Literature DB >> 17142126

Simple anthropometric measures identify fasting hyperinsulinemia and clustering of cardiovascular risk factors in Asian Indian adolescents.

Anoop Misra1, Malini Madhavan, Naval K Vikram, Ravindra M Pandey, Vibha Dhingra, Kalpana Luthra.   

Abstract

Correlations of easily measurable parameters of obesity (body mass index [BMI], waist circumference [WC], and subscapular skinfold thickness) with fasting hyperinsulinemia and cardiovascular risk factors (CRFs) have not been investigated in adolescents. We evaluated the screening performance of 3 anthropometric measurements, BMI, WC, and subscapular skinfold thickness, in identifying fasting hyperinsulinemia and clustering of CRFs in 680 male and 521 female adolescents and young adults aged 14 to 18 years in a cross-sectional population survey. CRFs considered were hypercholesterolemia, hypertriglyceridemia, low levels of high-density lipoprotein cholesterol, impaired fasting blood glucose, hypertension, and fasting hyperinsulinemia. The ability of the anthropometric measurements to identify the clustering of CRFs without (cluster 1) and with fasting hyperinsulinemia (cluster 2), and fasting hyperinsulinemia alone was evaluated. BMI, WC, and subscapular skinfold thickness identified the clustering of CRFs and fasting hyperinsulinemia better in males than in females. Among individual risk factors, WC was better in identifying the presence of 3 or more risk factors in cluster 1 for both males and females, and in cluster 2 in females. Subscapular skinfold thickness was better than BMI and WC in identifying hyperinsulinemia in males, and the presence of 3 or more risk factors in cluster 2 in females. All 3 measurements were more accurate in identifying fasting hyperinsulinemia than presence of 3 or more CRFs in either cluster 1 or cluster 2 with higher odds ratio for males. This study shows gender differences in identification of insulin resistance and clustering of CRFs by using simple anthropometric parameters in Asian Indian adolescents. These simple measurements are useful for preventing and predicting cardiovascular risk and for generating a correct definition of the metabolic syndrome.

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Year:  2006        PMID: 17142126     DOI: 10.1016/j.metabol.2006.06.029

Source DB:  PubMed          Journal:  Metabolism        ISSN: 0026-0495            Impact factor:   8.694


  14 in total

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2.  Is waist circumference a better predictor of insulin resistance than body mass index in U.S. adolescents?

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4.  Protocol for a prospective, observational, deep phenotyping study on adipose epigenetic and lipidomic determinants of metabolic homoeostasis in South Asian Indians: the Indian Diabetes and Metabolic Health (InDiMeT) study.

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5.  Can body mass index, waist circumference, waist-hip ratio and waist-height ratio predict the presence of multiple metabolic risk factors in Chinese subjects?

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7.  Body Fat Patterning, Hepatic Fat and Pancreatic Volume of Non-Obese Asian Indians with Type 2 Diabetes in North India: A Case-Control Study.

Authors:  Anoop Misra; Shajith Anoop; Seema Gulati; Kalaivani Mani; Surya Prakash Bhatt; Ravindra Mohan Pandey
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8.  Predictors of metabolic syndrome in the Iranian population: waist circumference, body mass index, or waist to hip ratio?

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Review 9.  Obesity and dyslipidemia in South Asians.

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Journal:  Nutrients       Date:  2013-07-16       Impact factor: 5.717

10.  Agreement between Framingham Risk Score and United Kingdom Prospective Diabetes Study Risk Engine in Identifying High Coronary Heart Disease Risk in North Indian Population.

Authors:  Dipika Bansal; Ramya S R Nayakallu; Kapil Gudala; Rajavikram Vyamasuni; Anil Bhansali
Journal:  Diabetes Metab J       Date:  2015-07-08       Impact factor: 5.376

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