Researchers Identify 3 Type 2 Diabetes Subtypes
Researchers Identify 3 Type 2 Diabetes Subtypes
By Dennis Thompson
HealthDay Reporter
THURSDAY, Oct. 29, 2015 (HealthDay News) -- Medical data routinely gathered from millions of patients can be used to detect previously unseen patterns in chronic diseases such as type 2 diabetes, a new study suggests.
As a result, researchers say they've identified three distinct subgroups of type 2 diabetics by combing through the health records of more than 11,000 patients.
Each of the subgroups faces unique health problems related to type 2 diabetes, and shares common genetic traits that can explain those challenges, said senior study author Joel Dudley. Dudley is director of biomedical informatics and an assistant professor of genetics and genomic sciences at Mount Sinai School of Medicine in New York City.
"Not only did the clinical data tell us those were meaningful groups, but the genetics pointed toward potential biological factors that explain these differences in clinical characteristics," Dudley said.
The three subtypes identified by the data analysis included a cluster made up of the youngest and most obese patients, who were more likely to suffer kidney disease and blindness, and a group at highest risk for cancer and heart disease. The other cluster tended to suffer from many different health problems, including heart disease, mental illness, allergy and HIV infection.
Based on these groupings, a doctor could recommend more aggressive cancer monitoring in some patients, while prescribing heart-healthy medications and lifestyle changes for others, Dudley said.
The study findings were published Oct. 28 in the journal Science Translational Medicine.
Not all experts are wowed by this data-mining approach, however. Dr. Robert Ratner, chief scientific and medical officer for the American Diabetes Association, said that it's a "major leap of faith from their study" to assume that mounds of data will provide more clarity.
"I don't disagree with them that understanding the multiple different forms of diabetes is critically important," Ratner said. "To be blunt, I'm not sure this analysis contributes very much."
Also, the study authors themselves admit that their patient sample was relatively small.
The researchers started with electronic health data from 11,210 patients at Mount Sinai, of whom 2,551 had been diagnosed with type 2 diabetes. The data included a full blood panel and a genetic analysis.
HealthDay Reporter
THURSDAY, Oct. 29, 2015 (HealthDay News) -- Medical data routinely gathered from millions of patients can be used to detect previously unseen patterns in chronic diseases such as type 2 diabetes, a new study suggests.
As a result, researchers say they've identified three distinct subgroups of type 2 diabetics by combing through the health records of more than 11,000 patients.
Each of the subgroups faces unique health problems related to type 2 diabetes, and shares common genetic traits that can explain those challenges, said senior study author Joel Dudley. Dudley is director of biomedical informatics and an assistant professor of genetics and genomic sciences at Mount Sinai School of Medicine in New York City.
"Not only did the clinical data tell us those were meaningful groups, but the genetics pointed toward potential biological factors that explain these differences in clinical characteristics," Dudley said.
The three subtypes identified by the data analysis included a cluster made up of the youngest and most obese patients, who were more likely to suffer kidney disease and blindness, and a group at highest risk for cancer and heart disease. The other cluster tended to suffer from many different health problems, including heart disease, mental illness, allergy and HIV infection.
Based on these groupings, a doctor could recommend more aggressive cancer monitoring in some patients, while prescribing heart-healthy medications and lifestyle changes for others, Dudley said.
The study findings were published Oct. 28 in the journal Science Translational Medicine.
Not all experts are wowed by this data-mining approach, however. Dr. Robert Ratner, chief scientific and medical officer for the American Diabetes Association, said that it's a "major leap of faith from their study" to assume that mounds of data will provide more clarity.
"I don't disagree with them that understanding the multiple different forms of diabetes is critically important," Ratner said. "To be blunt, I'm not sure this analysis contributes very much."
Also, the study authors themselves admit that their patient sample was relatively small.
The researchers started with electronic health data from 11,210 patients at Mount Sinai, of whom 2,551 had been diagnosed with type 2 diabetes. The data included a full blood panel and a genetic analysis.
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