Amanda H Anderson1, Dawei Xie2, Xue Wang2, Robin L Baudier3, Paula Orlandi2, Lawrence J Appel4, Laura M Dember5, Jiang He3, John W Kusek6, James P Lash7, Sankar D Navaneethan8, Akinlolu Ojo9, Mahboob Rahman10, Jason Roy11, Julia J Scialla12, James H Sondheimer13, Susan P Steigerwalt14, F Perry Wilson15, Myles Wolf16, Harold I Feldman17. 1. Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA; Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA. Electronic address: aanderson5@tulane.edu. 2. Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA. 3. Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA. 4. Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD. 5. Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA; Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA. 6. Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA. 7. Division of Nephrology, Department of Medicine, University of Illinois at Chicago College of Medicine, Chicago, IL. 8. Section of Nephrology, Department of Medicine, Baylor University College of Medicine, Houston, TX. 9. University of Kansas School of Medicine, Kansas City, KS. 10. Division of Nephrology and Hypertension, Department of Medicine, Case Western Reserve University School of Medicine, Cleveland, OH. 11. Department of Biostatistics and Epidemiology, Rutgers School of Public Health, Piscataway, NJ. 12. Division of Nephrology, Department of Medicine, and Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC; Department of Medicine, University of Virginia School of Medicine, Charlottesville, VA; Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA. 13. Division of Nephrology and Hypertension, Wayne State University School of Medicine, Detroit. 14. Division of Nephrology, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI. 15. Program of Applied Translational Research, Department of Medicine, Yale University School of Medicine, New Haven, CT. 16. Division of Nephrology, Department of Medicine, and Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC. 17. Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA; Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA; Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA.
Abstract
RATIONALE & OBJECTIVE: Identification of novel risk factors for chronic kidney disease (CKD) progression may inform mechanistic investigations and improve identification of high-risk subgroups. The current study aimed to characterize CKD progression across levels of numerous risk factors and identify independent risk factors for CKD progression among those with and without diabetes. STUDY DESIGN: The Chronic Renal Insufficiency Cohort (CRIC) Study is a prospective cohort study of adults with CKD conducted at 7 US clinical centers. SETTING & PARTICIPANTS: Participants (N=3,379) had up to 12.3 years of follow-up; 47% had diabetes. PREDICTORS: 30 risk factors for CKD progression across sociodemographic, behavioral, clinical, and biochemical domains at baseline. OUTCOMES: Study outcomes were estimated glomerular filtration rate (eGFR) slope and the composite of halving of eGFR or initiation of kidney replacement therapy. ANALYTICAL APPROACH: Stepwise selection of independent risk factors was performed stratified by diabetes status using linear mixed-effects and Cox proportional hazards models. RESULTS: Among those without and with diabetes, respectively, mean eGFR slope was-1.4±3.3 and-2.7±4.7mL/min/1.73m2 per year. Among participants with diabetes, multivariable-adjusted hazard of the composite outcome was approximately 2-fold or greater with higher levels of the inflammatory chemokine CXCL12, the cardiac marker N-terminal pro-B-type natriuretic peptide (NT-proBNP), and the kidney injury marker urinary neutrophil gelatinase-associated lipocalin (NGAL). Among those without diabetes, low serum bicarbonate and higher high-sensitivity troponin T, NT-proBNP, and urinary NGAL levels were all significantly associated with a 1.5-fold or greater rate of the composite outcome. LIMITATIONS: The observational study design precludes causal inference. CONCLUSIONS: Strong associations for cardiac markers, plasma CXCL12, and urinary NGAL are comparable to that of systolic blood pressure≥140mm Hg, a well-established risk factor for CKD progression. This warrants further investigation into the potential mechanisms that these markers indicate and opportunities to use them to improve risk stratification.
RATIONALE & OBJECTIVE: Identification of novel risk factors for chronic kidney disease (CKD) progression may inform mechanistic investigations and improve identification of high-risk subgroups. The current study aimed to characterize CKD progression across levels of numerous risk factors and identify independent risk factors for CKD progression among those with and without diabetes. STUDY DESIGN: The Chronic Renal Insufficiency Cohort (CRIC) Study is a prospective cohort study of adults with CKD conducted at 7 US clinical centers. SETTING & PARTICIPANTS: Participants (N=3,379) had up to 12.3 years of follow-up; 47% had diabetes. PREDICTORS: 30 risk factors for CKD progression across sociodemographic, behavioral, clinical, and biochemical domains at baseline. OUTCOMES: Study outcomes were estimated glomerular filtration rate (eGFR) slope and the composite of halving of eGFR or initiation of kidney replacement therapy. ANALYTICAL APPROACH: Stepwise selection of independent risk factors was performed stratified by diabetes status using linear mixed-effects and Cox proportional hazards models. RESULTS: Among those without and with diabetes, respectively, mean eGFR slope was-1.4±3.3 and-2.7±4.7mL/min/1.73m2 per year. Among participants with diabetes, multivariable-adjusted hazard of the composite outcome was approximately 2-fold or greater with higher levels of the inflammatory chemokine CXCL12, the cardiac marker N-terminal pro-B-type natriuretic peptide (NT-proBNP), and the kidney injury marker urinary neutrophil gelatinase-associated lipocalin (NGAL). Among those without diabetes, low serum bicarbonate and higher high-sensitivity troponin T, NT-proBNP, and urinary NGAL levels were all significantly associated with a 1.5-fold or greater rate of the composite outcome. LIMITATIONS: The observational study design precludes causal inference. CONCLUSIONS: Strong associations for cardiac markers, plasma CXCL12, and urinary NGAL are comparable to that of systolic blood pressure≥140mm Hg, a well-established risk factor for CKD progression. This warrants further investigation into the potential mechanisms that these markers indicate and opportunities to use them to improve risk stratification.
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