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Age, fat mass, hematocrit, and reduced handgrip strength are independent predictors that can accurately identify prevalent periodontitis in adults with obesity. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

May 18, 2026BMC Oral Health0 citationsOpen Access

Muscle weakness, greater fat mass, lower hematocrit levels, and advanced age in a diagnostic prediction model of periodontitis in adults with obesity: a cross-sectional study

KDKarina Sarno Paes Alves Dias VSVirgílio Santana-Júnior LPLuciana Mara Barbosa Pereira

Key Points

The aim is to develop and validate a diagnostic prediction model for periodontitis in adults with obesity using clinical and laboratory data.
Cross-sectional design involving adults receiving care at public ambulatory health services.
Data collection included demographic information, body composition metrics, handgrip strength, and comprehensive periodontal examinations.
A multivariable logistic regression model with four predictors was internally validated using 1,000 bootstrap resamples.
71.3% of participants were diagnosed with periodontitis (82/115).
The model retained age (adjusted OR = 1.13), fat mass (adjusted OR = 1.04), hematocrit (adjusted OR = 0.89), and reduced handgrip strength (adjusted OR = 5.22) as predictors.
Overall accuracy of the model was 80.0% with an AUC of 0.873, indicating excellent discrimination.

Abstract

BACKGROUND: Periodontitis is a chronic inflammatory disease that shares key biological pathways with obesity, particularly low-grade systemic inflammation and immune dysregulation. This study aimed to develop and internally validate a diagnostic prediction model for periodontitis in adults with obesity using routinely available clinical and laboratory parameters. METHODS: ) receiving care in public ambulatory health services. Data collection included demographic characteristics, anthropometric and body-composition measures, handgrip strength (HGS), hematological and biochemical parameters, oral-health behaviors and status, and a comprehensive periodontal examination. Periodontitis was diagnosed using clinical and radiographic criteria consistent with the 2017 World Workshop framework. Candidate independent predictors were pre-specified based on biological plausibility and feasibility in public primary health care settings. A pre-specified forced-entry multivariable logistic regression model was developed with a maximum of four predictors and internally validated using 1,000 bootstrap resamples. RESULTS: Periodontitis was diagnosed in 71.3% of participants (82/115). The final model retained age (adjusted OR = 1.13 per year; 95%CI = 1.08-1.20), fat mass (adjusted OR = 1.04 per kg; 95%CI = 0.99-1.09), hematocrit (adjusted OR = 0.89 per percentage point; 95%CI = 0.76-1.02), and reduced HGS (adjusted OR = 5.22; 95%CI = 1.80-17.30) as independent predictors. Apparent discrimination was excellent (AUC = 0.873; 95%CI = 0.807-0.938), with an optimism-corrected AUC of 0.865. Calibration was acceptable (Hosmer-Lemeshow p = 0.749), and overall accuracy was 80.0%, with sensitivity of 76.8% and specificity of 87.9%. A LASSO sensitivity analysis confirmed predictor robustness. A simplified exploratory screening score and a provisional nomogram were derived to improve interpretability. CONCLUSIONS: A parsimonious diagnostic prediction model identified age, fat mass, hematocrit, and HGS as independent predictors of prevalent periodontitis in adults with obesity. The model should be regarded as exploratory and hypothesis-generating. It may inform future risk-stratification strategies in public primary health care settings, but external validation and recalibration are required before clinical implementation.

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