease Control and Prevention, Zhengzhou 450016, China
| | - Rebecca Stevens
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Junshi Chen
- China National Center for Food Safety Risk Assessment, Beijing 100022, China
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Liming Li
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University Health Science Center, Beijing 100191, China; Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
| | - Canqing Yu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University Health Science Center, Beijing 100191, China; Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China.
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Hukerikar N, Hingorani AD, Asselbergs FW, Finan C, Schmidt AF. Prioritising genetic findings for drug target identification and validation. Atherosclerosis 2024; 390:117462. [PMID: 38325120 DOI: 10.1016/j.atherosclerosis.2024.117462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 01/25/2024] [Indexed: 02/09/2024]
Abstract
The decreasing costs of high-throughput genetic sequencing and increasing abundance of sequenced genome data have paved the way for the use of genetic data in identifying and validating potential drug targets. However, the number of identified potential drug targets is often prohibitively large to experimentally evaluate in wet lab experiments, highlighting the need for systematic approaches for target prioritisation. In this review, we discuss principles of genetically guided drug development, specifically addressing loss-of-function analysis, colocalization and Mendelian randomisation (MR), and the contexts in which each may be most suitable. We subsequently present a range of biomedical resources which can be used to annotate and prioritise disease-associated proteins identified by these studies including 1) ontologies to map genes, proteins, and disease, 2) resources for determining the druggability of a potential target, 3) tissue and cell expression of the gene encoding the potential target, and 4) key biological pathways involving the potential target. We illustrate these concepts through a worked example, identifying a prioritised set of plasma proteins associated with non-alcoholic fatty liver disease (NAFLD). We identified five proteins with strong genetic support for involvement with NAFLD: CYB5A, NT5C, NCAN, TGFBI and DAPK2. All of the identified proteins were expressed in both liver and adipose tissues, with TGFBI and DAPK2 being potentially druggable. In conclusion, the current review provides an overview of genetic evidence for drug target identification, and how biomedical databases can be used to provide actionable prioritisation, fully informing downstream experimental validation.
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Affiliation(s)
- Nikita Hukerikar
- Institute of Health Informatics, Faculty of Population Health Sciences, University College London, London, UK.
| | - Aroon D Hingorani
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK; The National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London, London, UK
| | - Folkert W Asselbergs
- Institute of Health Informatics, Faculty of Population Health Sciences, University College London, London, UK; Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK; Department of Cardiology, Division Heart and Lungs, University Medical Centre Utrecht, Utrecht University, Utrecht, the Netherlands; Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical, Centre, University of Amsterdam, Amsterdam, the Netherlands
| | - Chris Finan
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK; The National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London, London, UK; Department of Cardiology, Division Heart and Lungs, University Medical Centre Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Amand F Schmidt
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK; The National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London, London, UK; Department of Cardiology, Division Heart and Lungs, University Medical Centre Utrecht, Utrecht University, Utrecht, the Netherlands; Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical, Centre, University of Amsterdam, Amsterdam, the Netherlands
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Wei X, Sun D, Gao J, Zhang J, Zhu M, Yu C, Ma Z, Fu Y, Ji C, Pei P, Yang L, Millwood IY, Walters RG, Chen Y, Du H, Jin G, Chen Z, Hu Z, Li L, Shen H, Lv J, Ma H. Development and evaluation of a polygenic risk score for lung cancer in never-smoking women: A large-scale prospective Chinese cohort study. Int J Cancer 2024; 154:807-815. [PMID: 37846649 DOI: 10.1002/ijc.34765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 08/30/2023] [Accepted: 09/13/2023] [Indexed: 10/18/2023]
Abstract
The proportion of lung cancer in never smokers is rising, especially among Asian women, but there is no effective early detection tool. Here, we developed a polygenic risk score (PRS), which may help to identify the population with higher risk of lung cancer in never-smoking women. We first performed a large GWAS meta-analysis (8595 cases and 8275 controls) to systematically identify the susceptibility loci for lung cancer in never-smoking Asian women and then generated a PRS using GWAS datasets. Furthermore, we evaluated the utility and effectiveness of PRS in an independent Chinese prospective cohort comprising 55 266 individuals. The GWAS meta-analysis identified eight known loci and a novel locus (5q11.2) at the genome-wide statistical significance level of P < 5 × 10-8 . Based on the summary statistics of GWAS, we derived a polygenic risk score including 21 variants (PRS-21) for lung cancer in never-smoking women. Furthermore, PRS-21 had a hazard ratio (HR) per SD of 1.29 (95% CI = 1.18-1.41) in the prospective cohort. Compared with participants who had a low genetic risk, those with an intermediate (HR = 1.32, 95% CI: 1.00-1.72) and high (HR = 2.09, 95% CI: 1.56-2.80) genetic risk had a significantly higher risk of incident lung cancer. The addition of PRS-21 to the conventional risk model yielded a modest significant improvement in AUC (0.697 to 0.711) and net reclassification improvement (24.2%). The GWAS-derived PRS-21 significantly improves the risk stratification and prediction accuracy for incident lung cancer in never-smoking Asian women, demonstrating the potential for identification of high-risk individuals and early screening.
