Preliminary analysis of salivary microbiota in catathrenia (nocturnal groaning) using machine learning algorithms.

Abstract

Objectives: The present study aimed to characterize the salivary microbiota in patients with catathrenia and to longitudinally validate potential biomarkers after treatment with mandibular advancement devices (MAD).

Materials and methods: Twenty-two patients with catathrenia (12 M/10 F, median age 28 y) and 22 age-matched control volunteers (8 M/14 F, median age 30 y) were included in the cross-sectional study. Video/audio polysomnography was conducted for diagnosis. All patients received treatment with custom-fit MAD and were followed for one month. Ten patients (6 M/4 F) underwent post-treatment PSG. Salivary samples were collected, and microbial characteristics were analyzed using 16S rRNA gene sequencing. The 10-fold cross-validated XGBoost and nested Random Forest Classifier machine learning algorithms were utilized to identify potential biomarkers.

Results: In the cross-sectional study, patients with catathrenia had lower α-diversity represented by Chao 1, Faith's phylogenetic diversity (pd), and observed species. Beta-diversity based on the Bray-Curtis dissimilarities revealed a significant inter-group separation (p = 0.001). The inter-group microbiota distribution was significantly different on the phylum and family levels. The treatment of MAD did not alter salivary microbiota distribution significantly. Among the most important genera in catathrenia and control classification identified by machine learning algorithms, four genera, Alloprevotella, Peptostreptococcaceae_XI_G1, Actinomyces and Rothia, changed significantly with MAD treatment. Correlation analysis revealed that Alloprevotella was negatively related to the severity of catathrenia (r²= -0.63, p < 0.001).

Conclusions: High-throughput sequencing revealed that the salivary microbiota composition was significantly altered in patients with catathrenia. Some characteristic genera (Alloprevotella, Peptostreptococcaceae_XI_G1, Actinomyces, and Rothia) could be potential biomarkers sensitive to treatment. Future studies are needed to confirm and determine the mechanisms underlying these findings.