BMC oral health · 2026
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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Dias Karina Sarno Paes Alves, Santana-Júnior Virgílio, Pereira Luciana Mara Barbosa, Bittencourt Felipe Oliveira, Ruas Hérika Maria Silveira, Correa Gefter Thiago Batista, Duarte Stênio Fernando Pimentel, Benitez Juciane Fagundes Durães, Junior Renato Sobral Monteiro, Santos Sérgio Henrique Sousa, Haikal Desirée Sant'Ana, de Paula Alfredo Maurício Batista
Аннотация
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.
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.