Introduction

Diabetic peripheral neuropathies (DPN) are major complications of diabetes, resulting from nervous system damage. Symptoms include tingling, numbness, pain, or reduced reflexes.² While many children with Type 1 Diabetes (T1D) do not exhibit these symptoms, studies, including one from the Children’s Hospital of Eastern Switzerland (OKS), show a significant reduction in nerve conduction velocity (NCV) in young patients. Currently, there is no cure for DPN, and treatment is limited to supplements and pain management. Therefore, preventing and slowing DPN progression is crucial.

Objectives

This project aims to:

• Understand the physiological interplay between glucose fluctuations and nerve vitality.
• Explore additional lifestyle factors (physical activity, sleep, diet) as covariates.
• Develop preventative measures.

Specific goals include:

• Identify, benchmark, and develop novel digital biomarkers that capture short-term glycemic fluctuations and deepen the understanding of their influence on the vitality of the peripheral nervous system.
• Systematically track various lifestyle factors, including physical activity and nutrition, and incorporate them into the analyses to investigate their potential to slow down or even reverse the reduction in NCV.

Ultimately, we believe that this work will allow us to fundamentally change the way we evaluate the efficacy of diabetes treatment regiments and enable the development of improved automated insulin dosing algorithms and systems.

Approach

Building on previous work by Dr. med. Oberhauser and Dr. med. Broser,¹ this project collects longitudinal intensive data from various sources (CGM, wearables, clinical labs). Additionally, we are developing infrastructure and modular systems to support data exploration and hypothesis testing.

The system currently supports the upload, storing, pre-processing, and analysis of CGM data from various CGM manufacturers and software publishers (Medtronic CareLink, Dexcom Clarity, Abbott LibreView, and Glooko), clinical lab data like HbA1C and C-Peptide tests, nerve conduction study data, and sonography data. We are extending this system to include wearable data (e.g., physical activity) and dietary information.

Methodology

In this prospective study, participants are recruited from the T1D patient population at OKS. Inclusion criteria include diabetes duration over 6 months and CGM data availability of >80%. Exclusion criteria are other chronic conditions, premature birth, or a family history of neurological diseases. NCS is performed yearly to measure NCV in the peroneal, tibial, and median (motor + sensory) nerves. CGM data and lab tests (HbA1C) are collected during quarterly patient visits. We preprocess CGM data and calculate various glucose fluctuation metrics, including Standard Deviation (SD), Average Daily Risk Range (ADRR)³ , Glycemic Variability Percentage (GVP)⁴, Continuous Overall Net Glycemic Action (CONGA)⁵, and Mean Absolute Glucose (MAG)⁶, comparing their performance in explaining NCV variability. We are also developing and evaluating our own metrics for capturing short-term glycemic fluctuations.

Expected Outcomes and Scientific Contributions

Initial results are promising, indicating that fluctuation-based metrics correlate more highly with NCV reduction compared to established biomarkers like HbA1C or average glucose levels. However, simple metrics like glucose level SD, although better than HbA1C, are insufficient and more advanced metrics are required.

Our contributions include:

• Collecting holistic multivariate time series data, including glucose, lab, and nerve data, in an understudied group.
• Developing and evaluating new metrics for short-term glucose fluctuations and their correlation with NCV reduction, considering confounders like physical activity.
• Designing scalable infrastructure and systems to support this and future research projects.

We believe that by better understanding the physiology & development of the peripheral nervous system and the significant damage highly fluctuating glucose levels have even at a young age and with a short diabetes duration, we can fundamentally shift the way we assess the efficacy of treatment approaches and lay the foundation for more advanced and optimised insulin dosing algorithms.

July 8th, 2024: Kinderspital und HSG forschen zu Diabetes bei Kindern – SRF

July 4th, 2024: HSG and Children’s Hospital of Eastern Switzerland launch research project on type 1 diabetes in children – University of St. Gallen

July 4th, 2024: Forschungskooperation OKS und HSG – Ostschweizer Kinderspital

[1] Oberhauser SS, l’Allemand D, Willems EP, Gozzi T, Heldt K, Eilers M, Stasinaki A, Lütschg J, Broser PJ. Slowing of Peripheral Nerve Conduction Velocity in Children and Adolescents With Type 1 Diabetes Is Predicted by Glucose Fluctuations. Diabetes. 2023 Dec 1;72(12):1835-1840. doi: 10.2337/db23-0063. PMID: 37699386; PMCID: PMC10658059.

[2] Rodica Pop-Busui, Andrew J.M. Boulton, Eva L. Feldman, Vera Bril, Roy Freeman, Rayaz A. Malik, Jay M. Sosenko, Dan Ziegler; Diabetic Neuropathy: A Position Statement by the American Diabetes Association. Diabetes Care 1 January 2017; 40 (1): 136–154. https://doi.org/10.2337/dc16-2042

[3] Kovatchev BP, Otto E, Cox D, Gonder-Frederick L, Clarke W. Evaluation of a new measure of blood glucose variability in diabetes. Diabetes Care. 2006 Nov;29(11):2433-8. doi: 10.2337/dc06-1085. PMID: 17065680.

[4] Peyser TA, Balo AK, Buckingham BA, Hirsch IB, Garcia A. Glycemic Variability Percentage: A Novel Method for Assessing Glycemic Variability from Continuous Glucose Monitor Data. Diabetes Technol Ther. 2018 Jan;20(1):6-16. doi: 10.1089/dia.2017.0187. Epub 2017 Dec 11. PMID: 29227755; PMCID: PMC5846572.

[5] McDonnell CM, Donath SM, Vidmar SI, Werther GA, Cameron FJ. A novel approach to continuous glucose analysis utilizing glycemic variation. Diabetes Technol Ther. 2005 Apr;7(2):253-63. doi: 10.1089/dia.2005.7.253. PMID: 15857227.

[6] Hermanides J, Vriesendorp TM, Bosman RJ, Zandstra DF, Hoekstra JB, Devries JH. Glucose variability is associated with intensive care unit mortality. Crit Care Med. 2010 Mar;38(3):838-42. doi: 10.1097/CCM.0b013e3181cc4be9. PMID: 20035218.

Diabetic peripheral neuropathy is one of the most common long-term complications of diabetes. This project aims to investigate it’s causes and develop novel digital monitor biomarkers to assess the progression.

Marc-Robin Gruener, Dr. Mia Jovanova, Prof. Dr. Tobias Kowatsch

PD Dr. med. Philip Broser, Sandro Meier, Dr. Erin West

Prof. Dr. Dagmar L’Allemand, Dr. med. Sarah Oberhauser, Dr. med. Katrin Heldt, Dr. med. Miriam Eilers

Prof. Dr. med. Jürg Lütschg

October 2023 – September 2025

This project is funded by the Health Forward Grant of the University of St. Gallen. The Children’s Hospital of Eastern Switzerland, St.Gallen, provided additional seed funding.