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Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
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Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
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Development of Plasma Protein Classification Models for Alzheimer's Disease Using Multiple Machine Learning

Amy Tsurumi1, Catherine M Cahill2, Andy J Liu3,4

  • 1Department of Surgery, Massachusetts General Hospital and Harvard Medical School, 55 Fruit St., Boston, MA 02114, USA.

International Journal of Molecular Sciences
|December 11, 2025
PubMed
Summary

New Alzheimer's disease (AD) detection uses plasma biomarkers and machine learning for accurate, non-invasive diagnosis. Identified proteins like ANG-2 and EGF show promise for early detection and potential therapies.

Keywords:
Alzheimer’s diseaseagingbiomarkersdiagnosismachine learningneurodegenerationproteomics

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Area of Science:

  • Neuroscience
  • Biomarker Discovery
  • Computational Biology

Background:

  • Alzheimer's Disease (AD) diagnosis relies on invasive cerebrospinal fluid (CSF) biomarkers, causing patient discomfort.
  • Limitations in current detection methods present challenges for effective AD management.
  • Plasma-based biomarkers offer a less invasive, more cost-effective diagnostic alternative.

Purpose of the Study:

  • To develop and validate machine learning models for AD detection using plasma proteomic data.
  • To identify novel plasma protein biomarkers associated with Alzheimer's Disease.
  • To explore the relevance of identified biomarkers in AD pathogenesis and aging.

Main Methods:

  • Utilized a dataset of 120 plasma proteins from AD patients and cognitively normal individuals.
  • Applied diverse machine learning algorithms (EBlasso, EBEN, XGBoost, LightGBM, TabNet, TabPFN) for classification.
  • Performed gene ontology, pathway enrichment, and literature review to assess biomarker relevance.

Main Results:

  • Machine learning models achieved high diagnostic performance (AUROC and accuracy >0.9).
  • Consistently identified predictor proteins included Angiopoietin-2 (ANG-2), EGF, IL-1α, and PDGF-BB, with established links to AD.
  • The identified biomarker pool was significantly enriched with aging-related proteins (p=0.040).

Conclusions:

  • Cutting-edge algorithms enhance the development of plasma-based AD prediction models.
  • The identified proteins may serve as novel therapeutic or preventative targets for Alzheimer's Disease.
  • External validation in diverse populations is crucial to confirm the generalizability of these findings.