Abstract
Background
Identification of persons with chronic kidney disease (CKD) who are at highest risk to progress to end stage renal disease (ESRD) is necessary to reduce the burden of kidney failure. The relative utility of traditional markers of kidney function, including estimated glomerular filtration rate (GFR) and serum creatinine, and emerging markers of kidney function, including cystatin C and beta-trace protein (BTP), to predict ESRD and mortality has yet to be established.
Study Design
Randomized clinical trial followed by an observational cohort study.
Setting & Participants
865 African American individuals with hypertensive CKD enrolled in a clinical trial of two levels of blood pressure control and three different antihypertensive drugs as initial therapy and subsequently followed by an observational cohort study.
Predictors
Quintile of measured GFR (mGFR) by iothalamate clearance, serum creatinine, serum creatinine-based estimated GFR (eGFRSCr), cystatin C, and BTP.
Outcomes and Measurements
Incidence of ESRD and mortality.
Results
A total of 246 participants reached ESRD over a median follow-up of 102 months. The incidence rate of ESRD was higher with higher quintiles of each marker. The association between higher BTP and ESRD was stronger than those for the other markers, including mGFR. All the markers remained significantly associated with ESRD after adjustment for mGFR and relevant covariates (all p<0.05), with BTP retaining the strongest association (HR for highest versus lowest quintile, 5.7; 95% CI, 2.2-14.9). Associations with the combined endpoint of ESRD or mortality (n=390) were weaker, but remained significant for cystatin C (p=0.05) and BTP (p=0.004).
Limitations
The ability of these markers to predict ESRD and mortality in other racial and ethnic groups and among individuals with CKD due to other causes is unknown.
Conclusions
Plasma BTP and cystatin C may be useful adjuncts to serum creatinine and mGFR in evaluating risk for progression of kidney disease.
Keywords: End-stage renal disease, beta trace protein, cystatin C, serum creatinine, iothalamate glomerular filtration rate
Decreased kidney function increases risk of end-stage renal disease (ESRD) and all-cause mortality.1-3 ESRD, in turn, is associated with a substantially increased risk of mortality and morbidity.4 Accurate identification of persons with chronic kidney disease (CKD) who are at highest risk of progressing to ESRD is critical to tailor therapy to reduce its incidence and sequelae.
Directly determining measured glomerular filtration rate (mGFR) by iothalamate clearance is considered the gold standard method to assess kidney function. This procedure, however, is burdensome and impractical in many settings. Endogenous markers of kidney function are attractive alternatives in these settings. Serum creatinine and, more recently serum cystatin C, have most commonly been used to assess kidney function. Both markers, however, are influenced by factors other than kidney function, resulting in inaccurate estimates of GFR.5 For example, serum levels of creatinine are positively associated with greater muscle mass and dietary meat intake and inversely associated with concomitant illness.5 Although cystatin C is less sensitive to inter-individual differences in muscle mass, this marker is increased in persons with diabetes, inflammation, and higher body mass index.5
Beta trace protein (BTP) has been proposed as alternative marker of kidney function.6-8 BTP, also known as lipocalin prostaglandin D2 synthase, is a low molecular weight factor that is a member of the lipocalin protein family. Serum BTP levels were strongly correlated with GFR in small studies of kidney transplant patients and patients with CKD, though data are limited in other populations.9, 10 Limited data are available on the association of BTP levels with subsequent outcomes.
We compared the ability of mGFR, serum creatinine, estimated GFR based on serum creatinine (eGFRSCr), cystatin C, and BTP to predict ESRD and mortality among African Americans with hypertensive CKD enrolled in the African American Study of Kidney Disease and Hypertension (AASK) clinical trial and cohort study.
