diabetes detection – Misryoum reports how AI and wearable CGMs may spot diabetes risk earlier than standard blood sugar tests.
A diabetes diagnosis built mainly on blood sugar levels is increasingly being challenged by researchers who say it can miss people when prevention matters most.
For years. clinicians have relied on tests such as HbA1c. which estimates average glucose over the prior few months and helps determine whether results cross a clinical threshold.. Misryoum notes that while this approach has been widely used. it does not always capture the full picture. including how blood sugar behaves in day-to-day life or how certain medical and physiological factors can influence results.
That gap has fueled concerns about delayed detection, especially for people who may not fit the typical patterns that screening tools are designed to catch.
In Misryoum’s view, the bigger issue is time.. Persistently elevated glucose can quietly raise the risk of serious complications over the years. from cardiovascular problems to kidney damage and nerve issues.. Earlier identification can make prevention more realistic, including strategies aimed at slowing or stopping diabetes from progressing.
Meanwhile, interest is growing in more personalized detection methods that combine richer data with modern analytics.. Researchers are exploring how wearable continuous glucose monitors (CGMs) can reveal metabolic patterns in real time. potentially spotting risk signals well before conventional diagnosis of type 2 diabetes. which accounts for the majority of cases.
Misryoum insight: CGMs change the question from “What was your average glucose recently?” to “How does your body respond over time?” That shift is important because diabetes is not just a number, it is a process.
A team connected to Stanford has been developing an AI-based algorithm that studies patterns in CGM data to distinguish different forms of type 2 diabetes.. According to Misryoum. the approach aims to identify meaningful sub-patterns earlier than traditional testing. with the goal of helping clinicians and individuals target prevention more precisely.
The work also aligns with a broader trend: CGMs are becoming more accessible in some markets. including over-the-counter options in the US.. Misryoum adds that researchers envision CGMs playing a role in routine preventive care. such as periodic use rather than waiting for symptoms or screening flags.
Misryoum insight: If earlier, pattern-based detection becomes practical, it could help shift diabetes care from reacting to complications toward reducing risk before damage accumulates.