Diabetes and CKD - Pitfalls: Cystatin C

Cystatin C has been proposed as an alternative marker of kidney function and studies have shown that CyC is a better predictor of mortality that serum creatinine. Although, when first introduced, it was thought that CyC was not influenced by factors apart from renal function, this assumption has been questioned in the recent past.

CyC is a 13 kDa cysteine protease inhibitor that is produced by all nucleated cells. It is freely filtered at the glomerulus and then catabolized in the proximal tubule such that very little appears in the urine. CyC levels are affected by renal function but also independently influenced by age, gender, BMI, fat mass, triglycerides and the presence of diabetes. Interestingly, these are all components of the metabolic syndrome.

In 2011, a paper was published in Diabetologia that found that elevated levels of CyC were associated with an increased incidence of type II diabetes. The thought was that CyC was potentially involved in the pathogenesis of diabetes. In July, a paper was published in NDT that shed a bit more light on this issue. The authors reported the results of a 3-year study of French adults in whom the incidence of diabetes was low. In this study, in common with previous research, CyC predicted incident diabetes. However, when stratified by BMI, CyC predicted incident diabetes only in participants with a BMI >25 at baseline.

So what is the explanation for this? CyC secretion has been shown to be 2-3 times higher in obese patients than in non-obese patients. CyC is also highly expressed in subcutaneous adipose tissue. Data from the Framingham Heart Study has shown that adipose tissue was not associated with CKD using creatinine-based estimating equations while it was associated with CKD using a CyC-based equation. CyC may play a role in preventing inflammation associated with increased adiposity explaining the increased secretion in obese patients.

The implications of this are that, although CyC may predict diabetes, it is unlikely that it adds any more to prediction algorithms considering that it is not independent of BMI and the metabolic syndrome - both of which are well known to be associated with diabetes. The second implication is that the fact that CyC is better at predicting mortality than creatinine (at the same level of eGFR) is related to non-renal factors - again, adiposity and the metabolic syndrome. It similarly suggests that in obese patients, estimating equations that utilize CyC may not be as accurate as previously suggested. The search for a better biomarker of GFR continues...

Diabetes and CKD - Pitfalls: Estimating GFR

The routine use of estimating equations for GFR has revolutionized the way that we view renal disease over the last 15 years and although some argue that this has lead to overdiagnosis of CKD, I believe that this has been an extremely positive development both in clinical and research terms. One criticism of the MDRD equation in particular was that it did not perform well in patients with near normal GFR and the CKD-Epi equation was introduced, at least in part, because of this limitation. However, there remain concerns that in patients with diabetes, particularly in those with hyperfiltration, this formula still does not perform sufficiently well.

To answer this question researchers in Italy took patients from two clinical trials who had serial measured GFR for up to 8 years and compared the results with simultaneous estimates of GFR using the 14 different equations. Of the 600 patients included, 15% were hyperfiltering and 13% had a reduced GFR. Overall, all but one of the equations underestimated GFR in the group as a whole. The single equation that overestimated GFR (Ibrahim) tended to overestimate at all levels. The range of differences between the mGFR and eGFR was -40 to +20 ml/min/1.73m2 and the mean percent error (MPE) ranged from -28.14 to 0.98%. Not unexpectedly, the majority of the error was related to underestimation of GFR in patients with hyperfilatration (MPE -12.8 to -36.7%). It is notable that the MPE was lowest in participants with hyperfiltration using the CKD-Epi equation. In this group, the mean mGFR was 132 ml/min/1.73m2 while the mean eGFR ranged from 83-114 ml/min/1.73m2.

The bias was far lower for the normofiltration and low GFR groups. Because the authors had longitudinal data also, they were able to look at the ability of the formulas to measure GFR decline over time. Given that all of the equations underestimated GFR at baseline, it is unsurprising that there was systematic underestimation of GFR decline over time, particularly in the patients with hyperfiltration. This was less marked in the patients with CKD at baseline. Five of the equations actually estimated that GFR was increasing in the patients despite a consistent decline in mGFR.

This is all not to say that these formulas are not useful. It is always important to recognize the limitations of your tools and one of the major issues here is that creatinine is used as the marker of kidney function with all of the limitations that this introduces. It should also be said that although the agreement with mGFR might not be great, we know from large EPI studies that an eGFR of less than 60 ml/min/1.73m2 is associated with poorer outcomes and this is true no matter what the cause of the disease. The take home from this is that it is not possible to accurately diagnose hyperfiltration in diabetic patients without over nephropathy using current creatinine-based estimating equations and that other signs should be taken into account when assessing these patients.

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