[Show abstract][Hide abstract] ABSTRACT: Objective To use the rs1229984 variant in the alcohol dehydrogenase 1B gene (ADH1B) as an instrument to investigate the causal role of alcohol in cardiovascular disease. Design Mendelian randomisation meta-analysis of 56 epidemiological studies. Participants 261 991 individuals of European descent, including 20 259 coronary heart disease cases and 10 164 stroke events. Data were available on ADH1B rs1229984 variant, alcohol phenotypes, and cardiovascular biomarkers. Main outcome measures Odds ratio for coronary heart disease and stroke associated with the ADH1B variant in all individuals and by categories of alcohol consumption. Results Carriers of the A-allele of ADH1B rs1229984 consumed 17.2% fewer units of alcohol per week (95% confidence interval 15.6% to 18.9%), had a lower prevalence of binge drinking (odds ratio 0.78 (95% CI 0.73 to 0.84)), and had higher abstention (odds ratio 1.27 (1.21 to 1.34)) than non-carriers. Rs1229984 A-allele carriers had lower systolic blood pressure (−0.88 (−1.19 to −0.56) mm Hg), interleukin-6 levels (−5.2% (−7.8 to −2.4%)), waist circumference (−0.3 (−0.6 to −0.1) cm), and body mass index (−0.17 (−0.24 to −0.10) kg/m2). Rs1229984 A-allele carriers had lower odds of coronary heart disease (odds ratio 0.90 (0.84 to 0.96)). The protective association of the ADH1B rs1229984 A-allele variant remained the same across all categories of alcohol consumption (P=0.83 for heterogeneity). Although no association of rs1229984 was identified with the combined subtypes of stroke, carriers of the A-allele had lower odds of ischaemic stroke (odds ratio 0.83 (0.72 to 0.95)). Conclusions Individuals with a genetic variant associated with non-drinking and lower alcohol consumption had a more favourable cardiovascular profile and a reduced risk of coronary heart disease than those without the genetic variant. This suggests that reduction of alcohol consumption, even for light to moderate drinkers, is beneficial for cardiovascular health.
[Show abstract][Hide abstract] ABSTRACT: Michael V Holmes, assistant professor (joint first author)123, Caroline E Dale, research fellow (joint first author)4, Luisa Zuccolo, population health scientist fellow5, Richard J Silverwood, lecturer in medical statistics46, Yiran Guo, research associate78, Zheng Ye, investigator scientist9, David Prieto-Merino, lecturer in medical statistics4, Abbas Dehghan, assistant professor10, Stella Trompet, senior researcher11, Andrew Wong, senior study manager12, Alana Cavadino, statistician13, Dagmar Drogan, scientist14, Sandosh Padmanabhan, reader15, Shanshan Li, postdoctoral research fellow16, Ajay Yesupriya, health scientist17, Maarten Leusink, doctoral candidate18, Johan Sundstrom, senior epidemiologist19, Jaroslav A Hubacek, senior scientist20, Hynek Pikhart, senior lecturer21, Daniel I Swerdlow, clinician scientist1, Andrie G Panayiotou, lecturer in public health22, Svetlana A Borinskaya, leading researcher23, Chris Finan, bioinformatician1, Sonia Shah, postdoctoral research fellow24, Karoline B Kuchenbaecker, research associate in genetic epidemiology25, Tina Shah, postdoctoral research fellow1, Jorgen Engmann, data manager1, Lasse Folkersen, postdoctoral research fellow26, Per Eriksson, professor of cardiovascular medicine26, Fulvio Ricceri, epidemiologist, research fellow28, Olle Melander, professor27, Carlotta Sacerdote, medical epidemiologist28, Dale M Gamble, researcher29, Sruti Rayaprolu, researcher30, Owen A Ross, associate professor30, Stela McLachlan, data manager31, Olga Vikhireva, research associate21, Ivonne Sluijs, assistant professor32, Robert A Scott, senior investigator scientist9, Vera Adamkova, head of department33, Leon Flicker, professor of geriatric medicine34, Frank M van Bockxmeer, director of cardiovascular genetics laboratory35, Christine Power, professor of epidemiology and public health13, Pedro Marques-Vidal, associate professor of internal medicine36, Tom Meade, emeritus professor of epidemiology4, Michael G Marmot, director of UCL institute of Health Equity37, Jose M Ferro, professor of neurology3839, Sofia Paulos-Pinheiro, masters student4041, Steve E Humphries, professor of cardiovascular genetics at UCL42, Philippa J Talmud, professor of cardiovascular genetics42, Irene Mateo Leach, postdoctoral research fellow43, Niek Verweij, doctoral candidate43, Allan Linneberg, professor44, Tea Skaaby, doctoral candidate44, Pieter A Doevendans, chief cardiologist45, Maarten J Cramer, consultant cardiologist45, Pim van der Harst, cardiologist434647, Olaf H Klungel, associate professor of pharmacoepidemiologic methods18, Nicole F Dowling, epidemiologist17, Anna F Dominiczak, regius professor of medicine15, Meena Kumari, professor of biological and social epidemiology1, Andrew N Nicolaides, emeritus professor of vascular surgery, professor emeritus484950, Cornelia Weikert, scientist, group head14, Heiner Boeing, professor and head of department14, Shah Ebrahim, professor of public health4, Tom R Gaunt, senior lecturer in bioinformatics and molecular genetics5, Jackie F Price, clinical reader in epidemiology31, Lars Lannfelt, professor51, Anne Peasey, teaching fellow in social epidemiology21, Ruzena Kubinova, head of centre52, Andrzej Pajak, professor and head of department53, Sofia Malyutina, professor and head of laboratory5455, Mikhail I Voevoda, professor and director5456, Abdonas Tamosiunas, senior researcher57, Anke H Maitland-van der Zee, associate professor18, Paul E Norman, winthrop professor58, Graeme J Hankey, winthrop professor of neurology5960, Manuela M Bergmann, scientist14, Albert Hofman, professor of epidemiology10, Oscar H Franco, professor of preventative medicine10, Jackie Cooper, senior research fellow61, Jutta Palmen, senior research fellow42, Wilko Spiering, vascular medicine internist62, Pim A de Jong, radiologist63, Diana Kuh, professor of life course epidemiology and MRC unit director12, Rebecca Hardy, professor of epidemiology and medical statistics and MRC programme leader12, Andre G Uitterlinden, professor of complex genetics10, M Arfan Ikram, associate professor of neuroepidemiology10, Ian Ford, professor of biostatistics64, Elina Hyppönen, professor of nutritional and genetic epidemiology136566, Osvaldo P Almeida, director of research, professor and Winthrop chair of geriatric psychiatry346768, Nicholas J Wareham, professor and director of the MRC epidemiology unit9, Kay-Tee Khaw, professor of clinical gerontology69, Anders Hamsten, professor and team leader on behalf of IMPROVE study group*2670, Lise Lotte N Husemoen, senior research fellow44, Anne Tjønneland, research leader71, Janne S Tolstrup, research programme director72, Eric Rimm, associate professor of epidemiology and nutrition7374, Joline W J Beulens, assistant professor32, W M Monique Verschuren, deputy head75, N Charlotte Onland-Moret, assistant professor of genetic epidemiology32, Marten H Hofker, professor of molecular genetics76, S Goya Wannamethee, professor of epidemiology77, Peter H Whincup, professor of cardiovascular epidemiology78, Richard Morris, professor of medical statistics and epidemiology77, Astrid M Vicente, head of department407980, Hugh Watkins, professor of cardiovascular medicine and head of department8182, Martin Farrall, professor of cardiovascular genetics8182, J Wouter Jukema, professor of cardiology11, James Meschia, physician investigator29, L Adrienne Cupples, professor of biostatistics8384, Stephen J Sharp, senior statistician9, Myriam Fornage, professor of molecular medicine and human genetics85, Charles Kooperberg, full member86, Andrea Z LaCroix, professor of epidemiology86, James Y Dai, associate member of biostatistics86, Matthew B Lanktree, postdoctoral research fellow87, David S Siscovick, senior vice-president for research88, Eric Jorgenson, research scientist89, Bonnie Spring, professor of preventive medicine and director90, Josef Coresh, professor of epidemiology91, Yun R Li, medical and doctoral trainee7, Sarah G Buxbaum, assistant professor92, Pamela J Schreiner, professor93, R Curtis Ellison, professor of medicine and public health94, Michael Y Tsai, professor95, Sanjay R Patel, associate professor of medicine96104, Susan Redline, professor96, Andrew D Johnson, principal investigator84, Ron C Hoogeveen, assistant professor of medicine97, Hakon Hakonarson, associate professor of paediatrics and director of genomics7, Jerome I Rotter, director and professor98, Eric Boerwinkle, professor and director99, Paul I W de Bakker, professor of genetic epidemiology and bioinformatics32100, Mika Kivimaki, professor of social epidemiology21, Folkert W Asselbergs, consultant cardiologist4547101, Naveed Sattar, professor of metabolic medicine102, Debbie A Lawlor, professor of epidemiology5, John Whittaker, professor and vice president of statistical platforms and technologies at GSK4103, George Davey Smith, director of MRC integrative epidemiology unit5, Kenneth Mukamal, general internalist104, Bruce M Psaty, professor105106, James G Wilson, professor of physiology and biophysics107, Leslie A Lange, associate professor108, Ajna Hamidovic, assistant professor109, Aroon D Hingorani, professor of genetic