Machine Learning-Optimized Metabolic Biomarker Panel for Precision Screening of Early-Stage Pancreatic Cancer in New-Onset Diabetes

Weiliang Jiang¹Zhiyaun Cheng¹Rong Mu¹Haoran Sun¹Zihao Guo¹Guo Yu¹Dongyan Wang^2*Lijuan Yang^1*

• ¹Department of Gastroenterology, Shanghai General Hospital, Shanghai, China
• ²Shanghai Pudong New Area Gongli Hospital, Shanghai, China

New-onset diabetes (NOD) represents a high-risk population for pancreatic ductal adenocarcinoma (PDAC); however, robust early detection tools remain elusive. To address this challenge, we conducted serum metabolomic profiling using ultra-high-performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) in 133 NOD patients aged over 65 years: 60 with PDAC (PDAC+NOD) and 73 without (NOD). Multivariate orthogonal partial least squares discriminant analysis (OPLS-DA) and machine learning were employed to identify and optimize a diagnostic metabolic biomarker panel. Our analysis revealed 62 differentially expressed serum metabolites (P < 0.05, FDR-corrected), predominantly implicating branched-chain amino acid metabolism, primary bile acid biosynthesis, and sphingolipid signaling pathways. Notably, significant reductions in one-carbon metabolism-related metabolites (e.g., serine, glycine, homocysteine) were observed in PDAC+NOD patients. Feature selection yielded an optimized 5-metabolite panel comprising glycine, L-serine, L-methionine, L-homocysteine, and L-homocystine. This panel demonstrated high diagnostic accuracy in the validation cohort, achieving 75.0% accuracy in distinguishing PDAC+NOD from NOD patients, with an area under the curve (AUC) of 0.853 (95% CI: 0.786-0.920). These findings establish a foundational metabolic biomarker strategy for precision screening of early-stage PDAC in NOD populations. The dysregulated one-carbon metabolites provide novel mechanistic insights into PDAC pathogenesis and offer actionable targets for clinical assay development.