Beta-cell function in type 1 diabetes can be estimated from single time point biochemical and clinical parameters to evaluate disease progression and response to immune therapy.

Immune therapies for type 1 diabetes (T1D) are being tested for their ability to preserve pancreatic beta-cell function. Beta-cell function is most commonly measured using a meal test to determine the plasma C-peptide area under the curve (CP_AUC). This requires 7 blood samples and takes 3 hours to perform. A simpler measure of beta-cell function is needed to facilitate implementation of effective immune therapies in the clinic. To determine if CP_AUC could be reliably estimated using measurements from a single fasting clinical assessment, we developed and validated linear models using routine biochemical and clinical data from eight randomised therapy trials involving participants with recently-diagnosed T1D. A model based on fasting plasma C-peptide, fasting plasma glucose, HbA1c, disease duration, body mass index and insulin dose most accurately estimated loss of beta-cell function (area under ROC 0.89; 95% CI 0.87 to 0.92) and was superior to the reported ‘insulin dose-adjusted HbA1c’ (IDAA1C; area under ROC 0.72; 95% CI 0.68 to 0.76). When applied to data from randomized trials, model-estimated CP_AUC (CP_EST) reliably identified effective therapies and, using conservative estimates, required a 17% increase in sample size to meet statistical power requirements. Thus, CP_EST approximated from six parameters at a single, fasting time point accurately identifies loss of beta-cell function and is comparable to observed CP_AUC for identifying treatment effects. CP_EST could therefore be used routinely to measure of beta-cell function in the clinic and serve as a simpler primary outcome measure for trials of disease-modifying therapy in T1D.