Background: The relationship between obesity and breast cancer risk remains complex and inconsistently observed in epidemiological studies due to potential confounding and reverse causation. Comprehensive Mendelian randomization analyses of multiple anthropometric traits, particularly central adiposity measures, and their stratified effects by estrogen receptor (ER) subtype are still lacking.
Methods: We conducted a two-sample MR study to assess the causal relationship between nine obesity-related traits (e.g., Body Mass Index (BMI), Waist circumference (WC), basal metabolic rate) and breast cancer risk, overall and by ER subtype. Genetic instruments were selected from large-scale GWAS summary data of European ancestry. FinnGen R12 served as the discovery cohort and IEU OpenGWAS as replication. We applied Inverse Variance Weighted (IVW) as the primary method, supplemented by five other MR approaches and extensive sensitivity analyses for heterogeneity, pleiotropy, and colocalization.
Results: Genetically predicted higher BMI and WC were significantly associated with reduced risk of overall breast cancer in the discovery cohort (BMI: OR = 0.89, 95 % CI: 0.83-0.95; WC: OR = 0.84, 95 % CI: 0.77-0.93). These inverse associations were particularly consistent and robust for ER + breast cancer across both discovery and replication datasets. No significant associations were found for the other seven anthropometric traits. Colocalanalysis suggested shared genetic variants for some associations. Sensitivity analyses confirmed the robustness of the findings against pleiotropy and heterogeneity.
Conclusion: This study provides robust genetic evidence that higher BMI and WC are inversely associated with breast cancer risk, especially ER + disease. These findings highlight the importance of considering adiposity type and tumor subtype in understanding breast cancer etiology and suggest distinct underlying mechanisms warranting further investigation. Importantly, these results do not support recommending weight gain for cancer prevention; rather, they underscore the need for balanced health strategies that consider multiple risk factors.

