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APOEε4 potentiates amyloid β effects on longitudinal tau pathology

Nature Aging volume 3, pages 1210–1218 (2023)Cite this article

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Abstract

The mechanisms by which the apolipoprotein E ε4 (APOEε4) allele influences the pathophysiological progression of Alzheimer’s disease (AD) are poorly understood. Here we tested the association of APOEε4 carriership and amyloid-β (Aβ) burden with longitudinal tau pathology. We longitudinally assessed 94 individuals across the aging and AD spectrum who underwent clinical assessments, APOE genotyping, magnetic resonance imaging, positron emission tomography (PET) for Aβ ([18F]AZD4694) and tau ([18F]MK-6240) at baseline, as well as a 2-year follow-up tau-PET scan. We found that APOEε4 carriership potentiates Aβ effects on longitudinal tau accumulation over 2 years. The APOEε4-potentiated Aβ effects on tau-PET burden were mediated by longitudinal plasma phosphorylated tau at threonine 217 (p-tau217+) increase. This longitudinal tau accumulation as measured by PET was accompanied by brain atrophy and clinical decline. Our results suggest that the APOEε4 allele plays a key role in Aβ downstream effects on the aggregation of phosphorylated tau in the living human brain.

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Fig. 1: Longitudinal tau accumulation according to APOEε4 and Aβ statuses.
Fig. 2: APOEε4 potentiates Aβ effects on tau accumulation over 2 years.
Fig. 3: APOEε4-dependent effects of Aβ on tau accumulation occur through tau phosphorylation.
Fig. 4: Longitudinal tau accumulation is accompanied by brain atrophy and clinical decline.

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Data availability

The data from the TRIAD study used in the present work is not publicly available as the information could compromise the participants’ privacy. Therefore, the raw and analyzed data will be made available from the corresponding author (T.A.P.) upon reasonable request from a qualified academic investigator for the sole purpose of replicating the procedures and results presented in this article. Arrangements for data sharing for replication of the findings in the TRIAD dataset are subject to standard data-sharing agreements and further information can be found in the study’s website (https://triad.tnl-mcgill.com/).

Code availability

The codes used for the data analyses in the present work will be made available from the corresponding author (T.A.P.) upon request.

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Acknowledgements

We acknowledge all study participants and staff at the McGill Center for Studies in Aging. We thank Cerveau Technologies for the use of [18F]MK-6240. We also thank D. Jolly, A. Kostikov, M. Samoila-Lactatus, K. Ross, M. Boudjemeline and S. Li for assisting in the radiochemistry production, as well as R. Strauss, E. Strauss, G. Gagne, C. Mayhew, T. Vinet-Celluci, K. Wan, S. Sbeiti, M. Jin Joung, M. Olmand, R. Nazar, H.-H. Hsiao, R. Bouhachi and A. Aliaga for helping with the acquisition of the data. We thank the following funding agencies for their support: National Institutes of Health (grants R01AG075336 and R01AG073267 to T.A.P. and R01AG068398 to K.B.); Alzheimer’s Association (grants AARFD-22-974627 to B.B.; AACSF-20-648075 to T.A.P.; NIRG-12-92090 and NIRP-12-259245 to P.R.-N.; AARF-21-850325 to T.K.K.; AARFD-22-923814 to P.C.L.F.; AARGD-21-850670 to E.R.Z.; and ADSF-21-831376-C, ADSF-21-831381-C and ADSF-21-831377-C to H.Z.); Brain Canada Foundation (CFI Project 34874 and 33397 to P.R.-N.); CAPES (grant 88887.336490/2019-00 to B.B.); CNPq (grant 200691/2021-0 to J.P.F.-S. and 312306/2021-0 to E.R.Z.); Fonds de Recherche du Québec–Santé (Chercheur Boursier, grant 2020-VICO-279314 to P.R.-N.); CIHR-CCNA Canadian Consortium of Neurodegeneration in Aging (grants MOP-11-51-31; RFN 152985, 159815 and 162303 to P.R.-N.); Weston Brain Institute (grants 8400707, 8401154 and 8401103 to P.R.-N.); Colin Adair Charitable Foundation (grant to P.R.-N.); Swedish Research Council (grant 2021-03244 to T.K.K.; 2018-02532 to H.Z.; and 2017-00915 and 2022-00732 to K.B.); Wallenberg Scholar (grant 2022-01018 to H.Z.); BrightFocus Foundation (grant A2020812F to T.K.K.); Swedish Alzheimer Foundation (Alzheimerfonden; grant AF-930627 to T.K.K.; and AF-930351, AF-939721 and AF-968270 to K.B.); Swedish Brain Foundation (Hjärnfonden; grant FO2020-0240 to T.K.K.); Agneta Prytz-Folkes & Gösta Folkes Foundation (grant 2020-00124 to T.K.K.); European Union’s Horizon Europe research and innovation program (grant 101053962 to H.Z.); Swedish State Support for Clinical Research (grant ALFGBG-71320 to H.Z.); Alzheimer Drug Discovery Foundation (grant 201809-2016862 to H.Z. and RDAPB-201809-2016615 to K.B.); Bluefield Project, the Olav Thon Foundation, the Erling-Persson Family Foundation, Stiftelsen för Gamla Tjänarinnor, Hjärnfonden, Sweden (grant FO2022-0270 to H.Z.); European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie (grant 860197 (MIRIADE) to H.Z.); the European Union Joint Program–Neurodegenerative Disease Research (grant JPND2021-00694 to H.Z. and JPND2019-466-236 to K.B.); The UK Dementia Research Institute at UCL (grant UKDRI-1003 to H.Z.); National Academy of Neuropsychology (grant ALZ-NAN-22-928381 to E.R.Z.); Fundação de Amparo a pesquisa do Rio Grande do Sul (grant 21/2551-0000673-0 to E.R.Z.); Instituto Serrapilheira (grant Serra-1912-31365 to E.R.Z.); Hjärnfonden (grants FO2017-0243 and ALZ2022-0006 to K.B.); the Swedish state under the agreement between the Swedish government and the County Councils, the ALF agreement (grants ALFGBG-715986 and ALFGBG-965240 to K.B.); the Alzheimer’s Association 2021 Zenith Award (grant ZEN-21-848495 to K.B.); and the Alzheimer’s Association 2022–2025 grant (SG-23-1038904 QC to K.B.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Authors and Affiliations

