Quantitative susceptibility mapping demonstrates different patterns of iron overload in subtypes of early-onset Alzheimer’s disease.
Eur Radiol. 2022-07-26; 33(1): 184-195
DOI: 10.1007/s00330-022-09014-9
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Kuchcinski G(1)(2)(3), Patin L(4), Lopes R(5)(6), Leroy M(7), Delbeuck X(7), Rollin-Sillaire A(7)(8), Lebouvier T(5)(7)(8), Wang Y(9), Spincemaille P(9), Tourdias T(10)(11), Hacein-Bey L(12), Devos D(5)(13), Pasquier F(5)(7)(8), Leclerc X(5)(6)(4), Pruvo JP(5)(6)(4), Verclytte S(14).
Author information:
(1)Inserm, U1172 – LilNCog – Lille Neuroscience & Cognition, Univ Lille,
F-59000, Lille, France. .
(2)UMS 2014 – US 41 – PLBS – Plateformes Lilloises en Biologie & Santé, Univ
Lille, F-59000, Lille, France. .
(3)Department of Neuroradiology, CHU Lille, Rue Emile Laine, F-59000, Lille,
France. .
(4)Department of Neuroradiology, CHU Lille, Rue Emile Laine, F-59000, Lille,
France.
(5)Inserm, U1172 – LilNCog – Lille Neuroscience & Cognition, Univ Lille,
F-59000, Lille, France.
(6)UMS 2014 – US 41 – PLBS – Plateformes Lilloises en Biologie & Santé, Univ
Lille, F-59000, Lille, France.
(7)Memory Center – CNR MAJ, DISTALZ-LICEND, F-59000, Lille, France.
(8)Department of Neurology, CHU Lille, F-59000, Lille, France.
(9)Department of Radiology, Weill Cornell Medical College, New York, NY, USA.
(10)Neuroimagerie diagnostique et thérapeutique, CHU de Bordeaux, F-33000,
Bordeaux, France.
(11)Neurocentre Magendie, Inserm, U1215, Université de Bordeaux, F-33000,
Bordeaux, France.
(12)Radiology Department, University of California Davis School of Medicine,
Sacramento, CA, USA.
(13)Department of Pharmacology, CHU Lille, F-59000, Lille, France.
(14)Department of Imaging, Lille Catholic Hospitals, Lille Catholic University,
F-59000, Lille, France.
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.
© 2022. The Author(s), under exclusive licence to European Society of Radiology.
DOI: 10.1007/s00330-022-09014-9
PMID: 35881183 [Indexed for MEDLINE]