Type 2 Diabetes Patients Fall Into Three Groups, Say Scientists

Marcia Frellick

November 10, 2015

People with type 2 diabetes fall into three distinct groups, say researchers who have analyzed genotypes and data pulled from electronic health records (EHRs).

Knowing those groups and health risks associated with them may help provide information on the most appropriate treatment, they say.

Researchers compared several hundred variables, including laboratory test results, medication history, blood counts, metabolic panels, height, weight, and genotype data from 2551 patients with type 2 diabetes in the study by Li Li, MD, associate professor, department of genetics and genomic sciences, at the Icahn School of Medicine at Mount Sinai in New York, New York, and colleagues.

They found commonalities settled into three distinct clusters.

Those in group 1 were younger, more obese, and had higher relative risk for diabetic nephropathy and retinopathy; group 2 had higher risk for cancer and cardiovascular disease; risk in group 3 was associated most strongly with cardiovascular disease, neurological diseases, allergies, and HIV infections.

Their findings were published online October 28 in Science Translational Medicine.

Tailoring Treatment

Coauthor Joel Dudley, PhD, director of biomedical informatics and an assistant professor of genetics and genomic sciences at Mount Sinai, explained the findings to Medscape Medical News using cancer risk as an example.

He said that although doctors worry about the increased cancer risk among the diabetes population in general, knowing someone is in that specific high-risk group (group 2) could inform treatment.

That might mean, for instance, prescribing metformin, which reduces cancer risk, or increasing the number of cancer screenings, he said.

Dr Dudley said that they originally kept the genetic data separate and grouped patients according to the clinical data. When they analyzed the genetic data for these groups, they fell into the same clusters.

"They seemed to corroborate the clinical differences we found," he said. "It seems to imply that there is some biology driving these differences."

No Tests Available, Yet

So far, dividing patients into these groups is still in the research phase and no tests are currently available for doctors to assess into which group a patient falls.

Now that his team has published the methodology, Dr Dudley says he hopes large health systems that have such databanks and genetic information will say, "Why aren't we doing this?"

"We hope they will either open up their data to us or we can combine all our data together," he said.

He noted, however, that the study captured patients' data during one period, and it's possible that patients might later migrate between groups. He also acknowledges this is a small, retrospective trial and that prospective studies are needed to better pinpoint these patterns.

Doctors are lucky if they see patients once a year, he noted, but that's where personal monitoring devices may come in. If this study's results could be pulled not just from information gained from doctor visits but from real-time monitoring through smartwatches and other wearable devices, data points would expand exponentially.

In addition, as genome sequencing becomes less costly and more ubiquitous and EHRs get better at synthesizing data, medical researchers will have at their fingertips a wealth of information for treating type 2 diabetes and other diseases that retail giants have been using for years to better target products to consumers, he stressed.

This study was funded by the National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, and National Cancer Institute. The authors have reported no relevant financial relationships.

Sci Transl Med. 2015;7(311):311ra174. Abstract


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