Guided by the latest biomarker and imaging data, scientists have drafted a new set of diagnostic research criteria redefining Alzheimer disease as a condition that develops—and eventually could warrant intervention—decades prior to obvious symptoms. Most clinicians welcomed the changes, which were proposed last month at the International Conference on Alzheimer’s Disease (ICAD) in Honolulu, Hawaii, but some questioned the benefit of earlier diagnosis while there is yet no way to stop the disease in its tracks (see ARF related news story). Amid this debate looms the critical question of how well biomarkers can predict who among the cognitively normal is heading toward dementia and, eventually, full-blown AD. This report recaps a sampling of ICAD studies that address this issue. By and large, the data suggest that seniors who appear normal on cognitive tests, but nonetheless suspect their memory is off, or who have high brain amyloid or other pathological reads, may already be quietly on the wane.

If amyloid-positive “normals” are, in fact, in the earliest phase of a disease continuum, how might one design studies to assess treatment efficacy in the preclinical AD population? This question motivated a study by Michael Donohue, University of California, San Diego, and colleagues. The researchers divided control participants of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) into two groups—those with high amyloid burden, as assessed by cerebrospinal fluid (CSF) assays or positron emission tomography (PET) using the radiotracer Pittsburgh compound B (PIB), and those without. Among the candidate measures of disease progression, “we wanted to see if the amyloid-positives separate from the amyloid-negatives,” he said, as this would demonstrate the measure’s ability to capture “disease-specific” progression. Indeed, that is what they found. Compared with amyloid-negative research participants, amyloid-positive volunteers had greater hippocampal atrophy, more glucose hypometabolism (a measure of brain function judged by fluorodeoxyglucose, or FDG-PET), and more cognitive deterioration, measured by the Mini-Mental State Examination (MMSE) and Functional Activities Questionnaire (FAQ) scores, over a two-year period, Donohue reported. All told, the findings seem to underscore the usefulness of CSF amyloid measures or in vivo amyloid imaging. Using a single parameter, i.e., brain amyloid load, “you can identify a cohort of normals who show accelerated longitudinal decline on imaging and cognitive measures,” Donohue said, noting that this selection criterion may establish the feasibility of future preclinical AD trials.

For otherwise normal individuals, having a lot of brain amyloid seems to bode ill in other, subtler, ways, as well. The elderly often have trouble matching names to faces, in part because of age-related problems with their default network, a set of brain areas that are active during rest and suppressed when the person performs a focused, specific task. As detected by functional MRI (fMRI), people suppress this network less and less well as they age, and the dysfunction worsens in those with high amyloid burden. This trend came through in a study by Patrizia Vannini, Brigham and Women’s Hospital, Boston, and colleagues, who challenged 27 young and 41 older adults, all cognitively normal, with a task requiring them to learn the names associated with a series of faces. Consistent with prior research, seniors with amyloid-laden brains suppressed their default network less than did those without brain amyloid, who in turn suppressed to a smaller extent than did young participants. The novelty in the current research is that, after repeated trials of the face-name task, amyloid-free elderly showed reduced suppression for each repetition, while seniors with brain amyloid did not demonstrate this practice effect. Instead, they continued to engage their default network across repeated trials. Throughout the testing, both groups performed similarly on the task itself, Vannini noted in an e-mail to ARF. The findings suggest that amyloid pathology in cognitively intact seniors “is related to disrupted synaptic activity in the networks supporting memory function before any clinical symptoms are evident in these subjects,” she wrote.

