Movatterモバイル変換

[0]ホーム
URL:

Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Advertisement

Nature
  • Article
  • Published:

APOE4 leads to blood–brain barrier dysfunction predicting cognitive decline

Naturevolume 581, pages71–76 (2020)Cite this article

Subjects

Abstract

Vascular contributions to dementia and Alzheimer’s disease are increasingly recognized1,2,3,4,5,6. Recent studies have suggested that breakdown of the blood–brain barrier (BBB) is an early biomarker of human cognitive dysfunction7, including the early clinical stages of Alzheimer’s disease5,8,9,10. The E4 variant ofapolipoprotein E (APOE4), the main susceptibility gene for Alzheimer’s disease11,12,13,14, leads to accelerated breakdown of the BBB and degeneration of brain capillary pericytes15,16,17,18,19, which maintain BBB integrity20,21,22. It is unclear, however, whether the cerebrovascular effects ofAPOE4 contribute to cognitive impairment. Here we show that individuals bearingAPOE4 (with the ε3/ε4 or ε4/ε4 alleles) are distinguished from those withoutAPOE4 (ε3/ε3) by breakdown of the BBB in the hippocampus and medial temporal lobe. This finding is apparent in cognitively unimpairedAPOE4 carriers and more severe in those with cognitive impairment, but is not related to amyloid-β or tau pathology measured in cerebrospinal fluid or by positron emission tomography23. High baseline levels of the BBB pericyte injury biomarker soluble PDGFRβ7,8 in the cerebrospinal fluid predicted future cognitive decline inAPOE4 carriers but not in non-carriers, even after controlling for amyloid-β and tau status, and were correlated with increased activity of the BBB-degrading cyclophilin A-matrix metalloproteinase-9 pathway19 in cerebrospinal fluid. Our findings suggest that breakdown of the BBB contributes toAPOE4-associated cognitive decline independently of Alzheimer’s disease pathology, and might be a therapeutic target inAPOE4 carriers.

This is a preview of subscription content,access via your institution

Access options

Access through your institution

Access Nature and 54 other Nature Portfolio journals

Get Nature+, our best-value online-access subscription

9,800 Yen / 30 days

cancel any time

Subscription info for Japanese customers

We have a dedicated website for our Japanese customers. Please go tonatureasia.com to subscribe to this journal.

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: BBB breakdown in the HC and PHG inAPOE4 carriers increases with cognitive impairment, independently of CSF Aβ and tau status.
Fig. 2: Blood-brain barrier breakdown inAPOE4 carriers is independent of amyloid and tau accumulation in the brain.
Fig. 3: Elevated baseline CSF levels of sPDGFRβ predict cognitive decline inAPOE4 carriers.
Fig. 4: Elevated CSF sPDGFRβ, cyclophilin A and matrix metalloproteinase-9 inAPOE4 carriers.

Similar content being viewed by others

Data availability

All data generated and/or analysed during this study are either included in this article (and its Supplementary Information) or are available from the corresponding author on reasonable request. Source Data for Figs.1–4 are provided with the article.

Code availability

References

  1. Wardlaw, J. M. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration.Lancet Neurol.12, 822–838 (2013).

    Article PubMed PubMed Central  Google Scholar 

  2. Kapasi, A., DeCarli, C. & Schneider, J. A. Impact of multiple pathologies on the threshold for clinically overt dementia.Acta Neuropathol.134, 171–186 (2017).

    Article PubMed PubMed Central  Google Scholar 

  3. Iadecola, C. The neurovascular unit coming of age: a journey through neurovascular coupling in health and disease.Neuron96, 17–42 (2017).

    Article CAS PubMed PubMed Central  Google Scholar 

  4. Iturria-Medina, Y., Sotero, R. C., Toussaint, P. J., Mateos-Pérez, J. M. & Evans, A. C. Early role of vascular dysregulation on late-onset Alzheimer’s disease based on multifactorial data-driven analysis.Nat. Commun.7, 11934 (2016).

    Article ADS CAS PubMed PubMed Central  Google Scholar 

  5. Sweeney, M. D., Sagare, A. P. & Zlokovic, B. V. Blood–brain barrier breakdown in Alzheimer disease and other neurodegenerative disorders.Nat. Rev. Neurol.14, 133–150 (2018).

    Article CAS PubMed PubMed Central  Google Scholar 

  6. Sweeney, M. D. et al. Vascular dysfunction—the disregarded partner of Alzheimer’s disease.Alzheimers Dement.15, 158–167 (2019).

