Using metabolic syndrome traits for efficient detection of impaired glucose tolerance.

Objective: Efficient detection of impaired glucose tolerance (IGT) is needed to implement type 2 diabetes prevention interventions.
Research Design and Methods: We assessed the capacity of the metabolic syndrome (MetS) to identify IGT in a cross-sectional analysis of 3,326 Caucasian Framingham Offspring Study (FOS), 1,168 Caucasian and 1,812 Mexican-American San Antonio Heart Study (SAHS), 1,983 Mexico City Diabetes Study (MCDS), and 452 Caucasian, 407 Mexican-American, and 290 African-American Insulin Resistance Atherosclerosis Study (IRAS) men and women aged 30-79 years who had a clinical examination and an oral glucose tolerance test (OGTT) during 1987-1996. Those with diabetes treatment or fasting plasma glucose > or =7.0 mmol/l were excluded (MetS was defined by Third Report of the National Cholesterol Education Program's Adult Treatment Panel criteria and IGT as 2-h postchallenge glucose [2hPG] > or =7.8 mmol/l). We calculated positive (PPV) and negative predictive values (NPV), population attributable risk percentages (PAR%), age- and sex-adjusted odds ratios (ORs), and areas under the receiver operating characteristic curve (AROCs) associated with MetS traits.
Results: Among FOS, SAHS, and MCDS subjects, 24-43% had MetS and 15-23% had IGT (including 2-5% with 2hPG > or =11.1 mmol/l). Among those with MetS, OR for IGT were 3-4, PPV were 0.24-0.41, NPV were 0.84-0.91, and PAR% were 30-40%. Among subjects with MetS defined by impaired fasting glucose (IFG) and any two other traits, OR for IGT were 9-24, PPV were 0.62-0.89, NPV were 0.78-0.87, and PAR% were 3-12%. Among IRAS subjects, 24-34% had MetS and 37-41% had IGT. Among those with MetS, ORs for IGT were 3-6, PPVs were 0.57-0.73, and NPVs were 0.67-0.72. In logistic regression models, IFG, large waist, and high triglycerides were independently associated with IGT (AROC 0.71-0.83) in all study populations.
Conclusions: The MetS, especially defined by IFG, large waist, and high triglycerides, efficiently identifies subjects likely to have IGT on OGTT and thus be eligible for diabetes prevention interventions.