Methods
Study Design
AASK was a 3 × 2 factorial multicenter randomized clinical trial conducted from 1995 to 2001. The study was designed to test the effects of three anti-hypertensive medications, an ACE-inhibitor (ramipril), a β-blocker (metoprolol), and a calcium channel blocker (amlodipine) as initial therapy in a drug regimen, and two levels of blood pressure control (low: mean arterial pressure (MAP) of 92 mmHg or lower, and usual: MAP of 102-107 mmHg) on progression of chronic kidney disease. The study population included 1,094 non-diabetic, self-identified African Americans, aged 18 to 70 years, with an mGFR assessed by iothalamate clearance between 20 and 65 mL/min/1.73 m2 with no identified causes of chronic kidney disease other than hypertension. 11 At the conclusion of the trial, participants were invited to enroll in a cohort study, which was initiated to identify factors that predict progression of kidney disease within the setting of recommended management of CKD due to hypertension (i.e., use ACE inhibitors and ARB antihypertensive drugs and a low blood pressure goal (< 92 mmHg)). 10 At the end of the clinical trial a total 759 participants who had not yet reached ESRD were being followed; 689 enrolled in the cohort study. 12 Incidence of ESRD was defined as beginning maintenance dialysis or receipt of a kidney transplant.13, 14 Study participants were seen in clinic at 3, 6, and every six months thereafter during the clinical trial and at baseline and every 12 months thereafter during the cohort study. Details of the design of the clinical trial and cohort study have been previously published.15
Measurements of GFR and Endogenous Markers
Kidney function was measured twice by iothalamate clearance at baseline of the clinical trial. We used the second of these measurements for comparison to the single measurements of the other markers, and the mean of these two measurements for adjusted analyses. Serum creatinine, cystatin C, and BTP also were measured from samples obtained at baseline. Serum creatinine was measured in a central laboratory with the rate-Jaffe method with an alkaline picrate assay. Cystatin C and BTP were measured by particle-enhanced nephelometry (Dade/Siemens). Estimated GFR based on serum creatinine was calculated using an equation specific to the AASK population. 16
Other Measurements
The baseline visit consisted of a physical examination, questionnaires (including self-reporting of any history of cardiovascular disease), and 24-hour urine specimen collection on the day prior to their first pre-randomization mGFR, 14 measurement of blood pressure (using Hawksley random-zero sphygmomanometers), and performance of a 12-lead electrocardiogram (EKG). Urinary protein excretion was expressed as the urinary protein-creatinine ratio from a 24-hour urine collection.
Statistical Analysis
Correlation of log(mGFR) with the inverse of each marker was assessed. Linear or logistic regression was used to test for trends across quintiles of mGFR. Event-free survival time was defined from time of randomization to ESRD, mortality, censoring or administrative withdrawal at the end of the study (June, 2007). ESRD was censored at death and mortality was censored at ESRD. Cox proportional hazards competing risk models were used to account for this informative censoring. 17, 18 Multivariate models adjusted for age, sex, prevalent coronary heart disease, total high density lipoprotein (HDL) cholesterol, urinary protein-creatinine ratio, education, body mass index (BMI), smoking status, trial treatment group (i.e., blood pressure goal and anti-hypertensive medication), and mean baseline mGFR. The proportionality assumption was assessed using Schoenfeld residuals and log-log plots. 19 Model discrimination was assessed using Harrell's C statistic for fully-adjusted models. 20
The continuous net reclassification improvement [NRI(>0)] at 102 months of follow-up (median follow-up) was calculated from fully-adjusted Poisson models and defined as the sum of those classified upward to higher risk among those with an event plus those classified downward to a lower risk among non-events less the sum of those classified downward to lower risk among those with an event plus those classified to a higher risk among those without an event. 21 We also report the NRI among those participants with an event (NRIevent) and without an event (NRIevent-free). These models did not account for informative censoring. All analyses were conducted using Stata 10.1 software. 22
Results
Baseline Characteristics
Serum creatinine, cystatin C, and BTP measurements were available for 865 participants at baseline. The mean age of participants was 54.8 years and 61% were male (Table 1). The mean mGFR was 46.5 mL/min/1.73 m2. Lower mGFR was associated with younger age, female sex, and greater urinary protein-creatinine ratio. All of the other markers also were strongly associated with mGFR.
Table 1.
