A Mendelian Randomization Analysis of 55 Genetically Predicted Metabolic Traits with Breast Cancer Survival Outcomes in the Pathways Study

Abstract

Previous studies suggest associations of metabolic syndromes with breast cancer prognosis, yet the evidence is mixed. In recent years, the maturation of genome-wide association study findings has led to the development of polygenic scores (PGS) for many common traits, making it feasible to use Mendelian randomization to examine associations between metabolic traits and breast cancer outcomes. In the Pathways Study of 3,902 patients and a median follow-up time of 10.5 years, we adapted a Mendelian randomization approach to calculate PGS for 55 metabolic traits and tested their associations with seven survival outcomes. Multivariable Cox proportional hazards models were used to derive hazard ratios (HRs) and 95% confidence intervals (CIs) with adjustment for covariates. The highest tertile (T3)of PGS for cardiovascular disease was associated with shorter overall survival (HR=1.34, 95% CI= 1.11-1.61) and second primary cancer-free survival (HR=1.31,95% CI=1.12-1.53). PGS for hypertension (T3) was associated with shorter overall survival (HR=1.20, 95% CI=1.00-1.43), second primary cancer-free survival (HR=1.24, 95% CI=1.06-1.45), invasive disease-free survival (HR=1.18, 95% CI=1.01-1.38), and disease-free survival (HR=1.21, 95% CI=1.04-1.39). PGS for serum cystatin C levels (T3) was associated with longer disease-free survival (HR=0.82, 95% CI=0.71-0.95), breast event-free survival (HR=0.74, 95% CI=0.61-0.91), and breast cancer-specific survival (HR=0.72, 95% CI=0.54-0.95). The above associations were significant at a nominal P<0.05 level but not after correcting for multiple-testing (Bonferroni P<0.0009). Our analyses revealed notable associations of PGS for cardiovascular disease, hypertension, and cystatin C levels with breast cancer survival outcomes. These findings implicate metabolic traits in breast cancer prognosis.