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    ABSTRACT: Background Statins increase the risk of new-onset type 2 diabetes mellitus. We aimed to assess whether this increase in risk is a consequence of inhibition of 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR), the intended drug target. Methods We used single nucleotide polymorphisms in the HMGCR gene, rs17238484 (for the main analysis) and rs12916 (for a subsidiary analysis) as proxies for HMGCR inhibition by statins. We examined associations of these variants with plasma lipid, glucose, and insulin concentrations; bodyweight; waist circumference; and prevalent and incident type 2 diabetes. Study-specific effect estimates per copy of each LDL-lowering allele were pooled by meta-analysis. These findings were compared with a meta-analysis of new-onset type 2 diabetes and bodyweight change data from randomised trials of statin drugs. The effects of statins in each randomised trial were assessed using meta-analysis. Findings Data were available for up to 223 463 individuals from 43 genetic studies. Each additional rs17238484-G allele was associated with a mean 0·06 mmol/L (95% CI 0·05–0·07) lower LDL cholesterol and higher body weight (0·30 kg, 0·18–0·43), waist circumference (0·32 cm, 0·16–0·47), plasma insulin concentration (1·62%, 0·53–2·72), and plasma glucose concentration (0·23%, 0·02–0·44). The rs12916 SNP had similar effects on LDL cholesterol, bodyweight, and waist circumference. The rs17238484-G allele seemed to be associated with higher risk of type 2 diabetes (odds ratio [OR] per allele 1·02, 95% CI 1·00–1·05); the rs12916-T allele association was consistent (1·06, 1·03–1·09). In 129 170 individuals in randomised trials, statins lowered LDL cholesterol by 0·92 mmol/L (95% CI 0·18–1·67) at 1-year of follow-up, increased bodyweight by 0·24 kg (95% CI 0·10–0·38 in all trials; 0·33 kg, 95% CI 0·24–0·42 in placebo or standard care controlled trials and −0·15 kg, 95% CI −0·39 to 0·08 in intensive-dose vs moderate-dose trials) at a mean of 4·2 years (range 1·9–6·7) of follow-up, and increased the odds of new-onset type 2 diabetes (OR 1·12, 95% CI 1·06–1·18 in all trials; 1·11, 95% CI 1·03–1·20 in placebo or standard care controlled trials and 1·12, 95% CI 1·04–1·22 in intensive-dose vs moderate dose trials). Interpretation The increased risk of type 2 diabetes noted with statins is at least partially explained by HMGCR inhibition. Funding The funding sources are cited at the end of the paper.
    The Lancet. 09/2014;
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    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.
    BMJ. 07/2014; 349.
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    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/.
    BMJ. 07/2014; 349.
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    ABSTRACT: To investigate the association between serum 25-hydroxyvitamin D concentrations (25(OH)D) and mortality in a large consortium of cohort studies paying particular attention to potential age, sex, season, and country differences.
    BMJ Clinical Research 06/2014; 348:g3656. · 14.09 Impact Factor
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    ABSTRACT: To assess the seasonality of cardiovascular risk factors (CVRF) in a large set of population-based studies.
    Heart (British Cardiac Society) 05/2014; · 5.01 Impact Factor
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    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.
    Addiction 04/2014; · 4.58 Impact Factor
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    ABSTRACT: The aims of this study were to explore associations of the distance and use of urban green spaces with the prevalence of cardiovascular diseases (CVD) and its risk factors, and to evaluate the impact of the accessibility and use of green spaces on the incidence of CVD among the population of Kaunas city (Lithuania). We present the results from a Kaunas cohort study on the access to and use of green spaces, the association with cardiovascular risk factors and other health-related variables, and the risk of cardiovascular mortality and morbidity. A random sample of 5,112 individuals aged 45-72 years was screened in 2006-2008. During the mean 4.41 years follow-up, there were 83 deaths from CVD and 364 non-fatal cases of CVD among persons free from CHD and stroke at the baseline survey. Multivariate Cox proportional hazards regression models were used for data analysis. We found that the distance from people's residence to green spaces was not related to the prevalence of health-related variables. However, the prevalence of cardiovascular risk factors and the prevalence of diabetes mellitus were significantly lower among park users than among non-users. During the follow up, an increased risk of non-fatal and fatal CVD combined was observed for those who lived >=629.61 m from green spaces (3rd tertile of distance to green space) (hazard ratio (HR) = 1.36), and the risk for non-fatal CVD-for those who lived >=347.81 m (2nd and 3rd tertile) and were not park users (HR = 1.66) as compared to men and women who lived 347.8 m or less (1st tertile) from green space. Men living further away from parks (3rd tertile) had a higher risk of non-fatal and fatal CVD combined, compared to those living nearby (1st tertile) (HR = 1.51). Compared to park users living nearby (1st tertile), a statistically significantly increased risk of non-fatal CVD was observed for women who were not park users and living farther away from parks (2nd and 3rd tertile) (HR = 2.78). Our analysis suggests public health policies aimed at promoting healthy lifestyles in urban settings could produce cardiovascular benefits.
