A Two-Stage Plasma Biomarker Algorithm Reduces Amyloid PET Use and the p-Tau217 Gray Zone.
7 affiliations
- 1Department of Physiology and Neurobiology, UConn Computational Biology Core, University of Connecticut, Storrs, Connecticut, USA.
- 2Department of Neurobiology and Behavior, University of California Irvine, Irvine, California, USA.
- 3Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
- 4Department of Molecular and Cell Biology, University of Connecticut, Storrs, Connecticut, USA.
- 5Yale School of Medicine, Yale New Haven Health, New Haven, Connecticut, USA.
- 6VA Boston Healthcare System, Boston, Massachusetts, USA.
- 7Alzheimer's Disease Research Center, Boston University Chobanian and Avedisian School of Medicine, Boston, Massachusetts, USA.
Abstract
Objective
Plasma p-Tau217 leaves 30%-50% of patients in an indeterminate "gray zone" often requiring Aβ-PET. We developed GRAD (Gatekeeper-Reflex for Alzheimer's Disease), a two-stage Aβ-positivity prediction model to improve classification while reserving Aβ-PET for residual uncertainty.Methods
GRAD was developed in 145 cognitively impaired ADNI participants and externally validated in 1644 cognitively unimpaired A4/LEARN participants. Stage 1 used p-Tau217 alone; probabilities between 0.25 and 0.75 were considered indeterminate. Stage 2 used L2-regularized logistic regression combining plasma-biomarkers, genetic and demographic features. Aβ-PET served as the reference standard (ADNI florbetapir SUVR > 1.11; A4 Centiloid ≥ 20).Results
Full model AUC was 0.915 (95% CI, 0.859-0.961) internally and 0.879 (95% CI, 0.861-0.895) externally. In the external gray zone (n = 630), Stage 2 improved discrimination over p-Tau217 alone (AUC 0.793 vs. 0.719; p < 0.001), increased correct classification without PET from 41.4% to 56.2%, and reduced PET referral from 35.9% to 24.3%. In the p-Tau181 subset (n = 256), Stage 2 outperformed p-Tau181 (AUC 0.799 vs. 0.597; p < 0.001). Overall, 89.0% of ADNI and 90.7% of A4/LEARN participants were classified without PET; modeled testing costs were 44%-52% lower than p-Tau217 screening with PET confirmation of the gray zone.Interpretation
Reflex multimarker testing improves indeterminate p-Tau217 classification while retaining Aβ-PET for residual uncertainty. GRAD may reduce PET use and testing costs in memory-clinic triage, prevention-trial enrichment, and disease-modifying therapy workup.
Related
- Plasma biomarker strategies to reduce diagnostic uncertainty in p-tau217–based assessment of CSF amyloid positivity in secondary-care patients with MCI
- Direction-specific biomarker profiles and clinical significance of plasma p-tau217/amyloid PET discordance in cognitively normal adults.
- Analytical validation and amyloid-status discrimination of a high-throughput, research-use-only plasma p-Tau217 immunoassay
- Clinical performance and decision thresholds of an automated plasma p-tau217 assay for amyloid pathology in a Taiwanese memory clinic.
- Validation of plasma biomarker immunoassay‐based models for predicting brain amyloid positivity in cognitively impaired individuals
BibTeX
@article{parankusham2026twostage,
title = {{A Two-Stage Plasma Biomarker Algorithm Reduces Amyloid PET Use and the p-Tau217 Gray Zone.}},
author = {Harthik Parankusham and Casey Vanderlip and Colin Birkenbihl and Eashwar Krishna and Chizobam Ugboaja and Andrew E Budson and Brandon Frank and Alzheimer's Disease Neuroimaging Initiative},
journal = {Annals of clinical and translational neurology},
year = {2026},
month = oct,
doi = {10.1002/acn3.70560},
url = {https://europepmc.org/article/MED/42897700},
note = {CC BY},
}
RIS
TY - JOUR
TI - A Two-Stage Plasma Biomarker Algorithm Reduces Amyloid PET Use and the p-Tau217 Gray Zone.
AU - Harthik Parankusham
AU - Casey Vanderlip
AU - Colin Birkenbihl
AU - Eashwar Krishna
AU - Chizobam Ugboaja
AU - Andrew E Budson
AU - Brandon Frank
AU - Alzheimer's Disease Neuroimaging Initiative
PY - 2026
DA - 2026/10/19
JO - Annals of clinical and translational neurology
DO - 10.1002/acn3.70560
UR - https://europepmc.org/article/MED/42897700
AB - Objective Plasma p-Tau217 leaves 30%-50% of patients in an indeterminate "gray zone" often requiring Aβ-PET. We developed GRAD (Gatekeeper-Reflex for Alzheimer's Disease), a two-stage Aβ-positivity prediction model to improve classification while reserving Aβ-PET for residual uncertainty.Methods GRAD was developed in 145 cognitively impaired ADNI participants and externally validated in 1644 cognitively unimpaired A4/LEARN participants. Stage 1 used p-Tau217 alone; probabilities between 0.25 and 0.75 were considered indeterminate. Stage 2 used L2-regularized logistic regression combining plasma-biomarkers, genetic and demographic features. Aβ-PET served as the reference standard (ADNI florbetapir SUVR > 1.11; A4 Centiloid ≥ 20).Results Full model AUC was 0.915 (95% CI, 0.859-0.961) internally and 0.879 (95% CI, 0.861-0.895) externally. In the external gray zone (n = 630), Stage 2 improved discrimination over p-Tau217 alone (AUC 0.793 vs. 0.719; p < 0.001), increased correct classification without PET from 41.4% to 56.2%, and reduced PET referral from 35.9% to 24.3%. In the p-Tau181 subset (n = 256), Stage 2 outperformed p-Tau181 (AUC 0.799 vs. 0.597; p < 0.001). Overall, 89.0% of ADNI and 90.7% of A4/LEARN participants were classified without PET; modeled testing costs were 44%-52% lower than p-Tau217 screening with PET confirmation of the gray zone.Interpretation Reflex multimarker testing improves indeterminate p-Tau217 classification while retaining Aβ-PET for residual uncertainty. GRAD may reduce PET use and testing costs in memory-clinic triage, prevention-trial enrichment, and disease-modifying therapy workup.
SN - 2328-9503
ER -