Evidence map›Paper›PMID 42897700›Full record

ArticleAnnals of clinical and translational neurology2026

A Two-Stage Plasma Biomarker Algorithm Reduces Amyloid PET Use and the p-Tau217 Gray Zone.

Harthik Parankusham, Casey Vanderlip, Colin Birkenbihl, Eashwar Krishna, Chizobam Ugboaja, Andrew E Budson, Brandon Frank, Alzheimer's Disease Neuroimaging Initiative

Abstract read
In one paragraph

Article in Annals of clinical and translational neurology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Harthik ParankushamDepartment of Physiology and Neurobiology, UConn Computational Biology Core, University of Connecticut, Storrs, Connecticut, USA.ORCID https://orcid.org/0009-0006-2796-1436
Casey VanderlipDepartment of Neurobiology and Behavior, University of California Irvine, Irvine, California, USA.ORCID https://orcid.org/0000-0002-8233-5829
Colin BirkenbihlDepartment of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.ORCID https://orcid.org/0000-0002-7212-7700
Eashwar KrishnaDepartment of Molecular and Cell Biology, University of Connecticut, Storrs, Connecticut, USA.
Chizobam UgboajaYale School of Medicine, Yale New Haven Health, New Haven, Connecticut, USA.
Andrew E BudsonVA Boston Healthcare System, Boston, Massachusetts, USA.
Brandon FrankVA Boston Healthcare System, Boston, Massachusetts, USA.
Alzheimer's Disease Neuroimaging Initiative

Funding

Alzheimer's Disease Neuroimaging Initiative - SupplementU01AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE RES &EDUC · PI WEINER, MICHAEL W · 2004 to 2015
$121.0M
RL5: Research Education CoreP30AG072978 · NIA · BOSTON UNIVERSITY MEDICAL CAMPUS · PI Ann C. McKee · 2021 to 2026
$25.6M
CSRD VA I01 CX002400CSRD VA IK2 CX002625NIA NIH HHS P30 AG072978NIA NIH HHS U01 AG024904Veterans Health Administration I01 CX002400Veterans Health Administration IK2 CX002625
6 · The paper itself

Abstract

objectivePlasma 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.

methodsGRAD 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).

resultsFull 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.

interpretationReflex 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.

Indexed as

Alzheimer's diseaseamyloid PETcognitively unimpairedgray zonep‐Tau217

Identifiers

PMID42897700
PMCPMC13680551

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.