Hoffman, Kristi L.

Background: Pancreatitis significantly alters the microbial composition of the oral and intestinal compartments, causing dysbiosis that may contribute to disease mechanisms and potentially serve as a basis for diagnosis or treatment. Objective: To determine whether the oral or gut microbial signature can classify chronic pancreatitis (CP). Methods: Stool samples (n=707) were collected from participants in the Prospective Evaluation of Chronic Pancreatitis for Epidemiologic and Translational Studies (PROCEED). Samples were distributed among 200 healthy (HC), 310 CP, 49 acute pancreatitis (AP), and 148 recurrent acute pancreatitis (RAP). In addition, saliva samples were collected for a subset of participants (n=156). Whole genome sequencing was performed to assess microbiome composition. Machine learning algorithms were utilized to identify a signature with microbial features predictive of CP. Results: Gut alpha diversity was significantly decreased in AP, RAP, and CP compared with HC, with CP exhibiting the lowest diversity. In contrast, oral microbial diversity showed no significant variation across groups. Beta diversity analysis revealed distinct gut microbiome compositions between HC and pancreatitis subtypes, with CP showing the most pronounced differences. Random forest models using gut microbial species demonstrated robust predictive performance for CP using a minimum of 10 species (Area under the curve—AUC: 0.834; accuracy: 0.774). Despite similarities in gut microbiome composition across pancreatitis subtypes, a unique gut microbial signature for CP was identified highlighting the microbiome’s potential in CP diagnosis. Conclusion: Our study reveals a gut microbial signature predictive of CP using machine learning models in a large US multi-institutional cohort.

Objectives Chronic pancreatitis (CP) is a chronic fibroinflammatory condition of the pancreas difficult to diagnose in early stages. Novel biomarkers useful to facilitate early diagnosis or treatment responses may be found in biofluids. Although saliva can be easily and noninvasively collected from patients, useful salivary biomarkers from CP patients have not yet been identified. Methods Here, we analyzed the proteome by quantitative proteomics, cytokine/chemokine levels by Luminex analysis, prostaglandin E2 (PGE2) levels by a mass spectrometry-based assay, and bacterial species diversity by 16S ribosomal ribonucleic acid sequencing in saliva samples from confirmed CP patients and healthy controls. Results Our results indicate the presence of various differentially expressed proteins, cytokines/chemokines, and a loss of oral bacterial diversity in the saliva of CP patients. The PGE2 levels trend toward elevation in CP patients. Area under the receiver operating characteristic curve models for proteomic, cytokine, and PGE2 assays ranged from 0.59 to 0.90. Conclusions Collectively, our studies identify a range of putative CP biomarkers and alterations in human saliva requiring further validation. The biomarker discovery approaches we used might lead to identification of biomarkers useful for CP diagnosis and monitoring.

A major direction for future microbiome research is implementation of fecal sample collections in large-scale, prospective epidemiologic studies. Studying microbiome-disease associations likely requires microbial data to be pooled from multiple studies. Our findings suggest collection methods that are most optimal to be used standardly across future WGSS microbiome studies.