Research Article: An interpretable machine-learning model identifies obesity-related metabolic complications in children attending a tertiary obesity clinic
Abstract:
Childhood obesity-related metabolic complications are common in Northwest China, yet region-specific evidence and risk-stratification tools are lacking. In a single-visit cross-sectional study of 131 children (3–18 years) with overweight or obesity attending a Gansu maternal and child-care hospital, obesity-related metabolic complications (ORMC) were present in 31.3% (dyslipidaemia 16.0%, hyperuricaemia 9.2%, elevated blood pressure 7.6%). An interpretable model combining LASSO-selected adiposity measures (body mass index, neck circumference, body fat percentage) with penalised logistic regression achieved moderate, internally cross-validated discrimination (AUC 0.72, 95% CI 0.62-0.82) that awaits external validation, comparable to a random forest (0.73). Screen time was the strongest modifiable behavioural correlate. Routine metabolic screening and simple body-composition measures should be embedded in paediatric obesity clinics, with screen-time reduction as a counselling priority.
Introduction:
Childhood obesity has become one of the most serious public-health challenges of the twenty-first century. Between 1990 and 2022 the global prevalence of obesity among school-aged children and adolescents increased from 1.7% to 6.9% in girls and from 2.1% to 9.3% in boys, affecting approximately 160 million young people worldwide ( 1 , 2 ). In the United States, 16.9% of children and adolescents were already obese in 2011–2012 ( 3 ). China now carries the largest population of children with overweight or obesity…
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