Skip to main content

Advertisement

SpringerLink
Log in

Quantitative susceptibility mapping demonstrates different patterns of iron overload in subtypes of early-onset Alzheimer’s disease

  • Neuro
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

We aimed to define brain iron distribution patterns in subtypes of early-onset Alzheimer’s disease (EOAD) by the use of quantitative susceptibility mapping (QSM).

Methods

EOAD patients prospectively underwent MRI on a 3-T scanner and concomitant clinical and neuropsychological evaluation, between 2016 and 2019. An age-matched control group was constituted of cognitively healthy participants at risk of developing AD. Volumetry of the hippocampus and cerebral cortex was performed on 3DT1 images. EOAD subtypes were defined according to the hippocampal to cortical volume ratio (HV:CTV). Limbic-predominant atrophy (LPMRI) is referred to HV:CTV ratios below the 25th percentile, hippocampal-sparing (HpSpMRI) above the 75th percentile, and typical-AD between the 25th and 75th percentile. Brain iron was estimated using QSM. QSM analyses were made voxel-wise and in 7 regions of interest within deep gray nuclei and limbic structures. Iron distribution in EOAD subtypes and controls was compared using an ANOVA.

Results

Sixty-eight EOAD patients and 43 controls were evaluated. QSM values were significantly higher in deep gray nuclei (p < 0.001) and limbic structures (p = 0.04) of EOAD patients compared to controls. Among EOAD subtypes, HpSpMRI had the highest QSM values in deep gray nuclei (p < 0.001) whereas the highest QSM values in limbic structures were observed in LPMRI (p = 0.005). QSM in deep gray nuclei had an AUC = 0.92 in discriminating HpSpMRI and controls.

Conclusions

In early-onset Alzheimer’s disease patients, we observed significant variations of iron distribution reflecting the pattern of brain atrophy. Iron overload in deep gray nuclei could help to identify patients with atypical presentation of Alzheimer’s disease.

Key Points

• In early-onset AD patients, QSM indicated a significant brain iron overload in comparison with age-matched controls.

• Iron load in limbic structures was higher in participants with limbic-predominant subtype.

• Iron load in deep nuclei was more important in participants with hippocampal-sparing subtype.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Abbreviations

AD:

Alzheimer’s disease

CTV:

Cortical total volume

EOAD:

Early-onset Alzheimer’s disease

HpSpMRI :

Hippocampal-sparing

HV:

Hippocampal volume

LPMRI :

Limbic-predominant

MMSE:

Mini-mental status examination

NPI:

Neuropsychiatric inventory

QSM:

Quantitative susceptibility mapping

tADMRI :

Typical-AD

VAT:

Visual association test

VMI:

Visual-motor integration

References

  1. Ferreira D, Nordberg A, Westman E (2020) Biological subtypes of Alzheimer disease: a systematic review and meta-analysis. Neurology 94:436–448. https://doi.org/10.1212/WNL.0000000000009058

    Article  Google Scholar 

  2. Risacher SL, Anderson WH, Charil A et al (2017) Alzheimer disease brain atrophy subtypes are associated with cognition and rate of decline. Neurology 89:2176–2186

    Article  Google Scholar 

  3. Whitwell JL, Dickson DW, Murray ME et al (2012) Neuroimaging correlates of pathologically defined subtypes of Alzheimer’s disease: a case-control study. Lancet Neurol 11:868–877

    Article  Google Scholar 

  4. Ossenkoppele R, Cohn-Sheehy BI, La Joie R et al (2015) Atrophy patterns in early clinical stages across distinct phenotypes of Alzheimer’s disease. Hum Brain Mapp 36:4421–4437

    Article  Google Scholar 

  5. Palasi A, Gutierrez-Iglesias B, Alegret M et al (2015) Differentiated clinical presentation of early and late-onset Alzheimer’s disease: is 65 years of age providing a reliable threshold? J Neurol 262:1238–1246

    Article  CAS  Google Scholar 

  6. Kvello-Alme M, Bråthen G, White LR, Sando SB (2021) Time to diagnosis in young onset Alzheimer’s disease: a population-based study from Central Norway. J Alzheimers Dis 82:965–974. https://doi.org/10.3233/JAD-210090

