Literature DB >> 28502055

Cardiovascular Risk Stratification in Patients with Metabolic Syndrome Without Diabetes or Cardiovascular Disease: Usefulness of Metabolic Syndrome Severity Score.

Walter Masson1, Teo Epstein2, Melina Huerín2, Lorenzo Martín Lobo2, Graciela Molinero2, Adriana Angel2, Gerardo Masson2, Diana Millán2, Salvador De Francesca2, Laura Vitagliano2, Alberto Cafferata2, Pablo Losada2.   

Abstract

INTRODUCTION: The estimated cardiovascular risk determined by the different risk scores, could be heterogeneous in patients with metabolic syndrome without diabetes or vascular disease. This risk stratification could be improved by detecting subclinical carotid atheromatosis. AIMS: To estimate the cardiovascular risk measured by different scores in patients with metabolic syndrome and analyze its association with the presence of carotid plaque.
METHODS: Non-diabetic patients with metabolic syndrome (Adult Treatment Panel III definition) without cardiovascular disease were enrolled. The Framingham score, the Reynolds score, the new score proposed by the 2013 ACC/AHA Guidelines and the Metabolic Syndrome Severity Calculator were calculated. Prevalence of carotid plaque was determined by ultrasound examination. A Receiver Operating Characteristic analysis was performed.
RESULTS: A total of 238 patients were enrolled. Most patients were stratified as "low risk" by Framingham score (64%) and Reynolds score (70.1%). Using the 2013 ACC/AHA score, 45.3% of the population had a risk ≥7.5%. A significant correlation was found between classic scores but the agreement (concordance) was moderate. The correlation between classical scores and the Metabolic Syndrome Severity Calculator was poor. Overall, the prevalence of carotid plaque was 28.2%. The continuous metabolic syndrome score used in our study showed a good predictive power to detect carotid plaque (area under the curve 0.752).
CONCLUSION: In this population, the calculated cardiovascular risk was heterogenic. The prevalence of carotid plaque was high. The Metabolic Syndrome Severity Calculator showed a good predictive power to detect carotid plaque.

Entities:  

Keywords:  Cardiovascular risk estimation; Carotid atherosclerotic plaque; Metabolic syndrome; Risk scores

Mesh:

Year:  2017        PMID: 28502055     DOI: 10.1007/s40292-017-0209-0

Source DB:  PubMed          Journal:  High Blood Press Cardiovasc Prev        ISSN: 1120-9879


  29 in total

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Authors: 
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2.  Prediction of coronary heart disease using risk factor categories.

Authors:  P W Wilson; R B D'Agostino; D Levy; A M Belanger; H Silbershatz; W B Kannel
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Authors:  Salvatore Mottillo; Kristian B Filion; Jacques Genest; Lawrence Joseph; Louise Pilote; Paul Poirier; Stéphane Rinfret; Ernesto L Schiffrin; Mark J Eisenberg
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Review 5.  Metabolic syndrome, cardiovascular risk and screening for subclinical atherosclerosis.

Authors:  Shaista Malik; Nathan D Wong
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6.  Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation.

Authors:  K G Alberti; P Z Zimmet
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7.  Prognostic impact of metabolic syndrome by different definitions in a population with high prevalence of obesity and diabetes: the Strong Heart Study.

Authors:  Giovanni de Simone; Richard B Devereux; Marcello Chinali; Lyle G Best; Elisa T Lee; James M Galloway; Helaine E Resnick
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