Abstract
Background
The accuracy and precision of glomerular filtration rate (GFR) estimating equations based on plasma creatinine (GFRcr), cystatin C (GFRcys), and the combination of these markers (GFRcr-cys) has recently been assessed in HIV-infected individuals.
Methods
We compared the associations of baseline GFRcr, GFRcys, and GFRcr-cys (using the CKD-EPI equations) with mortality, cardiovascular events (CVE), and opportunistic diseases (OD) in the Strategies for the Management of Antiretroviral Therapy (SMART) study. We used Cox proportional hazards models to estimate unadjusted and adjusted hazard ratios per standard deviation (SD) change in GFR.
Results
4,614 subjects from the SMART trial with available baseline creatinine and cystatin C data were included in this analysis. Of these, 99 died, 111 had a CVE and 121 had an OD. GFRcys was weakly to moderately correlated with HIV RNA, CD4 cell count, high sensitivity C-reactive protein, interleukin-6, and D-dimer, while GFRcr had little or no correlation with these factors. GFRcys had the strongest associations with the three clinical outcomes, followed closely by GFRcr-cys, with GFRcr having the weakest associations with clinical outcomes. In a model adjusting for demographics, cardiovascular risk factors, HIV-related factors, and inflammation markers, a 1-SD lower GFRcys was associated with a 55% (95% confidence interval [CI], 27% -90%) increased risk of mortality, a 21% (95% CI, 0% -47%) increased risk of CVE, and a 22% (95% CI, 0% -48%) increased risk of OD.
Conclusions
Of the three CKD-EPI GFR equations, GFRcys had the strongest associations with mortality, CVE, and OD.
INTRODUCTION
Reduced kidney function is common in HIV-infected individuals (1). In clinical practice, glomerular filtration rate (GFR) is usually estimated with serum creatinine. However, variability in creatinine production, which is principally determined by muscle mass and diet, may affect the accuracy of GFR estimates (2). Cystatin C is an alternate GFR marker that is produced by all nucleated cells in the body at a constant rate and appears to be less affected by variation in muscle mass than creatinine (3). However, non-GFR determinants of cystatin C have not been fully elucidated and correlations have been reported between cystatin C and thyroid disease and inflammation markers (4–6).
The Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) has developed GFR estimating equations based on standardized creatinine (GFRcr), cystatin C (GFRcys), and the combination of the two markers (GFRcr-cys), using data from over 5000 participants in 13 studies who had GFR measured by urinary or plasma clearance of an exogenous filtration marker (7). These three estimating equations have recently been compared to plasma clearance of iohexol in a sample of HIV-infected subjects (8). In both the general population study (7) and the HIV study (8), the GFRcr-cys equation was the most accurate of the three, while the accuracy of GFRcr and GFRcys were similar.
Cystatin C has consistently been found to be a stronger predictor of all-cause mortality and cardiovascular events (CVE) than creatinine in the general population (9, 10). However, less is known about the association between cystatin C and clinical events in HIV-infected persons. In the present study, we compared the associations of baseline GFRcr, GFRcys, and GFRcr-cys with mortality, CVE, or opportunistic disease (OD) in the Strategies for the Management of Antiretroviral Therapy (SMART) study.
METHODS
Study cohort
SMART was a multi-center, international, randomized controlled trial in which HIV-infected participants were randomized to either continuous or episodic antiretroviral therapy. Results have been presented previously (11, 12). HIV-infected individuals were eligible to participate if they were 13 years of age or older, had a CD4 cell count > 350 cells/mm3, were willing to start, modify, or stop antiretroviral therapy, and were not pregnant or breast-feeding. In accordance with a recommendation from the data and safety monitoring board, the episodic treatment arm was discontinued on January 11, 2006 due to an increased risk for OD or death in this arm. Study follow-up was closed on July 11, 2007. The present analysis included subjects enrolled in SMART who consented to store plasma from which creatinine and cystatin C concentrations were measured.
Laboratory measurements and definitions
Plasma creatinine was measured at a central laboratory at the University of Florida using an enzymatic assay that was traceable to an isotope dilution mass spectrometry reference method (13). Other biomarkers were measured by the Laboratory for Clinical Biochemistry Research at the University of Vermont. Plasma cystatin C was measured with a BNII nephelometer (Dade Behring Inc., Deerfield, IL, US) that used a particle-enhanced immunonephelometric assay, and values were standardized to certified reference material from the Institute for Reference Materials and Measurements (14). GFRcr, GFRcys, and GFRcr-cys were calculated using the CKD-EPI equations based on creatinine alone, cystatin C alone, and both markers, respectively (7). High-sensitivity C-reactive protein (hs-CRP) was measured using the BNII nephelometer (N High Sensitivity CRP; Siemens Healthcare Diagnostics), interleukin-6 (IL-6) was measured by an ultrasensitive enzyme-linked immunosorbent assay (Quantikine HS Human IL-6Immunoassay; R&D Systems), and D-dimer was measured by an immunoturbidimetric assay (Lia-test D-DI; Diagnostica Stago).
