Prediabetes is an extremely heterogeneous metabolic disorder. Scientists from several partner institutes of the German Center for Diabetes Research (DZD)* have now used artificial intelligence (AI) to identify epigenetic markers that indicate an elevated risk of complications. A simple blood test could be sufficient to identify individuals at high risk of developing type 2 diabetes and its complications at an early stage. The study shows how data-driven approaches and molecular medicine interact in the diagnostic process.
Prediabetes opens up an important window of time for affected individuals to actively prevent the development of type 2 diabetes. Early lifestyle interventions can inhibit the progression of the metabolic disorder or even lead to remission. However, a reliable risk assessment is a crucial factor in this regard: While some people have only a low risk of disease, others have a high likelihood of developing diabetes or complications and require much stronger interventions to counteract the risk.
Prediabetes Clusters with Different Levels of Risk
Previous studies ** conducted by the DZD and its partners demonstrated that prediabetes can be classified into at least six clusters, which differ significantly in terms of metabolic profile, disease progression, and risk of complications: three with a moderate risk and three with a high risk of type 2 diabetes and complications. Assigning people to these clusters requires clinical examinations such as oral glucose tolerance tests, detailed insulin measurements, and imaging procedures.
"This detailed classification is of great value, but is simply too time-consuming for routine practice," explains Dr. Meriem Ouni, corresponding author of the study. She is a researcher at the German Institute of Human Nutrition Potsdam-Rehbrücke (DIfE), a partner of the DZD. Ouni: "For this reason, we wanted to examine whether risk groups could also be identified using easily accessible biomarkers in the blood."
1,557 Epigenetic Markers as a Biological Fingerprint
In the recently published study, the researchers combined blood-based DNA methylation analyses with state-of-the-art machine learning methods. They studied samples from participants across multiple study cohorts with a known prediabetes risk profile.
Their result: Using 1,557 epigenetic markers in the blood, they were able to correctly assign people to the high-risk clusters with an accuracy of around 90 percent—including in an independent validation cohort. It is particularly noteworthy that many of these markers are cluster-specific and reflect different biological signaling pathways.
Many of the identified markers were already known from previous epigenome-wide studies. They are associated with type 2 diabetes and chronic inflammation as well as heart and kidney disease—and could largely explain the heterogeneity of prediabetes.
Prospects: Easier Prevention, Wider Application
"Our results suggest that epigenetic markers in the blood are an effective early warning system," explains Prof. Annette Schürmann, Director of the DZD and last author of the study. These markers did not only reflect the current metabolic state but also provided indications of the future course of the disease. "They make it possible to identify people with a particularly high risk of diabetes and complications early on—even before severe metabolic deterioration occurs."
In the long term, this approach could fundamentally transform prevention and care for people with prediabetes. Instead of time-intensive and costly clinical examinations, a standardized blood test could conceivably allow for a differentiated risk assessment and much more targeted preventive measures than before. This would enable prevention to be started at an earlier stage and tailored more individually.
"Our next step is therefore to convert our insights into a practical test," explains Ouni. First of all, the number of markers should be selectively narrowed down. Building on this, the development of a custom-designed analysis chip is planned, enabling the simple and cost-effective identification of prediabetes risk clusters in routine diagnostics.
Original publication:
Singh, A., Schwartzenberg, R.Jv., Wagner, R. et al. Stratifying high-risk prediabetes clusters using blood-based epigenetic markers. Biomark Res (2026). https://doi.org/10.1186/s40364-025-00887-8
*Several partner institutes of the German Center for Diabetes Research (DZD) e.V. participated in the study:
• German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE)
• Institute for Diabetes Research and Metabolic Diseases (IDM) of Helmholtz Munich at the Eberhard-Karls-University of Tübingen
• German Diabetes Center in Düsseldorf (DDZ)
• Paul Langerhans Institute Dresden of Helmholtz Munich at the University Hospital and Faculty of Medicine Carl Gustav Carus of TU Dresden (PLID)
** https://pubmed.ncbi.nlm.nih.gov/33398163/
