- Genetic Markers
- Insulin Resistance and Brain Glucose Uptake
- Cerebrospinal Fluid (CSF) Biomarkers
- Imaging the Living Brain
- Combining Genetics, CSF, and Imaging Biomarkers
- Sensory Changes
- Motor Changes
Many researchers believe that treatments for Alzheimer’s are more likely to be effective if initiated early in the disease. It is now thought that Alzheimer’s-related changes in the brain can begin years, or even decades, before cognitive impairment becomes evident. Researchers are developing methods to detect these changes at their earliest stages. These efforts are designed to determine who is at the highest risk for Alzheimer’s so that possible treatments can be tested more rapidly and effectively, as well as to improve diagnosis in clinical practice to better serve patients and their families.
Brain Variability and Alzheimer’s Disease
It can be challenging to identify whether changes in brain structure, such as a loss of volume, are part of normal cognitive aging or caused by Alzheimer’s disease. Scientists are using advances in brain imaging to gain insight into the variability of the aging brain. Knowing the normal range of variation in brain volume in specific brain regions may one day help researchers identify abnormalities that may signify disease onset.
This composite image of the Alzheimer’s brain shows how the structure of certain brain regions can greatly vary between individuals while other regions are less variable. For example, the color pink indicates brain regions that vary the most from one person to another. This area of the brain is important to language.
This image indicates by color the variability of the human brain. Areas in blue are regions that do not differ much from person to person, but yellow and red indicate brain regions that vary greatly between individuals. This knowledge is important as researchers need to know whether the differences they find in an individual’s brain are normal for that region or a sign of abnormalities, such as Alzheimer’s disease.
Courtesy of Paul M. Thompson, PhD, and Arthur W. Toga, PhD, Laboratory of Neuro Imaging, UCLA.
Genotyping is a major tool for risk assessment, and tests of insulin resistance may also help predict Alzheimer’s risk. Scientists are currently exploring three main approaches to early diagnosis: measurements of biomarkers in cerebrospinal fluid (CSF), brain imaging, and standardized clinical tests of memory and thinking abilities to determine cognitive health. Through the National Institute on Aging (NIA)-led Alzheimer’s Disease Neuroimaging Initiative (ADNI) and other studies, these efforts are already showing some success, and scientists are beginning to explore the use of biomarkers and brain imaging in combination to predict disease risk. (See Supporting Infrastructure and Initiatives for more on ADNI.)
In addition, a growing body of research suggests that other early symptoms, including changes in sensory and motor function, may precede memory changes in Alzheimer’s.
The apolipoprotein E ε4 genotype is the strongest known genetic risk factor for late-onset Alzheimer’s disease. Given that mild cognitive impairment (MCI) is often a precursor to Alzheimer’s, past studies have produced surprisingly mixed results as to whether the ε4 allele also confers increased MCI risk. This may be because MCI can arise from multiple causes.
To further explore this issue, a team led by scientists at Cornell University, Ithaca, NY, studied nearly 850 people age 70 or older who were cognitively normal or had MCI (Brainerd et al., 2011). The group did not include people with symptoms of cognitive decline caused by stroke and other non-Alzheimer’s conditions. They found that those with MCI all met key diagnostic criteria for amnestic MCI (aMCI), the form of MCI that typically precedes Alzheimer’s disease, and the ε4 allele was seen at a significantly higher frequency than it was in the cognitively normal participants. This study indicates that the ε4 allele may be a reliable predictor of MCI.
Insulin is a hormone in blood that helps glucose (sugar) to enter cells and be used for energy—also known as “glucose uptake.” Insulin resistance, a condition in which the body has reduced glucose uptake, may lead to diabetes. It may also increase risk of Alzheimer’s disease, according to a University of Washington, Seattle, study (Baker et al., 2011).
