title: Impact of age on glomerular filtration estimates.
abstract: BACKGROUND
  Glomerular filtration decreases progressively with age in adults. Predictive equation should have proper modelling to adequately account for normal senescence.
  
  
  METHODS
  Corrected 24-h creatinine clearances (CCLs) were measured in a cohort of 773 outpatients from 18 to 90 years old. Multiple linear regression was used to model the effect of age on glomerular filtration. Comparisons were made with the simplified MDRD and the MAYO equations. Impact of the derived equation was tested in a second cohort of 7551 patients with normal serum creatinine.
  
  
  RESULTS
  While all equations show declining function with age, our results suggest that the GFR reduction is progressive after the age of 30 and continue to decline steadily after the age of 60. This leads to a convex curve in the multiple regression analysis that is best fitted by an equation including the quadratic term (age(2)). In contrast, the MDRD equation produces a faster decrease in early adulthood and a flatter curve after the age of 60 while the MAYO equation produces a more linear effect. MDRD results in the normal range are lower than those estimated by the MAYO equation. These equations, as applied on an independent cohort of 7551 normal outpatients from 18 to 102 years, produce different profile of evolution of GFR with age.
  
  
  CONCLUSIONS
  Inclusion of a quadratic term for age in the formula estimating GFR results in better modelling of the natural decline of renal function associated with ageing. Furthermore, as GFR steadily declines after the age of 30, a single cut-off value of GFR normality for all ages leads to underdiagnosis of young adults and over diagnosis of elderly individuals. Guidelines should take into account the observed reduction of kidney function with age in normal population for optimal evaluation of eGFR.
authors.affiliation: Service de Biochimie Médicale, CHUQ-L'Hôtel-Dieu de Québec, Québec, Canada. Pierre.douville@chuq.qc.ca
authors.forename: Pierre
authors.initials: P
authors.lastname: Douville
authors.forename: Ariane R
authors.initials: AR
authors.lastname: Martel
authors.forename: Jean
authors.initials: J
authors.lastname: Talbot
authors.forename: Simon
authors.initials: S
authors.lastname: Desmeules
authors.forename: Serge
authors.initials: S
authors.lastname: Langlois
authors.forename: Mohsen
authors.initials: M
authors.lastname: Agharazii
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000375:Aging / Q000502:physiology*; D015331:Cohort Studies; D003404:Creatinine / Q000097:blood / Q000378:metabolism; D005260:Female; D005919:Glomerular Filtration Rate; D006801:Humans; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D012016:Reference Values; D012044:Regression Analysis; D015203:Reproducibility of Results; D055815:Young Adult
chemical_list: D003404:Creatinine