Jordana B Cohen1, Wei Yang2, Liang Li3, Xiaoming Zhang2, Zihe Zheng2, Paula Orlandi2, Nisha Bansal4, Rajat Deo5, James P Lash6, Mahboob Rahman7, Jiang He8, Tariq Shafi9, Jing Chen8, Debbie L Cohen10, Kunihiro Matsushita11, Michael G Shlipak12, Myles Wolf13, Alan S Go14, Harold I Feldman2. 1. Renal-Electrolyte and Hypertension Division, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA; Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA. Electronic address: jco@pennmedicine.upenn.edu. 2. Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA. 3. Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX. 4. Division of Nephrology, Kidney Research Institute, University of Washington, Seattle, WA. 5. Division of Cardiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA. 6. Department of Medicine, Institute for Minority Health Research, University of Illinois at Chicago, Chicago, IL. 7. Department of Medicine, Case Western University, University Hospitals Case Medical Center, Cleveland, OH. 8. Departments of Epidemiology and Medicine, Tulane University, New Orleans, LA. 9. Division of Nephrology, Department of Medicine, University of Mississippi Medical Center, Jackson, MS. 10. Renal-Electrolyte and Hypertension Division, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA. 11. Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD. 12. Department of Medicine, University of California, San Francisco. 13. Division of Nephrology, Department of Medicine, and Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC. 14. Department of Medicine, University of California, San Francisco; Division of Research, Kaiser Permanente Northern California, Oakland, CA.
Abstract
RATIONALE & OBJECTIVE: Evaluating repeated measures of estimated glomerular filtration rate (eGFR) and urinary protein-creatinine ratio (UPCR) over time may enhance our ability to understand the association between changes in kidney parameters and cardiovascular disease risk. STUDY DESIGN: Prospective cohort study. SETTING & PARTICIPANTS: Annual visit data from 2,438 participants in the Chronic Renal Insufficiency Cohort (CRIC). EXPOSURES: Average and slope of eGFR and UPCR in time-updated, 1-year exposure windows. OUTCOMES: Incident heart failure, atherosclerotic cardiovascular disease events, death, and a composite of incident heart failure, atherosclerotic cardiovascular disease events, and death. ANALYTICAL APPROACH: A landmark analysis, a dynamic approach to survival modeling that leverages longitudinal, iterative profiles of laboratory and clinical information to assess the time-updated 3-year risk of adverse cardiovascular outcomes. RESULTS: Adjusting for baseline and time-updated covariates, every standard deviation lower mean eGFR (19mL/min/1.73m2) and declining slope of eGFR (8mL/min/1.73m2 per year) were independently associated with higher risks of heart failure (hazard ratios [HRs] of 1.82 [95% CI, 1.39-2.44] and 1.28 [95% CI, 1.12-1.45], respectively) and the composite outcome (HRs of 1.32 [95% CI, 1.11-1.54] and 1.11 [95% CI, 1.03-1.20], respectively). Every standard deviation higher mean UPCR (136mg/g) and increasing UPCR (240mg/g per year) were also independently associated with higher risks of heart failure (HRs of 1.58 [95% CI, 1.28-1.97] and 1.20 [95% CI, 1.10-1.29], respectively) and the composite outcome (HRs of 1.33 [95% CI, 1.17-1.50] and 1.12 [95% CI, 1.06-1.18], respectively). LIMITATIONS: Limited generalizability of annual eGFR and UPCR assessments; several biomarkers for cardiovascular disease risk were not available annually. CONCLUSIONS: Using the landmark approach to account for time-updated patterns of kidney function, average and slope of eGFR and proteinuria were independently associated with 3-year cardiovascular risk. Short-term changes in kidney function provide information about cardiovascular risk incremental to level of kidney function, representing possible opportunities for more effective management of patients with chronic kidney disease.
RATIONALE & OBJECTIVE: Evaluating repeated measures of estimated glomerular filtration rate (eGFR) and urinary protein-creatinine ratio (UPCR) over time may enhance our ability to understand the association between changes in kidney parameters and cardiovascular disease risk. STUDY DESIGN: Prospective cohort study. SETTING & PARTICIPANTS: Annual visit data from 2,438 participants in the Chronic Renal Insufficiency Cohort (CRIC). EXPOSURES: Average and slope of eGFR and UPCR in time-updated, 1-year exposure windows. OUTCOMES: Incident heart failure, atherosclerotic cardiovascular disease events, death, and a composite of incident heart failure, atherosclerotic cardiovascular disease events, and death. ANALYTICAL APPROACH: A landmark analysis, a dynamic approach to survival modeling that leverages longitudinal, iterative profiles of laboratory and clinical information to assess the time-updated 3-year risk of adverse cardiovascular outcomes. RESULTS: Adjusting for baseline and time-updated covariates, every standard deviation lower mean eGFR (19mL/min/1.73m2) and declining slope of eGFR (8mL/min/1.73m2 per year) were independently associated with higher risks of heart failure (hazard ratios [HRs] of 1.82 [95% CI, 1.39-2.44] and 1.28 [95% CI, 1.12-1.45], respectively) and the composite outcome (HRs of 1.32 [95% CI, 1.11-1.54] and 1.11 [95% CI, 1.03-1.20], respectively). Every standard deviation higher mean UPCR (136mg/g) and increasing UPCR (240mg/g per year) were also independently associated with higher risks of heart failure (HRs of 1.58 [95% CI, 1.28-1.97] and 1.20 [95% CI, 1.10-1.29], respectively) and the composite outcome (HRs of 1.33 [95% CI, 1.17-1.50] and 1.12 [95% CI, 1.06-1.18], respectively). LIMITATIONS: Limited generalizability of annual eGFR and UPCR assessments; several biomarkers for cardiovascular disease risk were not available annually. CONCLUSIONS: Using the landmark approach to account for time-updated patterns of kidney function, average and slope of eGFR and proteinuria were independently associated with 3-year cardiovascular risk. Short-term changes in kidney function provide information about cardiovascular risk incremental to level of kidney function, representing possible opportunities for more effective management of patients with chronic kidney disease.
