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Estimating the causal effect of BMI on mortality risk in people with heart disease, diabetes and cancer using Mendelian randomization

David Jenkins, Kaitlin H. Wade, David Carslake, Jack Bowden, Naveed Sattar, Ruth J. F. Loos, Nicholas J. Timpson, Matthew Sperrin, Martin K. Rutter

2021International Journal of Cardiology19 citationsDOIOpen Access PDF

Abstract

BACKGROUND: Observational data have reported that being overweight or obese, compared to being normal weight, is associated with a lower risk for death - the "obesity paradox". We used Mendelian randomization (MR) to estimate causal effects of body mass index (BMI) on mortality risks in people with coronary heart disease (CHD), type 2 diabetes mellitus (T2DM) or malignancy in whom this paradox has been often reported. METHODS: We studied 457,746 White British UK Biobank participants including three subgroups with T2DM (n = 19,737), CHD (n = 21,925) or cancer (n = 42,612) at baseline and used multivariable-adjusted Cox models and MR approaches to describe relationships between BMI and mortality risk. RESULTS: ). In all participants, MR analyses showed a positive linear causal effect of BMI on mortality risk (HR for mortality per unit higher BMI: 1.05; 95% CI: 1.03-1.08), also evident in people with CHD (HR: 1.08; 95% CI: 1.01-1.14). Point estimates for hazard ratios across all BMI values in participants with T2DM and cancer were consistent with overall positive linear effects but confidence intervals included the null. CONCLUSION: These data support the idea that population efforts to promote intentional weight loss towards the normal BMI range would reduce, not enhance, mortality risk in the general population including, importantly, individuals with CHD.

Topics & Concepts

MedicineMendelian randomizationOverweightBody mass indexHazard ratioPopulationInternal medicineProportional hazards modelDemographyObesityObesity paradoxObservational studyConfidence intervalEnvironmental healthGenetic variantsChemistrySociologyGenotypeBiochemistryGeneGenetic Associations and EpidemiologyBRCA gene mutations in cancerAdvanced Causal Inference Techniques