  7. Wang Y, Liu T (2015) Quantitative susceptibility mapping (QSM): decoding MRI data for a tissue magnetic biomarker. Magn Reson Med 73:82–101

    Article  CAS  Google Scholar 

  8. Moon Y, Han SH, Moon WJ (2016) Patterns of brain iron accumulation in vascular dementia and Alzheimer’s dementia using quantitative susceptibility mapping imaging. J Alzheimers Dis 51:737–745

    Article  CAS  Google Scholar 

  9. Spotorno N, Acosta-Cabronero J, Stomrud E et al (2020) Relationship between cortical iron and tau aggregation in Alzheimer’s disease. Brain 143:1341–1349

    Article  Google Scholar 

  10. Ayton S, Wang Y, Diouf I et al (2019) Brain iron is associated with accelerated cognitive decline in people with Alzheimer pathology. Mol Psychiatry 18:019–0375

    Google Scholar 

  11. Koss E, Edland S, Fillenbaum G et al (1996) Clinical and neuropsychological differences between patients with earlier and later onset of Alzheimer’s disease: a CERAD analysis, part XII. Neurology 46:136–141. https://doi.org/10.1212/WNL.46.1.136

    Article  CAS  Google Scholar 

  12. Marshall GA, Fairbanks LA, Tekin S et al (2007) Early-onset Alzheimer’s disease is associated with greater pathologic burden. J Geriatr Psychiatry Neurol 20:29–33. https://doi.org/10.1177/0891988706297086

    Article  Google Scholar 

  13. van Rooden S, Doan NT, Versluis MJ et al (2015) 7T T2*-weighted magnetic resonance imaging reveals cortical phase differences between early- and late-onset Alzheimer’s disease. Neurobiol Aging 36:20–26. https://doi.org/10.1016/j.neurobiolaging.2014.07.006

    Article  Google Scholar 

  14. McKhann GM, Knopman DS, Chertkow H et al (2011) The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7:263–269

    Article  Google Scholar 

  15. Dubois B, Feldman HH, Jacova C et al (2014) Advancing research diagnostic criteria for Alzheimer’s disease: the IWG-2 criteria. Lancet Neurol 13:614–629

    Article  Google Scholar 

  16. Tremblay-Mercier J, Madjar C, Das S et al (2021) Open science datasets from PREVENT-AD, a longitudinal cohort of pre-symptomatic Alzheimer’s disease. Neuroimage Clin 31:102733. https://doi.org/10.1016/j.nicl.2021.102733

  17. Marson DC, Dymek MP, Duke LW, Harrell LE (1997) Subscale validity of the Mattis Dementia Rating Scale. Arch Clin Neuropsychol 12:269–275

  18. Lindeboom J, Schmand B, Tulner L et al (2002) Visual association test to detect early dementia of the Alzheimer type. J Neurol Neurosurg Psychiatry 73:126–133

    Article  CAS  Google Scholar 

  19. Lim CY, Tan PC, Koh C et al (2015) Beery-Buktenica Developmental Test of Visual-Motor Integration (Beery-VMI): lessons from exploration of cultural variations in visual-motor integration performance of preschoolers. Child Care Health Dev 41:213–221

    Article  CAS  Google Scholar 

  20. Cummings JL, Mega M, Gray K et al (1994) The neuropsychiatric inventory: comprehensive assessment of psychopathology in dementia. Neurology 44:2308–2314. https://doi.org/10.1212/wnl.44.12.2308

    Article  CAS  Google Scholar 

  21. Manjon JV, Coupé P (2016) volBrain: an online MRI brain volumetry system. Front Neuroinform 10:30

  22. Liu J, Liu T, de Rochefort L et al (2012) Morphology enabled dipole inversion for quantitative susceptibility mapping using structural consistency between the magnitude image and the susceptibility map. Neuroimage 59:2560–2568

    Article  Google Scholar 

  23. Damulina A, Pirpamer L, Soellradl M et al (2020) Cross-sectional and longitudinal assessment of brain iron level in Alzheimer disease using 3-T MRI. Radiology 296:619–626