  1. Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA

    João Pedro Ferrari-Souza, Bruna Bellaver, Pâmela C. L. Ferreira, Guilherme Povala, Firoza Z. Lussier, Douglas T. Leffa, Cécile Tissot, Carolina Soares, Ann Cohen, William E. Klunk, Thomas K. Karikari, Victor L. Villemagne, Dana L. Tudorascu & Tharick A. Pascoal

  2. Graduate Program in Biological Sciences: Biochemistry, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil

    João Pedro Ferrari-Souza, Bruna Bellaver, Carolina Soares, Diogo O. Souza & Eduardo R. Zimmer

  3. Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer’s Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l’Ouest-de-l’Île-de-Montréal; Department of Neurology and Neurosurgery, Psychiatry and Pharmacology and Therapeutics, McGill University, Montreal, Quebec, Canada

    Andréa L. Benedet, Firoza Z. Lussier, Joseph Therriault, Cécile Tissot, Yi-Ting Wang, Mira Chamoun, Stijn Servaes, Arthur C. Macedo, Marie Vermeiren, Min Su Kang, Jenna Stevenson, Nesrine Rahmouni, Vanessa Pallen, Nina Margherita Poltronetti, Serge Gauthier & Pedro Rosa-Neto

  4. Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden

    Andréa L. Benedet, Thomas K. Karikari, Nicholas J. Ashton, Henrik Zetterberg & Kaj Blennow

  5. Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada

    Gleb Bezgin & Jean-Paul Soucy

  6. Artificial Intelligence and Computational Neurosciences Laboratory, Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada

    Min Su Kang

  7. LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada

    Min Su Kang

  8. Department of Neurology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA

    Oscar L. Lopez & Tharick A. Pascoal

  9. Neuroscience Biomarkers, Janssen Research and Development, La Jolla, CA, USA

    Gallen Triana-Baltzer, Ziad S. Saad & Hartmuth C. Kolb

  10. Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Gothenburg, Sweden

    Thomas K. Karikari, Henrik Zetterberg & Kaj Blennow

  11. Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA

    Dana L. Tudorascu

  12. Centre for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway

    Nicholas J. Ashton

  13. Department of Old Age Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK

    Nicholas J. Ashton

  14. Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK

    Henrik Zetterberg

  15. UK Dementia Research Institute at UCL, London, UK

    Henrik Zetterberg

  16. Hong Kong Center for Neurodegenerative Diseases, Hong Kong, China

    Henrik Zetterberg

  17. UW Department of Medicine, School of Medicine and Public Health, Madison, WI, USA

    Henrik Zetterberg

  18. Department of Pharmacology, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil

    Eduardo R. Zimmer

  19. Graduate Program in Biological Sciences: Pharmacology and Therapeuctis, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil

    Eduardo R. Zimmer

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  1. João Pedro Ferrari-Souza
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  2. Bruna Bellaver
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  14. Arthur C. Macedo
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  18. Jenna Stevenson
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  19. Nesrine Rahmouni
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  21. Nina Margherita Poltronetti
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  22. Ann Cohen
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  23. Oscar L. Lopez
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  26. Serge Gauthier
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  27. Diogo O. Souza
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  28. Gallen Triana-Baltzer
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  29. Ziad S. Saad
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  30. Hartmuth C. Kolb
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  31. Thomas K. Karikari
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  32. Victor L. Villemagne
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  33. Dana L. Tudorascu
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  34. Nicholas J. Ashton
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  35. Henrik Zetterberg
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  36. Kaj Blennow
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  37. Eduardo R. Zimmer
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  38. Pedro Rosa-Neto
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  39. Tharick A. Pascoal
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J.P.F.-S. and T.A.P. conceived the study. J.P.F.-S. and T.A.P. prepared the figures, tables and drafted the paper with input from B.B., P.C.L.F., A.L.B., G.P., F.Z.L., D.T.L., J.T., C.T., C.S., Y.-T.W., M.C., S.S., A.C.M., M.V., G.B., M.S.K., J.S., N.R., V.P., N.M.P., A.C., O.L.L., W.E.K., J.-P.S., S.G., D.O.S., G.T.-B., Z.S.S., H.C.K., T.K.K., V.L.V., D.L.T., N.J.A., H.Z., K.B., E.R.Z. and P.R.-N. J.P.F.-S., B.B., P.C.L.F., G.P., F.Z.L., D.T.L., J.T., C.T., C.S., Y.-T.W., M.C., S.S., A.C.M., M.V., G.B., M.S.K., J.S., N.R., V.P., N.M.P., A.C., O.L.L., W.E.K., J.-P.S., S.G., D.O.S., V.L.V., E.R.Z., P.R.-N. and T.A.P. contributed to the acquisition, processing and analysis of imaging data. J.P.F.-S., A.L.B., G.T.-B., Z.S.S., H.C.K., T.K.K., N.J.A., H.Z. and K.B. contributed to the analysis of the fluid biomarker data. T.A.P. supervised this work. D.L.T. assisted in the statistical analysis. All authors performed a critical review of the paper for intellectual content and approved the final paper draft.

Corresponding author

Correspondence to Tharick A. Pascoal.

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Competing interests

S.G. has served as a scientific advisor to Cerveau Technologies. N.J.A. has given lectures in symposia sponsored by Lilly and Quanterix. G.T.-B., Z.S.S. and H.C.K. are employees of Janssen R&D and receive salary and stock from its parent company, Johnson & Johnson. H.Z. has served at scientific advisory boards and/or as a consultant for Abbvie, Acumen, Alector, ALZPath, Annexon, Apellis, Artery Therapeutics, AZTherapies, CogRx, Denali, Eisai, Nervgen, Novo Nordisk, Passage Bio, Pinteon Therapeutics, Red Abbey Labs, reMYND, Roche, Samumed, Siemens Healthineers, Triplet Therapeutics and Wave, has given lectures in symposia sponsored by Cellectricon, Fujirebio, Alzecure, Biogen and Roche and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program. K.B. has served as a consultant, at advisory boards, or at data monitoring committees for Abcam, Axon, BioArctic, Biogen, JOMDD/Shimadzu, Julius Clinical, Lilly, MagQu, Novartis, Prothena, Roche Diagnostics and Siemens Healthineers and is a co-founder of BBS, which is a part of the GU Ventures Incubator Program. E.R.Z. serves on the scientific advisory board of Next Innovative Therapeutics. The other authors declare no competing interests.

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Nature Aging thanks Alifiya Kapasi and Christina Young for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Flowchart of included participants.

aOut of the 13 APOEε2 carriers who were excluded, twelve had the ε2/ε3 genotype (8 CU older adults, 2 with MCI, and 2 with AD dementia), and one had the ε2/ε4 genotype (individual with MCI). Abbreviations: AD = Alzheimer’s disease; ADAD = autosomal dominant Alzheimer’s disease; APOE = apolipoprotein E; Aβ = amyloid-β; CU = cognitively unimpaired; EOAD = early-onset Alzheimer’s disease; MCI = mild cognitive impairment; MRI = magnetic resonance imaging; PET = positron emission tomography; TRIAD = Translational Biomarkers in Aging and Dementia.

Extended Data Fig. 2 Sensitivity analyses testing the association of APOEε4 carriership and Aβ burden with tau accumulation including APOEε2 carriers.