epidemiology1, Børge G Nordestgaard, professor110111112, Martin Bobak, professor of epidemiology21, David A Leon, professor of epidemiology4, Claudia Langenberg, academic clinical lecturer9, Tom M Palmer, assistant professor in medical statistics113, Alex P Reiner, research professor86, Brendan J Keating, assistant professor in paediatrics and surgery27, Frank Dudbridge, professor of statistical genetics4, Juan P Casas, professor of epidemiology14 on behalf of The InterAct Consortium1Genetic Epidemiology Group, Institute of Cardiovascular Science, Department of Epidemiology and Public Health, University College London, UK2Department of Surgery, Penn Transplant Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104, USA3Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA4Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK5MRC Integrative Epidemiology Unit (IEU) at the Universty of Bristol, Oakfield House, Bristol BS8 2BN, UK6Centre for Statistical Methodology, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK7Center for Applied Genomics, Abramson Research Center, The Childrentextquoterights Hospital of Philadelphia, Philadelphia, USA8BGI-Shenzhen, Beishan Industrial Zone, Yantian District, Shenzhen 518083, China9MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooketextquoterights Hospital, Cambridge, UK10Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands11Department of Cardiology, Leiden University Medical Center, the Netherlands12MRC Unit for Lifelong Health and Ageing at UCL, London, UK13Centre for Paediatric Epidemiology and Biostatistics, UCL Institute of Child Health, London, UK14German Institute of Human Nutrition Potsdam-Rehbrücke, Arthur-Scheunert-Allee 114-116, 14558 Nuthetal, Germany15Institute of Cardiovascular and Medical Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G12 8TA, UK16Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA17Office of Public Health Genomics, Office of Epidemiology, Surveillance, and Laboratory Services, Centers for Disease Control and Prevention, Atlanta, GA 30333, USA18Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands19Department of Medical Sciences, Uppsala University, Uppsala University Hospital, SE-751 85 Uppsala, Sweden20Center for Experimental Medicine, Institute for Clinical and Experimental Medicine, Videnska 1958/9, Prague 4, 14021, Czech Republic21Department of Epidemiology and Public Health, University College London, London, WC1E 6BT, UK22Cyprus International Institute for Environmental and Public Health in association with the Harvard School of Public Health, Cyprus University of Technology, 3603 Limassol, Cyprus23Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, Russia24UCL Genetics Institute, Department of Genetics Environment and Evolution, London, WC1E 6BT, UK25Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK26Atherosclerosis Research Unit, Center for Molecular Medicine, Department of Medicine, Karolinska Institutet, Stockholm, Sweden27Department of Clinical Sciences, Lund University, Malmö, Sweden28Unit of Cancer Epidemiology, San Giovanni Battista Hospital and Center for Cancer Prevention (CPO-Piemonte), 10129, Torino, Italy29Mayo Clinic Department of Neurology, Jacksonville, FL 32224, USA30Department of Neuroscience, Mayo Clinic Florida, Jacksonville, FL, USA31Centre for Population Health Sciences, University of Edinburgh, Edinburgh EH8 9AG, UK32Julius Center for Health Sciences and Primary Care, University Medical Center, Utrecht, The Netherlands33Department of Preventive Cardiology, Institute for Clinical and Experimental Medicine, Prague 4, 14021, Czech Republic34Western Australian Centre for Health & Ageing, Centre for Medical Research, University of Western Australia, Perth, Australia35Department of Clinical Biochemistry, Royal Perth Hospital and School of Surgery, the University of Western Australia36Department of Internal Medicine, Internal Medicine, CHUV, Lausanne, Switzerland37UCL Institute of Health Equity, Department of Epidemiology & Public Health, London WC1E 7HB, UK38Instituto Medicina Molecular, Faculdade de Medicina Universidade de Lisboa, 1649-028 Lisbon, Portugal39Servico Neurologia, Hospital de Santa Maria, 1649-035 Lisbon, Portugal40Instituto Nacional de Saude Doutor Ricardo Jorge, 1649-016 Lisbon, Portugal41Faculdade Ciencias Universidade Lisboa, 1749-016 Lisbon, Portugal42Centre for Cardiovascular Genetics, Institute of Cardiovascular Science, University College London, London, UK43Department of Cardiology, University Medical Center Groningen, Groningen, The Netherlands44Research Centre for Prevention and Health, Capital Region of Denmark, Glostrup University Hospital, Glostrup, Denmark45Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht, The Netherlands46Department of Genetics, University Medical Center Groningen, Groningen, The Netherlands47Durrer Center for Cardiogenetic Research, ICIN-Netherlands Heart Institute, Utrecht, The Netherlands48Vascular Screening and Diagnostic Centre, Ayios Dometios, Nicosia, Cyprus49Deparment of Vascular Surgery, Imperial College, London, SW7 2BX, UK50Cyprus Cardiovascular Disease Educational and Research trust, Nicosia, Cyprus51Department of Public Health & Caring Sciences, Uppsala University, Uppsala University Hospital, SE-75185 Uppsala, Sweden52Centre for Health Monitoring, National Institute of Public Health, 100 42 Prague, Czech Republic53Department of Epidemiology and Population Studies, Institute of Public Health, Jagiellonian University Medical College, 31-531 Krakow, Poland54Institute of Internal and Preventative Medicine, Siberian Branch of Russian Academy of Medical Sciences, Novosibirsk, Russia, 63008955Dept of Internal Medicine, Novosibirsk State Medical University, Novosibirsk, Russia, 63009156Faculty of Medicine, Novosibirsk State University, Novosibirsk, Russia, 63009057Department of Population Studies, Institute of Cardiology, Lithuanian University of Health Sciences, Kaunas LT-50161, Lithuania58School of Surgery, University of Western Australia, Perth, Australia59Department of Neurology, Sir Charles Gairdner Hospital, Perth, Australia60School of Medicine and Pharmacology, The University of Western Australia, Nedlands, Perth, Australia61Centre for Cardiovascular Genetics, Institute of Cardiovascular Science, University College London, London, UK WC1E 6JF62Department of Vascular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands63Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands64Robertson Centre for Biostatistics, University of Glasgow, Glasgow, UK65School of Population Health and Sansom Institute for Health Research, University of South Australia, Adelaide SA 5000, Australia66South Australian Health and Medical Research Institute, Adelaide SA5000, Australia67School of Psychiatry & Clinical Neurosciences (M573), University of Western Australia, Perth 6009, Australia68Department of Psychiatry, Royal Perth Hospital, Perth, Australia69Department of Primary Care and Public Health and Primary Care, University of Cambridge, Cambridge, UK70Center for Molecular Medicine, Karolinska University Hospital Solna, Stockholm, Sweden71Danish Cancer Society, Strandboulevarden, Copenhagen, Denmark72National Institute of Public Health, University of Southern Denmark, Copenhagen, Denmark73Department of Epidemiology and Department of Nutrition, Harvard School of Public Health, Boston, MA, USA74Channing Division of Network Medicine, Brigham and Womentextquoterights Hospital and Harvard Medical School, Boston, MA, USA75National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands76Dept Pathology and Medical Biology, Medical Biology division, Molecular Genetics, University Medical Center Groningen and Groningen University, Groningen, The Netherlands77Department of Primary Care & Population Health, UCL, London, UK78Population Health Research Institute, St Georgetextquoterights, University of London, London, UK79Instituto Gulbenkian Ciencia, P-2780-156 Oeiras, Portugal80Biofig - Center for Biodiversity, Functional and Integrative Genomics, Campus da FCUL, 1749-016 Lisboa, Portugal81Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK82Department of Cardiovascular Medicine, University of Oxford, Oxford, UK83Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA84National Heart, Lung, and Blood Institutetextquoterights The Framingham Heart Study, Framingham, Massachusetts, USA85Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Texas, USA86Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA87Department of Medicine, McMaster University, Hamilton, Ontario, Canada L8S4L888New York Academy of Medicine, New York, NY 10021, USA89Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA90Northwestern University, Feinberg School of Medicine, Department of Preventive Medicine, Chicago, IL, USA91Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA92School of Health Sciences, Jackson State University, Jackson, MS, USA93School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA94Preventive Medicine and Epidemiology, Evans Department of Medicine, Boston University School of Medicine, Boston, Massachusetts, USA95Department of Laboratory Medicine and Pathology, University of Minnesota, USA96Division of