Franco Giulianini1, Bastiaan Geelhoed2, Kathryn L Lunetta3, Jeffrey R Misialek4, Maartje N Niemeijer5, Michiel Rienstra2, Lynda M Rose1, Albert V Smith6, Neal A Chatterjee1, Dan E Arking7, Patrick T Ellinor8, Jan Heeringa5, Honghuang Lin3,9, Steven A Lubitz8, Elsayed Z Soliman10, Niek Verweij2, Alvaro Alonso11, Emelia J Benjamin3, Vilmundur Gudnason6, Bruno H C Stricker5, Pim Van Der Harst2, Daniel I Chasman1, Christine M Albert1,12. 1. Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. 2. Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands. 3. The Framingham Heart Study, Framingham, MA, USA; Cardiology and Preventive Medicine Sections, Boston University School of Medicine, Epidemiology Department, Boston University School of Public Health, Boston, MA, USA. 4. Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA. 5. Department of Epidemiology, Erasmus Medical Center-University Medical Center, Rotterdam, The Netherlands. 6. Icelandic Heart Association, Research Institute, Kpoavogur, Iceland and University of Iceland, Reykjavik, Iceland. 7. McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. 8. Cardiovascular Research Center and Cardiac Arrhythmia Service, Massachusetts General Hospital, Boston, MA, USA and Program in Medical and Population Genetics, The Broad Institute of Harvard and MIT, Cambridge, MA, USA. 9. Computational Biomedicine Section, Boston University School of Medicine, Boston, MA, USA. 10. Epidemiological Cardiology Research Center (EPICARE), Wake Forest School of Medicine, Winston Salem, NC, USA. 11. Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA. 12. Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.
Abstract
BACKGROUND: Observational studies have identified an association between body mass index (BMI) and incident atrial fibrillation (AF). Inferring causality from observational studies, however, is subject to residual confounding, reverse causation, and bias. The primary objective of this study was to evaluate the causal association between BMI and AF by using genetic predictors of BMI. METHODS: We identified 51 646 individuals of European ancestry without AF at baseline from 7 prospective population-based cohorts initiated between 1987 and 2002 in the United States, Iceland, and the Netherlands with incident AF ascertained between 1987 and 2012. Cohort-specific mean follow-up ranged from 7.4 to 19.2 years, over which period there was a total of 4178 cases of incident AF. We performed a Mendelian randomization with instrumental variable analysis to estimate a cohort-specific causal hazard ratio for the association between BMI and AF. Two genetic instruments for BMI were used: FTO genotype (rs1558902) and a BMI gene score comprising 39 single-nucleotide polymorphisms identified by genome-wide association studies to be associated with BMI. Cohort-specific estimates were combined by random-effects, inverse variance-weighted meta-analysis. RESULTS: In age- and sex-adjusted meta-analysis, both genetic instruments were significantly associated with BMI (FTO: 0.43 [95% confidence interval, 0.32-0.54] kg/m2 per A-allele, P<0.001; BMI gene score: 1.05 [95% confidence interval, 0.90-1.20] kg/m2 per 1-U increase, P<0.001) and incident AF (FTO, hazard ratio, 1.07 [1.02-1.11] per A-allele, P=0.004; BMI gene score, hazard ratio, 1.11 [1.05-1.18] per 1-U increase, P<0.001). Age- and sex-adjusted instrumental variable estimates for the causal association between BMI and incident AF were hazard ratio, 1.15 (1.04-1.26) per kg/m2, P=0.005 (FTO) and 1.11 (1.05-1.17) per kg/m2, P<0.001 (BMI gene score). Both of these estimates were consistent with the meta-analyzed estimate between observed BMI and AF (age- and sex-adjusted hazard ratio 1.05 [1.04-1.06] per kg/m2, P<0.001). Multivariable adjustment did not significantly change findings. CONCLUSIONS: Our data are consistent with a causal relationship between BMI and incident AF. These data support the possibility that public health initiatives targeting primordial prevention of obesity may reduce the incidence of AF.
BACKGROUND: Observational studies have identified an association between body mass index (BMI) and incident atrial fibrillation (AF). Inferring causality from observational studies, however, is subject to residual confounding, reverse causation, and bias. The primary objective of this study was to evaluate the causal association between BMI and AF by using genetic predictors of BMI. METHODS: We identified 51 646 individuals of European ancestry without AF at baseline from 7 prospective population-based cohorts initiated between 1987 and 2002 in the United States, Iceland, and the Netherlands with incident AF ascertained between 1987 and 2012. Cohort-specific mean follow-up ranged from 7.4 to 19.2 years, over which period there was a total of 4178 cases of incident AF. We performed a Mendelian randomization with instrumental variable analysis to estimate a cohort-specific causal hazard ratio for the association between BMI and AF. Two genetic instruments for BMI were used: FTO genotype (rs1558902) and a BMI gene score comprising 39 single-nucleotide polymorphisms identified by genome-wide association studies to be associated with BMI. Cohort-specific estimates were combined by random-effects, inverse variance-weighted meta-analysis. RESULTS: In age- and sex-adjusted meta-analysis, both genetic instruments were significantly associated with BMI (FTO: 0.43 [95% confidence interval, 0.32-0.54] kg/m2 per A-allele, P<0.001; BMI gene score: 1.05 [95% confidence interval, 0.90-1.20] kg/m2 per 1-U increase, P<0.001) and incident AF (FTO, hazard ratio, 1.07 [1.02-1.11] per A-allele, P=0.004; BMI gene score, hazard ratio, 1.11 [1.05-1.18] per 1-U increase, P<0.001). Age- and sex-adjusted instrumental variable estimates for the causal association between BMI and incident AF were hazard ratio, 1.15 (1.04-1.26) per kg/m2, P=0.005 (FTO) and 1.11 (1.05-1.17) per kg/m2, P<0.001 (BMI gene score). Both of these estimates were consistent with the meta-analyzed estimate between observed BMI and AF (age- and sex-adjusted hazard ratio 1.05 [1.04-1.06] per kg/m2, P<0.001). Multivariable adjustment did not significantly change findings. CONCLUSIONS: Our data are consistent with a causal relationship between BMI and incident AF. These data support the possibility that public health initiatives targeting primordial prevention of obesity may reduce the incidence of AF.
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