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Affiliation(s)
- Xiaoxia Wei
- Department of Epidemiology and Biostatistics, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Dianjianyi Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Jiaxin Gao
- Department of Epidemiology and Biostatistics, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Jing Zhang
- Department of Epidemiology and Biostatistics, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Meng Zhu
- Department of Epidemiology and Biostatistics, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Zhimin Ma
- Department of Epidemiology and Biostatistics, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Yating Fu
- Department of Epidemiology and Biostatistics, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Chen Ji
- Department of Epidemiology and Biostatistics, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Pei Pei
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Ling Yang
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, UK
| | - Iona Y Millwood
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, UK
| | - Robin G Walters
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, UK
| | - Yiping Chen
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, UK
| | - Huaidong Du
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, UK
| | - Guangfu Jin
- Department of Epidemiology and Biostatistics, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, UK
| | - Zhibin Hu
- Department of Epidemiology and Biostatistics, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- State Key Laboratory of Reproductive Medicine, Center for Global Health, Nanjing Medical University, Nanjing, China
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Hongbing Shen
- Department of Epidemiology and Biostatistics, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
- Research Units of Cohort Study on Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing, China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Hongxia Ma
- Department of Epidemiology and Biostatistics, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
- Research Units of Cohort Study on Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing, China
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Xia C, Xu Y, Li H, He S, Chen W. Benefits and harms of polygenic risk scores in organised cancer screening programmes: a cost-effectiveness analysis. THE LANCET REGIONAL HEALTH. WESTERN PACIFIC 2024; 44:101012. [PMID: 38304718 PMCID: PMC10832505 DOI: 10.1016/j.lanwpc.2024.101012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 12/18/2023] [Accepted: 01/07/2024] [Indexed: 02/03/2024]
Abstract
Background While polygenic risk scores (PRS) could enable the streamlining of organised cancer screening programmes, its current discriminative ability is limited. We conducted a cost-effectiveness analysis to trade-off the benefits and harms of PRS-stratified cancer screening in China. Methods The validated National Cancer Center (NCC) modelling framework for six cancers (lung, liver, breast, gastric, colorectum, and oesophagus) was used to simulate cancer incidence, progression, stage-specific cancer detection, and risk of death. We estimated the number of cancer deaths averted, quality-adjusted life-years (QALY) gained, number needed to screen (NNS), overdiagnosis, and incremental cost-effectiveness ratio (ICER) of one-time PRS-stratified screening strategy (screening 25% of PRS-defined high-risk population) for a birth cohort at age 60 in 2025, compared with unstratified screening strategy (screening 25% of general population) and no screening strategy. We applied lifetime horizon, societal perspective, and 3% discount rate. An ICER less than $18,364 per QALY gained is considered cost-effective. Findings One-time cancer screening for population aged 60 was the most cost-effective strategy compared to screening at other ages. Compared with an unstratified screening strategy, the PRS-stratified screening strategy averted more cancer deaths (61,237 vs. 40,329), had a lower NNS to prevent one death (307 vs. 451), had a slightly higher overdiagnosis (14.1% vs. 13.8%), and associated with an additional 130,045 QALYs at an additional cost of $1942 million, over a lifetime horizon. The ICER for all six cancers combined was $14,930 per QALY gained, with the ICER varying from $7928 in colorectal cancer to $39,068 in liver cancer. ICER estimates were sensitive to changes in risk threshold and cost of PRS tools. Interpretation PRS-stratified screening strategy modestly improves clinical benefit and cost-effectiveness of organised cancer screening programmes. Reducing the costs of polygenic risk stratification is needed before PRS implementation. Funding The Chinese Academy of Medical Sciences, the Jing-jin-ji Special Projects for Basic Research Cooperation, and the Sanming Project of the Medicine in Shenzhen.
