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March 6, 2026Journal of the American Society of Nephrology

Systemic Proteome Profiling to Differentiate Primary Glomerular Diseases

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Authors

JOJ. OhKJKyeonghun JeongJKJung Hun Koh

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Overview

Large-scale proteome profiling differentiates glomerular diseases in Korean participants, suggesting new diagnostic pathways.

Key Points

  • The aim is to identify protein signatures that differentiate primary glomerular disease subtypes using systemic proteome profiling.
  • Conducted systemic proteome profiling of 5,416 plasma proteins in discovery (n=147) and validation (n=85) cohorts.
  • Utilized Olink Explore HT for high-throughput proteomics.
  • Developed a machine learning model with logistic regression and elastic net for classification of disease subtypes.
  • Evaluated model performance in distinguishing subtypes using AUROC method.
  • Distinct plasma proteome profiles were identified among glomerular disease subtypes.
  • Machine learning model performed robustly, achieving AUROC > 0.8 for prominent diseases.
  • Model correctly identified 93% of minimal change disease cases and 63% of IgA nephropathy cases.
  • Performance was limited for focal segmental glomerulosclerosis, with only 21% accuracy.

Cite This Study

Oh et al. (2026) studied this question.

synapsesocial.com/papers/69aa70d6531e4c4a9ff5af38https://doi.org/10.1681/asn.0000001054
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