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Diabetes Care. 2004 Sep;27(9):2234-40.

Cardiovascular disease risk factors predict the development of type 2 diabetes: the insulin resistance atherosclerosis study.

Author information

  • 1Department of Public Health Science, Biostatistics Section, Wake Forest University School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157, USA. rdagosti@wfubmc.edu

Abstract

OBJECTIVE:

In a few previous studies, cardiovascular disease (CVD) risk factors (RFs) have been shown to predict diabetes. Our objective was to determine whether the presence of CVD RFs predict the eventual development of diabetes after controlling for known RFs, such as directly measured insulin resistance and obesity.

RESEARCH DESIGN AND METHODS:

We studied 872 participants with normal or impaired glucose tolerance (IGT) who were enrolled at baseline in the Insulin Resistance Atherosclerosis Study (IRAS). Of these, 143 (16%) developed type 2 diabetes in 5 years. Using these participants, a series of logistic regression models were fit to address the question.

RESULTS:

Significant RFs for developing type 2 diabetes included high plasminogen activator inhibitor-1, hypertension, high triglycerides, low levels of HDL cholesterol, and IGT. The 5-year cumulative incidence of type 2 diabetes by the number of RFs (0-5) was as follows: no RFs, 11 of 230 = 5%; one RF, 31 of 278 = 11%; two RFs, 36 of 202 = 18%; three RFs, 41 of 110 = 37%; four RFs, 19 of 42 = 45%; and five RFs, 5 of 10 = 50% (P < 0.001). The odds ratio (OR) for conversion to type 2 diabetes for each additional RF was 2.1 (95% CI 1.78-2.46) after adjusting for age, sex, ethnicity, and center. After further adjustment for insulin resistance, determined by the frequently sampled intravenous glucose tolerance test and waist circumference, each additional CVD RF increased the risk of type 2 diabetes significantly (OR 1.81, 95% CI 1.49-2.20).

CONCLUSIONS:

Individuals with multiple CVD RFs are at increased risk of type 2 diabetes, which is only partially mediated by insulin resistance or central adiposity. This information should be useful for identifying high-risk patients for developing diabetes through RF assessments.

PMID:
15333490
[PubMed - indexed for MEDLINE]
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