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Assessing Extracellular Vesicle Proteins as Predictive Biomarkers for Developing Type 1 Diabetes.

Panshak P Dakup1, Lisa M Bramer1, Athena Schepmoes1

  • 1Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, USA.

Proteomics
|May 21, 2026
PubMed
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Plasma extracellular vesicles (EVs) hold promise for type 1 diabetes (T1D) biomarker discovery. Proteomics analysis of EVs identified 448 differentially abundant proteins, aiding in predicting T1D development with 81% accuracy.

Area of Science:

  • Biochemistry
  • Immunology
  • Proteomics

Background:

  • Type 1 diabetes (T1D) arises from autoimmune destruction of insulin-producing beta cells.
  • Predictive biomarkers are crucial for developing preventive therapies and understanding T1D's autoimmune trigger.
  • Plasma extracellular vesicles (EVs) are valuable sources for biomarker discovery due to their disease-specific cargo.

Purpose of the Study:

  • To evaluate plasma EV proteomics for identifying predictive biomarkers of T1D development.
  • To assess the potential of EV protein cargo to distinguish individuals with islet autoimmunity from controls.

Main Methods:

  • Plasma EVs were captured using strong anion exchange beads (Mag-Net) from individuals with islet autoimmunity (AAB+) and controls.
  • EV protein cargo was analyzed using mass spectrometry, identifying and quantifying thousands of proteins.

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  • Machine learning (random forest) was employed to test the predictive capacity of the identified proteins.

    • Main Results:

    • Over 5,480 proteins were identified in plasma EVs, significantly increasing proteome coverage.
    • 448 proteins were found to be differentially abundant between AAB+ individuals and controls, including 69 verified EV proteins.
    • A random forest model achieved an area under the receiver operating characteristic curve of 0.81 for predicting AAB+ status.

    Conclusions:

    • Plasma EV proteomics is a promising approach for identifying predictive biomarkers of T1D.
    • The identified differentially abundant proteins and pathways offer insights into T1D pathogenesis.
    • This methodology supports further research into early T1D detection and therapeutic strategies.