[Show abstract][Hide abstract] ABSTRACT: Background
Variation in genes involved in alcohol metabolism is associated with drinking patterns worldwide. We compared variation in these genes among the Inuit with published results from the general population of Denmark and, due to the Asian ancestry of the Inuit, with Han Chinese. We analysed the association between gene variations and drinking patterns among the Inuit.
We genotyped 4,162 Inuit participants from two population health surveys. Information on drinking patterns was available for 3,560. Seven single nucleotide polymorphisms (SNPs) were examined: ADH1B arg48his, ADH1C ile350val, ADH1C arg272gln, ALDH2 glu504lys, ALDH2 5’-UTR A-357G, ALDH1B1 ala86val and ALDH1B1 arg107leu.
The allele distribution differed significantly between Inuit and the general population of Denmark. A protective effect on heavy drinking was found for the TT genotype of the ALDH1B1 arg107leu SNP (OR = 0.59; 95% CI 0.37-0.92), present in 3% of pure Inuit and 37% of Danes. The ADH1C GG genotype was associated with heavy drinking and a positive CAGE test (OR 1.34; 95% CI 1.05-1.72). It was present in 27% of Inuit and 18% of Danes. The Asian genotype pattern with a high frequency of the ADH1B A allele and an ALDH2 gene coding for an inactive enzyme was not present in Greenland.
ADH1C and ALDH1B1 arg107leu SNPs play a role in the shaping of drinking patterns among the Inuit in Greenland. A low frequency of the ALDH1B1 arg107leu TT genotype compared with the general population in Denmark deserves further study. This genotype was protective of heavy drinking among the Inuit.
Drug and Alcohol Dependence 09/2014; · 3.28 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: In 11 680 individuals (18-85 years) maximal oxygen consumption (VO2max) was estimated indirectly in a maximal cycle test using a prediction model developed in a young population (15-28 years). A subsample of 182 individuals (23-77 years) underwent 2 maximal cycle tests with VO2max estimated indirectly in both tests and measured directly in one test. Agreement between the direct measurement and the indirect estimate of VO2max and repeatability of the indirect estimates of VO2max were examined by Bland-Altman plots, limits of agreement (LOA) and coefficient of repeatability (CR). The indirect method (mean VO2max=3 132 ml · min(-1)) underestimated VO2max as compared to the direct method (mean VO2max=3 190 ml · min(-1)) in men (bias: 58 ml · min(-1) (95% LOA-450 and 565)) and overestimated VO2max in women (mean VO2max=2 328 vs. 2 258 ml · min(-1), bias: - 70 ml · min(-1) (95% LOA-468 and 328)). The mean difference between the 2 indirect estimates was non-significant (men: - 11.9 ml · min(-1), women: 18.3 ml · min(-1)) with a CR of 279 ml · min(-1) (8.9%) in men and 274 ml · min(-1) (11.7%) in women. The validity of the indirect method was good despite minor sex-specific bias. Owing to this bias we suggest a new prediction model of VO2max. The maximal cycle test was highly repeatable.
International Journal of Sports Medicine 09/2014; · 2.37 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Recent studies suggest that physical inactivity as well as sitting time are associated with metabolic syndrome. Our aim was to examine joint associations of leisure time physical activity and total daily sitting time with metabolic syndrome.
[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: A variety of smoking cessation aids are available; however, the majority of smokers quit unaided. We know little of the differences between users and non-users of cessation support.
[Show abstract][Hide abstract] ABSTRACT: Objective
To study whether demographic and smoking-related characteristics are associated with participation (reach) in a smoking cessation trial and subsequent use (uptake) of two specific smoking interventions (internet-based program and proactive telephone counseling).
We used data from a four-arm randomized smoking cessation trial (2011). Participants (n = 1,809) were recruited among 9,924 smokers who previously participated in two health surveys in Denmark (2007-2008 and 2010). Interventions were: 1) an internet-based smoking cessation program 2) proactive telephone counseling, 3) reactive telephone counseling, 4) self-help booklet.