    Article PubMed PubMed Central  Google Scholar 

  7. Nation, D. A. et al. Blood–brain barrier breakdown is an early biomarker of human cognitive dysfunction.Nat. Med.25, 270–276 (2019).

    Article CAS PubMed PubMed Central  Google Scholar 

  8. Montagne, A. et al. Blood–brain barrier breakdown in the aging human hippocampus.Neuron85, 296–302 (2015).

    Article CAS PubMed PubMed Central  Google Scholar 

  9. van de Haar, H. J. et al. Neurovascular unit impairment in early Alzheimer’s disease measured with magnetic resonance imaging.Neurobiol. Aging45, 190–196 (2016).

    Article PubMed  Google Scholar 

  10. van de Haar, H. J. et al. Blood–brain barrier leakage in patients with early Alzheimer disease.Radiology281, 527–535 (2016).

    Article PubMed  Google Scholar 

  11. Corder, E. H. et al. Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer’s disease in late onset families.Science261, 921–923 (1993).

    Article ADS CAS PubMed  Google Scholar 

  12. Roses, A. D. Apolipoprotein E alleles as risk factors in Alzheimer’s disease.Annu. Rev. Med.47, 387–400 (1996).

    Article CAS PubMed  Google Scholar 

  13. Farrer, L. A. et al. Effects of age, sex, and ethnicity on the association between apolipoprotein E genotype and Alzheimer disease. A meta-analysis.J. Am. Med. Assoc.278, 1349–1356 (1997).

    Article CAS  Google Scholar 

  14. Genin, E. et al. APOE and Alzheimer disease: a major gene with semi-dominant inheritance.Mol. Psychiatry16, 903–907 (2011).

    Article CAS PubMed PubMed Central  Google Scholar 

  15. Hultman, K., Strickland, S. & Norris, E. H. The APOE ɛ4/ɛ4 genotype potentiates vascular fibrin(ogen) deposition in amyloid-laden vessels in the brains of Alzheimer’s disease patients.J. Cereb. Blood Flow Metab.33, 1251–1258 (2013).

    Article CAS PubMed PubMed Central  Google Scholar 

  16. Halliday, M. R. et al. Accelerated pericyte degeneration and blood–brain barrier breakdown in apolipoprotein E4 carriers with Alzheimer’s disease.J. Cereb. Blood Flow Metab.36, 216–227 (2016).

    Article CAS PubMed PubMed Central  Google Scholar 

  17. Salloway, S. et al. Effect of APOE genotype on microvascular basement membrane in Alzheimer’s disease.J. Neurol. Sci.203-204, 183–187 (2002).

    Article CAS PubMed  Google Scholar 

  18. Zipser, B. D. et al. Microvascular injury and blood–brain barrier leakage in Alzheimer’s disease.Neurobiol. Aging28, 977–986 (2007).

    Article CAS PubMed  Google Scholar 

  19. Bell, R. D. et al. Apolipoprotein E controls cerebrovascular integrity via cyclophilin A.Nature485, 512–516 (2012).

    Article ADS CAS PubMed PubMed Central  Google Scholar 

  20. Armulik, A. et al. Pericytes regulate the blood–brain barrier.Nature468, 557–561 (2010).

    Article ADS CAS PubMed  Google Scholar 

  21. Bell, R. D. et al. Pericytes control key neurovascular functions and neuronal phenotype in the adult brain and during brain aging.Neuron68, 409–427 (2010).

    Article CAS PubMed PubMed Central  Google Scholar 

  22. Nikolakopoulou, A. M. et al. Pericyte loss leads to circulatory failure and pleiotrophin depletion causing neuron loss.Nat. Neurosci.22, 1089–1098 (2019).

    Article CAS PubMed PubMed Central  Google Scholar 

  23. Jack, C. R. Jr et al. NIA-AA Research Framework: toward a biological definition of Alzheimer’s disease.Alzheimers Dement.14, 535–562 (2018).

    Article PubMed PubMed Central  Google Scholar 

  24. Pan, C. et al. Diagnostic values of cerebrospinal fluid T-Tau and Aβ42 using meso scale discovery assays for Alzheimer’s disease.J. Alzheimers Dis.45, 709–719 (2015).

    Article CAS PubMed PubMed Central  Google Scholar 

  25. Roe, C. M. et al. Amyloid imaging and CSF biomarkers in predicting cognitive impairment up to 7.5 years later.Neurology80, 1784–1791 (2013).

