Citations

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.

Fronto-temporal lobar degeneration (FTLD) is a form of dementia which may manifest through symptoms similar to Alzheimer’s, including language-specific impairment. Linguistic manifestations of the disorder are often described in subjective assessments of the disorder, but not always easily quantifiable using objective tests. This paper investigates a speech characteristic not yet assessed in FTLD patients, that of “temporal cycles” [Henderson et al. (1966); Butterworth and Goldman-Eisler (1979)]. Temporal cycles in the speech of healthy adults consist of alternating and roughly equal periods of fluent and hesitant speech [Roberts and Kirsner (2000)]. A time series analysis of temporal cycles was conducted using spontaneous speech from 45 adults diagnosed with FTLD. Patients’ cognitive functioning was assessed using the language-specific clinical dementia rating (CDR) scale by board-certified neurologists. Periodicity of temporal cycles was quantified using the proportion of the energy in the highest peak to the total energy in the power spectrum. It was found that this measure was correlated with independent CDR assessments. The results of this study indicate that temporal cycles may be used to characterize the effects of neurodegenerative disorders on speech communication. [Work supported by US NIA Grants Nos. R01-AG023195, P50-AG16574, P30-AG19610, and NIH NIA-1R01AG026390, and Univ. of MN Academic Health Center.]