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Published on: February 2, 2017
Assessment of Insulin Resistance and Body Composition in Children with Overweight and Obesity: A Pilot Study Using
Bogdan Mihai Pascu1,2,3, Anca Bălănescu1,3, Paul Cristian Bălănescu1
1Pediatric Department, Faculty of Medicine, University of Medicine and Pharmacy "Carol Davila", 030167 Bucharest, Romania.
Insights
High insulin resistance (IR) affects over half of overweight children, linked to factors like age and body composition. Bioelectrical impedance analysis (BIA) and lipid ratios show moderate predictive power for identifying IR in pediatric obesity.
Area of Science:
- Pediatric Endocrinology
- Metabolic Health
- Body Composition Analysis
Background:
- Childhood obesity increases risks for type 2 diabetes and cardiovascular disease via insulin resistance (IR).
- Standard BMI may not fully capture metabolic risk in children, especially those with atypical body fat distribution (TOFI).
- Early identification of IR is crucial for preventing long-term health complications.
Purpose of the Study:
- To determine the prevalence and predictors of IR in overweight/obese children.
- To evaluate the utility of conventional biomarkers and bioelectrical impedance analysis (BIA) for IR assessment.
- To explore the role of lipid ratios, fasting glucose, and principal component analysis (PCA) in metabolic phenotyping.
Main Methods:
- Retrospective analysis of 210 children (aged 1-18) with overweight/obesity.
- Data collection included anthropometrics, fasting labs, and BIA (Tanita PRO DC430 MA).
- IR defined as HOMA-IR > 2; ROC analysis used for predictive performance; PCA applied to BIA data.
Main Results:
- Insulin resistance (IR) was found in 54.8% of the cohort.
- IR correlated with higher age, pubertal status, ALT, LDL-C, triglycerides, and BIA-derived fat-free mass (FFM), total body water (TBW), and appendicular skeletal muscle mass (PMM).
- Triglyceride-to-HDL ratio and triglycerides showed moderate predictive power for IR; BIA metrics had similar performance. PCA identified fat-free mass and adiposity axes.
Conclusions:
- A high prevalence of IR exists in children with excess weight, underscoring the need for early screening.
- Integrating BIA and composite biomarkers can enhance early detection of metabolic dysfunction.
- PCA-derived body composition components may refine metabolic phenotyping in pediatric obesity management.
Abstract:
Background/Objectives: Childhood obesity is associated with early metabolic complications, particularly insulin resistance (IR), which significantly elevates the long-term risk for type 2 diabetes and cardiovascular disease. Standard measures such as BMI may inadequately capture metabolic risk, particularly in children with atypical phenotypes such as TOFI (Thin Outside, Fat Inside). This study aimed to evaluate the prevalence and predictors of IR in a pediatric population with overweight and obesity, using both conventional biomarkers and bioelectrical impedance analysis (BIA). We also examined the predictive value of lipid ratios and fasting glucose and applied Principal Component Analysis (PCA) to identify underlying body composition dimensions. Methods: A retrospective cohort of 210 children aged 1-18 years, assessed in a tertiary pediatric endocrinology center in Romania, was analyzed. Clinical data included anthropometric measures, fasting laboratory results, and body composition parameters obtained via Tanita PRO DC430 MA BIA. Insulin resistance was defined as HOMA-IR > 2. ROC analysis assessed the predictive performance of triglyceride-to-HDL (Tg/HDL) ratio, fasting glucose, and BIA metrics. PCA was applied to BIA variables to explore dimensional structure. Results: Insulin resistance was present in 54.8% of the cohort. It was significantly associated with higher age, pubertal status, ALT, LDL-cholesterol, triglycerides, and BIA-derived fat-free mass (FFM), TBW, and PMM. ROC analyses showed moderate predictive power for Tg/HDL (AUROC = 0.645) and triglycerides (AUROC = 0.656) in identifying IR. BIA metrics had comparable discriminatory performance (AUROC~0.61). PCA reduced eight BIA parameters into two components: a fat-free mass axis (TBW, FFM, PMM, WATERM) and an adiposity axis (BMI, FATP, FATM, WATERP). Conclusions: This study highlights the high burden of insulin resistance among children with excess weight and supports the integration of BIA and composite biomarkers into early screening protocols. PCA-derived components may improve metabolic phenotyping in pediatric obesity.
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