    Article CAS PubMed PubMed Central  Google Scholar 

  26. Montagne, A., Zhao, Z. & Zlokovic, B. V. Alzheimer’s disease: a matter of blood-brain barrier dysfunction?J. Exp. Med.214, 3151–3169 (2017).

    Article CAS PubMed PubMed Central  Google Scholar 

  27. Bennett, R. E. et al. Tau induces blood vessel abnormalities and angiogenesis-related gene expression in P301L transgenic mice and human Alzheimer’s disease.Proc. Natl Acad. Sci. USA115, E1289–E1298 (2018).

  28. Fouquet, M., Besson, F. L., Gonneaud, J., La Joie, R. & Chételat, G. Imaging brain effects of APOE4 in cognitively normal individuals across the lifespan.Neuropsychol. Rev.24, 290–299 (2014).

    Article PubMed  Google Scholar 

  29. Schultz, S. A. et al. Widespread distribution of tauopathy in preclinical Alzheimer’s disease.Neurobiol. Aging72, 177–185 (2018).

    Article CAS PubMed PubMed Central  Google Scholar 

  30. Miners, J. S., Kehoe, P. G., Love, S., Zetterberg, H. & Blennow, K. CSF evidence of pericyte damage in Alzheimer’s disease is associated with markers of blood–brain barrier dysfunction and disease pathology.Alzheimers Res. Ther.11, 81 (2019).

    Article CAS PubMed PubMed Central  Google Scholar 

  31. Stanciu, C., Trifan, A., Muzica, C. & Sfarti, C. Efficacy and safety of alisporivir for the treatment of hepatitis C infection.Expert Opin. Pharmacother.20, 379–384 (2019).

    Article CAS PubMed  Google Scholar 

  32. Morris, J. C. et al. The Uniform Data Set (UDS): clinical and cognitive variables and descriptive data from Alzheimer Disease Centers.Alzheimer Dis. Assoc. Disord.20, 210–216 (2006).

    Article ADS PubMed  Google Scholar 

  33. Morris, J. C. The Clinical Dementia Rating (CDR): current version and scoring rules.Neurology43, 2412–2414 (1993).

    Article CAS PubMed  Google Scholar 

  34. Nation, D. A. et al. Antemortem pulse pressure elevation predicts cerebrovascular disease in autopsy-confirmed Alzheimer’s disease.J. Alzheimers Dis.30, 595–603 (2012).

    Article PubMed PubMed Central  Google Scholar 

  35. Bangen, K. J. et al. Aggregate effects of vascular risk factors on cerebrovascular changes in autopsy-confirmed Alzheimer’s disease.Alzheimers Dement.11, 394–403.e1 (2015).

    Article PubMed  Google Scholar 

  36. Jak, A. J. et al. Quantification of five neuropsychological approaches to defining mild cognitive impairment.Am. J. Geriatr. Psychiatry17, 368–375 (2009).

    Article PubMed PubMed Central  Google Scholar 

  37. Jak, A. J. et al. Neuropsychological criteria for mild cognitive impairment and dementia risk in the Framingham heart study.J. Int. Neuropsychol. Soc.22, 937–943 (2016).

    Article PubMed PubMed Central  Google Scholar 

  38. Weintraub, S. et al. The Alzheimer’s Disease Centers’ Uniform Data Set (UDS): the neuropsychologic test battery.Alzheimer Dis. Assoc. Disord.23, 91–101 (2009).

    Article PubMed PubMed Central  Google Scholar 

  39. Besser, L. et al. Version 3 of the National Alzheimer’s Coordinating Center’s Uniform Data Set.Alzheimer Dis. Assoc. Disord.32, 351–358 (2018).

    Article PubMed PubMed Central  Google Scholar 

  40. Delis, D., Kramer, J., Kaplan, E. & Ober, B.California Verbal Learning Test (PsychCorp, 2000).

  41. Montagne, A. et al. Undetectable gadolinium brain retention in individuals with an age-dependent blood-brain barrier breakdown in the hippocampus and mild cognitive impairment.Alzheimers Dement.15, 1568–1575 (2019).

    Article PubMed PubMed Central  Google Scholar 

  42. Fischl, B. FreeSurfer.Neuroimage62, 774–781 (2012).

    Article PubMed  Google Scholar 

  43. Fischl, B. et al. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain.Neuron33, 341–355 (2002).

    Article CAS PubMed  Google Scholar 

  44. Desikan, R. S. et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest.Neuroimage31, 968–980 (2006).

    Article PubMed  Google Scholar 

  45. Fischl, B. & Dale, A. M. Measuring the thickness of the human cerebral cortex from magnetic resonance images.Proc. Natl Acad. Sci. USA97, 11050–11055 (2000).