Sleep and Circadian Disorders, Brigham and Womentextquoterights Hospital; Harvard Medical School, Boston USA97Baylor College of Medicine, Department of Medicine, Division of Atherosclerosis & Vascular Medicine, Houston, Texas 77030, USA98Institute for Translational Genomics and Population Sciences, Los Angeles BioMedical Research Institute and Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, Calif, USA99Division of Epidemiology, School of Public Health, University of Texas Health Science Center at Houston, Texas, USA100Department of Medical Genetics, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands101Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK102British Heart Foundation Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, UK103Genetics, R&D, GlaxoSmithKline, Stevenage, UK104Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA105Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services, University of Washington, Seattle, WA,USA106Group Health Research Institute, Group Health Cooperative, Seattle, WA, USA107Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS, USA108Department of Genetics, University of North Carolina School of Medicine at Chapel Hill, Chapel Hill, North Carolina 27514, USA109College of Pharmacy, The University of New Mexico, Albuquerque, NM, USA110The Copenhagen General Population Study, Herlev Hospital, Copenhagen, Denmark111Faculty of Health Sciences, Copenhagen University Hospital, University of Copenhagen,Copenhagen, Denmark112Department of Clinical Biochemistry, Herlev Hospital, Copenhagen University Hospital, Denmark113Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, UKCorrespondence to: J P Casas, Faculty of Epidemiology and Public Health, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK Juan-P.Casasatlshtm.ac.ukAccepted 21 May 2014Abstract Objective To use the rs1229984 variant in the alcohol dehydrogenase 1B gene (ADH1B) as an instrument to investigate the causal role of alcohol in cardiovascular disease. Design Mendelian randomisation meta-analysis of 56 epidemiological studies. Participants 261 991 individuals of European descent, including 20 259 coronary heart disease cases and 10 164 stroke events. Data were available on ADH1B rs1229984 variant, alcohol phenotypes, and cardiovascular biomarkers. Main outcome measures Odds ratio for coronary heart disease and stroke associated with the ADH1B variant in all individuals and by categories of alcohol consumption. Results Carriers of the A-allele of ADH1B rs1229984 consumed 17.2% fewer units of alcohol per week (95% confidence interval 15.6% to 18.9%), had a lower prevalence of binge drinking (odds ratio 0.78 (95% CI 0.73 to 0.84)), and had higher abstention (odds ratio 1.27 (1.21 to 1.34)) than non-carriers. Rs1229984 A-allele carriers had lower systolic blood pressure (-0.88 (-1.19 to -0.56) mm Hg), interleukin-6 levels (-5.2% (-7.8 to -2.4%)), waist circumference (-0.3 (-0.6 to -0.1) cm), and body mass index (-0.17 (-0.24 to -0.10) kg/m2). Rs1229984 A-allele carriers had lower odds of coronary heart disease (odds ratio 0.90 (0.84 to 0.96)). The protective association of the ADH1B rs1229984 A-allele variant remained the same across all categories of alcohol consumption (P=0.83 for heterogeneity). Although no association of rs1229984 was identified with the combined subtypes of stroke, carriers of the A-allele had lower odds of ischaemic stroke (odds ratio 0.83 (0.72 to 0.95)). Conclusions Individuals with a genetic variant associated with non-drinking and lower alcohol consumption had a more favourable cardiovascular profile and a reduced risk of coronary heart disease than those without the genetic variant. This suggests that reduction of alcohol consumption, even for light to moderate drinkers, is beneficial for cardiovascular health. Footnotes Members of the InterAct Consortium and IMPROVE study group are listed in the supplementary appendix. We thank Dr Kieran McCaul (Western Australian Centre for Health & Ageing, Centre for Medical Research, University of Western Australia, Perth, Western Australia, Australia) for help with analysis of the Health in Men Study (HIMS) cohort. Contributors: All coauthors satisfy the recommendations outlined in the ICMJE Recommendations 2013. All coauthors provided substantial contributions to the conception or design of the work or acquisition, analysis, or interpretation of data for the work, and helped with drafting the work or revising it critically for important intellectual content. All coauthors approve this version of the manuscript and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. MVH, CED, and JPC are guarantors for the study, had full access to all of the data in the study, and take responsibility for the integrity of the data and the accuracy of the data analysis. Funding of individuals Dr Michael V. Holmes is funded by a UK Medical Research Council (MRC) population health scientist fellowship (G0802432). Dr Abbas Dehghan is supported by NWO grant (veni, 916.12.154) and the EUR Fellowship. Dr James Meschia receives support from a Clinical Investigator grant from the Mayo Foundation for Medical Education and Research. Prof Mika Kivimaki was supported by the Medical Research Council; the British Heart Foundation; the Economic and Social Research Council; the National Heart Lung and Blood Institute (NHLBI: HL36310); and the National Institute on Aging (AG13196), US, NIH. Prof. Dr. J. W. Jukema is an Established Clinical Investigator of the Netherlands Heart Foundation (grant 2001 D 032). Dr Owen Ross is funded by the James and Ester King Foundation and the Florida State Department of Health, the American Heart Association and the Myron and Jane Hanley Award in Stroke research. Prof Sir Michael Marmot is supported by a Medical Research Council Professorship. Dr Johan Sundstrom is supported by the Swedish Heart-Lung Foundation (grant 20041151), the Swedish Research Council (grant 2007-5942). Dr. Alex Reiner was supported by a contract HHSN268200900009C from the NIH National Heart Lung and Blood Institute. Dr James Y. Dai was supported by a R01 grant from the National Heart Lung and Blood Institute (HL 114901). Prof Hugh Watkins and Prof Martin Farrall are members of the Oxford British Heart Foundation (BHF) Centre of Research Excellence. Dr Daniel Swerdlow was supported by a MRC doctoral training award, and acknowledges support of the UCL MBPhD programme. Prof Frank Dudbridge is supported by a MRC grant (G1000718). Dr Jaroslav Hubacek was supported by MH CZ - DRO (quotedblbaseInstitute for Clinical and Experimental Medicine - IKEM, IN 00023001textquotedblleft). Dr Richard Silverwood is supported by the UK Economic and Social Research Council (NCRM Pathways node, ES/I025561/2). Professor Steve E. Humphries is supported by the British Heart Foundation (PG/2008/008). Prof Kuh, Prof Hardy and Dr Wong were supported by the Medical Research Council (MC_UU_12019/1). Dr Folkert W. Asselbergs is supported by National Institute of Health Research University College London Hospitals Biomedical Research Centre and Netherlands Heart Foundation (2014T001). Dr. Jorgenson is supported by the National Institute on Alcohol Abuse and Alcoholism (NIAAA: AA021223-01). Ajna Hamidovic was funded by MD Scientist Fellowship in Genetic Medicine (Northwestern Memorial Foundation) and the National Research Service Award F32DA024920 (NIH/NIDA; Ajna Hamidovic). Dr. Springtextquoterights work is supported by NIH HL075451. This work was supported in part by BHF Programme Grant RG/10/12/28456. Professors Lawlor and Davey Smith and Dr Zuccolo work in a research unit that receives funding from the UK Medical Research Council (MC_UU_12013/1 and MC_UU_12013/5). Dr. Buxbaumtextquoterights research is supported in part by P20MD006899 awarded by the National Institute on Minority Health and Health Disparities of the National Institutes of Health. Professors Aroon D. Hingorani and Juan P Casas are supported by the National Institute of Health Research University College London Hospitals Biomedical Research Centre. Funding of studies ALSPAC: We are extremely grateful to all of the families who took part in this study, the midwives for recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses. The research leading to the specific results from ALSPAC in this paper received funding from the Wellcome Trust (WT088806 and WT087997MA). The UK Medical Research Council and Wellcome Trust (092731), together with the University of Bristol, provide core support for the ALSPAC study. ARIC: The Atherosclerosis Risk in Communities Study is carried out as a collaborative study supported by National Heart, Lung, and Blood Institute contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HHSN268201100012C), R01HL087641, R01HL59367 and R01HL086694; National Human Genome Research Institute contract U01HG004402; and National Institutes of Health contract HHSN268200625226C. The authors thank the staff and participants of the ARIC study for their important contributions. Infrastructure was partly supported by Grant Number UL1RR025005, a component of the National Institutes of Health and NIH Roadmap for Medical Research; BWHHS: The British Womentextquoterights Heart and Health Study has been supported by funding from the British Heart Foundation (BHF) (grant PG/09/022) and the UK Department of Health Policy Research Programme (England) (grant 0090049). The BWHHS HumanCVD data were funded by the BHF (PG/07/131/24254); We thank all BWHHS participants, the general practitioners and their staff who have supported data collection since the study inception; BRHS: The British Regional Heart Study has been supported by programme grant funding from the British Heart Foundation (RG/08/013/25942); CARe: wishes to acknowledge the support of the National Heart, Lung and Blood Institute and the contributions of the research institutions, study investigators, field staff, and study participants in creating this resource for biomedical research (NHLBI contract number HHSN268200960009C); CARDIA: CARDIA is supported by contracts N01-HC-48047, N01-HC-48048, N01-HC-48049, N01-HC-48050 and N01-HC-95095 from the National Heart, Lung, and Blood Institute/National Institutes of Health; CFS: The Cleveland Family Study (CFS) was supported by grant HL46380 from the National Heart, Lung, and Blood Institute (NHLBI); CGPS: This study was supported by Herlev Hospital, Copenhagen University Hospital, The Copenhagen County Research Fund, and The Danish Medical Research Council; CHS: This research was supported by contracts HHSN268201200036C, HHSN268200800007C, N01 HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086, N01HC65226, and grant HL080295 from the National Heart, Lung, and Blood Institute (NHLBI), with additional contribution from the National Institute of Neurological Disorders and Stroke (NINDS). Additional support was provided by AG023629 from the National Institute on Aging (NIA). A full list of principal CHS investigators and institutions can be found at CHS-NHLBI.org; Cyprus: The Cyprus Study has been supported by the Cyprus Cardiovascular Disease Educational and Research Trust (CCDERT) and Joint Cyprus Research Promotion Foundation, Ministry of Health and Cyprus Heart Foundation grant No 41/5PE as well as Research Promotion Foundation grants (PENEK 05/04 and YGEIA 04/06); EAS: The EAS was funded by the British Heart Foundation (Programme Grant RG/98002); ELSA: Samples from the English Longitudinal Study of Ageing (ELSA) DNA Repository (EDNAR), received support under a grant (AG1764406S1) awarded by the National Institute on Ageing (NIA). ELSA was developed by a team of researchers based at the National Centre for Social Research, University College London and the Institute of Fiscal Studies. The data were collected by the National Centre for Social Research.; EPIC InterAct: We thank all EPIC participants and staff for their contribution to the study. We thank staff from the Technical, Field Epidemiology and Data Functional Group Teams of the MRC Epidemiology Unit in Cambridge, UK, for carrying out sample preparation, DNA provision and quality control, genotyping and data-handling work. The InterAct study received funding from the European Union (Integrated Project LSHM-CT-2006-037197 in the Framework Programme 6 of the European Community); EPIC Netherlands: We thank Statistics Netherlands and Netherlands Cancer Registry (NKR) for follow-up data on cancer, cardiovascular disease, vital status and causes of death. Supported by the European Commission: Public Health and Consumer Protection Directorate 1993-2004; Research Directorate-General 2005; Dutch Ministry of Public Health, Welfare and Sports; Netherlands Cancer Registry; LK Research Funds; Dutch Prevention Funds; Dutch Zorg Onderzoek Nederland; and World Cancer Research Fund (The Netherlands) (to the European Prospective Investigation into Cancer and Nutrition-Netherlands study). The EPIC-NL study was funded by textquoteleftEurope against Cancertextquoteright Programme of the European Commission (SANCO), Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch Cancer Society; ZonMW the Netherlands Organisation for Health Research and Development, World Cancer Research Fund (WCRF) (The Netherlands). Genotyping was funded by IOP Genomics grant IGE05012 from Agentschap NL; EPIC Norfolk: We thank all study participants and the general practitioners and the EPIC-Norfolk study team for their helpful input. The EPIC-Norfolk study is supported by programme grants from the Medical Research Council and Cancer Research UK; EPIC Potsdam: The recruitment phase of the EPIC-Potsdam Study was supported by the Federal Ministry of Science, Germany (01 EA 9401), and the European Union (SOC 95201408 05F02). The follow-up was supported by the German Cancer Aid (70-2488-Ha I) and the European Community (SOC 98200769 05F02). The present study was supported by the Federal Ministry of Education and Research (0312750B). Mercodia provided the oxLDL kits free of charge. JS and AFHP were supported by German Research Federal Ministry (BMBF), JS was supported by a Heisenberg-Professorship (SP716/1-1) and clinical research groups of the German Research Foundation (DFG; KFO192/1 and 218/1). JS, AFHP and MM were also supported by a graduate school of the DFG (GK1208); EPIC Turin: The EPIC Turin study is funded by grants from the Associazione Italiana per le Ricerche sul Cancro, Italy and grants from the Compagnia di San Paolo, Turin, Italy; FHS: The Framingham Heart Study began in 1948 with the recruitment of an original cohort of 5,209 men and women (mean age 44 years; 55 percent women). In 1971 a second generation of study participants was enrolled; this cohort consisted of 5,124 children and spouses of children of the original cohort. The mean age of the offspring cohort was 37 years; 52 percent were women. A third generation cohort of 4,095 children of offspring cohort participants (mean age 40 years; 53 percent women) was enrolled beginning in 2002. At each clinic visit, a medical history was obtained with a focus on cardiovascular content, and participants underwent a physical examination including measurement of height and weight from which BMI was calculated; HAPIEE: This study was supported by Wellcome Trust textquoteleftDeterminants of Cardiovascular Diseases in Eastern Europe: A multi-centre cohort studytextquoteright [grants 064947/Z/01/Z; and 081081/Z/06/Z]; the MacArthur Foundation textquoteleftMacArthur Initiative on Social Upheaval and Healthtextquoteright [grant 712058]; the National Institute on Ageing textquoteleftHealth disparities and aging in societies in transition (the HAPIEE study)textquoteright [grant 1R01 AG23522]; and a project from the Ministry of Health, Czech Republic, for the development of the research organization No. 00023001 (IKEM, Prague, Czech Republic). We would like to thank researchers, interviewers and participants in Novosibirsk, Krakow, Kaunas, Hav'iv rov/Karviná, Jihlava, Úst'i nad Labem, Liberec, Hradec Králové, and Kromev r'iz.; HIMS: National Health and Medical Research Council (NHMRC) project grants 279408, 379600, 403963, 513823 and 634492; HPFS/NHS: We would like to thank Hardeep Ranu and Pati Soule from the DF/HCC Genotyping Core for genotyping and data management. This study was supported by research grants HL35464, CA55075, CA87969, AA11181, and HL34594 from the National Institute of Health, Bethesda; M.D; IMPROVE: This study was supported by the European Commission (Contract number: QLG1- CT- 2002- 00896), Ministero della Salute Ricerca Corrente, Italy, the Swedish Heart-Lung Foundation, the Swedish Research Council (projects 8691 and 0593), the Foundation for Strategic Research, the Stockholm County Council (project 562183), the Foundation for Strategic Research, the Academy of Finland (Grant $#$110413) and the British Heart Foundation (RG2008/014). None of the aforementioned funding organizations or sponsors has had a specific role in design or conduct of the study, collection, management, analysis, or interpretation of the data, or preparation, review, or approval of the manuscript; Inter99: The Inter99 study was supported by the Danish Medical Research Council, the Danish Centre for Evaluation and Health Technology Assessment, Copenhagen County, the Danish Heart Foundation, the Danish Pharmaceutical Association, the Health Insurance Foundation, the Augustinus Foundation, the Ib Henriksens foundation and the Beckett Foundation. The present study was further supported by the Danish Diabetes Association (grant No. 32, December 2005) and the Health Insurance Foundation (grant No. 2010 B 131); ISGS/SWISS: ISGS (Grant Number R01 42733) and SWISS (R01 NS39987) were funded by grants from the National Institute of Neurological Disorders and Stroke (US); Izhevsk: The Izhevsk Family Studies was funded by a UK Wellcome Trust programme grant (078557); MDC: This work was supported by the Swedish Medical Research Council; by the Swedish Heart and Lung Foundation; by the Medical Faculty of Lund University, Malmo University Hospital; by the Albert Pahlsson Research Foundation; by the Crafoord foundation; by the Ernhold Lundstroms Research Foundation, the Region Skane; by the Hulda and Conrad Mossfelt Foundation; by the King Gustaf V and Queen Victoria Foundation; by the Lennart Hanssons Memorial Fund; and by the Marianne and Marcus Wallenberg Foundation. Genotyping was supported