Characterising oral microbial signatures for periodontal disease in the NHANES population
DOI:
https://doi.org/10.2340/aos.v85.45662Keywords:
Oral microbiota, periodontal disease, 16S rRNA gene, NHANES, machine learningAbstract
Objective: This study aims to characterise the relationship between the oral microbiota and periodontal disease (PD) by leveraging the latest and largest oral microbiota database from the US National Health and Nutrition Examination Survey (NHANES).
Methods: This study represented a secondary analysis of publicly available data from the NHANES 2009–2012 cycle. Within this dataset, subjects with PD and periodontally healthy controls were identified. Oral rinse samples were collected by the original NHANES study, which also performed polymerase chain reaction (PCR) amplification targeting the V4 hypervariable region of the 16S rRNA gene, sequencing, and subsequent construction of amplicon sequence variant (ASV) tables with taxonomic classification using the SILVA reference database. Venn diagram was generated to illustrate the overlap of differentially relative abundant genera identified by the Wilcoxon test, STAMP, and Linear discriminant analysis effect size (LEfSe). These results were integrated to calculate the Microbial Dysbiosis Index (MDI). To reliably distinguish PD, four supervised machine learning (ML) algorithms were employed, SHapley Additive exPlanations (SHAP) was utilised to explain the model.
Results: A Venn diagram identified 19 core genera. Subjects in the case group exhibited a significantly higher MDI compared to controls (t = 8.536, P < 0.001), with an area under the curve (AUC) of 0.595 (95% confidence interval [CI]: 0.574–0.622). ML models, particularly XGBoost, demonstrated strong predictive performance (AUC: 0.958, 95% CI: 0.950–0.966) for PD classification. SHAP analysis highlighted important microbial taxa, including Treponema_2 and Prevotella.
Conclusion: This study comprehensively investigated the oral microbiota’s association with PD, identifying potential biomarkers for diagnosis and targeted interventions.
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