    Article ADS CAS PubMed PubMed Central  Google Scholar 

  46. Fischl, B., Sereno, M. I., Tootell, R. B. & Dale, A. M. High-resolution intersubject averaging and a coordinate system for the cortical surface.Hum. Brain Mapp.8, 272–284 (1999).

    Article CAS PubMed PubMed Central  Google Scholar 

  47. Dinov, I. et al. Neuroimaging study designs, computational analyses and data provenance using the LONI pipeline.PLoS ONE 5, e13070 (2010).

    Article ADS PubMed PubMed Central CAS  Google Scholar 

  48. Sepehrband, F. et al. Neuroanatomical morphometric characterization of sex differences in youth using statistical learning.Neuroimage172, 217–227 (2018).

    Article PubMed  Google Scholar 

  49. Cabeen, R. P., Laidlaw, D. H. & Toga, A. W. Quantitative imaging toolkit: software for interactive 3D visualization, data exploration, and computational analysis of neuroimaging datasets.Proc.Intl Soc. Magnetic Resonance in Medicine (ISMRM)vol. 2854 (2018).

  50. Jenkinson, M., Bannister, P., Brady, M. & Smith, S. Improved optimization for the robust and accurate linear registration and motion correction of brain images.Neuroimage17, 825–841 (2002).

    Article PubMed  Google Scholar 

  51. Bullich, S. et al. Optimal reference region to measure longitudinal amyloid-β change with18F-florbetaben PET.J. Nucl. Med.58, 1300–1306 (2017).

    Article CAS PubMed  Google Scholar 

  52. Marquié, M. et al. Lessons learned about [F-18]-AV-1451 off-target binding from an autopsy-confirmed Parkinson’s case.Acta Neuropathol. Commun.5, 75 (2017).

    Article PubMed PubMed Central CAS  Google Scholar 

  53. Mishra, S. et al. AV-1451 PET imaging of tau pathology in preclinical Alzheimer disease: defining a summary measure.Neuroimage161, 171–178 (2017).

    Article CAS PubMed  Google Scholar 

  54. TCW, J. et al. Cholesterol and matrisome pathways dysregulated in human ε4 glia. Preprint athttps://www.biorxiv.org/content/10.1101/713362v1 (2019).

  55. Faal, T. et al. Induction of mesoderm and neural crest-derived pericytes from human pluripotent stem cells to study blood-brain barrier interactions.Stem Cell Reports12, 451–460 (2019).

    Article CAS PubMed PubMed Central  Google Scholar 

  56. Aggarwal, C. C.Outlier Analysis (Springer, 2013).

  57. Sagare, A. P., Sweeney, M. D., Makshanoff, J. & Zlokovic, B. V. Shedding of soluble platelet-derived growth factor receptor-β from human brain pericytes.Neurosci. Lett.607, 97–101 (2015).

    Article CAS PubMed PubMed Central  Google Scholar 

  58. Glickman, M. E., Rao, S. R. & Schultz, M. R. False discovery rate control is a recommended alternative to Bonferroni-type adjustments in health studies.J. Clin. Epidemiol.67, 850–857 (2014).

    Article PubMed  Google Scholar 

Download references

Acknowledgements

The work of B.V.Z. is supported by National Institutes of Health (NIH) grant nos. R01AG023084, R01NS034467, R01AG039452, 5P01AG052350 and 5P50AG005142, in addition to an Alzheimer’s Association strategic 509279 grant, Cure Alzheimer’s Fund, the Foundation Leducq Transatlantic Network of Excellence for the Study of Perivascular Spaces in Small Vessel Disease reference no. 16 CVD 05, and Open Philanthropy. D.A.N. is supported by NIH grant nos. R01AG060049, R01AG064228, P01AG052350 and P50AG016573, Alzheimer’s Association grant AARG-17–532905 and Alzheimer’s Association strategic grant 509279. D.P.B and M.G.H. are supported by the L. K. Whittier Foundation, grant nos. P01AG052350, R01AG054434 and R01AG055770. Enrolment of participants into the WashU Knight ADRC is supported by NIH grant nos. P50AG05681, P01AG03991 and P01AG026276 (J.C.M.). E.M.R. is supported by National Institute of Aging (NIA) grant nos. P30AG19610 and R01AG031581, in addition to the state of Arizona. Enrolment of participants into the USC ADRC is supported by NIH grant no. 5P50AG005142 (H.C.C.). Avid Radiopharmaceuticals, a wholly owned subsidiary of Eli Lilly and Company, provided doses of FBP and financial support for FBP scanning at the WashU site. Avid Radiopharmaceuticals also provided the WashU site with AV1451 precursor and technology transfer for producing the tracer on site. Avid Radiopharmaceuticals was not involved in data analysis or interpretation.