Reach (defined as the proportion accepting to participate in the trial of those invited) was highest among persons aged 40-59, women, heavy smokers and persons with long education. Among trial participants, uptake (defined as any use of the specific intervention at one-month follow-up) was 69% for the internet-based program, 74% and 9 % for proactive and reactive telephone counseling, and 84% for the self-help booklet. Young age was associated with uptake of the internet-based program and short education was associated with using proactive telephone counseling.
Internet-based interventions and proactive telephone counseling appeal to different age and educational groups. Further, offering similar intervention content by a proactive and a reactive approach can be associated with different intervention uptake.
[Show abstract][Hide abstract] ABSTRACT: Evidence suggests that sitting time is adversely associated with health risks. However, previous epidemiological studies have mainly addressed mortality whereas little is known of the risk of coronary heart disease. This study aimed to investigate total sitting time and risk of myocardial infarction, coronary heart disease incidence and all-cause mortality.
In the Danish Health Examination Survey (DANHES) conducted in 2007-2008 we tested the hypothesis that a higher amount of daily total sitting time is associated with greater risk of myocardial infarction, coronary heart disease and all-cause mortality. The study population consisted of 71,363 men and women aged 18-99 years without coronary heart disease. Participants were followed for myocardial infarction, coronary heart disease and mortality in national registers to August 10, 2012. Cox regression analyses were performed with adjustment for potential confounders and multiple imputation for missing values.
During a mean follow-up period of 5.4 years 358 incident cases of myocardial infarction, 1,446 of coronary heart disease, and 1,074 deaths from all causes were registered. The hazard ratios associated with 10 or more hours of daily sitting compared to less than 6 hours were 1.38 (95% CI: 1.01, 1.88) for myocardial infarction, 1.07 (95% CI: 0.91, 1.27) for coronary heart disease and 1.31 (95% CI: 1.09, 1.57). Compared to sitting less than 6 hours per day and being physically active in leisure time, the hazard ratios of sitting more than 10 hours per day and also being physically inactive in leisure time were 1.80 (95% CI: 1.15, 2.82) for myocardial infarction, 1.42 (95% CI: 1.11, 1.81) for coronary heart disease, and 2.29 (95% CI: 1.82, 2.89) for all-cause mortality.
The results suggest that a higher amount of daily total sitting time is associated with all-cause mortality, particularly among inactive adults. In relation to coronary heart, disease results were less clear. This paper adds new evidence to the limited data on the evidence of sitting time and cardiovascular disease and mortality.
International Journal of Behavioral Nutrition and Physical Activity 02/2014; 11(1):13. · 3.68 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The aim was to compare the effectiveness of untailored text messages for smoking cessation to tailored text messages delivered at a higher frequency. From February 2007 to August 2009, 2030 users of an internet-based smoking cessation program with optional text message support aged 15-25 years were consecutively randomized to versions of the program that offered either tailored or untailored text messages. Thirty-day point abstinence from smoking was measured self-reportedly at 12-months follow-up. Response rates were 36.3% and 38.1% in the tailored and untailored group, respectively. We analyzed the entire study population, as well as those opting for text messages (n = 1619). In intention-to-treat analysis with multiple imputation of missing data, the odds ratio for 30-day point abstinence was 1.28 (95% CI 0.91-2.08) for the tailored compared with untailored messages. When restricting the analysis to those who had chosen to receive text messages, the corresponding odds ratio was 1.45 (95% CI 1.01-2.08). The higher long-term quit rates in the group receiving the tailored text messages compared with untailored text messages in the restricted analysis indicated that tailoring and higher frequency of text messages increases quit rates among young smokers.
Health Education Research 01/2014; · 1.66 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The belief that alcohol makes you cheerful is one of the main reasons for engaging in high-risk drinking, especially among young adults. The aim of the study was to investigate the association between blood alcohol content (BAC) and cheerfulness, focus distraction, and sluggishness among students attending high school parties.