The researchers used FDG-PET scanning to measure glucose uptake in the brains of 23 cognitively normal older adults (average age, 74) with prediabetes or early diabetes. They found that greater insulin resistance was associated with an Alzheimer’s-like pattern of reduced glucose uptake in brain regions important for learning and memory. Compared to healthy individuals, those with prediabetes and early diabetes showed smaller increases in brain glucose uptake during a memory task and also performed more poorly on the task. This study suggests that insulin resistance is a marker of Alzheimer’s risk in cognitively normal individuals and is associated with subtle cognitive impairments at the earliest stages of the disorder, even before the onset of MCI.
The use of CSF protein biomarkers, such as beta-amyloid, tau, and phospho-tau, has shown great promise for early Alzheimer’s disease diagnosis and the selection of at-risk subjects for clinical trials. In people with Alzheimer’s, CSF beta-amyloid is decreased, and total tau and phospho-tau are increased compared with older people free of the disease. In this study, a team led by investigators at The Johns Hopkins University, Baltimore, analyzed CSF biomarker data from ADNI for nearly 200 people with aMCI (Okonkwo et al., 2011). They found that abnormal beta-amyloid levels predicted a faster rate of cognitive decline and a greater risk of progression to dementia, but abnormal tau levels did not. This indicates that people with aMCI who have abnormal CSF beta-amyloid levels may be ideal candidates for clinical trials and treatments developed to halt progression.
Researchers continue to search for imaging biomarkers (brain changes visible by MRI or other imaging methods) that can predict who will develop Alzheimer’s. Scientists at Massachusetts General Hospital, Boston, and Rush University Medical Center, Chicago, identified a “signature” of structural changes in the brains of cognitively normal older people that strongly predicted which of them would develop Alzheimer’s (Dickerson et al., 2011). They had previously identified nine regions of the cerebral cortex that thinned out in people with mild Alzheimer’s.
In this study of 65 cognitively normal study participants, the researchers identified a subgroup who showed significant thinning in the same cortical areas. They determined that this group was more than three times more likely to develop Alzheimer’s dementia over the next 10 years. The cortical thinning signature appears to predict risk for Alzheimer’s more accurately than shrinkage of the hippocampus, a more commonly studied MRI biomarker for the disorder.
Positron emission tomography (PET) imaging of brain beta-amyloid is increasingly used in research for detecting early stages of Alzheimer’s. Pittsburgh Compound B, or PiB, is the radioactive agent commonly used in PET imaging to “light up” amyloid levels in the brain. However, the use of PiB imaging is largely restricted to major research centers due to PiB’s short half-life: its signal fades by half within 20 minutes, so it must be made onsite. A team led by researchers at Avid Radiopharmaceuticals reported results with a new beta-amyloid tracer, florbetapir (also known as Amyvid), which has a much longer half-life of 110 minutes (Clark et al., 2011).
The researchers analyzed florbetapir-PET images from nearly 30 people who died within several months after their PET scans were done, and they also performed post mortem analyses of the brains to look for Alzheimer’s pathology. About half of the group had Alzheimer’s confirmed at autopsy. The researchers found a 96 percent agreement in determining the presence of Alzheimer’s pathology between the florbetapir-PET images and post mortem results. At this time, florbetapir is approved by the U.S. Food and Drug Administration to help rule out Alzheimer's as a cause of memory and behavior changes.
This image of the brain shows abnormal deposits of amyloid, a hallmark of Alzheimer’s disease. Yellow or white areas indicate higher concentrations of amyloid; dark red indicates lower levels. The gray areas are free of the deposits.
Courtesy of the University of Pittsburgh Amyloid Imaging Group.
Acetylcholine is an important neurotransmitter critical to attention and memory. In Alzheimer’s disease, brain cells that produce acetylcholine are known to die. Research has shown that as the disorder progresses, neurons lose their stores of choline acetyltransferase (ChAT), the enzyme that produces acetylcholine. The loss of ChAT activity is associated with cognitive impairment. Indeed, University of Pittsburgh, PA, researchers studied the precuneus region, which plays a role in attention and memory.
The researchers found reduced ChAT in the precuneus region at autopsy in people with Alzheimer’s but not in those with MCI (Ikonomovic et al., 2011). In addition, this decline occurred only after significant beta-amyloid had accumulated in the precuneus. This finding suggests that the window of time immediately following the onset of amyloid accumulation in the precuneus may be a particularly critical period for drugs that enhance acetylcholine production.
