Metabolic phenotyping of saliva to identify possible biomarkers of periodontitis using proton nuclear magnetic resonance

Aim The aim of this study was to propose biomarker candidates for periodontitis via untargeted metabolomics analysis. Materials and methods Metabolic profiling was performed using saliva samples from 92 healthy controls (H) and 129 periodontitis patients (P) in the discovery cohort using proton nuclear magnetic resonance spectroscopy. Random forest was applied to identify metabolites that significantly differentiated the control group from the periodontitis group. Candidate metabolites were then validated in an independent validation cohort. Results In the discovery set, the metabolic profiles of the P group were clearly separated from those of the H group. A total of 31 metabolites were identified in saliva, and 7 metabolites were selected as candidate biomarkers. These metabolites were further confirmed in the validation set. Ethanol, taurine, isovalerate, butyrate, and glucose were finally confirmed as biomarkers. Furthermore, the biomarker panel showed more than 0.9 of the area under curve value in both discovery and validation sets, indicating that panels were more effective than individual metabolites for diagnosing periodontitis. Conclusions We identified five metabolite biomarkers that discriminated patients with periodontitis from healthy controls in two independent cohorts. These biomarkers have the potential for periodontal screening, detection of periodontitis, and monitoring of the outcome of periodontal therapy.

Automated summary

This study aimed to propose biomarker candidates for periodontitis through untargeted metabolomics analysis. 5 metabolites were identified as biomarkers that distinguished periodontitis patients from healthy controls, showing potential applications in periodontal screening, detection, and monitoring of treatment outcomes.