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 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.