Anthropometric Characteristics of Triple-Negative Breast Cancer Patients by Menopausal Status: Evidence from the Population-Based Multicentric Study—MCC-Spain

Background/Objectives: This study aimed to analyze the relationship between various anthropometric measurements (Body Mass Index (BMI), Clínica Universidad de Navarra-Body Adiposity Estimator (CUNBAE), hip and waist circumference (WC), weight, and height) and Triple-Negative Breast Cancer (TNBC) according to menopausal status. Methods: A total of 113 TNBC cases and 226 matched controls from the MCC-Spain study were included. Controls were matched by age, educational level, family history, and province. Conditional logistic regression models, stratified by menopausal status, were used to estimate adjusted Odds Ratios (aORs) and their 95% Confidence Intervals (95% CIs) for the association between anthropometric measures and TNBC risk. Results: A divergent non-significant trend was observed: compared to their respective controls, premenopausal cases tended to have lower mean anthropometric measurements (except height), while postmenopausal cases showed higher means. No statistically significant associations were observed for individual measures derived from logistic regressions. However, when comparing women with normal BMI and normal WC (the reference group), a non-significant association of risk was found in those premenopausal women who were centrally obese (normal weight/high WC) (aOR = 1.79; 95% CI = 0.17–18.29), but the combination of overweight and a large WC showed an aOR of 0.22 (95% CI = 0.03–1.68) before menopause. In contrast, the combination of overweight and a high WC showed a statistically significant adjusted OR of 3.28 in postmenopausal women (95% CI = 1.10–9.81). Conclusions: Our findings suggest that the relationship between adiposity and TNBC is inverse in premenopausal women and direct in postmenopausal women, highlighting the importance of considering both body fat distribution and menopausal status when evaluating TNBC. However, our findings are limited by low statistical power, which may have led to a lack of statistical significance, and there is a need for larger, collaborative studies.