RESEARCHERS from the University of Bristol, UK, have uncovered that factors beyond carbohydrate intake significantly influence blood glucose levels in people with Type 1 diabetes (T1D). These findings suggest that current automated insulin delivery (AID) systems, which predominantly rely on carbohydrate data, may miss critical information necessary for optimal glucose regulation.
The study analysed data from individuals with T1D using OpenAPS, an automated insulin delivery system. It aimed to identify and understand patterns in insulin needs, uncovering that unexpected variations are as frequent as well-documented ones.
The team noted that “the results support our hypothesis that factors beyond carbohydrates play a substantial role in euglycemia; the state when blood glucose levels are within the standard range. However, without measurable information about these factors, AID systems cautiously adjust insulin, which can lead to blood glucose levels becoming too low or too high.”
T1D is a chronic condition where the body produces insufficient insulin, a hormone essential for regulating blood glucose. Treatment typically involves injecting or pumping insulin, with precise dosing required to balance carbohydrate intake. However, other influences, such as exercise, hormones, and stress, significantly affect insulin needs, making glucose regulation a complex and often unpredictable process.
The study’s findings underscore the heterogeneity of insulin needs among individuals with T1D and the importance of personalised treatment approaches. While AID systems offer advancements in T1D management, they are limited by their reliance on incomplete data, which hinders accurate blood glucose forecasting.
Degen added: “Our study highlights that managing Type 1 diabetes is far more complex than counting carbs. The richness of insights gained from studying AID data is worth the effort. What surprised us most was the variety of patterns observed, even within our relatively small and homogenous group of participants. It’s clear that one size doesn’t fit all.”
The research team is now advancing methods to analyse real-life medical data, including irregular sampling and missing information, to better understand the factors influencing insulin needs. They aim to develop innovative techniques for segmenting and clustering multivariate time series data.
To drive future innovations in personalised diabetes care, the team seeks open-access AID datasets covering a diverse range of participants and sensor measurements. They also plan to collaborate with experts in time series analysis and machine learning to uncover causal relationships behind observed patterns.
This study marks a step forward in improving T1D management and highlights the potential for tailored approaches that consider the full complexity of factors affecting blood glucose regulation.
Victoria Antoniou, EMJ
Reference
Degen I et al. Beyond expected patterns in insulin needs of people with Type 1 diabetes: temporal analysis of automated insulin delivery data. JMIR Pub. 2024;5:e44384.
