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Estimating Residual Kidney Function: Present and Future Challenge

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Abstract

Residual kidney function is a major prognosis factor in patients with end-stage renal disease under hemodialysis or peritoneal dialysis. Advances in later years promoted residual kidney function protection as an adequacy target and the advocacy of incremental dialysis, utilizing its assessment as a parameter of individualized dialysis schedules. Glomerular filtration rate measurement is only a dimension of kidney function neglecting the share of tubular function, with several dialytic limitations. The need for interdialytic urine collections to quantify residual kidney function, by the mean of urea and creatinine clearances, is cumbersome and prone to errors in dialysis patients. This review will approach residual kidney function estimation without urine collection, mainly with biomarkers such as cystatin C, beta-2 microglobulin, and beta-trace protein, as well as the behavior of these molecules on various dialysis modalities, their non-renal determinants, and its potential use for patient risk stratification. Multi-frequency bioimpedance analysis is also described as a promising approach to estimate residual kidney function, being an opportunity to highlight the relevant link between volume balance and diuresis. We conclude that standard glomerular filtration rate estimation formulas are not sufficiently accurate for residual kidney function assessment. There is a need for innovative tools that consider glomerular and interstitial function to be implement in clinical practice, therefore the new equations already developed and approached in this review should be validated in larger cohorts.

Introduction

Chronic kidney disease (CKD) is a public health problem that affects 11–13% of the world’s adult population [1] and has a close association with progression to end-stage renal disease (ESRD), cardiovascular disease, and increased risk of death [2, 3]. In spite of some advances, there are still limitations on the assessment of kidney function, especially in the latter stages of CKD. Residual kidney function (RKF) in patients with ESRD is associated with better survival in both hemodialysis and peritoneal dialysis [4, 5], with many clinical advantages including easier volume control, less inflammation, better nutrition, and improved phosphate levels and endocrine function [6]. Preservation of RKF is recommended as a parameter of adequate dialysis, with individualized schedules of treatment. In the last decades, there has been a growing interest on finding a gold standard for RKF estimation, particularly in patients undergoing dialysis, after the recognition of its biologic relevance and the increased use of its obligatory evaluation to prescribe incremental dialysis schedules. This review considers the assessment of kidney function during this transition period and showcases the latest advances on this clinically relevant subject.

Residual Kidney Function Is More than Glomerular Filtration Rate

Urine output is variable throughout the ESRD spectrum, ranging from normal levels to anuria; it is determined not only by glomerular filtration rate (GFR) but also by the rate of tubular reabsorption. This RKF has an importance that is disproportionately high relative to its measured value in uremic solute removal. The removal of slowly diffusing intracellular uremic compounds is dialysis treatment time dependent; however, glomerular filtration is continuous (as opposed to the 10–15 h week−1 for intermittent dialysis) and RKF can more effectively clear these compounds [4, 7]. Measured RKF also underestimates the removal of toxins that can be cleared by tubular secretion (p-cresol sulfate, indoxyl sulfate). Glomerular permeability to large molecules (such as beta-2 microglobulin) is also greater than that of dialysis membranes; such toxins are not effectively removed by dialysis [8] but are removed by RKF [9, 10]. There has been major interest on middle-molecule clearance, which is not achieved by standard low-flux hemodialysis and is dependent of RKF. RKF has been better studied for peritoneal dialysis (PD) than hemodialysis (HD) and is routinely included for dialysis dosage [7]; however, its value has shown to be important for HD as well [11]. RKF is strongly associated with improved survival in both modalities: each 1 mL min−1 1.73 m−2 higher creatinine clearance is associated with 12–44% lower risk of death [4, 9]. Another major advantage of RKF is in fluid and electrolyte balance. It allows a lower rate of fluid removal by dialysis which translates to lower risk of interdialytic hypotension and thus less myocardial stunning, ischemia, and mortality [12]. There has been a growing interest on incremental dialysis, which depends on the presence and continuous evaluation of residual native kidney function. It is widely accepted that RKF declines more slowly in PD than in HD. Furthermore, long frequent hemodialysis [11] as well as low-flux hemodialysis [13] correlate with accelerated loss of residual kidney function and consequently higher mortality. Given this, it has been recommended by the Kidney Disease Outcome Quality Initiative (KDOQI) Clinical Practice Guideline for Hemodialysis Adequacy (level 1C) to inform patients about the risk of loss of residual kidney function on long standing hemodialysis and to decrease number and duration of dialysis when RKF is still present [11]. The role of tubulointerstitial changes in the progression of kidney disease and the trajectory of RKF also after dialysis initiation also must be taken into account [14].

The clinical relevance of RKF in both peritoneal and hemodialysis, independently of specific schedules in each of the modalities, calls for a revised and integrated concept of adequacy: this should include the dimensions of renal protection, kidney function estimation, renal tubular secretion of uremic toxins, volume balance, and sodium removal, as well as acknowledging the serious limitation of Kt/V urea as an estimate of uremic solutes removal.

Residual Kidney Function Assessment

GFR is generally accepted as the best overall measurement of kidney function [15]. Its assessment is crucial for clinical practice, approach to kidney disease, drug dosing, and also for the management of prognosis [16]. However, CKD is not merely a filtration disorder, but a complex disease, based on kidney structural changes and which can affect endocrine and metabolic functions, as well as reabsorption and secretion. This applies generally to all stages of kidney disease; however, the limitations of GFR in assessing kidney function should be especially considered in ESRD.

GFR is preferably estimated with the clearance of exogenous filtration markers that are eliminated by the kidney through filtration only. In clinical practice, urea and creatinine clearances are measured with urine collection prone to serious limitations [13, 17]. GFR estimations can be made without urine collection [16], with equations based on measurement of creatinine and other variables such as age, sex, and ethnicity [17, 18]. Non-GFR determinants however can affect the plasma concentration of creatinine, due to physiological processes that also affect its value, such as its generation by muscle and other kidney functions as tubular reabsorption or secretion and extrarenal elimination [19].