Author information

Author notes

  1. These authors contributed equally: Axel Montagne, Daniel A. Nation, Abhay P. Sagare, Giuseppe Barisano, Melanie D. Sweeney

Authors and Affiliations

  1. Department of Physiology and Neuroscience, Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

    Axel Montagne, Daniel A. Nation, Abhay P. Sagare, Giuseppe Barisano, Melanie D. Sweeney, Ararat Chakhoyan, Maricarmen Pachicano, Amy R. Nelson, Yining Chen & Berislav V. Zlokovic

  2. Alzheimer’s Disease Research Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

    Daniel A. Nation, Elizabeth Joe, Lina M. D’Orazio, John M. Ringman, Lon S. Schneider, Helena C. Chui, Judy Pa, Meng Law, Arthur W. Toga & Berislav V. Zlokovic

  3. Department of Psychological Science, University of California, Irvine, Irvine, CA, USA

    Daniel A. Nation

  4. Institute for Memory Disorders and Neurological Impairments, University of California, Irvine, Irvine, CA, USA

    Daniel A. Nation

  5. Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

    Elizabeth Joe, Lina M. D’Orazio, John M. Ringman, Lon S. Schneider & Helena C. Chui

  6. Huntington Medical Research Institutes, Pasadena, CA, USA

    David P. Buennagel & Michael G. Harrington

  7. Department of Radiology, Washington University School of Medicine, St Louis, MO, USA

    Tammie L. S. Benzinger

  8. The Hope Center for Neurodegenerative Disorders, Washington University School of Medicine, St Louis, MO, USA

    Tammie L. S. Benzinger & Anne M. Fagan

  9. Department of Neurology, Washington University School of Medicine, St Louis, MO, USA

    Anne M. Fagan & John C. Morris

  10. The Knight Alzheimer’s Disease Research Center, Washington University School of Medicine, St Louis, MO, USA

    Anne M. Fagan & John C. Morris

  11. Department of Psychiatry and Behavioral Sciences, University of Southern California, Los Angeles, CA, USA

    Lon S. Schneider

  12. Banner Alzheimer Institute, Phoenix, AZ, USA

    Eric M. Reiman

  13. Department of Neurology, Mayo Clinic, Scottsdale, AZ, USA

    Richard J. Caselli

  14. Department of Neuroscience & Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Julia TCW

  15. Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Julia TCW

  16. Laboratory of Neuroimaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

    Judy Pa & Arthur W. Toga

  17. Molecular Imaging Center, Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

    Peter S. Conti

  18. Department of Neurological Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

    Meng Law

  19. Department of Neuroscience and Radiology, Monash University, Alfred Health, Melbourne, Victoria, Australia

    Meng Law

Authors
  1. Axel Montagne

    You can also search for this author inPubMed Google Scholar

  2. Daniel A. Nation

    You can also search for this author inPubMed Google Scholar

  3. Abhay P. Sagare

    You can also search for this author inPubMed Google Scholar

  4. Giuseppe Barisano

    You can also search for this author inPubMed Google Scholar

  5. Melanie D. Sweeney

    You can also search for this author inPubMed Google Scholar

  6. Ararat Chakhoyan

    You can also search for this author inPubMed Google Scholar

  7. Maricarmen Pachicano

    You can also search for this author inPubMed Google Scholar

  8. Elizabeth Joe

    You can also search for this author inPubMed Google Scholar

  9. Amy R. Nelson

    You can also search for this author inPubMed Google Scholar

  10. Lina M. D’Orazio

    You can also search for this author inPubMed Google Scholar

  11. David P. Buennagel

    You can also search for this author inPubMed Google Scholar

  12. Michael G. Harrington

    You can also search for this author inPubMed Google Scholar

  13. Tammie L. S. Benzinger

    You can also search for this author inPubMed Google Scholar

  14. Anne M. Fagan

    You can also search for this author inPubMed Google Scholar

  15. John M. Ringman

    You can also search for this author inPubMed Google Scholar

  16. Lon S. Schneider

    You can also search for this author inPubMed Google Scholar

