Unseen woman pricking their finger to test their blood sugar level
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Researchers from the University of Edinburgh, Scotland, say they have developed a method of analyzing DNA methylation from a blood sample that, along with other patient risk factors, can improve the ability to predict the development of type 2 diabetes within the next 10 years.

Current prediction tools for type 2 diabetes include, age, sex, BMI, and a family history of the disease, but the addition of DNA methylation data provided a more accurate prediction.

The team used their findings, published today in the journal Nature Aging, to estimate the predictive performance using a hypothetical screening of 10,000 people where one-in-three people develop type 2 diabetes over the ensuing 10 years. Using their method, the investigators correctly classified an additional 449 people at risk when incorporating the DNA methylation data compared with screening using current methods.

To conduct the study, the investigators used the data of 14,613 volunteers enrolled in the Generation Scotland study—a population health research project to investigate the causes of diseases, help guide priorities of the nationalized health system, as well as inform future treatments and health policies. The study also repeated its analysis, this time using the data of 1,451 people from a German study to discover if their method could be used in other populations.

“It is promising that our findings were observed in the Scottish and German studies with both showing an improvement in prediction above and beyond commonly used risk factors. Delaying onset is important as diabetes is a risk factor for other common diseases, including dementias,” said Yipeng Cheng, a PhD student from the University of Edinburgh’s Centre for Genomic and Experimental Medicine.

The hope is that including the new methylation data in patient risk assessment of developing type 2 diabetes could help doctors and patients do more to prevent the onset of the disease and, in turn, reduce the economic and health burden it causes.

While the research team focused on type 2 diabetes for their study, there is hope that the method employed for this risk assessment could be applicable in other areas.

“Similar approaches could be taken for other common diseases to generate broad health predictors from a single blood or saliva sample,” said Prof. Riccardo Marioni, also from the University of Edinburgh’s Centre for Genomic and Experimental Medicine and the study’s lead author. “We are incredibly grateful for our study volunteers who make this research possible—the more people that join our study, the more precisely we can identify signals that will help delay or reduce the onset of diseases as we age.”

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