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

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.

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.

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.

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.