    Environmental Health 03/2014; 13(1):20. · 2.71 Impact Factor
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    ABSTRACT: The study aimed to examine the prevalence of depressive symptoms and their correlates in urban middle-aged and elderly Lithuanian adults. Data from the survey was collected within the framework of the international project HAPIEE (Health, Alcohol and Psychosocial Factors in Eastern Europe). A random sample of 7,115 individuals aged 45-72 years was screened in 2006-2008. Depressive symptoms were differently associated with independent variables by sex. In men, deprivation (OR 1.85, 95 % CI 1.54-2.17), being divorced (OR 2.34, 95 % CI 1.61-3.39) or widowed (OR 3.64, 95 % CI 2.40-5.52), physical inactivity (OR 1.30, 95 % CI 1.02-1.65), having a history of spine and joint disease (OR 1.72, 95 % CI 1.36-2.17), average perceived health (OR 2.14, 95 % CI 1.55-2.95), poor perceived health (OR 5.13, 95 % CI 3.39-7.76), average quality of life (OR 2.0, 95 % CI 1.55-2.95), or poor quality of life (OR 8.86, 95 % CI 5.19-15.13) were significantly associated with depressive symptoms. In women, deprivation (OR 1.28, 95 % CI 1.15-1.43), being widowed (OR 1.52, 95 % CI 1.23-1.88), mean dose of alcohol per occasion 40-79.9 g (OR 1.65, 95 % CI 1.18-2.30) and more than 80 g (OR 2.09, 95 % CI 1.14-3.82), physical inactivity in leisure time (OR 1.27, 95 % CI 1.04-1.57), having a history of spine and joint disease (OR 1.26, 95 % CI 1.06-1.51), average perceived health (OR 2.56, 95 % CI 1.89-2.72), poor perceived health (OR 5.07, 95 % CI 3.62-7.11), average quality of life (OR 2.27, 95 % CI 1.89-2.72), or poor quality of life (OR 7.21, 95 % CI 4.73-11.00) were significantly associated with depressive symptoms. Health status and lifestyle factors are associated with depressive symptoms. Associations between depressive symptoms and long-term health problems are partially mediated by self-rated quality of life and self-rated health.
    Social Psychiatry 02/2014; · 2.05 Impact Factor
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    ABSTRACT: To investigate age-related shifts in the relative importance of SBP and DBP as predictors of cardiovascular mortality and all-cause mortality and whether these relations are influenced by other cardiovascular risk factors. Using 42 cohorts from the MORGAM Project with baseline between 1982 and 1997, 85 772 apparently healthy Europeans and Australians aged 19-78 years were included. During 13.3 years of follow-up, 9.2% died (of whom 7.2% died due to stroke and 21.1% due to coronary heart disease, CHD). Mortality risk was analyzed using hazard ratios per 10-mmHg/5-mmHg increase in SBP/DBP by multivariate-adjusted Cox regressions, including SBP and DBP simultaneously. Because of nonlinearity, SBP and DBP were analyzed separately for blood pressure (BP) values above and below a cut-point wherein mortality risk was the lowest. For the total population, significantly positive associations were found between stroke mortality and SBP [hazard ratio = 1.19 (1.13-1.25)] and DBP at least 78 mmHg [hazard ratio = 1.08 (1.02-1.14)], CHD mortality and SBP at least 116 mmHg [1.20 (1.16-1.24)], and all-cause mortality and SBP at least 120 mmHg [1.09 (1.08-1.11)] and DBP at least 82 mmHg [1.03 (1.02-1.05)]. BP values below the cut-points were inversely related to mortality risk. Taking into account the age × BP interaction, there was a gradual shift from DBP (19-26 years) to both DBP and SBP (27-62 years) and to SBP (63-78 years) as risk factors for stroke mortality and all-cause mortality, but not CHD mortality. The age at which the importance of SBP exceeded DBP was for stroke mortality influenced by sex, cholesterol, and country risk. Age-related shifts to the superiority of SBP exist for stroke mortality and all-cause mortality, and for stroke mortality was this shift influenced by other cardiovascular risk factors.
