Genius, PatriciaCalle, M LuzRodríguez-Fernández, BlancaMinguillon, CarolinaCacciaglia, RaffaeleGarrido-Martin, DiegoEsteller, ManelNavarro, ArcadiGispert, Juan DomingoVilor-Tejedor, Natalia2025-06-172025-06-172025-02Alzheimers Dement. 2025 Feb;21(2):e14490.https://hdl.handle.net/20.500.12105/26761Traditional multivariate methods for neuroimaging studies overlook the interdependent relationship between brain features. This study addresses this gap by analyzing relative brain volumetric patterns to capture how Alzheimer's disease (AD) and genetics influence brain structure along the disease continuum. This study analyzed data from participants across the AD continuum from the Alzheimer's and Families (ALFA) and Alzheimer's Disease Neuroimaging Initiative (ADNI) studies. Compositional data analysis (CoDA) was exploited to examine relative brain volumetric variations that (1) were linked to different AD stages compared to cognitively unimpaired amyloid-β-negative (CU A-) individuals and (2) varied by AD genetic risk. Disease stage-specific compositional brain scores were identified, differentiating CU A- individuals from those in more advanced stages. Genetic risk-stratified models revealed a broader genetic landscape affecting brain morphology in AD, beyond the well-known apolipoprotein E ε4 allele. CoDA emerges as an alternative multivariate framework to deepen understanding of AD-related structural changes and support targeted interventions for those at higher genetic risk.The research leading to these results has received funding from “la Caixa” Foundation (ID 100010434), under agreement LCF/PR/GN17/50300004, the Health Department of the Catalan Government (Health Research and Innovation Strategic Plan (PERIS) 2016-2020 grant# SLT002/16/00201), and the Alzheimer’s Association and an international anonymous charity foundation through the TriBEKa Imaging Platform project (TriBEKa-17-519007). Additional support has been received from the Universities and Research Secretariat, Ministry of Business and Knowledge of the Catalan Government under the grant no. 2021 SGR 00913. All CRG authors acknowledge the support of the Spanish Ministry of Science, Innovation, and universities to the EMBL partnership, the Centro de Excelencia Severo Ochoa, and the CERCA Programme/Generalitat de Catalunya. N.V.-T. was supported by the Spanish Ministry of Science and Innovation—State Research Agency (IJC2020-043216-I/MCIN/AEI/10.13039/501100011033) and the European Union «NextGenerationEU»/PRTR and currently receives funding from the Spanish Research Agency MICIU/AEI/10.13039/501100011033 (grant RYC2022-038136-I cofunded by the European Union FSE+ and grant PID2022-143106OA-I00 cofunded by the European Union FEDER).engVoRhttp://creativecommons.org/licenses/by-nc-nd/4.0/Alzheimer's disease genetic predispositionbrain imaging geneticscompositional brain scorecompositional data analysismulti phenotype analysisneurodegenerationpolygenic risk scoringAttribution-NonCommercial-NoDerivatives 4.0 International39868465Alzheimers & Dementiaopen access