CVE were prospectively defined in SMART as death due to cardiovascular disease, clinical myocardial infarction, silent myocardial infarction (serial change on annual study electrocardiogram without interim clinical history of myocardial infarction), stroke, or coronary artery revascularization procedure. OD in SMART were prospectively defined and can be found in the web appendix for the initial results paper (12). Deaths, CVE, and OD were adjudicated by an Endpoint Review Committee that was blinded to study arm.
Statistical methods
We compared categorical and continuous variables in included and excluded SMART participants with likelihood ratio chi-square and Wilcoxon rank sum tests, respectively. We calculated Spearman’s rank correlation coefficients between the three GFR estimates and selected other variables. We calculated event rates per 1,000 person-years for all-cause mortality, CVE, and OD by decreasing tertiles of GFR. We used Cox proportional hazards models to estimate crude and adjusted hazard ratios (HR) for covariates of interest. We calculated HRs per standard deviation lower of each GFR measure to facilitate direct comparison.
We assessed how unadjusted HR for the three GFR equations changed when adjusted for different groups of covariates by constructing separate models with 1) demographic and traditional cardiovascular disease risk factors, including age, gender, race, smoking, diabetes mellitus, history of cardiovascular disease, blood pressure lowering drugs, lipid lowering drugs, total cholesterol, and high density lipoprotein cholesterol; 2) HIV-related factors, including antiretroviral therapy use at baseline, exposure to abacavir prior to baseline, study arm assignment, baseline CD4 count, nadir CD4 count, baseline HIV RNA, history of injection drug use, and hepatitis C infection; 3) inflammation markers, including hs-CRP, IL-6, and D-dimer; and 4) a fully adjusted model with all factors from models 1 to 3.
We evaluated whether the associations between GFR estimates and clinical outcomes differed according to treatment arm assignment (continuous or episodic antiretroviral therapy) by including interaction terms in regression models. As a sensitivity analysis, we repeated analyses in the subgroup assigned to continuous antiretroviral therapy. To assess whether estimating equations that used cystatin C had improved discriminatory ability for the risk of clinical outcomes, we calculated integrated discrimination improvement (IDI) statistics (15) i) between a model with both GFRcr and GFRcys and a model with GFRcr only, and ii) between a model with both GFRcys and GFRcr-cys and a model with GFRcys only. We used logistic regression approximation of survival analysis, which assumed equal follow-up of all subjects. The IDI is a measure of the degree to which an extended model improves sensitivity without sacrificing specificity compared to a nested simpler model.
RESULTS
Study subjects
After excluding 858 participants because of missing values for creatinine (n=341), cystatin C (n=48), or both (n=469) at baseline, a total of 4614 participants were included in the analysis. Excluded subjects with missing data either did not provide consent for stored specimens or stored specimens had been used in prior analyses. Missing baseline GFR marker data varied by region, with subjects from Asian sites more likely to be excluded for missing data (Table 1). Compared with included subjects, excluded subjects were more likely to be female, black, injection drug users, have diabetes, and have hepatitis C. Compared with included subjects, excluded subjects were less likely to be taking antiretroviral therapy at baseline and had lower CD4 cell counts. During trial follow-up, participants that were excluded from the present analysis had statistically significantly higher rates of death, CVE, and OD than included subjects.
Table 1.
