Morgan E Grams1,2, Aditya Surapaneni2, Jingsha Chen2, Linda Zhou2, Zhi Yu2, Diptavo Dutta3, Paul A Welling4, Nilanjan Chatterjee3, Jingning Zhang3, Dan E Arking5, Teresa K Chen4, Casey M Rebholz2, Bing Yu6, Pascal Schlosser2,7, Eugene P Rhee8, Christie M Ballantyne9, Eric Boerwinkle6,10, Pamela L Lutsey11, Thomas Mosley12, Harold I Feldman13, Ruth F Dubin14, Peter Ganz14, Hongzhe Lee13, Zihe Zheng13, Josef Coresh4,2,3. 1. Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland mgrams2@jhmi.edu. 2. Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland. 3. Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland. 4. Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland. 5. McKusick-Nathans Institute, Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland. 6. Department of Epidemiology, Human Genetics and Environmental Sciences, University of Texas Health Sciences Center at Houston School of Public Health, Houston, Texas. 7. Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany. 8. Nephrology Division and Endocrine Unit, Massachusetts General Hospital, Boston, Massachusetts. 9. Department of Medicine, Baylor College of Medicine, Houston, Texas. 10. Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas. 11. Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, Minnesota. 12. Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi. 13. Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania. 14. Department of Medicine, University of California San Francisco, San Francisco, California.
Abstract
BACKGROUND: Proteomic profiling may allow identification of plasma proteins that associate with subsequent changesin kidney function, elucidating biologic processes underlying the development and progression of CKD. METHODS: We quantified the association between 4877 plasma proteins and a composite outcome of ESKD or decline in eGFR by ≥50% among 9406 participants in the Atherosclerosis Risk in Communities (ARIC) Study (visit 3; mean age, 60 years) who were followed for a median of 14.4 years. We performed separate analyses for these proteins in a subset of 4378 participants (visit 5), who were followed at a later time point, for a median of 4.4 years. For validation, we evaluated proteins with significant associations (false discovery rate <5%) in both time periods in 3249 participants in the Chronic Renal Insufficiency Cohort (CRIC) and 703 participants in the African American Study of Kidney Disease and Hypertension (AASK). We also compared the genetic determinants of protein levels with those from a meta-analysis genome-wide association study of eGFR. RESULTS: In models adjusted for multiple covariates, including baseline eGFR and albuminuria, we identified 13 distinct proteins that were significantly associated with the composite end point in both time periods, including TNF receptor superfamily members 1A and 1B, trefoil factor 3, and β-trace protein. Of these proteins, 12 were also significantly associated in CRIC, and nine were significantly associated in AASK. Higher levels of each protein associated with higher risk of 50% eGFR decline or ESKD. We found genetic evidence for a causal role for one protein, lectin mannose-binding 2 protein (LMAN2). CONCLUSIONS: Large-scale proteomic analysis identified both known and novel proteomic risk factors for eGFR decline.
BACKGROUND: Proteomic profiling may allow identification of plasma proteins that associate with subsequent changesin kidney function, elucidating biologic processes underlying the development and progression of CKD. METHODS: We quantified the association between 4877 plasma proteins and a composite outcome of ESKD or decline in eGFR by ≥50% among 9406 participants in the Atherosclerosis Risk in Communities (ARIC) Study (visit 3; mean age, 60 years) who were followed for a median of 14.4 years. We performed separate analyses for these proteins in a subset of 4378 participants (visit 5), who were followed at a later time point, for a median of 4.4 years. For validation, we evaluated proteins with significant associations (false discovery rate <5%) in both time periods in 3249 participants in the Chronic Renal Insufficiency Cohort (CRIC) and 703 participants in the African American Study of Kidney Disease and Hypertension (AASK). We also compared the genetic determinants of protein levels with those from a meta-analysis genome-wide association study of eGFR. RESULTS: In models adjusted for multiple covariates, including baseline eGFR and albuminuria, we identified 13 distinct proteins that were significantly associated with the composite end point in both time periods, including TNF receptor superfamily members 1A and 1B, trefoil factor 3, and β-trace protein. Of these proteins, 12 were also significantly associated in CRIC, and nine were significantly associated in AASK. Higher levels of each protein associated with higher risk of 50% eGFR decline or ESKD. We found genetic evidence for a causal role for one protein, lectin mannose-binding 2 protein (LMAN2). CONCLUSIONS: Large-scale proteomic analysis identified both known and novel proteomic risk factors for eGFR decline.
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