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. 2022 Jul;18(7):1370-1382.
doi: 10.1002/alz.12480. Epub 2021 Oct 14.

Contribution of Alzheimer's biomarkers and risk factors to cognitive impairment and decline across the Alzheimer's disease continuum

Duygu Tosun  1   2 , Zeynep Demir  2 , Dallas P Veitch  1   2 , Daniel Weintraub  3 , Paul Aisen  4 , Clifford R Jack Jr  5 , William J Jagust  6 , Ronald C Petersen  7   8 , Andrew J Saykin  9   10   11 , Leslie M Shaw  12 , John Q Trojanowski  12 , Michael W Weiner  1   2 ; Alzheimer's Disease Neuroimaging Initiative
Affiliations

Contribution of Alzheimer's biomarkers and risk factors to cognitive impairment and decline across the Alzheimer's disease continuum

Duygu Tosun et al. Alzheimers Dement. 2022 Jul.

Abstract

Introduction: Amyloid beta (Aβ), tau, and neurodegeneration jointly with the Alzheimer's disease (AD) risk factors affect the severity of clinical symptoms and disease progression.

Methods: Within 248 Aβ-positive elderly with and without cognitive impairment and dementia, partial least squares structural equation pathway modeling was used to assess the direct and indirect effects of imaging biomarkers (global Aβ-positron emission tomography [PET] uptake, regional tau-PET uptake, and regional magnetic resonance imaging-based atrophy) and risk-factors (age, sex, education, apolipoprotein E [APOE], and white-matter lesions) on cross-sectional cognitive impairment and longitudinal cognitive decline.

Results: Sixteen percent of variance in cross-sectional cognitive impairment was accounted for by Aβ, 46% to 47% by tau, and 25% to 29% by atrophy, although 53% to 58% of total variance in cognitive impairment was explained by incorporating mediated and direct effects of AD risk factors. The Aβ-tau-atrophy pathway accounted for 50% to 56% of variance in longitudinal cognitive decline while Aβ, tau, and atrophy independently explained 16%, 46% to 47%, and 25% to 29% of the variance, respectively.

Discussion: These findings emphasize that treatments that remove Aβ and completely stop downstream effects on tau and neurodegeneration would only be partially effective in slowing of cognitive decline or reversing cognitive impairment.

Keywords: Alzheimer's Disease Assessment Scale-Cognitive Subscale; Preclinical Alzheimer Cognitive Composite; amyloid beta; atrophy; cognition; magnetic resonance imaging; positron emission tomography; tau; white matter lesions.

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Conflict of interest statement