Authors: Thomas J Hoerger; Sean A Simpson; Benjamin O Yarnoff; Meda E Pavkov; Nilka Ríos Burrows; Sharon H Saydah; Desmond E Williams; Xiaohui Zhuo Journal: Am J Kidney Dis Date: 2014-11-05 Impact factor: 8.860
Authors: Marshall Joffe; Chi-yuan Hsu; Harold I Feldman; Matthew Weir; J R Landis; L Lee Hamm Journal: Am J Nephrol Date: 2010-04-14 Impact factor: 3.754
Authors: Amanda Hyre Anderson; Wei Yang; Chi-yuan Hsu; Marshall M Joffe; Mary B Leonard; Dawei Xie; Jing Chen; Tom Greene; Bernard G Jaar; Patricia Kao; John W Kusek; J Richard Landis; James P Lash; Raymond R Townsend; Matthew R Weir; Harold I Feldman Journal: Am J Kidney Dis Date: 2012-06-02 Impact factor: 8.860
Authors: Aminu K Bello; Brenda Hemmelgarn; Anita Lloyd; Matthew T James; Braden J Manns; Scott Klarenbach; Marcello Tonelli Journal: Clin J Am Soc Nephrol Date: 2011-04-28 Impact factor: 8.237
Authors: Mirela Dobre; Wei Yang; Jing Chen; Paul Drawz; L Lee Hamm; Edward Horwitz; Thomas Hostetter; Bernard Jaar; Claudia M Lora; Lisa Nessel; Akinlolu Ojo; Julia Scialla; Susan Steigerwalt; Valerie Teal; Myles Wolf; Mahboob Rahman Journal: Am J Kidney Dis Date: 2013-03-13 Impact factor: 8.860
Authors: Tanvir C Turin; Josef Coresh; Marcello Tonelli; Paul E Stevens; Paul E de Jong; Christopher K T Farmer; Kunihiro Matsushita; Brenda R Hemmelgarn Journal: Kidney Int Date: 2013-01-23 Impact factor: 10.612
Authors: Harold I Feldman; Lawrence J Appel; Glenn M Chertow; Denise Cifelli; Borut Cizman; John Daugirdas; Jeffrey C Fink; Eunice D Franklin-Becker; Alan S Go; L Lee Hamm; Jiang He; Tom Hostetter; Chi-Yuan Hsu; Kenneth Jamerson; Marshall Joffe; John W Kusek; J Richard Landis; James P Lash; Edgar R Miller; Emile R Mohler; Paul Muntner; Akinlolu O Ojo; Mahboob Rahman; Raymond R Townsend; Jackson T Wright Journal: J Am Soc Nephrol Date: 2003-07 Impact factor: 10.121
Authors: Nancy S Sung; William F Crowley; Myron Genel; Patricia Salber; Lewis Sandy; Louis M Sherwood; Stephen B Johnson; Veronica Catanese; Hugh Tilson; Kenneth Getz; Elaine L Larson; David Scheinberg; E Albert Reece; Harold Slavkin; Adrian Dobs; Jack Grebb; Rick A Martinez; Allan Korn; David Rimoin Journal: JAMA Date: 2003-03-12 Impact factor: 56.272
Authors: Donna K Arnett; Roger S Blumenthal; Michelle A Albert; Andrew B Buroker; Zachary D Goldberger; Ellen J Hahn; Cheryl Dennison Himmelfarb; Amit Khera; Donald Lloyd-Jones; J William McEvoy; Erin D Michos; Michael D Miedema; Daniel Muñoz; Sidney C Smith; Salim S Virani; Kim A Williams; Joseph Yeboah; Boback Ziaeian Journal: Circulation Date: 2019-03-17 Impact factor: 29.690
Authors: Kunihiro Matsushita; Josef Coresh; Yingying Sang; John Chalmers; Caroline Fox; Eliseo Guallar; Tazeen Jafar; Simerjot K Jassal; Gijs W D Landman; Paul Muntner; Paul Roderick; Toshimi Sairenchi; Ben Schöttker; Anoop Shankar; Michael Shlipak; Marcello Tonelli; Jonathan Townend; Arjan van Zuilen; Kazumasa Yamagishi; Kentaro Yamashita; Ron Gansevoort; Mark Sarnak; David G Warnock; Mark Woodward; Johan Ärnlöv Journal: Lancet Diabetes Endocrinol Date: 2015-05-28 Impact factor: 32.069