Participants included 230 students attending high school parties. BAC, measured by use of a breath analyzer, self-reported cheerfulness (on a score from 0 to 16), focus distraction (score from 0 to 8), and sluggishness (score from 0 to 4) were assessed several times during the party. Data were analyzed by means of linear regression, including robust standard errors and stratified on sex.
For girls, cheerfulness increased up to a BAC of 0.113 g% and decreased at higher BACs. At BACs of 0.020, 0.050, 0.100, and 0.150 g% cheerfulness was 11.0 (95% confidence interval [CI]: 10.4 to 11.6), 12.4 (95% CI: 11.8 to 12.9), 13.5 (95% CI: 13.0 to 14.0), and 13.1 (95% CI: 11.9 to 14.4), respectively. For boys, the association was linear with an increase of 0.18 points in cheerfulness (95% CI: 0.01 to 0.36) for every 0.010 g% increase in BAC. Focus distraction increased with increasing BAC: 0.22 (95% CI: 0.16 to 0.28) and 0.24 (95% CI: 0.14 to 0.33) points for girls and boys, respectively, per 0.010 g% increase in BAC. The degree of sluggishness increased only slightly with increasing BAC with 0.02 (95% CI: 0.02 to 0.05) and 0.03 (95% CI: -0.01 to 0.07) points for every 0.010 g% increase in BAC for girls and boys, respectively.
Cheerfulness increased up to a certain BAC value for girls, while it increased linearly for boys. Focus distraction increased with increasing BAC.
Alcoholism Clinical and Experimental Research 10/2013; · 3.31 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The aim of the study was to quantify alcohol-attributable and -preventable mortality, totally and stratified on alcohol consumption in Denmark 2010, and to estimate alcohol-related mortality assuming different scenarios of changes in alcohol distribution in the population. We estimated alcohol-attributable and -preventable fractions based on relative risks of conditions causally associated with alcohol from meta-analyses and information on alcohol consumption in Denmark obtained from 14,458 participants in the Danish National Health Survey 2010 and corrected for adult per capita consumption. Cause-specific mortality data were obtained from the Danish Register of Causes of Death. In total, 1,373 deaths among women (5.0 % of all deaths) and 2,522 deaths among men (9.5 % of all deaths) were attributable to alcohol, while an estimated number of 765 (2.8 %) and 583 (2.2 %) deaths were prevented by alcohol. Of the alcohol-attributable deaths, 73 and 81 % occurred within the high alcohol consumption group (>14/21 drinks/week for women/men). A reduction of 50 % in the alcohol consumption was associated with a decrease of 1,406 partly alcohol-attributable deaths (46 %) and 37 alcohol-preventable deaths (3 %). Total compliance with sensible drinking guidelines with a low risk limit (<7/14 drinks/week) and a high risk limit (<14/21 drinks/week) was associated with a reduction of 2,380 and 1,977 alcohol-attributable deaths, respectively. In summary, 5.0 % of deaths among women and 9.5 % of deaths among men were attributable to alcohol in Denmark 2010. The minority of Danish women and men had high alcohol consumption (16 and 26 %). However, the majority of all alcohol-attributable deaths among women and men were caused by high consumption (73 and 81 %).
European Journal of Epidemiology 10/2013; · 5.15 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: To investigate the joint association between self-reported physical activity as well as cardiorespiratory fitness and self-rated health among healthy women and men.
Data from 10,416 participants in The Danish Health Examination Survey 2007-2008 which took part in 13 Danish municipalities were analyzed. Leisure time physical activity level and self-rated health was based on self-reported questionnaire data. Optimal self-rated health was defined as "very good" or "good" self-rated health. Cardiorespiratory fitness (mL O2* min-1*kg-1) was estimated from maximal power output in a maximal cycle exercise test.
