Key result
TyG and TyG-WC indexes show moderate diagnostic accuracy for metabolic syndrome in pediatric obesity.
Why the study?
Do TyG, TyG-WC, BAI, FGIR, and QUICKI indexes accurately identify metabolic syndrome in children and adolescents with obesity?
Cross-Sectional (n=758)
No
Do TyG, TyG-WC, BAI, FGIR, and QUICKI indexes accurately identify metabolic syndrome in children and adolescents with obesity?
Effect estimate: AUC 0.75 and 0.76
TyG and TyG-WC indexes offer moderate discriminative ability for identifying metabolic syndrome in children and adolescents with obesity, serving as potential supportive screening tools.
Pediatric obesity is closely associated with metabolic syndrome (MetS), a condition linked to increased cardiometabolic risk. Early identification of high-risk individuals remains challenging. This study aimed to evaluate the diagnostic performance of selected anthropometric, metabolic dysfunction and insulin resistance indexes for identifying MetS in children and adolescents with obesity. Methods: In this retrospective, cross-sectional, single-center study, 758 children and adolescents with obesity (mean age 14.8 ± 2.1 years; 59.9% females) hospitalized for a body weight-reduction program were included. MetS was defined according to International Diabetes Federation criteria, in which central obesity is a mandatory diagnostic component. The triglyceride–glucose (TyG), TyG–waist circumference (TyG-WC), body adiposity index (BAI), fasting glucose-to-insulin ratio (FGIR), and quantitative insulin sensitivity check index (QUICKI) were calculated. Receiver operating characteristic curve analysis was used to assess their discriminative ability. Results: The prevalence of MetS was 27.8% and was significantly higher in males thanfemales (34.9% vs. 23.1%, p < 0.0001). TyG and TyG-WC showed the best discriminative performance (AUC 0.75 and 0.76, respectively), although with only moderate sensitivity and specificity. FGIR and QUICKI demonstrated lower accuracy (AUC 0.64 and 0.63), whereas BAI showed no discriminative ability (AUC 0.48). These findings were consistent across sexes, although sex-specific differences in both MetS prevalence and optimal cut-off values were observed. Correlation analyses confirmed moderate associations between TyG-based indexes and MetS, whereas other indexes showed weaker relationships. Conclusions: In the present cohort of children and adolescents with obesity, TyG and TyG-WC showed the best performance in identifying MetS compared with the other evaluated indexes. However, their performance remained moderate, and the proposed cut-off values require validation in independent populations. These indexes may represent simple supportive screening and risk-stratification tools but should be used alongside comprehensive clinical assessment and established diagnostic criteria rather than as stand-alone diagnostic measures. These data cannot be made publicly available as they include sensitive information, but they can be made available upon reasonable request of interested researchers to the corresponding author, who will forward a data transfer agreement request to the relevant Ethical Committee. Requests can be addressed to Dr. Alessandro Sartorio (sartorio@auxologico.it).
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Alessandro Sartório (2026) conducted a cross-sectional in Metabolic syndrome in pediatric obesity (n=758). TyG and TyG-WC indexes vs. BAI, FGIR, and QUICKI indexes was evaluated on Discriminative ability for identifying metabolic syndrome (AUC 0.75 and 0.76). TyG and TyG-WC indexes showed the best discriminative performance for identifying metabolic syndrome in pediatric obesity (AUC 0.75 and 0.76), though accuracy remained moderate.
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