Enterprise AI Analysis

Non-invasive liquid biopsy based on metabolomic profiling improves diagnosis and early warning of severe acute pancreatitis

To identify novel diagnostic biomarkers for acute pancreatitis (AP) and facilitate the early prediction of severe AP (SAP), this investigation characterized the serum metabolomic profiles of patients across distinct disease phases and integrated metabolomics with artificial intelligence to construct bile acid-based predictive models. The observational protocol was registered with the Chinese Clinical Trial Registry (ChiCTR2000034117) on June 24, 2020. Comparative metabolomic analysis revealed significant alterations in 303 metabolites and 461 lipid species in AP. Subsequent weighted gene coexpression network analysis demonstrated robust correlations between clinical parameters and specific metabolic clusters, particularly bile acids (BAs) and lipid species. Targeted quantification of 63 BAs was subsequently performed within a multicentre validation cohort (n = 948). Machine learning algorithms applied to these data facilitated the derivation of two distinct BA panels. The first panel, comprising nine BAs, demonstrated high diagnostic accuracy for AP, including among individuals with negative conventional enzymatic biomarkers, and effectively discriminated AP from acute cholangitis, as reflected by elevated area under the curve (AUC) values. A second panel, consisting of 13 BAs, reliably identified patients at elevated risk for SAP progression. Collectively, these results validate the translational potential of machine learning-driven metabolic biomarkers for the precision management of acute abdominal conditions, underscore the clinical utility of BAs as promising diagnostic and prognostic biomarkers in acute pancreatitis, and provide a new paradigm for the development of dynamic risk early-warning systems.