  17. John C. Morris

    You can also search for this author inPubMed Google Scholar

  18. Eric M. Reiman

    You can also search for this author inPubMed Google Scholar

  19. Richard J. Caselli

    You can also search for this author inPubMed Google Scholar

  20. Helena C. Chui

    You can also search for this author inPubMed Google Scholar

  21. Julia TCW

    You can also search for this author inPubMed Google Scholar

  22. Yining Chen

    You can also search for this author inPubMed Google Scholar

  23. Judy Pa

    You can also search for this author inPubMed Google Scholar

  24. Peter S. Conti

    You can also search for this author inPubMed Google Scholar

  25. Meng Law

    You can also search for this author inPubMed Google Scholar

  26. Arthur W. Toga

    You can also search for this author inPubMed Google Scholar

  27. Berislav V. Zlokovic

    You can also search for this author inPubMed Google Scholar

Contributions

A.M., D.A.N., A.P.S., G.B., M.D.S. and B.V.Z. designed the research study and analysed and interpreted the data. A.M., D.A.N., A.P.S., G.B., M.D.S., A.C., M.P. and Y.C. performed the experiments and analysed the data. A.M. and G.B. performed the MRI analysis. A.M., G.B. and A.C. performed the PET analysis. A.P.S., M.D.S. and M.P. performed the biofluids analysis. D.A.N. performed the neuropsychological analysis. A.P.S., Y.C., B.V.Z. and J.TCW. contributed to human iPSC-pericyte experiments. L.M.D. and A.R.N. prepared and submitted the study to the IRB. M.P., E.J., D.P.B., M.G.H., T.L.S.B., A.M.F., J.M.R., L.S.S., J.C.M., E.M.R., R.J.C., H.C.C., J.TCW., J.P., P.S.C., M.L. and A.W.T. recruited the participants and performed and provided the imaging scans. A.P.S., G.B., M.G.H., T.L.S.B., A.M.F., J.M.R., L.S.S., J.C.M., E.M.R., R.J.C., H.C.C., J.TCW., P.S.C. and A.W.T. provided critical reading of the manuscript. A.M. and D.A.N. contributed to manuscript writing and B.V.Z. wrote the manuscript.

Corresponding author

Correspondence toBerislav V. Zlokovic.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review informationNature thanks Ronald Thomas, Yi-Fen Yen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Regional BBBKtrans constant in eight additional brain regions inAPOE4 carriers and non-carriers (APOE3) with CDR status 0 and 0.5.

BBBKtrans constant in the ITG (a), superior frontal gyrus (SFG,b), caudate nucleus (CN,c), thalamus (Thal,d), striatum (Str,e), subcortical watershed normal-appearing white matter (Subcort. WS NAWM,f), corpus callosum (CC,g), and internal capsule (IC,h) in individuals with CDR 0 bearingAPOE3 (black,n = 128) andAPOE4 (red,n = 68), and with CDR 0.5 bearingAPOE3 (black,n = 14) andAPOE4 (red,n = 25). Continuous lines, median; dotted lines, IQR. Significance by ANCOVAs for main effects and post hoc comparisons controlling for age, sex, and education.

Extended Data Fig. 2 BBB breakdown in the HC and PHG inAPOE4 carriers increases with cognitive domain impairment.

a,b,Ktrans constant in the HC (a) and PHG (b) in individuals with no cognitive domains impaired bearingAPOE3 (black,n = 70) orAPOE4 (red,n = 40); one cognitive domain impaired bearingAPOE3 (n = 18) orAPOE4 (n = 21); and two or more cognitive domains impaired bearingAPOE3 (n = 7) orAPOE4 (n = 12). Continuous lines, median; dotted lines, IQR.c,d,Ktrans (estimated marginal mean ± s.e.m.from ANCOVA models corrected for age, sex, education, CSF Aβ1–42 and pTau status, and HC and PHG volumes) in the HC (c) and PHG (d) in individuals with no cognitive domains impaired bearingAPOE3 (n = 70) orAPOE4 (n = 40); one cognitive domain impaired bearingAPOE3 (n = 18) orAPOE4 (n = 21); and two or more cognitive domains impaired bearingAPOE3 (n = 7) orAPOE4 (n = 12). Significance by ANCOVA for main effects and post hoc comparisons controlling for age, sex, and education. All ANCOVA omnibus tests remained significant at FDR threshold of 0.05.

Extended Data Fig. 3 Regional BBBKtrans constant in eight additional brain regions inAPOE4 carriers andAPOE3 carriers with different degrees of cognitive domain impairment.

