Exploring metabolomics for colorectal cancer risk prediction: evidence from the UK Biobank and ESTHER cohorts

Abstract

Abstract Background While metabolic pathway alterations are linked to colorectal cancer (CRC), the predictive value of pre-diagnostic metabolomic profiling in CRC risk assessment remains to be clarified. This study evaluated the predictive performance of a metabolomics risk panel (MRP) both independently and in combination with established risk factors. Methods We derived, internally validated (IV), and externally validated (EV) a metabolomics risk panel (MRP) for CRC from data of the UK Biobank (UKB) and the German ESTHER cohort. Baseline blood samples were assessed for 249 metabolites using nuclear magnetic resonance spectroscopy analysis. We applied LASSO Cox proportional hazards regression to identify metabolites for inclusion in the MRP and evaluated the model performance using the concordance index (C-index). We compared the performance of the MRP to an environmental risk panel (ERP; sex, age, body mass index, smoking status, and alcohol consumption) and a genetic risk panel (GRP; polygenic risk score). Results The study included 154,892 participants of the UKB cohort (mean age at baseline 54.5 years; 55.5% female) with 1879 incident CRC and 3242 participants of the ESTHER cohort (mean age 61.5 years; 52.2% female) with 103 CRC cases. Twenty-three metabolites, primarily amino acid and lipid-related metabolites, were selected for the MRP, showing moderate predictive performance (C-index 0.60 [IV] and 0.54 [EV]). The ERP and GRP showed superior performance, with C-index values of 0.73 (IV) and 0.69 (EV). Adding the MRP to these risk models did not change the C-indices in both cohorts. Conclusions Genetic and environmental risk information provided strong predictive accuracy for CRC risk, with no improvements from adding metabolomics data. These findings suggest that metabolomics data may have limited impact on enhancing established CRC risk models in clinical practice.

Keywords

• Colorectal cancer
• Metabolomics
• Risk stratification
• Biomarkers