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The Association of Periodontal Inflammation and Systemic Health Indicators: A Machine Learning Approach.

Yumeng Yan¹, Praveen Sharma^2,3,4, Jeanie Suvan⁵

¹Periodontology Unit, UCL Eastman Dental Institute, London, UK.

Journal of Clinical Periodontology

|July 23, 2025

Summary

This study confirms a link between periodontal inflammation and systemic inflammation using machine learning. Predictive models can identify periodontitis using systemic health indicators, aiding early detection in healthcare settings.

Keywords:

C‐reacive protein inflammation periodontal inflamed surface area periodontitis systemic diseases

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Area of Science:

Oral Health
Systemic Health
Biostatistics

Background:

Periodontitis is linked to systemic inflammation, impacting overall health.
Understanding this bidirectional relationship is crucial for public health.
Machine learning offers novel approaches to analyze complex health data.

Purpose of the Study:

To investigate the association between periodontal inflammation and systemic inflammation markers.
To explore the link between periodontal inflammation and metabolic health indicators.
To develop and validate machine learning models for predicting periodontal status using systemic health data.

Main Methods:

Cross-sectional cohort data (N=667) analyzed using regression models.
Machine learning classifiers (RF, SVM, GB) used to predict systemic inflammation (CRP) and periodontitis.
Models validated on large, nationally representative datasets (NHANES).

Main Results:

Confirmed a significant nonlinear relationship between periodontal inflamed surface area (PISA) and C-reactive protein (CRP) levels.
Support Vector Machine (SVM) model demonstrated high accuracy in predicting systemic inflammation (AUC=0.74).
SVM model effectively predicted periodontitis status from systemic indicators (AUC=0.82), validated externally.

Conclusions:

Machine learning models confirm a robust link between periodontal and systemic inflammation.
Predictive models using systemic parameters can aid in identifying periodontitis cases.
These models show potential for primary care screening and confirming the periodontitis-systemic health link.