Recent functional connectivity MRI (fcMRI) studies by Prashanthi Vemuri, Mayo Clinic, Rochester, Minnesota, and colleagues also seem to bear out the notion that amyloid buildup takes a toll on brain networks early in the disease process. While most fcMRI studies have focused on the default-mode network, showing disrupted connectivity in amyloid-positive seniors (Hedden et al., 2009; Sheline et al., 2010), Vemuri’s team looked at global functional connectivity. They analyzed people with AD or mild cognitive impairment (MCI), and cognitively normal elderly, from the Mayo Clinic Study of Aging. At a day-long pre-ICAD imaging meeting, Vemuri reported that among amyloid-positive research participants, global connectivity is high in controls, lower in people with MCI, and further reduced in the AD group. This suggests that functional connectivity drops off with clinical deterioration, and that fcMRI could serve as an AD marker. Within the control group, though, a measure of global functional connectivity differed between amyloid-positive and amyloid-negative subjects in an unexpected fashion—the former had higher connectivity. This suggests a possible compensatory mechanism early in the disease process that warrants validation through further study, Vemuri wrote in an e-mail to ARF.

Similarly, a study by Gaël Chetelat, who moved from Austin Health, Melbourne, Australia, to Inserm-EPHE-University of Caen, France, stratified control subjects from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study into high and low brain amyloid groups, and saw a not-so-straightforward pattern. Compared to amyloid-negative normal participants, those with high amyloid burden had better episodic memory and greater temporal gray matter volume at baseline. The researchers took this to mean that people with more temporal gray matter could tolerate more Aβ. By contrast, within the group of participants with subjective cognitive impairment (i.e., those who appeared normal on cognitive tests but themselves thought they had problems with their memory), the amyloid-positive subset had more gray matter atrophy.

Together, these and other studies are making a case that amyloid deposits are worrisome despite their presence in some 15 to 40 percent of seniors who do fine on standard memory tests (Aizenstein et al., 2008; see also ARF related conference story). But the science is not clear-cut at this point. “If you have symptomatic MCI (mild cognitive impairment) and you have amyloid in your brain, that's not a good thing,” said William Klunk, University of Pittsburgh, Pennsylvania, in a phone interview. “Your chances of converting to dementia over the next two to three years are very high. We can say that with a high degree of certainty in MCI.” However, for those who have yet to develop memory problems, the significance of a high amyloid load is murkier. “Some studies have shown slightly smaller brain size in these folks, others a little less metabolism,” Klunk said of amyloid-positive normals. “But these are very slight changes, so while we think these are the people who will progress to MCI and to AD, we don't have that data in hand yet.” The evidence points to amyloid accumulating in the brain more than a decade ahead of symptoms, yet live brain imaging techniques have only recently emerged on the research scene. Swedish investigators performed the first PIB scan in 2002, and it wasn’t until several years later that others began doing the same. Thus, as for whether amyloid deposition reliably predicts dementia years down the road, “we really haven’t studied enough normal people throughout the natural history of the disease to have time to know,” Klunk told ARF.

The situation becomes more complex when one considers the multitude of factors that could influence rate of decline in normals. “There are good things—for example, having more gray matter or higher network density—that mitigate decline,” Klunk said, noting that one of the PIB-positive normal controls in the 2002 Swedish cohort was still cognitively intact as of early 2010, eight years after his initial scan. “But there are also bad things—such as strokes, vascular disease, and head injury—that speed your progress through the asymptomatic stage into the symptomatic stage.”

Furthermore, brain amyloid isn’t the only predictive biomarker. Others can also signal impending drops in cognition, it seems, and figuring out how certain markers work together or complement each other is a key challenge in the field (see ARF Live Discussion on biomarkers). At the ICAD pre-meeting on imaging, Adam Fleisher of Banner Alzheimer’s Institute, Phoenix, Arizona, presented new data using a statistical algorithm to measure how well PIB-PET and FDG-PET data correlate with a known AD predictor, i.e., ApoE4 status, in cognitively normal elderly. Converting complex neuroimaging datasets into numerical scores, the scientists saw that amyloid load and glucose metabolism individually tracked with ApoE4 gene dose, and that using both imaging modalities further improved the correlation. “By combining the two using statistics, we could distill the patterns of amyloid imaging and hypometabolism into a single score, and that score was predictive for how much genetic risk of AD the person had,” Fleisher told ARF. The recent analysis also showed that amyloid deposition and glucose hypometabolism occur in different parts of the brain, with the exception of the hard-hit precuneus, where PIB and FDG signals converged. “In these cognitively normal people, areas of brain dysfunction are not the same areas in which amyloid was being deposited,” Fleisher said. “This suggests that maybe we don't have a clear understanding of how amyloid plaque deposition affects brain networks prior to dementia. It may be more indirect than we realize.”

