ScholarWorks@중앙대학교

초록

Background: Primary glomerulonephritis (GN) is a heterogeneous group of kidney disorders where understanding their pathophysiology remains incomplete. Despite the diagnostic potential of high-throughput proteomics, constrained proteomic depth and a reliance on binary comparisons have left the feasibility of using systemic signatures to differentiate multiple GN subtypes largely unexplored. Methods: To identify protein signatures that noninvasively differentiate major primary glomerular disease subtypes and provide mechanistic insights, we performed large-scale systemic proteome profiling of 5,416 plasma proteins via Olink Explore HT in a discovery cohort (n=147) and an external validation cohort (n=85) of Korean participants (mean age, 41±13 years; 46% female). The study population included patients with four GN subtypes—focal segmental glomerulosclerosis, IgA nephropathy, minimal change disease, and membranous nephropathy—alongside healthy controls. We developed a machine learning model using logistic regression with elastic net regularization to classify disease groups based on proteomic profiles and evaluated its performance in the independent validation cohort. Results: Plasma proteome profiles were distinct among disease subtypes, emerging as a significant source of data variation independent of conventional markers such as eGFR or proteinuria levels. The machine learning model performed robustly in both the discovery and validation cohorts, achieving an area under the receiver operating characteristic curve (AUROC) > 0.8 for differentiating minimal change disease, membranous nephropathy, and IgA nephropathy. The model, even without clinical information, correctly identified 93% of minimal change disease cases (14 of 15) and 63% of IgA nephropathy cases (20 of 32), but its performance was limited for focal segmental glomerulosclerosis, with only 21% of cases (3 of 14) correctly classified. Functional analysis of key proteins highlighted distinct biological pathways, such as hemostasis in minimal change disease. Conclusions: We identified distinct systemic proteome signatures for primary glomerular diseases, where disease subtype served as a major determinant of proteomic variance alongside conventional clinical markers. Machine learning models demonstrated robust discriminatory performance for minimal change disease, membranous nephropathy, and IgA nephropathy, underscoring the potential for proteome-based classification.

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