Alzheimer disease (AD) encompasses multiple biological scales, spanning molecular factors, cells, tissues, and behavioral manifestations. The interplay among these scales in shaping the clinical phenotype is not yet fully comprehended. In particular, there is great interest in understanding the heterogeneity of the clinical aspects of AD in order to improve treatment and prevention, by targeting those aspects most susceptible to the disease. Here we employed a systems biology approach to address this issue, utilizing multilayer network analysis and deep phenotyping. This integrative analysis incorporated genomics, cerebrospinal fluid biomarkers, tau and amyloid beta, PET imaging, brain MRI data, risk factors, and clinical information (cognitive tests scores, Clinical Dementia Rating and clinical diagnosis) obtained through the ADNI collaboration. Multilayer networks were built based on mutual information between the elements of each layer and between layers. Boolean simulations allowed us to identify paths that transmit dynamic information across layers. The most prominent path that significantly predicted the average cognitive phenotype included the PET radiotracer fluorodeoxyglucose (FDG) in the posterior cingulate. Combinations of different symptomatic variables, mainly related to mental health (depression, mood swings, drowsiness) and vascular features (hypertension, cardiovascular history), were also part of the paths explaining the average phenotype. Our results show that integrating the flow of information across biological scales reveals relevant paths for AD, which can be subsequently explored as potential biomarkers or therapeutic targets.

PV has received consultancy fees and holds stocks in Bionure Investment, Accure Therapeutics, Attune Neurosciences, QMENTA, CLight, NeuroPrex, Spiral Therapeutics, and Adhera Health, none related to this study. PV holds patent rights and has received royalties and consultancy fees from Oculis Holding AG for using OCS-05 (aka BN201) to treat optic neuritis (NCT04762017). JDG is currently an employee at Astra-Zeneca, but his contribution were done before joining the company. All the other authors have no conflict of interest.

This work was supported by project PID2021-127311NB-I00 financed by the Spanish573 Ministry of Science and Innovation, the Spanish State Research Agency and FEDER574 (MICIN/AEI/10.13039/ 501100011033/FEDER), by the Maria de Maeztu Programme575 for Units of Excellence in R&D (project CEX2018-000792-M), and by the ICREA576 Academia programme

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Data used in preparation of this article were obtained from the Alzheimer Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how to apply/ADNI Acknowledgement List.pdf

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Data used in preparation of this article were obtained from the Alzheimer Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how to apply/ADNI Acknowledgement List.pdf

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