A strong dose-response relation between cardiorespiratory fitness and self-rated health as well as between physical activity level and self-rated health among both women and men was found. Within categories of physical activity, odds ratios (ORs) for optimal self-rated health increased with increasing categories of cardiorespiratory fitness, and vice versa. Hence, participants who were moderately/vigorously physically active and had a high cardiorespiratory fitness had the highest OR for optimal self-rated health compared with sedentary participants with low cardiorespiratory fitness (OR=12.2, 95% CI: 9.3-16.1).
Although reluctant to conclude on causality, this study suggests that an active lifestyle as well as good cardiorespiratory fitness probably increase self-rated health.
[Show abstract][Hide abstract] ABSTRACT: Alcohol hangover is a growing research area, but differences across the life span have not been assessed. Here, we test the hypothesis that the severity of hangovers depends on age.
A cross-sectional study of 51,645 men and women aged 18 to 94 years old, who participated in the population-based Danish Health Examination Study (DANHES) in Denmark between 2007 and 2008, formed the database for our study.
The incidence of severe hangover was lower among older than younger participants. Odds ratios for experiencing severe hangover following an episode of binge drinking were 6.8, 4.8, 3.0, and 2.0 among the 18 to 29, 30 to 39, 40 to 49, and 50 to 59-year-old men, compared with those aged 60+ years. For women, similar results were obtained. This finding could not be explained by the usual amount of alcohol consumption, frequency of binge drinking, or the proportion of alcohol consumed with meals.
We found that hangover following engagement in binge drinking is much more common in the young than in the older age groups.
Alcoholism Clinical and Experimental Research 09/2013; · 3.31 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: To systematically review and summarize the evidence of an association between preoperative smoking status and postoperative complications elaborated on complication type.
The conclusions of studies examining the association between preoperative smoking and postoperative complications are inconsistent, thus there is a need for a review and meta-analysis to summarize the existing evidence.
A systematic review and meta-analysis based on a search in MEDLINE, EMBASE, CINAHL, and PsycINFO. Included were original studies of the association between smoking status and postoperative complications occurring within 30 days of operation. In total, 9354 studies were identified and reviewed for eligibility and data were extracted. Forest plots and summarized relative risks (RR) including 95% confidence intervals (CIs) were estimated for various complication types.
Of the 9354 identified studies, 107 studies were included in the meta-analyses and based on these, 157 data sets were extracted. Preoperative smoking was associated with an increased risk of various postoperative complications including general morbidity (RR = 1.52, 95% CI: 1.33-1.74), wound complications (RR = 2.15, 95% CI: 1.87-2.49), general infections (RR = 1.54, 95% CI: 1.32-1.79), pulmonary complications (RR = 1.73, 95% CI: 1.35-2.23), neurological complications (RR = 1.38, 95% CI: 1.01-1.88), and admission to intensive care unit (RR = 1.60, 95% CI: 1.14-2.25). Preoperative smoking status was not observed to be associated with postoperative mortality, cardiovascular complications, bleedings, anastomotic leakage, or allograft rejection.
Preoperative smoking was found to be associated with an increased risk of the following postoperative complications: general morbidity, wound complications, general infections, pulmonary complications, neurological complications, and admission to the intensive care unit.
[Show abstract][Hide abstract] ABSTRACT: Objectives. We investigated associations of smoking and coronary heart disease (CHD) by age. Methods. Data came from the Pooling Project on Diet and Coronary Heart Disease (8 prospective studies, 1974-1996; n = 192 067 women and 74 720 men, aged 40-89 years). Results. During follow-up, 4326 cases of CHD were reported. Relative to never smokers, CHD risk among current smokers was highest in the youngest and lowest in the oldest participants. For example, among women aged 40 to 49 years the hazard ratio was 8.5 (95% confidence interval [CI] = 5.0, 14) and 3.1 (95% CI = 2.0, 4.9) among those aged 70 years or older. The largest absolute risk differences between current smokers and never smokers were observed among the oldest participants. Finally, the majority of CHD cases among smokers were attributable to smoking. For example, attributable proportions of CHD by age group were 88% (40-49 years), 81% (50-59 years), 71% for (60-69 years), and 68% (70+ years) among women who smoked. Conclusions. Among smokers, the majority of CHD cases are attributable to smoking in all age groups. Smoking prevention is important, irrespective of age. (Am J Public Health. Published online ahead of print June 13, 2013: e1-e7. doi:10.2105/AJPH.2012.301091).
