Abstract
BACKGROUND: Biomarkers for estimating reduced glucose tolerance, insulin sensitivity, or impaired insulin secretion would be clinically useful, since these physiologic measures are important in the pathogenesis of type 2 diabetes mellitus. METHODS: We conducted a cross-sectional study in which 94 individuals, of whom 84 had 1 or more risk factors and 10 had no known risk factors for diabetes, underwent oral glucose tolerance testing. We measured 34 protein biomarkers associated with diabetes risk in 250-μL fasting serum samples. We applied multiple regression selection techniques to identify the most informative biomarkers and develop multivariate models to estimate glucose tolerance, insulin sensitivity, and insulin secretion. The ability of the glucose tolerance model to discriminate between diabetic individuals and those with impaired or normal glucose tolerance was evaluated by area under the ROC curve (AUC) analysis. RESULTS: Of the at-risk participants, 25 (30%) were found to have impaired glucose tolerance, and 11 (13%) diabetes. Using molecular counting technology, we assessed multiple biomarkers with high accuracy in small volume samples. Multivariate biomarker models derived from fasting samples correlated strongly with 2-h postload glucose tolerance (R(2) = 0.45, P < 0.0001), composite insulin sensitivity index (R(2) = 0.91, P < 0.0001), and insulin secretion (R(2) = 0.45, P < 0.0001). Additionally, the glucose tolerance model provided strong discrimination between diabetes vs impaired or normal glucose tolerance (AUC 0.89) and between diabetes and impaired glucose tolerance vs normal tolerance (AUC 0.78). CONCLUSIONS: Biomarkers in fasting blood samples may be useful in estimating glucose tolerance, insulin sensitivity, and insulin secretion.