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@INPROCEEDINGS{Biel:283095,
author = {Biel, Davina and Steward, Anna and Dewenter, Anna and
Dehsarvi, Amir and Zhu, Zeyu and Roemer-Cassiano, Sebastian
and Frontzkowski, Lukas and Hirsch, Fabian and Brendel,
Matthias and Franzmeier, Nicolai},
title = {{P}lasma p‐tau217 as a suitable biomarker for monitoring
cognitive changes in {A}lzheimer’s disease},
journal = {Alzheimer's and dementia},
volume = {21},
number = {Suppl 8},
issn = {1552-5260},
reportid = {DZNE-2025-01502},
pages = {e110180},
year = {2025},
abstract = {With the approval of anti-amyloid therapies in Alzheimer's
disease (AD), surrogate biomarkers are urgently needed to
monitor treatment effects that translate into clinical
benefits. Candidate biomarkers, including amyloid-PET,
tau-PET, plasma phosphorylated tau (p-tau), and MRI-assessed
atrophy, capture core pathophysiological changes in AD.
While cross-sectional biomarker assessments are critical for
diagnosis and staging, biomarker change rates may better
reflect disease dynamics, making them more suitable for
monitoring treatment efficacy. Therefore, we determined
which biomarker most effectively tracks cognitive changes in
AD, identifying those best suited for efficient monitoring
of disease-modifying treatments.We leveraged ADNI (N = 108)
and A4 (N = 151) participants with longitudinal AD biomarker
data (global amyloid-PET, temporal meta tau-PET, plasma
p-tau217, MRI-assessed cortical thickness in the AD
signature region) together with cognitive assessments (ADNI:
MMSE, ADAS13, CDR-SB; A4: MMSE, PACC). Linear mixed models
were used to calculate change rates for biomarkers and
cognition. To test whether biomarker changes track cognitive
decline, linear models were applied, to test biomarker
change rates as a predictor of cognitive change rates.
Standardized beta values from bootstrapped linear models
were extracted to compare the strengths of correlations
between biomarkers and cognitive decline. For non-parametric
comparisons, $95\%$ confidence intervals (CIs) of
standardized beta values were compared. Models were
controlled for age, sex, education, and baseline cognition,
with ADNI models additionally adjusted for clinical
status.In both cohorts, changes in temporal tau-PET, plasma
p-tau217, and MRI-assessed cortical thickness were
associated with cognitive decline (ADNI: Figure 1; A4:
Figure 2). Amyloid-PET changes showed no significant
association with cognitive changes (ADNI: Figure 1A+F+K; A4:
Figure 2A+F). Bootstrapping confirmed that tau-PET, plasma
p-tau217, and cortical thickness track cognitive decline,
but not amyloid-PET (ADNI: Figure 1E+J+O; A4: Figure 2E+J).
Overlapping CIs for tau-PET and plasma p-tau217 indicated
comparable predictive accuracy.Our findings demonstrate that
tau-PET and plasma p-tau217 are robust biomarkers for
monitoring cognitive changes, with plasma p-tau217 offering
a cost-effective, scalable alternative for clinical use.
Changes in amyloid-PET do not reliably reflect cognitive
decline, limiting its utility as a treatment monitoring
tool. Although cortical thickness correlates with cognitive
changes, its application is limited by pseudoatrophy and
volume loss induced by anti-amyloid antibody treatments.},
month = {Jul},
date = {2025-07-27},
organization = {Alzheimer’s Association
International Conference, Toronto
(Canada), 27 Jul 2025 - 31 Jul 2025},
keywords = {Humans / Alzheimer Disease: diagnostic imaging / Alzheimer
Disease: pathology / Male / Female / tau Proteins: blood /
Biomarkers: blood / Aged / Magnetic Resonance Imaging /
Positron-Emission Tomography / Longitudinal Studies / Aged,
80 and over / Brain: diagnostic imaging / Brain: pathology /
Amyloid beta-Peptides / tau Proteins (NLM Chemicals) /
Biomarkers (NLM Chemicals) / Amyloid beta-Peptides (NLM
Chemicals)},
cin = {AG Haass},
ddc = {610},
cid = {I:(DE-2719)1110007},
pnm = {352 - Disease Mechanisms (POF4-352)},
pid = {G:(DE-HGF)POF4-352},
typ = {PUB:(DE-HGF)1 / PUB:(DE-HGF)16},
pubmed = {pmid:41433469},
pmc = {pmc:PMC12725594},
doi = {10.1002/alz70862_110180},
url = {https://pub.dzne.de/record/283095},
}