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Recent advancements in artificial intelligence have led to the development of a model capable of predicting diabetes risk by analyzing glucose spikes. Researchers at Scripps Research have uncovered that integrating data from continuous glucose monitors (CGMs) with other health metrics can provide a more comprehensive assessment of an individual's likelihood of developing diabetes.
Traditionally, the diagnosis of type 2 diabetes or prediabetes relies heavily on the HbA1c test, which measures average blood glucose levels over several months. However, this method does not effectively identify individuals at high risk of transitioning from healthy to prediabetic or from prediabetic to diabetic states. The new AI model aims to fill this gap by utilizing real-time glucose data, gut microbiome information, dietary habits, physical activity levels, and genetic factors.
According to the researchers, individuals with identical HbA1c scores can exhibit vastly different risk profiles for diabetes. The AI model assesses various parameters, such as the duration of glucose spikes and the body's ability to return to baseline levels after a spike, to identify those on a fast track to diabetes. By leveraging a multifaceted approach to data collection, the researchers hope to enhance the understanding of diabetes progression and enable earlier clinical interventions.
Fluctuations in blood sugar levels are normal, particularly post-meal; however, frequent and pronounced spikes may indicate a failure in the body's ability to manage glucose effectively. In healthy individuals, blood sugar levels typically rise and fall smoothly, while those at risk of diabetes may experience sharper and more frequent spikes that take longer to normalize. The study reveals that monitoring daily fluctuations in glucose can offer crucial insights into metabolic health, potentially enabling the early identification of at-risk individuals.
This research is part of the PRediction Of Glycemic RESponse Study (PROGRESS), a digital clinical trial that successfully engaged over 1,000 participants from across the United States through social media outreach. Participants included individuals diagnosed with prediabetes or diabetes, as well as healthy controls. Over ten days, participants utilized Dexcom G6 CGMs to monitor their glucose levels while tracking their food intake and physical activity. They also provided biological samples, including blood, saliva, and stool, for analysis.
Researchers utilized this rich dataset to train an AI model designed to differentiate between individuals with type 2 diabetes and those who are healthy. One significant finding was the length of time it took for blood glucose levels to return to normal after a spike. In individuals with type 2 diabetes, this process often exceeded 100 minutes, whereas healthier individuals returned to baseline levels much more quickly. Additionally, a diverse gut microbiome and higher levels of physical activity were associated with better glucose control, while a higher resting heart rate correlated with increased diabetes risk.
Crucially, the AI model was able to identify diabetes risk even in individuals with normal HbA1c levels. By examining prediabetic participants, the researchers found that some exhibited metabolic profiles similar to those with diabetes, while others resembled healthy individuals, despite comparable lab results. This granularity in risk assessment could allow healthcare providers to tailor interventions and lifestyle modifications according to individual risk levels.
The study's findings represent a snapshot in time, and researchers plan to continue monitoring participants to determine if the AI model's predictions correlate with actual disease progression. The model has shown promise through validation with additional patient data from Israel, suggesting its potential for broader clinical application.
Future iterations of the AI model may be utilized by healthcare professionals or individuals using CGMs at home to evaluate metabolic risk and understand how daily lifestyle choices impact their likelihood of developing diabetes. The overarching goal is to empower individuals with greater insight and control over their health, as diabetes often develops gradually over time, and these tools can facilitate earlier detection and more effective intervention strategies.
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