Baseline characteristics
| Quintile of mGFR (in mL/min/1.73 m2) | ||||||
|---|---|---|---|---|---|---|
| 1 (≥58.8) | 2 (51.1-58.7) | 3 (43.2-51.1) | 4 (32.4-43.1) | 5 (<32.3) | p-trend | |
| No. | 173 | 173 | 173 | 173 | 173 | - |
| Age, years | 55.9 +/- 10.9 | 56.7 +- 9.6 | 55.5 +/- 10.3 | 53.1 +/- 10.0 | 52.7 +/- 11.6 | <0.001 |
| Female | 30.1 | 35.2 | 42.8 | 45.1 | 41.6 | 0.01 |
| Prevalent heart disease | 49.1 | 47.4 | 56.1 | 55.5 | 50.3 | 0.4 |
| Current smoking | 24.3 | 27.2 | 27.2 | 32.9 | 29.5 | 0.1 |
| Body mass index, kg/m2 | 30.6 +/- 5.8 | 31.0 +/- 6.3 | 30.6 +/- 6.0 | 30.2 +/- 6.8 | 30.1 +/- 6.9 | 0.2 |
| Less than high school education | 34.1 | 46.8 | 42.2 | 37.0 | 36.4 | 0.7 |
| Non-HDL cholesterol, mg/dL | 163.6 +/- 41.7 | 166.6 +/- 44.2 | 165.7 +/- 44.6 | 157.7 +/- 42.3 | 163.8 +/- 49.8 | 0.4 |
| UPCR (mg/g) | 3.7 [1.9; 9.4] | 4.3 [2.2; 11.3] | 5.7 [2.6; 24.7] | 13.4 [4.6; 57.7] | 40.6 [11.2; 106.8] | <0.001 |
| mGFR, mL/min/1.73 m2 | 62.3 +/- 2.6 | 55.0 +/- 2.2 | 47.4 +/- 2.2 | 37.7 +/- 3.1 | 26.9 +/- 3.5 | - |
| SCr, mg/dL | 1.54 +/- 0.31 | 1.64 +/- 0.33 | 1.79 +/- 0.47 | 2.18 +/- 0.58 | 2.94 +/- 0.78 | <0.001 |
| eGFRSCr, mL/min/1.73 m2 | 54.5 +/- 7.1 | 50.7 +/- 7.0 | 47.0 +/- 7.3 | 40.1 +/- 7/2 | 31.5 +/- 6.1 | <0.001 |
| SCysC, mg/L | 1.24 +/- 0.22 | 1.37 +/- 0.28 | 1.52 +/- 0.30 | 1.88 +/- 0.42 | 2.43 +/- 0.53 | <0.001 |
| BTP, mg/dL | 0.94 +/- 0.20 | 1.06 +/- 0.28 | 1.21 +/- 0.30 | 1.55 +/- 0.44 | 2.17 +/- 0.60 | <0.001 |
Continuous variables are given as mean +/- standard deviation or median [25th; 75th percentile]; categorical variables as percentage.
eGFRSCr: Estimated glomerular filtration rate based on serum creatinine; HDL, high-density lipoprotein; mGFR, measured glomerular filtration rate; UPCR, urinary protein-creatinine ratio; SCr, serum creatinine; SCysC, serum cystatin C; BTP, beta-trace protein.
Conversion factors for units: non-HDL cholesterol in mg/dL to mmol/L, x0.02586, SCr in mg/dL to micromol/L, x88.4; GFR in mL/min/1.73 m2 to mL/s/1.73 m2, x0.01667
Serum creatinine had the weakest correlation with mGFR (0.65), whereas eGFRSCr had the highest correlation (0.77; Table 2). Cystatin C and BTP were less well correlated with mGFR (0.73 and 0.71, respectively), but were most strongly correlated with each other (0.79).
Table 2.
