title: Tool to assess likelihood of fasting glucose impairment (TAG-IT).
abstract: OBJECTIVE
  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 diagnosis 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.
authors.affiliation: Department of Family and Community Medicine, University of Missouri, Columbia, MO 65212, USA. koopmanr@health.missouri.edu
authors.forename: Richelle J
authors.initials: RJ
authors.lastname: Koopman
authors.forename: Arch G
authors.initials: AG
authors.lastname: Mainous
authors.forename: Charles J
authors.initials: CJ
authors.lastname: Everett
authors.forename: Rickey E
authors.initials: RE
authors.lastname: Carter
mesh_terms: D000328:Adult; D001786:Blood Glucose / Q000032:analysis*; D015992:Body Mass Index; D003430:Cross-Sectional Studies; D003920:Diabetes Mellitus / Q000097:blood / Q000175:diagnosis; D005215:Fasting / Q000097:blood*; D005260:Female; D005951:Glucose Tolerance Test; D006801:Humans; D006943:Hyperglycemia / Q000097:blood / Q000175:diagnosis*; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D012680:Sensitivity and Specificity; D012737:Sex Factors; D055815:Young Adult
chemical_list: D001786:Blood Glucose