Short-term Variability in Measures of Glycemia and Implications for the Classification of Diabetes

BACKGROUND: Short-term variability in measures of glycemia has important implications for the diagnosis of diabetes mellitus and the conduct and interpretation of epidemiologic studies. Our objectives were to characterize the within-person variability in fasting glucose, 2-hour glucose, and hemoglobin A1c (HbA1c) levels and to assess the impact of using repeated measurements for classification of diabetes. METHODS: We analyzed repeated measurements from 685 fasting participants without diagnosed diabetes from the National Health and Nutrition Examination Survey III Second Examination, a substudy conducted from 1988 to 1994 in which repeated examinations were conducted approximately 2 weeks after the original examination. RESULTS: Two-hour glucose levels had substantially more variability (within-person coefficient of variation CV(w), 16.7%; 95% confidence interval CI, 15.0 to 18.3) compared with either fasting glucose (CV(w), 5.7%; 95% CI, 5.3 to 6.1) or HbA1c (CV(w,) 3.6%; 95% CI, 3.2 to 4.0) levels. The proportion of persons with a fasting glucose level of 126 mg/dL or higher (to convert to millimoles per liter, multiply by 0.0555) on the first test who also had a second glucose level of 126 mg/dL or higher was 70.4% (95% CI, 49.8% to 86.2%). Results were similar using the 2-hour glucose cutoff point of 140 mg/dL or higher. The prevalence of undiagnosed diabetes using a single fasting glucose level of 126 mg/dL or higher was 3.7%. If a second fasting glucose level of 126 mg/dL or higher was used to confirm the diagnosis (American Diabetes Association guidelines), the prevalence decreased to 2.8% (95% CI, 1.5% to 4.0%), a 24.4% decrease. CONCLUSIONS: We found high variability in 2-hour glucose levels relative to fasting glucose levels and high variability in both of these relative to HbA1c levels. Our findings suggest that studies that strictly apply guidelines for the diagnosis of diabetes (2 glucose measurements) may arrive at substantially different prevalence estimates compared with studies that use only a single measurement.