Integrated meta-analysis and exploratory small-sample machine learning to evaluate curcumin against osteoporosis: a preclinical evidence-based study.

Purpose: To systematically evaluate the anti-osteoporotic efficacy of curcumin in preclinical models and utilize an exploratory small-sample machine learning (ML) framework to examine potential contributors to heterogeneity and generate preliminary dose-duration hypotheses. Methods: Following PRISMA guidelines, we searched eight databases for controlled Results: Twenty-three studies were included. Meta-analysis revealed that curcumin significantly elevated femoral BMD [standardized mean difference (SMD) = 2.73, P < 0.001], preserved trabecular microarchitecture, and enhanced biomechanical strength. Curcumin was also associated with changes in bone-remodeling and oxidative stress-related markers. Based on only 23 study-level observations, exploratory ML suggested that body weight had the largest model-dependent contribution (mean |SHAP| = 0.201). GPR modeling suggested a bell-shaped dose-response relationship, with the fitted response reaching a local maximum at an HED of approximately 32 mg/(kg·d) and an intervention duration of 8-12 weeks. These model-derived findings should not be interpreted as validated optimal dose or duration estimates. Conclusion: Curcumin exerts potent, multi-target osteoprotective effects that are associated with improved bone remodeling and oxidative stress-related indices. The exploratory integration of ML with meta-analysis suggested that biological characteristics and dosage may contribute to variability in treatment effects. However, because the ML component was constrained by the limited number of study-level observations, these model-derived findings should be regarded as hypothesis-generating signals and should not be interpreted as independently validated predictors or clinical dosing recommendations. Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier CRD420251269951.