Can Metric Parameter Combining Metabolic Syndrome Components Usefully Predict Coronary Artery Disease?
|Sarraj Mohamed Youssef1*, Najah Mohamed1, Slimani Afef1, Ben Hamda Khaldoun3, Neffati Fadoua2, Najjar Mohamed Fadhel2 and Slimane Mohamed Naceur1|
|1Research Unit genetic and biological factors of atherosclerosis, Medicine Faculty, University of Monastir, Tunisia|
|2Laboratory of Biochemistry and Toxicology of the University Hospital of Monastir, Tunisia|
|3Department of Cardiology of the University Hospital of Monastir, Tunisia|
|Corresponding Author :||Mohamed Youssef Sarraj
Research Unit 05/UR/09-12: Genetic
and Biological Factors of Atherosclerosis
Medicine Faculty, University of Monastir, Tunisia
E-mail: [email protected]
|Received April 01, 2013; Accepted April 22, 2013; Published April 27, 2013|
|Citation: Youssef SM, Mohamed N, Afef S, Khaldoun BH, Fadoua N, et al. (2013) Can Metric Parameter Combining Metabolic Syndrome Components Usefully Predict Coronary Artery Disease? J Metabolic Synd 2:119. doi:10.4172/2167- 0943.1000119|
|Copyright: © 2013 Youssef SM, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.|
Aims: We have investigated to what extent Metabolic Syndrome (MS) is related to Coronary Artery Disease (CAD) incidence and we tried to determine a metric parameter combining MS quantitative components to be used as a screening tool to diagnose CAD.
Materials and methods: 239 patients and 244 control subjects were investigated for clinical, biochemical, anthropometric and angiographic information. CAD is defined as 50% stenosis on the left main coronary artery or multiple significant (≥ 70% stenosis) in more than one coronary artery. The diagnosis of MS was based on the IDF and AHA/NHLBI definition. The computer model HOMA 2 was used to determine HOMA-β, HOMA-S and HOMA-IR. Triglycerides (TG), High Density Lipoprotein cholesterol (cHDL), Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP), HOMA-IR and Waist Circumference (WC) were used to calculate the different MS markers. The area under curve of ROC curves were used to compare the powers of these MS markers.
Results: MS was significantly related to the CAD. Each MS quantitative component was a significant discriminating factor for CAD. FPG followed by SBP were the principal predictive factors of CAD. A metric parameter combing MS qualitative components [(TG/cHDL) × (HOMA-IR × WC)] + SBP was more accurate to estimate CAD risk. Its cut-off point was 247.1
Conclusion: MS was associated with CAD. This marker, with sensitivity and specificity of 86.2 and 73.0 per cent can be used either to diagnose or to predict CAD incidence.