Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026
GPND-AI NULISA: A 15-Protein AI classifier for diagnosis and co-pathology profiling across neurodegenerative diseases.
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Xu Ying, Denkinger Marisa N, Liu Menghan, Gong Katherine, Chen Yike, Western Daniel, Timsina Jigyasha, Cheng Yuchen, Xie Yunchang, Mu Rui, Budde John, Beach Thomas G, Serrano Geidy E, Reiman Eric M, Singh Alpana, Alfradique-Dunham Isabel, Benzinger Tammie L S, Schindler Suzanne E, Morris John C, Holtzman David M, Perlumtter Joel S, Snider B Joy, Campbell Meghan C, Kotzbauer Paul T, Ashton Nicholas J, Cruchaga Carlos
Аннотация
INTRODUCTION: 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.
METHODS: 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.
RESULTS: 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.
DISCUSSION: 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.
METHODS: 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.
RESULTS: 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.
DISCUSSION: 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.