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Publications

Journal Article Preprint Epublish

PubMed: 40630573 DOI: 10.1101/2025.06.27.25330344

Benchmarking the AI-based diagnostic potential of plasma proteomics for neurodegenerative disease in 17,170 people.

medRxiv

Authors: Lijun An, Alexa Pichet-Binette, Ines Hristovska, Gabriele Vilkaite, Xiao Yu, Bart Smets, Rowan Saloner, Shinya Tasaki, Ying Xu, Varsha Krish, Farhad Imam, Shorena Janelidze, Danielle van Westen, Erik Stomrud, Christopher D Whelan, Sebastian Palmqvist, Rik Ossenkoppele, Niklas Mattsson-Carlgren, Oskar Hansson, Jacob W Vogel

Affiliations

  • Department of Clinical Sciences Malmö, SciLifeLab, Lund University, Lund, Sweden.
  • Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden.
  • Department of Physiology and Pharmacology, Université de Montréal, Montréal, QC, Canada.
  • Centre de recherche de l'institut universitaire de gériatrie de Montréal (CRIUGM), Montréal, QC, Canada.
  • Janssen Pharmaceutica NV, a Johnson & Johnson company, Beerse, Belgium.
  • Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, CA, USA.
  • Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA.
  • Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA.
  • NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA.
  • Gates Ventures, Seattle, WA, USA.
  • Department of Diagnostic Radiology, Clinical Sciences, Lund University, Lund, Sweden.
  • Image and Function, SkÃ¥ne University Hospital, Lund, Sweden.
  • Memory Clinic, SkÃ¥ne University Hospital, Malmö, Sweden.
  • Neuroscience Data Science, Janssen Research & Development, Cambridge, MA, USA.
  • Amsterdam Neuroscience, Neurodegeneration, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.
  • Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, Netherlands.

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.

MeSH

Pathology, Biomarkers, Work, Proteomics, Diagnosis, Artificial Intelligence, Neurodegenerative Diseases, Benchmarking, Plasma