Authors
Naser Elkum, Abdelilah Aboussekhra
Published in
Current obesity reports. Volume 15. Issue 1. Sep 11, 2026. Epub Sep 11, 2026.
Abstract
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.
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.
PMID:
42726171
Bibliographic data and abstract were imported from PubMed on 12 Sep 2026.
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