To diagnose both sort 2 diabetes or pre-diabetes, clinicians usually depend on a lab worth often known as HbA1c. This take a look at captures an individual’s common blood glucose ranges over the last few months. However HbA1c can not predict who’s at highest threat of progressing from wholesome to prediabetic, or from prediabetic to full-blown diabetes.
Now, scientists at Scripps Analysis have found that synthetic intelligence can use a mixture of different information—together with real-time glucose ranges from wearable screens—to offer a extra nuanced view of diabetes threat.
The brand new mannequin, described in Nature Medication, makes use of steady glucose monitor (CGM) information alongside intestine microbiome, weight loss program, bodily exercise and genetic data. It flags early indicators of diabetes threat that normal HbA1c exams could miss.
“We confirmed that two individuals with the identical HbA1c rating can have very completely different underlying threat profiles,” says co-senior writer Giorgio Quer, the director of synthetic intelligence and assistant professor of Digital Medication at Scripps Analysis. “By bringing in additional information—how lengthy glucose spikes take to resolve, what occurs to glucose in a single day, what the meals consumption is, and even what’s taking place within the intestine—we are able to begin to inform who’s on a quick monitor to diabetes and who is not.”
“Finally, the objective of this work is to get a greater understanding of what’s driving diabetes development and the way we are able to intervene earlier within the clinic,” provides co-senior writer Ed Ramos, the senior director of digital medical trials at Scripps Analysis.
Whereas some variation in blood sugar is totally regular—particularly after consuming—frequent or exaggerated glucose spikes generally is a signal that the physique is struggling to handle sugar successfully. In wholesome people, blood sugar usually rises and falls easily. However in individuals vulnerable to diabetes, these spikes can grow to be sharper, extra frequent or slower to resolve, even earlier than routine lab exams like HbA1c decide up an issue. The brand new research exhibits that monitoring these day-to-day dynamics gives a way more detailed view of an individual’s metabolic well being, and may assist establish bother earlier.
The findings are a results of a multi-year, digital analysis program known as the PRediction Of Glycemic RESponse Examine (PROGRESS). The research used social media outreach to enroll greater than 1,000 individuals from throughout the U.S. in a totally distant medical trial. Contributors included individuals with diagnoses of both pre-diabetes or diabetes, in addition to wholesome people.
For ten days, they wore Dexcom G6 CGMs, tracked their meals and train, and despatched in samples of their blood, saliva and stool for testing. The researchers additionally had entry to contributors’ digital well being information, which included earlier lab values and diagnoses made by medical practitioners.
“This was a extremely pioneering effort within the distant medical trial house,” says Ramos. “We needed to design a research that contributors might full fully on their very own—from making use of sensors to amassing and delivery organic samples—with out ever visiting a clinic. That degree of self-guided participation required a very completely different sort of infrastructure than common.”
Utilizing the information, the researchers skilled an AI mannequin to differentiate individuals with sort 2 diabetes from wholesome people.
One of many clearest alerts of diabetes threat that the researchers discovered was the time it took for a blood sugar spike to return to regular. In individuals with sort 2 diabetes, it usually took 100 minutes or extra for blood sugar to lower after a spike, whereas more healthy people returned to baseline a lot quicker. The research additionally discovered that individuals with a extra numerous intestine microbiome and better exercise degree tended to have higher glucose management, whereas the next resting coronary heart fee was linked to diabetes.
Importantly, the AI mannequin did not simply detect threat in individuals with already elevated HbA1c. When utilized to pre-diabetic people, it discovered that some appeared metabolically just like these with diabetes, whereas others resembled wholesome people, regardless of having related lab values. This degree of granularity might assist clinicians personalize therapy—specializing in way of life adjustments or early therapies for sufferers with the best threat of illness development.
Whereas the present research was a snapshot in time, the researchers are persevering with to observe contributors to see whether or not the mannequin’s predictions translate to real-world illness development. In addition they validated the mannequin utilizing a separate set of affected person information from Israel, strengthening its potential for broader medical use.
The group envisions future variations of the mannequin being utilized by clinicians, and even people utilizing CGMs at dwelling, to evaluate metabolic threat and monitor how day by day selections have an effect on diabetes.
“Finally, that is about giving individuals extra perception and management,” says Quer. “Diabetes does not simply seem at some point—it builds slowly, and we now have the instruments to detect it earlier and intervene smarter.”
Extra data:
Mattia Carletti et al, Multimodal AI correlates of glucose spikes in individuals with regular glucose regulation, pre-diabetes and sort 2 diabetes, Nature Medication (2025). DOI: 10.1038/s41591-025-03849-7
Quotation:
AI mannequin makes use of glucose spikes to disclose hidden diabetes threat earlier than signs seem (2025, July 31)
retrieved 31 July 2025
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