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Kilpeläinen, T.

Publications and source records attributed to Kilpeläinen, T..

2 recordsLinked to original sources

Estimating the causal relationship between smoking and abdominal adiposity using Mendelian randomization

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.

genomics↗

Multi-ancestry analysis of gene-sleep interactions in 126,926 individuals identifies multiple novel blood lipid loci that contribute to our understanding of sleep-associated adverse blood lipid profile

Both short and long sleep are associated with an adverse lipid profile, likely through different biological pathways. To provide new insights in the biology of sleep-associated adverse lipid profile, we conducted multi-ancestry genome-wide sleep-SNP interaction analyses on three lipid traits (HDL-c, LDL-c and triglycerides). In the total study sample (discovery + replication) of 126,926 individuals from 5 different ancestry groups, when considering either long or short total sleep time interactions in joint analyses, we identified 49 novel lipid loci, and 10 additional novel lipid loci in a restricted sample of European-ancestry cohorts. In addition, we identified new gene-sleep interactions for known lipid loci such as LPL and PCSK9. The novel gene-sleep interactions had a modest explained variance in lipid levels: most notable, gene-short-sleep interactions explained 4.25% of the variance in triglyceride concentration. Collectively, these findings contribute to our understanding of the biological mechanisms involved in sleep-associated adverse lipid profiles.

genomics↗