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  5. Compositional structural brain signatures capture Alzheimer's genetic risk on brain structure along the disease<i>continuum</i>

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Preprint
en
2024

Compositional structural brain signatures capture Alzheimer's genetic risk on brain structure along the disease<i>continuum</i>

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en
2024
DOI: 10.1101/2024.05.08.24307046

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Manel Esteller
Manel Esteller

University of Barcelona

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Patricia Genius
M. Luz Calle
Blanca Rodríguez‐Fernández
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Abstract

Abstract INTRODUCTION Traditional brain imaging genetics studies have primarily focused on how genetic factors influence the volume of specific brain regions, often neglecting the overall complexity of brain architecture and its genetic underpinnings. METHODS This study analyzed data from participants across the Alzheimer’s disease (AD) continuum from the ALFA and ADNI studies. We exploited compositional data analysis to examine relative brain volumetric variations that (i) differentiate cognitively unimpaired (CU) individuals, defined as amyloid-negative (A-) based on CSF profiling, from those at different AD stages, and (ii) associated with increased genetic susceptibility to AD, assessed using polygenic risk scores. RESULTS Distinct brain signatures differentiated CU A-individuals from amyloid-positive MCI and AD. Moreover, disease stage-specific signatures were associated with higher genetic risk of AD. DISCUSSION The findings underscore the complex interplay between genetics and disease stages in shaping brain structure, which could inform targeted preventive strategies and interventions in preclinical AD.

How to cite this publication

Patricia Genius, M. Luz Calle, Blanca Rodríguez‐Fernández, Carolina Minguillón, Raffaele Cacciaglia, Diego Garrido-Martín, Manel Esteller, Arcadi Navarro, Juan Domingo Gispert, Natalia Vilor-Tejedor (2024). Compositional structural brain signatures capture Alzheimer's genetic risk on brain structure along the disease<i>continuum</i>. , DOI: https://doi.org/10.1101/2024.05.08.24307046.

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Publication Details

Type

Preprint

Year

2024

Authors

10

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.1101/2024.05.08.24307046

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