Precision Metabolic and Bariatric Medicine: Reconciling Predictive Machine Learning with Explanatory Biostatistics to Validate the Independent Effect of a 10.5% Preoperative TBWL Threshold.

Published in Obesity surgery. Abstract: Preoperative total body weight loss (PreopTBWL%) is widely used before metabolic and bariatric surgery (MBS), yet its long-term prognostic value remains debated. This study integrated machine learning (ML), unsupervised clustering, and linear mixed-effects modeling (LMM) to define data-driven preoperative targets and characterize multi-year postoperative weight trajectories. We analyzed prospectively collected data and electronic medical records from 802 adults undergoing primary laparoscopic sleeve gastrectomy, primary Roux-en-Y gastric bypass, or revision/conversion MBS within a structured, protein-focused preoperative lifestyle program. Five machine learning models were trained with nested cross-validation to predict each postoperative year. Each model used only patients with that year's outcome recorded (608 at Year 1 to 83 at Year 6). From Year 2 onward, earlier postoperative measurements for those patients were included as predictors. Priority was given to Years 1-4. Top-performing SHAP profiles guided feature selection for confirmatory longitudinal LMMs. Although the LMM framework spanned the full 6-year horizon (858 repeated measures from 420 active patients), the most robust inferences were restricted to Years 1-4 because of substantial attrition thereafter. Youden Index optimization, segmented regression, and unsupervised k-means clustering on UMAP embeddings were used to define precise PreopTBWL% targets and map distinct multi-year phenotypic subgroups. Machine lea...