Relationship between glycaemic variability and hyperglycaemic clamp-derived functional variables in (impending) type 1 diabetes

Aims/hypothesis We examined whether measures of glycaemic variability (GV), assessed by continuous glucose monitoring (CGM) and self-monitoring of blood glucose (SMBG), can complement or replace measures of beta cell function and insulin action in detecting the progression of preclinical disease to type 1 diabetes. Methods Twenty-two autoantibody-positive (autoAb + ) first-degree relatives (FDRs) of patients with type 1 diabetes who were themselves at high 5-year risk (50%) for type 1 diabetes underwent CGM, a hyperglycaemic clamp test and OGTT, and were followed for up to 31 months. Clamp variables were used to estimate beta cell function (first-phase [AUC 5–10 min ] and second-phase [AUC 120–150 min ] C-peptide release) combined with insulin resistance (glucose disposal rate; M 120–150 min ). Age-matched healthy volunteers ( n = 20) and individuals with recent-onset type 1 diabetes ( n = 9) served as control groups. Results In autoAb + FDRs, M 120–150 min below the 10th percentile (P10) of controls achieved 86% diagnostic efficiency in discriminating between normoglycaemic FDRs and individuals with (impending) dysglycaemia. M 120–150 min outperformed AUC 5–10 min and AUC 120–150 min C-peptide below P10 of controls, which were only 59–68% effective. Among GV variables, CGM above the reference range was better at detecting (impending) dysglycaemia than elevated SMBG (77–82% vs 73% efficiency). Combined CGM measures were equally efficient as M 120–150 min (86%). Daytime GV variables were inversely correlated with clamp variables, and more strongly with M 120–150 min than with AUC 5–10 min or AUC 120–150 min C-peptide. Conclusions/interpretation CGM-derived GV and the glucose disposal rate, reflecting both insulin secretion and action, outperformed SMBG and first- or second-phase AUC C-peptide in identifying FDRs with (impending) dysglycaemia or diabetes. Our results indicate the feasibility of developing minimally invasive CGM-based criteria for close metabolic monitoring and as outcome measures in trials.