Data Sheet 1_Development of a predictive model for pancreatic cancer integrating serum metabolomics and polygenic risk score.docx

Model performance was assessed using the C-index and time-dependent area under the curve (AUC).</p>Results<p>During the follow-up period, 878 incident PC cases were identified. In total, 15 clinical biomarkers and 55 metabolites were significantly associated with PC risk, with the majority of lipid- and lipoprotein-related metabolites showing inverse associations. Subsequently, 52 metabolites were selected to construct the MRS.

The C-index increased from 0.722 in the base model including age and sex to 0.755 with the addition of clinical biomarkers and to 0.768 after further incorporating the PRS; the addition of the MRS yielded a C-index of 0.769, with improved risk reclassification. The integrated model showed good discrimination, with time-dependent AUCs of 0.812, 0.808, 0.768, and 0.783 at 1, 3, 5, and 10 years, respectively.</p>Conclusions<p>These findings support the utility of clinical and genetic factors for PC risk assessment, while the additional predictive value of metabolomic profiling warrants further validation.</p>