. Prevalence Estimates of Amyloid Abnormality Across the Alzheimer Disease Clinical Spectrum. JAMA Neurol. 2022 Mar 1;79(3):228-243. PubMed.

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  1. This is an important initiative, and we are pleased to be able to contribute from the UCSF ADRC. The prevalence estimates from this large and international dataset will doubtlessly serve as benchmarks for clinical interpretation of amyloid biomarkers, clinical trial design, and, hopefully, implementation of effective therapies.

    With the larger dataset, it is nice to see both replication of findings from the 2015 meta-analysis, as well as to have adequate power to detect more subtle effects (e.g., the protective effect of E2/E4 vs E3/E4 genotype). It’s also nice to see congruence between visual and quantitative approaches to classifying amyloid PET scans—we are finding the same in IDEAS—as visual read is still the standard in clinical practice.

    Though this is an important contribution, the study has limitations that need to be addressed in future work. First, 94.6 percent of all participants are from North America or Europe, and race and ethnicity were not reported in most cohorts. I strongly agree with the editorial by Young and Mormino that we must do better as a field in recruiting a more diverse, global, and representative sample of research participants to AD biomarker studies.

    Second, the CSF analysis is based primarily on the older Ineptest assay. The field is moving rapidly toward more precise, high-sensitivity immunoassays, and toward CSF ratios (Aβ42/40 or p-tau/Aβ42) rather than Aβ42 as a stand-alone measure to define amyloid status. The emphasis on the higher sensitivity of CSF compared to PET needs to be counterbalanced by the higher specificity of PET for clinically meaningful overall AD neuropathology (Lesman-Segev et al., 2021). This is a critical point, especially for interpreting the clinical significance of a positive amyloid biomarker in regard to a patient’s cognitive impairment.

    References:

    . Diagnostic Accuracy of Amyloid versus 18 F-Fluorodeoxyglucose Positron Emission Tomography in Autopsy-Confirmed Dementia. Ann Neurol. 2021 Feb;89(2):389-401. Epub 2020 Dec 7 PubMed.

    View all comments by Gil Rabinovici
  2. We acknowledge that this is an impressive feat of global collaborative science.

    It's interesting that reanalysis of CSF cutoffs leads to increasing prevalence. To some extent this is similar to situations where we simply change cut points for conditions such as diabetes and hypertension with millions overnight coming into that diagnostic category. Clearly, population cohorts such as CFAS give much higher prevalences of "amyloid" pathology (including Aβ deposition, not all of which is in a true amyloid β-sheet state) at death.

    This correlates relatively poorly with dementia status. Some of the existing in-life biomarkers may lack specificity and fail to predict progression (Dubois et al., 2021). Presumably, as in-life techniques become more sensitive, the prevalence that this gives will continue to rise, so prediction of dementia status and progression may require other approaches unless nearly everyone over 65 is to be labelled as having AD. This is important in thinking about the current debate of how AD is defined. 

    The age effect is interesting, not just in terms of rising prevalence but in the convergence between demented and non-demented individuals. We report this effect pathologically in the NEJM paper (Savva et al., 2009). The clinical and demographic context in which these biomarkers are used are really important to being able to interpret the findings. 

    The paper also talks about prodromal/preclinical AD. With ever-increasing biomarker sensitivity, we would expect such approaches to report increasing prevalences of AD over time until “everyone has it” (Brayne and Calloway, 1988). But, as with changing criteria for conditions such as diabetes and hypertension, clinical context and burden of pathology remain important, as not everyone with AD pathology will have, or develop, dementia as the clinical syndrome. Nor are they necessarily on a trajectory of cognitive decline (an interesting area for further work).

    The issue of preclinical AD, that is, changes that are on a trajectory to develop clinical AD-type dementia, still need be defined and validated in “usual” aging populations rather than in volunteer cohorts. We may have a situation where near 100 percent of over 70s could be labelled as such. As we have remarked in editorials and commentaries in the past, this may be good for pharma companies and their countries’ government exchequer, but not necessarily good for health and well-being of the population, including the specificity and meaning of diagnostic labels applied to individuals. 

    The researchers want to show results for the AD spectrum, but it is likely that the use of clinical/research cohorts with many selection/exclusion criteria will not reveal the true continuum of cognition or biomarker status. By applying filters based on discrete phenotypes, and exclusion of other diagnoses/comorbidities, the results are likely to artificially enhance the separation of amyloid prevalence between those defined as normal, MCI, or AD dementia. Out of the 85 cohorts, only three are described as population cohorts, representing fewer than 500 of the 19,097 participants (2.5 percent). Even with such filters, with increasing age amyloid prevalence becomes unlikely to distinguish between clinical AD dementia and MCI, and those with normal cognition approach overlap. These boundaries would likely be more blurred in unselected cohorts, where increasing dementia with age is due to multiple underlying pathologies, and the ability of any one type to predict/explain dementia reduces. 

