Evaluating the utility of self-reported questionnaire data to screen for dysglycemia in young adults: Findings from the US National Health and Nutrition Examination Survey.

Dysglycemia, including prediabetes and type 2 diabetes, is dangerous and widespread. Yet, the condition is transiently reversible and sequelae preventable, prompting the use of prediction algorithms to quickly assess dysglycemia status through self-reported data. However, as current algorithms have largely been developed in older populations, their application to younger adults is uncertain considering associations between risk factors and dysglycemia vary by age. We sought to identify sex-specific predictors of current dysglycemia among young adults and evaluate their ability to screen for prediabetes and undiagnosed diabetes. We analyzed 2005-2014 data from the National Health and Nutrition Examination Survey for 3251 participants aged 20-39, who completed an oral glucose tolerance test (OGTT), had not been diagnosed with diabetes, and, for females, were not pregnant. Sex-specific stepwise logistic models were fit with predictors identified from univariate analyses. Risk scores were developed using adjusted odds ratios and model performance was assessed using area under the curve (AUC) measures. The OGTT identified 906 (27.9%) and 78 (2.4%) participants with prediabetes or undiagnosed diabetes, respectively. Predictors of dysglycemia status for males were BMI, age, race, and first-degree family history of diabetes, and, in addition to those, education, delivered baby weight, waist circumference, and vigorous physical activity for females. Our male- and female-specific models demonstrated improved validity to assess dysglycemia presence among young adults relative to the widely-used American Diabetes Association test (AUC = 0.69 vs. 0.61; 0.92 vs. 0.71, respectively). Thus, age-specific scoring algorithms employing questionnaire data show promise and are effective in identifying dysglycemia among young adults.