Prediagnostic Plasma Nutrimetabolomics and Prostate Cancer Risk: A Nested Case–Control Analysis Within the EPIC Study.

Simple Summary: Previous studies using a metabolomics approach in relation to prostate cancer (PCa) risk assessment have focused on endogenous metabolism. As far as we know, the associations between circulating metabolites derived from exogenous sources, such as diet or lifestyle, and PCa risk have not been widely studied. In our work, we examined the associations between endogenous and exogenous plasma metabolites and PCa risk in a case–control study conducted in the frame of the EPIC study. The main findings of our study include novel negative and positive associations of food metabolites, such as cyclamate and biomarkers of plant-based foods, respectively, with PCa cancer risk. Furthermore, a few microbial metabolites were associated with PCa risk, suggesting the potential role of microbiota in prostate cancer development. Our results reinforce the use of nutritional metabolomics to deepen the understanding of the complex role of diet and its potential mechanisms of action in prostate carcinogenesis. Background and Objective: Nutrimetabolomics may reveal novel insights into early metabolic alterations and the role of dietary exposures on prostate cancer (PCa) risk. We aimed to prospectively investigate the associations between plasma metabolite concentrations and PCa risk, including clinically relevant tumor subtypes. Methods: We used a targeted and large-scale metabolomics approach to analyze plasma samples of 851 matched PCa case–control pairs from the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Associations between metabolite concentrations and PCa risk were estimated by multivariate conditional logistic regression analysis. False discovery rate (FDR) was used to control for multiple testing correction. Results: Thirty-one metabolites (predominately derivatives of food intake and microbial metabolism) were associated with overall PCa risk and its clinical subtypes (p < 0.05), but none of the associations exceeded the FDR threshold. The strongest positive and negative associations were for dimethylglycine (OR = 2.13; 95% CI 1.16–3.91) with advanced PCa risk (n = 157) and indole-3-lactic acid (OR = 0.28; 95% CI 0.09–0.87) with fatal PCa risk (n = 57), respectively; however, these associations did not survive correction for multiple testing. Conclusions: The results from the current nutrimetabolomics study suggest that apart from early metabolic deregulations, some biomarkers of food intake might be related to PCa risk, especially advanced and fatal PCa. Further independent and larger studies are needed to validate our results. [ABSTRACT FROM AUTHOR]