A recent study from the University of Bristol has unveiled crucial insights into the complexities of managing type 1 diabetes (T1D), challenging the assumption that a universal treatment approach can suffice. Researchers found that factors beyond carbohydrate intake play a significant role in blood glucose regulation, highlighting a gap in current automated insulin delivery (AID) systems, which rely primarily on carb-counting to adjust insulin levels.
The research, led by Isabella Degen from Bristol’s Faculty of Science and Engineering, analyzed data from individuals with T1D using OpenAPS, an advanced AID system. The study identified unexpected fluctuations in insulin needs as frequently as the well-known patterns triggered by carbohydrate consumption. The results, published as a preprint in JMIRx Med, suggest that elements such as exercise, hormones, and stress—factors not typically measured by AID systems—have a substantial impact on blood glucose levels.
Degen emphasized that current AID systems lack the ability to account for these external influences, leading to cautious insulin adjustments that sometimes cause blood glucose levels to swing too high or too low. “The study reinforces our hypothesis that euglycemia—maintaining blood glucose within the target range—is influenced by more than just carbohydrate intake,” said Degen. “Without better data on these factors, insulin management becomes far more imprecise.”
Type 1 diabetes, a condition where the body fails to produce sufficient insulin, requires careful management of insulin doses, typically through injections or pumps, to balance blood glucose levels. While carbohydrate intake has long been the cornerstone of insulin dosing, other variables like physical activity, stress, and hormonal changes can unpredictably alter insulin requirements. Despite advances in AID systems, blood glucose regulation remains a complex and often trial-and-error process.
The findings underscore the variability of insulin needs among individuals with T1D, reinforcing the idea that treatment plans must be highly personalized. Researchers are now calling for the development of methods to measure and quantify these other influencing factors to enhance insulin dosing accuracy. This could also improve blood glucose forecasting, which, according to the study, is not always reliable with data on insulin and carbohydrates alone.
“Managing type 1 diabetes is far more complex than simply counting carbs,” Degen noted. “The breadth of insights we gained from studying AID data offers a unique opportunity to refine diabetes management. What struck us most was the diversity of insulin patterns we observed, even in a relatively homogeneous group. Clearly, there is no ‘one size fits all’ approach.”
The team is now focused on advancing techniques to analyze real-world medical data, which is often irregular and incomplete. They are developing new methods for segmenting and clustering time-series data to uncover more precise patterns. To further their research, the team is seeking open-access AID datasets that incorporate a wide range of sensor measurements from diverse T1D patients.
Looking ahead, Degen and her colleagues aim to collaborate with experts in time-series analysis and machine learning to overcome the challenges posed by inconsistent data. Their goal is to uncover the causal factors behind insulin fluctuations, ultimately leading to more effective, personalized care for individuals with type 1 diabetes.
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