CSF proteomics identifies early changes in autosomal dominant Alzheimer’s disease

In this high-throughput proteomic study of autosomal dominant Alzheimer's disease (ADAD), we sought to identify early biomarkers in cerebrospinal fluid (CSF) for disease monitoring and treatment strategies. We examined CSF proteins in 286 mutation carriers (MCs) and 177 non-carriers (NCs). The developed multi-layer regression model distinguished proteins with different pseudo-trajectories between these groups. We validated our findings with independent ADAD as well as sporadic AD datasets and employed machine learning to develop and validate predictive models. Our study identified 137 proteins with distinct trajectories between MCs and NCs, including eight that changed before traditional AD biomarkers. These proteins are grouped into three stages: early stage (stress response, glutamate metabolism, neuron mitochondrial damage), middle stage (neuronal death, apoptosis), and late presymptomatic stage (microglial changes, cell communication). The predictive model revealed a six-protein subset that more effectively differentiated MCs from NCs, compared with conventional biomarkers.

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Publication Details

Journal
Cell
Published
2024-09-26
DOI
https://doi.org/10.1016/j.cell.2024.08.049
Citations
49
Primary Topic
Alzheimer's disease research and treatments
Type
article
Field-Weighted Citation Impact
16.00

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article

CSF proteomics identifies early changes in autosomal dominant Alzheimer’s disease

Alan E. Renton, Ignacio Álvarez, Qisi Wu, Richard J. Perrin et al.
49 citations
Cell
Alzheimer's disease research and treatments
16.00
article

CSF proteomics identifies early changes in autosomal dominant Alzheimer’s disease

Alan E. Renton, Ignacio Álvarez, Qisi Wu, Richard J. Perrin, Ezequiel Surace, Nicholas T. Seyfried, Randall John Bateman, Daniel Western, Nelly Joseph‐Mathurin, Allan I. Levey, Pau Pástor, Tony Wyss‐Coray, Johannes Levin, Chengran Yang, Yan V. Sun, Erik C. B. Johnson, Muhammad Ali, Darío Maschi, Ciyang Wang, Yueyao Wang, John Budde, Rawan Tarawneh, Gregory Scott Day, John C. Morris, Ricardo Allegri, Eric McDade, Aleksandra Beric, Priyanka Gorijala, María Victoria Fernández, John M. Ringman, Alison Goate, Brian A. Gordon, Jorge J. Llibre‐Guerra, Haiyan Liu, Yuanyuan Shen, Menghan Liu, Jigyasha Timsina, Anh Do, Gyujin Heo
article en
49 citations

Abstract

In this high-throughput proteomic study of autosomal dominant Alzheimer's disease (ADAD), we sought to identify early biomarkers in cerebrospinal fluid (CSF) for disease monitoring and treatment strategies. We examined CSF proteins in 286 mutation carriers (MCs) and 177 non-carriers (NCs). The developed multi-layer regression model distinguished proteins with different pseudo-trajectories between these groups. We validated our findings with independent ADAD as well as sporadic AD datasets and employed machine learning to develop and validate predictive models. Our study identified 137 proteins with distinct trajectories between MCs and NCs, including eight that changed before traditional AD biomarkers. These proteins are grouped into three stages: early stage (stress response, glutamate metabolism, neuron mitochondrial damage), middle stage (neuronal death, apoptosis), and late presymptomatic stage (microglial changes, cell communication). The predictive model revealed a six-protein subset that more effectively differentiated MCs from NCs, compared with conventional biomarkers.

CellVol. 187(22)
Emory University (US), University of New Mexico (US), Neurosciences Institute (US), Washington University in St. Louis (US), Keck Hospital of USC (US), Jacksonville College (US), LMU Klinikum (DE), University Hospital Mútua de Terrassa (ES), Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia (AR), Mallinckrodt (United States) (US), Instituto de Neurología Cognitiva (AR), Fundació ACE (ES), The Affiliated Yongchuan Hospital of Chongqing Medical University (CN), Institut d'Investigació en Ciències de la Salut Germans Trias i Pujol (ES), Mayo Clinic in Florida (US), Hospital Universitari Germans Trias i Pujol (ES), Ludwig-Maximilians-Universität München (DE), Chongqing Medical University (CN), Stanford University (US), Icahn School of Medicine at Mount Sinai (US)
Alzheimer's Association, Eli Lilly and Company, AstraZeneca, Sanofi, Hope Center for Neurological Disorders, Eisai, Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas, Grifols, National Institutes of Health, National Institute on Aging
Openalex Percentile: Top 1%
Alzheimer's disease research and treatments
16.00
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