Literature DB >> 19177154

For estimating creatinine clearance measuring muscle mass gives better results than those based on demographics.

Andrew D Rule1, Kent R Bailey, Gary L Schwartz, Sundeep Khosla, John C Lieske, L Joseph Melton.   

Abstract

Estimation of creatinine clearance requires knowledge of creatinine generation which can vary in different groups of patients. Since the main source of creatinine is muscle we used dual-energy X-ray absorptiometry to measure the mass of muscle in a cohort of adult men and women in Rochester, Minnesota. Serum and 24 h urinary creatinines were measured directly. The urinary creatinine was estimated using equations based on age and gender and muscle mass in order to calculate creatinine clearance. Among 664 subjects with a mean age of 55+/-20 years, 51% of whom were women, the model fit for urinary creatinine estimated with age and gender (R2=0.359) was similar to that estimated with measured muscle mass (R2=0.359). The likelihood of chronic kidney disease (creatinine clearance of less than 60 ml/min per 1.73 m2) in older subjects was highest with equations that used age, and likelihood of CKD in women was highest with equations that used gender. The outcomes of mortality and cardiovascular disease had stronger associations with decreased creatinine clearance calculated with age and gender than by the clearance calculated with muscle mass. This could be explained by age being a potent predictor of mortality and cardiovascular disease independent of urinary creatinine, muscle mass, and gender. Our study shows that the likelihood of chronic kidney disease in the elderly and in women and the risk of adverse outcomes may be inflated by equations that use patient demographics to estimate creatinine generation.

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Year:  2009        PMID: 19177154      PMCID: PMC2681487          DOI: 10.1038/ki.2008.698

Source DB:  PubMed          Journal:  Kidney Int        ISSN: 0085-2538            Impact factor:   10.612


  43 in total

1.  K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification.

Authors: 
Journal:  Am J Kidney Dis       Date:  2002-02       Impact factor: 8.860

Review 2.  Epidemiology of sarcopenia.

Authors:  L J Melton; S Khosla; B L Riggs
Journal:  Mayo Clin Proc       Date:  2000-01       Impact factor: 7.616

3.  Excerpts from the United States Renal Data System 2003 Annual Data Report: atlas of end-stage renal disease in the United States.

Authors:  Allan J Collins; Bertram Kasiske; Charles Herzog; Shu-Cheng Chen; Susan Everson; Edward Constantini; Richard Grimm; Marshall McBean; Jay Xue; Blanche Chavers; Arthur Matas; Willard Manning; Thomas Louis; Wei Pan; Jiannong Liu; Suying Li; Tricia Roberts; Frederick Dalleska; Jon Snyder; James Ebben; Eric Frazier; Daniel Sheets; Roger Johnson; Shuling Li; Stephan Dunning; Delaney Berrini; Haifeng Guo; Craig Solid; Cheryl Arko; Frank Daniels; Xinyue Wang; Beth Forrest; David Gilbertson; Wendy St Peter; Pamela Frederick; Paul Eggers; Lawrence Agodoa
Journal:  Am J Kidney Dis       Date:  2003-12       Impact factor: 8.860

4.  Drawbacks of the use of indirect estimates of renal function to evaluate the effect of risk factors on renal function.

Authors:  Jacobien C Verhave; Ron T Gansevoort; Hans L Hillege; Dick De Zeeuw; Gary C Curhan; Paul E De Jong
Journal:  J Am Soc Nephrol       Date:  2004-05       Impact factor: 10.121

5.  Definition and classification of chronic kidney disease: a position statement from Kidney Disease: Improving Global Outcomes (KDIGO).

Authors:  Andrew S Levey; Kai-Uwe Eckardt; Yusuke Tsukamoto; Adeera Levin; Josef Coresh; Jerome Rossert; Dick De Zeeuw; Thomas H Hostetter; Norbert Lameire; Garabed Eknoyan
Journal:  Kidney Int       Date:  2005-06       Impact factor: 10.612

6.  The progression of chronic kidney disease: a 10-year population-based study of the effects of gender and age.

Authors:  B O Eriksen; O C Ingebretsen
Journal:  Kidney Int       Date:  2006-01       Impact factor: 10.612

7.  Calibration and random variation of the serum creatinine assay as critical elements of using equations to estimate glomerular filtration rate.

