Aug 2026· Alzheimer's Research & Therapy· 0 citations
TL;DR
Ratios combining biomarkers of both amyloid and tau, such as Aβ42/pTau, consistently outperformed individual biomarkers in detecting amyloid and tau PET positivity and enhanced clinical utility by highlighting ambiguous cases requiring further assessment.
Abstract
Accurate detection of Alzheimer’s disease (AD) pathophysiological changes is crucial for research and clinical practice. While positron emission tomography (PET) imaging is an established reference standard for detecting brain amyloid and tau deposition, cerebrospinal fluid (CSF) biomarkers offer a more accessible alternative. This study aimed to identify optimal CSF biomarkers and thresholds for predicting tau and amyloid PET positivity in a memory clinic population.
This cross-sectional, observational study included 220 participants from a memory clinic who underwent amyloid or tau PET scans and CSF biomarker analysis using either Fujirebio INNOTEST or LUMIPULSE assays. CSF biomarker performance was assessed via receiver operating characteristic (ROC) analysis. Double-threshold approaches were implemented to address classification uncertainty.
Ratios combining biomarkers of both amyloid and tau, such as Aβ42/pTau, consistently outperformed individual biomarkers in detecting amyloid and tau PET positivity. For amyloid PET, the Aβ42/pTau ratio achieved an AUC of 0.93 (95% CI: 0.86–0.99) with INNOTEST and an AUC of 0.89 (95% CI: 0.8–0.98) with LUMIPULSE. For tau PET, the Aβ42/pTau ratio achieved an AUC of 0.85 (95% CI: 0.77–0.93) with INNOTEST and an AUC of 0.97 (95% CI: 0.9-1.00) with LUMIPULSE. Double thresholds enabled classification based on predefined sensitivity and specificity, and can be used by clinicians to quickly assess a subject’s likelihood of positivity based on their biomarker levels.
CSF biomarker ratios such as Aβ42/pTau provide high predictive accuracy for brain amyloid and tau deposition. Implementing double thresholds enhances clinical utility by highlighting ambiguous cases requiring further assessment.
Alzheimer's disease (AD) is biologically defined by amyloid beta and tau pathology. Available biomarkers enable early diagnosis but differ in targets, accessibility, and utility. This review synthesizes evidence for plasma biomarkers, magnetic resonance imaging (MRI), and positron emission tomography (PET) to develop a...
Jia-Yu Liu, Chou Xu, Yu-Xin Wang et al.· Alzheimer's & Dementia· 0 citations
BackgroundAlzheimer's disease (AD) is increasingly defined biologically. Amyloid positron emission tomography (PET) is a validated biomarker; however, evidence from truly consecutive, real-world memory-clinic cohorts spanning subjective cognitive decline (SCD), mild cognitive impairment (MCI), and early AD remains limi...
Heii Arai, Satoko Aiba, Hideo Yamamoto et al.· Journal of Alzheimer's Disea...· 0 citations
BACKGROUND
Implementation of disease-modifying anti-amyloid therapies for Alzheimer's disease remains constrained by the cost and procedural burden of positron emission tomography and cerebrospinal fluid testing. This review evaluates blood-based biomarkers in therapeutic pathways.
METHODS
PubMed/MEDLINE and Embase w...
Blood-based biomarkers could broaden access to Alzheimer’s disease diagnostics, but their ability to approximate tau PET, and to address gray-zone results, remains uncertain. We evaluated plasma p-tau217 against tau PET using a harmonized ADNI baseline cohort and an independently derived temporal meta-ROI tau-PET thres...
OBJECTIVE
Recent studies suggest that combining plasma phosphorylated tau (p-tau) with β-amyloid (Aβ) may improve diagnosis accuracy for Alzheimer's disease (AD). However, the cross-sectional and longitudinal concordance of these markers with Aβ positron emission tomography (PET) positivity remains incompletely underst...
Mingxing Jiang, Guo-Yu Lan, Jiayi Zhu et al.· Annals of Neurology· 0 citations
Purpose Machine learning to enable precise, non-invasive detection of cerebral amyloid-beta (Aβ) pathology by integrating cognitive assessments, plasma biomarkers, and structural neuroimaging. Methods We developed an explainable multimodal machine-learning framework to predict amyloid PET visual read status using plasm...
Thony Yan Liang, Xue-Ting Cui, Micheal Adeyosoye et al.· Frontiers in Neuroscience· 0 citations
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