CSF total tau as a proxy of synaptic degeneration

Aug 29, 2025·
Carolina Soares
,
Bruna Bellaver
,
Pamela C. L. Ferreira
,
Guilherme Povala
,
Cristiano Schaffer Aguzzoli
,
João Pedro Ferrari-Souza
,
Hussein Zalzale
,
Firoza Z. Lussier
,
Francieli Rohden
,
Sarah Abbas
Guilherme Bauer-Negrini
Guilherme Bauer-Negrini
,
Douglas Teixeira Leffa
,
Andréa L. Benedet
,
Rebecca Langhough
,
Tobey J. Betthauser
,
Bradley T. Christian
,
Rachael E. Wilson
,
Dana L. Tudorascu
,
Pedro Rosa-Neto
,
Thomas K. Karikari
,
Henrik Zetterberg
,
Kaj Blennow
,
Eduardo R. Zimmer
,
Sterling C. Johnson
,
Tharick A. Pascoal
· 0 min read
DOI
Abstract
Cerebrospinal fluid (CSF) total tau (t-tau) is considered a biomarker of neuronal degeneration alongside brain atrophy and fluid neurofilament light chain protein (NfL) in biomarker models of Alzheimer’s disease (AD). However, previous studies show that CSF t-tau correlates strongly with synaptic dysfunction/degeneration biomarkers like neurogranin (Ng) and synaptosomal-associated protein 25 (SNAP25). Here, we compare the association between CSF t-tau and synaptic degeneration and axonal/neuronal degeneration biomarkers in cognitively unimpaired and impaired groups from two independent cohorts. We observe a stronger correlation between CSF t-tau and synaptic biomarkers than neurodegeneration biomarkers in both groups. Synaptic biomarkers explain a greater proportion of variance in CSF t-tau levels compared to neurodegeneration biomarkers. Notably, CSF t-tau levels are elevated in individuals with abnormalities only in synaptic biomarkers, but not in individuals with abnormalities only in neurodegeneration biomarkers. Our findings suggest that CSF t-tau is a closer proxy for synaptic degeneration than for axonal/neuronal degeneration.
Type
Publication
Nature Communications
Status
Peer-reviewed
publications
Guilherme Bauer-Negrini
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.