Cognitive tests are frequently used to evaluate new therapeutics in clinical trials. However, a volunteer’s performance on a cognitive test may vary depending on the conditions under which the test is administered, and some test results may not accurately reflect their ability to carry out everyday tasks and social activities.
Investigators at the University of California, Berkeley, found that FDG-PET imaging, which measures glucose metabolism in the brain, was a more reliable tool than a commonly used cognitive test for monitoring Alzheimer’s progression (Landau et al., 2011). The researchers studied more than 300 volunteers enrolled in ADNI over 2 years. Individuals who had low brain glucose metabolism at the start of the study were more likely to show subsequent cognitive and functional decline. Moreover, statistical analyses indicated that FDG-PET data would be more sensitive than the ADAS-Cog, a cognitive test frequently used for assessing drug treatments.
Researchers are interested in learning how different Alzheimer’s biomarkers interact with each other and with genetic factors to predict disease progression. In one study, Alzheimer’s Disease Neuroimaging Initiative (ADNI) investigators studied interactions among CSF beta-amyloid levels, gene risk factors (in particular, the APOE ε4 gene), and hippocampal volume loss (Chiang et al., 2011). The study involved nearly 300 older volunteers with normal cognition, MCI, or Alzheimer’s.
For all groups, participants who had lower (i.e., abnormal) beta-amyloid levels in CSF at the start of the study showed greater loss of hippocampal volume over a 1-year period. In the MCI group, APOE ε4 carriers showed greater hippocampal volume loss than non-carriers, and individuals with both the APOE ε4 allele and low beta-amyloid showed greater hippocampal volume loss than would be expected from either risk factor alone. These results suggest abnormal beta-amyloid processing may accelerate degenerative brain changes associated with the APOE ε4 allele.
This diagram illustrates how Alzheimer’s-related changes in the brain may contribute to disease progression, from normal cognitive aging to early mild cognitive impairment (eMCI) to late MCI (LMCI) to Alzheimer’s dementia. The curves represent the sequence in which specific markers may play a role in disease progression. This model suggests that different imaging tools, measurements, and biochemical biomarkers may serve as predictors (measures that predict future change) and outcomes (measures that detect change) at different stages in the transition from normal aging to MCI to dementia. The NIA-led ADNI study is gathering data to test this model.
Courtesy of Paul Aisen, M.D., Alzheimer's Disease Cooperative Study, University of California, San Diego.
Loss of the sense of smell (olfaction) is often an early symptom in Alzheimer’s disease. Similarly, in mouse models, changes in the olfactory system are an early symptom of degenerative changes. In a study led by researchers at the Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, abnormal network activity was detected in the olfactory bulbs of Alzheimer’s model mice as early as 3 months of age (around the time of puberty in mice) and coincided with the first signs of beta-amyloid accumulation (Wesson et al., 2011). The researchers noted these changes occurred before the appearance of beta-amyloid in other brain regions and the onset of cognitive deficits.
Treating the mice with a drug that promotes beta-amyloid degradation reversed the olfactory network abnormalities and restored the mice’s ability to detect odors. This study suggests we may be able to reverse the early olfactory impairment that occurs in Alzheimer’s, and that more refined methods for testing the olfactory network in humans with increased sensitivity and specificity might help detect the early stages of the disease.
Declining gait speed may serve as a predictor of cognitive decline in older adults. However, clinicians do not routinely assess gait speed and may have difficulty distinguishing sudden changes from those that occur slowly over time. Oregon Health Sciences University, Portland, researchers reported on the use of an in-home device that monitored gait speed by collecting measurements unobtrusively from infrared motion sensors (Austin et al., 2011). The researchers developed mathematical formulas for analyzing changes in gait speed over time. They also identified parameters associated with a sudden decrease in speed (for example, in a volunteer who suffered a stroke) versus slower decline (in a participant who was diagnosed with MCI toward the end of the monitoring period).
These methods suggest that assessing changes in gait speed might help identify people at risk of cognitive impairment and other adverse health outcomes.