Turning Challenges Into Change: What’s Solvable in Diabetes Care?
Key takeaways:
- During a recent diaTribe panel, experts shared insights on improving access to the newest technologies and treatments to advance global diabetes care.
- Experts also highlighted the potential of AI tools to transform diabetes prevention and management, while discussing updated terminology like time in normoglycemia (TING) to better reflect treatment goals.
- The conversation underscored the need for greater access, continued research, and collaboration to improve diabetes management.
At ATTD 2025, the diaTribe Foundation hosted Solvable Problems, a compelling panel featuring esteemed women at the forefront of diabetes research. During the event, experts discussed opportunities to improve diabetes care with advanced technologies, including the use of artificial intelligence, continuous glucose monitoring (CGM), and renaming key glycemic targets.
“At diaTribe, we believe that all of the biggest challenges in diabetes are, in fact, solvable – and solving them requires innovation, collaboration, and relentless commitment to improving lives,” said Jim Carroll, CEO of diaTribe, in his opening remarks.
The panel was moderated by Dr. Tadej Battelino, head of the Department of Pediatric and Adolescent Endocrinology at UMC Ljubljana in Slovenia, and included:
- Dr. Julia Mader, associate professor of medicine at the Division of Endocrinology and Diabetology at the Medical University of Graz in Austria
- Dr. Katrien Benhalima, endocrinologist at Katholieke Universiteit Leuven in Belgium
- Elaine Chow, clinical associate professor in the Department of Medicine & Therapeutics at The Chinese University of Hong Kong
Barriers to adopting new technologies and treatments
Despite significant technological advances over the past decade, adoption is still a major challenge in many parts of the world. One of the key obstacles, Mader said, is reimbursement and funding for these technologies.
“I think it’s our role, jointly with people living with diabetes, to really engage with the public and policymakers to make change happen,” she said.
Benhalima noted similar limitations with access to GLP-1 medications, with production shortages and issues with reimbursement making it difficult to ensure availability.
“We can offer it to a group of people with type 2 diabetes, but it’s not reimbursed for type 1 diabetes yet – so they have to pay out of pocket,” Benhalima said.
Chow echoed this concern, explaining that while Hong Kong has access to newer medications, access to CGM remains limited, creating an imbalance in blood sugar monitoring.
CGM as a critical tool for high-risk populations
Chow also highlighted the value of CGM for people with chronic kidney disease (CKD), particularly within Asian populations, where diabetes-related CKD is more common. Traditional markers like A1C are often unreliable in CKD due to factors like altered red cell turnover and iron therapy, she explained.
On the other hand, CGM goes beyond tracking average glucose by also identifying highs and lows, which people with CKD are especially prone to. Chow emphasized that people need this technology and the monitoring that goes with it.
“If you have a CGM, you’re your own doctor every day,” she said.
Mader also proposed the proactive use of CGM for people with prediabetes so that they can see how their glucose values are evolving over time. She also mentioned that investing in sensors at this stage could lead to major savings by preventing costly complications down the road.
CGM provides a comprehensive set of metrics that offer a real-time, dynamic view of how blood sugar levels change over time. One of these metrics is time in tight range, which is the percentage of time a person's blood glucose levels stay within the target range of 70-140 mg/dL.
One of the noteworthy takeaways during the ATTD 2025 conference was the anticipated renaming of time in tight range to “time in normoglycemia,” or TING. Battelino explained that TING reflects the ultimate goal – healthy blood sugar levels. He added that experts from regulatory agencies have responded positively to the term, seeing it as a clear and meaningful outcome in diabetes management.
The promise of AI in predicting diabetes risk
Looking ahead, Benhalima explored the emerging role of AI in managing gestational diabetes and predicting long-term risk.
“We are just beginning to see what AI can do,” she said. “Even with the general risk factors and prediction models we currently have, we cannot know what the exact risks are for adverse pregnancy outcomes for an individual, or even what the long-term risk is for developing type 2 diabetes after pregnancy.”
While there are several studies using machine learning and looking at new ways of prediction, Benhalima said the studies are often small and lacking external validation. She suggested that investment is needed in large clinical trials using AI, as this could shape outcomes and policy in meaningful ways.
Benhalima also emphasized that more evidence and better technology is needed to support automated insulin delivery (AID) in pregnancy for women with type 1 diabetes, as many still struggle to meet narrower blood sugar targets. For type 2 diabetes, she said that the potential of personalized treatment and AI lies in preventing the disease rather than just treating it.
Expanding on this, Chow discussed the opportunity for AI to help shape new lifestyle habits by making suggestions in real-time – for example, encouraging exercise after a high-carb meal. She added that these types of interventions could have a big impact on day-to-day diabetes management.
The bottom line
The Solvable Problems panel underscored a shared belief: That with the right investments in technology, collaboration, and advocacy, the diabetes community can overcome its most pressing challenges.
From expanding CGM access to leveraging AI and renaming clinical metrics, each insight brought the conversation closer to a future with more personalized, preventative, and accessible diabetes care.
Learn more about advancements in diabetes technology and care here: