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

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 (Aβ42/Aβ40). 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 Aβ positivity at a cognitive disorder unit, which would lead to reduce the CSF analysis.