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There are few preventative therapies, and the Pooled Cohort equations to Prevent Heart Failure (PCP-HF) perform poorly in the setting of CKD. New drug targets and better risk stratification are urgently needed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this analysis of incident HF, SomaScan V4.0 (4638 proteins) was analysed in 2906 participants of the Chronic Renal Insufficiency Cohort (CRIC) with validation in the Atherosclerosis Risk in Communities (ARIC) study. The primary outcome was 14-year incident HF (390 events); secondary outcomes included 4-year HF (183 events), HF with reduced ejection fraction (137 events), and HF with preserved ejection fraction (165 events). Mendelian randomization and Gene Ontology were applied to examine causality and pathways. The performance of novel multi-protein risk models was compared to the PCP-HF risk score.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Over 200 proteins were associated with incident HF after adjustment for estimated glomerular filtration rate at P &amp;lt; 1 \u00d7 10\u22125. After adjustment for covariates including N-terminal pro-B-type natriuretic peptide, 17 proteins remained associated at P &amp;lt; 1 \u00d7 10\u22125. Mendelian randomization associations were found for six proteins, of which four are druggable targets: FCG2B, IGFBP3, CAH6, and ASGR1. For the primary outcome, the C-statistic (95% confidence interval [CI]) for the 48-protein model in CRIC was 0.790 (0.735, 0.844) vs. 0.703 (0.644, 0.762) for the PCP-HF model (P = .001). C-statistic (95% CI) for the protein model in ARIC was 0.747 (0.707, 0.787).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Large-scale proteomics reveal novel circulating protein biomarkers and potential mediators of HF in CKD. Proteomic risk models improve upon the PCP-HF risk score in this population.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/eurheartj\/ehae288","type":"journal-article","created":{"date-parts":[[2024,4,25]],"date-time":"2024-04-25T15:43:29Z","timestamp":1714059809000},"page":"2752-2767","source":"Crossref","is-referenced-by-count":33,"title":["Incident heart failure in chronic kidney disease: proteomics informs biology and risk stratification"],"prefix":"10.1093","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0498-1980","authenticated-orcid":false,"given":"Ruth F","family":"Dubin","sequence":"first","affiliation":[{"name":"Division of Nephrology, University of Texas Southwestern Medical Center , 5323 Harry Hines Blvd, H5.122E, Dallas, TX 75390 , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rajat","family":"Deo","sequence":"additional","affiliation":[{"name":"Division of Cardiovascular Medicine, Perelman School of Medicine at the University of Pennsylvania , Philadelphia, PA , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Ren","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania , Philadelphia, PA , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianqiao","family":"Wang","sequence":"additional","affiliation":[{"name":"Harvard T.H. 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