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: Thyroid dysfunction, particularly hypothyroidism, has been associated with endometrial cancer in observational studies; however, these findings may be confounded by obesity, an endometrial cancer risk factor. To clarify these associations, we performed Mendelian randomisation analysis, a genetic approach that mitigates confounding and reverse causation analyses. : We accessed European-ancestry GWAS summary statistics for endometrial cancer (12,270 cases; 46,126 controls), endometrioid (8758 cases), and non-endometrioid (1230 cases) subtypes. Thyroid dysfunction phenotype and BMI GWAS data were predominantly from individuals of European descent. We used these datasets to conduct univariable and multivariable Mendelian randomisation analyses incorporating body mass index (BMI). : Our main finding was a causal association between hypothyroidism and decreased risk of endometrial cancer (OR = 0.93; 95% CI 0.89-0.97; = 3.96 × 10). Subtype analysis revealed a decreased risk of the most common histological subtype, endometrioid endometrial cancer, and a similar protective association for Hashimoto's thyroiditis, an autoimmune disease and common cause of hypothyroidism. Sensitivity analyses confirmed the robustness of the associations. Further analyses revealed that while BMI was causally associated with hypothyroidism risk, both BMI and hypothyroidism independently influenced endometrial cancer risk. : Our study has identified hypothyroidism as a protective factor for endometrial cancer, challenging previous observational associations and highlighting potential confounding by obesity. Further investigation into immune mechanisms, particularly those linked to Hashimoto's thyroiditis, may provide insights into the biological pathways underlying endometrial cancer development.
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http://dx.doi.org/10.3390/biomedicines13071729 | DOI Listing |
Bioact Mater
December 2025
Division of Cancer Immunology and Microbiology, Medicine and Oncology Integrated Service Unit, School of Medicine, University of Texas Rio Grande Valley, McAllen, TX, USA.
The endometrium is a vital mucosal tissue which undergoes cyclical regeneration, differentiation, and remodeling upon hormonal, cellular, and molecular signaling networks. Dysregulation of these processes can trigger a range of pathological conditions including chronic inflammatory disorders, hyperplastic lesions, malignancies, and infertility, necessitating the need for effective therapeutic interventions. Furthermore, we are still dependent on conventional treatment modalities which are often constrained by inefficient drug biodistribution, systemic toxicity, and emergence of therapeutic resistance.
View Article and Find Full Text PDFHealth Equity
August 2025
Department of Obstetrics and Gynecology, University of Washington, Seattle, Washington, USA.
Objectives: Black patients have the highest mortality rate from endometrial cancer (EC), and yet remain underrepresented in EC research. Thus, currently published symptom patterns may not be comprehensive for this population. The purpose of this study is to analyze symptomatology among Black patients with EC in the Guidelines for Ultrasound in the Detection of Early Endometrial Cancer study and to compare with those undergoing benign hysterectomy.
View Article and Find Full Text PDFGynecol Oncol Rep
October 2025
University of California, Irvine, Irvine, CA, USA.
Obesity is a well-established risk factor for endometrial cancer, driven by chronic inflammation, insulin resistance, and excess estrogen. As the global obesity epidemic continues to worsen, effective weight management plays a crucial role in reducing both incidence and progression. Recent pharmacotherapy advancements, particularly GLP-1 receptor agonists, show promising weight loss effects by modulating appetite and metabolism.
View Article and Find Full Text PDFProteomics Clin Appl
September 2025
AIBioMed Research Group, Taipei Medical University, Taipei, Taiwan.
Background: Endometrial carcinoma (EC) represents a significant clinical challenge due to its pronounced molecular heterogeneity, directly influencing prognosis and therapeutic responses. Accurate classification of molecular subtypes (CNV-high, CNV-low, MSI-H, POLE) and precise tumor mutational burden (TMB) assessment is crucial for guiding personalized therapeutic interventions. Integrating proteomics data with advanced machine learning (ML) techniques offers a promising strategy for achieving precise, clinically actionable classification and biomarker discovery in EC.
View Article and Find Full Text PDFInt J Gynecol Cancer
August 2025
Radiation Department, A.O. S. Croce e Carle Teaching Hospital, Cuneo CN, Italy.