Leveraging OGTT derived metabolic features to detect Binge-eating disorder in individuals with high weight: a "seek out" machine learning approach.

ABSTRACT: Binge eating disorder (BED) carries a 6 times higher risk for obesity and accounts for roughly 30% of type 2 diabetes cases. Timely identification of early glycemic disturbances and comprehensive treatment can impact on the likelihood of associated metabolic complications and the overall outcome. In this study, machine learning techniques were applied to static and dynamic glucose-derived measures to detect BED among 281 individuals with high weight. Data from the classic (2 h) and the extended (5 h) glucose load were computed by multiple algorithms and two models with the most relevant features were trained to detect BED within the sample. The models were then tested on an independent cohort (N = 21). The model based on the 5 h-long glucose load exhibited the best performance (sensitivity

SUBMITTER: Rania M 

PROVIDER: S-EPMC11836435 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature