Grégory Kuchcinski1,2,3, Lucas Patin4, Renaud Lopes5,6, Mélanie Leroy7, Xavier Delbeuck7, Adeline Rollin-Sillaire7,8, Thibaud Lebouvier5,7,8, Yi Wang9, Pascal Spincemaille9, Thomas Tourdias10,11, Lotfi Hacein-Bey12, David Devos5,13, Florence Pasquier5,7,8, Xavier Leclerc5,6,4, Jean-Pierre Pruvo5,6,4, Sébastien Verclytte14. 1. Inserm, U1172 - LilNCog - Lille Neuroscience & Cognition, Univ Lille, F-59000, Lille, France. gregory.kuchcinski@univ-lille.fr. 2. UMS 2014 - US 41 - PLBS - Plateformes Lilloises en Biologie & Santé, Univ Lille, F-59000, Lille, France. gregory.kuchcinski@univ-lille.fr. 3. Department of Neuroradiology, CHU Lille, Rue Emile Laine, F-59000, Lille, France. gregory.kuchcinski@univ-lille.fr. 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.
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.
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.
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