Abstract
BackgroundGenetic 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.MethodsThe 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.ResultsThirty-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.ConclusionsIn 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.
Highlights
Genetic and lifestyle factors have considerable effects on obesity and related diseases, yet their effects in a clinical cohort are unknown
There was an interaction between the obesity polygenic risk score (PRS) and obesity lifestyle risk index on Body mass index (BMI) (Pint = 7.1 × 10−6)
In an analysis of adult patients in a clinical biobank, we observed (1) that an obesity PRS was robustly associated with clinically measured BMI; (2) an interaction between an obesity PRS and an obesity lifestyle risk index, such that among patients with a higher obesity genetic risk, an obesogenic lifestyle exacerbated the genetic risk, regardless of patient morbidity; (3) in phenome-wide association analyses (PheWAS), that an obesity PRS was associated with novel and known diseases spanning multiple categories; and (4) that an obesogenic lifestyle did not modify the associations between an obesity PRS and disease outcomes derived from billing codes
Summary
Genetic and lifestyle factors have considerable effects on obesity and related diseases, yet their effects in a clinical cohort are unknown. For the adoption of personalized medicine into healthcare practice, examining the transferability of obesity genetic findings in a healthcare setting is essential to inform clinical action. Both genetic and lifestyle factors have considerable effects on obesity and related diseases. Twin studies support the role of an obesogenic environment on the phenotypic effects of obesity-related genes [11] Based on these findings from general community settings of population-based cohorts, emphasizing a healthy lifestyle among genetically at-risk individuals may be clinically impactful, but this approach has not been tested in patient biobanks. Elucidating the relationship between the genetics of obesity and other diseases may help to prioritize diseases that will inevitably increase in prevalence because of the obesity epidemic
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