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Identifying protein biomarkers of diet, and their associations with these outcomes, can elucidate biologic mechanisms through which diet improves prognosis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Using data from the Chronic Renal Insufficiency Cohort study of adults with CKD (\n                      <jats:italic toggle=\"yes\">n<\/jats:italic>\n                      =2217, mean age 59 years), we examined cross-sectional associations between 4954 plasma proteins and four dietary patterns: Healthy Eating Index-2020, Alternative Healthy Eating Index-2010, Dietary Approaches to Stop Hypertension, and alternate Mediterranean diet. Relative values of proteins were determined using an aptamer-based assay. Dietary intake was assessed using a food frequency questionnaire. We used multivariable linear regression to identify proteins associated with diet, and Cox proportional hazards regression to assess longitudinal associations between diet-related proteins, CKD progression, and mortality. Elastic net regression was used to select subsets of proteins that are associated with these outcomes.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      At a false discovery rate-adjusted\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt; 0.05, 199 proteins were associated with \u22651 dietary pattern and 18 were associated with all patterns. Over 7 years of median follow-up, 824 CKD progression events occurred. Twenty-one proteins were associated with CKD progression at\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt; 2.5\u00d710\n                      <jats:sup>\u22124<\/jats:sup>\n                      (=0.05\/199), of which eight were selected in elastic net regression: follistatin-related protein 3, glutaredoxin-1, asialoglycoprotein receptor 1, extracellular superoxide dismutase [Cu-Zn], zinc-\n                      <jats:italic toggle=\"yes\">\u03b1<\/jats:italic>\n                      -2-glycoprotein, IL-18 receptor 1, ecto-ADP-ribosyltransferase 3, and ephrin type-A receptor 1. All were inversely associated with healthy dietary patterns and associated with higher risk of CKD progression. Thirty proteins were associated with all-cause mortality, of which 14 were selected by elastic net regression.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Large-scale proteomics analyses identified potential protein biomarkers of healthy dietary patterns that were associated with CKD progression and mortality in adults with CKD. Their functions, including regulating blood lipids, insulin sensitivity, vascular homeostasis, inflammation, and oxidative stress, may represent mechanisms through which diet improves disease course.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Podcast<\/jats:title>\n                    <jats:p>This article contains a podcast at https:\/\/dts.podtrac.com\/redirect.mp3\/www.asn-online.org\/media\/podcast\/CJASN\/2026_04_16_CJASNApr.21.4.41620.mp3<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2215\/cjn.0000000941","type":"journal-article","created":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T18:01:55Z","timestamp":1764612115000},"page":"553-566","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":0,"title":["Proteomic Markers of Dietary Patterns, CKD Progression, and Mortality in the CRIC 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Maryland"},{"name":"Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland"},{"name":"Division of General Internal Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2557-5161","authenticated-orcid":false,"given":"Sarah","family":"Schrauben","sequence":"additional","affiliation":[{"name":"Departments of Medicine and Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8670-3644","authenticated-orcid":false,"given":"Ana C.","family":"Ricardo","sequence":"additional","affiliation":[{"name":"Division of Nephrology, Department of Medicine, University of Illinois Chicago, Chicago, Illinois"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4699-5395","authenticated-orcid":false,"given":"Panduranga","family":"Rao","sequence":"additional","affiliation":[{"name":"Division of Nephrology, Department of Medicine, University of Michigan, Ann Arbor, Michigan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9059-9998","authenticated-orcid":false,"given":"Mirela","family":"Dobre","sequence":"additional","affiliation":[{"name":"Division of Nephrology and Hypertension, University Hospital Cleveland Medical Center, Case Western Reserve University, Cleveland, Ohio"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nishigandha","family":"Pradhan","sequence":"additional","affiliation":[{"name":"Division of Nephrology and Hypertension, University Hospital Cleveland Medical Center, Case Western Reserve University, Cleveland, 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