Investigating family history of diabetes as a predictor of fasting insulin and fasting glucose activity in a sample of healthy weight adults.

ABSTRACT

AIMS: Type 2 diabetes is a major public health problem for the global community. Having a family history of diabetes significantly increases risk for diabetes development and understanding how family history contributes to diabetes risk could lead to more effective prevention efforts for at-risk individuals. In a previous study, we showed family history of diabetes is a significant predictor of fasting insulin in healthy weight children. The present study aimed to use the National Health and Nutrition Examination Survey (NHANES 2017) to apply similar multiple regression models to a population of healthy weight adults to determine if family history is a significant predictor of fasting glucose and fasting insulin. METHODS: Fasting glucose (mg/dL) and fasting insulin (pmol/L) were used as dependent variables in each model, respectively, with family history of diabetes as the independent variable. Covariates for each model included age, gender, race/ethnicity, waist circumference, and macronutrient intake. RESULTS: The model significantly predicted the variance of fasting glucose [(F(11,364) = 34.80, p < 0.001, R2 = 0.2342] and fasting insulin [F(11,343) = 17.58, p < 0.001, R2 = 0.1162]. After adjusting for covariates, family history was a significant predicator of fasting glucose (p = 0.0193) as well as age, gender, non-Hispanic black ethnicity, waist circumference, and fat intake. Significant predictors of fasting insulin included gender and waist circumference, but not family history (p = 0.8264). In addition, fasting glucose was higher in individuals with a family history of diabetes (p = 0.033). CONCLUSIONS: These results add to the understanding of how family history influences the biomarkers that contribute to diabetes development. Knowledge of how family history of diabetes relates to fasting insulin and fasting glucose activity in healthy weight individuals can be used to design personalized screening and early prevention strategies.