Correlation among kidney function markers
| Log (mGFR) | 1 / SCr | Log (eGFRSCr) | 1 / SCysC | 1 / BTP | mGFR | |
|---|---|---|---|---|---|---|
| 1 / SCr | 0.66 | 1.00 | - | - | - | - |
| Log (eGFRSCr) | 0.78 | 0.86 | 1.00 | - | - | - |
| 1 / SCysC | 0.72 | 0.70 | 0.73 | 1.00 | - | - |
| 1 / BTP | 0.71 | 0.70 | 0.71 | 0.79 | 1.00 | - |
| mGFR | 0.99 | 0.65 | 0.77 | 0.73 | 0.71 | 1.00 |
mGFR: measured glomerular filtration rate; BTP: beta-trace protein; SCr, serum creatinine; SCysC, serum cystatin C; eGFRSCr: estimated glomerular filtration rate based on serum creatinine
Association with ESRD
A total of 246 participants reached ESRD over a mean follow-up of 102.7 months. Higher quintiles of each marker were strongly associated with ESRD, with cystatin C and BTP having the strongest associations, which reached statistical significance among the second quintile (Table 3). Adjustment for mGFR attenuated these associations, but each marker remained significantly associated with ESRD. Subsequent adjustment for covariates attenuated these associations further, but all remained significantly associated with ESRD, with BTP retaining the strongest association. Harrell's C statistic was slightly higher for BTP (0.857) than for mGFR (0.852), serum creatinine (0.849), eGFRSCr (0.853), and cystatin C (0.851).
Table 3.
Incidence rate ratio for ESRD.
| Quintile of Kidney Function Marker | p-trend | |||||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | ||
| Unadjusted | ||||||
| mGFR | Ref | 1.38 (0.71, 2.68) | 2.68 (1.48, 4.84) | 5.41 (3.12, 9.39) | 12.96 (7.52, 22.31) | <0.001 |
| SCr | Ref | 1.36 (0.77, 2.41) | 1.90 (1.07, 3.38) | 4.64 (2.79, 7.73) | 10.57 (6.54, 17.11) | <0.001 |
| eGFRSCr | Ref | 1.28 (0.65, 2.50) | 2.44 (1.34, 4.47) | 4.90 (2.80, 8.60) | 13.70 (7.99, 23.49) | <0.001 |
| SCysC | Ref | 3.92 (1.79, 8.59) | 4.77 (2.21, 10.29) | 10.78 (5.17, 22.45) | 25.27 (12.25, 52.12) | <0.001 |
| BTP | Ref | 3.96 (1.62, 9.69) | 7.21 (3.06, 17.00) | 13.22 (5.72, 30.53) | 37.75 (16.59, 85.90) | <0.001 |
| Adjusted for mGFR | ||||||
| SCr | Ref | 1.09 (0.61, 1.94) | 1.22 (0.67, 2.21) | 1.90 (1.08, 3.33) | 2.92 (1.66, 5.14) | <0.001 |
| eGFRSCr | Ref | 1.01 (0.51, 2.00) | 1.47 (0.79, 2.73) | 1.96 (1.05, 3.64) | 3.65 (1.90, 7.01) | <0.001 |
| SCysC | Ref | 3.70 (1.68, 8.18) | 2.90 (1.32, 6.37) | 4.45 (2.05, 9.65) | 5.85 (2.59, 13.24) | <0.001 |
| BTP | Ref | 3.68 (1.50, 9.04) | 5.10 (2.13, 12.22) | 6.69 (2.76, 16.20) | 13.10 (5.30, 32.42) | <0.001 |
| Adjusted for covariates* and mGFR | ||||||
| SCr | Ref | 0.85 (0.47, 1.55) | 0.97 (0.52, 1.81) | 1.15 (0.62, 2.14) | 1.67 (0.83, 3.37) | 0.03 |
| eGFRSCr | Ref | 1.15 (0.57, 2.30) | 1.30 (0.67, 2.50) | 1.81 (0.94, 3.51) | 3.13 (1.57, 6.26) | <0.001 |
| SCysC | Ref | 2.94 (1.26, 6.83) | 2.04 (0.88, 4.72) | 2.17 (0.92, 5.12) | 3.12 (1.27, 7.62) | 0.04 |
| BTP | Ref | 3.22 (1.27, 8.16) | 3.02 (1.21, 7.49) | 3.69 (1.46, 9.33) | 5.70 (2.19, 14.87) | <0.001 |
Values shown are incidence rate ratio (95% confidence interval).
eGFRSCr, serum creatinine-based estimated glomerular filtration rate; BTP, Beta-trace Protein; SCysC, serum cystatin C; SCr, serum creatinine; mGFR, measured glomerular filtration rate; ESRD, end-stage renal disease.
