Application of Obtainable Biological Agent Characteristics in Efficacy Stratification of Oral Anti-Obesity Drugs

The aim of this study is to develop a "patient-friendly, accurate screening application for oral anti-obesity medicines" (AOMs). It transforms gut microbiome information from high-cost mechanistic research into an optional add-on input and constructs a surrogate feature system that remains functional without omics testing. We followed 260 obese individuals who were treated orally/injected with AOMs (mainly GLP-1 agonists) for 16 weeks, collecting baseline fecal 16S/metagenomic data, continuous glucose monitoring (CGM), lipid metabolism indices, and patient reported outcomes (PROs). First, we constructed a microbiota-metabolic pathway, and further refined the surrogate variables under "non-omics conditions" (dietary structure questionnaire, postprandial blood glucose fluctuations, history of gastrointestinal symptoms, sleep/activity characteristics). The app implemented a two-layer model: the baseline layer predicts treatment response using only low-burden features; the enhanced layer calibrates prediction confidence and refines population stratification when omics data is available.Results demonstrated: the baseline layer predicted a 12 – 16 week reduction of 7% or more (AUC = 0.82), whereas the enhanced layer, including the microbial-pathway score, raised AUC to 0.87 and significantly reduced uncertainty in borderline samples (18% reduction in ECE) .This study showed that microbiome information was more suitable as an "enhanced module" for the application, which improved the reliability and interpretability of precise screening without compromising patient availability.