[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: Peripheral inflammatory markers are elevated in patients with dementia. In order to assess their etiologic role, we examined whether interleukin-6 (IL-6) and C-reactive protein (CRP) measured in midlife predict concurrently assessed cognition and subsequent cognitive decline.METHODS: Mean value of IL-6 and CRP, assessed on 5,217 persons (27.9% women) in 1991-1993 and 1997-1999 in the Whitehall II longitudinal cohort study, were categorized into tertiles to examine 10-year decline (assessments in 1997-1999, 2002-2004, and 2007-2009) in standardized scores (mean = 0, SD = 1) of memory, reasoning, and verbal fluency using mixed models. Mini-Mental State Examination (MMSE) was administered in 2002-2004 and 2007-2009; decline ≥3 points was modeled with logistic regression. Analyses were adjusted for baseline age, sex, education, and ethnicity; further analyses were also adjusted for smoking, obesity, Framingham cardiovascular risk score, and chronic diseases (cancer, coronary heart disease, stroke, diabetes, and depression).RESULTS: In cross-sectional analysis, reasoning was 0.08 SD (95% confidence interval [CI] -0.14, -0.03) lower in participants with high compared to low IL-6. In longitudinal analysis, 10-year decline in reasoning was greater (ptrend = 0.01) among participants with high IL-6 (-0.35; 95% CI -0.37, -0.33) than those with low IL-6 (-0.29; 95% CI -0.31, -0.27). In addition, participants with high IL-6 had 1.81 times greater odds ratio of decline in MMSE (95% CI 1.20, 2.71). CRP was not associated with decline in any test.CONCLUSIONS: Elevated IL-6 but not CRP in midlife predicts cognitive decline; the combined cross-sectional and longitudinal effects over the 10-year observation period corresponded to an age effect of 3.9 years.
[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: Previous studies have shown that psychological well being is associated with reduced risk of cardiovascular disease. However, whether well being might be specifically associated with reduced risk of hypertension has not been rigorously investigated in prospective studies.
This study examined the prospective association between two measures of psychological well being and incident hypertension.
Participants were 6384 healthy British civil servants aged 39-63 from the Whitehall II cohort. Psychological well being (emotional vitality and optimism) and cardiovascular risk factors (demographic characteristics, health status, health behaviors, psychological ill being) were assessed during the 1991-1994 baseline. Incident hypertension was defined by clinical measures of SBP or DBP at least 140/90 mmHg, self-reported physician-diagnosed hypertension, or treatment for hypertension. Follow-up assessments of hypertension took place approximately every 3 years through 2002-2004. Cox proportional hazards regression models estimated hazard ratios.
There were 2304 cases of incident hypertension during the follow-up period. High versus low emotional vitality was associated with a significantly reduced risk of hypertension in an age-adjusted model (hazard ratio = 0.89; 95% confidence interval 0.80-0.98). This association was maintained after controlling for demographic characteristics and health status, but was slightly attenuated after adjusting for health behaviors and ill being. Optimism was not significantly associated with hypertension.
High emotional vitality was associated with reduced hypertension risk; favorable health behaviors explained only part of the relationship. Associations did not differ by age, were similar for men and women, and were maintained after accounting for ill being.
Journal of hypertension 06/2014; 32(6):1222-1228. · 4.02 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: We sought association of genetic variants in the renin-angiotensin system (RAS) and vitamin D system with acute pancreatitis (AP) development and severity.
The endocrine RAS is involved in circulatory homeostasis through the pressor action of angiotensin II at its AT1 receptor. However, local RAS regulate growth and inflammation in diverse cells and tissues, and their activity may be suppressed by vitamin D. Intrapancreatic angiotensin II generation has been implicated in the development of AP.
Five hundred forty-four white patients with AP from 3 countries (United Kingdom, 22; Germany, 136; and The Netherlands 386) and 8487 control subjects (United Kingdom 7833, The Netherlands 717) were genotyped for 8 polymorphisms of the RAS/vitamin D systems, chosen on the basis of likely functionality.
The angiotensin-converting enzyme I (rather than D) allele was significantly associated with alcohol-related AP when all cohorts were combined (P = 0.03). The renin rs5707 G (rather than A) allele was associated with AP (P = 0.002), infected necrosis (P = 0.025) and mortality (P = 0.046).
The association of 2 RAS polymorphisms with AP suggests the need for further detailed analysis of the role of RAS/vitamin D in the genesis or severity of AP, particularly given the ready potential for pharmacological manipulation of this system using existing marketed agents. However, further replication studies will be required before any such association is considered robust, particularly given the significant heterogeneity of AP causation and clinical course.
[Show abstract][Hide abstract] ABSTRACT: To use Mendelian randomisation to assess whether any versus no alcohol intake causes either increased or reduced cognitive function.
Mendelian randomization using a genetic variant related to alcohol intake (ADH1B rs1229984) was used to obtain unbiased estimates of the association between any alcohol intake and either higher or lower cognitive performance.
Europe PARTICIPANTS: More than 34,000 adults.
Any versus no alcohol intake in the previous week was measured by questionnaire. Cognitive function was assessed in terms of immediate and delayed word recall, verbal fluency and processing speed.
Having consumed any vs no alcohol was associated with higher scores by 0.17 standard deviations (SD) (95% confidence interval [CI] 0.15, 0.20) for immediate recall, 0.17 SD (95%CI 0.14, 0.19) for delayed recall, 0.17 SD (95%CI 0.14, 0.19) for verbal fluency and 0.12 SD (95%CI 0.09, 0.15) for processing speed. The minor allele of rs1229984 was associated with reduced odds of consuming any alcohol (odds ratio 0.87; 95% CI 0.80, 0.95; P=0.001; R2=0.1%; F-statistic=47). In Mendelian randomisation analysis, the minor allele was not associated with any cognitive test score, and instrumental variable analysis suggested no causal association between alcohol consumption and cognition: -0.74 SD (95%CI -1.88, 0.41) for immediate recall, -1.09 SD (95%CI -2.38, 0.21) for delayed recall, -0.63 SD (95%CI -1.78, 0.53) for verbal fluency and -0.16 SD (95%CI -1.29, 0.97) for processing speed.
Consuming alcohol in some quantity does not appear to affect cognitive ability.
[Show abstract][Hide abstract] ABSTRACT: The risk of type 2 diabetes among obese adults who are metabolically healthy has not been established. We systematically searched Medline (1946–August 2013) and Embase (1947–August 2013) for prospective studies of type 2 diabetes incidence (defined by blood glucose levels or self-report) among metabolically healthy obese adults (defined by body mass index [BMI] and normal cardiometabolic clustering, insulin profile or risk score) aged ≥18 years at baseline. We supplemented the analysis with an original effect estimate from the English Longitudinal Study of Ageing (ELSA), with metabolically healthy obesity defined as BMI ≥ 30 kg m−2 and <2 of hypertension, impaired glycaemic control, systemic inflammation, adverse high-density lipoprotein cholesterol and adverse triglycerides. Estimates from seven published studies and ELSA were pooled using random effects meta-analyses (1,770 healthy obese participants; 98 type 2 diabetes cases). The pooled adjusted relative risk (RR) for incident type 2 diabetes was 4.03 (95% confidence interval = 2.66–6.09) in healthy obese adults and 8.93 (6.86–11.62) in unhealthy obese compared with healthy normal-weight adults. Although there was between-study heterogeneity in the size of effects (I2 = 49.8%; P = 0.03), RR for healthy obesity exceeded one in every study, indicating a consistently increased risk across study populations. Metabolically healthy obese adults show a substantially increased risk of developing type 2 diabetes compared with metabolically healthy normal-weight adults. Prospective evidence does not indicate that healthy obesity is a harmless condition.
[Show abstract][Hide abstract] ABSTRACT: The metabolically healthy obese (MHO) phenotype refers to obese individuals with a favourable metabolic profile. Its prognostic value is unclear and may depend on the health outcome being examined. We examined the association of MHO phenotype with incident cardiovascular disease (CVD) and type 2 diabetes.
Body mass index and metabolic health, assessed using the Adult Treatment Panel-III (ATP-III) criteria, were assessed on 7122 participants (69.7% men) from the Whitehall II study, aged 39-63 years in 1991-93. Incident CVD (coronary heart disease or stroke) and type 2 diabetes were ascertained from medical screenings (every 5 years), hospital data, and registry linkage until 2009. A total of 657 individuals (9.2% of the cohort) were obese and 42.5% of these were classified as MHO in 1991-93. Over the median follow-up of 17.4 years, there were 828 incident cases of CVD and 798 incident cases of type 2 diabetes. Compared with metabolically healthy normal weight individuals, MHO subjects were at increased risk for CVD (HR = 1.97, 95% CI: 1.38-2.80) and type 2 diabetes (3.25, 95% CI: 2.32-4.54). There was excess risk in metabolically unhealthy obese compared with MHO for type 2 diabetes (1.98, 95% CI: 1.39-2.83) but not CVD (1.23, 95% CI: 0.81-1.87). Treating all measures as time varying covariates produced similar findings.