Adjusted for age, sex, prevalent coronary heart disease, total high density lipoprotein cholesterol, urinary protein-creatinine ratio, education, body mass index, smoking status, blood pressure goal, anti-hypertensive medication, and mean baseline glomerular filtration rate
Net Reclassification Improvement (NRI) for ESRD
The addition of any individual marker significantly improved risk classification. The addition of eGFR quintiles to a model with mGFR and covariates had an NRI(>0) of 0.56 (p<0.001; NRIevent = 0.30, NRIevent-free = 0.26). Including BTP resulted in an NRI(>0) of 0.40 (p<0.001; NRIevent = 0.27, NRIevent-free = 0.13). Serum creatinine improved classification of those without an event (NRIevent-free = 0.21) more than those with an event (NRIevent = 0.08), whereas cystatin C improved classification of those with an event (NRIevent = 0.35), but not those without an event (NRIevent-free = -0.10).
Association with Mortality or ESRD
Results were similar for analyses of mortality or ESRD (n=390). Each marker was significantly associated with the combined outcome in crude analyses and after adjustment for mGFR, but only cystatin C and BTP remained significantly associated after further adjustment for covariates (Table 4). BTP was most strongly associated with the combined outcome. The models had lower discriminatory power for the combined outcome than for ESRD alone (Harrell's C for mGFR=0.769, compared to 0.754 for serum creatinine, 0.757 for eGFRSCr, 0.758 for cystatin C and 0.761 for BTP).
Table 4.
Incidence rate ratio for ESRD or mortality.
| Quintile of Kidney Function Marker | p-trend | |||||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | ||
| Unadjusted | ||||||
| mGFR | Ref | 1.71 (1.10, 2.71) | 2.61 (1.71, 4.01) | 5.00 (3.35, 7.46) | 9.90 (6.68, 14.67) | <0.001 |
| SCr | Ref | 1.44 (0.98, 2.11) | 1.71 (1.15, 2.54) | 3.64 (2.54, 5.22) | 5.88 (4.17, 8.29) | <0.001 |
| eGFRSCr | Ref | 1.09 (0.72, 1.66) | 2.00 (1.37, 2.93) | 3.14 (2.19, 4.51) | 6.08 (4.29, 8.62) | <0.001 |
| SCysC | Ref | 1.91 (1.23, 2.96) | 2.54 (1.67, 3.87) | 4.89 (3.29, 7.28) | 9.52 (6.45, 14.05) | <0.001 |
| BTP | Ref | 1.99 (1.28, 3.11) | 2.76 (1.81, 4.29) | 4.86 (3.22, 7.33) | 11.18 (7.51, 16.65) | <0.001 |
| Adjusted for mGFR | ||||||
| SCr | Ref | 1.18 (0.81, 1.74) | 1.14 (0.76, 1.71) | 1.66 (1.11, 2.47) | 1.78 (1.17, 2.72) | 0.002 |
| eGFRSCr | Ref | 0.84 (0.55, 1.28) | 1.16 (0.78, 1.73) | 1.21 (0.79, 1.85) | 1.44 (0.89, 2.31) | 0.04 |
| SCysC | Ref | 1.79 (1.15, 2.78) | 1.69 (1.09, 2.60) | 2.41 (1.55, 3.73) | 2.88 (1.75, 4.75) | <0.001 |
| BTP | Ref | 1.79 (1.15, 2.80) | 1.99 (1.28, 3.10) | 2.57 (1.63, 4.04) | 4.05 (2.46, 6.66) | <0.001 |
| Adjusted for covariates* and mGFR | ||||||
| SCr | Ref | 0.98 (0.65, 1.47) | 0.97 (0.63, 1.51) | 1.17 (0.74, 1.85) | 1.25 (0.74, 2.13) | 0.25 |
| eGFRSCr | Ref | 0.84 (0.55, 1.28) | 1.14 (0.76, 1.70) | 1.16 (0.76, 1.78) | 1.41 (0.87, 2.29) | 0.07 |
| SCysC | Ref | 1.38 (0.88, 2.16) | 1.41 (0.91, 2.19) | 1.50 (0.95, 2.37) | 1.77 (1.06, 2.97) | 0.05 |
| BTP | Ref | 1.63 (1.04, 2.56) | 1.56 (1.00, 2.44) | 1.76 (1.10, 2.80) | 2.32 (1.38, 3.92) | 0.004 |
Values shown are incidence rate ratio (95% confidence interval).
eGFRSCr, serum creatinine-based estimated glomerular filtration rate; BTP, Beta-trace Protein; SCysC, serum cystatin C; SCr, serum creatinine; mGFR, measured glomerular filtration rate; ESRD, end-stage renal disease.
