Beta cell function in type 1 diabetes determined from clinical and fasting biochemical variables

Aims/hypothesis: Beta cell function in type 1 diabetes is commonly assessed as the average plasma C-peptide concentration over 2 h following a mixed-meal test (CP_AVE). Monitoring of disease progression and response to disease-modifying therapy would benefit from a simpler, more convenient and less costly measure. Therefore, we determined whether CP_AVE could be reliably estimated from routine clinical variables. Methods: Clinical and fasting biochemical data from eight randomised therapy trials involving participants with recently diagnosed type 1 diabetes were used to develop and validate linear models to estimate CP_AVE and to test their accuracy in estimating loss of beta cell function and response to immune therapy. Results: A model based on disease duration, BMI, insulin dose, HbA_1c, fasting plasma C-peptide and fasting plasma glucose most accurately estimated loss of beta cell function (area under the receiver operating characteristic curve [AUROC] 0.89 [95% CI 0.87, 0.92]) and was superior to the commonly used insulin-dose-adjusted HbA_1c (IDAA1c) measure (AUROC 0.72 [95% CI 0.68, 0.76]). Model-estimated CP_AVE (CP_EST) reliably identified treatment effects in randomised trials. CP_EST, compared with CP_AVE, required only a modest (up to 17%) increase in sample size for equivalent statistical power. Conclusions/interpretation: CP_EST, approximated from six variables at a single time point, accurately identifies loss of beta cell function in type 1 diabetes and is comparable to CP_AVE for identifying treatment effects. CP_EST could serve as a convenient and economical measure of beta cell function in the clinic and as a primary outcome measure in trials of disease-modifying therapy in type 1 diabetes.