    Article  Google Scholar 

  24. Zhu WZ, Zhong WD, Wang W et al (2009) Quantitative MR phase-corrected imaging to investigate increased brain iron deposition of patients with Alzheimer disease. Radiology 253:497–504

    Article  Google Scholar 

  25. Acosta-Cabronero J, Williams GB, Cardenas-Blanco A et al (2013) In vivo quantitative susceptibility mapping (QSM) in Alzheimer’s disease. PloS One 8:e81093. https://doi.org/10.1371/journal.pone.0081093

    Article  Google Scholar 

  26. Bartzokis G, Sultzer D, Mintz J et al (1994) In vivo evaluation of brain iron in Alzheimer’s disease and normal subjects using MRI. Biol Psychiatry 35:480–487. https://doi.org/10.1016/0006-3223(94)90047-7

    Article  CAS  Google Scholar 

  27. Behrens TEJ, Johansen-Berg H, Woolrich MW et al (2003) Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging. Nat Neurosci 6:750–757. https://doi.org/10.1038/nn1075

    Article  CAS  Google Scholar 

  28. Tziortzi AC, Haber SN, Searle GE et al (2013) Connectivity-based functional analysis of dopamine release in the striatum using diffusion-weighted MRI and positron emission tomography. Cereb Cortex 24:1165–1177. https://doi.org/10.1093/cercor/bhs397

    Article  Google Scholar 

  29. Winkler AM, Ridgway GR, Webster MA et al (2014) Permutation inference for the general linear model. Neuroimage 92:381–397

    Article  Google Scholar 

  30. Meadowcroft MD, Connor JR, Smith MB, Yang QX (2009) MRI and histological analysis of beta-amyloid plaques in both human Alzheimer’s disease and APP/PS1 transgenic mice. J Magn Reson Imaging 29:997–1007

    Article  Google Scholar 

  31. Zeineh MM, Chen Y, Kitzler HH et al (2015) Activated iron-containing microglia in the human hippocampus identified by magnetic resonance imaging in Alzheimer disease. Neurobiol Aging 36:2483–2500. https://doi.org/10.1016/j.neurobiolaging.2015.05.022

    Article  CAS  Google Scholar 

  32. Cho H, Seo SW, Kim J-H et al (2013) Amyloid deposition in early onset versus late onset Alzheimer’s disease. J Alzheimers Dis JAD 35:813–821. https://doi.org/10.3233/JAD-121927

    Article  CAS  Google Scholar 

  33. Cho H, Seo SW, Kim J-H et al (2013) Changes in subcortical structures in early- versus late-onset Alzheimer’s disease. Neurobiol Aging 34:1740–1747. https://doi.org/10.1016/j.neurobiolaging.2013.01.001

    Article  Google Scholar 

  34. Harper L, Bouwman F, Burton EJ et al (2017) Patterns of atrophy in pathologically confirmed dementias: a voxelwise analysis. J Neurol Neurosurg Psychiatry 88:908–916. https://doi.org/10.1136/jnnp-2016-314978

    Article  Google Scholar 

  35. Janocko NJ, Brodersen KA, Soto-Ortolaza AI et al (2012) Neuropathologically defined subtypes of Alzheimer’s disease differ significantly from neurofibrillary tangle-predominant dementia. Acta Neuropathol 124:681–692

    Article  Google Scholar 

  36. Murray ME, Graff-Radford NR, Ross OA et al (2011) Neuropathologically defined subtypes of Alzheimer’s disease with distinct clinical characteristics: a retrospective study. Lancet Neurol 10:785–796. https://doi.org/10.1016/S1474-4422(11)70156-9

    Article  Google Scholar 

  37. Rüb U, Stratmann K, Heinsen H et al (2016) Hierarchical distribution of the tau cytoskeletal pathology in the thalamus of Alzheimer’s disease patients. J Alzheimers Dis JAD 49:905–915. https://doi.org/10.3233/JAD-150639

    Article  CAS  Google Scholar 

  38. Wang Z, Zeng Y-N, Yang P et al (2019) Axonal iron transport in the brain modulates anxiety-related behaviors. Nat Chem Biol 15:1214–1222. https://doi.org/10.1038/s41589-019-0371-x

