Blood Test Could Identify High Risk Individuals for Type 2 Diabetes

While some individuals remain stable for years, others rapidly deteriorate despite having similar clinical measurements. Reliable early risk assessment is essential because timely lifestyle interventions can delay or even prevent disease progression. Researchers have now shown that specific epigenetic patterns in blood, analyzed using artificial intelligence (AI), can identify individuals at particularly high risk through a simple blood test.

The latest research by investigators at the German Center for Diabetes Research (DZD) (Neuherberg, Germany) builds on their previous work, which demonstrated that prediabetes can be divided into six distinct clusters with markedly different risks of type 2 diabetes and complications. However, assigning individuals to these clusters currently requires extensive clinical testing, including glucose tolerance tests, insulin measurements, and imaging.

To overcome these limitations, the researchers explored whether blood-based molecular signals could replicate this risk stratification. They combined DNA methylation profiling from blood samples with advanced machine learning algorithms to capture epigenetic patterns associated with different prediabetes risk clusters.

The researchers analyzed blood samples from multiple cohorts with well-characterized prediabetes profiles. Using 1,557 DNA methylation markers, the AI model was able to assign individuals to high-risk prediabetes clusters with approximately 90% accuracy. The findings, published in Diabetologia, were confirmed in an independent validation cohort, demonstrating the robustness of the approach.

Notably, many of the identified markers were cluster-specific and linked to biological pathways associated with inflammation, type 2 diabetes, cardiovascular disease, and kidney disease. This molecular fingerprint explained much of the biological diversity seen in prediabetes. The findings suggest that epigenetic blood markers can act as an early warning system, identifying people at high risk even before significant metabolic decline occurs.

This could fundamentally change prediabetes care by replacing complex diagnostic procedures with a standardized blood test. Such an approach would allow clinicians to tailor preventive strategies, intensifying interventions only for those who need them most. The researchers now plan to refine the marker set and translate their findings into a practical diagnostic tool. The next step involves reducing the number of markers and developing a dedicated analysis chip suitable for routine clinical use.

“Our results suggest that epigenetic markers in the blood are an effective early warning system,” said Prof. Annette Schürmann, Director of the DZD and last author of the study. “They make it possible to identify people with a particularly high risk of diabetes and complications early on—even before severe metabolic deterioration occurs.”

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