    The results by APOE genotype are a reminder to interpret the overall findings in light of the fact that many of the cohorts are enriched for APOE e4 carriers (by design or self-selection), as shown by the cohort-level prevalences of e4 often being higher than expected population frequencies. The paper highlights some of the contrasts with Mayo Clinic Study of Aging estimates, including higher prevalence at younger ages and in the MCI group, which may in part be explained by APOE e4. 

    Given the rising prevalence of amyloid even in the cognitively normal with age, who in these studies represent the cognitively fittest end of the spectrum, we are reminded how problematic biomarker definitions of AD are. They may label a large proportion of the population as preclinical/prodromal AD, even when many will live out their lives without clinical dementia. 

    The paper recognizes its limitation regarding the lack of data on race/ethnicity. This raises a caveat when it comes to clinical trial recruitment, which the study aims to inform, in light of robust criticism of the aducanumab trials on these grounds: "approval was based on trials that were not inclusive of the people who bear a disproportionate burden of the disease" (Manly and Glymour, 2021). 

    References:

    . Clinical diagnosis of Alzheimer's disease: recommendations of the International Working Group. Lancet Neurol. 2021 Jun;20(6):484-496. Epub 2021 Apr 29 PubMed.

    . Age, neuropathology, and dementia. N Engl J Med. 2009 May 28;360(22):2302-9. PubMed.

    . Normal ageing, impaired cognitive function, and senile dementia of the Alzheimer's type: a continuum?. Lancet. 1988 Jun 4;1(8597):1265-7. PubMed.

    . What the Aducanumab Approval Reveals About Alzheimer Disease Research. JAMA Neurol. 2021 Nov 1;78(11):1305-1306. PubMed.

    View all comments by Steve Wharton
  3. I would like to congratulate Willemijn Jansen, Olin Janssen, and the leaders of the Amyloid Biomarker Study for another organizational tour de force. (By way of disclosure, I also thank them for enabling us to provide data from one of the 85 cohorts used in the meta-analysis described in this article.)

    Their participant-level meta-analysis provides prevalence estimates for Aβ positivity, stratified for clinical severity (cognitively unimpaired, mild cognitive impairment, dementia), age, sex, APOE genotype, and geographic region, using PET and CSF biomarker data from an unprecedented number of persons around the world.

    Findings from this study will have an important impact on the field. Among other things, they could help to inform AD treatment and prevention trial design and recruitment, clinical care strategies, and the health-economic impact of AD-modifying and prevention therapies now in development. Equally important, the strategies used to generate their biomarker findings could provide an operational roadmap for other efforts of this kind.

    In 2015, the study leaders reported findings from a meta-analysis of Aβ PET data from 7,583 persons without dementia (Jansen et al., 2015) and 1,359 persons with dementia (Ossenkopele et al., 2015). They have since extended their findings to 19,097 persons with no cognitive impairment, mild cognitive impairment and dementia (MCI), including 10,139 who had PET and 8,895 who had CSF biomarkers of Aβ plaque deposition.

    As noted in the article, the thoughtful editorial by Cristina Young and Beth Mormino, and Gil Rabinovici’s Alzforum comment, the report has limitations, the most glaring of which is the unacceptable paucity of participants from underrepresented ethnic and racial groups. This limitation needs to be better addressed in all research studies and clinical trials.

    When emerging blood-based biomarkers (BBBMs) for Aβ positivity and the diagnosis of AD are further developed, tested, and extended to underrepresented groups, there will be an opportunity to use the strategy deployed by this team to provide information about BBBM measurements of amyloid positivity and the diagnosis of AD in even larger and more representative populations.

    As noted in the article, editorial, and comment, there are also some technical limitations that could have influenced  the reported prevalence estimates. Despite those, participant numbers matter, and the perfect should not be the enemy of the good when it comes to those technical issues. The findings of this meta-analysis are still important for stakeholders in the field.

    References:

    . Prevalence of cerebral amyloid pathology in persons without dementia: a meta-analysis. JAMA. 2015 May 19;313(19):1924-38. PubMed.

    . Prevalence of amyloid PET positivity in dementia syndromes: a meta-analysis. JAMA. 2015 May 19;313(19):1939-49. PubMed.

    View all comments by Eric M. Reiman

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