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26.01.2026 11:05
Prediabetes: Blood-based epigenetic markers enable more precise risk assessment
Prediabetes is an extremely heterogeneous metabolic disorder. Scientists from several partner institutes of the German Center for Diabetes Research (DZD) eV* have now used artificial intelligence (AI) to identify epigenetic markers that indicate an increased risk of secondary diseases. Even a simple blood test could be enough to early identify people at high risk of developing type 2 diabetes and its complications. The study shows how data-driven approaches and molecular medicine interact in diagnostics.
Prediabetes opens up an important window of opportunity for those affected to specifically prevent the development of type 2 diabetes. Early lifestyle interventions can slow the progression of the metabolic disorder or even enable remission.
However, a reliable risk assessment is crucial for this: While some people only have a low risk of developing the disease, others are very likely to develop diabetes or secondary diseases – and need significantly stronger interventions to counteract this.
Prediabetes clusters with different levels of risk
Previous studies** by the DZD and its partners had shown that prediabetes can be divided into at least six clusters that differ significantly in metabolic profile, disease progression and risk of complications: three with a moderate and three with a high risk of type 2 diabetes or complications. Assigning people to these clusters requires clinical investigations such as oral glucose tolerance tests, detailed insulin measurements and imaging tests.
“This detailed classification is very valuable, but simply too complex for daily routine,” explains Dr. Meriem Ouni, corresponding author of the study. She conducts research at the German Institute for Nutritional Research Potsdam-Rehbrücke (DIfE), a partner of the DZD. Ouni: “That’s why we wanted to check whether the risk groups could also be identified using easily accessible biomarkers in the blood.”
1,557 epigenetic markers as a biological fingerprint
In the study that has now been published, the researchers combined blood analyzes for DNA methylation with modern machine learning methods. To do this, they examined samples from people from several study cohorts with a known prediabetes risk profile.
Their result: With 1,557 epigenetic markers in the blood, they are able to correctly assign people to high-risk clusters with an accuracy of around 90 percent – even in an independent validation cohort. What is particularly noteworthy is that many of these markers are cluster specific and reflect different biological signaling pathways.
Many of the markers identified were known from previous epigenome-wide studies. They are associated with type 2 diabetes, chronic inflammation, and heart and kidney disease – and could largely explain the heterogeneity of prediabetes.
Perspective: Easier prevention, broader application
“Our results indicate that epigenetic markers in the blood are a powerful early warning system,” explains Prof. Annette Schürmann, board member at the DZD and last author of the study. These markers not only reflected the current metabolic state, but also provided clues about the future course of the disease. “They make it possible to identify people with a particularly high risk of diabetes and complications at an early stage – even before serious metabolic disorders occur.”
In the long term, this approach could fundamentally change the prevention and care of people with prediabetes. Instead of time-consuming and cost-intensive clinical examinations, a standardized blood test would be conceivable that would enable a differentiated risk assessment and control preventive measures in a much more targeted manner than before. This would allow prevention to begin earlier and be tailored more individually
“Our next step is to translate our findings into a practical test,” explains Ouni. First of all, the number of markers should be specifically limited. Building on this, the development of a tailor-made analysis chip is planned, which will allow simple and cost-efficient identification of prediabetes risk clusters in routine diagnostics.
The German Institute for Nutritional Research Potsdam-Rehbrücke (DIfE) is a member of the Leibniz Association. It researches the causes of nutrition-related diseases in order to develop new strategies for prevention, therapy and nutritional recommendations. His research interests include the causes and consequences of metabolic syndrome, a combination of obesity (obesity), hypertension (high blood pressure), insulin resistance and dyslipidemia, the role of nutrition in healthy aging, and the mechanisms of food selection and precision nutrition. www.dife.de
The German Center for Diabetes Research (DZD) eV is one of the eight German centers for health research. It brings together experts in the field of diabetes research and combines basic research, epidemiology and clinical application. The aim of the DZD is to make a significant contribution to the successful, tailor-made prevention, diagnosis and treatment of diabetes mellitus using a novel, integrative research approach. Members of the network are Helmholtz Munich – German Research Center for Health and Environment, the German Diabetes Center DDZ in Düsseldorf, the German Institute for Nutritional Research DIfE in Potsdam-Rehbrücke, the Institute for Diabetes Research and Metabolic Diseases of Helmholtz Munich at the Eberhard Karls University of Tübingen and the Paul Langerhans Institute Dresden of Helmholtz Munich at the Carl Gustav Carus University Hospital of the TU Dresden, associated partners the universities in Heidelberg, Cologne, Leipzig, Lübeck and Munich as well as other project partners. www.dzd-ev.de
Scientific contacts:
dr. Meriem Ouni
German Institute for Nutritional Research Potsdam-Rehbrücke (DIfE)
Head of the junior research group Epigenetics of Obesity and Diabetes
Tel.: +49 33200 88-2505
Email:
meriem.ouni@dife.de
Original publication:
Singh, A., Schwartzenberg, R.Jv., Wagner, R. et al. Stratifying high-risk prediabetes clusters using blood-based epigenetic markers. Biomark Res (2026).
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