by the British Heart Foundation (grant number CH/98001 to A.F.D., RG/07/005/23633 to A.F.D., S.P.); MESA: The Multi-Ethnic Study of Atherosclerosis Study (MESA) is a multicenter prospective cohort study initiated to study the development of subclinical cardiovascular disease. A total of 6814 women and men between the age of 45 and 84 year were recruited for the first examination between 2000 and 2002. Participants were recruited in six US cities (Baltimore, MD; Chicago, IL; Forsyth County, NC; Los Angeles County, CA; Northern Manhattan, NY; and St. Paul, MN). This study was approved by the institutional review boards of each study site, and written informed consent was obtained from all participants. This cohort was genotyped as part of the National Heart Lung and Blood Institutetextquoterights (NHLBI) Candidate Gene Association Resource (CARe) (Musunuru, K., Lettre, G., Young, T., Farlow, D.N., Pirruccello, J.P., Ejebe, K.G., Keating, B.J., Yang, Q., Chen, M.H., Lapchyk, N. et al. Candidate gene association resource (CARe): design, methods, and proof of concept. Circ. Cardiovasc. Genet, 3, 267-275.); MRC 1958BC: Dr Sue Ring and Dr Wendy McArdle (University of Bristol) and Mr Jon Johnson (Centre for Longitudinal Studies, Institute of Education, London) are thanked for help with data linkage. The study was supported by the Academy of Finland (12926) and the Medical Research Council (MRC G0601653 and SALVE/PrevMedsyn). The Medical Research Council funded the 2002-2004 clinical follow-up of the 1958 birth cohort (grant G0000934). This research used resources provided by the Type 1 Diabetes Genetics Consortium, a collaborative clinical study sponsored by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institute of Allergy and Infectious Diseases, National Human Genome Research Institute, National Institute of Child Health and Human Development, and Juvenile Diabetes Research Foundation International (JDRF) and supported by U01 DK062418. This study makes use of data generated by the Wellcome Trust Case-Control Consortium. A full list of investigators who contributed to generation of the data is available from the Wellcome Trust Case-Control Consortium website(www.wtccc.org.uk). Funding for the project was provided by the Wellcome Trust under award 076113. Work at the Centre for Paediatric Epidemiology and Biostatistics benefits from funding support from the MRC in its capacity as the MRC Centre of Epidemiology for Child Health. Research at the University College London Institute of Child Health and Great Ormond Street Hospital for Children NHS Trust benefits from R&D funding received from the NHS Executive; MRC NSHD: Supported by Medical Research Council -- MC_UU_12019/1. We are very grateful to the members of this birth cohort for their continuing interest and participation in the study. We would like to acknowledge the Swallow group, UCL, who performed the DNA extractions; NHANES III: The findings and conclusions in this report are those of the author(s) and do not necessarily represent the views of the Centers for Disease Control and Prevention; NORDIL: This work was supported by the British Heart Foundation (grant number CH/98001 to A.F.D., RG/07/005/23633 to A.F.D., S.P.) and a Special Project, for genotyping of the Swedish extremes from the NORDIL and MDC cohorts; and by Pharmacia. We thank Professor Thomas Hedner (Department of Clinical Pharmacology, Sahlgrenska Academy, Gotheburg, Sweden) and Professor Sverre Kjeldsen (Ullevaal University Hospital, University of Oslo, Oslo, Norway), who are investigators of the NORDIL study. Professor Kjeldsen is also an investigator of the ASCOT trial; NPHS II: NPHS-II was supported by the British Medical Research Council, the US National Institutes of Health (grant NHLBI 33014), and Du Pont Pharma, Wilmington, Delaware; Portuguese stroke: Instituto Nacional de Saude Doutor Ricardo Jorge; PREVEND: PREVEND genetics is supported by the Dutch Kidney Foundation (Grant E033), The Netherlands organisation for health research and development (ZonMw grant 90.700.441), and the Dutch Inter University Cardiology Institute Netherlands (ICIN); PROCARDIS: PROCARDIS was supported by the EU FP7 Program (LSHM-CT-2007-037273), AstraZeneca, the British Heart Foundation, the Oxford BHF Centre of Research Excellence, the Wellcome Trust core award (090532/Z/09/Z), the Swedish Research Council, the Knut and Alice Wallenberg Foundation, the Swedish Heart-Lung Foundation, the Torsten and Ragnar Söderberg Foundation, the Strategic Cardiovascular Program of Karolinska Institutet and Stockholm County Council, the Foundation for Strategic Research and the Stockholm County Council (560283); PROSPER: The PROSPER study was supported by an investigator initiated grant obtained from Bristol-Myers Squibb and by grants from the Interuniversity Cardiology Institute of the Netherlands (ICIN) and the Durrer Center for Cardiogenetic Research both Institutes of the Netherlands Royal Academy of Arts and Sciences (KNAW), the Netherlands Heart Foundation, the Center for Medical Systems Biology (CMSB), a center of excellence approved by the Netherlands Genomics Initiative/Netherlands Organisation for Scientific Research (NWO), the Netherlands Consortium for Healthy Ageing (NCHA). The research leading to these results has received funding from the European Uniontextquoterights Seventh Framework Programme (FP7/2007-2013) under grant agreement ntextdegree HEALTH-F2-2009-223004 and by the Netherlands Genomics Initiative (Netherlands Consortium for Healthy Aging grant 050-060-810); Rotterdam: The Rotterdam Study is supported by the Erasmus Medical Center and Erasmus University Rotterdam; the Netherlands Organization for Scientific Research (NWO); the Netherlands Organization for Health Research and Development (ZonMw); the Research Institute for Diseases in the Elderly (RIDE); the Netherlands Heart Foundation; the Ministry of Education, Culture and Science; the Ministry of Health Welfare and Sports; the European Commission; and the Municipality of Rotterdam. Support for genotyping was provided by the Netherlands Organisation of Scientific Research NWO Investments (nr. 175.010.2005.011, 911-03-012), the Research Institute for Diseases in the Elderly (014-93-015; RIDE2), the Netherlands Genomics Initiative (NGI)/Netherlands Consortium for Healthy Aging (NCHA) project nr. 050-060-810; SMART: SMART GENETICS was financially supported by BBMRI-NL, a Research Infrastructure financed by the Dutch government (NWO 184.021.007); TPT: TPT was funded by the Medical Research Council, the British Heart Foundation, DuPont Pharma and Bayer Corporation; UCP: The UCP study was funded by Veni grant Organization for Scientific Research (NWO), Grant no. 2001.064 Netherlands Heart Foundation (NHS), and TI Pharma Grant T6-101 Mondriaan. The department of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, has received unrestricted research funding from the Netherlands Organisation for Health Research and Development (ZonMW), the Dutch Health Care Insurance Board (CVZ), the Royal Dutch Pharmacists Association (KNMP), the private-public funded Top Institute Pharma (www.tipharma.nl, includes co-funding from universities, government, and industry), the EU Innovative Medicines Initiative (IMI), EU 7th Framework Program (FP7), the Dutch Medicines Evaluation Board, the Dutch Ministry of Health and industry (including GlaxoSmithKline, Pfizer, and others); Whitehall II: The Whitehall II study and Mika Kivimaki were supported by the Medical Research Council; the British Heart Foundation; the Economic and Social Research Council; the National Heart Lung and Blood Institute (NHLBI: HL36310); and the National Institute on Aging (AG13196), US, NIH; WHI: The WHI program is funded by the National Heart, Lung, and Blood Institute, National Institutes of Health, U.S. Department of Health and Human Services through contracts HHSN268201100046C, HHSN268201100001C, HHSN268201100002C, HHSN268201100003C, HHSN268201100004C, and HHSN271201100004C. A listing of WHI investigators can be found at https://cleo.whi.org/researchers/Documents%20%20Write%20a%20Paper/WHI%20Investigator%20Short%20List.pdf. Statement of independence from funders: All researchers acted independently of study funders. The study funders played no role in study design and the collection, analysis, and interpretation of data and the writing of the article and the decision to submit it for publication. None of the funders influenced the data analysis or interpretation of results. The comments made in this paper are those of the authors and not necessarily those of any funders. Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf and declare: Prof Whittaker is 90% employed by GlaxoSmithKline and own shares in GlaxoSmithKline. All other coauthors report no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work. Data sharing statement: No additional data available Transparency declaration: The lead authors, MVH, CED, and JPC (the manuscripttextquoterights guarantors) affirm that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained. This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 3.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/3.0/.
