Diabetes has traditionally been classified into two types — Type 1, which typically develops in childhood, and Type 2, which is often linked to obesity and tends to occur later in life. However, scientists have discovered that not all Type 2 diabetes patients are alike, with variations in factors such as body weight, age of onset, and other characteristics. There has been an increasing push to subclassify Type 2 diabetes, which accounts for 95% of all diabetes cases, in order to better understand the risk of related conditions, including cardiovascular, kidney, liver, and eye complications, and identify the underlying physiology of each individual's diabetes. At present, diabetes diagnosis relies solely on blood glucose levels, which can be measured through a simple blood test. However, these tests do not provide insight into the biological mechanisms behind elevated blood sugar. Understanding the underlying physiology requires metabolic tests, which are typically conducted in a research setting, but these tests are costly, cumbersome, and not feasible for routine clinical use.

On the other hand, continuous glucose monitors, which are available over the counter, can track high blood sugar and provide more detailed information about a person’s metabolic biology. Insulin, a hormone produced by the pancreas, regulates blood glucose by helping cells absorb it and use it for energy. When the pancreas produces insufficient insulin — a condition known as insulin deficiency — blood glucose levels rise. Insulin resistance, a common marker of diabetes, occurs when cells fail to respond to insulin, causing glucose to accumulate in the blood. Type 2 diabetes can also result from a defect in the production of incretin, a hormone released by the gut after eating that prompts the pancreas to secrete insulin, or from insulin resistance in the liver. Each of these four physiological subtypes of diabetes may require different therapeutic approaches.

Researchers from Stanford Medicine (Stanford, CA, USA) have now developed an AI-based algorithm that uses data from continuous glucose monitors to differentiate between three of the four most common Type 2 diabetes subtypes. The researchers sought to determine whether a widely used device, such as a continuous glucose monitor, could provide data with hidden signals that correlate to different subtypes of diabetes. The monitor, which is worn on the upper arm, measures real-time fluctuations in blood sugar levels. When people consume a glucose drink, their blood sugar levels typically spike, but the intensity and pattern of these spikes can differ between individuals.

In a study involving 54 participants — 21 with prediabetes and 33 healthy individuals — the researchers applied an AI-powered algorithm to identify patterns in the peaks and troughs of blood sugar levels that correspond to various Type 2 diabetes subtypes. Participants who used the continuous glucose monitors also underwent the oral glucose test at a doctor’s office. When the data from the continuous glucose monitors were compared with clinical data and other metabolic biomarkers, the algorithm was able to predict metabolic subtypes, such as insulin resistance and beta-cell deficiency, with greater accuracy than traditional metabolic tests.

The tool, detailed in a paper published in Nature Biomedical Engineering, was able to accurately detect and identify subtypes approximately 90% of the time. In addition to providing higher-resolution data for individuals with diabetes or prediabetes, the continuous glucose monitor offers additional benefits. Even if someone with insulin resistance doesn’t develop diabetes, it remains important to identify the condition, as insulin resistance is a risk factor for several other health issues, such as heart disease and fatty liver disease. The researchers plan to continue testing the algorithm on individuals diagnosed with Type 2 diabetes and hope that the broad availability of the technology will improve access to care, especially for patients who are unable to attend doctor’s appointments. They also view this technology as an invaluable health tool for individuals who face economic or geographical barriers to accessing health care.

“People have looked at that for decades and have found certain parameters that indicate insulin resistance or beta cell dysfunction, which are the main drivers of diabetes,” said Tracey McLaughlin, MD, a professor of endocrinology. “But now we have the monitors, and you can get a much more nuanced picture of the glucose pattern which predicts these subtypes with greater accuracy and can be done at home.”

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