Influence of age and measure of eGFR on the association between renal function and cardiovascular events.
OBJECTIVE
This study investigates whether the association between estimated GFR (eGFR) and cardiovascular (CV) outcome differs for different measures of eGFR and different age groups.


METHODS
Between 1997 and 1998, 8047 participants visited our outpatient clinic for measurement of serum creatinine, serum cystatin C, urinary creatinine, and urinary albumin excretion. GFR was estimated by the Modification of Diet in Renal Disease formula, the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula, a cystatin C-based formula, a formula combining serum creatinine and cystatin C, and 24-hour creatinine clearance. Subjects had follow-up on CV events until 2005.


RESULTS
During follow-up, 530 subjects had a CV event. The association between eGFR and CV events was significantly modified by age, except when GFR was estimated by 24-hour creatinine clearance. In subjects <60 years of age, all measures of eGFR were independently and significantly associated with CV events, whereas in subjects ≥60 years of age only 24-hour creatinine clearance had a weak but significant association with CV events. For all measures and all levels of eGFR, subjects with elevated levels of albuminuria were at higher risk of CV events compared with subjects with normoalbuminuria.


CONCLUSIONS
In the general population, all measures of eGFR are independently and significantly associated with CV events in individuals <60 years of age, but in subjects ≥60 years of age, only 24-hour creatinine clearance is. In general, the association between eGFR and risk of CV events is weaker in elderly subjects than in younger subjects.
van der Velde
Marije
M
Division of Nephrology, Department of Internal Medicine, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
Bakker
Stephan J L
SJ
de Jong
Paul E
PE
Gansevoort
Ron T
RT
D000328:Adult; D000367:Age Factors; D000419:Albuminuria / Q000209:etiology / Q000503:physiopathology; D015415:Biomarkers / Q000097:blood / Q000652:urine; D002318:Cardiovascular Diseases / Q000209:etiology* / Q000503:physiopathology; D016009:Chi-Square Distribution; D003404:Creatinine / Q000097:blood / Q000652:urine; D055316:Cystatin C / Q000097:blood; D005260:Female; D005919:Glomerular Filtration Rate; D006801:Humans; D007668:Kidney / Q000378:metabolism / Q000503:physiopathology*; D007674:Kidney Diseases / Q000150:complications* / Q000378:metabolism / Q000503:physiopathology; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D009426:Netherlands; D016016:Proportional Hazards Models; D011446:Prospective Studies; D018570:Risk Assessment; D012307:Risk Factors; D013997:Time Factors
D015415:Biomarkers; C525030:CST3 protein, human; D055316:Cystatin C; D003404:Creatinine