Promising clinical tools for specific Alzheimer disease diagnosis from plasma pTau217 and ApoE genotype in a cognitive disorder unit.

Autores de IIS La Fe

• Lourdes Álvarez Sánchez

  Autor

• María Del Carmen Peña Bautista

  Autor

• Laura Ferré González

  Autor

• Angel Balaguer Timor

  Autor

• Julian Luis Amengual Roig

  Autor

• 

  Miguel Baquero Toledo

  Autor

• 

  María Consuelo Cháfer Pericás

  Autor

Grupos

• Grupo de Investigación en Enfermedad de Alzheimer (GINEA)

Abstract

Alzheimer's disease (AD) diagnosis relies on cerebrospinal fluid (CSF) biomarkers or amyloid PET. Alternatives for AD diagnosis from blood samples are needed to develop a fully-automated early-diagnosis approach, potentially implemented in a cognitive disorder unit. Plasma p-Tau217 was determined in patients diagnosed with AD (n = 134) or non-AD (n = 132), from CSF biomarkers (Aß42/Aß40). A logistic regression model was developed. The predictive performance was assessed using a training set (70% of data) and internally validated with a test set (30% of data) and 1000 iterations. A nomogram and a double cut-off strategy were proposed to visualize the model results, and stratify patients (AD, non-AD, uncertain), respectively. The model (plasma p-Tau217, ApoE, age) showed satisfactory performance (AUC 0.94, sensitivity 0.85, specificity 0.89); so, together with the corresponding nomogram, it could be applied in specialized clinical contexts. The model including only plasma p-Tau217 (AUC 0.93, sensitivity 0.72, specificity 0.96) would be a useful approach in less specialized clinics. The corresponding two-cut-off strategy for the first model were as AD probability (< 0.41 non-AD, > 0.57 AD). This study provided clinical tools (nomogram, double cut-off) for identifying Aß positivity at a cognitive disorder unit, which would lead to reduce the CSF analysis.

© 2025. The Author(s).

Keywords

• Alzheimer disease; Biomarkers; Diagnosis; Implementation; Plasma; Predictive model; Validation