The metabolic syndrome. Lancet

Division of Endocrinology, Metabolism and Diabetes, University of Colorado at Denver and Health Sciences Center, PO Box 6511, MS 8106, Aurora, CO 80045, USA.
The Lancet (Impact Factor: 45.22). 04/2005; 365(9468):1415-28. DOI: 10.1016/S0140-6736(05)66378-7
Source: PubMed


The metabolic syndrome is a common metabolic disorder that results from the increasing prevalence of obesity. The disorder is defined in various ways, but in the near future a new definition(s) will be applicable worldwide. The pathophysiology seems to be largely attributable to insulin resistance with excessive flux of fatty acids implicated. A proinflammatory state probably contributes to the syndrome. The increased risk for type 2 diabetes and cardiovascular disease demands therapeutic attention for those at high risk. The fundamental approach is weight reduction and increased physical activity; however, drug treatment could be appropriate for diabetes and cardiovascular disease risk reduction.

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Available from: Robert Eckel, Dec 18, 2014
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    • "In general, the higher the waist circumference and body mass index, the greater the risk of T2DM. The incidence of T2DM is five times higher in patients with metabolic syndrome (MS), compared to a group of patients without the syn- drome[16]. "

    Full-text · Article · Jan 2016 · Open Journal of Endocrine and Metabolic Diseases
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    • "The lifestyle of the 21st century, characterized by people sedentary routine and diets rich in fat and carbohydrates, has contributed to an increase in the occurrence of metabolic syndromes , including type 2 diabetes mellitus, dyslipidemia, obesity and other cardiovascular diseases (Eckel et al., 2005). Several of the metabolic pathways involved in these disorders are regulated by Peroxisome Proliferator-Activated Receptors (PPARs), which are members of the superfamily of the nuclear receptors and function as transcription factors activated by several synthetic and natural ligands (Forman et al., 1997). "
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    ABSTRACT: Peroxisome Proliferator-Activated Receptors (PPARs) are ligand-dependent transcription factors that control various functions in human organism, including the control of glucose and lipid metabolism. PPARγ is a target of TZD agonists, clinically used to improve insulin sensitivity whereas fibrates, PPARα ligands, lower serum triglyceride levels. We report here the structural studies of GL479, a synthetic dual PPARα/γ agonist, designed by a combination of clofibric acid skeleton and a phenyldiazenyl moiety, as bioisosteric replacement of stilbene group, in complex with both PPARα and PPARγ receptors. GL479 was previously reported as a partial agonist of PPARγ and a full agonist of PPARα with high affinity for both PPARs. Our structural studies reveal different binding modes of GL479 to PPARα and PPARγ, which may explain the distinct activation behaviors observed for each receptor. In both cases the ligand interacts with a Tyr located at helix 12 (H12), resulting in the receptor active conformation. In the complex with PPARα, GL479 occupies the same region of the ligand-binding pocket (LBP) observed for other full agonists, whereas GL479 bound to PPARγ displays a new binding mode. Our results indicate a novel region of PPARs LBP that may be explored for the design of partial agonists as well dual PPARα/γ agonists that combine, simultaneously, the therapeutic effects of the treatment of insulin resistance and dyslipidemia. Copyright © 2015. Published by Elsevier Inc.
    Full-text · Article · Jul 2015 · Journal of Structural Biology
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    • "Metabolic syndrome (MS) is a complex disorder encompassing a cluster of metabolic abnormalities characterized by central obesity, hyperglycemia, hypertension, and dyslipidemia [1]. Particularly, progression of the pathophysiological state of MS is a consequence of the complex and interrelation of genetic and environmental factors including insulin resistance (IR), adiposity, dyslipidemia, endothelial dysfunction, elevated blood pressure, and chronic state [2]. "
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    ABSTRACT: Aims: This study proposes a computational method for determining the prevalence of metabolic syndrome (MS) and to predict its occurrence using the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) criteria. The Random Forest (RF) method is also applied to identify significant health parameters. Materials and Methods: We used data from 5,646 adults aged between 18-78 years old residing in Bangkok who had received an annual health check-up in 2008. MS was identified using the NCEP ATP III criteria. The RF method was applied to predict the occurrence of MS and to identify important health parameters surrounding this disorder. Results: The overall prevalence of MS was 23.70% (34.32% for males and 17.74% for females). RF accuracy for predicting MS in an adult Thai population was 98.11%. Further, based on RF, triglyceride levels were the most important health parameter associated with MS. Conclusion: RF was shown to predict MS in an adult Thai population with an accuracy >98% and triglyceride levels were identified as the most informative variable associated with MS. Therefore, using RF to predict MS may be potentially beneficial in identifying MS status for preventing the development of diabetes mellitus and cardiovascular diseases.
    Full-text · Article · Jun 2015 · The Scientific World Journal
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