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.

Keywords: BMI; Electronic health records; Gene-lifestyle interaction; Genetic risk; Lifestyle; Obesity; Obesogenic behaviors; Phenome-wide association study.

Fig. 1

Associations of obesity genetic risk and obesity lifestyle risk index with clinically measured BMI with effect modification by comorbidity in the Mass General Brigham Biobank. A Associations of the obesity PRS with clinically measured BMI in all 33,511 patients and associations stratified by lowest and highest morbidity based on the Charlson Comorbidity Index (10-yr survival probability). Effect estimates are derived from a multivariable linear regression model for BMI adjusted for age, sex, genotyping array, and 5 PCs of ancestry per SD of the PRS. B Association of the obesity lifestyle risk index with clinically measured BMI in all 17,040 patients and associations stratified by lowest and highest morbidity. Effect estimates are derived from a multivariable linear regression model for BMI adjusted for age and sex per SD of the obesity lifestyle risk index. Abbreviations: polygenic risk score (PRS), principal components (PCs), standard deviation (SD)

Fig. 2

Average clinically measured BMI by lowest and highest decile of obesity genetic risk and by obesity lifestyle risk index in an electronic health record biobank (n =17,040). P_int value is for the interaction term between the PRS and the obesity lifestyle risk index (both continuous) on BMI in a multivariable linear regression model adjusted for age, sex, genotyping array, and 5 PCs of ancestry adding both the PRS and the index as covariates. The obesity lifestyle risk index was standardized to have a mean of 0 and a standard deviation of 1 then dichotomized by the median and presented as low (less obesogenic behaviors) and high (more obesogenic behaviors). Abbreviations: polygenic risk score (PRS), principal components (PCs)

Fig. 3

Interaction between obesity genetic risk and obesity lifestyle risk index on clinically measured BMI in an electronic health record biobank (n =17,040). A Interactions and associations of the obesity PRS with clinically measured BMI stratified by low and high obesity lifestyle risk index (low vs. high obesogenic behaviors). Effect estimates (Beta) are derived from a multivariable linear regression model for BMI adjusted for age, sex, genotyping array, and 5 PCs of ancestry per SD of the polygenic risk score. P_int value is for the interaction term between the PRS and the obesity lifestyle risk index (both continuous) on BMI in the multivariable linear regression model with the PRS and the index added as covariates. B Interaction and associations stratified by lowest and highest morbidity based on the Charlson Comorbidity Index (10-yr survival probability). Effect estimates are derived from a multivariable linear regression model for BMI adjusted for age, sex, genotyping array, and 5 PCs of ancestry per SD of the PRS. P_int value is for the interaction term between the PRS and the obesity lifestyle risk index (both continuous) on BMI in a multivariable linear regression model with the PRS and the index added as covariates. Abbreviations: confidence interval (CI), polygenic risk score (PRS), principal components (PCs), standard deviation (SD)

Fig. 4

Phenome-wide association results for the obesity PRS (n =33,511). A Manhattan plot showing phenome-wide associations between the obesity PRS and 675 disease outcomes grouped by their broad disease groups on the x-axis and the -log₁₀P value of the association on the y-axis. The horizontal red line represents the Bonferroni corrected P value cut-off (P value =1.49 × 10⁻⁴). Each disease outcome is represented by either an upward or downward triangle indicating a positive or negative association, respectively. B Pie chart summarizing distribution of significant PheWAS findings across disease groups. Abbreviations: phenome-wide association study (PheWAS), polygenic risk score (PRS)

Fig. 5

Phenome-wide associations between obesity PRS and disease outcomes and interactions between obesity PRS and obesity lifestyle risk index on disease outcomes. Disease outcomes were limited to 40 significant findings from obesity PRS PheWAS. Disease outcomes are color-coded by their corresponding disease groups as described in the shared legend. PheWAS association models were adjusted for age, sex, genotyping array, and 5 PCs of ancestry. PheWAS association results are presented as OR (95%) and corresponding P value comparing highest (Q10) to lowest (Q1 - reference) decile of the obesity PRS. In interaction analyses, an interaction term between the PRS and the index was added and both the PRS and the index were added as covariates. P_int value are P values for the interaction term between the continuous PRS and obesity lifestyle risk index. Interactions were considered significant at P_int < 0.00125 accounting for 40 tests. Abbreviations: odds ratio (OR), phenome-wide association study (PheWAS), polygenic risk score (PRS), quartile (Q)