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Benchmarking the AI-based diagnostic potential of plasma proteomics for neurodegenerative disease in 17,187 people

An, Lijun LU orcid ; Pichet Binette, Alexa LU ; Hristovska, Ines LU ; Vilkaite, Gabriele LU ; Xiao, Yu LU ; Zendehdel, Romina LU ; Dong, Zijian ; Smets, Bart ; Saloner, Rowan and Tasaki, Shinya , et al. (2025)
Abstract
Co-pathology is a common feature of neurodegenerative diseases that complicates diagnosis, treatment and clinical management. However, sensitive, specific and scalable biomarkers for in vivo pathological diagnosis are not available for most neurodegenerative neuropathologies. Here, we present ProtAIDe-Dx, a deep joint-learning model trained on 17,170 patients and controls that uses plasma proteomics to provide simultaneous probabilistic diagnosis across six conditions associated with dementia in aging. ProtAIDe-Dx achieves cross-validated balanced classification accuracy of 69%-96% and AUCs > 79% across all conditions. The model's diagnostic probabilities highlighted subgroups of patients with co-pathologies, and were associated with... (More)
Co-pathology is a common feature of neurodegenerative diseases that complicates diagnosis, treatment and clinical management. However, sensitive, specific and scalable biomarkers for in vivo pathological diagnosis are not available for most neurodegenerative neuropathologies. Here, we present ProtAIDe-Dx, a deep joint-learning model trained on 17,170 patients and controls that uses plasma proteomics to provide simultaneous probabilistic diagnosis across six conditions associated with dementia in aging. ProtAIDe-Dx achieves cross-validated balanced classification accuracy of 69%-96% and AUCs > 79% across all conditions. The model's diagnostic probabilities highlighted subgroups of patients with co-pathologies, and were associated with pathology-specific biomarkers in an external sample, even among cognitively unimpaired people. Model interpretation revealed a suite of protein networks marking shared and specific biological processes across diseases, and identified novel and previously described proteins discriminating each diagnosis. ProtAIDe-Dx significantly improved biomarker-based differential diagnosis in a memory clinic sample, pinpointing proteins leading to diagnostic decisions at an individual level. Together, this work highlights the promise of plasma proteomics to improve patient-level diagnostic work-up with a single blood draw. (Less)
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organization
publishing date
type
Working paper/Preprint
publication status
published
subject
pages
45 pages
publisher
medRxiv
external identifiers
  • pmid:40630573
DOI
10.1101/2025.06.27.25330344
language
English
LU publication?
yes
id
5a5bca4f-c0b6-47e5-b5a1-7f7d47a95bb8
date added to LUP
2026-03-31 17:10:57
date last changed
2026-04-01 06:49:17
@misc{5a5bca4f-c0b6-47e5-b5a1-7f7d47a95bb8,
  abstract     = {{Co-pathology is a common feature of neurodegenerative diseases that complicates diagnosis, treatment and clinical management. However, sensitive, specific and scalable biomarkers for in vivo pathological diagnosis are not available for most neurodegenerative neuropathologies. Here, we present ProtAIDe-Dx, a deep joint-learning model trained on 17,170 patients and controls that uses plasma proteomics to provide simultaneous probabilistic diagnosis across six conditions associated with dementia in aging. ProtAIDe-Dx achieves cross-validated balanced classification accuracy of 69%-96% and AUCs > 79% across all conditions. The model's diagnostic probabilities highlighted subgroups of patients with co-pathologies, and were associated with pathology-specific biomarkers in an external sample, even among cognitively unimpaired people. Model interpretation revealed a suite of protein networks marking shared and specific biological processes across diseases, and identified novel and previously described proteins discriminating each diagnosis. ProtAIDe-Dx significantly improved biomarker-based differential diagnosis in a memory clinic sample, pinpointing proteins leading to diagnostic decisions at an individual level. Together, this work highlights the promise of plasma proteomics to improve patient-level diagnostic work-up with a single blood draw.}},
  author       = {{An, Lijun and Pichet Binette, Alexa and Hristovska, Ines and Vilkaite, Gabriele and Xiao, Yu and Zendehdel, Romina and Dong, Zijian and Smets, Bart and Saloner, Rowan and Tasaki, Shinya and Xu, Ying and Krish, Varsha and Imam, Farhad and Janelidze, Shorena and van Westen, Danielle and Stomrud, Erik and Whelan, Christopher D. and Palmqvist, Sebastian and Ossenkoppele, Rik and Mattsson-Carlgren, Niklas and Hansson, Oskar and Vogel, Jacob}},
  language     = {{eng}},
  month        = {{10}},
  note         = {{Preprint}},
  publisher    = {{medRxiv}},
  title        = {{Benchmarking the AI-based diagnostic potential of plasma proteomics for neurodegenerative disease in 17,187 people}},
  url          = {{http://dx.doi.org/10.1101/2025.06.27.25330344}},
  doi          = {{10.1101/2025.06.27.25330344}},
  year         = {{2025}},
}