Abu-El-Haija, Maisam

INTRODUCTION: Reliable biomarkers for the diagnosis of chronic pancreatitis (CP) and pancreatic fibrosis severity are lacking, hindering effective treatment and management. Histologic fibrosis is a hallmark of late-stage CP, but noninvasive methods to evaluate fibrosis progression are limited. We used urine proteomics to discover biomarkers that identify patients with CP and predict fibrosis severity. METHODS: We performed a cross-sectional study of 130 total subjects (CP n = 50) selected based on clinical criteria in a tertiary care setting. Urine proteomics samples were quantified using data-independent acquisition mass spectrometry. Differential biomarker candidates were identified with false discovery rate-corrected pairwise comparisons. These proteins were validated with an independent paired urine and plasma sample cohort (n = 36). Machine learning was used to develop a protein panel that predicted Ammann scores for patients with histologic fibrosis. RESULTS: We found 34 proteins consistently differentially expressed between CP and controls in pairwise false discovery rate-controlled tests. Of these, 25 urine proteins outperformed 19 previously suggested CP blood-based biomarkers in an independent validation cohort. Isocitrate dehydrogenase (IDH1), calcyphosin (CAPS), synuclein gamma (SNCG), and protein S100-P (S100P) all produced receiver operator curve area under the curve values >0.95, while the best plasma marker was interleukin 2 receptor subunit alpha (receiver operator curve area under the curve = 0.80). A 12-protein panel of identified markers predicted fibrosis severity with a linear correlation R 2 value of 0.61. DISCUSSION: We identified a panel of proteins that may diagnose CP in children and developed a model to predict pancreatic fibrosis severity, offering promising tools for improving diagnostics and patient care.