The assessment of residual kidney function in hemodialysis is recommended by the European Best Practice Guidelines and is made by the mean urinary clearance of urea and/or creatinine from an interdialytic urine collection corrected to body surface area. Since urea and creatinine levels vary over this period, this mean level should account for the post-HD concentration immediately after dialysis and the pre-HD immediately before the next cycle (evidence level C) [20]. KDOQI Guidelines for Hemodialysis Adequacy state that in patients with significant residual kidney function (residual urea clearance > 2 mL min−1 1.73 m−2), the dose of hemodialysis may be reduced if this value is measured periodically to avoid inadequate dialysis (recommendation level not graded) [11]. Residual function has been considered for many years. In peritoneal dialysis, current guidelines suggest that it should be measured at least every 6 months, ideally every 2–4 months, from urea in dialysate and urine. In these patients, urea clearance is usually measured and incorporated into the Kt/V urea with the total renal and dialytic clearances used to determine dialysis dose. However, since urea is absorbed by the renal tubules, it underestimates GFR by about 40%, which therefore underestimates its contribution to uremic solute removal. This underestimate has often been considered favorably as it provides a margin of safety given that abrupt declines in RKF may occur and go unrecognized. To get a true estimate of GFR in peritoneal dialysis, there is the same recommendation for the use of the mean of urea and creatinine clearance, which more closely reflects actual GFR [7].

In clinical practice, for estimation of GFR in other stages of CKD, two equations are mainly used: The Modification of Diet in Renal Disease (MDRD) Study and the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI). MDRD equation has been widely used in clinical practice since 2000 [21]; however, CKD-EPI GFR estimating equation is considered to have superior accuracy [16, 17]. Both account for creatinine, sex, race, and age. The main issue with residual kidney function assessment in patients undergoing dialysis is that the MDRD Study equation and the CKD-EPI equations cannot estimate GFR in pre-dialysis, as creatinine is lowered by treatment [22]. Therefore, interdialytic urine collections are needed to calculate the mean of urea and creatinine clearances.

Urine collections are typically collected over the full (approximately 44 h) interdialytic period and are prone to errors leading to over or underestimation of RKF [23]. There are several limitations to this process: if a patient is treated with intermittent dialysis, there will be considerable variation of urine production throughout the week, assuming a 3× a week schedule. There are also issues as to when to evaluate urea levels (given its post-dialysis rebound) and the less-than-ideal relationship between more accurate investigative measures of GFR and those obtained by averaging urea and creatinine clearances. This cumbersome and problematic process for patients and physicians limits RKF assessment.

Novel Filtration Markers on the Evaluation of Residual Kidney Function

Creatinine is not an ideal endogenous filtration marker of GFR [24] since it is secreted variably by the tubules [25] and in ESRD also undergoes elimination by the GI tract [26]. Investigation has risen on the search for new filtration markers that improve the estimated GFR, especially in ESRD, decreasing the impact of non-GFR determinants [27].

New equations can be useful not only for CKD patients but also for other special cases which are not covered by the present creatinine-based GFR-estimating formulas, such as non-white, non-African American, despite ethnic correction [28, 29]. During the past decade, alternative markers for GFR, such as cystatin C (CysC), B2 microglobulin (B2M), beta-trace protein (BTP), and others have emerged [27, 30,31,32,33,34]. These new markers are not physiologically inert like serum creatinine but are enzymatic components of important biological pathways. Other advantage is their function as early urinary markers of tubular dysfunction, presumably accounting for the tubulointerstitial share of RKF in later stage of CKD. Cystatin C and B2 microglobulin are freely filtered, reabsorbed, and metabolized by proximal tubular cells and therefore absent from the urine of patients without tubular dysfunction [24].

Cystatin C

Cystatin C is a 120 amino acid non-glycosylated protein (13.3 kDa), expressed in all nucleated cells, with multiple biologic functions: modulation of the immune system, antibacterial and antiviral activity, and response to brain injury [35]. One of the advantages of this marker for estimating kidney function is that after being freely filtered in the glomerulus it is then absorbed in the kidney tubules, where it is fully degraded locally, with no active tubular secretion or significant extrarenal elimination [36]. Recent studies have shown that cystatin C is strongly associated with risk of cardiovascular disease [30, 31] and may also be a better predictor of non-cardiovascular disease, such as pulmonary, cancer, and infection [37].

There are several factors that may be non-GFR determinants of cystatin C such as inflammation (C-reactive protein), obesity, thyroid dysfunction, use of corticosteroid, current smoking, and male sex [36, 38,39,40,41], and therefore, reduce its accuracy on these patients. These associations may reflect higher metabolic rate in men vs. women and in smokers vs. non-smokers, which may also contribute to stronger association of low estimated GFR by cystatin (eGFRCys) with cardiovascular disease and mortality [30, 41]. However, some studies have contradictory results and do not show clear association between cystatin C and inflammation or sex [23, 42]. A major advantage of cystatin over creatinine level is that its value is independent of muscle mass and nutritional status; therefore, it is more accurate in a greater range of body types, including pediatric patients and elders [36]. Cystatin C equations also show better results of eGFR in non-white and non-African-American ethnicities [28, 29]. Similarly to other middle-molecules it is not removed by low-flux dialysis, but is removed by high-flux and hemodiafiltration [8]. Its levels during hemodiafiltration are inversely correlated with diuresis [43] and its’ values undergo a rebound in the post dialytic period, remaining stable in the interdialytic period. However, the use of cystatin C in dialytic patients is limited since non-renal clearance predominates over renal clearance and has significant interindividual variation [44]. Regarding peritoneal dialysis, its removal is highly dependent of RKF with very low peritoneal clearance [45, 46]. However, clearance of cystatin C was reported to be higher during continuous ambulatory peritoneal dialysis (CAPD) when compared with continuous cycling peritoneal dialysis (CCPD), but still the proportion of peritoneal clearance was only around 3–20% of total Cys C clearance [47].

