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Integrated meta-analysis and exploratory small-sample machine learning to evaluate curcumin against osteoporosis: a preclinical evidence-based study
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DOI：10.3389/fendo.2026.1893878.png)
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Abstract
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PurposeTo 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.MethodsFollowing PRISMA guidelines; we searched eight databases for controlled in vivo animal studies in validated osteoporosis models that compared curcumin monotherapy with saline or vehicle controls and reported extractable bone-related outcomes; including bone mineral density (BMD) and trabecular microarchitecture. Methodological quality was assessed using the SYRCLE tool. In addition to random-effects meta-analysis; an exploratory ML framework incorporating SHapley Additive exPlanations (SHAP) and Gaussian Process Regression (GPR) was employed to examine associations between nine study-level features and effect estimates and to explore potential non-linear dose–response patterns after normalization to the Human Equivalent Dose (HED).ResultsTwenty-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.ConclusionCurcumin 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 registrationhttps://www.crd.york.ac.uk/prospero/; identifier CRD420251269951.
Keywords:
machine learning
curcumin
meta-analysis
osteoporosis
preclinical evidence
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