Incident heart failure in chronic kidney disease: proteomics informs biology and risk stratification

Ruth F. Dubin, Division of Nephrology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, H5.122E, Dallas, TX 75390, USA.
Rajat Deo, Division of Cardiovascular Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
Yue Ren, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Jianqiao Wang, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Alexander R. Pico, Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA, USA.
Josyf C. Mychaleckyj, Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA.
Julia Kozlitina, McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Victoria Arthur, Division of Cardiology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Hongzhe Lee, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Amil Shah, Division of Cardiology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Harold Feldman, Patient-Centered Outcomes Research Institute, Washington, DC, USA.
Nisha Bansal, Division of Nephrology, University of Washington Medical Center, Seattle, WA, USA.
Leila Zelnick, Division of Nephrology, University of Washington Medical Center, Seattle, WA, USA.
Panduranga Rao, Division of Nephrology, University of Michigan, Ann Arbor, MI, USA.
Nidhi Sukul, Division of Nephrology, University of Michigan, Ann Arbor, MI, USA.
Dominic S. Raj, Division of Kidney Diseases and Hypertension, George Washington University School of Medicine, Washington, DC, USA.
Rupal Mehta, Division of Nephrology and Hypertension, Northwestern University Feinberg School of Medicine, USA.
Sylvia E. Rosas, Joslin Diabetes Center, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
Zeenat Bhat, Division of Nephrology, University of Michigan, Ann Arbor, MI, USA.
Matthew R. Weir, Division of Nephrology, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA.
Jiang He, Department of Epidemiology, Tulane University, New Orleans, LA, USA.
Jing Chen, Department of Epidemiology, Tulane University, New Orleans, LA, USA.
Mayank Kansal, Division of Cardiology, University of Illinois College of Medicine, Chicago, IL, USA.
Paul L. Kimmel, Division of Kidney, Urologic, and Hematologic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA.
Vasan S. Ramachandran, University of Texas School of Public Health San Antonio and the University of Texas Health Sciences Center in San Antonio, Section of Preventive Medicine and Epidemiology, Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
Sushrut S. Waikar, Section of Nephrology, Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
Mark R. Segal, Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA.
Peter Ganz, Division of Cardiology, University of California San Francisco, San Francisco, CA, USA.

Document Type

Journal Article

Publication Date

5-17-2024

Journal

European heart journal

DOI

10.1093/eurheartj/ehae288

Keywords

Chronic kidney disease; Heart failure; Mendelian randomization; Risk model

Abstract

BACKGROUND AND AIMS: Incident heart failure (HF) among individuals with chronic kidney disease (CKD) incurs hospitalizations that burden patients and health care systems. 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. METHODS: 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. RESULTS: Over 200 proteins were associated with incident HF after adjustment for estimated glomerular filtration rate at P < 1 × 10-5. After adjustment for covariates including N-terminal pro-B-type natriuretic peptide, 17 proteins remained associated at P < 1 × 10-5. 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). CONCLUSIONS: 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.

Department

Medicine

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