title: Chronic kidney disease in octogenarians.
abstract: OBJECTIVE
  There are limited data on the prevalence of chronic kidney disease (CKD) and its clinical importance in the very old. We examined the prevalence of CKD in octogenarians and its association with cardiovascular disease (CVD).
  
  
  METHODS
  In a cross-sectional analysis of 1028 participants from the Cardiovascular Health Study All Stars, we evaluated association of prevalent CKD with CVD using multivariable logistic regression. CKD was defined as eGFR of <60 ml/min per 1.73 m(2). GFR was estimated using CKD-Epi creatinine and cystatin C equations that incorporate coefficients for age, gender, and race (eGFR(EPI), eGFR(CYS3var)) and the one-variable cystatin C equation (eGFR(CYS1var)). Prevalent CVD was defined as a composite of coronary heart disease, heart failure, and stroke.
  
  
  RESULTS
  Mean age was 86 years, 64% were women, 86% were Caucasians, 14% had diabetes, and 39% had prevalent CVD. Mean eGFR(EPI), eGFR(CYS3var), and eGFR(CYS1var) were 59, 62, and 70 ml/min per 1.73 m(2), and 51%, 46%, and 33% had CKD, respectively. Associations of CKD with CVD varied by equation in adjusted analyses: CKD(EPI) (OR, 1.53; 95% CI, 1.15 to 2.03), CKD(CYS3var) (OR, 1.67; 95% CI, 1.25, 2.23), and CKD(CYS1var) (OR, 2.09; 95% CI, 1.55, 2.83).
  
  
  CONCLUSIONS
  Reduced eGFR is highly prevalent in octogenarians, and the eGFR(CYS1var) equation yielded the lowest prevalence of CKD but the strongest association with prevalent CVD. Because there are no validated estimating equations in the elderly, estimation of kidney function on the basis of on any one equation should be interpreted with caution.
authors.affiliation: Tufts Medical Center, Boston, MA 02111, USA.
authors.forename: Shani
authors.initials: S
authors.lastname: Shastri
authors.forename: Hocine
authors.initials: H
authors.lastname: Tighiouart
authors.forename: Ronit
authors.initials: R
authors.lastname: Katz
authors.forename: Dena E
authors.initials: DE
authors.lastname: Rifkin
authors.forename: Linda F
authors.initials: LF
authors.lastname: Fried
authors.forename: Michael G
authors.initials: MG
authors.lastname: Shlipak
authors.forename: Anne B
authors.initials: AB
authors.lastname: Newman
authors.forename: Mark J
authors.initials: MJ
authors.lastname: Sarnak
mesh_terms: D000367:Age Factors; D000369:Aged, 80 and over; D000704:Analysis of Variance; D015415:Biomarkers / Q000097:blood; D002318:Cardiovascular Diseases / Q000097:blood / Q000453:epidemiology* / Q000503:physiopathology; D016009:Chi-Square Distribution; D002908:Chronic Disease; D003404:Creatinine / Q000097:blood; D003430:Cross-Sectional Studies; D055316:Cystatin C / Q000097:blood; D003920:Diabetes Mellitus / Q000453:epidemiology; D005260:Female; D005919:Glomerular Filtration Rate; D006801:Humans; D007668:Kidney / Q000503:physiopathology; D007674:Kidney Diseases / Q000097:blood / Q000453:epidemiology* / Q000503:physiopathology; D016015:Logistic Models; D008297:Male; D008954:Models, Biological; D015995:Prevalence; D018570:Risk Assessment; D012307:Risk Factors; D014481:United States / Q000453:epidemiology
chemical_list: D015415:Biomarkers; C525030:CST3 protein, human; D055316:Cystatin C; D003404:Creatinine