The most recent guidelines recommend the use of cystatin C formulas for confirmation of GFR, in adults with eGFRcr of 45–59 mL min−1 1.73 m−2 without markers of kidney damage [16, 48]. However, even with the association of creatinine and cystatin C, the accuracy of eGFR vs. measured GFR (mGFR) and its use for clinical decision making, especially for ESRD remain elusive [27, 49, 50], making the evaluation of novel filtration markers a growing area of research.

RKF should be preferably evaluated with combined glomerular and tubular function assessment since at such an advanced stage of CKD, interstitial lesions play a role in both excretion and endocrine kidney abilities.

Beta-Trace Protein

BTP is a 168-amino acid glycoprotein with varying molecular weight between 23 and 29 kDa, also known as lipocalin-type prostaglandin D synthase [32, 51]. BTP promotes the conversion of prostaglandin H2 to prostaglandin D2 [32] and can be found in a number of organs including the brain, retina, testes, heart, and kidney [52]. The major sources of circulating BTP are on the central nervous system, and it is one of two proteins found in human cerebral spinal fluid (CSF) and not in blood [24]. Its serum level can be used to estimate GFR in non-dialysis patients, with rising values associated with declining kidney function [53]. BTP, however, has greater within-person variability than other markers [54]. BTP levels have stronger association than creatinine to CKD progression and mortality in adults with hypertensive kidney disease and non-diabetic kidney disease and also to all-cause and CDV mortality in adults on hemodialysis [30, 33, 52, 55, 56], likely related to its involvement in prostaglandin biosynthesis [24]. Unlike cystatin C and B2M, BTP does not undergo significant tubular reabsorption in healthy tubules; however, its levels also rise in CKD, even though it is always present in urine. One explanation could be because of increased local production due to physiologic stress, independent of changes in GFR or tubular function [24]. BTP is not removed by high- or low- flux dialysis and is only partially removed by hemodiafiltration [8, 55]. Regarding peritoneal dialysis, it appears to remain in a steady state; however, there is not much information about its clearance by this modality [57]. Non-GFR determinants of BTP include: age (with weaker association than creatinine), sex, race, urine protein, and weight [23, 32, 41]. BTP might also be affected by corticosteroids, therefore its use might not be appropriate in patients receiving this treatment [23]. BTP has low correlation to creatinine after adjustment to mGFR, therefore it might be useful as an addition to creatinine based GFR estimation equations in CKD [41]. It could be considered for estimation of residual kidney function particularly in hemodialysis and peritoneal dialysis patients, given the stability throughout the dialytic and interdialytic period [57].

Beta-2 Microglobulin

B2M is a 100-amino acid protein, 12–16 kDa protein, and is a component of the class I major histocompability molecules present on all nucleated cells [58]. The serum concentration of both B2M and BTP are generally more strongly associated than creatinine with mortality, end stage renal disease and cardiovascular disease in both general population and CKD studies [30, 33, 34, 52]. Non-GFR determinants of B2M include urine protein and smoking, as well as age, sex and race in a lesser extent than creatinine [27, 41]. There is high correlation between B2M and cystatin C, suggesting that B2M may replace cystatin C if not available [41]. B2M levels increase with progressive kidney failure, inflammation and malignancy and correlate with residual kidney function in both hemodialysis and peritoneal dialysis, being RKF the most significant determinant of B2M levels in HD patients [23, 52, 59]. Its clearance in low-flux dialysis is lower than high-flux with mean pre-dialysis B2M in low-flux HD 6 mg L−1 higher [13]. There was also correlation of long term high flux dialysis (> 3.7 years) and hemodiafiltration with lower levels of B2M over time which translates to lower mortality [59, 60]. In a study by Cheung et al., regarding the results of the HEMO study, it was found that patients with B2M values of 42.5–50 mg L−1 had 60% higher risk of death than those with < 27.5 mg L−1 [13]. Regarding peritoneal dialysis, B2M has a similar behavior to cystatin C, since they have similar molecular weights, which difficult the diffusive and convective transport across the pores of the peritoneal membrane. Both rely heavily on RKF for excretion and have lower clearance than creatinine and urea [46, 61]. There appears to be a relationship between duration of dialysis and clearance of middle molecules, depending mainly on the total dwell hours of PD and not on the number of exchanges of peritoneal dialysate, with higher removal achieved by CAPD (Table 1) [61, 63].

Table 1 Elimination of middle molecules by dialysis modalities

Non-GFR Determinants

Some current studies have tried to estimate GFR with BTP, B2M, and cystatin C, aiming to overcome creatinine’s non-GFR determinants [27, 64, 65]. As already discussed, BTP, B2M, and cystatin C are less influenced by age, sex, and race than creatinine. BTP and B2M do not vary across different body mass indexes or diabetes; however, in spite of these qualities, they do not improve GFR estimation beyond creatinine and cystatin C equations [27]. Cystatin C, B2M, and BTP are also affected by non-GFR determinants that should be considered when developing new equations [41]. One advantage of the BTP-B2M GFR-estimating equations is that they do not require age, sex, or race and therefore can be used to access eGFR without the use of demographics, especially race [27]. Since BTP increases with the use of steroids, B2M with malignancy and both potentially with inflammation, failure to account for these non-GFR determinants can limit the use of both markers in estimating GFR.

Risk Stratification

BTP, B2M and the association of all four markers (BTP, B2M, Creatinine and Cystatin C) are independently associated with risk of ESRD and all-cause mortality; B2M and the 4-marker composite were also associated with risk of cardiovascular events, evidencing greater association than with estimated GFR(creatinine) alone, therefore another possible relevant use for these markers is risk stratification [31, 66]. Notably, these associations were independent of mGFR, indicating that non-GFR determinants also contribute to outcome in CKD [31]. These and other uses of BTP-B2M equations should, nevertheless, be weighed against their additional high cost and availability.