Ktrans constant in the ITG (a), SFG (b), CN (c), thalamus (d), striatum (e), subcortical WS NAWM (f), CC (g), and IC (h) in individuals with no cognitive domains impaired bearingAPOE3 (black,n = 70) orAPOE4 (red,n = 40): one cognitive domain impaired bearingAPOE3 (n = 18) orAPOE4 (n = 21); and two or more cognitive domains impaired bearingAPOE3 (n = 7) orAPOE4 (n = 12). Continuous lines, median; dotted lines, IQR. Significance tests from ANCOVAs for main effects and post hoc comparisons controlling for age, sex, and education.

Extended Data Fig. 4 Regional BBBKtrans constant in all studied brain regions inAPOE4 carriers and APOE3 carriers in relation to vascular risk factors.

Ktrans constant in the HC (a), PHG (b), ITG (c), SFG (d), CN (e), thalamus (f), striatum (g), subcortical WS NAWM (h), CC (i), and IC (j) inAPOE3 (green,n = 80) andAPOE4 (brown,n = 42) carriers with 0–1 vascular risk factors (VRFs), andAPOE3 (n = 58) andAPOE4 (n = 51) carriers with 2+ VRFs. Continuous lines, medians; dotted lines, IQR. Significance by ANCOVAs for main effects and post hoc comparisons controlling for age, sex, and education.

Extended Data Fig. 5 Amyloid and tau PET analysis inAPOE4 carriers and correction of18F-AV1451 off-target binding in the choroid plexus.

All studies were performed in individuals with CDR score 0. Amyloid and tau PET studies were conducted using18F- FBB or18F- FBP, and18F- AV1451, respectively. For amyloid PET data analysis, FBP and FBB data sets were combined.a, Uptake of amyloid tracers by the OFC inAPOE4 (n = 29) relative toAPOE3 (n = 45) carriers (voxel-wise two-sample one-tailedt-tests).b, Representative amyloid PET SUVR maps from anAPOE3 homozygote (top) and anAPOE4 carrier (APOE4) (bottom). Slices 1 and 2, regions of interest (ROIs) for amyloid PET and BBB DCE-MRI scans (seee). Arrow, amyloid tracer uptake by OFC. TheAPOE3 andAPOE4 representative images used FBP.c, Uptake of tau tracer shows undetectable tau accumulation inAPOE3 (n = 60) orAPOE4 (n = 37) carriers (voxel-wise two-sample one-tailedt-tests).d, Representative tau PET SUVR maps fromAPOE3 (top) andAPOE4 (bottom) carriers. Slices 1 and 1′, ROIs for tau PET and BBB DCE-MRI scans, respectively (seee).e, Coronal 3D scans of regions studied in Fig.2: HC (red), PHG (green), medial OFC (yellow), and ITG (blue).f, Correction of18F-AV1451 off-target binding in the choroid plexus. Step 1, HC masks were generated from the 3D T1-weighted magnetization prepared–rapid gradient echo (MP-RAGE). Step 2, CP masks were generated from the T1-weighted VIBE post-GBCA image (flip angle, 15°). Step 3, HC and CP masks were overlaid (arrowheads, red). Step 4, areas of CP overlap with HC masks (arrowheads, yellow) were subtracted to obtain CP-corrected HC tau PET signal after adding 6-mm voxel size on top of CP mask generated from DCE data.g, Representative images of HC tau PET signal before (top) and after (bottom) applying the CP correction (arrows and white dotted lines show overlap between HC and CP).

Extended Data Fig. 6 CSF biomarkers of glia and inflammatory response and endothelial and neuronal cell injury inAPOE4 andAPOE3carriers.

a, CSF astrocytic S100B levels in individuals with CDR 0 bearingAPOE3 (black,n = 77) orAPOE4 (red,n = 41), and with CDR 0.5 bearingAPOE3 (n = 39) orAPOE4 (n = 32).b, CSF IL6 levels in individuals with CDR 0 bearingAPOE3 (n = 71) orAPOE4 (n = 47), and with CDR 0.5 bearingAPOE3 (n = 34) orAPOE4 (n = 32).c, CSF IFNγ levels in individuals with CDR 0 bearingAPOE3 (n = 54) orAPOE4 (n = 29), and with CDR 0.5 bearingAPOE3 (n = 25) orAPOE4 (n = 17).d, CSF IL1β levels in individuals with CDR 0 bearingAPOE3 (n = 43) orAPOE4 (n = 18), and with CDR 0.5 bearingAPOE3 (n = 17) orAPOE4 (n = 13). (e) CSF TNFα levels in individuals with CDR 0 bearingAPOE3 (n = 70) orAPOE4 (n = 46), and with CDR 0.5 bearingAPOE3 (n = 34) orAPOE4 (n = 32).f, CSF soluble intercellular adhesion molecule 1 (sICAM1) levels in individuals with CDR 0 bearingAPOE3 (n = 77) orAPOE4 (n = 40), and with CDR 0.5 bearingAPOE3 (n = 39) orAPOE4 (n = 33).g, CSF NSE levels in individuals with CDR 0 bearingAPOE3 (n = 47) orAPOE4 (n = 32), and with CDR 0.5 bearingAPOE3 (n = 29) orAPOE4 (n = 29). Continuous lines, median; dotted lines, IQR.a andb had one outlier each, which were removed before statistical analysis (see Methods). Significance by ANCOVAs for main effects and post hoc comparisons controlling for age, sex, and education.

