Glial reactivity correlates with synaptic dysfunction across aging and Alzheimer's disease
Jul 1, 2025·,,,,,,,,,,
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Francieli Rohden
Pamela C. L. Ferreira
Bruna Bellaver
João Pedro Ferrari-Souza
Cristiano Schaffer Aguzzoli
Carolina Soares
Sarah Abbas
Hussein Zalzale
Guilherme Povala
Firoza Z. Lussier
Douglas Teixeira Leffa
Guilherme Bauer-Negrini
Nesrine Rahmouni
Cécile Tissot
Joseph Therriault
Stijn Servaes
Jenna Stevenson
Andréa L. Benedet
Nicholas J. Ashton
Thomas K. Karikari
Dana L. Tudorascu
Henrik Zetterberg
Kaj Blennow
Eduardo R. Zimmer
Diogo Souza
Pedro Rosa-Neto
Tharick A. Pascoal
Abstract
Previous studies suggest glial and neuronal changes may trigger synaptic dysfunction in Alzheimer’s disease (AD), but the link between their markers and synaptic abnormalities in the living brain remains unclear. We investigated the association between glial reactivity and synaptic dysfunction biomarkers in cerebrospinal fluid (CSF) from 478 individuals in cognitively unimpaired (CU) and cognitively impaired (CI) individuals. We measured amyloid-β (Aβ), phosphorylated tau (pTau181), astrocyte reactivity (GFAP), microglial activation (sTREM2), and synaptic markers (GAP43, neurogranin). CSF GFAP levels were associated with presynaptic and postsynaptic dysfunction, independent of cognitive status or Aβ presence. CSF sTREM2 levels were related to presynaptic markers in cognitively unimpaired and impaired Aβ+ individuals, and to postsynaptic markers in cognitively impaired Aβ+ individuals. Notably, CSF pTau mediated the relationships between GFAP or sTREM2 and synaptic dysfunction. Our findings, validated in two independent cohorts (TRIAD and ADNI), reveal a distinct pattern of glial contribution to synaptic degeneration.
Type
Publication
Nature Communications
Status
Peer-reviewed
Authors
Biomedical Data Scientist
Computational neuroscientist working at the intersection of machine learning, biomedical
imaging, and human genetics in neurodegenerative disease. My work applies deep learning to
high-dimensional medical images, harmonizes imaging measurements across sites and acquisition
protocols, and integrates imaging with genomic, proteomic, and longitudinal clinical data to
characterise Alzheimer’s disease and related dementias, with particular focus on fluid and
imaging biomarkers of neurodegeneration.