New Equations to Estimate Residual Kidney Function in Dialysis

Serum concentrations of the markers previously approached are correlated with measured GFR and their low or inexistent removal by dialysis turns them to be good markers for RKF, being a possible solution for its assessment without 24 h urine collection [23, 24]. Use of cystatin C for estimation of residual kidney function has been studied, with equations proposed by Hoek et al. in 2007 and Yang et al. in 2011 [67, 68]. Hoek et al. compares his new formula based on cystatin C with the MDRD equation and residual glomerular filtration rate (rGFR) estimated by the mean clearance of creatinine and urea corrected by body surface area and tested it on both HD and PD patients. At the time of the article CKD-EPI equation was not yet developed and the results were promising for cystatin C, resulting in better accuracy and precision than the MDRD formula [67]. In 2011, Yang et al. produce a new Cyst C-based equation and compare it to the Hoek et al.’s equation, MDRD and rGFR estimated by urea/creatinine in PD patients and state that Cyst-based equations have superior sensitivity and specificity for identifying significant RKF (> 2 mL min−1 1.732−1) and 30–50% higher accuracy than the Hoek formula [68]. More recently both equations were externally validated and compared to the CKD-EPI equation: it was concluded that cystatin C-derived equations outperform CKD-EPI, as well as MDRD, when estimating rGFR, although, still overestimate its value [49, 50].

We believe that, in spite of such overestimation, serial measurements in patients under dialysis could be used to monitor RKF trajectory and adjust dialysis schedule, while avoiding the cumbersome urine collection.

The clinical relevance of this translates in updated investigations. In the later years, there have been other attempts at producing equations that accurately estimate RKF. Vilar et al. [60] studied cystatin C and B2M on high-flux dialysis and hemodiafiltration (HDF) patients. This equation could be used to identify patients with significant RFK, with a specificity of 90% and a sensitivity of 65%. In this case, B2M levels < 19.15 mg L−1 could identify RKF ≥ 2 mL min−1 1.732−1, the cut-off defined by KDIGO guidelines [48].

On the same track, BTP and B2M are promising candidates as predictors of RKF [52]. BTP is a promising marker for RKF estimations, without necessity for timed urine collection, in peritoneal dialysis and hemodialysis [8, 55, 57]; however, its levels are not as accurate in patients receiving hemodiafiltration [57].

Different studies show different results for these two molecules (Tables 2 and 3). It has been shown that inclusion of both BTP and B2M into regression equations, can better estimate RKF than either alone [52]. Shafi et al. published various equations based on serum cystatin C, BTP, and B2M, as well as urea and creatinine and compared it with CKD-EPI creatinine equation. These equations reflect higher accuracy for detection of rGFR [23]. This study has demonstrated that these low molecular weight proteins can have better performance than those including metabolites such as creatinine and urea and also have high diagnostic accuracy for identifying patients with CLurea ≥ 2 mL min−1 and therefore can be used in place of timed urine collections [23]. In hemodialysis patients, BTP shows increased association with mortality risk [55, 56]. Wong et al. suggest that serum levels of BTP and B2M may not be accurate enough to replace the standard estimation of GFR using creatinine and urea clearance, nevertheless in their cohort of n = 40 HD patients, combined BTP/B2M equation correctly identified 95% of patients with residual urea clearance > 2 mL min−1 1.73 m−2, which could potentially suggest its use for KDIGO incremental dialysis algorithm [52].

Table 2 Non-glomerular filtration rate (GFR) determinants and risk association of middle molecules
Table 3 Equations for estimation of residual glomerular filtration rate

The advantages of these new equations are that clearance of urea can be estimated without urine collection, based on serum values, and then used to adjust dialysis dose. BTP equations are not influenced by diet and dialysis schedules, but there is still a need for research to determine whether dialysis dose can be safely modified with estimating equations instead of timed-urine collections. Based on this information, serum BTP equations may be the most reliable for assessing residual kidney function in dialysis patients. However, BTP assays are not readily available, contrary with B2M and cystatin C [23].

Shafi et al. propose the use of their urea + creatinine equation, which considers dialysis determinants, as a screening tool to estimate RKF in patients with self-reported urine output ≥ 250 mL (1 cup day−1) and further use of low molecular weight proteins, mainly BTP, for more reliable estimation and clinical decision making. This equation might be better than CKD-EPI because it was optimized to reflect non-renal (dyalitic) clearance. Precision and accuracy were better using BTP + B2M than urea + creatinine equations, especially in patients treated with hemodialysis. However, the equations overestimated the change in clearance overtime and therefore there is still a need for improvement for individual patient use. The use of these biomarkers is still inaccessible for clinical practice, but it is time to invest in them and find a useful formula to evaluate RKF [23].

In 2018, Beberashvili et al. have tried a different approach to the assessment of RKF. As already discussed, RKF is variable throughout the interdialytic period, mainly due to changes in hydration status. This hydration status is not only variable during the hemodialysis sessions but also over the years of treatment, with increasing interdialytic water-weight gain, is related to loss of kidney function. Patients with good RKF will have less variation in hydration status [4]. Changes in body-fluid compartments could be assessed with multi-frequency bioimpedance analysis (BIA) and therefore it was hypothesized that measuring body-fluid immediately before and after dialysis, could be a good procedure to estimate RKF. With BIA, Beberashvili et al. accessed BMI, body surface area, body cell mass, lean body mass, and extracellular water to total body water ratio. Values of resistance (R) at 5 and 100 Hz—inversely related to tissue water content, reactance (Xc) at 5 and 50 Hz—proportional to the cell membranes, phase angle (PhA) at 5 and 50 Hz—indicator of membrane integrity and water distribution between intra- and extra-cellular spaces—were obtained and an equation was proposed (see Table 3), then applied to a validation group and compared with rGFR estimated by mean urea and creatinine clearance corrected by body surface area. This process was repeated 2 weeks later. The new equation showed high diagnostic accuracy (85% of sensitivity and 89% of specificity) to estimate RKF at a cut-off of > 2 mL min−1 1.73 m−2 and reproducibility over time. This study also tried to answer if the residual urinary volume could correlate with residual kidney function assessed by BIA, in response to a recent study that concluded that residual urine volume has a stronger association with mortality than rGFR [70]. It was proposed that 200 mL day−1 could correspond to rGFR (urea/creatinine) of 2 mL min−1 1.73 m−2 with a sensitivity of 94% and specificity of 72%. Nevertheless, since for estimation of rGFR with the mean of urea and creatinine clearance, urinary volume is taken into account, this relation could be misleading. There is room for investigation of the relationship between residual urinary volume and residual kidney function. This method was validated in a small cohort and should be evaluated in wider populations with various co-morbidities [69].