[Show abstract][Hide abstract] ABSTRACT: Background
Low plasma 25-hydroxyvitamin D (25[OH]D) concentration is associated with high arterial blood pressure and hypertension risk, but whether this association is causal is unknown. We used a mendelian randomisation approach to test whether 25(OH)D concentration is causally associated with blood pressure and hypertension risk.
In this mendelian randomisation study, we generated an allele score (25[OH]D synthesis score) based on variants of genes that affect 25(OH)D synthesis or substrate availability (CYP2R1 and DHCR7), which we used as a proxy for 25(OH)D concentration. We meta-analysed data for up to 108 173 individuals from 35 studies in the D-CarDia collaboration to investigate associations between the allele score and blood pressure measurements. We complemented these analyses with previously published summary statistics from the International Consortium on Blood Pressure (ICBP), the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium, and the Global Blood Pressure Genetics (Global BPGen) consortium.
In phenotypic analyses (up to n=49 363), increased 25(OH)D concentration was associated with decreased systolic blood pressure (β per 10% increase, −0·12 mm Hg, 95% CI −0·20 to −0·04; p=0·003) and reduced odds of hypertension (odds ratio [OR] 0·98, 95% CI 0·97—0·99; p=0·0003), but not with decreased diastolic blood pressure (β per 10% increase, −0·02 mm Hg, −0·08 to 0·03; p=0·37). In meta-analyses in which we combined data from D-CarDia and the ICBP (n=146 581, after exclusion of overlapping studies), each 25(OH)D-increasing allele of the synthesis score was associated with a change of −0·10 mm Hg in systolic blood pressure (−0·21 to −0·0001; p=0·0498) and a change of −0·08 mm Hg in diastolic blood pressure (−0·15 to −0·02; p=0·01). When D-CarDia and consortia data for hypertension were meta-analysed together (n=142 255), the synthesis score was associated with a reduced odds of hypertension (OR per allele, 0·98, 0·96—0·99; p=0·001). In instrumental variable analysis, each 10% increase in genetically instrumented 25(OH)D concentration was associated with a change of −0·29 mm Hg in diastolic blood pressure (−0·52 to −0·07; p=0·01), a change of −0·37 mm Hg in systolic blood pressure (−0·73 to 0·003; p=0·052), and an 8·1% decreased odds of hypertension (OR 0·92, 0·87—0·97; p=0·002).
Increased plasma concentrations of 25(OH)D might reduce the risk of hypertension. This finding warrants further investigation in an independent, similarly powered study.
[Show abstract][Hide abstract] ABSTRACT: Little is known about genes regulating male puberty. Further, while many identified pubertal timing variants associate with age at menarche, a late manifestation of puberty, and body mass, little is known about these variants' relationship to pubertal initiation or tempo. To address these questions, we performed genome-wide association meta-analysis in over 11,000 European samples with data on early pubertal traits, male genital and female breast development, measured by the Tanner scale. We report the first genome-wide significant locus for male sexual development upstream of MKL2 (P=8.9 x 10(-9)), a menarche locus tagging a developmental pathway linking earlier puberty with reduced pubertal growth (P=4.6 x 10(-5)) and short adult stature (P=1.1 x 10(-11)) in both males and females. Furthermore, our results indicate that a proportion of menarche loci are important for pubertal initiation in both sexes.Consistent with epidemiological correlations between increased prepubertal body mass and earlier pubertal timing in girls, BMI-increasing alleles correlated with earlier breast development. In boys, some BMI-increasing alleles associated with earlier, and others with delayed, sexual development; these genetic results mimic the controversy in epidemiological studies, some of which show opposing correlations between prepubertal BMI and male puberty. Our results contribute to our understanding of the pubertal initiation program in both sexes, and indicate that although mechanisms regulating pubertal onset in males and females may largely be shared, the relationship between body mass and pubertal timing in boys may be complex and requires further genetic studies.
Human Molecular Genetics 04/2014; · 7.69 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Low vitamin D status has been shown to be a risk factor for several metabolic traits such as obesity, diabetes and cardiovascular disease. The biological actions of 1, 25-dihydroxyvitamin D, are mediated through the vitamin D receptor (VDR), which heterodimerizes with retinoid X receptor, gamma (RXRG). Hence, we examined the potential interactions between the tagging polymorphisms in the VDR (22 tag SNPs) and RXRG (23 tag SNPs) genes on metabolic outcomes such as body mass index, waist circumference, waist-hip ratio (WHR), high- and low-density lipoprotein (LDL) cholesterols, serum triglycerides, systolic and diastolic blood pressures and glycated haemoglobin in the 1958 British Birth Cohort (1958BC, up to n = 5,231). We used Multifactor- dimensionality reduction (MDR) program as a non-parametric test to examine for potential interactions between the VDR and RXRG gene polymorphisms in the 1958BC. We used the data from Northern Finland Birth Cohort 1966 (NFBC66, up to n = 5,316) and Twins UK (up to n = 3,943) to replicate our initial findings from 1958BC.
After Bonferroni correction, the joint-likelihood ratio test suggested interactions on serum triglycerides (4 SNP - SNP pairs), LDL cholesterol (2 SNP - SNP pairs) and WHR (1 SNP - SNP pair) in the 1958BC. MDR permutation model testing analysis showed one two-way and one three-way interaction to be statistically significant on serum triglycerides in the 1958BC. In meta-analysis of results from two replication cohorts (NFBC66 and Twins UK, total n = 8,183), none of the interactions remained after correction for multiple testing (Pinteraction >0.17).
Our results did not provide strong evidence for interactions between allelic variations in VDR and RXRG genes on metabolic outcomes; however, further replication studies on large samples are needed to confirm our findings.
[Show abstract][Hide abstract] ABSTRACT: Aim
25-hydroxyvitamin D (25OHD) concentrations have been shown to be associated with major clinical outcomes, with a suggestion that individual risk may vary according to common genetic differences in the vitamin D receptor (VDR) gene. Hence, we tested for the interactions between two previously studied VDR polymorphisms and 25OHD on metabolic and cardiovascular disease-related outcomes in a large population-based study.
Interactions between two previously studied VDR polymorphisms (rs7968585 and rs2239179) and 25OHD concentrations on metabolic and cardiovascular disease-related outcomes such as obesity- (body mass index, waist circumference, waist-hip ratio (WHR)), cardiovascular- (systolic and diastolic blood pressure), lipid- (high- and low-density lipoprotein, triglycerides, total cholesterol), inflammatory- (C-reactive protein, fibrinogen, insulin growth factor-1, tissue plasminogen activator) and diabetes- (glycated haemoglobin) related markers were examined in the 1958 British Birth cohort (n up to 5160). Interactions between each SNP and 25OHD concentrations were assessed using linear regression and the likelihood ratio test.
After Bonferroni correction, none of the interactions reached statistical significance except for the interaction between the VDR SNP rs2239179 and 25OHD concentrations on waist-hip ratio (WHR) (P = 0.03). For every 1 nmol/L higher 25OHD concentrations, the association with WHR was stronger among those with two major alleles (−4.0%, P = 6.26e−24) compared to those with either one or no major alleles (−2.3%, P ≤ 8.201e−07, for both) of the VDR SNP rs2239179.