Extended Data Fig. 7 Decreased CSF Aβ1–42 and increased pTau levels inAPOE4 carriers with cognitive impairment.

a, CSF Aβ1–42 levels in individuals with CDR 0 bearingAPOE3 (black,n = 141) orAPOE4 (red,n = 83) and with CDR 0.5 bearingAPOE3 (n = 39) orAPOE4 (n = 41).b, CSF Aβ1–42 levels inAPOE3 (n = 89) andAPOE4 (n = 55) carriers with no cognitive domains impaired,APOE3 (n = 29) andAPOE4 (n = 31) carriers with one cognitive domain impaired, andAPOE3 (n = 17) andAPOE4 (n = 14) carriers with two or more cognitive domains impaired.c, CSF Aβ1-42 levels (estimated marginal means ± s.e.m. from ANCOVA models corrected for age, sex, education, and CSF sPDGFRβ levels) in individuals with CDR 0 bearingAPOE3 (n = 141) orAPOE4 (n = 83) and with CDR 0.5 bearingAPOE3 (n = 39) orAPOE4 (n = 41).d, CSF pTau levels in individuals with CDR 0 bearingAPOE3 (n = 141) orAPOE4 (n = 82) and with CDR 0.5 bearingAPOE3 (n = 39) orAPOE4 (n = 43).e, CSF pTau levels inAPOE3 (n = 89) andAPOE4 (n = 56) carriers with no cognitive domains impaired,APOE3 (n = 29) andAPOE4 (n = 30) carriers with one cognitive domain impaired, andAPOE3 (n = 17) andAPOE4 (n = 15) carriers with two or more cognitive domains impaired.f, CSF pTau levels (estimated marginal means ± s.e.m. from ANCOVA models corrected for age, sex, education, and CSF sPDGFRβ levels) in individuals with CDR 0 bearingAPOE3 (n = 141) orAPOE4 (red,n = 82) and with CDR 0.5 bearingAPOE3 (n = 39) orAPOE4 (n = 43). Violin plots: continuous lines, median; dotted lines, IQR. CSF Aβ1–42 and pTau values were log10-transformed before statistical analysis because they had a non-normal distribution. Significance tests from ANCOVAs for main effects and post hoc comparisons controlling for age, sex, and education.

Extended Data Fig. 8 Full scans of western blots.

Full scans of western blots for CypA shown in Fig.4m (top).

Extended Data Table 1APOE3 andAPOE4 carriers studied for regional BBB permeability changes by DCE-MRI
Extended Data Table 2APOE3 andAPOE4 carriers studied for regional amyloid or tau brain accumulation by PET and BBB permeability changes by DCE-MRI
Extended Data Table 3APOE3 andAPOE4 carriers studied for CSF sPDGFRβ levels

Supplementary information

Supplementary Information

This file contains Supplementary Tables 1-5, Supplementary Methods, a Supplementary Discussion and Supplementary References.

Rights and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Montagne, A., Nation, D.A., Sagare, A.P.et al.APOE4 leads to blood–brain barrier dysfunction predicting cognitive decline.Nature581, 71–76 (2020). https://doi.org/10.1038/s41586-020-2247-3

Download citation

This article is cited by

Access through your institution
Buy or subscribe

Associated content

Collection

Aging, longevity and age-related diseases

Risk factor for Alzheimer’s disease breaks the blood–brain barrier

  • Makoto Ishii
  • Costantino Iadecola
NatureNews & Views

Advertisement

Search

Advanced search

Quick links

Nature Briefing

Sign up for theNature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox.Sign up for Nature Briefing
[8]ページ先頭
©2009-2025 Movatter.jp