ICAD featured other efforts to flesh out how well various AD biomarkers forecast fading cognition in normal seniors. For their part, Susan Landau of the University of California, Berkeley, and colleagues examined baseline brain glucose metabolism (FDG-PET) and hippocampal volume (structural MRI), as well as ApoE status, in 92 ADNI normals. “We classified the ADNI normals as 'abnormal' or 'normal' on each of those three variables, and looked at whether that status at baseline predicted change on ADAS-cog over about a 2.5-year period,” Landau told ARF in a post-meeting phone interview. For ApoE status, “abnormal” simply meant having an E4 allele, whereas non-carriers were designated “normal.” For the two imaging measures, the researchers determined cut points, i.e., values that distinguish the “abnormal” and “normal” categories, from external cohorts, and then applied these to the ADNI normals.

Of the three biomarkers tested, only hippocampal volume ended up predicting ADAS-cog change if the normals were considered as a group. However, if stratified into high- and low-performing subgroups according to baseline scores on the Auditory Verbal Learning Test, which measures long-term memory, the findings came out entirely different. Among high performers, none of the biomarkers predicted cognitive change, whereas in the low-performing group, all three had predictive power. In particular, low performers who had abnormal hippocampal volumes and at least one E4 allele saw their ADAS-cog scores plummet 2.3 points per year more than did low performers with normal hippocampal volume and ApoE status. “The bottom line is that baseline biomarker status was more useful at predicting cognitive change in the low-performing subpopulation of normals, which may include individuals with early, subclinical pathology,” Landau said.

Even without formal cognitive testing, older adults who merely suspect their memory is off could, in fact, be on to something. Analyzing more than a thousand seniors (ages 60 and up) from the AIBL study at baseline and 18 months later, Jonathan Foster of the Health Department of Western Australia, Cassandra Szoeke of University of Melbourne, and colleagues found that memory complainers and non-complainers did not differ in brain amyloid load by PIB-PET. However, the complainers had worse performance on category fluency and Boston naming tests when they were initially tested, Szoeke reported at ICAD. Those reporting memory loss at baseline also seemed more likely to decline to MCI over time, though the number of converts was too small to be statistically significant (3.7 percent of memory complainers converted to MCI, compared with 1.2 percent of non-complainers), she said.

Though far from cut and dry, the evidence to date does seem to converge on the idea that biomarkers can help identify not only which cognitively normal elderly people are most likely to decline, but also which measures may best track this deterioration as the disease progresses. “It could take quite a while to understand the full natural history of the cognitive normal stage of AD pathology,” Klunk said. “But we have to understand this asymptomatic stage so we're in a position to understand the effects of treatments when we do have attractive therapies for prevention trials. We have to know what the natural history is going in, so we'll be that much more able to understand whether a drug is having an effect.”—Esther Landhuis.


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News Citations

  1. Noisy Response Greets Revised Diagnostic Criteria for AD
  2. HAI Chicago: PIB in Healthy People

Webinar Citations

  1. Together at Last, Top Five Biomarkers Model Stages of AD

Paper Citations

  1. . Disruption of functional connectivity in clinically normal older adults harboring amyloid burden. J Neurosci. 2009 Oct 7;29(40):12686-94. PubMed.
  2. . Amyloid plaques disrupt resting state default mode network connectivity in cognitively normal elderly. Biol Psychiatry. 2010 Mar 15;67(6):584-7. PubMed.
  3. . Frequent amyloid deposition without significant cognitive impairment among the elderly. Arch Neurol. 2008 Nov;65(11):1509-17. PubMed.

External Citations

  1. Alzheimer’s Disease Neuroimaging Initiative

Further Reading