Authors
Ying Xu, Marisa N Denkinger, Menghan Liu, Katherine Gong, Yike Chen, Daniel Western, Jigyasha Timsina, Yuchen Cheng, Yunchang Xie, Rui Mu, John Budde, Thomas G Beach, Geidy E Serrano, Eric M Reiman, Alpana Singh, Isabel Alfradique-Dunham, Tammie L S Benzinger, Suzanne E Schindler, John C Morris, David M Holtzman, Joel S Perlumtter, B Joy Snider, Meghan C Campbell, Paul T Kotzbauer, Nicholas J Ashton, Carlos Cruchaga
Published in
Alzheimer's & dementia : the journal of the Alzheimer's Association. Volume 22. Issue 5. Pages e71420.
Abstract
Accurate clinical diagnosis of neurodegenerative diseases remains challenging, particularly when individuals have mixed pathologies. We implemented the generalizable protein-based neurodegenerative disease artificial intelligence (GPND-AI) classifier using the NUcleic acid-Linked Immuno-Sandwich Assay (NULISA) central nervous system (CNS) panel to classify Alzheimer's disease, Parkinson's disease, frontotemporal dementia, dementia with Lewy bodies, and healthy controls, while disentangling mixed pathologies.
Proteomic and clinical information from the Charles F. and Joanne Knight Alzheimer's Disease Research Center (Knight-ADRC) and Movement Disorder Clinic were used to train and test the GPND-AI classifier. External validation was performed in a Banner Sun Health Research Institute cohort and additional Knight-ADRC samples with neuropathologically confirmed diagnoses.
GPND-AI identified 15 proteins that achieve an area under the curve (AUC) of 0.955 and 92.3% accuracy across five diagnostic categories. In validation cohort, predicted co-pathologies significantly correlated with clinical characteristics.
GPND-AI identified a 15-protein panel that accurately classifies individuals across the four major neurodegenerative diseases. Validation against neuropathology-confirmed diagnoses supports the utility of proteomics-based approaches for mapping disease-specific and co-existing neurodegenerative processes.
PMID:
42050390
Bibliographic data and abstract were imported from PubMed on 29 Apr 2026.
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