American Journal of Public Health 06/2013; · 3.93 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: PURPOSE: Risk factors for adolescent alcohol use are typically conceptualized at the individual level, and school- and community-level risk factors have received little attention. Based on the theoretical understanding of youth alcohol consumption as a reflection of community social practice, we analyzed whether adolescent drunkenness was related to community-level adult alcohol use (AAC), when taking individual and school-level risk factors for drunkenness into account. Furthermore, we investigated whether the association between community-level AAC and adolescent drunkenness was attenuated after inclusion of parental drinking. METHODS: We used data from three sources: data about adolescent drunkenness from the Health Behavior in School-Aged Children 2010 survey (N = 2,911; 13- to 15-year-olds nested in 175 school classes and 51 schools); data about community-level AAC derived from the Danish National Health Survey 2010 (177,639 participants); and data on school-level variables from Health Behavior in School-Aged Children School Leader Survey 2010. We performed multilevel logistic regression analysis with data from students nested within school classes and schools. RESULTS: Overall, 33.5% of students had been drunk twice or more. High community-level AAC was significantly associated with adolescent drunkenness (odds ratio [95% confidence interval], 1.94 [1.21-3.11]). Parental drinking was strongly related to adolescent drunkenness but did not attenuate the relationship between community-level AAC and adolescent drunkenness. We found no association between adolescent drunkenness and school-level variables (youth friendly environment, alcohol education, and exposure to alcohol outlets). CONCLUSIONS: Adolescent drunkenness was associated with community-level AAC and was not explained by parental drinking.
Journal of Adolescent Health 06/2013; · 2.75 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: OBJECTIVE:: To systematically review and summarize the evidence of the association between preoperative alcohol consumption and postoperative complications elaborated on complication type. BACKGROUND:: Conclusions in studies on preoperative alcohol consumption and postoperative complications have been inconsistent. METHODS:: A systematic review and meta-analysis based on a search in MEDLINE, EMBASE, CINAHL, and PsycINFO citations. Included were original studies of the association between preoperative alcohol consumption and postoperative complications occurring within 30 days of the operation. In total, 3676 studies were identified and reviewed for eligibility, and data were extracted. Forest plots and pooled relative risks (RRs), including 95% confidence intervals (CIs), were estimated for several complication types. RESULTS:: Fifty-five studies provided data for estimates. Preoperative alcohol consumption was associated with an increased risk of various postoperative complications, including general morbidity (RR = 1.56; 95% CI: 1.31-1.87), general infections (RR = 1.73; 95% CI: 1.32-2.28), wound complications (RR = 1.23; 95% CI: 1.09-1.40), pulmonary complications (RR = 1.80; 95% CI: 1.30-2.49), prolonged stay at the hospital (RR = 1.24; 95% CI: 1.18-1.31), and admission to intensive care unit (RR = 1.29; 95% CI: 1.03-1.61). Clearly defined high alcohol consumption was associated with increased risk of postoperative mortality (RR = 2.68; 95% CI: 1.50-4.78). Low to moderate preoperative alcohol consumption and postoperative complications did not seem to be associated; however, very few studies were included in the analyses hereof. CONCLUSIONS:: Preoperative alcohol consumption was associated with an increased risk of general postoperative morbidity, general infections, wound complications, pulmonary complications, prolonged stay at the hospital, and admission to intensive care unit.