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New methods may allow earlier diagnosis of type 1 diabetes
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CHICAGO — The development of models to predict progression to type 1 diabetes could possibly allow clinicians to diagnose type 1 diabetes earlier in life in some individuals, a speaker said here at the ADA Scientific Sessions.
During a presentation, Jay M. Sosenko, MD, professor of medicine and epidemiology at the University of Miami, discussed the development of a risk score based on data from the Diabetes Prevention Trial of Type 1 Diabetes (DPT-1), linking certain factors to an eventual diagnosis of type 1 diabetes. The risk score included variables such as BMI, age, fasting C-peptide levels, a measure of overall C-peptide production and a measure of overall glucose obtained via oral glucose tolerance tests (OGTT).
To validate the risk score, or DPTRS, Sosenko and colleagues applied the model to data from the large, multicenter TrialNet Natural History Study, also called the Pathway to Prevention Study, to analyze how well the score identified participants’ progression to type 1 diabetes. TrialNet seeks to identify treatments that might prevent or delay the onset of type 1 diabetes in high-risk participants, pancreatic autoantibody-positive relatives of patients with type 1 diabetes.
According to the results, the DPTRS was highly predictive of which participants would ultimately receive a diagnosis of type 1 diabetes in TrialNet. Of particular importance, Sosenko said, was the risk score’s potential to identify patients at high risk for type 1 diabetes but with normal glucose levels. Age played a significant role.
“If we compare those with dysglycemia [indicative of type 1 diabetes risk] with those who have normal glucose tolerance but a high risk score, we see a large difference in age,” Sosenko said. “The individuals with normal glucose levels and a higher risk score are much younger. Basically, this shows we’re missing some children at high risk by just looking at dysglycemia.”
Sosenko noted that using adult glucose criteria for children may be inappropriate.
The researchers also developed a 60-minute index based on purely metabolic factors, discounting variables such as BMI and age, to predict progression to type 1 diabetes in those who underwent 2-hour OGTTs in DPT-1. They found that a 60-minute index value of 2.30 was at least as predictive of a diagnosis of type 1 diabetes as a 2-hour glucose ≥200 mg/dL, a standard criterion. Additionally, by using an alternative algorithm, which also includes the 2-hour glucose criterion, some individuals could possibly be diagnosed a year earlier.
“The alternative algorithm could reduce inconvenience and lower costs if used as an endpoint in prevention trials by decreasing the number of tests and shortening follow-up,” he said. “It could also be considered for clinical application when treatments are available for the preservation of insulin secretion.”
The use of the DPTRS and the index are limited to autoantibody-positive relatives of type 1 diabetes patients who participate in research programs; they represent a minority of those who develop type 1 diabetes. Although these predictive models are not yet ready for clinical application, they harbor significant potential, Sosenko said.
“We are trying to find a threshold that might be high enough, one that might indicate that someone is inevitably going to get diabetes,” Sosenko said at a press conference. “I can’t say we’ve accomplished that, but we would like to identify people earlier if we can capture patients earlier, they’ll have more insulin available and we can possibly treat them at an earlier stage of the disease process.” – by Melissa Foster
For more information:
Sosenko JM. Joint ADA/JDRF symposium — Global epidemiology of type 1 diabetes and implications for public health. Presented at: ADA Scientific Sessions; June 21-25, 2013; Chicago.
Disclosure: Sosenko reports no relevant financial disclosures.