Molecular Imaging News
June 14, 2006
New Study Finds PET Imaging of Value in Tracking Diabetes Progression
Columbia University Medical Center
"Diabetes is the only major disease with a death rate that continues to be on the upswing," proclaimed a front-page New York Times story this week. Now, for the first time, researchers at Columbia University Medical Center have identified a reliable, non-invasive imaging method that may eventually enable more precise care of people with diabetes by measuring their quantity of insulin-producing pancreatic beta cells.
A preclinical study demonstrating that beta cells can be non-invasively imaged in rats is being published in the June Journal of Clinical Investigation. Led by a Columbia University Medical Center research team, the paper details the application of a PET imaging method, already widely used for brain imaging, for beta cell mass measurement in diabetes.
Diabetes results when insulin-producing beta cells, located within the pancreas, fail. A goal of treatment is to increase, or at least preserve, the number of insulin-producing beta cells. For years, doctors have been stymied trying to develop a screening method to measure beta cells: the pancreas, located deep within the abdomen, is largely inaccessible to biopsies.
Paul E. Harris, PhD, principal investigator, and his research team at Columbia identified a molecule expressed in beta cells, VMAT2, which also happens to be expressed in tissues of the central nervous system. By injecting the rats with a radioactive form of DTBZ, which binds directly to the VMAT2 within beta cells, they're able to easily see and analyze the beta cells during PET scans.
This imaging method will be studied for the first time in human subjects in a trial expected to begin within the next few weeks at the Naomi Berrie Diabetes Center, Columbia University Medical Center. The goals are to assess the quantity of beta cells in healthy individuals and patients with type I diabetes.
The long-term possibilities include providing more detailed evaluations of people at high risk for diabetes—to develop predictive models of who may be diagnosed with diabetes later in life, and to monitor disease progression and response to therapy.