Characterization of salivary microbiota in elderly patients with type 2 diabetes mellitus: a matched case–control study

• Published in: Clinical oral investigations Vol. 26; no. 1; pp. 493 - 504
• Main Authors: Omori, Michi, Kato-Kogoe, Nahoko, Sakaguchi, Shoichi, Kamiya, Kuniyasu, Fukui, Nozomu, Gu, Yan-Hong, Nakamura, Shota, Nakano, Takashi, Hoshiga, Masaaki, Imagawa, Akihisa, Kit, Chee Hoe, Tamaki, Junko, Ueno, Takaaki
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2022; Springer Nature B.V
• Subjects: Aged; Case-Control Studies; Comparative analysis; Dentistry; Diabetes; Diabetes mellitus (non-insulin dependent); Diabetes Mellitus, Type 2; Humans; Learning algorithms; Machine learning; Medicine; Metagenomics; Microbiota; Microorganisms; Older people; Oral hygiene; Original Article; Patients; Prediction models; RNA, Ribosomal, 16S - genetics; rRNA 16S; Saliva; Oral microbiota; Type 2 diabetes mellitus; Saliva; Elderly; 16S rRNA metagenomic analysis
• ISSN: 1432-6981; 1436-3771; 1436-3771
• DOI: 10.1007/s00784-021-04027-y

Objective The importance of oral health in type 2 diabetes mellitus (T2DM) is widely recognized; however, oral microbiota characteristics associated with T2DM in the elderly population are not well-understood. This study was conducted to evaluate the characteristics of the salivary microbiota in elderly Japanese patients with T2DM. Methods Saliva samples were collected from 42 elderly Japanese patients with T2DM and 42 age- and sex-matched subjects without T2DM (control). 16S ribosomal RNA metagenomic analysis and comparative analysis of both groups were performed. Random forest classification by machine learning was performed to discriminate between the salivary microbiota in the two groups. Results There were significant differences in the overall salivary microbiota structure between the T2DM and control groups (beta diversity; unweighted UniFrac distances, p  = 0.001; weighted UniFrac distances, p  = 0.001). The phylum Firmicutes was abundant in patients with T2DM, whereas the phylum Bacteroidetes was abundant in controls. The T2DM prediction model by random forest based on salivary microbiota data was verified with a high predictive potential in five cross-validation tests (area under the curve (AUC) = 0.938 (95% CI, 0.824–1.000)). Conclusion Characterization revealed that the salivary microbiota profile of the elderly patients with T2DM is significantly distinct from that of the controls. Clinical relevance These data indicate the necessity of oral health management based on the characteristics of the salivary microbiota in elderly patients with T2DM. Our findings will contribute to future research on the development of new diagnostic and therapeutic methods for this purpose.