(a) Violin plot of temporal meta-ROI tau-PET SUVR change across groups. The horizontal line inside each box depicts the median, and box ends represent the 25th and 75th percentiles, with whiskers extending to 1.5 × interquartile range. The horizontal dashed line represents the threshold for tau accumulation. Groups were compared using two-sided analysis of covariance with Tukey’s multiple comparisons test. (b) The figure shows the ORs from logistic regression on being classified as tau accumulator. Only the concomitant presence of Aβ+ and APOEε4 carriership was associated with higher odds of being classified as tau accumulator (Aβ- APOEε4 noncarrier: reference group; Aβ- APOEε4 carrier: OR = 1.7, 95% CI 0.2 to 9.9, P = 0.582; Aβ+ APOEε4 noncarrier: OR = 1.7, 95% CI 0.3 to 9.6, P = 0.559; and Aβ+ APOEε4 carrier: OR = 21.4, 95% CI 3.7 to 159.8, P = 0.001; Supplementary Fig. 1b). (c) The scatter-plot displays the association between global Aβ-PET SUVR and temporal meta-ROI tau-PET SUVR change in APOEε4 noncarriers (blue) and carriers (red). The error bands indicate the 95% CI. Density plots along the x and y axes provide the data distribution. The β estimate and P value were computed from a linear regression model assessing the interaction of APOEε4 status and global Aβ-PET burden on longitudinal tau-PET. Regressions were two-sided and adjusted for age, sex, diagnosis, and baseline tau-PET burden. The interaction model also accounted for APOEε4 status and global Aβ-PET burden main effects. Analyses were conducted including individuals bearing the ε2 allele of the APOE gene who were excluded from the main analyses (70 CU older adults, 28 with MCI, and 9 with AD dementia). Abbreviations: AD = Alzheimer’s disease; APOEε4 = apolipoprotein E ε4; Aβ = amyloid-β; CI = confidence interval; CU = cognitively unimpaired; MCI = mild cognitive impairment; OR = odds ratio; PET = positron emission tomography; ROI = region of interest; SUVR = standardized uptake value ratio.

Extended Data Fig. 3 Association of APOEε4 carriership and Aβ burden with tau accumulation in the subgroup of non-demented participants.

(a) Violin plot of temporal meta-ROI tau-PET SUVR change across groups. The horizontal line inside each box depicts the median, and box ends represent the 25th and 75th percentiles, with whiskers extending to 1.5 × interquartile range. The horizontal dashed line represents the threshold for tau accumulation. Groups were compared using two-sided analysis of covariance with Tukey’s multiple comparisons test. (b) The figure shows the ORs from logistic regression on being classified as tau accumulator. Only the concomitant presence of Aβ+ and APOEε4 carriership was associated with higher odds of being classified as tau accumulator (Aβ- APOEε4 noncarrier: reference group; Aβ- APOEε4 carrier: OR = 1.5, 95% CI 0.2 to 9.2, P = 0.696; Aβ+ APOEε4 noncarrier: OR = 0.9, 95% CI 0.1 to 7.8, P = 0.910; and Aβ+ APOEε4 carrier: OR = 18.8, 95% CI 2.0 to 228.7, P = 0.013; Supplementary Fig. 1c). (c) The scatter-plot displays the association between global Aβ-PET SUVR and temporal meta-ROI tau-PET SUVR change in APOEε4 noncarriers (blue) and carriers (red). The error bands indicate the 95% CI. Density plots along the x and y axes provide the data distribution. The β estimate and P value were computed from a linear regression assessing the interaction of APOEε4 status and global Aβ-PET burden on longitudinal tau-PET. Regressions were two-sided and adjusted for age, sex, diagnosis, and baseline tau-PET burden. The interaction model also accounted for APOEε4 status and global Aβ-PET burden main effects. Analyses were conducted in the subgroup of non-demented participants (62 CU older adults and 25 with MCI). Abbreviations: APOEε4 = apolipoprotein E ε4; Aβ = amyloid-β; CI = confidence interval; CU = cognitively unimpaired; MCI = mild cognitive impairment; OR = odds ratio; PET = positron emission tomography; ROI = region of interest; SUVR = standardized uptake value ratio.

Extended Data Fig. 4 Plasma p-tau217+ change according to longitudinal tau accumulation status.

Violin plot of plasma p-tau217+ change across groups defined based on longitudinal tau accumulation status. The horizontal line inside each box depicts the median, and box ends represent the 25th and 75th percentiles, with whiskers extending to 1.5 × interquartile range. Groups were compared using two-sided analysis of covariance. The P value was computed from a model adjusted for age, sex, diagnosis, and baseline plasma p-tau217+ levels. Analyses were conducted on a subset of 65 individuals (Supplementary Table 4). Abbreviation: p-tau217+ = phosphorylated tau at threonine 217.

Supplementary information

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Supplementary Tables 1–12 and Fig. 1.

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Ferrari-Souza, J.P., Bellaver, B., Ferreira, P.C.L. et al. APOEε4 potentiates amyloid β effects on longitudinal tau pathology. Nat Aging 3, 1210–1218 (2023). https://doi.org/10.1038/s43587-023-00490-2

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