For type 2 diabetes, the MHO phenotype is associated with lower risk than the metabolically unhealthy obese, but for CVD the risk is as elevated in both obesity phenotypes.
European Heart Journal 03/2014; · 14.10 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: We examined the effect of salivary cortisol on cognitive performance and decline in 3229 adults (79% men), mean age 61 years. Six saliva samples over the day along with a cognition test battery were administered twice in 5 years. In fully-adjusted cross-sectional analyses from 2002 to 2004, higher waking cortisol was associated with higher reasoning score (β = 0.08, 95% confidence interval: 0.01, 0.15) but this finding was not replicated using data from 2007 to 2009. Over the mean 5 years follow-up there was decline in all cognitive tests but this decline did not vary as a function of cortisol levels; the exception was among APOE e4 carriers where a flatter diurnal slope and higher bedtime cortisol were associated with faster decline in verbal fluency. Changes in cortisol measures between 2002/2004 and 2007/2009 or chronically elevated levels were not associated with cognitive performance in 2007/2009. These results, based on a large sample of community-dwelling adults suggest that variability in hypothalamic-pituitary-adrenal function is not a strong contributor to cognitive aging.
Neurobiology of aging 03/2014; · 5.94 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: -Secretory phospholipase A2 (sPLA2) enzymes are considered to play a role in atherosclerosis. sPLA2 activity encompasses several sPLA2 isoenzymes, including sPLA2-V. While observational studies show strong association between elevated sPLA2 activity and CHD, no assay to measure sPLA2-V levels exists and the only evidence linking the sPLA2-V isoform to atherosclerosis progression comes from animal studies. In the absence of an assay that directly quantifies sPLA2-V levels, we used PLA2G5 mRNA levels in a novel, modified Mendelian randomization approach to investigate the hypothesized causal role of sPLA2-V in coronary heart disease (CHD) pathogenesis.
-Using data from the Advanced Study of Aortic Pathology, we identified the single nucleotide polymorphism (SNP) in PLA2G5 showing strongest association with PLA2G5 mRNA expression levels, as a proxy for sPLA2-V levels. We tested the association of this SNP with sPLA2 activity and CHD events in four prospective and 14 case-control studies with 27,230 events and 70,500 controls. rs525380C>A showed the strongest association with PLA2G5 mRNA expression (P=5.1x10(-6)). There was no association of rs525380C>A with plasma sPLA2 activity (difference in geometric mean of sPLA2 activity per rs525380 A-allele 0.4% (95%CI: -0.9%, 1.6%), P=0.56). In meta-analyses, the odds ratio for CHD per A allele was 1.02 (95% CI: 0.99, 1.04; P=0.20).
-This novel approach for SNP selection for this modified Mendelian randomization analysis showed no association between rs525380 (the lead SNP for PLA2G5 expression, a surrogate for sPLA2-V levels) and CHD events. The evidence does not support a causal role for sPLA2-V in CHD.
[Show abstract][Hide abstract] ABSTRACT: The correlation between objective and self-reported measures of physical activity varies between studies. We examined this association and whether it differed by demographic factors or socioeconomic status (SES). Data were from 3,975 Whitehall II (United Kingdom, 2012-2013) participants aged 60-83 years, who completed a physical activity questionnaire and wore an accelerometer on their wrist for 9 days. There was a moderate correlation between questionnaire- and accelerometer-assessed physical activity (Spearman's r = 0.33, 95% confidence interval: 0.30, 0.36). The correlations were higher in high-SES groups than in low-SES groups (P 's = 0.02), as defined by education (r = 0.38 vs. r = 0.30) or occupational position (r = 0.37 vs. r = 0.29), but did not differ by age, sex, or marital status. Of the self-reported physical activity, 68.3% came from mild activities, 25% from moderate activities, and only 6.7% from vigorous activities, but their correlations with accelerometer-assessed total physical activity were comparable (range of r 's, 0.21-0.25). Self-reported physical activity from more energetic activities was more strongly associated with accelerometer data (for sports, r = 0.22; for gardening, r = 0.16; for housework, r = 0.09). High-SES persons reported more energetic activities, producing stronger accelerometer associations in these groups. Future studies should identify the aspects of physical activity that are most critical for health; this involves better understanding of the instruments being used.
