Effects of ApoE4 and ApoE2 genotypes on subcortical magnetic susceptibility and microstructure in 27,535 participants from the UK Biobank
Open accessAbstract
Disrupted iron homeostasis is associated with several neurodegenerative diseases, including Alzheimer's disease (AD), and may be partially modulated by genetic risk factors. Here we evaluated whether subcortical iron deposition is associated with ApoE genotype, which substantially affects risk for late-onset AD. We evaluated differences in subcortical quantitative susceptibility mapping (QSM), a type of MRI sensitive to cerebral iron deposition, between either ApoE4 (E3E4+E4E4) or ApoE2 (E2E3+E2E2) carriers and E3 homozygotes (E3E3) in 27,535 participants from the UK Biobank (age: 45-82 years). We found that ApoE4 carriers had higher hippocampal (d=0.036; p=0.012) and amygdalar (d=0.035; p=0.013) magnetic susceptibility, particularly individuals aged 65 years or older, while those carrying ApoE2 (which protects against AD) had higher QSM only in the hippocampus (d=0.05; p=0.006), particularly those under age 65. Secondary diffusion MRI microstructural associations in these regions revealed greater diffusivity and less diffusion restriction in E4 carriers, however no differences were detected in E2 carriers. Disease risk conferred by ApoE4 may be linked with higher subcortical iron burden in conjunction with inflammation or neuronal loss in aging individuals, while ApoE2 associations may not necessarily reflect unhealthy iron deposits earlier in life.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
More recent investigations have interrogated cortical or subcortical gray matter with inconclusive findings.
“more recent investigations have interrogated cortical or subcortical gray matter with inconclusive findings [9, 10].”
From Effects of APOE2 and APOE4 on brain microstructure in older adults: modification by age, sex, and cognitive status (2024) · cites this paper as [10]
The Methods and Discussion (pages unavailable in our copy) show that this study investigated subcortical gray matter, and the Discussion notes inconsistent effects on diffusion metrics and a need for replication. But it also reports an association between ApoE4 and higher subcortical iron burden; it does not characterize its overall findings as inconclusive or investigate cortical gray matter. (LLM, conf=0.81)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain
Zhang, H., Schneider, T., Wheeler-Kingshott, C. A., & Alexander, D. C. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.03.072 · Cited by 3,288 · see citing works
Multimodal population brain imaging in the UK Biobank prospective epidemiological study
Miller, K. L., Alfaro-Almagro, F., Bangerter, N. K., Thomas, D. L., Yacoub, E., Xu, J., Bartsch, A. J., Jbabdi, S., Sotiropoulos, S. N., Andersson, J. L. R., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4393 · Cited by 2,020 · see citing works
The role of iron in brain ageing and neurodegenerative disorders
Ward, R. J., Zucca, F. A., Duyn, J. H., Crichton, R. R., & Zecca, L. (2014) · The Lancet Neurology · doi:10.1016/s1474-4422(14)70117-6 · Cited by 1,819 · see citing works
Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank
Alfaro‐Almagro, F., Jenkinson, M., Bangerter, N. K., Andersson, J., Griffanti, L., Douaud, G., Sotiropoulos, S. N., Jbabdi, S., Hernández-Fernández, M., Vallée, E., et al. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.10.034 · Cited by 1,563 · see citing works