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Affiliation(s)
- Changfa Xia
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yongjie Xu
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - He Li
- Office of National Cancer Regional Medical Centre in Liaoning Province, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Siyi He
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wanqing Chen
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Turnbull I, Camm CF, Halsey J, Du H, Bennett DA, Chen Y, Yu C, Sun D, Liu X, Li L, Chen Z, Clarke R. Correlates and consequences of atrial fibrillation in a prospective study of 25 000 participants in the China Kadoorie Biobank. EUROPEAN HEART JOURNAL OPEN 2024; 4:oeae021. [PMID: 38572088 PMCID: PMC10989653 DOI: 10.1093/ehjopen/oeae021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 02/08/2024] [Accepted: 02/15/2024] [Indexed: 04/05/2024]
Abstract
Aims The prevalence of atrial fibrillation (AF) is positively correlated with prior cardiovascular diseases (CVD) and CVD risk factors but is lower in Chinese than Europeans despite their higher burden of CVD. We examined the prevalence and prognosis of AF and other electrocardiogram (ECG) abnormalities in the China Kadoorie Biobank. Methods and results A random sample of 25 239 adults (mean age 59.5 years, 62% women) had a 12-lead ECG recorded and interpreted using a Mortara VERITAS™ algorithm in 2013-14. Participants were followed up for 5 years for incident stroke, ischaemic heart disease, heart failure (HF), and all CVD, overall and by CHA2DS2-VASc scores, age, sex, and area. Overall, 1.2% had AF, 13.6% had left ventricular hypertrophy (LVH), and 28.1% had ischaemia (two-thirds of AF cases also had ischaemia or LVH). The prevalence of AF increased with age, prior CVD, and levels of CHA₂DS₂-VASc scores (0.5%, 1.3%, 2.1%, 2.9%, and 4.4% for scores <2, 2, 3, 4, and ≥5, respectively). Atrial fibrillation was associated with two-fold higher hazard ratios (HR) for CVD (2.15; 95% CI, 1.71-2.69) and stroke (1.88; 1.44-2.47) and a four-fold higher HR for HF (3.79; 2.21-6.49). The 5-year cumulative incidence of CVD was comparable for AF, prior CVD, and CHA₂DS₂-VASc scores ≥ 2 (36.7% vs. 36.2% vs. 37.7%, respectively) but was two-fold greater than for ischaemia (19.4%), LVH (18.0%), or normal ECG (14.1%), respectively. Conclusion The findings highlight the importance of screening for AF together with estimation of CHA₂DS₂-VASc scores for prevention of CVD in Chinese adults.
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Affiliation(s)
- Iain Turnbull
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Headington, Oxford, OX3 7LF, UK
| | - Christian Fielder Camm
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Headington, Oxford, OX3 7LF, UK
| | - Jim Halsey
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Headington, Oxford, OX3 7LF, UK
| | - Huaidong Du
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Headington, Oxford, OX3 7LF, UK
| | - Derrick A Bennett
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Headington, Oxford, OX3 7LF, UK
| | - Yiping Chen
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Headington, Oxford, OX3 7LF, UK
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, Peking University, Beijing, China
- Department of Epidemiology and Biostatistics, Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
| | - Dianyianji Sun
- Department of Epidemiology and Biostatistics, Peking University, Beijing, China
- Department of Epidemiology and Biostatistics, Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
| | - Xiaohong Liu
- Medical Records Archive, Pengzhou Traditional Medicine Hospital, Penzhou, China
| | - Liming Li
- Department of Epidemiology and Biostatistics, Peking University, Beijing, China
- Department of Epidemiology and Biostatistics, Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Peking University, Beijing, China
| | - Zhengming Chen
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Headington, Oxford, OX3 7LF, UK
| | - Robert Clarke
- Clinical Trial Service Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Headington, Oxford, OX3 7LF, UK
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Yuan H, Hill EA, Kyle SD, Doherty A. A systematic review of the performance of actigraphy in measuring sleep stages. J Sleep Res 2024:e14143. [PMID: 38384163 DOI: 10.1111/jsr.14143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 11/29/2023] [Accepted: 12/20/2023] [Indexed: 02/23/2024]
Abstract
The accuracy of actigraphy for sleep staging is assumed to be poor, but examination is limited. This systematic review aimed to assess the performance of actigraphy in sleep stage classification of adults. A systematic search was performed using MEDLINE, Web of Science, Google Scholar, and Embase databases. We identified eight studies that compared sleep architecture estimates between wrist-worn actigraphy and polysomnography. Large heterogeneity was found with respect to how sleep stages were grouped, and the choice of metrics used to evaluate performance. Quantitative synthesis was not possible, so we performed a narrative synthesis of the literature. From the limited number of studies, we found that actigraphy-based sleep staging had some ability to classify different sleep stages compared with polysomnography.
