An assessment tool known as Tool to Assess Likelihood of Fasting Glucose ImpairmenT (TAG-IT) is effective in screening patients of varying ethnic backgrounds for prediabetes, according to the results of a new study. Researchers say the test is an improvement over using BMI alone or using a list of risk factors.
"Fifty-four million people in the United States have impaired fasting glucose (IFG); if it is identified, they may benefit from prevention strategies that can minimize progression to diabetes, morbidity, and mortality," write Richelle J. Koopman, MD, MS, from the University of Missouri in Columbia, and colleagues. "We created a tool to identify those likely to have undetected hyperglycemia….We then validated TAG-IT in a second population-based sample, and compared TAG-IT with BMI [body mass index] alone for the ability to predict IFG and undiagnosed diabetes."
Using existing data from the National Health and Nutrition Examination Survey (NHANES) 1999 to 2004, this cross-sectional analysis examined 4045 US adults aged 20 to 64 years who were not diagnosed with diabetes but who had a fasting plasma glucose measurement. The investigators developed a logistic regression model predicting IFG and undiagnosed diabetes from characteristics that are self-reported or measured without laboratory testing. On the basis of this model, TAG-IT was developed, validated with use of NHANES III, and compared with BMI alone. Subsets based on race and ethnicity were also examined.
Factors that were most predictive of IFG and included in the final version of TAG-IT were age, sex, BMI, family history of diabetes, resting heart rate, and history of hypertension (or measured high blood pressure). Area under the curve (AUC) for TAG-IT was 0.740, which was significantly better than BMI alone (AUC, 0.644).
For an aggressive case-finding strategy, a score of 5 or higher yielded 87.0% sensitivity. If high specificity is preferred to minimize additional testing and false-positive results, a score of 8 (78.8% specificity) or 9 (87.9% specificity) could be used.
"The TAG-IT efficiently identifies those most likely to have abnormal fasting glucose and can be used as a decision aid for screening in clinical and population settings, or as a prescreening tool to help identify potential participants for research," the study authors write. "The TAG-IT represents an improvement over BMI alone or a list of risk factors in both its utility in younger adult populations and its ability to provide clinicians and researchers with a strategy to assess the risks of combinations of factors."
Limitations of TAG-IT were that it was developed from cross-sectional data and examines only the present risk for elevated fasting plasma glucose levels vs a future risk for disease, use of only fasting plasma glucose level as an outcome vs impaired glucose tolerance, and race or ethnicity not included as a predictor.
"TAG-IT can be readily and immediately applied in clinical settings, can aid in the identification of potential research participants with IFG, can be widely applied in practices using electronic health records, and can improve the efficiency of population-based screening, including community and Web-based applications," the study authors conclude.
This is a cross-sectional analysis of existing data from NHANES, a stratified multistage representative probability sample of the adult, noninstitutionalized US population, to identify predictors of IFG and to create and validate the TAG-IT using another subset of NHANES data.
- The predictors of TAG-IT are age, sex, BMI, family history of diabetes, resting heart rate, and history of hypertension.
- TAG-IT can be used for screening or case-finding to identify individuals at high risk for IFG.
Ann Fam Med. 2008;6:555-561.