alexa The Relationship between Metabolic Syndrome and Markers of Cardiometabolic Disease among Canadian Adults
ISSN: 2155-6156

Journal of Diabetes & Metabolism
Open Access

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Research Article

The Relationship between Metabolic Syndrome and Markers of Cardiometabolic Disease among Canadian Adults

Darren R. Brenner1,2#, Paul Arora1,2#, Bibiana Garcia-Bailo1,3#, Howard Morrison4, Ahmed El-Sohemy3, Mohamed Karmali1,2,3 and Alaa Badawi1*

1Office of Biotechnology, Genomics and Population Health, Public Health Agency of Canada, Canada

2Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada

3Department of Nutritional Sciences, University of Toronto, Toronto, ON, Canada

4Centre for Chronic Disease Prevention and Control, Public Health Agency of Canada, Canada

#Equal Contribution

*Corresponding Author:
Alaa Badawi, PhD
Office of Biotechnology
Genomics and Population Health
Public Health Agency of Canada
11-101, 180 Queen Street West
Toronto, ON, Canada M5V 3L7
E-mail: [email protected]

Received date October 24, 2011; Accepted date December 12, 2011; Published date December 18, 2011

Citation: Brenner DR, Arora P, Garcia-Bailo B, Morrison H, El-Sohemy A, et al. (2011) The Relationship between Inflammation, Metabolic Syndrome and Markers of Cardiometabolic Disease among Canadian Adults. J Diabetes Metab S2:003. doi:10.4172/2155-6156.S2-003

Copyright: © 2011 Brenner DR, 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.

Abstract

Background: The metabolic syndrome (MetS) is a well-established risk factor for cardiometabolic disease. However, the association between MetS, and its components, with the metabolic phenotypes and inflammatory markers that are risk factor for cardiometabolic disease has not been explored in the general population. The present study examines this association among Canadian adults and explores the changes in the profile of a number of metabolic and inflammatory markers associated with cardiometabolic disease at various MetS stages.

Methods: Serum levels of apolipoprotein A1 and B (Apo-A1, -B), total:HDL-cholesterol (HDL-C) ratio, C-reactive protein (CRP), fibrinogen, glycosylated haemoglobin (HbA1c) and homocysteine were determined in 1,818 non-diabetic adults (16-79 years of age) from the Canadian Health Measures Survey (CHMS). The definition of MetS components was based on the National Cholesterol Education Program, Adult Treatment Panel III criteria. Taylor-series expansion methods for complex survey data were used to estimate variances. Generalized linear models adjusted for age, sex, physical activity, smoking status, use of medications and ethnicity were used to quantify the relationship between the metabolic phenotypes and inflammatory markers associated with risk to cardiometabolic disease and the number of MetS components.

Results: The prevalence of the MetS (i.e., with three or more MetS components) among the study subjects was 8.9%, with 31.8% having at least one component. As expected, metabolic markers such as total: HDL-C, Apo-B and HbA1c were all significantly increased as the number of MetS components increased whereas Apo-A was decreased. We also observed a significant association between the number of MetS components and the serum levels of inflammatory biomarkers such as CRP and fibrinogen, but not homocysteine. Mean serum levels of these markers were significantly elevated as the numbers of MetS components increased. Strong correlations were noted between CRP, fibrinogen, and homocysteine and the individual components of the MetS.

Conclusions: There is an apparent profile of metabolic phenotypes and inflammatory biomarkers, known to be related to the cardiometabolic disease risk, that emerges as MetS manifests with increasing the number of its components. These findings may permit proposing a metabolic trait that predisposes to MetS and may permit developing an effective approach for early risk prediction and intervention.

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