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Affiliation(s)
- Hang Yuan
- Big Data Institute, University of Oxford, Oxford, UK
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Elizabeth A Hill
- Sir Jules Thorn Sleep and Circadian Neuroscience Institute, University of Oxford, Oxford, UK
| | - Simon D Kyle
- Sir Jules Thorn Sleep and Circadian Neuroscience Institute, University of Oxford, Oxford, UK
| | - Aiden Doherty
- Big Data Institute, University of Oxford, Oxford, UK
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
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Yang J, Luo S, Liu Y, Hong M, Qiu X, Lin Y, Zhang W, Gao P, Li Z, Hu Z, Xia M. Cohort Profile: South China Cohort. Int J Epidemiol 2024; 53:dyae028. [PMID: 38412541 DOI: 10.1093/ije/dyae028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 02/06/2024] [Indexed: 02/29/2024] Open
Affiliation(s)
- Jialu Yang
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health and Department of Nutrition, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Shiyun Luo
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health and Department of Nutrition, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Yan Liu
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health and Department of Nutrition, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Minghuang Hong
- Department of Clinical Trial Centre, Sun Yat-sen University Cancer Centre, Guangzhou, China
| | - Xiaoqiang Qiu
- Department of Epidemiology and Health Statistics, School of Public Health, Guangxi Medical University, Nanning, China
| | - Yingzi Lin
- School of Tropical Medicine, Hainan Medical University, Haikou, China
| | - Weisen Zhang
- Molecular Epidemiology Research Centre, Guangzhou Twelfth People's Hospital, Guangzhou, China
| | - Peisong Gao
- Luohu People's Hospital, Shen Zhen Luohu Hospital Group, Shenzhen, China
| | - Zhibin Li
- First Affiliated Hospital of Xiamen University, Xiamen, China
| | - Zhijian Hu
- School of Public Health, Fujian Medical University, Fuzhou, China
| | - Min Xia
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health and Department of Nutrition, School of Public Health, Sun Yat-sen University, Guangzhou, China
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Pang Y, Lv J, Wu T, Yu C, Guo Y, Chen Y, Yang L, Millwood IY, Walters RG, Yang X, Stevens R, Clarke R, Chen J, Li L, Chen Z, Kartsonaki C. Associations of diabetes, circulating protein biomarkers, and risk of pancreatic cancer. Br J Cancer 2024; 130:504-510. [PMID: 38129526 PMCID: PMC10844301 DOI: 10.1038/s41416-023-02533-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 11/21/2023] [Accepted: 11/30/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND Type 2 diabetes (T2D) is associated with higher risk of pancreatic cancer (PC), but the underlying mechanisms are not fully understood. METHODS We conducted a case-subcohort study involving 610 PC cases and 623 subcohort participants with 92 protein biomarkers measured in baseline plasma samples. Genetically-instrumented T2D was derived using 86 single-nucleotide polymorphisms (SNPs), including insulin resistance (IR) SNPs. RESULTS In observational analyses of 623 subcohort participants (mean age, 52 years; 61% women), T2D was positively associated with 13 proteins (SD difference: IL6: 0.52 [0.23-0.81]; IL10: 0.41 [0.12-0.70]), of which 8 were nominally associated with incident PC. The 8 proteins potentially mediated 36.9% (18.7-75.0%) of the association between T2D and PC. In MR, no associations were observed for genetically-determined T2D with proteins, but there were positive associations of genetically-determined IR with IL6 and IL10 (SD difference: 1.23 [0.05-2.41] and 1.28 [0.31-2.24]). In two-sample MR, fasting insulin was associated with both IL6 and PC, but no association was observed between IL6 and PC. CONCLUSIONS Proteomics were likely to explain the association between T2D and PC, but were not causal mediators. Elevated fasting insulin driven by insulin resistance might explain the associations of T2D, proteomics, and PC.
