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Aims Whether high body mass index (BMI) causally influences development and prognosis of heart failure has implications for clinical practice. We tested the hypotheses that high BMI causally influences heart failure incidence and mortality. Methods and results Using observational and Mendelian randomization causal, genetic analyses, we studied 106 121 individuals from the Copenhagen General Population Study, 18 407 from the Copenhagen City Heart Study, and 977 323 from publicly available databases. In observational analyses in the Copenhagen studies with 10 years of median follow-up, multivariable adjusted hazard ratios per 1 kg/m2 increment of BMI were 1.06 (95% confidence interval: 1.05–1.07; P < 0.001; n = 124 528; events = 6589) for heart failure incidence, 1.04 (1.03–1.06; P < 0.001; n = 124 528; events = 1237) for heart failure mortality, and 1.01 (1.00–1.01; P < 0.001; n = 124 528; events = 24 144) for all-cause mortality. In genetic analyses in the Copenhagen studies, the age and sex adjusted causal risk ratios per 1 kg/m2 increment of BMI were 1.19 (1.05–1.36; P = 0.008; n = 118 200; events = 6541) for heart failure incidence, 1.27 (0.82–1.98; P = 0.28; n = 118 200; events = 889) for heart failure mortality, and 1.11 (1.02–1.22; P = 0.022; n = 118 200; events = 16 814) for all-cause mortality. Finally, combining genetic data from the Copenhagen studies, the Genetic Investigation of ANthropometric Traits, the Heart Failure Molecular Epidemiology for Therapeutic Targets, and the UK Biobank, the unadjusted causal risk ratios per 1 kg/m2 increment of BMI were 1.39 (1.27–1.52; P < 0.001; n = 1 095 523; events = 53 850) for heart failure incidence, 1.18 (1.00–1.38; P = 0.05; n = 576 853; events = 2373) for heart failure mortality, and 1.02 (1.00–1.04; P = 0.03; n = 576 853; events = 44 734) for all-cause mortality. Conclusion High BMI causally increases the risk of both heart failure incidence and mortality. [ABSTRACT FROM AUTHOR]

The Mendelian randomization approach is an epidemiological study design incorporating genetic information into traditional epidemiological studies to infer causality of biomarkers, risk factors, or lifestyle factors on disease risk. Mendelian randomization studies often draw on novel information generated in genome-wide association studies on causal associations between genetic variants and a risk factor or lifestyle factor. Such information can then be used in a largely unconfounded study design free of reverse causation to understand if and how risk factors and lifestyle factors cause cardiovascular disease. If causation is demonstrated, an opportunity for prevention of disease is identified; importantly however, before prevention or treatment can be implemented, randomized intervention trials altering risk factor levels or improving deleterious lifestyle factors needs to document reductions in cardiovascular disease in a safe and side-effect sparse manner. Documentation of causality can also inform on potential drug targets, more likely to be successful than prior approaches often relying on animal or cell studies mainly. The present review summarizes the history and background of Mendelian randomization, the study design, assumptions for using the design, and the most common caveats, followed by a discussion on advantages and disadvantages of different types of Mendelian randomization studies using one or more samples and different levels of information on study participants. The review also provides an overview of results on many of the risk factors and lifestyle factors for cardiovascular disease examined to date using the Mendelian randomization study design. [ABSTRACT FROM AUTHOR]