Adjusted for age, sex, prevalent coronary heart disease, total high density lipoprotein cholesterol, urinary protein-creatinine ratio, education, body mass index, smoking status, blood pressure goal, anti-hypertensive medication, and mean baseline glomerular filtration rate
Net Reclassification Improvement (NRI) for Mortality or ESRD
Improvements in reclassification also were lower for the combined outcome than for ESRD alone. Addition of BTP to a model with mGFR and covariates resulted in an NRI(>0) of 0.29 (p<0.001; NRIevent = 0.27, NRIevent-free = 0.02). Additional of cystatin C improved classification of those with an event (NRIevent = 0.30) but not those without an event (NRIevent-free = -0.16), as did eGFRSCr (NRIevent = 0.14, NRIevent-free = -0.036), whereas serum creatinine improved classification of those without an event (NRIevent-free = 0.22), but not those with an event (NRIevent = -0.09).
Discussion
Identification of individuals with chronic kidney disease who are most likely to progress to ESRD or mortality is central in the clinical management of CKD. Such identification, however, is hampered by the shortcomings of current methods to assess kidney function by serum creatinine.23, 24 This has led to the search for other biomarkers of kidney function. Long-term follow-up of a cohort of African Americans with hypertensive chronic kidney disease afforded us the opportunity to compare the usefulness of mGFR, serum creatinine, eGFRSCr, cystatin C, and BTP to predict ESRD and mortality.
Higher concentrations of each marker were significantly associated with higher risk of incident ESRD, but BTP predicted ESRD more strongly than did the other markers, including mGFR. Each of the other markers remained significantly associated with ESRD after adjustment for baseline mGFR, but BTP was again more strongly associated with ESRD than were the other markers. The addition of any marker to a model with covariates and mGFR significantly improved risk classification of study participants. Higher concentrations of each marker were also significantly associated with ESRD or mortality, but these associations were weaker than for ESRD alone. Again, BTP was more strongly associated with this outcome than were the other markers, remaining significant after adjustment for mGFR and relevant covariates.
Synthesis of beta-trace protein occurs predominately in the central nervous system and it is one of the main components of cerebrospinal fluid.25 It may be a useful marker of kidney function because of its low molecular weight (23-29 kDa) and its lack of affinity for protein binding.25, 26 It acts as a prostaglandin D synthase, promoting conversion of prostaglandin H2 to prostaglandin D2.8 Previous studies on BTP and kidney function have reported that an eGFR equation based on serum BTP had lower bias and greater precision than eGFR based on serum creatinine. 27 It is uncertain, however, what non-filtration factors affect circulating levels of BTP.
A recent study of 227 European men and women with nondiabetic CKD and reduced kidney function at baseline explored the ability of serum creatinine, cystatin C, and BTP to predict CKD progression, defined as doubling of baseline creatinine and/or ESRD.28 The concentrations of all three markers were significantly associated with GFR cross-sectionally. All three markers also were similarly predictive of CKD progression. Our results are in general agreement with these findings, though in our study BTP predicted ESRD more strongly than did cystatin C, serum creatinine, or eGFRSCr. The results for serum creatinine as a predictor of outcomes in the previous study may be biased by using a change in serum creatinine as one component of the outcome definition. Other studies have reported that serum creatinine is less sensitive to small changes in kidney function compared to cystatin C and BTP.6, 29, 30 The range of GFR levels among the AASK population (i.e., 20-65 mL/min/1.73 m2), however, is generally within the range in which serum creatinine is a relatively accurate estimate of GFR.