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Affiliation(s)
- Yuanjie Pang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, 38 Xueyuan Road, 100191, Beijing, China.
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China.
| | - Jun Lv
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, 38 Xueyuan Road, 100191, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Ting Wu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, 38 Xueyuan Road, 100191, Beijing, China
| | - Canqing Yu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, 38 Xueyuan Road, 100191, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Yu Guo
- National Clinical Research Center of Cardiovascular Diseases, National Center for Cardiovascular Diseases, Beijing, China
- Fuwai Hospital, Chinese Academy of Medical Sciences, 167 Beilishi Road, Xicheng District, 100037, Beijing, China
| | - Yiping Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU) at the University of Oxford, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Ling Yang
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU) at the University of Oxford, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Iona Y Millwood
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU) at the University of Oxford, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Robin G Walters
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU) at the University of Oxford, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Xiaoming Yang
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, Oxford, UK
| | - Rebecca Stevens
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, Oxford, UK
| | - Robert Clarke
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, Oxford, UK
| | - Junshi Chen
- National Center for Food Safety Risk Assessment, 37 Guangqu Road, 100021, Beijing, China
| | - Liming Li
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, 38 Xueyuan Road, 100191, Beijing, China.
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China.
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China.
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU) at the University of Oxford, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Christiana Kartsonaki
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU) at the University of Oxford, Nuffield Department of Population Health, University of Oxford, Oxford, UK
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Wang X, Chen L, Shi K, Lv J, Sun D, Pei P, Yang L, Chen Y, Du H, Liu J, Yang X, Barnard M, Chen J, Chen Z, Li L, Yu C. Diabetes and chronic kidney disease in Chinese adults: a population-based cohort study. BMJ Open Diabetes Res Care 2024; 12:e003721. [PMID: 38267203 PMCID: PMC10823934 DOI: 10.1136/bmjdrc-2023-003721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 12/05/2023] [Indexed: 01/26/2024] Open
Abstract
INTRODUCTION Cohort evidence of the association of diabetes mellitus (DM) with chronic kidney disease (CKD) is limited. Previous studies often describe patients with kidney disease and diabetes as diabetic kidney disease (DKD) or CKD, ignoring other subtypes. The present study aimed to assess the prospective association of diabetes status (no diabetes, pre-diabetes, screened diabetes, previously diagnosed controlled/uncontrolled diabetes with/without antidiabetic treatment) and random plasma glucose (RPG) with CKD risk (including CKD subtypes) among Chinese adults. RESEARCH DESIGN AND METHODS The present study included 472 545 participants from the China Kadoorie Biobank, using baseline information on diabetes and RPG. The incident CKD and its subtypes were collected through linkage with the national health insurance system during follow-up. Cox regression models were used to calculate the HR and 95% CI. RESULTS During 11.8 years of mean follow-up, 5417 adults developed CKD. Screened plus previously diagnosed diabetes was positively associated with CKD (HR=4.52, 95% CI 4.23 to 4.83), DKD (HR=33.85, 95% CI 29.56 to 38.76), and glomerulonephritis (HR=1.66, 95% CI 1.40 to 1.97). In those with previously diagnosed diabetes, participants with uncontrolled diabetes represented higher risks of CKD, DKD, and glomerulonephritis compared with those with controlled RPG. The risk of DKD was found to rise in participants with pre-diabetes and increased with the elevated RPG level, even in those without diabetes. CONCLUSIONS Among Chinese adults, diabetes was positively associated with CKD, DKD, and glomerulonephritis. Screen-detected and uncontrolled DM had a high risk of CKD, and pre-diabetes was associated with a greater risk of DKD, highlighting the significance of lifelong glycemic management.
