Obesity-related Metabolic Dysfunction and Triple-negative Breast Cancer: Epidemiologic Signals, Mechanistic Pathways, and Opportunities for Prevention

PURPOSE OF REVIEW: Obesity is an established risk factor for postmenopausal hormone receptor-positive breast cancer, largely through estrogen-mediated pathways. Emerging evidence suggests that obesity-related metabolic dysfunction may also contribute to the risk and aggressive biology of receptor-negative breast cancer, particularly TNBC, among premenopausal women and populations undergoing rapid metabolic transition. This review evaluates epidemiologic, mechanistic, and translational evidence linking metabolic dysfunction to TNBC. RECENT FINDINGS: Observational studies and pooled analyses report modest but reproducible associations between adiposity and TNBC, stronger in some metabolically high-risk populations but heterogeneous across studies. BMI-based Mendelian randomization generally yields inverse associations with overall breast cancer, whereas bariatric-surgery studies suggest lower overall incidence without establishing TNBC-specific effects. These discordant causal-inference findings do not demonstrate that obesity directly causes TNBC. Mechanistic studies indicate that adipose inflammation, adipokine imbalance, insulin/IGF-1 signaling, and immune remodeling can promote inflammatory signaling, metabolic adaptation, and epithelial plasticity. These processes support biologic plausibility but do not demonstrate de novo conversion to a receptor-negative subtype in vivo. Current evidence supports a model in which obesity-related metabolic dysfunction may create a breast microenvironment favoring tumor-promoting inflammation and aggressive features associated with TNBC. Priorities include refined metabolic phenotyping, biomarker-driven risk stratification, validation in human-relevant models, and biomarker-enriched feasibility studies testing whether modifying metabolic dysfunction alters intermediate metabolic or tissue endpoints. An evidence-graded framework is needed to distinguish association, biologic plausibility, and causal inference.