Development and clinical validation of blood-based multibiomarker models for the evaluation of brain amyloid pathology
Listed in
This article is not in any list yet, why not save it to one of your lists.Abstract
Background and Objectives
Plasma biomarkers provide new tools to evaluate patients with mild cognitive impairment (MCI) for Alzheimer’s disease (AD) pathology. Such tools are needed for anti-amyloid therapies that require efficient and accurate diagnostic evaluation to identify potential treatment candidates. This study sought to develop and evaluate the clinical performance of a multi-marker combination of plasma beta-amyloid 42/40 (Aβ42/40), ptau-217, and APOE genotype to predict amyloid PET positivity in a diverse cohort of patients at a memory clinic and evaluate >4,000 results from “real-world” specimens submitted for high-throughput clinical testing.
Methods
Study participants were from the 1Florida AD Research Center (ADRC). Demographics, clinical evaluations, and amyloid PET scan data were provided with plasma specimens for model development for the intended-use cohort (MCI/AD: n=215). Aβ42/40 and ApoE4 proteotype (reflecting high-risk APOE ɛ4 alleles) were measured by mass spectrometry and ptau-217 by immunoassay. A likelihood score model was determined for each biomarker separately and in combination. Model performance was optimized using 2 cutpoints, 1 for high and 1 for low likelihood of PET positivity, to attain ≥90% specificity and sensitivity. These cutpoints were applied to categorize 4,326 real-world specimens and an expanded cohort stratified by cognitive status (normal cognition [NC], MCI, AD).
Results
For the intended-use cohort (46.0% prevalence of PET-positivity), a combination of Aβ42/40, ptau-217, and APOE4 allele count provided the best model with a receiver operating characteristic area under the curve (ROC-AUC) of 0.942 and with 2 cutpoints fixed at 91% sensitivity and 91% specificity yielding a high cutpoint with 88% positive predictive value (PPV) and 87% accuracy and a low cutpoint with 91% negative predictive value (NPV) and 85% accuracy.
Incorporating APOE4 allele count also reduced the percentage of patients with indeterminate risk from 15% to 10%. The cutpoints categorized the real-world clinical specimens as having 42% high, 51% low, and 7% indeterminate likelihood for PET positivity and differentiated between NC, MCI, and AD dementia cognitive status in the expanded cohort.
Discussion
Combining plasma biomarkers Aβ42/40, ptau-217, and APOE4 allele count is a scalable approach for evaluating patients with MCI for suspected AD pathology.
Key Takeaways
The approval of disease-modifying therapies for Alzheimer’s disease ushers in the need for accessible, affordable, and accurate blood-based testing for Alzheimer’s pathology.
Models implementing multiple analytes have demonstrated high performance in identifying patients with brain amyloid pathology.
We developed high-throughput, robust, multiple-analyte assays and models aimed at predicting the likelihood of amyloid PET positivity.
We report two models with excellent performance in alignment with current recommendations for blood-based testing.
Aβ42/40 + ptau-217 + APOE4 allele count provided the best prediction for amyloid PET positivity when sensitivity and specificity were both fixed at 91%.
