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.

(Click on images to enlarge)

Time for a change?

This month's issue of NDT has an interesting debate concerning whether or not clinical laboratories should start reporting CKD-EPI GFR instead of MDRD. The pro side is here while the con side is here. Basically, the argument for changing is that there is less bias in the CKD-EPI equation, it is more accurate at higher GFRs and more accurately classifies patients as stage 3 as opposed to stage 2 (in terms of overall prognosis). The counter-argument is that, although there is a slight decrease in bias associated with the use of CKD-EPI, it is not any more precise than MDRD - this is more a fault of creatinine as a test of renal function rather than a specific problem with the equations. It should also be mentioned that the CKD-EPI equation is not necessarily better in all circumstances - as documented by Leo in this post about renal transplant recipients.

To (perhaps) settle the argument on one side, the moderator of the debate wrote a commentary and came down on the side of changing to the CKD-EPI equation. The argument is that, even if the improvement is slight, we, as a nephrology community, should not settle for something that is clearly inferior in most circumstances. MDRD was developed in a population of patients with CKD and therefore does not accurately reflect GFR in healthy populations. For this reason, in the research community, there has been a move towards more use of CKD-EPI in the recent past as it is more appropriate for epidemiologic research. The switch to CKD-EPI would not require the use of any new analytes - a simple change in coding in the computers reporting results. In fact, a number of organizations have already switched.

Two other things to mention. Neither equation has been properly validated in Asian populations and this needs to be remedied. Secondly, the role for cystatin C-based or combination equations is still uncertain. Cystatin C is a better predictor of outcomes than creatinine but there are many non-GFR determinants of cystatin C that are likely biasing this and are not related to renal function. Also, the cystatin C test is expensive and has not been fully standardized. There may be a place for the combination equation in patients with borderline GFRs (45-60) in whom the diagnosis of CKD is uncertain.

MDRD vs CKD-EPI in Transplantation

With the results of the eAJKD brackets posted by Gearoid, I thought this article might be pertinent to stimulate further the debate... This article just came out on Transplantation and is a well designed study in which the performance of the CKD-EPI equation is compared with the MDRD Study equation in 825 stable kidney transplant recipients.
GFR was measured by urinary clearance of inulin (n=488) and plasma clearance of 51Cr-EDTA (n=337).
The results showed that bias was significantly lower for MDRD Study equation compared with CKD-EPI creatinine to estimate the GFR. This superiority translated into a better accuracy (80% and 74% for the MDRD and CKD-EPI creatinine, respectively). The best performance of the MDRD Study equation was confirmed both in the subgroups of patients with mGFR below 60 mL/min/1.73 m2 and between 60 and 90 mL/min/1.73 m2. For mGFR above 90 mL/min/1.73 m2, there were no significant differences between the two equations in terms of performance.
The data also bring us back to the main concern about using creatinine and how poor of a marker it is for renal function. About 30% are misclassified in the CKD stages...  The battle is far from over...

Still mysterious: the elusive circulating factor for FSGS

Important new findings were recently published in relation to proteinuria and FSGS, which are definitely of interest to our community.

First, the punch line:

There is new evidence for a “circulating factor” in recurrent FSGS in a fascinating case of a re-transplanted kidney (here)

BUT

There is growing evidence that suPAR is a non-specific marker of kidney disease and therefore not likely to be the “circulating factor.”(here)

In fact, it appears that it is non-specifically found in CKD, and correlates with a declining GFR.

Now for some details:

The re-transplanted kidney

A letter to the NEJM editor (here) describes an amazing case of resolution of recurrent FSGS after re-transplantation. 

A 27 year old patient with primary FSGS receiving a kidney from his healthy 24 year old sister developed proteinuria in the nephrotic range (up to 25 g/day!) within 2 days of transplantation, and had no improvement after plasmapheresis and standard immunosuppressive treatment. A renal biopsy confirmed foot process effacement, the first hallmark of recurrent podocyte damage heralding recurrent FSGS. Incredibly, with all appropriate consents and institutional approval, the transplant team removed the allograft from Patient 1 and re-transplanted it into another patient who had ESRD due to diabetes. Within 3-4 days, the proteinuria resolved and a repeat biopsy showed resolution of foot process effacement and re-establishment of a normal podocyte architecture. Eight months later, Patient 2 is reported to be doing very well, with good allograft function and no proteinuria.

This case demonstrates in a remarkable way that recurrent FSGS results from an elusive “factor” rapidly produced by the recipient (with primary FSGS), and that the allograft itself can remain fully functional if removed from the influence of this “factor” and placed in another patient.

suPAR is not suPER specific

What may have seemed to be exciting news in 2011, namely the notion that soluble uPAR may be predictive of recurrent FSGS (here), appears to be unfortunately evolving into yet another unsuccessful attempt to identify the ever elusive circulating factor.

Recent work published in Kidney International by Maas et al. (here) confirms that suPAR is not able to distinguish between idiopathic FSGS, secondary FSGS or minimal change disease. 

This is actually not surprising, because a closer look at the clinical data in Wei et al. (here) reveals that the admittedly arbitrary cut-off for separating primary FSGS from all other glomerular disease (3000 pg/ml) did not hold up when tested among their patient cohorts with idiopathic, recurrent versus non-recurrent FSGS (all had suPAR> 3000 pg/ml, thus suPAR could not predict the recurrent from the non-recurrent cases). 

The second figure in the Maas et al. paper may help explain this conundrum: they show a negative correlation between suPAR and eGFR, meaning that as GFR drops, suPAR levels rise, which essentially means that suPAR is simply a marker of CKD.

Future work will no doubt continue to address these issues, but the apparent lack of specificity of suPAR for FSGS casts serious doubt on its proposed role as the circulating factor.

So, the search is still on!!!