How Does The NIA-AA Framework Measure Up Against Real Data?
This past spring, the National Institute on Aging and Alzheimer’s Association proposed a new research framework in an effort to nudge the field toward a biological definition of Alzheimer’s disease (Apr 2018 news). The framework defined AD as the presence of plaque and tangle pathology, regardless of symptoms, and offered a systematic definition of pathological changes based on biomarkers for brain amyloid, tau, and neurodegeneration. In parallel, it outlined a six-stage symptomatic scheme that covers the spectrum from cognitively normal to severe dementia. The package came aligned with recent FDA guidance on clinical trials for pre-dementia (Mar 2018 news).
How does this framework hold up in real life? Does it even jibe with natural history data? At the Clinical Trials on Alzheimer’s Disease conference, held October 24–27 in Barcelona, Spain, the field got some initial answers to these questions. From early data testing the framework in large research cohorts assembled to date, it appears the biomarkers do a good job of predicting impending decline in people who are already cognitively impaired. On the other hand, an initial attempt at defining tests that enable staging of symptoms highlighted the critical need for measures that can tell apart the earliest phases of disease, and pick out the first, subtle signs of decline.
- A/T/N biomarkers predict progression to dementia fairly well.
- No single cognitive test tracks symptoms across all proposed stages.
- More sensitive tests are needed for the earliest two stages.
Leslie Shaw, University of Pennsylvania, Philadelphia, showed that a combination of brain amyloid, tau tangles, and neurodegeneration predicted prognosis in participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Shaw analyzed data from all 505 participants with a clinical diagnosis of mild cognitive impairment (MCI) at baseline, who underwent a spinal tap and an FDG-PET scan. He classified them according to the A/T/N scheme, using CSF Aβ1-42 for brain amyloid (A), CSF pTau181 for aggregated tau (T), and FDG-PET for neurodegeneration (N). Each participant’s values were deemed normal or abnormal based on cutoffs determined in ADNI. The binary classification of each of these three biomarker types bins people into eight possible profiles, ranging from completely negative (A-/T-/N-) to triply positive (A+/T+/N+). Shaw asked whether a person’s specific combination predicted his or her progression to AD dementia, and rate of decline in memory, cognition, and function.
Among these 505 people with MCI, 58 percent were above the cutoff for amyloid positivity based on CSF Aβ1-42. Their rate of progression to dementia increased with each additional positive marker: Only 11.6 percent of people with A+/T-/N- developed dementia over four years, but 40 percent did when T was also positive, and 80.9 percent when all three were positive. Shaw called this consistent with the idea that amyloid positivity on its own represents an early disease stage with a long horizon to dementia, while T and N reflect a later stage with additional downstream tau pathology and neurodegeneration.
People with brain amyloid and low FDG PET, but negative tau (A+/T-/N+), also showed substantial rates of decline, with 56.4 percent reaching dementia in four years. Under the NIA-AA criteria, this group is considered to have Alzheimer’s disease, which requires abnormal amyloid and tau for the diagnosis. They were on a trajectory of decline from a different disease, said Shaw. To understand which one, researchers urgently need α-synuclein, TDP-43, and vascular dementia markers.
In contrast, all amyloid-negative groups progress more slowly, ranging from 8.1 percent progression to dementia in triple-negative participants, to 16.5 for the A-/T+/N-, and 28.4 or 31 percent for A-/T+/N+ and A-/T-/N+, respectively.
How about annual rates of decline for MMSE, CDR-SB, and functional scores? They largely paralleled the rates of progression across the groups, Shaw said.
What’s the take-home? “If a person is amyloid-positive, it’s important to know other things, too,” Shaw said. “If we only know amyloid, we have a huge range of progression rates. But we can refine these progression rate predictions when we have baseline data for other markers, too.”
In other words, a person’s biomarker evaluation should include amyloid and tau status, and markers for neurodegeneration. How the “N” is measured will depend on local circumstances, Shaw said. The A/T/N approach makes a patient’s staging granular and improves his or her individual risk assessment. At this point, the field needs to do this work with other cohorts and other biomarkers for the A/T/N categories, Shaw concluded.
Enter Samantha Burnham, Commonwealth Scientific and Industrial Research Organization, Highett, Australia. At CTAD, she reported a similar analysis of A/T/N biomarkers in the Australian Imaging, Biomarker & Lifestyle Study of Aging. Her results in cognitively normal participants and those with MCI echoed Shaw’s, though with her smaller sample size, the differences were less dramatic.
Burnham analyzed data from 200 AIBL participants who had undergone lumbar puncture and were followed for a mean of 4.5 years, including 27 with AD, 33 with MCI, and 140 who were cognitively normal. Like Shaw, her group measured CSF Aβ1-42 for A and pTau181 for T, but for N they assayed CSF total tau and based their cutoff on a comparison of the AD and normal controls in their study.
For her analysis, Burnham collapsed the eight categories into four. They were: all normal markers, Alzheimer's pathological change (A+ only), AD (A+/T+), and non-AD pathological change (A-). Among the cognitively normal group, 67 percent were amyloid-negative, and nearly half of those had three normal biomarkers. In the MCI group, 75 percent were amyloid-positive, and half the MCIs were positive for all three biomarkers. In the AD group, 66 percent were triply positive.
