Mount Sinai Study Uses Big Data to Classify Type 2 Diabetes Patients Into Distinct Groups

Type 2 diabetes (T2D) behaves differently from patient to patient. But now, a team of researchers from the Icahn School of Medicine at Mount Sinai Hospital in New York has brought more clarity to this concept, with data that show T2D patients fall into 3 distinct groups.

The study, appearing in Science Translational Medicine, embraces the goals of President Obama’s precision medicine initiative by assembling data from electronic medical records (EMR) and genotype data from 2500 people with T2D who were treated at Mount Sinai.

All patients with T2D have diminished ability to use insulin efficiently, or no ability at all. This occurs for a variety of reasons, is associated with obesity and lack of exercise. While the risk of developing T2D increases with age, more young people are being diagnosed. Some patients are more likely than others to have comorbidities or develop disabling complications. Understanding which ones face the greatest risk could help physicians target these patients for closer monitoring and intervention.

By evaluating both the genetic information, health status, and other data points, the researchers were able to link patients to other similar patients, and eventually put patients into clusters. As this process progressed, 3 distinct subtypes emerged. According to the abstract, the Mount Sinai team identified more than 300 single nucleotide polymorphisms that were specific to each subtype.

The 3 groups were:

· Subtype 1 patients were younger, with higher risk of obesity, kidney disease, and retina problems that can progress to blindness. This group had lower white blood cell counts.

· Subtype 2 patients had lower body mass index (BMI) than the group overall, but more risk of developing cancer or heart disease.

· Subtype 3 patients had a high risk of heart disease as well, but also a high risk of mental illness and allergies.

The researchers called for more work with larger groups of patients to confirm their findings—and to figure out why these subtypes form.

“These distinctions might call for tailored treatment regimens rather than a one-size-fits-all approach for T2D,” the authors wrote. “Although a larger sample size is needed to determine causal relationships, this study demonstrates the potential of precision medicine.”

Reference

Li L, Cheng WY, Glicksberg BS, et al. Identification of type 2 diabetes subgroups through topological analysis of patient similarity [published online October 28, 2015]. Sci Transl Med. 2015; doi:10.1126/scitranslmed.aaa9364.