Nature or nurture: genetic and environmental predictors of adiposity gain in adults

Abstract

Background: Previous prediction models for adiposity gain have not yet achieved sufficient predictive ability for clinical relevance. We investigated whether traditional and genetic factors accurately predict adiposity gain.

Methods: A 5-year gain of ≥5% in body mass index (BMI) and waist-to-hip ratio (WHR) from baseline were predicted in mid-late adulthood individuals (median of 55 years old at baseline). Proportional hazards models were fitted in 245,699 participants from the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort to identify robust environmental predictors. Polygenic risk scores (PRS) of 5 proxies of adiposity [BMI, WHR, and three body shape phenotypes (PCs)] were computed using genetic weights from an independent cohort (UK Biobank). Environmental and genetic models were validated in 29,953 EPIC participants.

Findings: Environmental models presented a remarkable predictive ability (AUC_BMI: 0.69, 95% CI: 0.68-0.70; AUC_WHR: 0.75, 95% CI: 0.74-0.77). The genetic geographic distribution for WHR and PC1 (overall adiposity) showed higher predisposition in North than South Europe. Predictive ability of PRSs was null (AUC: ∼0.52) and did not improve when combined with environmental models. However, PRSs of BMI and PC1 showed some prediction ability for BMI gain from self-reported BMI at 20 years old to baseline observation (early adulthood) (AUC: 0.60-0.62).

Interpretation: Our study indicates that environmental models to discriminate European individuals at higher risk of adiposity gain can be integrated in standard prevention protocols. PRSs may play a robust role in predicting adiposity gain at early rather than mid-late adulthood suggesting a more important role of genetic factors in this life period.

Funding: French National Cancer Institute (INCA_N°2019-176) 1220, German Research Foundation (BA 5459/2-1), Instituto de Salud Carlos III (Miguel Servet Program CP21/00058).

Keywords: Adiposity gain; Environmental factors; Polygenic risk scores; Prediction.

Fig. 1

Time-dependent Area Under the Curve (AUC) for the prediction of BMI and WHR gain in a period of 5 years for the environmental model, PRS of BMI, WHR, and body shape phenotypes (PC1-3), and environmental and PRS models combined. Environmental models for BMI and WHR gain were developed in 245,699 and 44,190 individuals, respectively. All models were validated in 29,953 for BMI gain and 6456 for WHR gain. BMI, body mass index; PC, principal component; PRS, polygenic risk score; WHR, waist-to-hip ratio.

Fig. 2

Genetic predisposition by European country to BMI (dark blue), WHR (orange), PC1 (light blue), PC2 (green), and PC3 (red) in the EPIC cohort (top) and 1000 Genomes Project (bottom). PRS were developed in 29,953 and 396 EPIC and 1000 Genomes Project individuals, respectively. BMI, body mass index; PC, principal component; PRS, polygenic risk score; WHR, waist-to-hip ratio.

Fig. 3

Associations between the polygenic risk scores of BMI, WHR, and body shape phenotypes (PC1—3) and BMI and WHR gain in EPIC (dark purple) and UK Biobank (light purple). Associations were performed in 29,953 for BMI gain and 6456 for WHR gain. BMI, body mass index; CI, confidence interval; HR, hazard ratio; PC, principal component; PRS, polygenic risk score; WHR, waist-to-hip ratio.

Fig. 4

Time-dependent Area Under the Curve (AUC) for the prediction of BMI gain in a period of 5 years in mid-late and early adulthood for the PRS of BMI. Models were validated in 29,953 and 12,386 for mid-late and early adulthood, respectively. BMI, body mass index; PRS, polygenic risk score.