Genome-wide association studies of brain imaging phenotypes in UK Biobank
Elliott, L. T., Sharp, K., Alfaro-Almagro, F., Shi, S., Miller, K. L., Douaud, G., Marchini, J., & Smith, S. M. (2018) · Nature · doi:10.1038/s41586-018-0571-7 · Cited by 834 · see citing works
Automated brain extraction of multisequence MRI using artificial neural networks
Isensee, F., Schell, M., Pflüger, I., Brugnara, G., Bonekamp, D., Neuberger, U., Wick, A., Schlemmer, H., Heiland, S., Wick, W., et al. (2019) · Human Brain Mapping · doi:10.1002/hbm.24750 · Cited by 731 · see citing works
Quantitative susceptibility mapping: current status and future directions
Haacke, E. M., Liu, S., Buch, S., Zheng, W., Wu, D., & Ye, Y. (2015) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2014.09.004 · Cited by 511 · see citing works
A histochemical study of iron, transferrin, and ferritin in Alzheimer's diseased brains
Connor, J. R., Menzies, S. L., Martin, S. M. S., & Mufson, E. J. (1992) · Journal of Neuroscience Research · doi:10.1002/jnr.490310111 · Cited by 468 · see citing works
Liu, T., Wisnieff, C., Lou, M., Chen, W., Spincemaille, P., & Wang, Y. (2012) · Magnetic Resonance in Medicine · doi:10.1002/mrm.24272 · Cited by 362 · see citing works
Overview of quantitative susceptibility mapping
Deistung, A., Schweser, F., & Reichenbach, J. R. (2017) · NMR in Biomedicine · doi:10.1002/nbm.3569 · Cited by 338 · see citing works
Ayton, S., Faux, N. G., Bush, A. I., Initiative, A. D. N., Weiner, M. W., Aisen, P. S. S., Petersen, R. C., Jack, C. R., Jagust, W., Trojanowki, J. Q., et al. (2015) · Nature Communications · doi:10.1038/ncomms7760 · Cited by 330 · see citing works
Self-citationAPOE2: protective mechanism and therapeutic implications for Alzheimer’s disease
Li, Z., Shue, F., Zhao, N., Shinohara, M., & Bu, G. (2020) · Molecular Neurodegeneration · doi:10.1186/s13024-020-00413-4 · Cited by 306 · see citing works
In Vivo Quantitative Susceptibility Mapping (QSM) in Alzheimer's Disease
Acosta‐Cabronero, J., Williams, G. B., Cárdenas‐Blanco, A., Arnold, R. J., Lupson, V. C., & Nestor, P. J. (2013) · PLoS ONE · doi:10.1371/journal.pone.0081093 · Cited by 288 · see citing works
Raven, E. P., Lu, P. H., Tishler, T. A., Heydari, P., & Bartzokis, G. (2013) · Journal of Alzheimer s Disease · doi:10.3233/jad-130209 · Cited by 270 · see citing works
Design and Validation of Diffusion MRI Models of White Matter
Jelescu, I. O., & Budde, M. D. (2017) · Frontiers in Physics · doi:10.3389/fphy.2017.00061 · Cited by 240 · see citing works
Background field removal by solving the Laplacian boundary value problem
Zhou, D., Liu, T., Spincemaille, P., & Wang, Y. (2014) · NMR in Biomedicine · doi:10.1002/nbm.3064 · Cited by 238 · see citing works
Ravanfar, P., Loi, S. M., Syeda, W. T., Rheenen, T. E. V., Bush, A. I., Desmond, P. M., Cropley, V., Lane, D. J. R., Opazo, C., Moffat, B. A., et al. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.618435 · Cited by 231 · see citing works
Reduced Hippocampal Volume in Healthy Young ApoE4 Carriers: An MRI Study
O’Dwyer, L., Lamberton, F., Matura, S., Tanner, C. J., Scheibe, M., Miller, J., Rujescu, D., Prvulovic, D., & Hampel, H. J. (2012) · PLoS ONE · doi:10.1371/journal.pone.0048895 · Cited by 190 · see citing works
Colocalization of cerebral iron with Amyloid beta in Mild Cognitive Impairment
Bergen, J. M. G. V., Li, X., Hua, J., Schreiner, S. J., Steininger, S. C., Quevenco, F. C., Wyss, M. T., Gietl, A. F., Treyer, V., Leh, S. E., et al. (2016) · Scientific Reports · doi:10.1038/srep35514 · Cited by 181 · see citing works