Dr. Weintraub reports grants from NIA, Department of Veterans Affairs, Fox Foundation, Acadia Pharmaceuticals, and IPMDS. All payments made to U. Pennsylvania, compensation for licensing of QUIP, QUIP‐RS, and PDAQ‐15 through the University of Pennsylvania. He received personal consulting compensation from Acadia, Aptinyx, CHDI Foundation, Clintrex LLC (Otsuka), Eisai, Great Lake Neurotechnologies, Janssen, Sage, Scion, Signant Health, Sunovion, and Vanda. He serves as Chair DSMB for ATRI and ADCS. Dr. Aisen has received support over the last 36 months from payments to his institution from NIA, FNIH, Alzheimer's Association, Janssen, Lilly, Merck, and Eisai. He has served on the advisory boards of Biogen, Merck, Roche, Abbvie, Rainbow Medical, ImmunoBrain Checkpoint, Shionogi, and has no other support or conflicts of interest to declare. Dr. Jack was supported in the current work by NIH funding. Over the last 36 months, he has received support from NIH grants and the Alexander Family Alzheimer's Disease Research Professorship of the Mayo Clinic to his institution and has served on the advisory boards of iDMC and Roche with no payments made. He serves on an independent data monitoring board for Roche, has consulted for and served as a speaker for Eisai, and consulted for Biogen, but he receives no personal compensation from any commercial entity. He has no other support or conflicts of interest to declare. Dr. Jagust has received support over the last 36 months from grants to his institution (NIH grants R01 AG034570 [Dr. Jagust], R01AG062542 [Dr. Jagust], U24 AG067418 [Dr. Jagust], P01AG019724 [Dr. Bruce Miller], R01 AG031164 [Dr. Matthew Walker], RF1 AG054019 [Dr. Matthew Walker], U01 AG024904 [Dr. Weiner], RF1 AG054106‐01A1 [Dr. Matthew Walker], R44AG046025‐03 [Dr. Daojing Wang], R01 AG061303 [Dr. Lexin Li], MH112775 [Dr. Ming Hsu], 1R01AG062689‐01 [Dr. Landau], AG062624 [Dr. José Luchsinger], and R01AG069090), direct consulting fees (Biogen, Bioclinica, Genentech/Roche, CuraSen, Grifols), and has served on the advisory board of the Alzheimer's Prevention Initiative. He has no other support or conflicts of interest to declare. Dr. Petersen over the past 36 months received grants through his institution (P30 AG062677, U01 AG006786); licenses or royalties from Oxford University Press and UpToDate; and consulting fees from Roche, Merck, Biogen, Genentech, and Eisai. He served on the advisory board of Genentech and has no other support or conflicts of interest to declare. Dr. Saykin was supported in this work by grants from NIH and Department of Defense (NIH grants U01 AG024904, P30 AG010133, R01 AG019771, R01 LM013463, R01 LM011360 and DoD grants W81XWH‐13‐1‐0259 and W81XWH‐12‐2‐0012), and over the past 36 months received support from grants to his institution (as detailed above). He served on the Bayer Oncology Advisory Board and received PET tracer precursor from Eli Lilly/Avid Radiopharmaceuticals. He reports personal fees from Arkley BioTek and Springer Nature, outside the submitted work. He has no other support or conflicts of interest to declare. Dr. Shaw has received over the past 36 months grants through his institution (NIH grants U01 AG024904 (ADNI3), UPenn ADRC NIA grant for Biomarker Core; Michael J. Fox Foundation for Parkinson's Research for AD biomarker studies; Roche IIS for AD biomarker studies), fees for the Biogen Teaching program on AD Biomarkers, and travel funds from NIA ADNI3 Biomarker Core. He has served on the Roche Advisory Board, LEADS Advisory Board, and Fujirebio Advisory Board. He received in kind support from Roche (immunoassay reagents and equipment) for ADNI3. He has no other support or conflicts of interest to declare. Dr. Trojanowski has received over the past 36 months grants through his institution (AG10124). He has no other support or conflicts of interest to declare. Dr. Weiner is the Principal Investigator of NIH funded grants. Over the past 36 months he received funding administered through his institutions (NIH grants: 1RF1AG059009‐01 and 1R01AG058676‐01A1; CA Dept. of Health grant: 19‐10616; NIH Subaward from Dr. Richard Gershon: 1U2CA060426‐01), consulting fees (Cerecin/Accera, Inc., BioClinica, Nestle/Nestec, Roche, Genentech, NIH, The Buck Institute for Research on Aging, FUJIFILM‐Toyama Chemical [Japan], Garfield Weston, Baird Equity Capital, University of Southern California [USC], Cytox, and Japanese Organization for Medical Device Development, Inc. [JOMDD] and T3D Therapeutics), and payment for lecturing (The Buck Institute for Research on Aging). He holds stock options in Anven, Alzheon, and Aleca. He receives other grant support for his work (NIH: 5U19AG024904‐14; 1R01AG053798‐01A1; R01 MH098062; U24 AG057437‐01; 1U2CA060426‐01; 1R01AG058676‐01A1; and 1RF1AG059009‐01, DOD: W81XWH‐15‐2‐0070; 0W81XWH‐12‐2‐0012; W81XWH‐14‐1‐0462; W81XWH‐13‐1‐0259, PCORI: PPRN‐1501‐26817, California Dept. of Public Health: 16‐10054, U. Michigan: 18‐PAF01312, Siemens: 444951‐54249, Biogen: 174552, Hillblom Foundation: 2015‐A‐011‐NET, Alzheimer's Association: BHR‐16‐459161; The State of California: 18‐109929). He also receives support from Johnson & Johnson, Kevin and Connie Shanahan, GE, VUmc, Australian Catholic University (HBI‐BHR), The Stroke Foundation, and the Veterans Administration. He has served on advisory boards for Eli Lilly, Cerecin/Accera, Roche, Alzheon, Inc., Merck Sharp & Dohme Corp., Nestle/Nestec, PCORI/PPRN, Dolby Family Ventures, National Institute on Aging (NIA), Brain Health Registry, and ADNI. He has no other support or conflicts of interest to declare.