Cystatin C has been proposed as a better marker of kidney function than serum creatinine, in part because it is less sensitive to differences in muscle mass.31, 32 Cystatin C is a stronger predictor of cardiovascular events, cardiovascular mortality, and all-cause mortality than is serum creatinine or eGFR based on serum creatinine.33, 34 Among 825 participants with stage 3 or 4 non-diabetic CKD in the Modification of Diet in Renal Disease (MDRD) Study, 1/cystatin C had a stronger association with all-cause mortality and CVD mortality than did 1/creatinine.31 The associations of 1/creatinine with kidney failure, however, was slightly stronger than that for 1/cystatin C (relative risk per 1 standard deviation of 2.81 for 1/creatinine and 2.36 for 1/cystatin C). Similarly, in our study, the association between cystatin C and ESRD was generally similar to the association between serum creatinine and ESRD, but cystatin C was more strongly associated with the combined outcome of ESRD or death than was serum creatinine or eGFRSCr.
A limited number of previous studies have found serum creatinine, cystatin C, and beta trace protein to better predict kidney outcomes than mGFR. 28, 31 In our study, beta trace protein and cystatin C predicted ESRD more strongly than did mGFR. Each of the endogenous markers also predicted ESRD after adjustment for mGFR. This may be related to imprecision in mGFR measurement, including hourly and daily variations and deviations from measurement protocol. 35 Previous studies have shown that between-day coefficients of variation (CV) can vary between 6.3-16.6% when using I-iothalamate clearance.36, 37 We were not able to explicitly test this hypothesis. Alternatively, these markers may represent non-GFR factors that are related to progression to ESRD. It is important to note that the inclusion criteria for the AASK study required participants to have an mGFR between 20 and 65 mL/min per 1.73 m2. This restricted range of mGFR values may limit the maximum statistical association between mGFR and outcomes. No such restriction was placed on the other markers, allowing them to have their full statistical association with outcomes. This may explain, in part, the residual association between these markers and outcomes after adjustment for mGFR.
In our study, ESRD was defined by kidney transplant or initiation of dialysis. As the decision to implement either treatment is likely to be influenced by the physicians’ interpretation of the measured mGFR, serum creatinine, and eGFR, we could expect that the association between these markers and ESRD would be biased upwards. Despite this bias, higher BTP was more strongly associated with ESRD than these markers, even after adjustment for mGFR.
Each of the endogenous markers was reasonably well-correlated with mGFR. Serum creatinine had a weaker correlation with mGFR than did cystatin C or BTP. However, estimated GFR based on serum creatinine, age, and sex had the strongest correlation with mGFR. This may be due to the strong dependency of creatinine on muscle mass, for which age and sex act as proxies in the GFR-estimating equations. The correlation of eGFRSCr with mGFR may be higher than in other studies, as the estimating equation used was developed in a pilot study for the AASK trial.16
The results of our analysis are promising but are limited by use of a select population. The AASK study enrolled African Americans with hypertensive kidney disease with no other identifiable cause of kidney disease. Further work needs to be done in a more generalizable population. The ability of these markers to predict mortality, cardiovascular disease, and other outcomes remains to be better studied. The use of the AASK study has distinctive advantages, as well. The study includes a high-risk group with well-controlled blood pressure. Baseline characteristics, including mGFR and proteinuria, were systematically collected and outcomes were carefully and thoroughly ascertained over a long follow-up period.
In summary, we found that BTP predicts ESRD more strongly than other markers of kidney function evaluated, including cystatin C, serum creatinine, eGFRSCr and mGFR. The results of this study suggest that, although these endogenous markers have relatively similar correlations with mGFR, the markers may be differentially associated with progression to ESRD, and that BTP and cystatin C may be useful adjuncts to serum creatinine or mGFR in evaluating risk for progression of kidney disease. Further work needs to be done to determine the best way to combine information from various markers to evaluate risk for specific adverse outcomes.
