The relationship between metabolic syndrome and left ventricular mass index in obese children
To investigate the relationships between metabolic syndrome (MS), other metabolic features and left ventricular mass index (LVMI) in a population of obese children and adolescents with MS. Two hundred and eight obese children and adolescents (119 females and 89 males, mean age: 11.9±2.7 years) and c...
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Published in | Journal of clinical research in pediatric endocrinology Vol. 3; no. 3; pp. 132 - 138 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Turkey
Galenos Publishing
2011
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Subjects | |
Online Access | Get full text |
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Summary: | To investigate the relationships between metabolic syndrome (MS), other metabolic features and left ventricular mass index (LVMI) in a population of obese children and adolescents with MS.
Two hundred and eight obese children and adolescents (119 females and 89 males, mean age: 11.9±2.7 years) and control subjects (24 females and 26 males, mean age: 11.4±2.9 years) were enrolled in the study. The insulin sensitivity index and LVMI were determined. The International Diabetes Federation criteria were used to diagnose MS.
The obese patients were divided into MS group (n=55) and non-MS (n=153) group. The values of LVMI in the MS group were significantly higher than those in the non-MS group (p=0.014). The present LVMI cut-off point of 33 g/m² for the diagnosis of MS yielded a sensitivity of 97% and a specificity of 98%. LVMI was found to be positively correlated in univariate analysis with height, weight, body mass index (BMI) SDS, fasting insulin level, homeostasis model assessment of insulin resistance (HOMA-IR) and fasting glucose to insulin ratio (FGIR) and negatively correlated with quantitative insulin sensitivity check index (QUICK-I).
We suggest that our optimal LVMI cut-off value for identifying MS may be considered as a sensitive index in screening obese children and adolescents for pediatric MS. Assessment of LVMI in obese children and adolescents may be used as a tool in predicting the presence of MS and its associated cardiovascular risks. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 1308-5727 1308-5735 |
DOI: | 10.4274/jcrpe.v3i3.26 |