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Updated: Jul 1, 2026

Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies
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Screening for Periodontitis Using Blood Biomarkers and Demographic Data: A Machine Learning Study.

Seongwon Choi, Daniel Oh, Rami Rashed

    Oral Health & Preventive Dentistry
    |May 20, 2026
    PubMed
    Summary
    This summary is machine-generated.

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    Routine blood tests and demographics can screen for periodontitis, a common gum disease linked to other health issues. This non-dental approach aids early detection, especially for those avoiding dental care.

    Area of Science:

    • Biomedical Science
    • Public Health
    • Preventive Medicine

    Background:

    • Periodontitis is a prevalent chronic inflammatory condition.
    • It is associated with systemic diseases like diabetes and cardiovascular disease.
    • Current diagnosis relies on dental exams, often missed by at-risk populations.

    Purpose of the Study:

    • To evaluate the efficacy of routine blood biomarkers and demographic data for screening moderate-to-severe periodontitis.
    • To explore a non-dental screening method for periodontitis.
    • To identify key predictors for periodontitis risk.

    Main Methods:

    • Utilized National Health and Nutrition Examination Survey (NHANES) data (N=3,338).
    • Trained an XGBoost classifier on 77 features including demographics, complete blood count, glycemic markers (HbA1c), and heavy metals.
    Keywords:
    NHANESblood biomarkersmachine learningperiodontitispreventionscreening

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  • Assessed model performance using Accuracy, Precision, Recall, and F1 score; employed SHAP for interpretability.

    • Main Results:

    • The model achieved 61.1% Accuracy, 57.4% Precision, 94.6% Recall, and 71.5% F1 score.
    • Performance was higher in males (F1=78.2%) than females (F1=63.9%).
    • Key predictors identified were age, gender, blood cadmium, blood lead, and HbA1c.

    Conclusions:

    • Routine blood biomarkers and demographics offer a feasible non-dental screening strategy for periodontitis in primary care.
    • The model's high recall minimizes false negatives, identifying at-risk individuals.
    • This approach supports integrating oral health into general medical care, especially for underserved populations.