Associations between brain iron deposition and structural Alzheimer’s disease signature in cognitively unimpaired adults

Background Iron dyshomeostasis appears to be an early event in the Alzheimer’s continuum. However, little is known how incipient cerebral iron deposition affects brain structure and function in cognitively unimpaired (CU) individuals. Here, we assessed the relationship between mean intensities in su...

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Published in:Alzheimer's & dementia 2022-12, Vol.18 (S1), p.n/a
Main Authors: Stankeviciute, Laura, Falcon, Carles, Operto, Grégory, Rojas, Santiago, Grau‐Rivera, Oriol, Garcia, Marina, Shekari, Mahnaz, Niñerola‐Baizán, Aida, Perissinotti, Andrés, Minguillón, Carolina, Fauria, Karine, Molinuevo, Jose Luis, Zetterberg, Henrik, Blennow, Kaj, Suárez‐Calvet, Marc, Cacciaglia, Raffaele, Gispert, Juan Domingo
Format: Article
Language:English
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Summary:Background Iron dyshomeostasis appears to be an early event in the Alzheimer’s continuum. However, little is known how incipient cerebral iron deposition affects brain structure and function in cognitively unimpaired (CU) individuals. Here, we assessed the relationship between mean intensities in subcortical nuclei, a proxy measure for iron content, and regional cortical thickness as well as brain metabolism in middle‐aged CU individuals and sought for interactions with biomarkers of Alzheimer’s pathology. Method We included 288 CU adults from the ALFA+ cohort (Table 1). Mean intensities were quantified from T2‐weighted magnetic resonance images in 18 a priori defined subcortical nuclei. Due to the high correlations among regional intensities, we performed a principal component analysis to obtain a single measurement of cerebral iron load (Table 2). Cortical thickness (CT) of the “Alzheimer’s disease (AD) signature” composite region of interest (ROI) was assessed using FreeSurfer v6.0. [18F]fluorodeoxyglucose (FDG) PET scans were used to calculate the standardized uptake value ratio (SUVr) within a meta ROI. We created separate general linear models where cortical thickness and PET meta‐ROIs were the dependent variables, while the first principal component (PC1), age, sex, APOE‐ε4 status, and education level were entered as predictors. In additional models, CSF levels of amyloid‐beta ratio (Aβ42/40) and phosphorylated tau (p‐tau) were entered as covariates. We also tested the interaction between PC1 and CSF Aβ42/40 and pTau status. Statistical significance was set at p
ISSN:1552-5260
1552-5279
DOI:10.1002/alz.066766