We found no evidence for VDR polymorphisms acting as major modifiers of the association between 25OHD concentrations and cardio-metabolic risk. Interaction between VDR SNP rs2239179 and 25OHD on WHR warrants further confirmation.
[Show abstract][Hide abstract] ABSTRACT: The pandemic increase in obesity is inversely associated with vitamin D levels. While a higher BMI was causally related to lower 25-hydroxyvitamin D (25(OH)D), no evidence was obtained for a BMI lowering effect by higher 25(OH)D. Some of the physiological functions of 1,25(OH)2D3 (1,25-dihydroxycholecalciferol or calcitriol) via its receptor within the adipose tissue have been investigated such as its effect on energy balance, adipogenesis, adipokine, and cytokine secretion. Adipose tissue inflammation has been recognized as the key component of metabolic disorders, e.g., in the metabolic syndrome. The adipose organ secretes more than 260 different proteins/peptides. However, the molecular basis of the interactions of 1,25(OH)2D3, vitamin D binding proteins (VDBPs) and nuclear vitamin D receptor (VDR) after sequestration in adipose tissue and their regulations are still unclear. 1,25(OH)2D3 and its inactive metabolites are known to inhibit the formation of adipocytes in mouse 3T3-L1 cell line. In humans, 1,25(OH)2D3 promotes preadipocyte differentiation under cell culture conditions. Further evidence of its important functions is given by VDR knock out (VDR(-/-)) and CYP27B1 knock out (CYP27B1 (-/-)) mouse models: Both VDR(-/-) and CYP27B1(-/-) models are highly resistant to the diet induced weight gain, while the specific overexpression of human VDR in adipose tissue leads to increased adipose tissue mass. The analysis of microarray datasets from human adipocytes treated with macrophage-secreted products up-regulated VDR and CYP27B1 genes indicating the capacity of adipocytes to even produce active 1,25(OH)2D3. Experimental studies demonstrate that 1,25(OH)2D3 has an active role in adipose tissue by modulating inflammation, adipogenesis and adipocyte secretion. Yet, further in vivo studies are needed to address the effects and the effective dosages of vitamin D in human adipose tissue and its relevance in the associated diseases.
[Show abstract][Hide abstract] ABSTRACT: Hypovitaminosis D has been linked with poor cognitive function, particularly in older adults, but studies lack a lifespan approach; hence, the effects of reverse causality remain unknown. In the present study, we aimed to assess the relationship between 25-hydroxyvitamin D (25(OH)D) concentrations and subsequent cognitive performance in mid-adulthood and the influence of earlier life factors, including childhood cognitive ability, on this association. Information for the present study was obtained from the members of the 1958 British birth cohort (n 6496). Serum 25(OH)D concentration, indicating vitamin D status, was measured at age 45 years. Verbal memory (immediate and delayed word recall), verbal fluency (animal naming) and speed of processing were tested at age 50 years. Information on childhood cognitive ability, educational attainment, vitamin D-related behaviours and other covariates was collected prospectively from participants throughout their life. Childhood cognitive ability and educational attainment by age 42 years were strongly correlated with cognitive performance at age 50 years and with several vitamin D-related behaviours in mid-adulthood, but not with 25(OH)D concentrations at age 45 years. Participants with both low ( < 25 nmol/l) and high ( ≥ 75 nmol/l) 25(OH)D concentrations at age 45 years performed significantly worse on immediate word recall. The associations attenuated after adjustment for childhood cognitive ability, education, and socio-economic position; however, for the immediate word recall test, there was a non-linear association with 25(OH)D after further adjustment for obesity, menopausal status, smoking, alcohol consumption, physical activity and depressive symptoms at age 45 years (P curvature= 0·01). The present study demonstrated that 25(OH)D concentrations were non-linearly associated with immediate word recall in mid-life. A clarification of the level of 25(OH)D concentrations that is most beneficial for predicting better cognitive performance in mid-life is required.
The British journal of nutrition 10/2013; · 3.45 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Lower maternal vitamin D status in pregnancy may be associated with increased offspring cardiovascular risk in later life, but evidence for this is scant. We examined associations of maternal total 25-hydroxyvitamin D (25(OH)D) in pregnancy with offspring cardiovascular risk factors assessed in childhood and adolescence.
A longitudinal, prospective study.
The study was based on data from mother-offspring pairs in the Avon Longitudinal Study of Parents and Children (ALSPAC), a UK prospective population-based birth cohort (N=4109).
Offspring cardiovascular risk factors were measured in childhood (mean age 9.9 years) and in adolescence (mean age 15.4 years): blood pressure, lipids, apolipoproteins (at 9.9 years only), glucose and insulin (at 15.4 years only), C reactive protein (CRP), and interleukin 6 (at 9.9 years only) were measured.
After adjustments for potential confounders (maternal age, education, body mass index (BMI), smoking, physical activity, parity, socioeconomic position, ethnicity, and offspring gestational age at 25(OH)D sampling; gender, age, and BMI at outcome assessment), maternal 25(OH)D was inversely associated with systolic blood pressure (-0.48 mm Hg difference per 50 nmol/L increase in 25(OH)D; 95% CI -0.95 to -0.01), Apo-B (-0.01 mg/dL difference; 95% CI -0.02 to -0.001), and CRP (-6.1% difference; 95% CI -11.5% to -0.3%) at age 9.9 years. These associations were not present for risk factors measured at 15.4 years, with the exception of a weak inverse association with CRP (-5.5% difference; 95% CI -11.4% to 0.8%). There was no strong evidence of associations with offspring triglycerides, glucose or insulin.
Our findings suggest that fetal exposure to 25(OH)D is unlikely to influence cardiovascular risk factors of individuals later in life.
[Show abstract][Hide abstract] ABSTRACT: Triglycerides are transported in plasma by specific triglyceride-rich lipoproteins; in epidemiological studies, increased triglyceride levels correlate with higher risk for coronary artery disease (CAD). However, it is unclear whether this association reflects causal processes. We used 185 common variants recently mapped for plasma lipids (P < 5 × 10(-8) for each) to examine the role of triglycerides in risk for CAD. First, we highlight loci associated with both low-density lipoprotein cholesterol (LDL-C) and triglyceride levels, and we show that the direction and magnitude of the associations with both traits are factors in determining CAD risk. Second, we consider loci with only a strong association with triglycerides and show that these loci are also associated with CAD. Finally, in a model accounting for effects on LDL-C and/or high-density lipoprotein cholesterol (HDL-C) levels, the strength of a polymorphism's effect on triglyceride levels is correlated with the magnitude of its effect on CAD risk. These results suggest that triglyceride-rich lipoproteins causally influence risk for CAD.
[Show abstract][Hide abstract] ABSTRACT: Levels of low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, triglycerides and total cholesterol are heritable, modifiable risk factors for coronary artery disease. To identify new loci and refine known loci influencing these lipids, we examined 188,577 individuals using genome-wide and custom genotyping arrays. We identify and annotate 157 loci associated with lipid levels at P < 5 × 10(-8), including 62 loci not previously associated with lipid levels in humans. Using dense genotyping in individuals of European, East Asian, South Asian and African ancestry, we narrow association signals in 12 loci. We find that loci associated with blood lipid levels are often associated with cardiovascular and metabolic traits, including coronary artery disease, type 2 diabetes, blood pressure, waist-hip ratio and body mass index. Our results demonstrate the value of using genetic data from individuals of diverse ancestry and provide insights into the biological mechanisms regulating blood lipids to guide future genetic, biological and therapeutic research.
[Show abstract][Hide abstract] ABSTRACT: Vitamin D is essential for a wide range of physiological processes including immune function and calcium homeostasis. Recent investigations have identified candidate genes which are strongly linked to concentrations of 25-hydroxyvitamin D. Since there is insufficient UVB radiation to induce year-round cutaneous synthesis of vitamin D at latitudes distant from the equator it is likely that these genes were subject to forces of natural selection. We used the fixation index (FST) to measure differences in allele frequencies in 993 individuals from ten populations to identify the presence of evolutionary selection in genes in the vitamin D pathway. We then explored the length of haplotypes in chromosomes to confirm recent positive selection.