Conclusion

Assessing residual kidney function is of major importance because it combines not only glomerular filtration rate but also tubular role in fluid and sodium removal and active tubular secretion of uremic toxins. RKF loss has impact on patient survival both in peritoneal and hemodialysis patients. This evidence calls for revised and integrated concepts of adequacy that should include renal protection and estimation of function in both modalities of dialysis. Its evaluation is also critical for incremental dialysis implementation and has been recommended by the most recent guidelines. The mean creatinine and urea clearance with interdialytic urine collections is the standard for RFK assessment; however, this can be impractical. Creatinine and urea being affected by dialysis and general equations for estimation of GFR on other stages of kidney disease do not apply to ESRD. Clinicians should move from the GFR approach to valuate tubular biomarkers of kidney function. New equations identify correctly patients with > 2 mL min−1 1.73 m−2 which could be an advantage for incremental dialysis. Besides CVD, prognostic information beyond RKF measurement with such biomarkers may add on patient risk stratification. Nevertheless, middle molecules utilized on these equations are expensive and of difficult use for clinical practice. Body-fluid assessment with BIA is an opportunity to highlight the link between fluid balance and residual urinary volume and could be a new way to approach the issue, but it still requires validation on larger populations. A task force should be put on RKF, which could improve dialytic patients’ survival. This is an area of investigation that still has many factors to develop; however, using accessible markers that do not require special collaboration from patients, excessive biological samples or that are easily available in the clinical practice, should be the future on the evaluation of chronic kidney disease.

References

  1. 1.

    Hill NR, Fatoba ST, Oke JL, Hirst JA, O’Callaghan CA, Lasserson DS, et al. Global prevalence of chronic kidney disease—a systematic review and meta-analysis. PLoS One. 2016;11(7):–e0158765. https://doi.org/10.1371/journal.pone.0158765.

  2. 2.

    Matsushita K, van der Velde M, Astor BC, Woodward M, Levey AS, de Jong PE, et al. Association of estimated glomerular filtration rate and albuminuria with all-cause and cardiovascular mortality in general population cohorts: a collaborative meta-analysis. Lancet. 2010;375(9731):2073–81. https://doi.org/10.1016/s0140-6736(10)60674-5.

  3. 3.

    Coresh J, Turin TC, Matsushita K, Sang Y, Ballew SH, Appel LJ, et al. Decline in estimated glomerular filtration rate and subsequent risk of end-stage renal disease and mortality. Jama. 2014;311(24):2518–31. https://doi.org/10.1001/jama.2014.6634.

  4. 4.

    Termorshuizen F, Dekker FW, van Manen JG, Korevaar JC, Boeschoten EW, Krediet RT. Relative contribution of residual renal function and different measures of adequacy to survival in hemodialysis patients: an analysis of the Netherlands Cooperative Study on the adequacy of Dialysis (NECOSAD)-2. J Am Soc Nephrol. 2004;15(4):1061–70.

  5. 5.

    Ramspek CL, Nacak H, van Diepen M, van Buren M, Krediet RT, Rotmans JI, et al. Pre-dialysis decline of measured glomerular filtration rate but not serum creatinine-based estimated glomerular filtration rate is a risk factor for mortality on dialysis. Nephrol Dial Transplant. 2017;32(1):89–96. https://doi.org/10.1093/ndt/gfw236.

  6. 6.

    Wang M, Obi Y, Streja E, Rhee CM, Lau WL, Chen J, et al. Association of parameters of mineral bone disorder with mortality in patients on hemodialysis according to level of residual kidney function. Clin J Am Soc Nephrol. 2017;12(7):1118–27. https://doi.org/10.2215/cjn.11931116.

  7. 7.

    Peritoneal Dialysis Adequacy Work Group. Clinical practice guidelines for peritoneal dialysis adequacy. Am J Kidney Dis. 2006;48(Suppl 1):S98–129. https://doi.org/10.1053/j.ajkd.2006.04.006.

  8. 8.

    Lindstrom V, Grubb A, Alquist Hegbrant M, Christensson A. Different elimination patterns of beta-trace protein, beta2-microglobulin and cystatin C in haemodialysis, haemodiafiltration and haemofiltration. Scand J Clin Lab Invest. 2008;68(8):685–91. https://doi.org/10.1080/00365510802047693.

  9. 9.

    Bargman JM, Thorpe KE, Churchill DN. Relative contribution of residual renal function and peritoneal clearance to adequacy of dialysis: a reanalysis of the CANUSA study. J Am Soc Nephrol. 2001;12(10):2158–62.

  10. 10.

    Lowenstein J, Grantham JJ. Residual renal function: a paradigm shift. Kidney Int. 2017;91(3):561–5. https://doi.org/10.1016/j.kint.2016.09.052.

  11. 11.

    National Kidney Foundation. KDOQI clinical practice guideline for hemodialysis adequacy: 2015 update. Am J Kidney Dis. 2015;66(5):884–930. https://doi.org/10.1053/j.ajkd.2015.07.015.

  12. 12.

    Burton JO, Jefferies HJ, Selby NM, McIntyre CW. Hemodialysis-induced cardiac injury: determinants and associated outcomes. Clin J Am Soc Nephrol. 2009;4(5):914–20. https://doi.org/10.2215/cjn.03900808.

  13. 13.

