Classification of type 2 diabetes

According to a new study, the classification of type 2 diabetes patients into subgroups based on genetic variations and related complications have important implications in the prevention and management of the disease.

 

Type 2 diabetes is a multifactorial disease with a variety of symptoms and related medical complications. It is one of the leading causes of death in the United States. Researchers from the Icahn School of Medicine at Mt. Sinai in New York examined electronic medical records and genetic variations of more than 11 000 type 2 diabetes patients to determine whether the range of diabetes-related complications correlate with inherent biological differences in patients.

From 11 000 medical records, approximately 2 500 patients with similar type 2 diabetes-related diseases clustered into three subtypes. Patients in subtype 1 were the youngest, with highest body weight and blood sugar concentration. The diseases associated with this group included upper respiratory infections, eye disease, liver disease, and kidney problems. Subtype 2 had the highest number of patients with lung cancer, tuberculosis, and heart disease. Patients in subtype 3 were more likely to suffer from mental illness, substance abuse, HIV infection, allergies, and high blood pressure.

The study showed that genetic variations correlated with diabetes-related diseases in patients. Genetic variations in biological pathways involved in liver and kidney function and sugar metabolism associated with diseases found in subtype 1 patients. Similar genetic findings were observed in subtypes 2 and 3.

The results from this study showed that the classification of type 2 diabetes into three subgroups could have implications in the future management of this disease and could be used to develop personalized medical treatments appropriate to the patient.

 

 

 

Li L, Cheng WY, Glicksberg BS, Gottesman O, Tamier R, Chen R, Bottinger EP, Dudley JT. Identification of type 2 diabetes subgroups through topological analysis of patient similarity. Sci Transl Med. Published online on 28 October 2015; 7(311): 311ra174. DOI: 10.1126/scitranslmed.aaa9364

 

 

 

 

 

 

Written by: Ana Victoria Pilar, PhD

 

 

 

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