    Journal of Hypertension 02/2014; · 4.22 Impact Factor
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    ABSTRACT: This study investigated the trends and levels of the prevalence of health factors, and the association of all-cause and cardiovascular (CVD) mortality with healthy levels of combined risk factors among Lithuanian urban population. Data from five general population surveys in Kaunas, Lithuania, conducted between 1983 and 2008 were used. Healthy factors measured at baseline include non-smoking, normal weight, normal arterial blood pressure, normal level of total serum cholesterol, normal physical activity and normal level of fasting glucose. Among 9,209 men and women aged 45-64 (7,648 were free from coronary heart disease (CHD) and stroke at baseline), 1,219 death cases from any cause, 589 deaths from CVD, and 342 deaths from CHD occurred during follow up. Cox proportional hazards regression was used to estimate the association between health factors and mortality from all causes, CVD and CHD. Between 1983 and 2008, the proportion of subjects with 6 healthy levels of risk factors was higher in 2006-2008 than in 1983-1984 (0.6% vs. 0.2%; p = 0.09), although there was a significant increase in fasting glucose and a decline in intermediate physical activity. Men and women with normal or intermediate levels of risk factors had significantly lower all-cause, CVD and CHD mortality risk than persons with high levels of risk factors. Subjects with 5-6 healthy factors had hazard ratio (HR) of CVD mortality 0.35 (95% confidence interval (CI) 0.15-0.83) compared to average risk in the whole population. The hazard ratio for CVD mortality risk was significant in men (HR 0.34, 95% CI 0.12-0.97) but not in women (HR 0.38, 95% CI 0.09-1.67). An inverse association of most healthy levels of cardiovascular risk factors with risk of all-cause and CVD mortality was observed in this urban population-based cohort. A greater number of cardiovascular health factors were related with significantly lower risk of CVD mortality, particularly among men.
    PLoS ONE 01/2014; 9(12):e114283. · 3.53 Impact Factor
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    ABSTRACT: The SCORE scale predicts the 10-year risk of fatal atherosclerotic cardiovascular disease (CVD), based on conventional risk factors. The high-risk version of SCORE is recommended for Central and Eastern Europe and former Soviet Union (CEE/FSU), due to high CVD mortality rates in these countries. Given the pronounced social gradient in cardiovascular mortality in the region, it is important to consider social factors in the CVD risk prediction. We investigated whether adding education and marital status to SCORE benefits its prognostic performance in two sets of population-based CEE/FSU cohorts. The WHO MONICA (MONItoring of trends and determinants in CArdiovascular disease) cohorts from the Czech Republic, Poland (Warsaw and Tarnobrzeg), Lithuania (Kaunas), and Russia (Novosibirsk) were followed from the mid-1980s (577 atherosclerotic CVD deaths among 14,969 participants with non-missing data). The HAPIEE (Health, Alcohol, and Psychosocial factors In Eastern Europe) study follows Czech, Polish (Krakow), and Russian (Novosibirsk) cohorts from 2002-05 (395 atherosclerotic CVD deaths in 19,900 individuals with non-missing data). In MONICA and HAPIEE, the high-risk SCORE ≥5% at baseline strongly and significantly predicted fatal CVD both before and after adjustment for education and marital status. After controlling for SCORE, lower education and non-married status were significantly associated with CVD mortality in some samples. SCORE extension by these additional risk factors only slightly improved indices of calibration and discrimination (integrated discrimination improvement <5% in men and ≤1% in women). Extending SCORE by education and marital status failed to substantially improve its prognostic performance in population-based CEE/FSU cohorts.
    PLoS ONE 01/2014; 9(4):e94344. · 3.53 Impact Factor
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    ABSTRACT: The aim of this study was to provide reliable information on dyslipidaemias, to estimate the trend of the prevalence of dyslipidaemias and other selected cardiovascular disease (CVD) risk factors at population level, and to evaluate the risk of all-cause and CVD mortality in relation to presence of mixed dyslipidaemias and other CVD risk factors.
