Interaction of obesity polygenic score with lifestyle risk factors in an electronic health record biobank

Abstract

Background Genetic and lifestyle factors have considerable effects on obesity and related diseases, yet their effects in a clinical cohort are unknown. This study in a patient biobank examined associations of a BMI polygenic risk score (PRS), and its interactions with lifestyle risk factors, with clinically measured BMI and clinical phenotypes. Methods The Mass General Brigham (MGB) Biobank is a hospital-based cohort with electronic health record, genetic, and lifestyle data. A PRS for obesity was generated using 97 genetic variants for BMI. An obesity lifestyle risk index using survey responses to obesogenic lifestyle risk factors (alcohol, education, exercise, sleep, smoking, and shift work) was used to dichotomize the cohort into high and low obesogenic index based on the population median. Height and weight were measured at a clinical visit. Multivariable linear cross-sectional associations of the PRS with BMI and interactions with the obesity lifestyle risk index were conducted. In phenome-wide association analyses (PheWAS), similar logistic models were conducted for 675 disease outcomes derived from billing codes. Results Thirty-three thousand five hundred eleven patients were analyzed (53.1% female; age 60.0 years; BMI 28.3 kg/m2), of which 17,040 completed the lifestyle survey (57.5% female; age: 60.2; BMI: 28.1 (6.2) kg/m2). Each standard deviation increment in the PRS was associated with 0.83 kg/m2 unit increase in BMI (95% confidence interval (CI) =0.76, 0.90). There was an interaction between the obesity PRS and obesity lifestyle risk index on BMI. The difference in BMI between those with a high and low obesogenic index was 3.18 kg/m2 in patients in the highest decile of PRS, whereas that difference was only 1.55 kg/m2 in patients in the lowest decile of PRS. In PheWAS, the obesity PRS was associated with 40 diseases spanning endocrine/metabolic, circulatory, and 8 other disease groups. No interactions were evident between the PRS and the index on disease outcomes. Conclusions In this hospital-based clinical biobank, obesity risk conferred by common genetic variants was associated with elevated BMI and this risk was attenuated by a healthier patient lifestyle. Continued consideration of the role of lifestyle in the context of genetic predisposition in healthcare settings is necessary to quantify the extent to which modifiable lifestyle risk factors may moderate genetic predisposition and inform clinical action to achieve personalized medicine.

Study of the Combined Effect of Maternal Tobacco Smoking and Polygenic Risk Scores on Birth Weight and Body Mass Index in Childhood

“…The best PRSs were robustly associated with the phenotypic traits but only explained ∼4% of the phenotypic variation ( R 2 ). In HELIX, the variation explained by the PRS of BMI that included 60,993 SNPs ( R 2 = 4.7%) was in the range or slightly lower than previous estimations in children (R 2 = 3%, 2 M SNPs ( Odintsova et al, 2021 ); R 2 = 11%, 2.1 M SNPs ( Hüls et al, 2021 )), in adolescents ( R 2 = 6.5%, 941 SNPs; Xie et al, 2020 ), or in adult individuals ( R 2 = 2.9%, 97 SNPs ( Dashti et al, 2022 ); R 2 = 5.2%, 376 SNPs ( Sulc et al, 2020 ); R 2 = 6.7%, 2 M SNPs ( Odintsova et al, 2021 ); R 2 = 7.8%, 2.1 M SNPs ( Khera et al, 2019 )). In Khera et al, children in the 10th percentile of the PRS for BMI, which included 2.1 M SNPs, weighed 3.5 kg more than children in the lowest percentile.…”

“…Similarly, in our data, the PRS of BMI was not correlated with the PRS of BW or vice-versa. In contrast, the PRSs of BMI-related traits (BMI, FM, and WC) were correlated among them, suggesting pleiotropic genetic effects underlying these phenotypic associations, as suggested earlier ( Vogelezang et al, 2020 ; Dashti et al, 2022 ). In our study and using the PRSice v2 tool, the addition of more SNPs in the PRS of BMI did not increase the predictive power.…”

“…The GxE analyses showed low evidence of interactive effects for BMI-related traits between the PRSs and maternal smoking during pregnancy. In contrast, other studies have reported GxE interactions between BMI genetic predisposition with a PRS including 2.1 M SNPs, and geographic region in Europe, parental education, fiber intake, or screen time ( Hüls et al, 2021 ), and between a PRS with 97 SNPs and obesity lifestyle risk index that includes alcohol intake, education, exercise, sleep habits, shift work, and own smoking ( Dashti et al, 2022 ). In addition, a recent study, based on a new statistical method that does not require environmental factors to be measured, has reported that GxE interactions account for 1.9% of the BMI variance, while the PRS alone for 5.2% ( Sulc et al, 2020 ).…”

“…In particular in the case of BW, but also for other traits where the phenotype is influenced by the direct effect of the fetal genotype and the indirect effect of the maternal genotype that controls the intrauterine environment, pleiotropy has an additional degree of complexity ( Warrington et al, 2019 ). Through a phenotype-wide association study (PheEWAS), a PRS for BMI was found to be related to 40 disease outcomes, spanning endocrine/metabolic, circulatory, and other disease groups ( Dashti et al, 2022 ).…”

“…Finally, PRS can also be used to stratify the environmental risk to disease by conducting gene by environmental (GxE) interaction analyses ( Domingue et al, 2020 ; Lewis and Vassos, 2020 ). Some studies have shown the interaction of PRSs for BMI with specific environmental factors such as parental education ( Hüls et al, 2021 ), with obesity lifestyle index ( Dashti et al, 2022 ), or with all potential environmental exposures estimated from the data ( Sulc et al, 2020 ).…”