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Affiliation(s)
- Xue Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Lu Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Kexiang Shi
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Dianjianyi Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Pei Pei
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Ling Yang
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Yiping Chen
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Huaidong Du
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Jiaqiu Liu
- NCDs Prevention and Control Department, Pengzhou CDC, Pengzhou, China
| | - Xiaoming Yang
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Maxim Barnard
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Junshi Chen
- China National Center for Food Safety Risk Assessment, Beijing, China
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
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Bhalala OG, Watson R, Yassi N. Multi-Omic Blood Biomarkers as Dynamic Risk Predictors in Late-Onset Alzheimer's Disease. Int J Mol Sci 2024; 25:1231. [PMID: 38279230 PMCID: PMC10816901 DOI: 10.3390/ijms25021231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 01/17/2024] [Accepted: 01/18/2024] [Indexed: 01/28/2024] Open
Abstract
Late-onset Alzheimer's disease is the leading cause of dementia worldwide, accounting for a growing burden of morbidity and mortality. Diagnosing Alzheimer's disease before symptoms are established is clinically challenging, but would provide therapeutic windows for disease-modifying interventions. Blood biomarkers, including genetics, proteins and metabolites, are emerging as powerful predictors of Alzheimer's disease at various timepoints within the disease course, including at the preclinical stage. In this review, we discuss recent advances in such blood biomarkers for determining disease risk. We highlight how leveraging polygenic risk scores, based on genome-wide association studies, can help stratify individuals along their risk profile. We summarize studies analyzing protein biomarkers, as well as report on recent proteomic- and metabolomic-based prediction models. Finally, we discuss how a combination of multi-omic blood biomarkers can potentially be used in memory clinics for diagnosis and to assess the dynamic risk an individual has for developing Alzheimer's disease dementia.
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Affiliation(s)
- Oneil G. Bhalala
- Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Parkville 3052, Australia; (R.W.); (N.Y.)
- Department of Neurology, Melbourne Brain Centre at The Royal Melbourne Hospital, University of Melbourne, Parkville 3050, Australia
| | - Rosie Watson
- Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Parkville 3052, Australia; (R.W.); (N.Y.)
- Department of Medicine, The Royal Melbourne Hospital, University of Melbourne, Parkville 3050, Australia
| | - Nawaf Yassi
- Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Parkville 3052, Australia; (R.W.); (N.Y.)
- Department of Neurology, Melbourne Brain Centre at The Royal Melbourne Hospital, University of Melbourne, Parkville 3050, Australia
- Department of Medicine, The Royal Melbourne Hospital, University of Melbourne, Parkville 3050, Australia
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49
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Allen NE, Lacey B, Lawlor DA, Pell JP, Gallacher J, Smeeth L, Elliott P, Matthews PM, Lyons RA, Whetton AD, Lucassen A, Hurles ME, Chapman M, Roddam AW, Fitzpatrick NK, Hansell AL, Hardy R, Marioni RE, O’Donnell VB, Williams J, Lindgren CM, Effingham M, Sellors J, Danesh J, Collins R. Prospective study design and data analysis in UK Biobank. Sci Transl Med 2024; 16:eadf4428. [PMID: 38198570 PMCID: PMC11127744 DOI: 10.1126/scitranslmed.adf4428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 12/13/2023] [Indexed: 01/12/2024]
Abstract
Population-based prospective studies, such as UK Biobank, are valuable for generating and testing hypotheses about the potential causes of human disease. We describe how UK Biobank's study design, data access policies, and approaches to statistical analysis can help to minimize error and improve the interpretability of research findings, with implications for other population-based prospective studies being established worldwide.
