Estimated GFR Associates with Cardiovascular Risk Factors Independently of Measured GFR

Abstract

Estimation of the GFR (eGFR) using creatinine-or cystatin C-based equations is imperfect, especially when the true GFR is normal or near-normal. Modest reductions in eGFR from the normal range variably predict cardiovascular morbidity. If eGFR associates not only with measured GFR (mGFR) but also with cardiovascular risk factors, the effects of these non-GFR-related factors might bias the association between eGFR and outcome. To investigate these potential non-GFR-related associations between eGFR and cardiovascular risk factors, we measured GFR by iohexol clearance in a sample from the general population (age 50 to 62 years) without known cardiovascular disease, diabetes, or kidney disease. Even after adjustment for mGFR, eGFR associated with traditional cardiovascular risk factors in multiple regression analyses. More risk factors influenced cystatin C-based eGFR than creatinine-based eGFR, adjusted for mGFR, and some of the risk factors exhibited nonlinear effects in generalized additive models (P Ͻ 0.05). These results suggest that eGFR, calculated using standard creatinine-or cystatin C-based equations, partially depends on factors other than the true GFR. Thus, estimates of cardiovascular risk associated with small changes in eGFR must be interpreted with caution.

Filtration Markers May Have Prognostic Value Independent of Glomerular Filtration Rate

“…In a recent study in a general population cohort, CVD risk factors were associated with higher cystatin C independent of measured GFR, suggesting that they are associated with non-GFR determinants of cystatin C and could possibly affect its association with prognosis. 30 In our additional analyses, further adjustment for CVD risk factors and other variables did not attenuate the risk estimates for cystatin C, but this does not eliminate the possibility that the observed results could be due, at least in part, to residual confounding by GFR, or due to collinearity between cystatin C and GFR such that the models are unstable.…”

GFR Estimating Equations

“…Second, hyperfiltration by mGFR or urinary creatinine clearance is associated with CKD risk factors and complications (obesity, diabetes, hypertension, left ventricular hypertrophy) (20)(21)(22)(23)(24), with developing albuminuria (25)(26)(27)(28), with structural abnormalities in the kidney (cysts, enlarged kidneys, and decreased glomerular density) (29)(30)(31)(32)(33), and with subsequent GFR decline and ESRD (29,34,35). Although eGFR Cr also identifies hyperfiltration as an increasedrisk state (16,24,28,35,36), eGFR Cys identifies hyperfiltration as a low-risk state (Figure 3) (16,23,24,35).…”

“…Cystatin C has an exponential coefficient of about 21.3 with mGFR (4,18,39), but the reasons for this are not clear. Evidence suggests that the non-GFR determinants (possibly endogenous generation rate) for increased cystatin C levels are associated with obesity, inflammation, and atherosclerosis (10,24,(40)(41)(42)(43), findings common among patients with CKD. However, if CKD leads to increased cystatin C generation, then an exponential coefficient .21 with mGFR would be expected, not the 21.3 with mGFR that has been detected.…”

“…eGFR and mGFR can be simultaneously modeled as dependent variables to minimize their error using generalized estimating equations (10). Alternatively, "error-in-variables" statistical models can be used to account for mGFR error (24). Finally, studies that obtain repeated measures of mGFR and eGFR can use various averaging techniques to minimize error.…”

“…Nonetheless, the optimal discrimination of CKD outcomes (criterion 2) is arguably what matters most with the application of endogenous filtration markers to patient care. The problem is that some markers, such as cystatin C, are superior to mGFR in discriminating prognosis (24,35,48,49). In such cases, the answer may not be with the eGFR construct.…”