Baseline characteristics of HIV-infected participants in the Strategies for Management of Antiretroviral Therapy (SMART) trial that were included in and excluded from the current analysis
| Characteristics at randomization | Included (n=4614) |
Excluded (n=858) |
P valuea |
|---|---|---|---|
| Episodic therapy arm, n (%) | 2322 (50.3) | 430 (50.1) | 0.911 |
| Age, years, median (IQR) | 44 (38, 50) | 43 (37, 50) | 0.122 |
| Female, n (%) | 1170 (25.4) | 316 (36.8) | <0.001 |
| Race, n (%) | <0.001 | ||
| Black | 1278 (27.7) | 317 (36.9) | |
| Asian | 79 (1.7) | 145 (16.9) | |
| White | 2765 (59.9) | 276 (32.2) | |
| Other | 492 (10.7) | 120 (14.0) | |
| Hepatitis C seropositive, n (%) | 621 (13.5) | 189 (22.0) | <0.001 |
| Hepatitis B surface antigen positive, n (%) | 103 (2.2) | 31 (3.6) | 0.023 |
| Past or current smoker, n (%) | 3057 (66.3) | 516 (60.1) | <0.001 |
| Diabetes, n (%) | 291 (6.3) | 75 (8.7) | 0.011 |
| Lipid lowering drug use, n (%) | 762 (16.5) | 104 (12.1) | <0.001 |
| Blood pressure lowering drug use, n (%) | 850 (18.4) | 173 (20.2) | 0.233 |
| History of injection drug use, n (%) | 415 (9.0) | 117 (13.6) | <0.001 |
| Total cholesterol, mg/dl, median (IQR) | 192 (164, 222) | 185 (159, 217) | 0.002 |
| HDL cholesterol, mg/dl, median (IQR) | 40 (33, 51) | 43 (34, 53) | 0.007 |
| Taking antiretroviral therapy, n (%) | 3913 (84.8) | 677 (78.9) | <0.001 |
| Prior abacavir exposure, n (%) | 1401 (30.4) | 181 (21.1) | 0.002 |
| CD4 count, cells/mm3, median (IQR) | 602 (470, 799) | 569 (441, 743) | <0.001 |
| Nadir CD4 count, cells/mm3, median (IQR) | 250 (152, 358) | 250 (164, 361) | 0.633 |
| HIV RNA, log10 copies/mL, median (IQR) | 1.90 (1.70, 2.83) | 1.90 (1.70, 3.38) | 0.161 |
| Continent | <0.001 | ||
| Africa | 65 (1.4) | 3 (0.3) | |
| America | 3105 (67.3) | 534 (62.2) | |
| Asia | 47 (1.0) | 140 (16.3) | |
| Australia | 158 (3.4) | 19 (2.2) | |
| Europe | 1239 (26.9) | 162 (18.9) | |
| History of cardiovascular disease, n (%) | 170 (3.7) | 28 (3.3) | 0.539 |
| Outcomes during study follow-up | |||
| Death | 99 (2.1) | 68 (7.9) | <0.001 |
| Cardiovascular disease event | 111 (2.4) | 35 (4.1) | 0.008 |
| Opportunistic disease | 121 (2.6) | 42 (4.9) | <0.001 |
| Creatinine, mg/dL, median (IQR) | 0.78 (0.67, 0.89) | 0.84 (0.69, 0.96)b | 0.137 |
| Cystatin C, mg/L, median (IQR) | 0.81 (0.71, 0.92) | 0.83 (0.73, 0.95)c | 0.008 |
| GFR creatinine, mL/min/1.73m2 | |||
| Median (IQR) | 111 (100, 121) | 104 (87, 118)b | 0.028 |
| ≥ 90 | 4072 (88.3) | 34 (70.8)b | |
| 60–89 | 472 (10.2) | 12 (25.0)b | |
| < 60 | 70 (1.5) | 2 (4.2)b | 0.002 |
| GFR cystatin C, mL/min/1.73m2 | |||
| Median (IQR) | 107 (90, 118) | 102 (84, 115)c | 0.003 |
| ≥ 90 | 3440 (74.6) | 231 (67.7)c | |
| 60–89 | 983 (21.3) | 88 (25.8)c | |
| < 60 | 190 (4.1) | 22 (6.5)c | 0.004 |
| GFR creatinine-cystatin C, mL/min/1.73m2 | |||
| Median (IQR) | 109 (96, 120) | -d | |
| ≥ 90 | 3883 (84.2) | -d | |
| 60–89 | 631 (13.7) | -d | |
| < 60 | 100 (2.2) | -d |
HDL, high density lipoprotein; GFR, glomerular filtration rate.
Likelihood ratio chi-square or Wilcoxon test, as appropriate.
Based on 48 excluded subjects who had creatinine data.
Based on 341 excluded subjects who had cystatin C data
No excluded subjects had both creatine and cystatin C data.
Correlations
GFRcr and GFRcys were moderately correlated with one another (rho 0.47, P<0.0001), while each was strongly correlated with GFRcr-cys (rho 0.76 and 0.91, respectively, P<0.0001 for both). GFRcr was weakly positively correlated with HIV RNA (rho 0.11, P<0.0001) and uncorrelated with CD4 (rho −0.02, P=0.22). GFRcys was weakly negatively correlated with HIV RNA (rho −0.21, P<0.0001) and weakly positively correlated with CD4 (rho 0.11, P<0.0001). GFRcys was moderately negatively correlated with hs-CRP, IL-6, and D-dimer (rho range −0.41 to −0.21, all P<0.0001), while GFRcr had minimal to no correlation with these inflammation/coagulation markers (rho range −0.09 to 0, P range <0.0001 to 0.87).
