title: Cystatin C identifies chronic kidney disease patients at higher risk for complications.
abstract: Although cystatin C is a stronger predictor of clinical outcomes associated with CKD than creatinine, the clinical role for cystatin C is unclear. We included 11,909 participants from the Multi-Ethnic Study of Atherosclerosis (MESA) and the Cardiovascular Health Study (CHS) and assessed risks for death, cardiovascular events, heart failure, and ESRD among persons categorized into mutually exclusive groups on the basis of the biomarkers that supported a diagnosis of CKD (eGFR <60 ml/min per 1.73 m(2)): creatinine only, cystatin C only, both, or neither. We used CKD-EPI equations to estimate GFR from these biomarkers. In MESA, 9% had CKD by the creatinine-based equation only, 2% had CKD by the cystatin C-based equation only, and 4% had CKD by both equations; in CHS, these percentages were 12, 4, and 13%, respectively. Compared with those without CKD, the adjusted hazard ratios (HR) for mortality in MESA were: 0.80 (95% CI 0.50 to 1.26) for CKD by creatinine only; 3.23 (95% CI 1.84 to 5.67) for CKD by cystatin C only; and 1.93 (95% CI 1.27 to 2.92) for CKD by both; in CHS, the adjusted HR were 1.09 (95% CI 0.98 to 1.21), 1.78 (95% CI 1.53 to 2.08), and 1.74 (95% CI 1.58 to 1.93), respectively. The pattern was similar for cardiovascular disease (CVD), heart failure, and kidney failure outcomes. In conclusion, among adults diagnosed with CKD using the creatinine-based CKD-EPI equation, the adverse prognosis is limited to the subset who also have CKD according to the cystatin C-based equation. Cystatin C may have a role in identifying persons with CKD who have the highest risk for complications.
authors.affiliation: San Francisco Veterans Affairs Medical Center, San Francisco, California, USA. carmenalicia.peralta@ucsf.edu
authors.forename: Carmen A
authors.initials: CA
authors.lastname: Peralta
authors.forename: Ronit
authors.initials: R
authors.lastname: Katz
authors.forename: Mark J
authors.initials: MJ
authors.lastname: Sarnak
authors.forename: Joachim
authors.initials: J
authors.lastname: Ix
authors.forename: Linda F
authors.initials: LF
authors.lastname: Fried
authors.forename: Ian
authors.initials: I
authors.lastname: De Boer
authors.forename: Walter
authors.initials: W
authors.lastname: Palmas
authors.forename: David
authors.initials: D
authors.lastname: Siscovick
authors.forename: Andrew S
authors.initials: AS
authors.lastname: Levey
authors.forename: Michael G
authors.initials: MG
authors.lastname: Shlipak
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D015415:Biomarkers / Q000378:metabolism; D002318:Cardiovascular Diseases / Q000453:epidemiology; D002908:Chronic Disease; D003404:Creatinine / Q000378:metabolism; D055316:Cystatin C / Q000378:metabolism*; D018450:Disease Progression; D005260:Female; D005919:Glomerular Filtration Rate / Q000502:physiology; D006333:Heart Failure / Q000453:epidemiology; D006801:Humans; D007674:Kidney Diseases / Q000150:complications* / Q000175:diagnosis / Q000378:metabolism*; D007676:Kidney Failure, Chronic / Q000453:epidemiology; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D012189:Retrospective Studies; D012307:Risk Factors
chemical_list: D015415:Biomarkers; D055316:Cystatin C; D003404:Creatinine