HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Figures Only Full Text Full Text (PDF) In Brief Supplemental data: TAG-IT Tool TRACK Discussion: Submit a Comment TRACK Discussion: View Comments Alert me when this article is cited Alert me when TRACK Comments are posted Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Download to citation manager Citing Articles Citing Articles via HighWire Google Scholar Articles by Koopman, R. J. Articles by Carter, R. E. PubMed PubMed Citation Articles by Koopman, R. J. Articles by Carter, R. E.

Tool to Assess Likelihood of Fasting Glucose ImpairmenT (TAG-IT)

¹ Department of Family and Community Medicine, University of Missouri, Columbia
² Department of Family Medicine, Medical University of South Carolina, Charleston
³ Biostatistics, Bioinformatics, and Epidemiology, Medical University of South Carolina, Charleston

CORRESPONDING AUTHOR: Richelle J. Koopman, MD, Department of Family and Community Medicine, University of Missouri, MA306-N Medical Science Building, DC032.00, Columbia, MO 65212, koopmanr{at}health.missouri.edu

PURPOSE 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. We created a tool to identify those likely to have undetected hyperglycemia.

METHODS We undertook a cross-sectional analysis of existing data from the National Health and Nutrition Examination Survey (NHANES) 1999–2004 of 4,045 US adults aged 20 to 64 years who did not have a disgnosis of diabetes who had a measured fasting plasma glucose. Using characteristics that are self-reported or measured without laboratory data, we developed a logistic regression model predicting IFG and undiagnosed diabetes. Based on this model, we created TAG-IT (the Tool to Assess likelihood of fasting Glucose ImpairmenT), validated it using NHANES III, examined race and ethnicity subsets, and compared it with body mass index (BMI) alone.

RESULTS Predictors in the final tool were age, sex, BMI, family history of diabetes, resting heart rate, and history of hypertension (or measured high blood pressure), which yielded an area under the curve (AUC) of 0.740, significantly better than BMI alone (AUC = 0.644).

CONCLUSIONS 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 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.

Key Words: Hyperglycemia • impaired fasting glucose • risk assessment • diabetes mellitus • NHANES • nutrition surveys

This article has been cited by other articles:

				K. C. Stange In This Issue: Cohort Studies Ann. Fam. Med, November 1, 2008; 6(6): 482 - 482. [Full Text] [PDF]

TRACK Comments: