Screening for Periodontitis Using Blood Biomarkers and Demographic Data: A Machine Learning Study

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Sensitivity 94.6%, Specificity ~35% (calculated), F1 score 71.5%

• Primary Outcome: Diagnostic accuracy for moderate-to-severe periodontitis screening

• Nnt Or Sensitivity: Sensitivity 94.6%, Positive predictive value 57.4%

• Confidence Interval: Not reported

Clinical Application

Moderate - requires integration of machine learning tools into clinical workflows and comprehensive blood panels Could complement RACGP preventive care guidelines. Integration with My Health Record and PBS pathology services feasible but requires validation in Australian populations Adults ≥30 years in primary care settings with routine blood work

Abstract

PURPOSE: Periodontitis is a common chronic disease associated with systemic conditions such as diabetes and cardiovascular disease. Diagnosis typically relies on dental examinations and radiographs, which may be underutilised by individuals who avoid, delay, or lack dental care. This study evaluated the potential of routine blood biomarkers and demographic data for screening moderate-to-severe periodontitis. METHODS AND MATERIALS: Data were obtained from the National Health and Nutrition Examination Survey (NHANES) 2011-2012 adults aged ≥ 30 years (N = 3,338). Periodontal status was classified using CDC/AAP definitions. An XGBoost classifier was trained on 77 features spanning demographics, complete blood count, glycemic and hemo-globin markers, heavy metals, and additional biochemical assays. Model performance was assessed with Accuracy, Precision, Recall, and F1 score. SHapley Additive exPlanations (SHAP) were used for interpretability. Subgroup analyses were conducted by age and gender. RESULTS: The model achieved 61.1% Accuracy, 57.4% Precision, 94.6% Recall, and an F1 score of 71.5%. Performance was higher in males (F1 = 78.2) than in females (F1 = 63.9%). SHAP identified age, gender, blood cadmium, blood lead, and glycohaemoglobin (HbA1c) as top predictors. CONCLUSION: Routine blood biomarkers and demographics can be leveraged for non-dental screening of periodontitis, offering a feasible preventive strategy in primary care. The model achieved high recall, minimising false negatives, and identified biologically plausible predictors. This approach may help flag at-risk individuals, particularly in underserved populations, and support integration of oral health into general medical care.

References

1. 1.Choi, S., Oh, D., Rashed, R., Hemadeh, R., Kim, Y. J., & Oyoyo, U. (2026). Screening for periodontitis using blood biomarkers and demographic data: A machine learning study. Oral Health & Preventive Dentistry. https://doi.org/10.3290/j.ohpd.c_2684