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000283146 037__ $$aDZNE-2026-00042
000283146 041__ $$aEnglish
000283146 082__ $$a610
000283146 1001_ $$aBiel, Davina$$b0
000283146 1112_ $$aAlzheimer’s Association International Conference$$cToronto$$d2025-07-27 - 2025-07-31$$gAAIC 25$$wCanada
000283146 245__ $$aPlasma p ‐tau 217 as a suitable biomarker for monitoring cognitive changes in Alzheimer's disease
000283146 260__ $$c2025
000283146 3367_ $$0PUB:(DE-HGF)1$$2PUB:(DE-HGF)$$aAbstract$$babstract$$mabstract$$s1767880784_14470
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000283146 520__ $$aWith 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.
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000283146 650_7 $$2NLM Chemicals$$aBiomarkers
000283146 650_7 $$2NLM Chemicals$$atau Proteins
000283146 650_7 $$2NLM Chemicals$$aAmyloid beta-Peptides
000283146 650_2 $$2MeSH$$aHumans
000283146 650_2 $$2MeSH$$aBiomarkers: blood
000283146 650_2 $$2MeSH$$aMale
000283146 650_2 $$2MeSH$$aFemale
000283146 650_2 $$2MeSH$$aAlzheimer Disease: diagnostic imaging
000283146 650_2 $$2MeSH$$aAlzheimer Disease: pathology
000283146 650_2 $$2MeSH$$aAlzheimer Disease: metabolism
000283146 650_2 $$2MeSH$$atau Proteins: blood
000283146 650_2 $$2MeSH$$aAged
000283146 650_2 $$2MeSH$$aMagnetic Resonance Imaging
000283146 650_2 $$2MeSH$$aPositron-Emission Tomography
000283146 650_2 $$2MeSH$$aLongitudinal Studies
000283146 650_2 $$2MeSH$$aCognitive Dysfunction: diagnostic imaging
000283146 650_2 $$2MeSH$$aAmyloid beta-Peptides
000283146 650_2 $$2MeSH$$aAged, 80 and over
000283146 650_2 $$2MeSH$$aBrain: pathology
000283146 650_2 $$2MeSH$$aBrain: diagnostic imaging
000283146 7001_ $$aSteward, Anna$$b1
000283146 7001_ $$aDewenter, Anna$$b2
000283146 7001_ $$aDehsarvi, Amir$$b3
000283146 7001_ $$aZhu, Zeyu$$b4
000283146 7001_ $$aRoemer-Cassiano, Sebastian$$b5
000283146 7001_ $$aFrontzkowski, Lukas$$b6
000283146 7001_ $$aHirsch, Fabian$$b7
000283146 7001_ $$0P:(DE-2719)9001539$$aBrendel, Matthias$$b8$$udzne
000283146 7001_ $$aFranzmeier, Nicolai$$b9
000283146 773__ $$0PERI:(DE-600)2201940-6$$a10.1002/alz70856_104771$$gVol. 21, no. S2, p. e104771$$nS2$$pe104771$$tAlzheimer's and dementia$$v21$$x1552-5260$$y2025
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