A paper detailing the research published Dec. 23 in Nature Biomedical Engineering. McLaughlin and Snyder are co-senior authors. Ahmed Metwally, PhD, a former postdoctoral scholar at Stanford Medicine who is now a research scientist at Google, is the lead author.
Delineating details of diabetes
Currently, diagnosing diabetes is based solely on the level of glucose in the blood and can be made through a simple blood draw. “But those tests reveal little about the biology underlying high blood sugar,” McLaughlin said. “Understanding the physiology behind it requires metabolic tests done in a research setting, but the tests are cumbersome and expensive and not practical for use in the clinic.”
However, continuous glucose monitors, available over the counter, can test for high blood sugar and compile more detailed information about their metabolic biology.
Insulin, a hormone made in the pancreas, regulates the levels of glucose, or sugar, in the bloodstream by encouraging cells to absorb it and use it as energy. If the pancreas does not make enough insulin, known as insulin deficiency, blood glucose rises. Insulin resistance, a common marker of diabetes, occurs when cells don’t respond to the cues from insulin, which also results in a buildup of blood glucose.
Type 2 diabetes can also result from a defect in the production of incretin, a hormone released by the gut after eating that stimulates insulin secretion from the pancreas, or by insulin resistance in the liver. Each of these four physiologic subtypes of diabetes might respond to different therapies.
Testing the algorithm
McLaughlin and Snyder wondered whether a common gadget like a continuous glucose monitor could produce data with hidden signals correlating to the different subtypes of diabetes. The device, which users attach to their upper arm, measures the rise and fall of blood sugar levels in real time. People who drink a glucose drink often show a spike in their blood glucose, but the level and pattern of those spikes varies from one person to the next.
In a study of 54 participants, 21 of whom had prediabetes and 33 of whom were healthy, the researchers applied an artificial intelligence-powered algorithm to identify patterns within peaks and dips that corresponded to different subtypes of Type 2 diabetes.
Participants who used the continuous glucose monitors also underwent the oral glucose test performed at a doctor’s office. “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,” McLaughlin said. “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.”
“Stanford University, officially Leland Stanford Junior University, is a private research university in Stanford, California. The campus occupies 8,180 acres, among the largest in the United States, and enrols over 17,000 students.”
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