AddictionGenomicsPeer-reviewed journal article
Estimating causality between smoking and abdominal obesity by Mendelian randomization
Germán D. CarrasquillaiD, Mario García‐UreñaiD, María J Romero-LadoiD, Tuomas O. KilpeläineniD
Abstract
Background and AimsSmokers tend to have a lower body weight than non-smokers, but also more abdominal fat. It remains unclear whether the relationship between smoking and abdominal obesity is causal. Previous Mendelian randomization studies have investigated this relationship by relying on a single genetic variant for smoking heaviness. This approach is sensitive to pleiotropic effects and may produce imprecise causal estimates. We aimed to assess causality between smoking and abdominal obesity using multiple genetic instruments. MethodsWe used GWAS results for smoking initiation (n=1,232,091), lifetime smoking (n=462,690) and smoking heaviness (n=337,334) as exposure traits, and waist-hip ratio (WHR) and waist and hip circumferences (WC and HC) (n up to 697,734), with and without adjustment for body mass index (adjBMI), as outcome traits. We implemented Mendelian randomization using the CAUSE and LHC-MR methods that instrument smoking using genome-wide data. ResultsBoth CAUSE and LHC-MR indicated a positive causal effect of smoking initiation on WHR (0.13 [95%CI 0.10, 0.16] and 0.49 [0.41, 0.57], respectively) and WHRadjBMI (0.07 [0.03, 0.10] and 0.31 [0.26, 0.37]). Similarly, they indicated a positive causal effect of lifetime smoking on WHR (0.35 [0.29, 0.41] and 0.44 [0.38, 0.51]) and WHRadjBMI (0.18 [0.13, 0.24] and 0.26 [0.20, 0.31]). In follow-up analyses, smoking particularly increased visceral fat. There was no evidence of a mediating role by cortisol or sex hormones. ConclusionsSmoking initiation and higher lifetime smoking may lead to abdominal fat distribution. The increase in abdominal fat due to smoking was characterized by an increase in visceral fat. Thus, efforts to prevent and cease smoking can have the added benefit of reducing abdominal fat.
- organism: human
- assay: GWAS summary statistics
- research focus: GWAS / association mapping
- research focus: benchmarking
- approach: statistical model
- approach: Bayesian inference
- Citations
- 40
- FWCI
- 10.58
- Field percentile
- top 1%
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