Preliminary analysis of salivary microbiota in catathrenia (nocturnal groaning) using machine learning algorithmsOriginal paper
Karen Pendergrass is a microbiome researcher specializing in microbiome-targeted interventions (MBTIs). She systematically analyzes scientific literature to identify microbial patterns, develop hypotheses, and validate interventions. As the founder of the Microbiome Signatures Database, she bridges microbiome research with clinical practice. In 2012, based on her own investigative research, she became the first documented case of FMT for Celiac Disease, four years before the first published case study.
Read MoreWhat was studied?
Researchers compared salivary microbiota between 22 patients with catathrenia (nocturnal groaning), diagnosed by video and audio polysomnography, and 22 age matched healthy controls.
How was it studied?
Saliva samples underwent 16S rRNA gene sequencing. Patients were treated with custom fit mandibular advancement devices for one month, and 10 patients had repeat polysomnography and sampling afterward. XGBoost and nested Random Forest machine learning models identified candidate bacterial biomarkers.
What did they find?
Catathrenia patients had lower alpha diversity (Chao 1, Faith's phylogenetic diversity, observed species) and a distinct overall community structure (Bray Curtis, p = 0.001), differing at the phylum and family level. Mandibular advancement device treatment did not significantly shift overall microbiota composition, but four genera, Alloprevotella, Peptostreptococcaceae_XI_G1, Actinomyces, and Rothia, changed significantly with treatment. Alloprevotella abundance correlated inversely with catathrenia severity (r2 = -0.63, p < 0.001).
Why it matters
These findings suggest salivary microbiota, particularly Alloprevotella, could serve as a treatment responsive biomarker for catathrenia, though the authors call for further mechanistic study.