Oral Multi-niche Microbiota and Age Stratification: A Novel Strategy for Precision Screening of Periodontitis

Background: periodontitis is a prevalent chronic inflammatory disease driven by oral microbiota dysbiosis, with profound oral and systemic impacts that impair healthy aging. Conventional screening methods are invasive and delay early detection, while microbiota-based strategies show promise for non-invasive screening—yet the roles of oral niche heterogeneity and age-related microbial shifts remain unclear. Methods: a total of 120 participants (20–90 years) were classified into low-risk (non-periodontal, NP, n=67) and high-risk (periodontal, P, n=53) groups based on clinical and radiological indices. Non-invasive samples (saliva, dental plaque, tongue plaque) were collected for 16S rRNA gene sequencing. Bioinformatic analyses and machine learning models were used to evaluate diagnostic efficacy, with age stratification (≤50 years vs. >50 years). Results: age differed significantly between NP and P groups (p=0.003). Age stratification improved model accuracy, with salivary microbiota showing optimal diagnostic efficacy (AUC=0.807), outperforming dental plaque (max AUC=0.780) and tongue plaque (AUC=0.753). Distinct site-specific biomarkers were identified, and beta-diversity analysis revealed significant microbial community differences across sites (PERMANOVA, p=0.001). Conclusions: oral niche heterogeneity and age stratification are critical for microbiota-based periodontitis screening. Saliva is the optimal non-invasive sample, providing robust evidence for developing precise, accessible diagnostic tools for community and primary healthcare settings.