    Cheung AK, Rocco MV, Yan G, Leypoldt JK, Levin NW, Greene T, et al. Serum beta-2 microglobulin levels predict mortality in dialysis patients: results of the HEMO study. J Am Soc Nephrol. 2006;17(2):546–55. https://doi.org/10.1681/asn.2005020132.

  14. 14.

    Group tNS, Leffondre K, Loubère L, Boucquemont J, Stengel B, Metzger M, et al. Identifying subgroups of renal function trajectories. Nephrol Dial Transplant. 2017;32(suppl_2):ii185–93. https://doi.org/10.1093/ndt/gfw380.

  15. 15.

    Stevens LA, Coresh J, Greene T, Levey AS. Assessing kidney function—measured and estimated glomerular filtration rate. N Engl J Med. 2006;354(23):2473–83. https://doi.org/10.1056/NEJMra054415.

  16. 16.

    Levey AS, Inker LA, Coresh J. GFR estimation: from physiology to public health. Am J Kidney Dis. 2014;63(5):820–34. https://doi.org/10.1053/j.ajkd.2013.12.006.

  17. 17.

    Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF 3rd, Feldman HI, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150(9):604–12.

  18. 18.

    James G, Sealey J, Alderman M, Ljungman S, Mueller FB, Pecker M, et al. A longitudinal study of urinary creatinine and creatinine clearance in normal subjects: race, sex, and age differences. Am J Hypertens. 1988;1:124–31. https://doi.org/10.1093/ajh/1.2.124.

  19. 19.

    Stevens LA, Levey AS. Measured GFR as a confirmatory test for estimated GFR. J Am Soc Nephrol. 2009;20(11):2305–13. https://doi.org/10.1681/asn.2009020171.

  20. 20.

    European Best Practice Guidelines Expert Group on Hemodialysis, European Renal Association. Section I. Measurement of renal function, when to refer and when to start dialysis. Nephrol Dial Transplant. 2002;17(Suppl 7):7–15.

  21. 21.

    Levey AS, Greene T, Kusek JW, Beck GJ, Group MS. Simplified equation to predict glomerular filtration rate from serum creatinine [Abstract]. J Am Soc Nephrol. 2000;11:155A.

  22. 22.

    Levey AS, Coresh J, Greene T, Stevens LA, Zhang YL, Hendriksen S, et al. Using standardized serum creatinine values in the modification of diet in renal disease study equation for estimating glomerular filtration rate. Ann Intern Med. 2006;145(4):247–54.

  23. 23.

    Shafi T, Michels WM, Levey AS, Inker LA, Dekker FW, Krediet RT, et al. Estimating residual kidney function in dialysis patients without urine collection. Kidney Int. 2016;89(5):1099–110. https://doi.org/10.1016/j.kint.2015.10.011.

  24. 24.

    White CA, Ghazan-Shahi S, Adams MA. Beta-trace protein: a marker of GFR and other biological pathways. Am J Kidney Dis. 2015;65(1):131–46. https://doi.org/10.1053/j.ajkd.2014.06.038.

  25. 25.

    Shemesh O, Golbetz H, Kriss JP, Myers BD. Limitations of creatinine as a filtration marker in glomerulopathic patients. Kidney Int. 1985;28(5):830–8. https://doi.org/10.1038/ki.1985.205.

  26. 26.

    Hankins DA, Babb AL, Uvelli DA, Scribner BH. Creatinine degradation I: the kinetics of creatinine removal in patients with chronic kidney disease. Int J Artif Organs. 1981;4(1):35–9.

  27. 27.

    Inker LA, Tighiouart H, Coresh J, Foster MC, Anderson AH, Beck GJ, et al. GFR estimation using β-trace protein and β(2)-microglobulin in CKD. Am J Kidney Dis. 2016;67(1):40–8. https://doi.org/10.1053/j.ajkd.2015.07.025.

  28. 28.

    Kabasawa A, Konta T, Suzuki N, Kamei K, Watanabe S, Araumi A, et al. The association between glomerular filtration rate estimated using different equations and mortality in the Japanese community-based population: the Yamagata (Takahata) study. Dis Markers. 2018;2018:9191832. https://doi.org/10.1155/2018/9191832.

  29. 29.

    Bukabau JB, Sumaili EK, Cavalier E, Pottel H, Kifakiou B, Nkodila A, et al. Performance of glomerular filtration rate estimation equations in Congolese healthy adults: the inopportunity of the ethnic correction. PLoS One. 2018;13(3):e0193384. https://doi.org/10.1371/journal.pone.0193384.

  30. 30.

    Astor BC, Shafi T, Hoogeveen RC, Matsushita K, Ballantyne CM, Inker LA, et al. Novel markers of kidney function as predictors of ESRD, cardiovascular disease, and mortality in the general population. Am J Kidney Dis. 2012;59(5):653–62. https://doi.org/10.1053/j.ajkd.2011.11.042.

  31. 31.

    Foster MC, Coresh J, Hsu CY, Xie D, Levey AS, Nelson RG, et al. Serum beta-trace protein and beta2-microglobulin as predictors of esrd, mortality, and cardiovascular disease in adults with CKD in the Chronic Renal Insufficiency Cohort (CRIC) Study. Am J Kidney Dis. 2016;68(1):68–76. https://doi.org/10.1053/j.ajkd.2016.01.015.

  32. 32.

    Filler G, Kusserow C, Lopes L, Kobrzynski M. Beta-trace protein as a marker of GFR—history, indications, and future research. Clin Biochem. 2014;47(13–14):1188–94. https://doi.org/10.1016/j.clinbiochem.2014.04.027.

  33. 33.

    Bhavsar NA, Appel LJ, Kusek JW, Contreras G, Bakris G, Coresh J, et al. Comparison of measured GFR, serum creatinine, cystatin C, and beta-trace protein to predict ESRD in African Americans with hypertensive CKD. Am J Kidney Dis. 2011;58(6):886–93. https://doi.org/10.1053/j.ajkd.2011.07.018.

