Algorithm Improves Blood Sugar Control in Type 2 Diabetes | UVA Health News
A new algorithm developed by researchers at the University of Virginia’s Center for Diabetes Technology, when paired with a continuous glucose monitor (CGM), significantly improved blood sugar management for adults with type 2 diabetes in a recent clinical trial. The system provides personalized insulin dose recommendations, offering a potential advancement over traditional self-monitoring methods.
The study, published in Diabetes Technology & Therapeutics, involved 30 participants who were randomly assigned to either use the algorithm-driven recommendations for 16 weeks or continue self-adjusting their insulin doses based on blood sugar readings. Participants utilizing the algorithm saw their average time spent within a safe blood sugar range increase dramatically, from 54.1% to 75.3%. In contrast, those relying on self-monitoring experienced a more modest improvement, from 50.2% to 55.3%.
“These results clearly show that diabetes technology and advanced algorithms can be leveraged to great effects, well beyond the classical paradigm of automated insulin delivery,” said Marc D. Breton, PhD, the study’s lead author and associate director of research at the UVA Center for Diabetes Technology. “As continuous glucose monitoring and connected medical devices become ubiquitous, we have the opportunity to provide highly personalized advice and monitoring to people with diabetes and guide their use of insulin and medications. Showing the impact of these technologies in early insulin therapy (only one dose a day) opens the door to helping the vast majority of people using insulin, well beyond what we were able to achieve with automated insulin delivery.”
Many individuals newly diagnosed with type 2 diabetes initially manage their condition with medications aimed at lowering blood sugar. However, the effectiveness of these medications often diminishes over time, necessitating the introduction of insulin. Adjusting insulin dosages – a process known as insulin titration – can be complex and time-consuming for both patients and healthcare providers and lacks a standardized approach.
This challenge prompted Anas El Fathi, PhD, a UVA Health researcher, to create the algorithm. It analyzes data from a CGM over a two-week period to generate weekly recommendations for insulin dose adjustments. “From a medical point of view, it was fascinating to see that the algorithm was not only better than the standardized insulin titration recommendations, but also how well the technology was accepted by the participants with type 2 diabetes,” said Ralf Nass, MD, a UVA Health researcher and study co-author. “This type of technology has the potential to help physicians enable their patients to achieve better glycemic control faster by using a personalized approach.”
The UVA Center for Diabetes Technology is currently recruiting adults aged 30-80 with type 2 diabetes for participation in a separate clinical study, focusing on a “Patient Centered Multi-Agent Decision Support System (PCM-DSS) for Healthcare Providers” (TREAT2D). The study, identified as IRB-HSR#302621 and NCT07063420, involves the use of a study-provided CGM and personal insulin pen over approximately five months. Participants receive $200 in compensation. Information is available by emailing Carlene Alix.
Researchers emphasize that further, larger clinical trials are needed to validate the algorithm’s effectiveness across diverse populations. Breton stated, “It is only the very beginning of these efforts. With early demonstration behind us, we can focus on robust approaches that will be effective with more varied populations. Integrating recently developed data-driven methodologies, especially digital twins, to further improve our capacity to tailor diabetes managements to individuals is likely to once more revolutionize diabetes care.”
The research team included El Fathi, Nass, Carol J. Levy, Camilla Levister, Grenye O’Malley, Nirali A. Shah, Shaziah Hassan, Cheryl Quainoo, Chaitanya L.K. Koravi, Taylor N. Nguyen, Giulio Matteo Santini, Emma Emory, Carlene Alix, Dillon K. Flanagan, David Fulkerson, Mary Clancy Oliveri, Christian Laugesen, Jonas K. Lineolov, Peter W. Hansen and Breton. The clinical trial received financial support from Novo Nordisk.