aceIdentification of plasma biomarkers for chronic kidney disease by integrating proteomics and machine learning
Background Chronic kidney disease (CKD) is a major global health burden, and early identification of individuals at risk remains challenging. This study aimed to identify plasma proteomic biomarkers associated with incident CKD and develop a machine learning-based prediction model integrating proteomic and clinical data. Methods We conducted a prospective cohort study of 27,429 UK Biobank participants without baseline CKD after excluding those with estimated glomerular filtration rate Results A total of 205 prot…
In a cohort study of 27,429 UK Biobank participants, adding four plasma proteins to a demographic model raised discrimination for incident chronic kidney disease to an AUC of 0.830, from 0.805 for demographics alone.
The improved prediction was assessed internally, and the authors say external validation is needed before the panel can be used clinically.
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- Peer-reviewedKidney Research and Clinical Practice2026-11-02
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Truvace Impact Record TRV-2026-2410, v1: “Identification of plasma biomarkers for chronic kidney disease by integrating proteomics and machine learning.” Truvace, 2026-11-07. /record/TRV-2026-2410 (accessed on the day you copy it). sha256 1c871eb854a19ed7…
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