Cited by 735Diabetes, Cardiovascular Risks, and LipoproteinsDiabetes Management and ResearchHealth Promotion and Cardiovascular Prevention

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Does the Metabolic Syndrome Improve Identification of Individuals at Risk of Type 2 Diabetes and/or Cardiovascular Disease?

Stern, Michael P., Williams, Ken, González-Villalpando, Clicerio, Hunt, Kelly J., Haffner, Steven M.·2004·Diabetes Care

DOI 10.2337/diacare.27.11.2676

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Abstract

OBJECTIVE—The metabolic syndrome has been promoted as a method for identifying high-risk individuals for type 2 diabetes and cardiovascular disease (CVD). We therefore sought to compare this syndrome, as defined by the National Cholesterol Education Program, to the Diabetes Predicting Model and the Framingham Risk Score as predictors of type 2 diabetes and CVD, respectively. RESEARCH DESIGN AND METHODS—A population-based sample of 1,709 initially nondiabetic San Antonio Heart Study (SAHS) participants were followed for 7.5 years, 195 of whom developed type 2 diabetes. Over the same time interval, 156 of 2,570 SAHS participants experienced a cardiovascular event. A population-based sample of 1,353 initially nondiabetic Mexico City Diabetes Study (MCDS) participants were followed for 6.5 years, 125 of whom developed type 2 diabetes. Baseline measurements included medical history, age, sex, ethnicity, smoking status, BMI, blood pressure, fasting and 2-h plasma glucose levels, and fasting serum total and HDL cholesterol and triglycerides. RESULTS—The sensitivities for predicting diabetes with the metabolic syndrome were 66.2 and 62.4% in the SAHS and the MCDS, respectively, and the false-positive rates were 27.8 and 38.7%, respectively. The sensitivity and false-positive rates for predicting CVD with the metabolic syndrome in the SAHS were 67.3 and 34.2%, respectively. At corresponding false-positive rates, the two predicting models had significantly higher sensitivities and, at corresponding sensitivities, significantly lower false-positive rates than the metabolic syndrome for both end points. Combining the metabolic syndrome with either predicting model did not improve the prediction of either end point. CONCLUSIONS—The metabolic syndrome is inferior to established predicting models for either type 2 diabetes or CVD.

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Authors: Stern, M. P., Williams, K., González-Villalpando, C., Hunt, K. J., & Haffner, S. M.
Affiliations: Division of Clinical Epidemiology, Department of Medicine, University of Texas Health Science Center, San Antonio, Texas
The University of Texas at San Antonio Health Science Center (US)
Centro de Estudios en Diabetes, The American British Cowdray Hospital and Unidades de Investigación Médica en Enfermedades Metabólicas y Epidemiología Clínica, Hospital Gabriel Mancera, Instituto Mexicano del Seguro Social, Mexico City, Mexico
ORCID: Hunt, Kelly J.
Venue: Diabetes Care
Publisher: American Diabetes Association
Published: 2004-11-01
Volume: 27
Issue: 11
Pages: 2676-2681
ISSN: 0149-5992 (ISSN-L)
1935-5548
DOI: 10.2337/diacare.27.11.2676
OpenAlex: W1975366890
Cited by: 735
References: 17
Citations by year: 2026: 5 · 2025: 10 · 2024: 19 · 2023: 15 · 2022: 19 · 2021: 21 · 2020: 13 · 2019: 24 · 2018: 29 · 2017: 33 · 2016: 35 · 2015: 48 · 2014: 39 · 2013: 40 · 2012: 43
FWCI: 31.54
Type: journal article
Language: en
Update notices: correction (2005-01-01)
Open access: closed
Locations: Diabetes Care (publishedVersion)
PubMed (publishedVersion)
Funders: National Heart, Lung, and Blood Institute (R01HL024799, R01HL036820)
Topics: Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Health Promotion and Cardiovascular Prevention
Keywords: metabolic syndrome, type 2 diabetes, cardiovascular disease, risk prediction, Framingham risk score, sensitivity and false positive rate, San Antonio Heart Study, risk stratification
MeSH terms: Adult, Cardiovascular Diseases, Diabetes Mellitus, Type 2, False Positive Reactions, Female, Humans, Male, Middle Aged, Models, Biological, Predictive Value of Tests, Risk, Likelihood Functions, Metabolic Syndrome

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Does the Metabolic Syndrome Improve Identification of Individuals at Risk of Type 2 Diabetes and/or Cardiovascular Disease?

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