  34. 34.

    Liabeuf S, Lenglet A, Desjardins L, Neirynck N, Glorieux G, Lemke HD, et al. Plasma beta-2 microglobulin is associated with cardiovascular disease in uremic patients. Kidney Int. 2012;82(12):1297–303. https://doi.org/10.1038/ki.2012.301.

  35. 35.

    Onopiuk A, Tokarzewicz A, Gorodkiewicz E. Chapter two—cystatin C: a kidney function biomarker. In: Makowski GS (ed) Advances in clinical chemistry, vol 68. Elsevier, pp 57-69. 2015. doi:https://doi.org/10.1016/bs.acc.2014.11.007.

  36. 36.

    McMahon GM, Waikar SS. Biomarkers in nephrology: core curriculum 2013. Am J Kidney Dis. 2013;62(1):165–78. https://doi.org/10.1053/j.ajkd.2012.12.022.

  37. 37.

    Fried LF, Katz R, Sarnak MJ, Shlipak MG, Chaves PHM, Jenny NS, et al. Kidney function as a predictor of noncardiovascular mortality. J Am Soc Nephrol. 2005;16(12):3728–35. https://doi.org/10.1681/asn.2005040384.

  38. 38.

    Ye Y, Gai X, Xie H, Jiao L, Zhang S. Impact of thyroid function on serum cystatin C and estimated glomerular filtration rate: a cross-sectional study. Endocr Pract. 2013;19(3):397–403. https://doi.org/10.4158/ep12282.Or.

  39. 39.

    Muntner P, Winston J, Uribarri J, Mann D, Fox CS. Overweight, obesity, and elevated serum cystatin C levels in adults in the United States. Am J Med. 2008;121(4):341–8. https://doi.org/10.1016/j.amjmed.2008.01.003.

  40. 40.

    Zhai JL, Ge N, Zhen Y, Zhao Q, Liu C. Corticosteroids significantly increase serum cystatin C concentration without affecting renal function in symptomatic heart failure. Clin Lab. 2016;62(1–2):203–7.

  41. 41.

    Liu X, Foster MC, Tighiouart H, Anderson AH, Beck GJ, Contreras G, et al. Non-GFR determinants of low-molecular-weight serum protein filtration markers in CKD. Am J Kidney Dis. 2016;68(6):892–900. https://doi.org/10.1053/j.ajkd.2016.07.021.

  42. 42.

    Grubb A, Björk J, Nyman U, Pollak J, Bengzon J, Östner G, et al. Cystatin C, a marker for successful aging and glomerular filtration rate, is not influenced by inflammation. Scand J Clin Lab Invest. 2011;71(2):145–9. https://doi.org/10.3109/00365513.2010.546879.

  43. 43.

    Balik M, Jabor A, Waldauf P, Kolar M, Pavlisova M, Brest'an D, et al. Cystatin C as a marker of residual renal function during continuous hemodiafiltration. Kidney Blood Press Res. 2005;28(1):14–9. https://doi.org/10.1159/000080936.

  44. 44.

    Vilar E, Boltiador C, Viljoen A, Machado A, Farrington K. Removal and rebound kinetics of cystatin C in high-flux hemodialysis and hemodiafiltration. Clin J Am Soc Nephrol. 2014;9(7):1240–7. https://doi.org/10.2215/cjn.07510713.

  45. 45.

    Kabanda A, Goffin E, Bernard A, Lauwerys R, van Ypersele de Strihou C. Factors influencing serum levels and peritoneal clearances of low molecular weight proteins in continuous ambulatory peritoneal dialysis. Kidney Int. 1995;48(6):1946–52.

  46. 46.

    Montini G, Amici G, Milan S, Mussap M, Naturale M, Rätsch I-M, et al. Middle molecule and small protein removal in children on peritoneal dialysis. Kidney Int. 2002;61(3):1153–9. https://doi.org/10.1046/j.1523-1755.2002.00216.x.

  47. 47.

    Steubl D, Roos M, Hettwer S, Angermann S, Wen M, Schmaderer C, et al. Comparison of peritoneal low-molecular-weight-protein-removal in CCPD and CAPD patients based on C-terminal agrin fragment clearance. Kidney Blood Press Res. 2016;41(2):175–85. https://doi.org/10.1159/000443419.

  48. 48.

    KDIGO. Clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int Suppl. 2012;3(1):19–62. https://doi.org/10.1038/kisup.2012.64.

  49. 49.

    Ahmadi F, Rahmani F, Lessan-Pezeshki M, Azmandian J. Utility of cystatin C-derived equations for evaluation of residual renal function in peritoneal dialysis patients. Ren Fail. 2015;37(1):50–6. https://doi.org/10.3109/0886022x.2014.964146.

  50. 50.

    Zhong H, Zhang W, Qin M, Gou Z, Feng P. Validation of cystatin C-based equations for evaluating residual renal function in patients on continuous ambulatory peritoneal dialysis. Nephrol Dial Transplant. 2017;32(6):1032–40. https://doi.org/10.1093/ndt/gfw096.

  51. 51.

    Hoffmann A, Nimtz M, Conradt HS. Molecular characterization of β-trace protein in human serum and urine: a potential diagnostic marker for renal diseases. Glycobiology. 1997;7(4):499–506. https://doi.org/10.1093/glycob/7.4.499.

  52. 52.

    Wong J, Sridharan S, Berdeprado J, Vilar E, Viljoen A, Wellsted D, et al. Predicting residual kidney function in hemodialysis patients using serum beta-trace protein and beta2-microglobulin. Kidney Int. 2016;89(5):1090–8. https://doi.org/10.1016/j.kint.2015.12.042.

  53. 53.

    Donadio C. Serum and urinary markers of early impairment of GFR in chronic kidney disease patients: diagnostic accuracy of urinary beta-trace protein. Am J Physiol Ren Physiol. 2010;299(6):F1407–23. https://doi.org/10.1152/ajprenal.00507.2009.