When Burnham looked at baseline cognitive performance for people in the normal cognition group, those with biomarker evidence of AD (A+/T+) performed at the lower end of the normal range on the AIBL-PACC cognitive composite and the MMSE. Burnham’s group sizes were small and the differences not statistically significant. Even so, this result was consistent with Shaw’s result of more positive biomarkers predicting worse cognitive performance, and with Reisa Sperling’s previous finding in A4 that cognitively normal people with both amyloid and tau pathology performed worse at baseline than people who only had brain amyloid.
Burnham had too few people progress to MCI or AD to draw conclusions about progression, but will do this analysis in larger cohorts, including in the new CONCORDE-AD network. Short for Connecting Cohorts to Diminish Alzheimer’s Disease, this public-private collaborative will combine data from AIBL, the Swedish BioFINDER study, and five other cohorts covering more than 20,000 people across the disease spectrum, in Australia, North America, and Europe (Burnham et al., 2018).
Burnham told the CTAD audience that CSF concentrations of pTau181 and total tau strongly correlate, indicating that total tau is not an appropriate measure of neurodegeneration. She is looking for other measures of N, including neurofilament light protein or structural MRI (Nov 2018 news).
What About Symptoms?
Besides biomarker-based classification, the NIA-AA framework also includes a six-stage ladder of symptomatic disease severity. The scheme postulates three stages of early disease, whereby people in stage 1 are completely cognitively normal with no complaints of memory problems; people in stage 2 report subjective memory concerns or display subtle abnormalities on sensitive cognitive tests, and people in stage 3 have obvious abnormalities on cognitive tests and mild functional impairment. Stages 4 to 6 encompass mild, moderate, and severe dementia.
How can scientists know which tests best identify people in each stage? Which tests best track a person over time as he or she moves from one stage to the next? That’s especially important in stages 1 and 2, as these people are increasingly becoming prime targets for enrollment in clinical trials.
Roos Jutten, VU University Medical Center, Amsterdam, pulled together test data on 1,213 people from four cohorts: the Harvard Aging Brain Study (HABS), ADNI, the National Alzheimer’s coordinating center (NACC), and the Amsterdam Dementia Cohort (ADC). All have brain amyloid as per PET or CSF, and had at least two cognitive assessments. For judging cognition, Jutten drew on the MMSE or Montreal Cognitive Assessment (MoCA) and devised a memory retention score based on items recalled from a story or list. She generated scores for subjective memory decline based on a visit to memory clinic or self-reported concerns, and for functional impairment based on the CDR-SB or global CDR score. Jutten created stage-specific cutoffs for each test and tested different ways of combining all the measures that would place the most people into one of the NIA-AA stages.
In the end, Jutten’s scheme placed 87 percent of participants clearly into one category or another. Most of the cognitively normal people, or those with subjective memory decline, fell into stage 1 or 2, while those formerly described as MCI mainly clustered in group 3. Thirteen percent of people could not be classified in this way, mostly because measures were not congruent. For example, a person with a high MMSE score but clear impairment on the CDR would not fit into a single category.
The different cohorts did not all shake out the same way. All HABS participants fell into stages 1 and 2, while in ADNI, most of the 258 subjects Jutten used were in stage 3. The NACC skewed toward stages 3 and 4, while the ADC contained no stage 1, and mostly stages 3 and 4.
How did the different tests change over time? Jutten compared baseline, one-, and three-year scores. For MMSE, the one-year decline was fastest for people in stage 4, and significant in stage 3. The MMSE clocked no change at one year in stages 1 or 2. After three years, Jutten did detect a drop in MMSE scores in stages 2, 3 and 4, but still no change in stage 1. Delayed recall was similar, with a measurable decline in test scores only in stages 3 and 4 after one or three years. Jutten also saw a ceiling effect on the MMSE in stages 1 and 2, and a floor effect on the delayed recall in stage 4.
Only the category fluency test registered change in stage 1 participants over time, and that took three years to detect. Jutten detected changes within one year on that test in stages 2, 3, and 4. Letter fluency only declined in stage 3 and 4 subjects after three years.
In essence, no single test picked up one-year change at each of the clinical stages. This means the field needs stage-specific cognitive measures, especially for stages 1 and 2, Jutten said. Her group will continue to investigate other available tests with the goal of combining them to create a sensitive, stage-specific battery that can then be compared to later outcomes. Researchers also need to investigate which changes are specific to AD, or change over time with aging alone, Jutten said.—Pat McCaffrey
- New Definition of Alzheimer’s Hinges on Biology, Not Symptoms
- FDA OKs Cognition as Sole Outcome Measure for Preclinical AD Trials
- Is NfL the New 'N' in A/T/N Classification of Alzheimer’s?
- Burnham SC, Coloma PM, Dartigues JF, Doody R, Hansson O, Helmer C, Kass JS, Masters CL, Palmqvist S, Pavlik VN, Petersen RC, Roberts RO, Schaeuble B, Sano M. Concorde-AD: An International Network of Cohorts for Better Understanding of Alzheimer’s Disease. Alzheimer's & Dementia: July 2018. 14 (7S) Part 28, p1465. Alzheimers Dement.
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