Literature DB >> 28899019

Cerebral quantitative susceptibility mapping predicts amyloid-β-related cognitive decline.

Scott Ayton1, Amir Fazlollahi2,3, Pierrick Bourgeat2,3, Parnesh Raniga2, Amanda Ng4, Yen Ying Lim1, Ibrahima Diouf1,2, Shawna Farquharson1,4, Jurgen Fripp2,3, David Ames5,6, James Doecke2,3, Patricia Desmond7, Roger Ordidge4, Colin L Masters1,3, Christopher C Rowe1,8, Paul Maruff1,9, Victor L Villemagne1,8, Olivier Salvado2,3, Ashley I Bush1,3.   

Abstract

See Derry and Kent (doi:10.1093/awx167) for a scientific commentary on this article.The large variance in cognitive deterioration in subjects who test positive for amyloid-β by positron emission tomography indicates that convergent pathologies, such as iron accumulation, might combine with amyloid-β to accelerate Alzheimer's disease progression. Here, we applied quantitative susceptibility mapping, a relatively new magnetic resonance imaging method sensitive to tissue iron, to assess the relationship between iron, amyloid-β load, and cognitive decline in 117 subjects who underwent baseline magnetic resonance imaging and amyloid-β positron emission tomography from the Australian Imaging, Biomarkers and Lifestyle study (AIBL). Cognitive function data were collected every 18 months for up to 6 years from 100 volunteers who were either cognitively normal (n = 64) or diagnosed with mild cognitive impairment (n = 17) or Alzheimer's disease (n = 19). Among participants with amyloid pathology (n = 45), higher hippocampal quantitative susceptibility mapping levels predicted accelerated deterioration in composite cognition tests for episodic memory [β(standard error) = -0.169 (0.034), P = 9.2 × 10-7], executive function [β(standard error) = -0.139 (0.048), P = 0.004), and attention [β(standard error) = -0.074 (0.029), P = 0.012]. Deteriorating performance in a composite of language tests was predicted by higher quantitative susceptibility mapping levels in temporal lobe [β(standard error) = -0.104 (0.05), P = 0.036] and frontal lobe [β(standard error) = -0.154 (0.055), P = 0.006]. These findings indicate that brain iron might combine with amyloid-β to accelerate clinical progression and that quantitative susceptibility mapping could be used in combination with amyloid-β positron emission tomography to stratify individuals at risk of decline.
© The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Entities:  

Keywords:  Alzheimer’s; MRI; Quantitative Susceptibility Mapping; cognitive decline; iron

Mesh:

Substances:

Year:  2017        PMID: 28899019     DOI: 10.1093/brain/awx137

Source DB:  PubMed          Journal:  Brain        ISSN: 0006-8950            Impact factor:   13.501


  80 in total

1.  Targeting Iron Dyshomeostasis for Treatment of Neurodegenerative Disorders.

Authors:  Niels Bergsland; Eleonora Tavazzi; Ferdinand Schweser; Dejan Jakimovski; Jesper Hagemeier; Michael G Dwyer; Robert Zivadinov
Journal:  CNS Drugs       Date:  2019-11       Impact factor: 5.749

2.  Automated adaptive preconditioner for quantitative susceptibility mapping.

Authors:  Zhe Liu; Yan Wen; Pascal Spincemaille; Shun Zhang; Yihao Yao; Thanh D Nguyen; Yi Wang
Journal:  Magn Reson Med       Date:  2019-08-11       Impact factor: 4.668

Review 3.  Treating Alzheimer's disease by targeting iron.

Authors:  Sara Nikseresht; Ashley I Bush; Scott Ayton
Journal:  Br J Pharmacol       Date:  2019-02-11       Impact factor: 8.739

Review 4.  Revisiting the intersection of amyloid, pathologically modified tau and iron in Alzheimer's disease from a ferroptosis perspective.

Authors:  Paul J Derry; Muralidhar L Hegde; George R Jackson; Rakez Kayed; James M Tour; Ah-Lim Tsai; Thomas A Kent
Journal:  Prog Neurobiol       Date:  2019-10-08       Impact factor: 11.685

5.  Effects of lipoic acid supplementation on age- and iron-induced memory impairment, mitochondrial DNA damage and antioxidant responses.

Authors:  Patrícia Molz; Betânia Souza de Freitas; Vanise Hallas Uberti; Kesiane Mayra da Costa; Luiza Wilges Kist; Maurício Reis Bogo; Nadja Schröder
Journal:  Eur J Nutr       Date:  2021-03-18       Impact factor: 5.614

6.  Low cortical iron and high entorhinal cortex volume promote cognitive functioning in the oldest-old.

Authors:  Jiri M G van Bergen; Xu Li; Frances C Quevenco; Anton F Gietl; Valerie Treyer; Sandra E Leh; Rafael Meyer; Alfred Buck; Philipp A Kaufmann; Roger M Nitsch; Peter C M van Zijl; Christoph Hock; Paul G Unschuld
Journal:  Neurobiol Aging       Date:  2017-12-20       Impact factor: 4.673

7.  Dipole modeling of multispectral signal for detecting metallic biopsy markers during MRI-guided breast biopsy: a pilot study.

Authors:  Sarah Eskreis-Winkler; Katherine Simon; Melissa Reichman; Pascal Spincemaille; Thanh Nguyen; Youngwook Kee; Junghun Cho; Paul J Christos; Michele Drotman; Martin R Prince; Elizabeth A Morris; Yi Wang
Journal:  Magn Reson Med       Date:  2019-10-21       Impact factor: 4.668

8.  High iron intake is associated with poor cognition among Chinese old adults and varied by weight status-a 15-y longitudinal study in 4852 adults.

Authors:  Zumin Shi; Ming Li; Youfa Wang; Jianghong Liu; Tahra El-Obeid
Journal:  Am J Clin Nutr       Date:  2019-01-01       Impact factor: 7.045

9.  Simultaneous quantitative susceptibility mapping and Flutemetamol-PET suggests local correlation of iron and β-amyloid as an indicator of cognitive performance at high age.

Authors:  J M G van Bergen; X Li; F C Quevenco; A F Gietl; V Treyer; R Meyer; A Buck; P A Kaufmann; R M Nitsch; P C M van Zijl; C Hock; P G Unschuld
Journal:  Neuroimage       Date:  2018-03-13       Impact factor: 6.556

10.  Altered brain iron content and deposition rate in Huntington's disease as indicated by quantitative susceptibility MRI.

Authors:  Lin Chen; Jun Hua; Christopher A Ross; Shuhui Cai; Peter C M van Zijl; Xu Li
Journal:  J Neurosci Res       Date:  2018-11-29       Impact factor: 4.164

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