Comparative Machine Learning Analysis of Saliva and Plaque Microbiomes in Children with Type 1 Diabetes

Background Type 1 diabetes (T1D) is associated with microbial dysbiosis. While most research has focused on the gut microbiome, limited data addresses the role of the oral microbiome in T1D. The oral and gut microbiomes overlap substantially, and the oral cavity may influence gut microbial composition. Saliva and dental plaque represent two distinct oral niches with unique microbial communities, but it remains unclear which better reflects systemic disease states like T1D. This study compares the performance of salivary and plaque microbiomes in classifying pediatric T1D status. Methods Paired saliva and plaque samples were collected from 46 children (23 with T1D, 23 healthy controls). Microbial DNA was extracted and sequenced targeting the 16S rRNA gene. Data were processed using QIIME 2 for taxonomic classification and centered log-ratio transformation. Alpha diversity, microbial abundance, and clustering analyses were performed to compare the oral microbiome between T1D and control groups. Random forest classifiers were used to evaluate and compare the predictive accuracy of saliva- and plaque-based models, both with and without clinical metadata. Results Saliva samples exhibited lower alpha diversity than plaque but had significantly higher bacterial load and total microbial abundance. Saliva-based models outperformed plaque-based models, achieving a classification accuracy of 94.2% with or without clinical metadata, compared to 73.3% accuracy for plaque-based models. ROC curve analysis further supported this difference, with saliva models reaching an AUC of approximately 0.94, versus 0.75 for plaque, indicating superior discriminative performance. UMAP clustering revealed more distinct separation of T1D and control groups in salivary profiles than in plaque. Feature importance analysis identified both unique and shared taxa predictive of T1D in each niche. Incorporating clinical and demographic metadata did not enhance model performance, underscoring the robustness and predictive strength of microbiome data alone. Conclusion The salivary microbiome is a more effective biospecimen than dental plaque for detecting T1D-associated microbial profiles in children. It offers superior classification accuracy and greater sensitivity in distinguishing T1D status, supporting saliva’s potential as a non-invasive, scalable medium for future microbiome-based monitoring.