Development and validation of a non-invasive prediction model for identifying high-risk children with metabolic

Yuan Xiaowu1, Dong Jian1, Wen Yizhu1

  • 1Department of Ultrasound, The First Affiliated Hospital, Shihezi University, Shihezi, China.

Frontiers in Pediatrics
|August 22, 2025
PubMed

Insights

A new health index can identify children with Metabolic dysfunction-associated fatty liver disease (MAFLD). This index uses factors like age, BMI, and sleep duration to detect MAFLD early in pediatric populations.

Area of Science:

  • Pediatric Endocrinology
  • Metabolic Disorders
  • Public Health

Background:

  • Metabolic dysfunction-associated fatty liver disease (MAFLD) is increasingly prevalent in children.
  • Early identification and risk stratification are crucial for effective management and prevention of pediatric MAFLD.

Purpose of the Study:

  • To investigate the prevalence and risk factors of MAFLD in children.
  • To develop and validate a novel health index scoring system for early MAFLD detection in pediatric populations.

Main Methods:

  • A cross-sectional study involving 2,190 children aged 6-18 years.
  • Data collection via questionnaires and anthropometric measurements.
  • Logistic regression and ROC curve analysis to identify risk factors and establish a health index.

Main Results:

  • MAFLD prevalence was 26.30% in the study cohort.
  • Key predictors identified include age, gender, BMI, WHR, WHtR, sleep duration, and dessert consumption.
  • The developed health index showed moderate predictive accuracy (AUC 0.72-0.74) with an optimal threshold of 11.5 points.

Conclusions:

  • Seven independent determinants of pediatric MAFLD were established.
  • The health index offers a clinically useful tool for early MAFLD screening in children.
  • This quantitative tool can enhance targeted prevention and resource allocation for childhood metabolic disorders.
Abstract