Biomarkers.

Area of Science:

• Neurology
• Biomarkers
• Alzheimer's Disease Research

Background:

• Early identification of amyloid-beta (Aβ) brain accumulation is vital for Alzheimer's disease (AD) diagnosis and management.
• Plasma biomarkers offer a promising non-invasive method for detecting Aβ accumulation.
• Current diagnostic methods often rely on more invasive procedures like CSF analysis.

Purpose of the Study:

• To develop and validate a predictive model for brain amyloid accumulation using plasma biomarkers.
• To assess the diagnostic performance and clinical utility of the developed model.
• To explore the potential of a multi-analyte plasma approach for non-invasive AD diagnostics.

Main Methods:

• A logistic regression model was constructed using plasma pTau-217, Aβ42/Aβ40 ratio, APOE genotype, and patient age.
• Amyloid status was determined by cerebrospinal fluid (CSF) Aβ42/Aβ40 measurements.
• Model performance was evaluated using AIC, accuracy, AUC, sensitivity, specificity, and predictive values in a cohort of 54 patients.

Main Results:

• The developed model achieved an AUC of 0.97 and 93% accuracy in predicting amyloid brain accumulation.
• The model demonstrated high Positive (96%) and Negative (89%) Predictive Values.
• The findings suggest a potential for a simplified, non-invasive diagnostic approach without requiring dual cutoffs in initial evaluations.

Conclusions:

• A plasma-based model integrating pTau-217, Aβ42/Aβ40, APOE genotype, and age is effective for predicting amyloid brain accumulation.
• This non-invasive approach shows significant statistical performance and clinical relevance for advancing AD diagnostics.
• Further validation in larger cohorts is warranted to confirm the broad clinical applicability of these plasma biomarkers.