Development and clinical validation of blood-based multibiomarker models for the evaluation of brain amyloid pathology

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 4 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.

Figure 1:

Receiver operating characteristic-area under the curve (ROC-AUC) analysis for individual and combined biomarkers for prediction of PET status. Abbreviations: Aβ, beta-amyloid; APOE4, APOE ε4; AUC, area under the curve.

Figure 2:

Boxplots of likelihood scores stratified by PET status for (A) ptau-217, (B) Aβ42/40, (C) Aβ42/40 + ptau-217, (D) Aβ42/40 + ptau-217 +APOE4 carrier status, and (E) Aβ42/40 + ptau-217 + APOE4 allele count. Orange dots represent individuals that are MCI; blue dots represent individuals that are AD. Horizontal dashed lines indicate high and low likelihood cutoffs. Results between these lines indicate neither a low nor high likelihood (ie, an indeterminate result).

Figure 3:

Distribution of model results for A) the intended use population, and B) real-world clinical specimens. Orange dots represent individuals that are amyloid PET negative; blue dots represent those that are amyloid PET positive in panel A. Grey dots represent individuals whose amyloid PET status is unknown. Horizontal dashed lines indicate high and low likelihood cutoffs.