Personalizing treatment of pancreatitis-associated chronic pain: the need for an integrated omics approach - Cole Myers; Cheyenna M. Espinoza; Aaron Clarke; Elizabeth R. Lusczek; Feng Xie; Brian T. Steffen; Zeribe C. Nwosu; Guru Trikudanathan; Geetha Saarunya; Melena Bellin; Gregory Beilman (Inflammation Research, 2026)
Background Chronic pancreatitis (CP) is a progressive inflammatory disorder characterized by debilitating chronic pain and substantial healthcare burden. Pain mechanisms in CP are heterogeneous and incompletely integrated into clinical decision-making. This narrative review synthesizes data from human CP cohorts and complementary experimental models to summarize inflammatory, neuropathic, and metabolic drivers of pancreatitis-associated pain and to evaluate how integrated multi-omics approaches may enable mechanism-based precision management. Current treatment relies on lifestyle modification, anatomy-guided interventions, stepwise pharmacologic escalation, and surgery for refractory cases. Emerging ion-channel–targeted therapies show promise, but inconsistent patient selection and limited biomarker guidance constrain therapeutic precision. Findings CP-associated pain arises from convergent inflammatory, neuroimmune, neuropathic, and metabolic pathways that promote peripheral and central sensitization with sustained neuroplastic remodeling. Advances in clinical phenotyping have improved characterization of pain subtypes; however, integration of biologic data remains limited. Genetic association studies increasingly implicate pathways linked to severe or persistent pain phenotypes. Omics investigations have identified candidate genomic, proteomic, and metabolomic signals that may support biologically informed stratification and treatment prediction. Nevertheless, most studies are cross-sectional, modality-specific, and derived from heterogeneous cohorts with inconsistent endpoints and limited external validation. Conclusions Integration of rigorous clinical phenotyping with longitudinal, multi-omics modeling provides a framework for developing testable, mechanism-based biomarkers to guide personalized analgesic and procedural strategies while supporting opioid-sparing care. Priorities include harmonization of multicenter datasets, standardized and longitudinal pain outcome measurement, expanded paired biospecimen collection, and external validation of predictive models. Such efforts may enable biologically grounded pain stratification and facilitate translation of biomarker-guided decision tools into routine clinical practice.