  54. 54.

    Selvin E, Juraschek SP, Eckfeldt J, Levey AS, Inker LA, Coresh J. Within-person variability in kidney measures. Am J Kidney Dis. 2013;61(5):716–22. https://doi.org/10.1053/j.ajkd.2012.11.048.

  55. 55.

    Gerhardt T, Poge U, Stoffel-Wagner B, Klein B, Klehr HU, Sauerbruch T, et al. Serum levels of beta-trace protein and its association to diuresis in haemodialysis patients. Nephrol Dial Transplant. 2008;23(1):309–14. https://doi.org/10.1093/ndt/gfm510.

  56. 56.

    Shafi T, Parekh RS, Jaar BG, Plantinga LC, Oberai PC, Eckfeldt JH, et al. Serum β-trace protein and risk of mortality in incident hemodialysis patients. Clin J Am Soc Nephrol. 2012;7(9):1435–45. https://doi.org/10.2215/CJN.02240312.

  57. 57.

    van Craenenbroeck AH, Bragfors-Helin AC, Qureshi AR, Lindholm B, Sjoberg B, Anderstam B, et al. Plasma beta-trace protein as a marker of residual renal function: the effect of different hemodialysis modalities and intra-individual variability over time. Kidney Blood Press Res. 2017;42(5):877–85. https://doi.org/10.1159/000484537.

  58. 58.

    Schardijn GH, Statius van Eps LW. Beta 2-microglobulin: its significance in the evaluation of renal function. Kidney Int. 1987;32(5):635–41.

  59. 59.

    Teruel-Briones JL, Fernandez-Lucas M, Rivera-Gorrin M, Ruiz-Roso G, Diaz-Dominguez M, Rodriguez-Mendiola N, et al. Progression of residual renal function with an increase in dialysis: haemodialysis versus peritoneal dialysis. Nefrologia. 2013;33(5):640–9. https://doi.org/10.3265/Nefrologia.pre2013.May.12038.

  60. 60.

    Vilar E, Boltiador C, Wong J, Viljoen A, Machado A, Uthayakumar A, et al. Plasma levels of middle molecules to estimate residual kidney function in Haemodialysis without urine collection. PLoS One. 2015;10(12):e0143813. https://doi.org/10.1371/journal.pone.0143813.

  61. 61.

    Bammens B, Evenepoel P, Verbeke K, Vanrenterghem Y. Removal of middle molecules and protein-bound solutes by peritoneal dialysis and relation with uremic symptoms. Kidney Int. 2003;64(6):2238–43. https://doi.org/10.1046/j.1523-1755.2003.00310.x.

  62. 62.

    Al-Wakeel JS, Hammad D, Memon NA, Tarif N, Shah I, Chaudhary A. Serum cystatin C: a surrogate marker for the characteristics of peritoneal membrane in dialysis patients. Saudi J Kidney Dis Transplant. 2009;20(2):227–31.

  63. 63.

    Brophy DF, Sowinski KM, Kraus MA, Moe SM, Klaunig JE, Mueller BA. Small and middle molecular weight solute clearance in nocturnal intermittent peritoneal dialysis. Peritoneal Dialysis Int: J Int Soc Peritoneal Dialysis. 1999;19(6):534–9.

  64. 64.

    White CA, Akbari A, Doucette S, Fergusson D, Hussain N, Dinh L, et al. A novel equation to estimate glomerular filtration rate using beta-trace protein. Clin Chem. 2007;53(11):1965–8. https://doi.org/10.1373/clinchem.2007.090126.

  65. 65.

    Stevens LA, Coresh J, Schmid CH, Feldman HI, Froissart M, Kusek J, et al. Estimating GFR using serum cystatin C alone and in combination with serum creatinine: a pooled analysis of 3,418 individuals with CKD. Am J Kidney Dis. 2008;51(3):395–406. https://doi.org/10.1053/j.ajkd.2007.11.018.

  66. 66.

    Tangri N, Inker LA, Tighiouart H, Sorensen E, Menon V, Beck G, et al. Filtration markers may have prognostic value independent of glomerular filtration rate. J Am Soc Nephrol. 2012;23(2):351–9. https://doi.org/10.1681/ASN.2011070663.

  67. 67.

    Hoek FJ, Korevaar JC, Dekker FW, Boeschoten EW, Krediet RT. Estimation of residual glomerular filtration rate in dialysis patients from the plasma cystatin C level. Nephrol Dial Transplant. 2007;22(6):1633–8. https://doi.org/10.1093/ndt/gfm027.

  68. 68.

    Yang Q, Li R, Zhong Z, Mao H, Fan J, Lin J, et al. Is cystatin C a better marker than creatinine for evaluating residual renal function in patients on continuous ambulatory peritoneal dialysis? Nephrol Dial Transplant. 2011;26(10):3358–65. https://doi.org/10.1093/ndt/gfr045.

  69. 69.

    Beberashvili I, Yermolayeva T, Katkov A, Garra N, Feldman L, Gorelik O, et al. Estimating of residual kidney function by multi-frequency bioelectrical impedance analysis in hemodialysis patients without urine collection. Kidney Blood Press Res. 2018;43(1):98–109. https://doi.org/10.1159/000487106.

  70. 70.

    Lee MJ, Park JT, Park KS, Kwon YE, Oh HJ, Yoo TH, et al. Prognostic value of residual urine volume, GFR by 24-hour urine collection, and eGFR in patients receiving dialysis. Clin J Am Soc Nephrol. 2017;12(3):426–34. https://doi.org/10.2215/cjn.05520516.

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Castro, I., Rodrigues, A. Estimating Residual Kidney Function: Present and Future Challenge. SN Compr. Clin. Med. 2, 140–148 (2020). https://doi.org/10.1007/s42399-019-00197-9

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Keywords

  • Residual kidney function
  • Dialysis
  • Peritoneal dialysis
  • Cystatin C
  • Beta-trace protein
  • Beta-2 microglobulin