Figures

FIGURE 1
FIGURE 1
A priori hypothesized biomarker pathways by which amyloid beta (Aβ)–tau–atrophy biomarkers might mediate the association of Alzheimer's disease (AD) risk factors and cognition. Rectangles represent manifest variables and ellipses represent latent variables. Each single‐headed arrow denotes a hypothesized unidirectional effect of one variable on another. For graphical simplicity, age, sex, education, and apolipoprotein E (APOE) ε4 is grouped although each AD risk factor is separately hypothesized to have unidirectional effect on white matter lesion (WML), cortical Aβ burden, tau latent variable (LV), atrophy LV, and cognitive outcome. Our analysis is premised on a conceptual Aβ–tau–atrophy pathologic pathway thought to mediate the association of AD risk factors and cognition. A priori, age, sex, years of education, and presence of APOE ε4 allele were specified to have direct effects on global Aβ, regional tau, regional atrophy, and WML, in addition to their direct effects on cognition. WML was hypothesized to have a direct effect on global Aβ, regional tau, and regional atrophy, in addition to its direct effect on cognition. Global Aβ was hypothesized to have a direct effect on regional tau and regional atrophy, in addition to its direct effect on cognition. In turn, the regional tau was hypothesized to have direct effect on regional atrophy, together with the direct effects of regional tau and regional atrophy on cognition. We note that the regional specificity of Aβ pathology was examined by including regional Aβ burden from all 31 ROIs instead of limiting the Aβ construct to the global cortical Aβ burden in the partial least squares structural equation modeling (PLS‐SEM). The estimated latent construct for the regional Aβ burden in the final PLS‐SEM involved all but bilateral entorhinal, amygdala, and hippocampus regions, suggesting the effect of Aβ being distributed across the cortex rather than localized in specific cortical regions in this cohort of all Aβ‐positive individuals. Therefore, Aβ construct of all PLS‐SEMs in this study was limited to global cortical Aβ burden.
FIGURE 2
FIGURE 2
Results of path analysis of combined Alzheimer's disease (AD) imaging biomarker pathways mediating the effect of AD risk factors on baseline cognitive outcome measure of modified Preclinical Alzheimer Cognitive Composite (mPACC) and Alzheimer's Disease Assessment Scale–Cognitive subscale (ADAS‐Cog) across the AD continuum. Goodness‐of‐fit was 0.43 for mPACC modeling and 0.42 for ADAS‐Cog modeling. Squares or rectangles represent manifest variables and brain maps represent latent variables (LV). Tau LV involved fusiform, inferior temporal, middle temporal, superior temporal, supramarginal, inferior parietal, superior frontal, and caudal middle frontal bilaterally, and left posterior cingulate, left superior parietal, right banks of superior temporal sulcus, and right precuenus. Atrophy LV involved amygdala, middle temporal, superior temporal, lateral orbitofrontal, parsopercularis, parstriangularis, supramarginal, and insula bilaterally, and left hippocampus, left entorhinal, right banks of superior temporal sulcus, right caudal middle frontal, right inferior temporal. Each single‐headed arrow denotes a hypothesized unidirectional effect of one variable on another. Numbers associated with effects are standardized regression coefficients or standardized factor loadings (i.e., from a latent variable to its indicators). Only the paths that were statistically significant at P < .05 are represented. Paths that were hypothesized but were not statistically significant at P < .05 are excluded. All AD imaging markers considered in this study, specifically greater global Aβ burden, tau LV with greater burden in the parietotemporal neocortical regions, and atrophy LV within the frontotemporal as well as parietal regions, together with presence of APOE ε4 allele had significant direct effects on greater baseline cognitive impairment measured by either mPACC or ADAS‐Cog. Fewer years of education, male sex, and greater white matter lesion (WML) had significant direct effects on worse baseline mPACC but not ADAS‐Cog. In addition to these direct effects on cognitive impairment, we also observed that advanced age had significant direct effects on greater cortical amyloid beta (Aβ), WML, and the atrophy LV, but not on the tau LV or baseline cognitive outcome measures, suggesting that the biomarker model mediated the effect of age on cognitive impairment. Fewer years of education had a significant direct effect on greater global Aβ, even though its direct effect on baseline cognitive impairment was only significant in the mPACC model. Similarly, presence of APOE ε4 allele had significant direct effects on greater global Aβ and neocortical tau LV, but not on the WML or atrophy LV. Greater WML had significant direct effects on both tau and atrophy LVs, but not on global Aβ, and its direct effect on baseline cognitive impairment was significant only for mPACC but not ADAS‐Cog. Greater global Aβ had significant direct effects on tau LV but not atrophy LV. Tau LV had a significant direct effect on atrophy LV, suggesting mediation of the effects of Aβ on atrophy by tau. IL2, indicator loading squared.
FIGURE 3
FIGURE 3
Direct and indirect effects of Alzheimer's disease (AD) imaging biomarkers (Aβ: global cortical amyloid beta burden, Tau: latent construct of the regional tau burden, and Atrophy: latent construct of the regional atrophy) on baseline cognitive impairment and longitudinal cognitive decline operationalized with modified Preclinical Alzheimer Cognitive Composite (mPACC) and Alzheimer's Disease Assessment Scale–Cognitive subscale (ADAS‐Cog). Confidence intervals were based on a bootstrapping procedure with 100 repetitions.
FIGURE 4
FIGURE 4
Results of path analysis of combined Alzheimer's disease (AD) imaging biomarker pathways mediating the effect of AD‐risk factors on longitudinal cognitive decline measure of modified Preclinical Alzheimer Cognitive Composite (ΔmPACC) and Alzheimer's Disease Assessment Scale–Cognitive subscale (ΔADAS‐Cog) across the AD continuum. Goodness‐of‐fit was 0.41 for both ΔmPACC modeling and ΔADAS‐Cog modeling. Squares or rectangles represent manifest variables and brain maps represent latent variables (LV). Tau LV involved fusiform, inferior temporal, middle temporal, superior temporal, supramarginal, inferior parietal, and posterior cingulate bilaterally, and left superior parietal, left superior frontal, right banks of superior temporal sulcus. Tau LV further involved bilateral caudal middle frontal, left pars opercularis, and right precuneus in ΔADAS‐Cog modeling. Atrophy LV involved hippocampus, amygdala, middle temporal, superior temporal, lateral orbitofrontal, pars opercularis, pars triangularis, and insula bilaterally, and right banks of superior temporal sulcus, right caudal middle frontal, and right supramarginal. Each single‐headed arrow denotes a hypothesized unidirectional effect of one variable on another. Numbers associated with effects are standardized regression coefficients or standardized factor loadings (i.e., from a latent variable to its indicators). Only the paths that were statistically significant at P < .05 are represented. Paths that were hypothesized but were not statistically significant at P < .05 are excluded. IL2, indicator loading squared.
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