Variants in TF and HFE Explain ∼40% of Genetic Variation in Serum-Transferrin Levels
Benyamin, B., McRae, A. F., Zhu, G., Gordon, S. D., Henders, A. K., Palotie, A. V., Peltonen, L., Martin, N. G., Montgomery, G. W., Whitfield, J. B., et al. (2008) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2008.11.011 · Cited by 169 · see citing works
Schiepers, O. J. G., Harris, S. E., Gow, A. J., Pattie, A., Brett, C. E., Starr, J. M., & Deary, I. J. (2011) · Molecular Psychiatry · doi:10.1038/mp.2010.137 · Cited by 159 · see citing works
A genome-wide meta-analysis yields 46 new loci associating with biomarkers of iron homeostasis
Bell, S., Rigas, A. S., Magnússon, M. K., Ferkingstad, E., Allara, E., Björnsdóttir, G., Ramond, A., Sørensen, E., Halldorsson, G. H., Paul, D. S., et al. (2021) · Communications Biology · doi:10.1038/s42003-020-01575-z · Cited by 157 · see citing works
Zeineh, M. M., Chen, Y., Kitzler, H. H., Hammond, R., Vogel, H., & Rutt, B. K. (2015) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2015.05.022 · Cited by 151 · see citing works
APOE genotype and cognitive functioning in a large age-stratified population sample.
Jorm, A. F., Mather, K. A., Butterworth, P., Anstey, K. J., Christensen, H. M., & Easteal, S. (2007) · Neuropsychology · doi:10.1037/0894-4105.21.1.1 · Cited by 151 · see citing works
Tesseur, I. C., Dorpe, J. V., Bruynseels, K., Bronfman, F. C., Sciot, R., Lommel, A. V., & LEUVEN, F. V. (2000) · American Journal Of Pathology · doi:10.1016/s0002-9440(10)64788-8 · Cited by 141 · see citing works
The effect of increased genetic risk for Alzheimer's disease on hippocampal and amygdala volume
Lupton, M. K., Strike, L. T., Hansell, N. K., Wen, W. X., Mather, K. A., Armstrong, N. J., Thalamuthu, A., McMahon, K. L., de Zubicaray, G. I., Assareh, A. A., et al. (2016) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2015.12.023 · Cited by 120 · see citing works
Self-citationThe Dmipy Toolbox: Diffusion MRI Multi-Compartment Modeling and Microstructure Recovery Made Easy
Fick, R., Wassermann, D., & Rachid Deriche, B. (2019) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2019.00064 · Cited by 89 · see citing works
Jahanshad, N., Omid Kohannim, A., Hibar, D. P., Stein, J. L., McMahon, K. L., de Zubicaray, G. I., Medland, S. E., Montgomery, G. W., Whitfield, J. B., Martin, N. G., et al. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1105543109 · Cited by 79 · see citing works
Self-citationHostage, C. A., Choudhury, K. R., Doraiswamy, P. M., Petrella, J. R., & Initiative, A. D. N. (2013) · PLoS ONE · doi:10.1371/journal.pone.0054483 · Cited by 71 · see citing works
Imaging Alzheimer's genetic risk using diffusion MRI: A systematic review
Harrison, J. R., Bhatia, S., Tan, Z. X., Mirza-Davies, A., Benkert, H., Tax, C. M. W., & Jones, D. K. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102359 · Cited by 48 · see citing works
Multimodal characterization of older APOE2 carriers reveals selective reduction of amyloid load
Grothe, M. J., Villeneuve, S., Dyrba, M., David Bartrés-faz, A., Wirth, M., Initiative, A. D. N., Initiative, A. D. N., Weiner, M. W., Aisen, P., Weiner, M. W., et al. (2017) · Neurology · doi:10.1212/wnl.0000000000003585 · Cited by 47 · see citing works
Self-citationKagerer, S. M., Bergen, J. M. G. V., Li, X., Quevenco, F. C., Gietl, A. F., Studer, S. T., Treyer, V., Meyer, R., Kaufmann, P. A., Nitsch, R. M., et al. (2020) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1002/dad2.12002 · Cited by 35 · see citing works
Zhang, Y., Wei, H., Cronin, M. J., He, N., Yan, F., & Liu, C. (2018) · Data in Brief · doi:10.1016/j.dib.2018.06.005 · Cited by 22 · see citing works
Ferreira, C., Baptista, R., Santos, M. O., da Costa, R. C., de Moura, J. P., & Gonçalves, L. M. M. (2019) · Journal of Lipids · doi:10.1155/2019/1698610 · Cited by 16 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Quantitative susceptibility mapping of brain iron in healthy aging and cognition
Madden, D. J., & Merenstein, J. L. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120401 · Cited by 95 · see citing works
Afsar, A., Castro, M. D. C. C., Soladogun, A. S., & Li, Z. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24087258 · Cited by 42 · see citing works
Reas, E. T., Triebswetter, C., Banks, S. J., & McEvoy, L. K. (2024) · Alzheimer's Research & Therapy · doi:10.1186/s13195-023-01380-w · Cited by 20 · see citing works
“However, others reported no difference in WM integrity for APOE2 carriers [7, 15] and one DTI study identified higher fractional anisotropy for APOE2 carriers than APOE3 homozygotes within a small cluster of the posterior cingulum [14].”
BackgroundResultThe Azalea Hypothesis of Alzheimer Disease: A Functional Iron Deficiency Promotes Neurodegeneration
LeVine, S. M. (2023) · The Neuroscientist · doi:10.1177/10738584231191743 · Cited by 8 · see citing works
Iron load in the normal aging brain measured with QSM and R2* at 7T: findings of the SENIOR cohort
Guevara, M., Roche, S., Brochard, V., Cam, D., Badagbon, J., Leprince, Y., Bottlaender, M., Cointepas, Y., Mangin, J., de Rochefort, L., et al. (2024) · Frontiers in Neuroimaging · doi:10.3389/fnimg.2024.1359630 · Cited by 4 · see citing works
Clocchiatti‐Tuozzo, S., Szejko, N., Rivier, C. A., Renedo, D. B., Huo, S., Sheth, K. N., Gill, T. M., & Falcone, G. J. (2024) · Journal of the American Geriatrics Society · doi:10.1111/jgs.19043 · Cited by 3 · see citing works
- Authors
- Nir, T. M., Zhu, A. H., Gari, I. B., Dixon, D., Islam, T., Villalon-Reina, J. E., Medland, S. E., Thompson, P. M., & Jahanshad, N.
- Affiliations
- University of Southern California (US)
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, 90292, USA
- QIMR Berghofer Medical Research Institute (AU)
- ORCID
- Venue
- Biocomputing 2022
- Publisher
- WORLD SCIENTIFIC
- Published
- 2021-11-28
- Volume
- 27
- Pages
- 121-132
- PMC
- PMC9009383
- OpenAlex
- W4200148621
- Cited by
- 6
- References
- 34
- Citations by year
- 2024: 3 · 2023: 3
- FWCI
- 1.89
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc
- Locations
- Pacific Symposium on Biocomputing (PSB) (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion, cc-by-nc)
- Funders
- National Institute on Aging (R01AG059874, T32AG058507, P30AG066530, U01AG068057)
- National Institute of Biomedical Imaging and Bioengineering (P41EB015922)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Neuroscience › Neurology › Neurological diseases and metabolism
- Keywords
- APOE ε4, APOE2, APOE genotype, quantitative susceptibility mapping, hippocampus, amygdala, Alzheimer's disease risk, iron homeostasis, brain aging, neuroinflammation
- MeSH terms
- Aged, Aged, 80 and over, Alzheimer Disease, Apolipoproteins E, Genotype, United Kingdom, Humans, Middle Aged, Biological Specimen Banks, Computational Biology, Apolipoprotein E4, Apolipoprotein E2
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
