Author Identifier (ORCID)
Abstract
Introduction: Blood biomarkers like phosphorylated tau (p-tau)217 offer high diagnostic accuracy for Alzheimer's disease (AD) but face implementation challenges in low-prevalence settings. We evaluated the Altoida NeuroMarker Platform as a digital cognitive triage tool before plasma p-tau217 testing.
Methods: XGBoost models were trained to predict mild cognitive impairment (MCI) and p-tau217 status in 688 individuals across Australia, Spain, and the United States using the Altoida NeuroMarker. p-tau217 status was dichotomized using a threshold of 0.04 pg/mL (LucentAD). We modeled a two-step workflow (Altoida, plasma p-tau217) to enrich downstream biomarker testing, assuming 30% prevalence of AD pathology.
Results: The Altoida NeuroMarker accurately identified MCI (receiver operating characteristic area under the curve [ROC AUC] = 0.89 ± 0.01) and p-tau217 elevation (ROC AUC = 0.77 ± 0.08). Predicted MCI was significantly associated with elevated pTau217 (p = 0.004). When used upstream, the Altoida NeuroMarker ruled out 64.4% of participants (negative predictive value 90.2%), shifting the pre-test probability of AD pathology to 66.6%, and improving the modeled positive predictive value of plasma p-tau217 from 81.7% to 95.3%.
Discussion: This workflow outlines a scalable path to enrich blood-based biomarker testing after digital cognitive screening.
Keywords
augmented reality, diagnosis, digital biomarkers, machine learning, mild cognitive impairment, phosphorylated tau 217
Document Type
Journal Article
Date of Publication
7-1-2026
Article Number
e70486
E-ISSN
23528729
Volume
18
Issue
3
Publication Title
Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring
Publisher
Wiley
School
Centre for Precision Health
RAS ID
102304
Funding Information
The BBRC's β-AARC study receives funding from the Catalan Health Department and Barcelona city council. D.A. acknowledges support from Instituto de Salud Carlos III (ISCIII), Spain (PI22/00611, PI25/00422, INT23/00048) and by the Department of Health, Generalitat de Catalunya PERIS program (SLT006/17/125, SLT042/25/000034). He also received support from the Department of Research and Universities from the Generalitat de Catalunya (2021 SGR 00979). O.G.R. acknowledges support from the Spanish Ministry of Science, Innovation and Universities (IJC2020-043417-I), the ISCIII, Spain (PI19/00117, PI24/00116), as well as from the Alzheimer's Association (2019-AARF-644568). M.S.C.’s affiliated institution received research funding, including a project grant and clinical trial support, from Roche Diagnostics. M.S.C. was granted a project and is a site investigator of a clinical trial (funded to the institution) by Roche Diagnostics. M.S.C. acknowledges in-kind support for research (to the institution) from ADx Neurosciences, Alamar Biosciences, ALZpath, Avid Radiopharmaceuticals, Eli Lilly, Fujirebio, Janssen Research & Development, Meso Scale Discovery, and Roche Diagnostics. I.R.B. acknowledges support from the ISCIII and the European Union (CM22/00052, CD25/00223), as well as from the Alzheimer's Association (AACSF-25-1486364). J.A. acknowledges support from the ISCIII, Spain (CM21/00243). J.F. acknowledges support from the Fondo de Investigaciones Sanitario, Instituto de Salud Carlos III (INT21/00073, PI20/01473, PI23/01786), the National Institutes of Health (1R01AG056850-01A1, R21AG056974, R01AG061566, 1R01AG081394-01, 1R61AG066543-01), the Department de Salut de la Generalitat de Catalunya (SLT006/17/00119), Fundación Tatiana Pérez de Guzmán el Bueno (IIBSP-DOW-2020-151), and the European Union Horizon 2020 Research and Innovation Programme (H2020-SC1-BHC-2018-2020).
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Recommended Citation
Vairavan, S., Griffin, N., Wilson, D., Triana‐Baltzer, G., Brugada‐Ramentol, V., Vera‐Campuzano, E., Alcolea, D., Fallone, S., Mackintosh, L., Mette, L., Doecke, J. D., Robertson, J., Ward, L., Benavides, G. S., Minguillon, C., Mas, C. P., Tort‐Merino, A., Balasa, M., Rivera, O. G., . . . Iulita, M. F. (2026). A scalable workflow using digital cognitive assessment and pTau217 for MCI detection. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, 18(3), e70486. https://doi.org/10.1002/dad2.70486