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Affiliation(s)
- Naomi E Allen
- UK Biobank Ltd, Stockport, UK
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Ben Lacey
- UK Biobank Ltd, Stockport, UK
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Deborah A Lawlor
- Population Health Science, Bristol Medical School University of Bristol, Bristol, UK
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
| | - Jill P Pell
- School of Health and Wellbeing, University of Glasgow, Scotland
| | - John Gallacher
- Department of Psychiatry, University of Oxford, Oxford, UK
- Dementias Platform UK, Department of Psychiatry, University of Oxford, Oxford, UK
| | - Liam Smeeth
- London School of Hygiene and Tropical Medicine, London, UK
| | - Paul Elliott
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- NIHR Health Protection Research Unit in Chemical Radiation Threats and Hazards, Imperial College London, UK
| | - Paul M Matthews
- UK Dementia Research Centre Institute and Department of Brain Sciences, Imperial College London, London, UK
| | - Ronan A Lyons
- Population Data Science, Swansea University Medical School, Swansea, Wales
| | - Anthony D Whetton
- Veterinary Health Innovation Engine, University of Surrey, Guildford, UK
| | - Anneke Lucassen
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Faculty of Medicine, Southampton University, Southampton, UK
| | - Matthew E Hurles
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | | | | | | | - Anna L Hansell
- Centre for Environmental Health and Sustainability, University of Leicester, Leicester, UK
| | - Rebecca Hardy
- School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, UK
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, University of Edinburgh, Edinburgh, Scotland
| | | | - Julie Williams
- UK Dementia Research Institute, Cardiff University, Cardiff, Wales
| | - Cecilia M Lindgren
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | | | | | - John Danesh
- British Heart Foundation 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
- National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, Cambridge, UK
| | - Rory Collins
- UK Biobank Ltd, Stockport, UK
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
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Hou L, Liu W, Sun W, Cao J, Shan S, Feng Y, Zhou Y, Yuan C, Li X, Song P. Lifetime cumulative effect of reproductive factors on ischaemic heart disease in a prospective cohort. Heart 2024; 110:170-177. [PMID: 37852733 PMCID: PMC10850633 DOI: 10.1136/heartjnl-2023-322442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 06/20/2023] [Indexed: 10/20/2023] Open
Abstract
OBJECTIVE This study aimed to examine the association between lifetime oestrogen exposure and ischaemic heart disease (IHD), based on the hypothesis that higher lifetime oestrogen exposure is linked to lower cardiovascular risk. METHODS In 2004-2008, lifetime cumulative exposure to reproductive factors was assessed among postmenopausal females from the China Kadoorie Biobank using reproductive lifespan (RLS), endogenous oestrogen exposure (EEE) and total oestrogen exposure (TEE). EEE was calculated by subtracting pregnancy-related and contraceptive use duration from RLS, while TEE by adding up the same components except for lactation. Incident IHD during follow-up (2004-2015) was identified. Stratified Cox proportional hazards models estimated the HRs and 95% CIs of IHD for RLS, EEE and TEE. RESULTS Among 118 855 postmenopausal females, 13 162 (11.1%) developed IHD during a median follow-up of 8.9 years. The IHD incidence rates were 13.0, 12.1, 12.5, 13.8 per 1000 person-years for RLS Q1-Q4, 15.8, 12.6, 11.3, 12.1 per 1000 person-years for EEE Q1-Q4 and 13.7, 12.3, 12.2, 13.4 per 1000 person-years for TEE Q1-Q4. The highest quartile (Q4) of RLS and TEE were associated with lower risks of IHD (adjusted HR (aHR) 0.95, 95% CI 0.91 to 1.00 and 0.92, 95% CI 0.88 to 0.97, respectively) compared with the lowest quartile (Q1). Longer EEE showed progressively lower risks of incident IHD (aHR 0.93, 95% CI 0.88 to 0.97; 0.88, 95% CI 0.84 to 0.93; 0.87, 95% CI 0.83 to 0.92 for Q2-Q4 vs Q1). CONCLUSIONS Longer RLS, TEE and EEE were associated with lower risks of IHD among Chinese postmenopausal females.
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Affiliation(s)
- Leying Hou
- Department of Social Medicine, School of Public Health and Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Wen Liu
- Department of Social Medicine, School of Public Health and Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Weidi Sun
- Department of Social Medicine, School of Public Health and Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Jin Cao
- Department of Social Medicine, School of Public Health and Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Shiyi Shan
- Department of Social Medicine, School of Public Health and Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yan Feng
- Department of Obstetrics, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yimin Zhou
- Department of Obstetrics, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Changzheng Yuan
- Department of Big Data in Health Science, School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Xue Li
- Department of Big Data in Health Science, School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Peige Song
- Department of Social Medicine, School of Public Health and Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
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