Flash glucose monitoring data analysed by detrended fluctuation function on beta-cell function and diabetes classification

Aim: We aimed to use data-driven glucose pattern analysis to unveil the correlation between the metrics reflecting glucose fluctuation and beta-cell function, and to identify the possible role of this metric in diabetes classification. Materials and methods: In total, 78 participants with type 1 diabetes and 59 with type 2 diabetes were enrolled in this study. All participants wore a flash glucose monitoring system, and glucose data were collected. A detrended fluctuation function (DFF) was utilized to extract glucose fluctuation information from flash glucose monitoring data and a DFF-based glucose fluctuation metric was proposed. Results: For the entire study population, a significant negative correlation between the DFF-based glucose fluctuation metric and fasting C-peptide was observed (r = −0.667; P <.001), which was larger than the correlation coefficient between the fasting C-peptide and mean amplitude of plasma glucose excursions (r = −0.639; P <.001), standard deviation (r = −0.649; P <.001), mean blood glucose (r = −0.519; P <.001) and time in range (r = 0.593; P <.001). As glucose data analysed by DFF revealed a clear bimodal distribution among the total participants, we randomly assigned the 137 participants into discovery cohorts (n = 100) and validation cohorts (n = 37) for 10 times to evaluate the consistency and effectiveness of the proposed metric for diabetes classification. The confidence interval for area under the curve according to the receiver operating characteristic analysis in the 10 discovery cohorts achieved (0.846, 0.868) and that for the 10 validation cohorts was (0.799, 0.862). In addition, the confidence intervals for sensitivity and specificity in the discovery cohorts were (75.5%, 83.0%), (81.3%, 88.5%) and (71.8%, 88.3%), (76.5%, 90.3%) in the validation cohorts, indicating the potential capacity of DFF in distinguishing type 1 and type 2 diabetes. Conclusions: Our study first proposed the possible role of data-driven analysis acquired glucose metric in predicting beta-cell function and diabetes classification, and a large-scale, multicentre study will be needed in the future.