Systemic Proteome Profiling to Differentiate Primary Glomerular Diseases - PubMed
3 days ago
- #glomerulonephritis
- #proteomics
- #machine learning
- Systemic proteome profiling was used to differentiate primary glomerular disease subtypes.
- The study involved 5,416 plasma proteins analyzed in discovery (n=147) and validation (n=85) cohorts.
- Four glomerulonephritis (GN) subtypes were studied: focal segmental glomerulosclerosis, IgA nephropathy, minimal change disease, and membranous nephropathy.
- A machine learning model achieved AUROC > 0.8 for differentiating minimal change disease, membranous nephropathy, and IgA nephropathy.
- The model correctly identified 93% of minimal change disease cases but only 21% of focal segmental glomerulosclerosis cases.
- Functional analysis revealed distinct biological pathways, such as hemostasis in minimal change disease.
- Proteome-based classification shows potential for noninvasive differentiation of GN subtypes.