Wearable Glucose Monitor Offers Less Invasive Approach to Assessing Diabetes Risk

Diabetes, often referred to as a "silent epidemic," is a growing global health issue with significant impacts on both health and the economy. Detecting impaired glucose regulation early — an intermediate stage between normal blood glucose levels and diabetes — is crucial in preventing or delaying the development of Type 2 diabetes. However, traditional diagnostic methods frequently miss early indicators because they rely on occasional blood samples rather than continuous monitoring. A new study has now introduced a simple, non-invasive method for assessing blood glucose regulation, a critical factor in determining diabetes risk. The approach, which utilizes continuous glucose monitoring (CGM) data, could enhance early detection and risk assessment for diabetes, eliminating the need for blood samples or complex and costly procedures.

Researchers from the University of Tokyo (Tokyo, Japan) aimed to find a practical alternative to traditional diabetes testing methods, which, while helpful, fail to capture the dynamic nature of glucose regulation in normal, everyday conditions. To achieve this, the team turned to CGM, a wearable device that continuously monitors glucose levels in real time, providing a more comprehensive view of blood glucose fluctuations throughout the day. The goal was to develop a CGM-based method to estimate the body's glucose handling capacity — how well it maintains stable glucose levels — without resorting to invasive procedures. The team analyzed data from 64 individuals who had no previous diabetes diagnosis, using CGM devices, oral glucose tolerance tests (OGTT), and clamp tests that assess insulin sensitivity and glucose metabolism. They then validated their results with an independent dataset and mathematical simulations.

Their findings revealed that AC_Var, a measure of glucose fluctuations, strongly correlates with the disposition index, which is a well-known predictor of future diabetes risk. Additionally, their model, which combines AC_Var with glucose standard deviation, proved to be more effective than traditional markers of diabetes, such as fasting blood glucose, HbA1c, and OGTT results, in predicting the disposition index. The researchers also demonstrated that their method was more accurate than conventional diagnostic indicators in predicting diabetes complications, including coronary artery disease. To make this approach more widely accessible, the research team developed a web application that allows individuals and healthcare providers to easily calculate these CGM-based indices.

“By analyzing CGM data with our new algorithm, we identified individuals with impaired glycemic control — even when standard diagnostic tests classified them as ‘normal’. This means we can potentially detect issues much earlier, creating an opportunity for preventive interventions before diabetes is diagnosed,” said Shinya Kuroda, a professor at the University of Tokyo’s Graduate School of Science and co-author of the current study. “Our ultimate goal is to provide a practical, accessible tool for widespread diabetes screening. By enabling early detection of glucose regulation abnormalities, we hope to prevent or delay disease onset and reduce long-term complications.”

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