Brain Imaging and Measurements of Proteins in Spinal Fluid May Improve Alzheimer’s Prediction and Diagnosis

Posted on: 16 July 2009

Changes in the brain measured with MRI and PET scans, combined with memory tests and detection of risk proteins in body fluids, may lead to earlier and more accurate diagnosis of Alzheimer’s Disease, according to new research reported this week at the Alzheimer’s Association 2009 International Conference on Alzheimer’s Disease (ICAD 2009) in Vienna  which included research by TCD’s Dr  Michael Ewers, senior research fellow at Trinity College Institute of Neuroscience  and Professor Harald Hampel Chair of Psychiatry.
The National Institute on Aging’s (NIA) Alzheimer’s Disease Neuroimaging Initiative (ADNI), data from which forms the basis of these three studies, is a $60 million, five-year, public-private partnership to test whether imaging technologies (such as MRI and PET), other biomarkers, and clinical and neuropsychological assessment can be combined to measure progression toward Alzheimer’s. ADNI is the first study to examine a number of candidate Alzheimer’s biomarkers in the same individuals. The study is expected to be a landmark for identifying Alzheimer’s biomarkers, with data widely available to researchers.

A biomarker is a substance or characteristic that can be objectively measured and evaluated as an indicator of normal body processes, disease processes, or the body’s response(s) to therapy. For example, blood pressure is a biomarker that indicates risk of cardiovascular disease. 
Memory Tests and Hippocampal Volume May Accurately Diagnose Early Alzheimer’s Disease
Researchers led by Dr  Michael Ewers, senior research fellow at Trinity College Institute of Neuroscience and   Professor Harald Hampel, Chair of Psychiatry at TCD, identified 345 Alzheimer’s Disease Neuroimaging Initiative  participants (81 with Alzheimer’s, 163 with amnestic mild cognitive impairment (MCI); 101 elderly healthy controls) on whom there was available data including (a) cerebrospinal fluid (CSF) concentration and ratios of Alzheimer’s related proteins: total tau, phosphorylated tau (p-tau181), and beta-amyloid (Aβ1-42), (b) MRI volume measures of certain sections of the brain, including the left and right hippocampus, entorhinal cortex, and medial temporal lobe, and (c) scores on certain standard memory, learning and brain function tests, including the Rey Auditory Verbal Learning test (RAVL) and the Alzheimer’s Disease Assessment Scale (ADAS). 
From this data they used statistical methods to identify the best set of predictors that correctly identified (a) healthy people versus those with Alzheimer’s, and (b) people with mild cognitive impairment (MCI) who progressed to Alzheimer’s (of which there were 50 people in the study who converted over the next year and a half).
“The clinical symptoms of MCI alone are not enough to allow for early diagnosis of Alzheimer’s,” Dr  Ewers said. “In fact, a substantial proportion of people with MCI may revert back to normal or may not develop Alzheimer’s for years. Thus, the challenging task is to discern which  people with MCI have the Alzheimer’s brain changes that may be responsible for their initial memory and thinking problems and their eventual progression to Alzheimer’s, so that they can be targeted for Alzheimer’s-specific treatments.”
The researchers found that results of three subunits of the memory tests could be combined to reach a classification accuracy of 89.9% for distinguishing people who progressed from MCI to Alzheimer’s versus healthy people. They found that by adding in results from MRI volume measurements of the left hippocampus – a brain region closely linked to memory and Alzheimer’s – they could increase classification accuracy to 94%. When, as a means to validate the findings, the same set of tests and measures was applied to distinguish the healthy people from those with Alzheimer’s, classification accuracy was 95.7%.
When the researchers also included measures of tau and beta amyloid in CSF and presence or absence of a known Alzheimer’s risk genotype (ApoE-e4), they could correctly identify people with MCI who progressed to Alzheimer’s within 1.5 years with 95.6% accuracy, but the model including only memory tests plus hippocampus was the most robust predictor set.
“Our results show that a relatively simple prediction model, including the combination of hippocampus volume measured by MRI with memory tests, may be able to accurately diagnose Alzheimer’s at a very early stage in the disease,” Dr  Ewers said. “We believe this is the first large-scale, multi-center study to use this variety of biomarker candidates in MCI and Alzheimer’s. This diagnostic model needs to be validated in autopsy-confirmed Alzheimer’s cases.”