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Brain-age prediction and its associations with glial and synaptic CSF markers

Authors :
Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
Universitat Politècnica de Catalunya. IDEAI-UPC - Intelligent Data sciEnce and Artificial Intelligence Research Group
Cumplido Mayoral, Irene
Milà Alomà, Marta
Falcón Falcón, Carles
Cacciaglia, Raffaele
Minguillón, Carolina
Fauria, Karine
Molinuevo Guix, José Luis
Kollmorgen, Gwendlyn
Suridjan, Ivonne
Wild, Norbert
Zetterberg, Henrik
Blennow, Kaj
Suarez-Calvet, Marc
Vilaplana Besler, Verónica
Domingo Gispert, Juan
Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
Universitat Politècnica de Catalunya. IDEAI-UPC - Intelligent Data sciEnce and Artificial Intelligence Research Group
Cumplido Mayoral, Irene
Milà Alomà, Marta
Falcón Falcón, Carles
Cacciaglia, Raffaele
Minguillón, Carolina
Fauria, Karine
Molinuevo Guix, José Luis
Kollmorgen, Gwendlyn
Suridjan, Ivonne
Wild, Norbert
Zetterberg, Henrik
Blennow, Kaj
Suarez-Calvet, Marc
Vilaplana Besler, Verónica
Domingo Gispert, Juan
Publication Year :
2023

Abstract

Background: MRI-derived brain-age prediction is a promising biomarker of biological brain aging. Accelerated brain aging has been found in Alzheimer’s disease (AD) and other neurodegenerative diseases. However, no previous studies have investigated the relationship between specific pathophysiological pathways in AD and biological brain aging. Here, we studied whether glial activation and synaptic dysfunction are associated with biological brain aging in the earliest stages of the Alzheimer’s continuum. Method: We included 418 cognitively unimpaired individuals (CU) from the ALFA+ study with available structural MRI, and CSF biomarkers of amyloid-ß (Aß42/40) and tau pathology (p-tau181), synaptic dysfunction (neurogranin, GAP43, SYT1, SNAP25), glial activation (sTREM2, YKL40, GFAP, interleukin-6 and S100b) and a-synuclein (Table 1). Aß42/40, neurogranin and the glial activation biomarkers were measured using the Roche NeuroToolKit. We computed brain-age delta as the difference between chronological and predicted brain-age. The latter was estimated using a previously pretrained machine learning algorithm on cerebral morphological measurements on individuals from the UKBioBank cohort (N = 22.000). General linear modeling was used to test the associations between CSF biomarkers and brain-age delta, adjusting by p-tau, age, APOE status and sex. For the biomarkers whose associations were significant, we evaluated the interaction term “biomarker” × AT status while adjusting by age, APOE status and sex. AT staging was performed using pre-established cut-off values. We then used hippocampal volume as a marker of AD-related neurodegeneration and repeated the same association studies with CSF biomarkers, adjusting by p-tau, age, APOE status, sex and TIV. Result: Brain-age delta was negatively associated with CSF sTREM2 (Padjusted<0.001), meaning that younger-appearing brains showed higher levels of this biomarker (Table 1). None of the other biomarkers survived multiple compar<br />Peer Reviewed<br />Postprint (author's final draft)

Details

Database :
OAIster
Notes :
5 p., application/pdf, English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1452496194
Document Type :
Electronic Resource