    PLoS ONE 01/2014; 9(6):e100158. · 3.53 Impact Factor
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    ABSTRACT: Relatively large socioeconomic inequalities in health and mortality have been observed in Central and Eastern Europe (CEE) and the former Soviet Union (FSU). Yet comparative data are sparse and virtually all studies include only education. The aim of this study is to quantify and compare socioeconomic inequalities in all-cause mortality during the 2000s in urban population samples from four CEE/FSU countries, by three different measures of socioeconomic position (SEP) (education, difficulty buying food and household amenities), reflecting different aspects of SEP. Data from the prospective population-based HAPIEE (Health, Alcohol, and Psychosocial factors in Eastern Europe) study were used. The baseline survey (2002-2005) included 16 812 men and 19 180 women aged 45-69 years in Novosibirsk (Russia), Krakow (Poland), Kaunas (Lithuania) and seven Czech towns. Deaths in the cohorts were identified through mortality registers. Data were analysed by direct standardisation and Cox regression, quantifying absolute and relative SEP differences. Mortality inequalities by the three SEP indicators were observed in all samples. The magnitude of inequalities varied according to gender, country and SEP measure. As expected, given the high mortality rates in Russian men, largest absolute inequalities were found among Russian men (educational slope index of inequality was 19.4 per 1000 person-years). Largest relative inequalities were observed in Czech men and Lithuanian subjects. Disadvantage by all three SEP measures remained strongly associated with increased mortality after adjusting for the other SEP indicators. The results emphasise the importance of all SEP measures for understanding mortality inequalities in CEE/FSU.
    Journal of epidemiology and community health 11/2013; · 3.04 Impact Factor
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    ABSTRACT: Abstract Background: Matrix metalloproteinases (MMP) are responsible for the degradation of extracellular matrix components and play an important role in the physiological and pathological remodeling of tissues. Purpose: To assess the impact of MMP-2 Rs2285053 (C -> T), MMP-3 Rs3025039 (5A -> 6A), and MMP-9 Rs3918242 (C -> T) single nucleotide polymorphism on the development of early age-related macular degeneration (AMD). Methods: The study group comprised 148 patients with AMD, and the control group enrolled 526 randomly selected persons. The genotyping of MMP-3 Rs3025039, MMP-2 Rs2285053, and MMP-9 Rs3918242 was performed by using the real-time PCR method. Results: The frequency of the MMP-2 (-735) C/T and MMP-3 (-1171) 5A/6A genotypes did not differ significantly between the patients with AMD and the control group, while the MMP-9 (-1562) C/C genotype was more frequently detected in patients with AMD than the control group (73.7% vs. 64.6%, p = 0.048). Logistic regression analysis showed that the MMP-9 (-1562) C/C genotype increased the likelihood of developing early AMD (OR = 1.51, 95% CI: 1.01-2.21; p = 0.046). After the subdivision into the groups by age, a significant difference only in the frequency of the MMP-9 (-1562) C/C genotype was found comparing the AMD patients and the control group younger than 65 years (79.7% vs. 66.4%, p = 0.039). Conclusions: Only MMP-9 Rs3918242 (C -> T) single nucleotide polymorphism was found to play a significant role in the development of AMD, and the effect was more pronounced at the age of less than 65 years.
    Ophthalmic Genetics 09/2013; · 1.07 Impact Factor
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    ABSTRACT: The Systematic COronary Risk Evaluation (SCORE) scale assesses 10 year risk of fatal atherosclerotic cardiovascular disease (CVD), based on conventional risk factors. The high-risk SCORE version is recommended for Central and Eastern Europe and former Soviet Union (CEE/FSU), but its performance has never been systematically assessed in the region. We evaluated SCORE performance in two sets of population-based CEE/FSU cohorts. The cohorts based on the World Health Organization MONitoring of trends and determinants in CArdiovascular disease (MONICA) surveys in the Czech Republic, Poland (Warsaw and Tarnobrzeg), Lithuania (Kaunas), and Russia (Novosibirsk) were followed from the mid-1980s. The Health, Alcohol, and Psychosocial factors in Eastern Europe (HAPIEE) study follows Czech, Polish (Krakow), and Russian (Novosibirsk) cohorts from 2002-05. In Cox regression analyses, the high-risk SCORE ≥5% at baseline significantly predicted CVD mortality in both MONICA [n = 15 027; hazard ratios (HR), 1.7-6.3] and HAPIEE (n = 20 517; HR, 2.6-10.5) samples. While SCORE calibration was good in most MONICA samples (predicted and observed mortality were close), the risk was underestimated in Russia. In HAPIEE, the high-risk SCORE overpredicted the estimated 10 year mortality for Czech and Polish samples and adequately predicted it for Russia. SCORE discrimination was satisfactory in both MONICA and HAPIEE. The high-risk SCORE underestimated the fatal CVD risk in Russian MONICA but performed well in most MONICA samples and Russian HAPIEE. This SCORE version might overestimate the risk in contemporary Czech and Polish populations.
