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GLP-1 Receptor Agonists and Obesity Related Cancers: What We Know So Far
Naga Praneeth Raja1,2, Nagapavani Kandagari3
1Division of Hematology and Oncology, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR, 97239, USA. nagapraneeth21d@gmail.com.
Purpose Of Review:
Obesity increases the risk of at least 13 malignancies. Glucagon-like peptide-1 receptor agonists (GLP-1RAs) produce substantial weight loss and improve metabolic and inflammatory pathways implicated in carcinogenesis. This review critically examines the preclinical, epidemiologic, randomized-trial, and emerging oncology evidence linking these agents to obesity-related cancer incidence and outcomes.
Recent Findings:
Large observational cohorts generally associate GLP-1RA exposure with a lower incidence of composite obesity-related cancers, with relative risk reductions of approximately 7-30% in most diabetes- and obesity-based populations. The most consistent favorable associations have been observed in hepatocellular, colorectal, endometrial, ovarian, and pancreatic cancers. However, these findings remain vulnerable to residual confounding, comparator selection, surveillance bias, and insufficient follow-up. Meta-analyses of randomized trials have not demonstrated a significant reduction in overall cancer incidence and have reported inconsistent site-specific signals, including thyroid and colorectal cancer. Preclinical studies support both indirect metabolic effects and potentially direct tumor and immune effects through insulin/IGF-1, cAMP/PKA, PI3K/Akt/mTOR, AMPK, and NF-κB signaling. Early retrospective studies in patients with active cancer suggest possible associations with improved survival or reduced progression, but causality has not been established. GLP-1RAs should currently be prescribed for approved metabolic indications rather than specifically for cancer prevention or treatment. Their role as metabolic modifiers of cancer risk and outcomes is promising but unresolved. Long-term prospective studies with cancer-specific endpoints, body-composition assessment, and integrated tumor and host biomarkers are needed to identify which populations and tumor types may benefit.
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