We find evidence of positive selection for DHCR7, which governs availability of 7-dehydrocholesterol for conversion to vitamin D3 by the action of sunlight on the skin. We show that extended haplotypes related to vitamin D status are highly prevalent at Northern latitudes (Europe 0.72, Northeast Asia 0.41). The common DHCR7 haplotype underwent a recent selective sweep in Northeast Asia, with relative extended haplotype homozygosity of 5.03 (99th percentile). In contrast, CYP2R1, which 25-hydroxylates vitamin D, is under balancing selection and we found no evidence of recent selection pressure on GC, which is responsible for vitamin D transport.
Our results suggest that genetic variation in DHCR7 is the major adaptation affecting vitamin D metabolism in recent evolutionary history which helped early humans to avoid severe vitamin D deficiency and enabled them to inhabit areas further from the equator.
[Show abstract][Hide abstract] ABSTRACT: Seasonal variations in health outcomes are commonly used to hypothesize a link with nutritional vitamin D (25-hydroxyvitamin D, 25(OH)D) status. The majority of vitamin D intake is from skin exposure to sunlight and varies seasonally in countries at a distance away from the Equator. However, despite the strong seasonality of vitamin D intake, no statistical method using cyclical patterns has been proposed to deduce an association between 25(OH)D and health indicators. Our motivation was to overcome the influence of related confounders, such as obesity, between 25(OH)D and health indicators: obesity would be expected to have little or no effect on the seasonal variations in 25(OH)D and in five inflammatory/hemostatic health outcomes (fibrinogen, tissue plasminogen activator [tPA], von Willebrand factor [vWF], C-reactive protein [CRP], and D-dimer). The data analyzed was from the 1958 British birth cohort biomedical survey (n = 6195) and the biomarkers were ascertained from blood drawn over an 18-mo period. We used mediation analysis to determine whether the seasonal variations of the outcomes were mediated by 25(OH)D to infer an association. The assumptions of mediation analysis fit naturally into the study's cross-sectional setting, where day of year of blood collection is the independent variable transformed by the harmonic function, and 25(OH)D is the mediator of the seasonal variation of the outcomes. The harmonic terms were tested to establish the presence of seasonal variation in the outcomes and 25(OH)D in order to determine whether the statistical mediation test could be applied. The data were collected over an 18-mo period and assayed in multiple batches to measure the serum biomarkers. When the assay batches were modeled as fixed effects, significant correlation was found with date of when blood was drawn. Thus, variation in assay batches was accounted for as random effects terms on the intercept in linear mixed-effects models. Inferences were based on tests from mediation analysis defined by the product of regression coefficients; we extended this test to allow for harmonic functions with multiple frequencies in order to statistically test the mediated effect through 25(OH)D. This was done using parametric bootstrap when the models were run in the Frequentist setting. We also replicated the analyses in the Bayesian setting to ascertain the change in amplitude of the seasonal variation that was due to 25(OH)D. Out of the five health outcomes, three (tPA, D-dimer, and fibrinogen) had significant seasonal associations that were partially mediated through 25(OH)D, one (vWF) had a seasonal pattern not mediated through 25(OH)D, and finally another (CRP) had no significant seasonal pattern. The association of 25(OH)D was strongest for tPA, and less so for D-dimer and fibrinogen. Our results and adaption of the mediation test show that there is broad potential in using seasonal variations of health indicators to deduce an association that may have not been affected by nonseasonal confounding.
Chronobiology International 06/2013; · 4.35 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Given the anthropometric differences between men and women and previous evidence of sex-difference in genetic effects, we conducted a genome-wide search for sexually dimorphic associations with height, weight, body mass index, waist circumference, hip circumference, and waist-to-hip-ratio (133,723 individuals) and took forward 348 SNPs into follow-up (additional 137,052 individuals) in a total of 94 studies. Seven loci displayed significant sex-difference (FDR<5%), including four previously established (near GRB14/COBLL1, LYPLAL1/SLC30A10, VEGFA, ADAMTS9) and three novel anthropometric trait loci (near MAP3K1, HSD17B4, PPARG), all of which were genome-wide significant in women (P<5×10(-8)), but not in men. Sex-differences were apparent only for waist phenotypes, not for height, weight, BMI, or hip circumference. Moreover, we found no evidence for genetic effects with opposite directions in men versus women. The PPARG locus is of specific interest due to its role in diabetes genetics and therapy. Our results demonstrate the value of sex-specific GWAS to unravel the sexually dimorphic genetic underpinning of complex traits.
[Show abstract][Hide abstract] ABSTRACT: Background:Few studies have investigated whether parental adiposity is associated with offspring cardiovascular health or the underlying pathways. Studying these associations may help to illuminate the paradox of increasing prevalence of obesity and declining trends in cardiovascular disease (CVD) mortality, which may be partially explained by beneficial adaptations to an obesogenic environment among people exposed to such environments from younger ages.Objective:To investigate associations between parental body mass index (BMI) and risk factors for CVD among their offspring in mid-life and to test whether associations of offspring BMI with CVD risk factors were modified by parental BMI.Methods:Data from parents and offspring in the 1958 British birth cohort were used (N=9328). Parental BMI was assessed when offspring were aged 11 years; offspring BMI, waist circumference and CVD risk factors (lipid levels, blood pressure, glycosylated haemoglobin (HbA1c) and inflammatory and haemostatic markers) were measured at 44-45 years.Results:Higher parental BMI was associated with less favourable levels of offspring risk factors for CVD. Most associations were maintained after adjustment for offspring lifestyle and socioeconomic factors but were largely abolished or reversed after adjustment for offspring adiposity. For some CVD risk factors, there was evidence of effect modification; the association between higher BMI and an adverse lipid profile among offspring was weaker if maternal BMI had been higher. Conversely, offspring BMI was more strongly associated with HbA1c if parental BMI had been higher.Conclusions:Intergenerational influences may be important in conferring the effect of high BMI on CVD risk among offspring.International Journal of Obesity advance online publication, 9 April 2013; doi:10.1038/ijo.2013.40.
International journal of obesity (2005) 04/2013; · 5.22 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Approaches exploiting trait distribution extremes may be used to identify loci associated with common traits, but it is unknown whether these loci are generalizable to the broader population. In a genome-wide search for loci associated with the upper versus the lower 5th percentiles of body mass index, height and waist-to-hip ratio, as well as clinical classes of obesity, including up to 263,407 individuals of European ancestry, we identified 4 new loci (IGFBP4, H6PD, RSRC1 and PPP2R2A) influencing height detected in the distribution tails and 7 new loci (HNF4G, RPTOR, GNAT2, MRPS33P4, ADCY9, HS6ST3 and ZZZ3) for clinical classes of obesity. Further, we find a large overlap in genetic structure and the distribution of variants between traits based on extremes and the general population and little etiological heterogeneity between obesity subgroups.
[Show abstract][Hide abstract] ABSTRACT: Several investigations have observed positive associations between good nutritional status, as indicated by micronutrients, and cognitive measures; however, these associations may not be causal. Genetic polymorphisms that affect nutritional biomarkers may be useful for providing evidence for associations between micronutrients and cognitive measures. As part of the Healthy Ageing across the Life Course (HALCyon) program, men and women aged between 44 and 90 y from 6 UK cohorts were genotyped for polymorphisms associated with circulating concentrations of iron [rs4820268 transmembrane protease, serine 6 (TMPRSS6) and rs1800562 hemochromatosis (HFE)], vitamin B-12 [(rs492602 fucosyltransferase 2 (FUT2)], vitamin D ([rs2282679 group-specific component (GC)] and β-carotene ([rs6564851 beta-carotene 15,15'-monooxygenase 1 (BCMO1)]. Meta-analysis was used to pool within-study effects of the associations between these polymorphisms and the following measures of cognitive capability: word recall, phonemic fluency, semantic fluency, and search speed. Among the several statistical tests conducted, we found little evidence for associations. We found the minor allele of rs1800562 was associated with poorer word recall scores [pooled β on z-score for carriers vs. noncarriers: -0.05 (95% CI: -0.09, -0.004); P = 0.03, n = 14,105] and poorer word recall scores for the vitamin D-raising allele of rs2282679 [pooled β per T allele: -0.03 (95% CI: -0.05, -0.003); P = 0.03, n = 16,527]. However, there was no evidence for other associations. Our findings provide little evidence to support associations between these genotypes and cognitive capability in older adults. Further investigations are required to elucidate whether the previous positive associations from observational studies between circulating measures of these micronutrients and cognitive performance are due to confounding and reverse causality.
Journal of Nutrition 03/2013; · 4.20 Impact Factor