ArticleJournal of paediatrics and child health2026
Role of Skinfold Thickness and MUAC in Predicting Insulin Resistance: ROC And Regression Analysis.
Article in Journal of paediatrics and child health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
introductionEarly detection of insulin resistance (IR) in children is essential to prevent long-term metabolic complications. Simple anthropometric measures-mid-upper arm circumference (MUAC) and skinfold thickness-are widely used, but their predictive value, especially when age-standardized, remains unclear. This study aimed to evaluate the performance of raw, SDS, and percentile MUAC and skinfold measurements in predicting IR in patients with obesity and adolescents.
methodsA cross-sectional study was conducted with 171 participants with obesity aged 6-18 years. Anthropometric measurements were recorded and converted to SDS and percentile values. Insulin resistance was evaluated using HOMA-IR with pubertal-stage-adjusted cutoffs. Logistic regression and ROC analyses were applied.
resultsInsulin resistance was present in 56.1% of participants. Both MUAC and skinfold thickness showed significant positive associations with IR; however, standardized measures (SDS and percentiles) demonstrated markedly stronger predictive value than raw measurements. Skinfold thickness SDS emerged as the strongest independent predictor of IR (OR = 12.04, p < 0.001), followed by MUAC SDS (OR = 7.28, p < 0.001). ROC analysis showed excellent discriminatory power for skinfold SDS (AUC = 0.82), with an optimal cutoff of 1.48 (76% sensitivity, 80% specificity). MUAC SDS also performed well (AUC = 0.76; cutoff = 1.10).
conclusionSkinfold thickness SDS and MUAC SDS are strong, practical, and non-invasive markers for predicting insulin resistance in paediatric patients with obesity. Standardized measurements clearly outperform raw values and should be integrated into routine clinical assessment to enhance early detection of metabolic risk.
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