Plasma pTau217 predicts continuous brain amyloid levels in preclinical and early Alzheimer's disease

Abstract

Background: This study investigated the potential of phosphorylated plasma Tau217 ratio (pTau217R) and plasma amyloid beta (Aβ) 42/Aβ40 in predicting brain amyloid levels measured by positron emission tomography (PET) Centiloid (CL) for Alzheimer's disease (AD) staging and screening.

Methods: Quantification of plasma pTau217R and Aβ42/Aβ40 employed immunoprecipitation-mass spectrometry. CL prediction models were developed on a cohort of 904 cognitively unimpaired, preclinical and early AD subjects and validated on two independent cohorts.

Results: Models integrating pTau217R outperformed Aβ42/Aβ40 alone, predicting amyloid levels up to 89.1 CL. High area under the receiver operating characteristic curve (AUROC) values (89.3% to 94.7%) were observed across a broad CL range (15 to 90). Utilizing pTau217R-based models for low amyloid levels reduced PET scans by 70.5% to 78.6%.

Discussion: pTau217R effectively predicts brain amyloid levels, surpassing cerebrospinal fluid Aβ42/Aβ40's range. Combining it with plasma Aβ42/Aβ40 enhances sensitivity for low amyloid detection, reducing unnecessary PET scans and expanding clinical utility.

Gov identifiers: NCT02956486 (MissionAD1), NCT03036280 (MissionAD2), NCT04468659 (AHEAD3-45), NCT03887455 (ClarityAD) HIGHLIGHTS: Phosphorylated plasma Tau217 ratio (pTau217R) effectively predicts amyloid-PET Centiloid (CL) across a broad spectrum. Integrating pTau217R with Aβ42/Aβ40 extends the CL prediction upper limit to 89.1 CL. Combined model predicts amyloid status with high accuracy, especially in cognitively unimpaired individuals. This model identifies subjects above or below various CL thresholds with high accuracy. pTau217R-based models significantly reduce PET scans by up to 78.6% for screening out individuals with no/low amyloid.

Keywords: blood‐based biomarkers; diagnosis; disease continuum; disease staging; patient monitoring; patient screening; prediction.

FIGURE 1

Relative influence of predictors in the model based on plasma pTau217R and Aβ42/Aβ40 is shown in (A), with pTau217R clearly shown as most dominant (>80% relative influence). The nature of the relationship between these biomarkers versus the predicted PET CL levels is shown in (B) and (C) for each subject (in black) and the average subject (in red) via these individual conditional expectation profiles. The prediction profile of each subject was centered by subtracting from the predicted PET CL level corresponding to the lowest value of the predictor.

FIGURE 2

Range of PET CL levels that can be reliably predicted by models based solely on (A) Aβ42/Aβ40, (B) pTau217R, and (C) combination of both. CL prediction range was estimated via five‐parameter logistic model using data pooled from VC‐1 and VC‐2. While the predicted upper plateau is higher using the model based solely on pTau217R, both models with pTau217R predict CL levels across a broad spectrum along the continuum from preclinical to early AD. The CL scale is anchored to 0, the average level in young healthy people expected to have no amyloid, and 100 in people with moderate AD. Since the anchors are averages, values for individual subjects can be less than 0 or over 100.

FIGURE 3

(A–H) Receiver operating characteristic (ROC) curves and area under these curves (AUROC) for predicting amyloid status for different CL thresholds (15, 30, 50, 70) for subjects in VC‐1 and VC‐2 using CL prediction models based on different combinations of Aβ42/Aβ40 and pTau217R. CL prediction model with pTau217R alone predicts amyloid status for wide range of CL thresholds. Adding Ab42/Ab40 significantly improves prediction for amyloid levels up to 40 CL (p < 0.05) in VC‐1 and improves up to 30 CL in VC‐2, but not significantly. Performance is well maintained for higher CL thresholds.

FIGURE 4

Percentage of PET scans saved through use of blood‐based biomarkers, specifically Aβ42/Aβ40 and pTau217R, via CL prediction models, illustrated here for two validation cohorts (VC‐1 and VC‐2). These models serve to rule out subjects who are amyloid‐negative while keeping a 5% false negative rate (95% sensitivity) across various CL thresholds. The depicted scenario involves conducting PET scans solely on subjects predicted to be amyloid‐positive, with blood‐based biomarkers employed as a preliminary screening tool to exclude amyloid‐negative subjects. In this context, the percentage savings in PET scans reflect the accurate prediction of true amyloid‐negative subjects by the models using blood‐based biomarkers, which aligns with the specificity (or 1 minus the false positive rate) of the model.