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Can the metabolic syndrome identify children with insulin resistance?
Valeria Hirschler1, Maria L Calcagno, Claudio Aranda
1Nutrition Department, Durand Hospital, Buenos Aires, Argentina. vhirschler@intramed.net
Insights
Pediatric metabolic syndrome criteria reliably identify insulin resistance in children. Homeostasis model assessment of insulin resistance (HOMA-IR) emerged as a key independent risk factor for metabolic syndrome in youth.
Area of Science:
- Pediatric Endocrinology
- Metabolic Disorders
- Cardiovascular Risk Factors
Background:
- Metabolic syndrome is linked to insulin resistance in adults.
- Pediatric metabolic syndrome criteria, adapted from adult guidelines, require validation for pediatric populations.
- Understanding pediatric metabolic syndrome is crucial for early intervention.
Purpose of the Study:
- To assess the reliability of pediatric metabolic syndrome criteria in identifying insulin resistance in children.
- To determine the association between metabolic syndrome components and insulin resistance.
- To identify independent risk factors for pediatric metabolic syndrome.
Main Methods:
- A cross-sectional study involving 167 children (aged 6.7 ± 3 years) categorized by BMI percentile: overweight (73), at risk of overweight (41), and normal-weight (53).
- Measurements included waist circumference, blood pressure, oral glucose tolerance test, C-reactive protein, adiponectin, insulin, and lipids.
- Homeostasis model assessment of insulin resistance (HOMA-IR) was calculated and analyzed against metabolic syndrome components and prevalence.
Main Results:
- The prevalence of metabolic syndrome was 11.3% overall and 21.9% in overweight children.
- Increased waist circumference and low high-density lipoprotein were common findings.
- Higher HOMA-IR values correlated significantly with the number of metabolic syndrome components (p < 0.0001).
- HOMA-IR was identified as the sole independent risk factor for metabolic syndrome in children, adjusted for age and sex (p = 0.007).
Conclusions:
- Insulin resistance, as indicated by HOMA-IR, plays a significant role in the pediatric metabolic syndrome.
- The established criteria appear reliable for identifying children at risk.
- Early detection of pediatric metabolic syndrome and insulin resistance may aid in predicting future cardiovascular disease and type 2 diabetes risk.
Objective:
The metabolic syndrome is associated with insulin resistance in adults. We defined pediatric metabolic syndrome using criteria analogous to Adult Treatment Panel III. The purpose of this study was to determine whether these criteria are reliable for insulin resistance in children.
Research Design And Methods:
Out of 167 children (6.7 +/- 3 yr), 73 overweight [body mass index (BMI) > 95 percentile], 41 at risk of overweight (BMI > 85 < 95 percentile), and 53 normal-weight (BMI < 85 percentile) children matched for sex and age were examined. The results for waist circumference, blood pressure, oral glucose tolerance test, C-reactive protein, adiponectin, insulin, and lipids were obtained.
Results:
There was a comparable prevalence of the metabolic syndrome in both sexes. The prevalence of the metabolic syndrome was 11.3% [95% confidence interval (CI) 6.56-16.19%] among the whole group and 21.9% (95% CI 12.24-31.0%) among overweight children. Waist circumference >75 percentile 53.2% (95% CI 45.73-60.86%) and low high-density lipoprotein 27.5% (95% CI 20.77-34.32%) were common in this sample. Compared with patients without any component of the metabolic syndrome, homeostasis model assessment insulin resistance (HOMA-IR) for patients with one through four components was higher (beta = 0.6, 95% CI 0.4-0.7, p < 0.0001, R(2) = 0.185). A logistic regression analysis using the metabolic syndrome as the dependent variable showed that HOMA-IR (odds ratio 1.52, 95% CI 1.2-2.0, p = 0.007) was the only independent risk factor for the metabolic syndrome, adjusted for age and sex.
Conclusions:
The importance of insulin resistance in the metabolic syndrome is supported by the results of logistic regression analysis. Early identification of children may be useful to predict future cardiovascular disease and type 2 diabetes.
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