JAMIA open
Inclusion of a multi-ancestry polygenic risk score into a machine learning weight gain prediction model: an observational analysis from the NIH All of Us Research Program
By Lauer KV, Stalter LN, Hanlon BM and 3 others.
Abstract
Objective
Obesity is a public health crisis with few effective preventative treatments limited by challenges in identifying who is at high risk of weight gain. To address this gap, our group has developed machine learning weight gain prediction models with modest predictive power. In this study, we aimed to build upon our prior work by incorporating a multi-ancestry polygenic risk score (MAPRS) into a machine learning model and hypothesized that its inclusion would improve weight gain prediction performance.
Materials and methods
Adults aged 18-70 years with weight measurements 2 years apart were identified within the NIH All of Us Research Program. Participants with a history of cancer, bariatric surgery, or pregnancy were excluded. XGBoost machine learning models were developed using demographics, vitals, laboratory data, obesity-related comorbidities, and MAPRS. The primary outcome was total body weight (TBW) gain of ≥10% over 2 years. Performance was compared using the area under the curves (AUCs).