EHRs, genomic data help ID new subgroups of Type 2 diabetes
Electronic health records combined with genotype data have identified new subtypes of Type 2 diabetes, potentially enabling clinicians to tailor medical care to patients based on their genomic differences, according to a new study in Science Translational Medicine.
The researchers, from the Icahn School of Medicine at Mount Sinai Medical Center in New York, conducted a network analysis of EHRs and genotype data for more than 11,000 patients. The patients grouped into three distinct subtypes based on data and genomic analysis, pinpointing common genetic variants of each subtype on the basis of distinct patterns of clinical characteristics and disease comorbidities.
Patients were more likely to suffer diabetic nephropathy and retinopathy in subtype 1; cancer and cardiovascular disease in subtype 2; and neurological disease, allergies and HIV infections in subtype 3. For each subtype, the researchers discovered unique genetic variants in hundreds of genes.
"This project demonstrates the very real promise of precision medicine to improve healthcare by tailoring diagnosis and treatment to each patient, as well as by learning from each patient," said paper author Joel Dudley, director of biomedical informatics at the Icahn School of Medicine.
The study also demonstrates the type of data analytics capabilities of EHRs, according to Ronald Tamler, M.D., co-author of the study and director of the Mount Sinai Clinical Diabetes Institute.
"Our approach demonstrates the potential to unlock clinically meaningful patient population subgroups from the wealth of information that is accumulating in electronic medical record systems," he said.
EHRs are expected to play a major role in the provision of precision medicine, intended to improve patient care by customizing treatment. The White House released its Precision Medicine Initiative earlier this year; the National Institutes of Health recently awarded grants to support research that incorporates DNA sequence information into EHRs.
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