Acknowledgements
The following individuals participated in the AASK study and are listed by study center (asterisks indicate principal investigators, and daggers study coordinators). Case Western Reserve University: J.T. Wright, Jr,* M. Rahman, R. Dancie,† L. Strauss; Emory University: J. Lea,* B. Wilkening,† A. Chapman, D. Watkins; Harbor–UCLA Medical Center: J.D. Kopple,* L. Miladinovich,† J. Choi, P. Oleskie, C. Secules; Harlem Hospital Center: V. Pogue,* D. Dowie,† H. Anderson, L. Herbert, R. Locko, H. Nurse, J.-T. Cheng, F. Darkwa, V. Dowdy, B. Nicholas; Howard University: O. Randall,* T. Retta, S. Xu,† M. Ketete, D. Ordor, C. Tilghman; Johns Hopkins University: E. Miller,* B. Astor, C. Diggs,† J. Charleston, C. Harris, T. Shields; L. Appel (steering committee); Charles R. Drew University: K. Norris,* D. Martins, M. Miller,† H. Howell, L. Pitts; Medical University of South Carolina: D. Cheek,* D. Brooks†; Meharry Medical College: M. Faulkner,* O. Adeyele, K. Phillips,† G. Sanford, C. Weaver; Morehouse School of Medicine: W. Cleveland,* K. Chapman, W. Smith,† S. Glover; Mount Sinai School of Medicine and University of Massachusetts: R. Phillips,* M. Lipkowitz, M. Rafey, A. Gabriel,† E. Condren, N. Coke; Ohio State University: L. Hebert,* G. Shidham, L. Hiremath,† S. Justice; University of Chicago: G. Bakris,* J. Lash, L. Fondren,† L. Bagnuolo, J. Cohan, A. Frydrych; University of Alabama, Birmingham: S. Rostand,* D. Thornley-Brown, B. Key†; University of California, San Diego: F.B. Gabbai,* D.T. O'Connor, B. Thomas†; University of Florida: C.C. Tisher,* G. Bichier, C. Sarmiento,† A. Diaz, C. Gordon; University of Miami: G. Contreras,* J. Bourgoignie, D. Florence-Green, J. Junco,† J. Vassallo; University of Michigan: K. Jamerson,* A. Ojo, T. Corbin, D. Cornish-Zirker,† T. Graham, W. Bloembergen; University of Southern California: S. Massry,* M. Smogorzewski, A. Richardson,† L. Pitts; University of Texas Southwestern Medical Center: R. Toto,* G. Peterson, R. Saxena, T. Lightfoot,† S.-A. Blackstone, C. Loreto; Vanderbilt University: J. Lewis,* G. Schulman, M. Sika,† S. McLeroy; NIDDK: L.Y. Agodoa, J.P. Briggs, J.W. Kusek; Data Coordinating Center (Cleveland Clinic Foundation): J. Gassman,* G. Beck, T. Greene, B. Hu, K. Brittain,† S. Sherer, L. Tuason, C. Kendrick, S. Bi, H. Litowitz, X. Liu, X. Wang, K. Wiggins, C.A. Tatum, N. Patterson; Central Biochemistry Laboratory: F. Van Lente, J. Waletzky, C. O'Laughlin, L. Burton; Scientific Advisory Committee: W. McClellan, L. Adams-Campbell, K. Faber-Langendoen, B. Kiberd, E. Lee, T. Meyer, D. Nathan, J. Stokes, H. Taylor, P.W. Wilson; Cardiovascular Research Foundation: T. deBacker, A. Lansky, S. Slack
We thank the staff and participants of the AASK Study for important contributions.
Support: This study was supported by cooperative agreements from NIDDK and other grants from the NIH: M01-RR00080, 5M01 RR00071, M01 00032, P20-RR11145, M01 RR00827, M01 RR00052, 2P20 R11104, and DK 2818-02. In addition, we gratefully acknowledge support from the Office of Research in Minority Health and the donation of drug and financial support from Pfizer Inc, AstraZeneca Pharmaceuticals, and King Pharmaceuticals Inc. The cystatin C and beta trace protein assays were funded by the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) through grants UO1 NIDDK 053869, UO1 NIDDK 067651, and UO1 NIDDK 35073. Dr Astor was supported in part by grant R21DK078218 from NIDDK. Dr Bhavsar was supported by the NIH/National Heart, Lung, and Blood Institute grant T32HL07024.
Footnotes
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