American journal of epidemiology 02/2014; · 5.59 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Systematic reviews examining associations of depressive disorder with coronary heart disease and stroke produce mixed results. Failure to consider reverse causation and dose-response patterns may have caused inconsistencies in evidence.
This prospective cohort study on depressive disorder, coronary heart disease, and stroke analysed reverse causation and dose-response effects using four 5-year and three 10-year observation cycles (total follow up 24 years) based on multiple repeat measures of exposure.
Participants in the Whitehall II study (n = 10,036, 31,395 person-observations, age at start 44.4 years) provided up to six repeat measures of depressive symptoms via the 30-item General Health Questionnaire (GHQ-30) and one measure via Center for Epidemiologic Studies Depression Scale (CES-D). The cohort was followed up for major coronary events (coronary death/nonfatal myocardial infarction) and stroke (stroke death/morbidity) through the national mortality register Hospital Episode Statistics, ECG-screening, medical records, and self-report questionnaires.
GHQ-30 caseness predicted stroke over 0-5 years (age-, sex- and ethnicity-adjusted HR 1.60, 95% CI 1.1-2.3) but not over 5-10 years (HR 0.94, 95% CI 0.6-1.4). Using the last 5-year observation cycle, cumulative GHQ-30 caseness was associated with incident coronary heart disease in a dose-response manner (1-2 times a case: HR 1.12, 95% CI 0.7-1.7; 3-4 times: HR 2.06, 95% CI 1.2-3.7), and CES-D caseness predicted coronary heart disease (HR 1.81, 95% CI 1.1-3.1).
There was evidence of a dose-response effect of depressive symptoms on risk of coronary heart disease. In contrast, prospective associations of depressive symptoms with stroke appeared to arise wholly or partly through reverse causation.
European journal of preventive cardiology. 02/2014;
[Show abstract][Hide abstract] ABSTRACT: Aims To investigate the causal role of high-density lipoprotein cholesterol (HDL-C) and triglycerides in coronary heart disease (CHD) using multiple instrumental variables for Mendelian randomization.
Methods and results We developed weighted allele scores based on single nucleotide polymorphisms (SNPs) with established associations with HDL-C, triglycerides, and low-density lipoprotein cholesterol (LDL-C). For each trait, we constructed two scores. The first was unrestricted, including all independent SNPs associated with the lipid trait identified from a prior meta-analysis (threshold P < 2 × 10−6); and the second a restricted score, filtered to remove any SNPs also associated with either of the other two lipid traits at P ≤ 0.01. Mendelian randomization meta-analyses were conducted in 17 studies including 62,199 participants and 12,099 CHD events. Both the unrestricted and restricted allele scores for LDL-C (42 and 19 SNPs, respectively) associated with CHD. For HDL-C, the unrestricted allele score (48 SNPs) was associated with CHD (OR: 0.53; 95% CI: 0.40, 0.70), per 1 mmol/L higher HDL-C, but neither the restricted allele score (19 SNPs; OR: 0.91; 95% CI: 0.42, 1.98) nor the unrestricted HDL-C allele score adjusted for triglycerides, LDL-C, or statin use (OR: 0.81; 95% CI: 0.44, 1.46) showed a robust association. For triglycerides, the unrestricted allele score (67 SNPs) and the restricted allele score (27 SNPs) were both associated with CHD (OR: 1.62; 95% CI: 1.24, 2.11 and 1.61; 95% CI: 1.00, 2.59, respectively) per 1-log unit increment. However, the unrestricted triglyceride score adjusted for HDL-C, LDL-C, and statin use gave an OR for CHD of 1.01 (95% CI: 0.59, 1.75).
Conclusion The genetic findings support a causal effect of triglycerides on CHD risk, but a causal role for HDL-C, though possible, remains less certain.
European Heart Journal 01/2014; · 14.10 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: To examine the association between alcohol consumption in midlife and subsequent cognitive decline.
