{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T20:50:56Z","timestamp":1792788656310,"version":"4.5.0"},"reference-count":27,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"9","funder":[{"name":"Ministry of Health and Welfare","award":["RS-2024-00403375"],"award-info":[{"award-number":["RS-2024-00403375"]}]},{"name":"Ministry of Health and Welfare","award":["RS-2024-00403492"],"award-info":[{"award-number":["RS-2024-00403492"]}]},{"name":"National Research Foundation of Korea","award":["RS-2024-00345867"],"award-info":[{"award-number":["RS-2024-00345867"]}],"id":[{"id":"https:\/\/ror.org\/013aysd81","id-type":"ROR","asserted-by":"crossref"}]},{"name":"Ministry of Health and Welfare","award":["RS-2025-25459535"],"award-info":[{"award-number":["RS-2025-25459535"]}]}],"content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":["JASN"],"published-print":{"date-parts":[[2026,9]]},"abstract":"\n Key Points<\/jats:title>\n \n \n \n Plasma proteome profiling identified distinct signatures across biopsy-proven primary glomerular disease subtypes.<\/jats:p>\n <\/jats:list-item>\n \n An elastic net model using 93 proteins classified primary glomerular disease subtypes and controls, with external validation.<\/jats:p>\n <\/jats:list-item>\n \n Integrating proteomics with machine learning yields biologically interpretable insights in primary glomerular diseases.<\/jats:p>\n <\/jats:list-item>\n <\/jats:list>\n <\/jats:p>\n <\/jats:sec>\n \n Background<\/jats:title>\n Primary GN is a heterogeneous group of kidney disorders where understanding of 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.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n \n To identify protein signatures that noninvasively differentiate major primary glomerular disease subtypes and provide mechanistic insights, we performed large-scale systemic proteome profiling of 5416 plasma proteins\n via<\/jats:italic>\n Olink Explore HT in a discovery cohort (\n n<\/jats:italic>\n =147) and an external validation cohort (\n n<\/jats:italic>\n =85) of Korean participants (mean age, 41\u00b113 years; 46% female). The study population included patients with four GN subtypes\u2014focal segmental glomerulosclerosis, IgA nephropathy, minimal change disease, and membranous nephropathy\u2014alongside healthy controls. We developed a machine learning (ML) 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.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n 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 ML model performed robustly in both the discovery and validation cohorts, achieving an area under the receiver operating characteristic curve >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 (three of 14) correctly classified. Functional analysis of key proteins highlighted distinct biologic pathways, such as hemostasis in minimal change disease.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n 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. ML models demonstrated robust discriminatory performance for minimal change disease, membranous nephropathy, and IgA nephropathy, underscoring the potential for proteome-based classification.<\/jats:p>\n <\/jats:sec>","DOI":"10.1681\/asn.0000001054","type":"journal-article","created":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T17:01:53Z","timestamp":1772643713000},"page":"1923-1935","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":2,"title":["Systemic Proteome Profiling to Differentiate Primary Glomerular Diseases"],"prefix":"10.1681","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-7497-1067","authenticated-orcid":false,"given":"Jae-ik","family":"Oh","sequence":"first","affiliation":[{"name":"Department of Translational Medicine, Seoul National University College of Medicine, Seoul, South Korea"},{"name":"Department of Internal Medicine, Seoul National University Hospital, Seoul, South 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University College of Medicine, Seoul, South Korea"},{"name":"Department of Microbiology and Immunology, Seoul National University College of Medicine, Seoul, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3199-635X","authenticated-orcid":false,"given":"Jeonghwan","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Seoul National University College of Medicine, Seoul, South Korea"},{"name":"Department of Internal Medicine, Seoul National University Boramae Medical Center, Seoul, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4714-1260","authenticated-orcid":false,"given":"Jung Pyo","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Seoul National University College of Medicine, Seoul, South Korea"},{"name":"Department of Internal Medicine, Seoul National University Boramae 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