Authors:  Josef Coresh; Brad C Astor; Geraldine McQuillan; John Kusek; Tom Greene; Frederick Van Lente; Andrew S Levey
Journal:  Am J Kidney Dis       Date:  2002-05       Impact factor: 8.860

8.  National Kidney Foundation practice guidelines for chronic kidney disease: evaluation, classification, and stratification.

Authors:  Andrew S Levey; Josef Coresh; Ethan Balk; Annamaria T Kausz; Adeera Levin; Michael W Steffes; Ronald J Hogg; Ronald D Perrone; Joseph Lau; Garabed Eknoyan
Journal:  Ann Intern Med       Date:  2003-07-15       Impact factor: 25.391

9.  Measured and estimated GFR in healthy potential kidney donors.

Authors:  Andrew D Rule; Hiie M Gussak; Gregory R Pond; Erik J Bergstralh; Mark D Stegall; Fernando G Cosio; Timothy S Larson
Journal:  Am J Kidney Dis       Date:  2004-01       Impact factor: 8.860

10.  Prevalence of chronic kidney disease and decreased kidney function in the adult US population: Third National Health and Nutrition Examination Survey.

Authors:  Josef Coresh; Brad C Astor; Tom Greene; Garabed Eknoyan; Andrew S Levey
Journal:  Am J Kidney Dis       Date:  2003-01       Impact factor: 8.860

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  47 in total

1.  Kidney function and risk triage in adults: threshold values and hierarchical importance.

Authors:  Robert N Foley; Changchun Wang; Jon J Snyder; Andrew D Rule; Allan J Collins
Journal:  Kidney Int       Date:  2010-08-18       Impact factor: 10.612

2.  Chronic kidney disease epidemic: myth and reality.

Authors:  Filippo Mangione; Antonio Dal Canton
Journal:  Intern Emerg Med       Date:  2011-10       Impact factor: 3.397

3.  GFR estimating equations: getting closer to the truth?

Authors:  Andrew D Rule; Richard J Glassock
Journal:  Clin J Am Soc Nephrol       Date:  2013-05-23       Impact factor: 8.237

4.  Chronic kidney disease: Automated eGFR reporting: good for the patient?

Authors:  Andrew D Rule; LaTonya J Hickson
Journal:  Nat Rev Nephrol       Date:  2009-12       Impact factor: 28.314

Review 5.  Effectiveness of targeted screening for chronic kidney disease in the community setting: a systematic review.

Authors:  Pankti A Gheewala; Syed Tabish R Zaidi; Matthew D Jose; Luke Bereznicki; Gregory M Peterson; Ronald L Castelino
Journal:  J Nephrol       Date:  2017-02-08       Impact factor: 3.902

6.  Risk factor profile for chronic kidney disease is similar to risk factor profile for small artery disease.

Authors:  Stephen T Turner; Andrew D Rule; Gary L Schwartz; Iftikhar J Kullo; Thomas H Mosley; Clifford R Jack; Sharon L R Kardia; Eric Boerwinkle; Kent R Bailey
Journal:  J Hypertens       Date:  2011-09       Impact factor: 4.844

7.  Relative performance of the MDRD and CKD-EPI equations for estimating glomerular filtration rate among patients with varied clinical presentations.

Authors:  Kazunori Murata; Nikola A Baumann; Amy K Saenger; Timothy S Larson; Andrew D Rule; John C Lieske
Journal:  Clin J Am Soc Nephrol       Date:  2011-07-07       Impact factor: 8.237

8.  Influence of urine creatinine on the relationship between the albumin-to-creatinine ratio and cardiovascular events.

Authors:  Caitlin E Carter; Ronald T Gansevoort; Lieneke Scheven; Hiddo J Lambers Heerspink; Michael G Shlipak; Paul E de Jong; Joachim H Ix
Journal:  Clin J Am Soc Nephrol       Date:  2012-03-01       Impact factor: 8.237

9.  Predictors of Augmented Renal Clearance in a Heterogeneous ICU Population as Defined by Creatinine and Cystatin C.

Authors:  Andrea M Nei; Kianoush B Kashani; Ross Dierkhising; Erin F Barreto
Journal:  Nephron       Date:  2020-05-19       Impact factor: 2.847

10.  Creatinine-Based and Cystatin C-Based GFR Estimating Equations and Their Non-GFR Determinants in Kidney Transplant Recipients.

Authors:  Mira T Keddis; Hatem Amer; Nikolay Voskoboev; Walter K Kremers; Andrew D Rule; John C Lieske
Journal:  Clin J Am Soc Nephrol       Date:  2016-06-23       Impact factor: 8.237

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