title: For estimating creatinine clearance measuring muscle mass gives better results than those based on demographics.
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.
authors.affiliation: Department of Internal Medicine, Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota, USA. rule.andrew@mayo.edu
authors.forename: Andrew D
authors.initials: AD
authors.lastname: Rule
authors.forename: Kent R
authors.initials: KR
authors.lastname: Bailey
authors.forename: Gary L
authors.initials: GL
authors.lastname: Schwartz
authors.forename: Sundeep
authors.initials: S
authors.lastname: Khosla
authors.forename: John C
authors.initials: JC
authors.lastname: Lieske
authors.forename: L Joseph
authors.initials: LJ
authors.lastname: Melton
mesh_terms: D015502:Absorptiometry, Photon / Q000379:methods; D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D002318:Cardiovascular Diseases; D002908:Chronic Disease; D015331:Cohort Studies; D003401:Creatine / Q000652:urine*; D005260:Female; D006801:Humans; D007674:Kidney Diseases / Q000150:complications / Q000175:diagnosis* / Q000401:mortality; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D009132:Muscles; D012737:Sex Factors; D015996:Survival Rate
chemical_list: D003401:Creatine