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Fluctuations in blood glucose levels are more than just an energy dip; they may soon serve as an early warning system for diabetes, potentially eliminating the need for painful blood tests.
Researchers from the University of Tokyo have unveiled a straightforward, noninvasive approach to evaluate blood glucose management, a critical component in assessing diabetes risk. This method, which utilizes continuous glucose monitoring (CGM) data, promises to enhance the early identification and evaluation of diabetes risk without depending on blood samples or intricate procedures.
This research appears in the journal Communications Medicine.
Diabetes, often referred to as a “silent epidemic,” is a growing global health issue with far-reaching effects on both individual health and the economy. Recognizing early signs of impaired glucose regulation, which exists between normal glucose levels and diabetes, is vital for preventing or postponing the onset of Type 2 diabetes. Traditional diagnostic measures frequently overlook these early indicators because they rely on intermittent blood testing rather than continuous assessment.
“Conventional diabetes screenings, while beneficial, fail to capture the dynamic and fluctuating nature of glucose regulation in real-time physiological conditions,” noted Shinya Kuroda, a professor at the University of Tokyo’s Graduate School of Science and co-author of the study.
In search of a more effective solution, the research team focused on CGM technology, which consistently monitors glucose levels in real-time, offering a comprehensive view of glucose fluctuations in daily life. Their aim was to establish a CGM-based technique to assess the body’s capacity to manage glucose levels without resorting to invasive testing.
The study involved 64 participants who had never been diagnosed with diabetes. The team utilized CGM devices alongside oral glucose tolerance tests (OGTT) and clamp tests, which measure insulin sensitivity and glucose metabolism. Their findings were validated with an independent dataset and mathematical modeling.
The results indicated that AC_Var, a metric indicating glucose-level variations, had a strong correlation with the disposition index, a recognized predictor of future diabetes risk. Furthermore, the researchers’ model, which integrated AC_Var with glucose standard deviation, surpassed traditional markers of diabetes risk—including fasting blood glucose, HbA1c, and OGTT—in its predictive power regarding the disposition index.
“Our analysis of CGM data using this algorithm allowed us to identify individuals with impaired glycemic control, even when they were classified as ‘normal’ by standard tests,” said Kuroda. “This advancement could facilitate earlier detection of issues, providing a chance for preventive measures before a diabetes diagnosis is made.”
The study also demonstrated that this innovative method was superior to conventional diagnostic markers in forecasting diabetes-related complications such as coronary artery disease. To promote wider access to this new methodology, the researchers have developed a web application that permits both individuals and healthcare providers to easily compute these CGM-based metrics.
“Our primary objective is to create a practical and accessible tool for diabetes screening across populations,” Kuroda emphasized. “By enabling the early identification of abnormalities in glucose regulation, we aim to prevent or delay the onset of diabetes and minimize long-term health complications.”
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