    Article  CAS  Google Scholar 

  39. Nikseresht S, Bush AI, Ayton S (2019) Treating Alzheimer’s disease by targeting iron. Br J Pharmacol 176:3622–3635. https://doi.org/10.1111/bph.14567

    Article  CAS  Google Scholar 

  40. Deh K, Kawaji K, Bulk M et al (2019) Multicenter reproducibility of quantitative susceptibility mapping in a gadolinium phantom using MEDI+0 automatic zero referencing. Magn Reson Med 81:1229–1236. https://doi.org/10.1002/mrm.27410

    Article  CAS  Google Scholar 

  41. Cogswell PM, Wiste HJ, Senjem ML et al (2021) Associations of quantitative susceptibility mapping with Alzheimer’s disease clinical and imaging markers. Neuroimage 224:117433

    Article  CAS  Google Scholar 

  42. van Rooden S, Versluis MJ, Liem MK et al (2014) Cortical phase changes in Alzheimer’s disease at 7T MRI: a novel imaging marker. Alzheimers Dement 10:e19–e26. https://doi.org/10.1016/j.jalz.2013.02.002

    Article  Google Scholar 

Download references

Funding

The authors state that this work has not received any funding.

Author information

Authors and Affiliations

  1. Inserm, U1172 – LilNCog – Lille Neuroscience & Cognition, Univ Lille, F-59000, Lille, France

    Grégory Kuchcinski, Renaud Lopes, Thibaud Lebouvier, David Devos, Florence Pasquier, Xavier Leclerc & Jean-Pierre Pruvo

  2. UMS 2014 – US 41 – PLBS – Plateformes Lilloises en Biologie & Santé, Univ Lille, F-59000, Lille, France

    Grégory Kuchcinski, Renaud Lopes, Xavier Leclerc & Jean-Pierre Pruvo

  3. Department of Neuroradiology, CHU Lille, Rue Emile Laine, F-59000, Lille, France

    Grégory Kuchcinski, Lucas Patin, Xavier Leclerc & Jean-Pierre Pruvo

  4. Memory Center – CNR MAJ, DISTALZ-LICEND, F-59000, Lille, France

    Mélanie Leroy, Xavier Delbeuck, Adeline Rollin-Sillaire, Thibaud Lebouvier & Florence Pasquier

  5. Department of Neurology, CHU Lille, F-59000, Lille, France

    Adeline Rollin-Sillaire, Thibaud Lebouvier & Florence Pasquier

  6. Department of Radiology, Weill Cornell Medical College, New York, NY, USA

    Yi Wang & Pascal Spincemaille

  7. Neuroimagerie diagnostique et thérapeutique, CHU de Bordeaux, F-33000, Bordeaux, France

    Thomas Tourdias

  8. Neurocentre Magendie, Inserm, U1215, Université de Bordeaux, F-33000, Bordeaux, France

    Thomas Tourdias

  9. Radiology Department, University of California Davis School of Medicine, Sacramento, CA, USA

    Lotfi Hacein-Bey

  10. Department of Pharmacology, CHU Lille, F-59000, Lille, France

    David Devos

  11. Department of Imaging, Lille Catholic Hospitals, Lille Catholic University, F-59000, Lille, France

    Sébastien Verclytte

Authors
  1. Grégory Kuchcinski
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lucas Patin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Renaud Lopes
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mélanie Leroy
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xavier Delbeuck
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Adeline Rollin-Sillaire
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Thibaud Lebouvier
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Yi Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Pascal Spincemaille
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Thomas Tourdias
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Lotfi Hacein-Bey
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. David Devos
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Florence Pasquier
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Xavier Leclerc
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Jean-Pierre Pruvo
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Sébastien Verclytte
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Grégory Kuchcinski.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Pr Sébastien Verclytte.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors has significant statistical expertise.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• prospective

• cross-sectional study / observational

• performed at one institution

Additional information

Publisher’s note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kuchcinski, G., Patin, L., Lopes, R. et al. Quantitative susceptibility mapping demonstrates different patterns of iron overload in subtypes of early-onset Alzheimer’s disease. Eur Radiol 33, 184–195 (2023). https://doi.org/10.1007/s00330-022-09014-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-022-09014-9

Keywords

Search

Navigation