Frontiers in pediatrics · 2025
Development and validation of a non-invasive prediction model for identifying high-risk children with metabolic dysfunction-associated fatty liver disease.
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Xiaowu Yuan, Jian Dong, Yizhu Wen, Ting Ma, Jin Tong, Juan Wei, Sirui Wang, Jinli Wang, Yuchen He, Huan Zhao, Yuhang Cheng, Jun Li
Аннотация
OBJECTIVE: This study aims to investigate the prevalence and risk factors of Metabolic dysfunction-associated fatty liver disease (MAFLD) in pediatric populations and establish a novel health index scoring system derived from key risk parameters for early identification of high-risk children with MAFLD.
METHOD: In this cross-sectional study, a systematic random sampling method was employed to recruit children (6-18 years) with MAFLD. Data collection involved standardized questionnaires and comprehensive anthropometric measurements. The prevalence of MAFLD was determined through epidemiological analysis. Both univariate and multivariate logistic regression models were systematically applied to identify independent risk factors (P < 0.05), with subsequent development of a health index scoring system. The optimal diagnostic threshold for the health index was established using receiver operating characteristic (ROC) curve analysis.
RESULTS: The study cohort comprised 2,190 pediatric participants, revealing an overall MAFLD prevalence of 26.30%. Significant demographic disparities were observed: males exhibited a higher prevalence than females. The age, BMI (Body Mass Index), Waist-Hip Ratio (WHR), and Waist-Height Ratio (WHtR) values of the MAFLD group were higher than those of the Non-MAFLD group, and the difference was statistically significant. Multivariable logistic regression subsequently identified seven independent predictors (P < 0.05), age (OR = 1.62, 95% CI 1.36, 1.92), gender (OR = 0.42, 95% CI 0.31,0.57), BMI (OR = 2.15, 95% CI 1.75, 2.64), WHR (OR = 2.10, 95% CI 1.64, 2.69), WHtR (OR = 4.01, 95% CI 3.07, 5.23), sleep duration (OR = 0.71, 95% CI 0.59, 0.85) and dessert consumption (OR = 1.46, 95% CI 1.17, 1.81). Health index demonstrated moderate predictive accuracy in both training (AUC = 0.72, 95% CI 0.68, 0.76) and validation cohorts (AUC = 0.74, 95% CI 0.70, 0.78) with optimal diagnostic threshold at 11.5 points. Calibration analysis revealed satisfactory model fit (Hosmer-Lemeshow χ 2 = 7.32, P = 0.12). Strong concordance was observed between dimension weights and regression coefficients (Pearson's r = 0.93, P < 0.001).
CONCLUSION: This study establishes seven independent determinants of MAFLD in pediatric populations: age, gender, BMI, waist-hip ratio, waist-height ratio, sleep duration, and frequent dessert consumption (P < 0.05). The health index demonstrates robust clinical utility for early detection, providing an evidence-based screening protocol for school health programs. Implementation of this quantitative tool could significantly enhance targeted prevention strategies and optimize resource allocation in childhood metabolic disorder surveillance in communities.
METHOD: In this cross-sectional study, a systematic random sampling method was employed to recruit children (6-18 years) with MAFLD. Data collection involved standardized questionnaires and comprehensive anthropometric measurements. The prevalence of MAFLD was determined through epidemiological analysis. Both univariate and multivariate logistic regression models were systematically applied to identify independent risk factors (P < 0.05), with subsequent development of a health index scoring system. The optimal diagnostic threshold for the health index was established using receiver operating characteristic (ROC) curve analysis.
RESULTS: The study cohort comprised 2,190 pediatric participants, revealing an overall MAFLD prevalence of 26.30%. Significant demographic disparities were observed: males exhibited a higher prevalence than females. The age, BMI (Body Mass Index), Waist-Hip Ratio (WHR), and Waist-Height Ratio (WHtR) values of the MAFLD group were higher than those of the Non-MAFLD group, and the difference was statistically significant. Multivariable logistic regression subsequently identified seven independent predictors (P < 0.05), age (OR = 1.62, 95% CI 1.36, 1.92), gender (OR = 0.42, 95% CI 0.31,0.57), BMI (OR = 2.15, 95% CI 1.75, 2.64), WHR (OR = 2.10, 95% CI 1.64, 2.69), WHtR (OR = 4.01, 95% CI 3.07, 5.23), sleep duration (OR = 0.71, 95% CI 0.59, 0.85) and dessert consumption (OR = 1.46, 95% CI 1.17, 1.81). Health index demonstrated moderate predictive accuracy in both training (AUC = 0.72, 95% CI 0.68, 0.76) and validation cohorts (AUC = 0.74, 95% CI 0.70, 0.78) with optimal diagnostic threshold at 11.5 points. Calibration analysis revealed satisfactory model fit (Hosmer-Lemeshow χ 2 = 7.32, P = 0.12). Strong concordance was observed between dimension weights and regression coefficients (Pearson's r = 0.93, P < 0.001).
CONCLUSION: This study establishes seven independent determinants of MAFLD in pediatric populations: age, gender, BMI, waist-hip ratio, waist-height ratio, sleep duration, and frequent dessert consumption (P < 0.05). The health index demonstrates robust clinical utility for early detection, providing an evidence-based screening protocol for school health programs. Implementation of this quantitative tool could significantly enhance targeted prevention strategies and optimize resource allocation in childhood metabolic disorder surveillance in communities.