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Background <p>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.</p> Methods <p>This cross-sectional study included 115 Brazilian adults with obesity (body mass index ≥ 30&#xa0;kg/m<sup>2</sup>) 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.</p> Results <p>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 <i>p</i> = 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.</p> Conclusions <p>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.</p>

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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

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  • 发表日期：

    2026年5月16日

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  • Karina Sarno Paes Alves Dias,
  • Virgílio Santana-Júnior,
  • Luciana Mara Barbosa Pereira,
  • Felipe Oliveira Bittencourt,
  • Hérika Maria Silveira Ruas,
  • Gefter Thiago Batista Correa,
  • Stênio Fernando Pimentel Duarte,
  • Juciane Fagundes Durães Benitez,
  • Renato Sobral Monteiro Junior,
  • Sérgio Henrique Sousa Santos,
  • Desirée Sant’Ana Haikal,
  • Alfredo Maurício Batista de Paula

摘要

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

This cross-sectional study included 115 Brazilian adults with obesity (body mass index ≥ 30 kg/m2) 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.