Data are from 5,054 men and 2,099 women from the Whitehall II cohort study with a mean age of 56 years (range 44-69 years) at first cognitive assessment. Alcohol consumption was assessed 3 times in the 10 years preceding the first cognitive assessment (1997-1999). Cognitive tests were repeated in 2002-2004 and 2007-2009. The cognitive test battery included 4 tests assessing memory and executive function; a global cognitive score summarized performances across these tests. Linear mixed models were used to assess the association between alcohol consumption and cognitive decline, expressed as z scores (mean = 0, SD = 1).
In men, there were no differences in cognitive decline among alcohol abstainers, quitters, and light or moderate alcohol drinkers (<20 g/d). However, alcohol consumption ≥36 g/d was associated with faster decline in all cognitive domains compared with consumption between 0.1 and 19.9 g/d: mean difference (95% confidence interval) in 10-year decline in the global cognitive score = -0.10 (-0.16, -0.04), executive function = -0.06 (-0.12, 0.00), and memory = -0.16 (-0.26, -0.05). In women, compared with those drinking 0.1 to 9.9 g/d of alcohol, 10-year abstainers showed faster decline in the global cognitive score (-0.21 [-0.37, -0.04]) and executive function (-0.17 [-0.32, -0.01]).
Excessive alcohol consumption in men (≥36 g/d) was associated with faster cognitive decline compared with light to moderate alcohol consumption.
[Show abstract][Hide abstract] ABSTRACT: Objectif
Le phénotype « obèses métaboliquement sains » désigne les sujets obèses ayant un profil métabolique favorable. Nous avons examiné l’association entre ce phénotype et la survenue d’un diabète de type 2 (DT2) d’une part, et d’un événement cardiovasculaire (ECV) d’autre part.
Matériels et méthodes
L’indice de masse corporelle (IMC) et le statut métabolique défini en utilisant le critère de l’ATP-III ont été mesurés chez 7 122 participants (69,7 % d’hommes) de la cohorte Whitehall II, entre 1991 et 1993. En croisant l’IMC (poids normal, surpoids, obésité) et le statut métabolique (sain/ anormal), six groupes ont été créés. Leur association avec la survenue d’un DT2 ou d’un ECV incident, chez des sujets indemnes à l’inclusion a été mesurée jusqu’en 2009 et analysée par des modèles de Cox (HR, IC 95 %).
Au total, 657 participants (9,2 %) étaient obèses et parmi eux 42,5 % étaient métaboliquement sains. Durant le suivi, 828 cas d’ECV et 798 DT2 ont été dénombrés. En comparaison aux individus métaboliquement sains de poids normal, les obèses métaboliquement sains avaient un risque élevé d’ECV: 1,97 (1,38–2,80) et de DT2 : 3,25 (2,32–4,54). En comparant les sujets obèses avec anomalies métaboliques aux sujets obèses métaboliquement sains, seul le risque de DT2 était augmenté : 1,98 (1,39–2,83).
Pour les événements de santé tels que le DT2, le phénotype obèse métaboliquement sain est associé à un risque moindre comparé aux obèses avec anomalies métaboliques. En revanche, aucune différence n’était observée entre les deux groupes pour la survenue d’un événement cardiovasculaire après 17 ans de suivi.
[Show abstract][Hide abstract] ABSTRACT: Objective
The role of sedentary behaviour in metabolically healthy obesity is unknown. We examined cross-sectional differences in television viewing time across metabolic and obesity phenotypes, hypothesizing that healthy obese individuals spend less time viewing television than their unhealthy counterparts.
A nationally representative sample of 4931 older adults in England (mean age 65.1; SD = 8.9 years) was drawn from the 2008/9 wave of the English Longitudinal Study of Ageing. Average weekly television viewing time was derived from two questions about weekday and weekend viewing. Obesity was defined as body mass index ≥ 30 kg/m2, and metabolically healthy as having ≤ 2 metabolic abnormalities (low HDL-cholesterol, high triglycerides, high blood pressure, hyperglycaemia, high inflammation).
After adjusting for covariates including chronic illness, functional limitations and physical activity, mean weekly viewing times were 4.7 (95% confidence interval 2.9, 6.5), 5.8 (2.5, 9.0) and 7.8 (5.7, 9.8) hours higher in unhealthy non-obese, healthy obese, and unhealthy obese groups respectively, compared to the healthy non-obese group (p for heterogeneity < 0.001).
A common type of leisure-time sedentary behaviour varies across metabolic and obesity phenotypes; however healthy obesity is not explained through differences in leisure-time sedentary behaviour.