A cell-based multivariate blood test (iCAP) for early detection of Alzheimer’s disease capturing non-amyloid-β biology

Abstract The indicator cell assay platform (iCAP) is a blood-based diagnostic approach that uses standardized cells as biosensors to transduce complex circulating signals into cell-based gene-expression profiles for multivariate disease classification. We developed an iCAP for early detection of Alzheimer’s disease (AD-iCAP) by incubating plasma from patients with early-stage AD (mild cognitive impairment or mild dementia) and non-affected controls with standardized neurons and training classifiers on transcriptomic responses. Modeling features were restricted to a predefined 84-gene panel derived independently by analyzing 5xFAD mouse plasma in the AD-iCAP. In this exploratory study, a model was trained and tested using 191 retrospectively collected human plasma samples across three cohorts. The classifier achieved AUC = 0.64 (95% CI 0.51–0.78) in an external validation set during model development (n = 82) and AUC = 0.77 (95% CI 0.57–0.96) in a blind test set (n = 23). The multivariate model incorporated 5 genes including AKT3 , GPR50 , PALLD , RGMA , and PCDH10 , implicated in AD, neuronal signaling and/or cytoskeletal dynamics. The cell-based approach is orthogonal to direct detection of circulating amyloid and tau, and these exploratory findings support further evaluation of AD-iCAP in prospective studies for combinatorial diagnostics and patient stratification for clinical trials.

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Journal
Scientific Reports
Published
2026-10-17
DOI
https://doi.org/10.1038/s41598-026-74186-z
Primary Topic
Dementia and Cognitive Impairment Research
Type
article
Field-Weighted Citation Impact
0.00

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article

A cell-based multivariate blood test (iCAP) for early detection of Alzheimer’s disease capturing non-amyloid-β biology

Vincent Doré, John D. Aitchison, Greg R. Savage, Julia Bomke et al.
Scientific Reports
Dementia and Cognitive Impairment Research
article

A cell-based multivariate blood test (iCAP) for early detection of Alzheimer’s disease capturing non-amyloid-β biology

Vincent Doré, John D. Aitchison, Greg R. Savage, Julia Bomke, Rebecca L. Rumble, Elaine R. Peskind, Scott Ayton, Carolyn Chadunow, Kelly Kathleen Pertile, Andrea Louey, Jürgen Fripp, Michael A. Vacher, Olivier Salvado, Lucy Mackintosh, Fergal J. Duffy, Steven John Collins, G. Adam Whitney, Nicola T. Lautenschlager, Kathryn A. Ellis, Tania Taddei, Ashley Ian Bush, Christine Thai, Pierrick Bourgeat, Leslie R. Miller, Kevin Taddei, Regan Tyrrell, Colin Louis Masters, Magdalene Soh, BRENDAN S. SILBERT, Fiona Lamb, Jason D. Berndt, Samantha L. Gardener, Victor L. Villemagne, Stephanie Ruth Rainey‐Smith, Lidija Milicic, Morgan E. Radler, Paul T. Maruff, Adrian Kamer, Joanne S. Robertson, Rosita Shishegar, Michael Weinborn, Tenielle Porter, Christopher James Fowler, Brett O. Trounson, Simon Matthew Laws, Robert D Grenfell, Amir Fazlollahi, Mark D. D'Ascenzo, Robert J. Lipshutz, Samuel A. Danziger, Elise Harrison, S. Gibson, Shiji Varghese, Jennifer J. Smith, Christopher C. Rowe, David Ames, Michael Woodward, James Doecke, Lis Evered, Yen Ying Lim, Lucy Lim, Mark Rodrigues, Liang Jin, Hamid R. Sohrabi, Ralph N. Martins, Ge Li, Ying Xia, Belinda M. Brown, Qiao-Xin Li, Yijun Qi
article en

Abstract

Abstract The indicator cell assay platform (iCAP) is a blood-based diagnostic approach that uses standardized cells as biosensors to transduce complex circulating signals into cell-based gene-expression profiles for multivariate disease classification. We developed an iCAP for early detection of Alzheimer’s disease (AD-iCAP) by incubating plasma from patients with early-stage AD (mild cognitive impairment or mild dementia) and non-affected controls with standardized neurons and training classifiers on transcriptomic responses. Modeling features were restricted to a predefined 84-gene panel derived independently by analyzing 5xFAD mouse plasma in the AD-iCAP. In this exploratory study, a model was trained and tested using 191 retrospectively collected human plasma samples across three cohorts. The classifier achieved AUC = 0.64 (95% CI 0.51–0.78) in an external validation set during model development (n = 82) and AUC = 0.77 (95% CI 0.57–0.96) in a blind test set (n = 23). The multivariate model incorporated 5 genes including AKT3 , GPR50 , PALLD , RGMA , and PCDH10 , implicated in AD, neuronal signaling and/or cytoskeletal dynamics. The cell-based approach is orthogonal to direct detection of circulating amyloid and tau, and these exploratory findings support further evaluation of AD-iCAP in prospective studies for combinatorial diagnostics and patient stratification for clinical trials.

Scientific Reports
Edith Cowan University (AU), Commonwealth Scientific and Industrial Research Organisation (AU), The University of Melbourne (AU), University of Pittsburgh (US), Murdoch University (AU), The University of Western Australia (AU), University of Washington (US), Curtin University (AU), Florey Institute of Neuroscience and Mental Health (AU), Austin Hospital (AU), Institute for Systems Biology (US), UNSW Sydney (AU), National Ageing Research Institute (AU), St Vincent's Hospital Melbourne (AU), VA Puget Sound Health Care System (US), VA Northwest Network (US), Australian e-Health Research Centre (AU), Australian Alzheimer’s Research Foundation (AU), PreCyte (United States) (US), Austin Health (AU), Data61 (AU), Seattle Children's Research Institute (US), Monash University (AU), Macquarie University (AU)
U.S. Department of Veterans Affairs, Alzheimer's Association, Washington University in St. Louis, University of Washington, Science and Industry Endowment Fund, Commonwealth Scientific and Industrial Research Organisation, Edith Cowan University, Dementia Collaborative Research Centres, Australia, National Institutes of Health, McDonnell Center for Systems Neuroscience, Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University in St. Louis, National Health and Medical Research Council, National Institute on Aging, National Cancer Institute
Good health and well-being
Openalex Percentile: Top 12%
Dementia and Cognitive Impairment Research
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