    European Heart Journal 06/2013; · 14.72 Impact Factor
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    ABSTRACT: Studies have indicated hazardous consumption of large quantities of alcohol among adults in Lithuania. We assessed the associations of alcohol consumption at baseline with cancer incidence among men in a population-based cohort study, using Cox models adjusted for smoking, education and body mass index. Attained age was used as a time-scale. During follow-up (1978-2008) 1,698 men developed cancer. A higher amount of alcohol consumption (≥140.1 g/week vs. 0.1-10.0 g/week) was positively associated with increased risk of total cancer [hazard ratio (HR) = 1.36, 95 % confidence interval (95 % CI) 1.11, 1.65], upper aerodigestive tract cancer (HR = 2.79, 95 % CI 1.23, 6.34) and alcohol-related cancers (i.e. oral cavity, pharynx, larynx, oesophagus, colorectal and liver cancer) (HR = 1.88, 95 % CI 1.25, 2.85). Compared to occasional drinkers (a few times/year), drinkers 2-7 times/week showed an increased risk of total (HR = 1.45, 95 % CI 1.16, 1.83), alcohol-related (HR = 1.83 95 % CI 1.14, 2.93) and other cancers (HR = 1.35, 95 % CI 1.04, 1.76). Our results showed no statistically significant associations between quantity of alcohol intake per one occasion and risk of cancer. About 13 % of total, 35 % of upper aerodigestive tract, 22 % of alcohol-related and 10 % of other cancer cases were due to alcohol consumption in this cohort of men.
    European Journal of Epidemiology 05/2013; · 5.12 Impact Factor
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    ABSTRACT: INTRODUCTION: The development of left ventricular remodelling after acute myocardial infarction is a predictor of heart failure and mortality. The purpose of the present study was to assess whether the polymorphism of angiotensinogen (AGT) gene with threonine (T) instead of methionine (M) at amino acid 235 in exon 2 (M235T) had effects on cardiac remodelling after acute myocardial infarction. METHODS: One hundred and forty-one patients (mean age 56.4±11.1 years) with a first acute myocardial infarction were enrolled. Within 24-72 hours of the onset of the symptoms and at a four month period two-dimensional echocardiography was performed. Remodelling was defined as a 20% increase from the baseline in left ventricular end-diastolic volume. The genotypes of the study group were compared with the reference group (n=1010) genotypes. AGT M235T polymorphism was determined using polymerase chain reaction amplification. RESULTS: At follow-up, 49 patients (34.7%) were classified as having left ventricular remodelling. Anterior localization of the infarct (p=0.008), leucocyte count at admission (p=0.040), global left ventricular longitudinal strain (p=0.021) and MM genotype of AGT (p=0.024) were independent predictors of ventricular remodelling after myocardial infarction. CONCLUSIONS: Anterior wall infarction, increased leucocyte count, decreased longitudinal strain of left ventricular and polymorphism of AGT M235T may predict remodelling after myocardial infarction.
