Machine Learning-Based Prediction of Comprehensive Lipid Response to Dietary Interventions in Overweight and Obese Women

Background: Inter-individual variability in lipid response to dietary interventions complicates cardiometabolic prevention in overweight and obese women. Although several dietary strategies improve lipid profiles on average, predictors of comprehensive, multi-marker lipid improvement remain unclear. Objective: To identify baseline clinical predictors of comprehensive lipid improvement across seven dietary interventions and to evaluate the performance of three machine learning (ML) classifiers in predicting a composite Global Score. Methods: This secondary analysis pooled individual-level data from 284 overweight or obese women enrolled in three randomized controlled trials (RCTs). Participants were assigned to continuous energy restriction (CER), intermittent energy restriction (IER), intermittent energy and carbohydrate restriction (IECR), IECR with added protein and fat (IECR+PF), high-carbohydrate, high-monounsaturated-fat, or daily energy-restriction diets. Eleven baseline clinical features served as predictors. Four binary lipid improvement scores (TC/HDL, LDL/HDL, non-HDL cholesterol, TG/HDL) were calculated from baseline to week 12, and a composite Global Score was defined as TRUE only when all four improved. Three ML classifiers (J48, Logistic Model Tree [LMT], Random Forest) were evaluated using stratified 10-fold cross-validation. Results: Overall, 30–35% achieved improvement in the Global Score. Improvement rates varied across diets, with High Mono and High Carb showing the highest rates (48.4%). LMT performed best (AUC = 0.66; accuracy = 70%). Baseline TG, BMI, age, total cholesterol, and weight were the strongest predictors. Conclusions: Comprehensive lipid improvement varies across dietary strategies and is influenced by baseline triglycerides, adiposity, age, and diet type. ML-based stratification may support personalized dietary prescriptions.