Background: Diabetic peripheral neuropathy is a common microvascular complication of type 1 diabetes (T1D) that can manifest early in pediatric patients. While chronic hyperglycemia is an established risk factor, the role of glucose variability in the development of neuropathy remains controversial, particularly in children and adolescents. Methods: In this single-center retrospective cross-sectional study, we evaluated associations between height-adjusted nerve conduction velocity (aNCV) and multiple glucose variability metrics in children and adolescents with type 1 diabetes. We analyzed continuous glucose monitoring data from the 90 days preceding standardized neurophysiological assessment to calculate established glucose variability metrics (SD, CONGA, ADRR, MAG, GVP) alongside our novel Glucose Fluctuation Moment Index (GFMI). The GFMI uniquely integrates both the velocity of glucose changes and their distance from the glycemic target. Using linear regression models adjusted for diabetes duration, we assessed the relationship between each glucose variability metric and aNCV, employing leave-one-out cross-validation to evaluate predictive accuracy despite limited sample size. Results: Among 42 eligible participants (mean age 12.2 ± 3.2 years, diabetes duration 4.1 ± 3.2 years), mean aNCV was negative in the peroneal nerve (-2.9 ± 2.9 m/s). Linear regression models adjusting for diabetes duration revealed no statistically significant associations between any glucose variability metrics (SD, CONGA1, CONGA24, ADRR, MAG, GVP, or GFMI) and peroneal nerve aNCV (all p>0.05). In predictive modeling using leave-one-out cross-validation, the Glycemic Variability Percentage (GVP) demonstrated the best predictive performance (RMSE 2.99 m/s with all observations, 2.46 m/s after removing three identified outliers), but offered minimal improvements (4.28 % and 6.08 %) over the baseline- (simply predicting the mean aNCV) or the glucose SD-based model. Outlier analysis revealed notable clinical factors potentially affecting nerve function, including physical activity. Conclusions: While early neurophysiological changes were observed in our pediatric T1D cohort, neither established glucose variability metrics nor our novel GFMI demonstrated superior predictive accuracy for the peroneal aNCV compared to the glucose SD, the clinically established GV measure, nor achieved clinically relevant accuracy. Further research with larger cohorts and longitudinal designs is needed to account for relevant confounding variables like C-peptide and physical activity to better understand these complex relationships.

MRG, MJ and TK are affiliated with the Centre for Digital Health Interventions (CDHI), a joint initiative of the Institute for Implementation Science in Health Care at the University of Zurich, the Department of Management, Technology, and Economics at ETH Zurich, and the School of Medicine and Institute of Technology Management at the University of St. Gallen. CDHI is partially funded by CSS, a Swiss health insurer; MTIP, a Swiss digital health investment company; and Mavie Next, an Austrian health provider. TK co-founded Pathmate Technologies in 2017, a university spin-off that creates and delivers digital clinical pathways. As of 2023, TK holds no shares nor any formal roles with Pathmate Technologies. Furthermore, Pathmate Technologies, CSS, MTIP, and Mavie Next were not involved in this research. The remaining authors declare that the research was conducted without any commercial or financial relationships that could be considered a potential conflict of interest.

This study was funded by the Children's Hospital of Eastern Switzerland and the Health Forward Grant by the University of St. Gallen.

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Ethikkommission Ostschweiz (EKOS) gave ethical approval for this work (EKOS 22/018) and the study was registered with the Swiss project database (BASEC ID: 2022-00216).

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