    Journal of Renin-Angiotensin-Aldosterone System 01/2013; · 2.29 Impact Factor
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    ABSTRACT: Mutation in SCARB1 gene, exon 8 rs5888, has been associated with altered lipid levels and cardiovascular risk in humans though the results have been inconsistent. We analysed the impact of SCARB1 single nucleotide polymorphism (SNP) rs5888 with plasma lipid profile and association with coronary artery disease (CAD) in a Lithuanian population characterized by high morbidity and mortality from CAD and high prevalence of hypercholesterolemia. The study included 1976 subjects from a random sample (reference group) and an myocardial infarction (MI) group of 463 patients. Genotyping of SCARB1 (rs5888) was carried out using the real-time polymerase chain reaction method. RESULTS/PRINCIPAL FINDINGS: Analysis of rs5888 C/T gene polymorphism in the reference group revealed that male TT genotype carriers (25-74 years) had significantly higher total cholesterol and triglyceride concentrations (5.70 mmol/l vs. 5.49 mmol/l; p = 0.036, and 1.70 mmol/l vs. 1.40 mmol/l, p = 0.023, respectively) than CT carriers and the oldest males (65-74 years) TT carriers had significantly higher high density lipoprotein cholesterol concentrations in comparison to heterozygous (1.52 mmol/l vs. 1.36 mmol/l, p = 0.033). The youngest female (25-44 years) TT genotype carriers had significantly lower low density lipoprotein cholesterol concentrations in comparison to C homozygous (2.59 mmol/l vs. 2.92 mmol/l, p = 0.023). The frequency of the SCARB1 TT genotype in the oldest male MI group (65-74 years) was significantly lower than in the corresponding reference group subjects (9.4% vs. 22.3%, p = 0.006). SCARB1 TT genotype was associated with decreased odds of MI in males aged 65-75 years (OR = 0.24, 95% CI 0.10-0.56, p = 0.001). SCARB1 polymorphism is associated with lipid metabolism and CAD in an age- and gender- dependent manner. Analysis of SCARB1 SNP rs5888 C/T genotypes revealed an atheroprotective phenotype of lipid profile in older men and in young women TT genotype carriers in the reference group. SCARB1 TT genotype was associated with decreased odds of MI in aged men.
    Lipids in Health and Disease 01/2013; 12:24. · 2.31 Impact Factor
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    ABSTRACT: BACKGROUND: The purpose of this study was to examine associations between cardiovascular risk factors and cognitive ability in middle aged and elderly Lithuanian urban population. METHODS: Data from the survey performed in the framework of the HAPIEE (Health, Alcohol, Psychosocial Factors in Eastern Europe) study were presented. A random sample of 7,087 individuals aged 45--72 years was screened in 2006--2008. RESULTS: The scores of immediate recall and delayed verbal recall, cognitive speed and attention were significantly lower in men than in women; yet numerical ability scores were higher in men. Significant associations between lowered cognitive functions and previous stroke (in male OR = 2.52; 95% CI = 1.75-3.64; in female OR = 2.45; 95% CI = 1.75, 3.64) as well as ischemic heart disease history (among male OR = 1.28; 95% CI = 1.03-1.60) have been determined. Higher level of physical activity in leisure time (among female OR = 1.32; 95% CI = 1.03-1.69), poor self-rated health (among male OR = 1.57; 95% CI = 1.15-2.14) and poor quality of life (in male OR = 1.67; 95% CI = 1.07-2.61; in female OR = 2.81; 95% CI = 1.92-4.11) were related to lowered cognitive function. CONCLUSIONS: The findings of the study suggest that associations between cardiovascular risk factors and lowered cognitive function among healthy middle-aged and elderly adults strongly depend on gender.
    BMC Neurology 11/2012; 12(1):149. · 2.56 Impact Factor
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    ABSTRACT: Background: Cancer of the pancreas is a relatively rare, but highly fatal cancer worldwide. Cigarette smoking has been recognized as an important risk factor, but the relation to other potential determinants is still inconsistent. We investigated the association between different lifestyle, biological and anthropometric factors and the risk of pancreatic cancer in a prospective population-based cohort study from Kaunas, Lithuania. Methods: Our study included 7132 urban men initially free from any diagnosed cancer, followed for up to 30 years. 77 incident cases of pancreatic cancer were identified. Cox proportional hazards regression models were used to estimate hazard ratios (HR) and corresponding 95% confidence intervals (95% CI). Results: Compared to never smokers, current smokers had a significantly increased risk of pancreatic cancer, HR was 1.79 (95% CI 1.03-3.09) after adjustment for age, body mass index, education and alcohol consumption. Among smokers, a significant association with higher smoking intensity was shown (≥20cigarettes/day: HR=2.60; 95% CI 1.42-4.76, P(trend)=0.046). We also observed a significantly increased risk for ≥30 pack-years of smoking (HR=2.24; 95% CI 1.12-4.49, P(trend)=0.16) and for age at starting smoking <18 years (HR=2.29; 95% CI 1.11-4.70, P(trend)=0.43) as compared to never smokers. Alcohol consumption, body mass index and total cholesterol level were not significantly associated with pancreatic cancer. Conclusions: Smoking significantly increases pancreatic cancer incidence and its high prevalence in Lithuania may partly explain high incidence of the disease. No convincing evidence was found that alcohol consumption, body mass index or serum cholesterol level were associated with pancreatic cancer risk, although the assessment was limited by the lack of statistical power.
    Cancer epidemiology. 10/2012;

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