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International Journal of Endocrinology
Volume 2012, Article ID 420792, 5 pages
Research Article

C-Peptide Versus Insulin: Relationships with Risk Biomarkers of Cardiovascular Disease in Metabolic Syndrome in Young Arab Females

College of Health Sciences, University of Sharjah, P.O. Box 27272, Sharjah, UAE

Received 11 April 2012; Revised 14 June 2012; Accepted 18 June 2012

Academic Editor: Panayota Mitrou

Copyright © 2012 A. Abdullah et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Background. Obesity is a major health concern and is associated with metabolic syndrome (MetS) that increases the risk for cardiovascular disease (CVD). Since little is known about the relationships between MetS components and CVD in overweight/obese young Arab females, our study aimed at examining these relationships and further to explore the associations between connecting peptide (C-peptide) and insulin with these biomarkers. Subjects and Methods. In this cross-sectional study, 80 apparently healthy young Arab females were recruited and grouped by their body mass index (BMI) into normal-weight (GI) and overweight/obese (GII) groups. Results. The two groups significantly differed in BMI, waist circumference (WC) and values of biomarkers, namely, leptin, fasting insulin, uric acid (UA), insulin resistance (HOMA-IR), C-peptide, high-sensitivity C-reactive protein (hs-CRP), high-density lipoprotein cholesterol (HDL-C), systolic blood pressure (SBP), and diastolic blood pressure (DBP). C-peptide significantly correlated with WC, leptin, UA, and HDL-C and was predicted by three biomarkers; UA, WC and HDL-C. Whereas, insulin significantly correlated with only two biomarkers including leptin and DBP and was predicted by UA and DBP. Conclusions. The present study highlighted the association between MetS and CVD in young Arab females and the possible role of C-peptide in the prediction of CVD.