ArticleEuropean journal of clinical investigation2025
New approach to specific Alzheimer's disease diagnosis based on plasma biomarkers in a cognitive disorder cohort.
Article in European journal of clinical investigation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Mapping Alzheimer's disease heterogeneity with molecular imaging biomarkers.European journal of clinical investigation · 2026Review
- Clinical validation of new alzheimer disease diagnosis tools based on plasma p-Tau217.Scientific reports · 2025Article
- Temporal Validation of a Plasma Diagnosis Approach for Early Alzheimer Disease Diagnosis in a Cognitive Disorder Unit.Journal of personalized medicine · 2025Article
- New approach to specific Alzheimer's disease diagnosis based on plasma biomarkers in a cognitive disorder cohort.European journal of clinical investigation · 2025Article
- Cross-platform analytical assessment of serum GFAP quantification in multiple sclerosis: SIMOA versus two automated immunoassays.Frontiers in neurology · 2025Article
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9 authors.
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Abstract
backgroundThe validation of a combination of plasma biomarkers and demographic variables is required to establish reliable cut-offs for Alzheimer's disease diagnosis (AD).
methodsPlasma biomarkers (Aβ42/Aβ40, p-Tau181, t-Tau, NfL, GFAP), ApoE genotype, and demographic variables were obtained from a retrospective clinical cohort of cognitive disorders (n = 478). These patients were diagnosed as AD (n = 254) or non-AD (n = 224) according to cerebrospinal fluid (CSF) Aβ42/Aβ40 levels. An analysis using a Ridge logistic regression model was performed to predict the occurrence of AD. The predictive performance of the model was assessed using the observations from a training set (70% of the sample) and validated using a test set (30% of the sample) in each group. Optimum cutoffs for the model were evaluated.
resultsThe model including plasma Aβ42/Aβ40, p-Tau181, GFAP, ApoE genotype and age was optimal for predicting CSF Aβ42/Aβ40 positivity (AUC .91, sensitivity .86, specificity .82). The model including only plasma biomarkers (Aβ42/Aβ40, p-Tau181, GFAP) provided reliable results (AUC .88, sensitivity .83, specificity .78). Also, GFAP, individually, showed the best performance in discriminating between AD and non-AD groups (AUC .859). The established cut-offs in a three-range strategy performed satisfactorily for the validated predictive model (probability) and individual plasma GFAP (concentration).
conclusionsThe plasma GFAP levels and the validated predictive model based on plasma biomarkers represent a relevant step toward the development of a potential clinical approach for AD diagnosis, which should be assessed in further research.
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