PET imaging studied as a way to gauge Alzheimer’s disease
Research is emerging that could enable scientists to use positron emission tomography to measure predictors of Alzheimer’s disease.
Research is emerging that could enable scientists to use positron emission tomography to measure predictors of Alzheimer’s disease.
Scientists have developed molecules that enable them to use PET imaging to measure levels of amyloid plaques—a protein in the liver kidneys, spleen and other tissues—and tau—which are proteins that help give shape to cells and cellular membranes.
This research could help scientists to track disease development and predict future loss of brain tissue in patients with Alzheimer’s disease. The brains of patients with Alzheimer’s have two distinct hallmarks. These are abnormal clumps of amyloid plaques, and bundles of tangled fibers called neurofibrillary, or tau tangles. These two changes disrupt nerve cells and over time causes the cells to die with the result being loss of brain tissue over time.
The study, led by Renaud La Joie, MD, and Gil Rabinovici, MD, at the University of California at San Francisco, was funded in part by the National Institutes of Health’s National Institutes on Aging.
More than 5 million persons in the United States have Alzheimer’s, and the number will rise as the present population ages. To understand whether tau tangles and amyloid plaques could predict loss of brain matter over time, researchers need to track tau and amyloid in the brain as disease develops.
A total of 32 patients with early stage Alzheimer’s underwent PET imaging to assess the levels and locations of amyloid and tau to start the study. They had MRI scans to calculate brain volume and 15 months later had a second MRI to measure loss of brain tissue.
Patients with higher levels of tau seen by PET imaging at the start of the study had a higher loss of brain matter by the second MRI. However, levels of amyloid measured when the study began were not strongly associated with subsequent brain changes.
Researchers estimated the tau patterns measured by PET imaging could explain about 40 percent of the difference in future brain degeneration, compared to only 3 percent for amyloid-PET.
Study participants were relatively young for people with the disease as two-thirds were under the age of 63 when enrolled and these younger patients had higher levels of tau and experienced a more rapid loss of brain tissue.
“The match between the spread of tau and what happened to the brain in the following year was really striking,” says Rabinovici. “Tau PET imaging predicted not only how much atrophy we would see, but also where it would happen. These predictions were much more powerful than anything we’ve been able to do with other imaging tools and add to evidence that tau is a major driver of the disease.”
Now, PET imaging of tau could help other clinical trials that are trying to target the tangles and such imaging could detect early response or non-response of patients to new treatments. But more work is required to understand other factors that can predict brain tissue loss with Alzheimer’s.
More information on the research was published on January 1, 2020, in Science Translational Medicine, available at stm.sciencemag.org.
Scientists have developed molecules that enable them to use PET imaging to measure levels of amyloid plaques—a protein in the liver kidneys, spleen and other tissues—and tau—which are proteins that help give shape to cells and cellular membranes.
This research could help scientists to track disease development and predict future loss of brain tissue in patients with Alzheimer’s disease. The brains of patients with Alzheimer’s have two distinct hallmarks. These are abnormal clumps of amyloid plaques, and bundles of tangled fibers called neurofibrillary, or tau tangles. These two changes disrupt nerve cells and over time causes the cells to die with the result being loss of brain tissue over time.
The study, led by Renaud La Joie, MD, and Gil Rabinovici, MD, at the University of California at San Francisco, was funded in part by the National Institutes of Health’s National Institutes on Aging.
More than 5 million persons in the United States have Alzheimer’s, and the number will rise as the present population ages. To understand whether tau tangles and amyloid plaques could predict loss of brain matter over time, researchers need to track tau and amyloid in the brain as disease develops.
A total of 32 patients with early stage Alzheimer’s underwent PET imaging to assess the levels and locations of amyloid and tau to start the study. They had MRI scans to calculate brain volume and 15 months later had a second MRI to measure loss of brain tissue.
Patients with higher levels of tau seen by PET imaging at the start of the study had a higher loss of brain matter by the second MRI. However, levels of amyloid measured when the study began were not strongly associated with subsequent brain changes.
Researchers estimated the tau patterns measured by PET imaging could explain about 40 percent of the difference in future brain degeneration, compared to only 3 percent for amyloid-PET.
Study participants were relatively young for people with the disease as two-thirds were under the age of 63 when enrolled and these younger patients had higher levels of tau and experienced a more rapid loss of brain tissue.
“The match between the spread of tau and what happened to the brain in the following year was really striking,” says Rabinovici. “Tau PET imaging predicted not only how much atrophy we would see, but also where it would happen. These predictions were much more powerful than anything we’ve been able to do with other imaging tools and add to evidence that tau is a major driver of the disease.”
Now, PET imaging of tau could help other clinical trials that are trying to target the tangles and such imaging could detect early response or non-response of patients to new treatments. But more work is required to understand other factors that can predict brain tissue loss with Alzheimer’s.
More information on the research was published on January 1, 2020, in Science Translational Medicine, available at stm.sciencemag.org.
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