GFR associations with clinical outcomes
Among subjects included in the analysis, 99 died, 111 experienced a CVE, and 121 experienced an OD. The crude rates of death, CVE, and OD are shown by descending tertiles of GFR for each of the three estimation equations in the Figure. Tertiles of both GFRcys and GFRcr-cys show monotonic relationships with all three outcomes. In contrast, tertiles of GFRcr have a monotonic relationship with CVE, but have a non-monotonic relationship with mortality and no apparent association with OD.
Figure.
Event rates for mortality (A; n=99), cardiovascular events (B; n=111), and opportunistic diseases (C; n=121) in 4,614 subjects enrolled in the Strategies for Management of Antiretroviral therapy (SMART) study are shown according to descending tertiles of GFR estimated by the CKD-EPI equations based on creatinine alone (diamonds; tertiles >117, 106–117, and <106 mL/min/1.73m2), cystatin C alone (squares; tertiles >114, 96–114, and <96 mL/min/1.73m2), and the combination of the two markers (triangles; tertiles >116, 102–116, and <102 mL/min/1.73m2). Error bars correspond to 95% confidence intervals.
Table 2 shows unadjusted and adjusted HR for each of the three clinical outcomes per standard deviation lower (18.3, 21.5, and 19.5 mL/min/1.73m2 for GFRcr, GFRcys, and GFRcr-cys, respectively) in the GFR measures. In unadjusted models, the GFR estimates were significantly associated with the three outcomes, with the exception of GFRcr, which was not significantly associated with OD. GFRcys had the strongest associations with the three clinical outcomes, GFRcr-cys had outcome associations that were slightly smaller than those of GFRcys, and GFRcr had the weakest associations – a pattern that persisted in adjusted models. Adjustment for covariates reduced the associations between the three GFR estimates and clinical outcomes. In the fully adjusted models, all three GFR equations remained statistically significantly associated with mortality, while only GFRcys remained statistically significantly associated with CVE and OD, although marginally so.
Table 2.
Associations of glomerular filtration rate estimates based on plasma creatinine, cystatin C, or the combination of these markers with clinical events in the Strategies for Management of Antiretroviral Therapy (SMART) study
| Outcome Biomarker |
Hazard ratio per standard deviation decrease in GFR (95% confidence interval) | ||||
|---|---|---|---|---|---|
| Unadjusted | Adjusted for demographics and cardiovascular disease risk factorsa |
Adjusted for HIV- related factorsb |
Adjusted for inflammation markersc |
Fully adjustedd | |
| Mortality | |||||
| GFR creatininee | 1.52 (1.31, 1.75) | 1.39 (1.18, 1.64) | 1.53 (1.33, 1.77) | 1.48 (1.28, 1.72) | 1.35 (1.12, 1.62) |
| GFR cystatin Ce | 1.85 (1.59, 2.15) | 1.65 (1.38, 1.97) | 1.83 (1.55, 2.14) | 1.75 (1.49, 2.06) | 1.55 (1.27, 1.90) |
| GFR creatinine-cystatin Ce | 1.74 (1.51, 1.99) | 1.57 (1.33, 1.86) | 1.72 (1.48, 1.98) | 1.66 (1.43, 1.93) | 1.50 (1.24, 1.82) |
| Cardiovascular disease events | |||||
| GFR creatininee | 1.41 (1.21, 1.63) | 1.16 (0.98, 1.38) | 1.37 (1.17, 1.59) | 1.38 (1.19, 1.61) | 1.11 (0.92, 1.33) |
| GFR cystatin Ce | 1.55 (1.33, 1.81) | 1.24 (1.04, 1.49) | 1.61 (1.38, 1.89) | 1.50 (1.27, 1.76) | 1.21 (1.00, 1.47) |
| GFR creatinine-cystatin Ce | 1.52 (1.31, 1.76) | 1.22 (1.03, 1.45) | 1.52 (1.31, 1.77) | 1.47 (1.26, 1.72) | 1.17 (0.97, 1.41) |
| Opportunistic diseases | |||||
| GFR creatininee | 1.10 (0.93, 1.31) | 1.06 (0.87, 1.29) | 1.11 (0.93, 1.32) | 1.08 (0.92, 1.28) | 1.04 (0.85, 1.28) |
| GFR cystatin Ce | 1.32 (1.13, 1.55) | 1.31 (1.09, 1.57) | 1.28 (1.08, 1.52) | 1.27 (1.08, 1.50) | 1.22 (1.00, 1.48) |
| GFR creatinine-cystatin Ce | 1.27 (1.08, 1.48) | 1.24 (1.04, 1.49) | 1.23 (1.04, 1.45) | 1.22 (1.04, 1.44) | 1.17 (0.96, 1.42) |
GFR, glomerular filtration rate estimated by CKD –EPI equations (7).
Adjusted for age, gender, race, smoking, diabetes mellitus, history of cardiovascular disease, blood pressure lowering drugs, lipid lowering drugs, total cholesterol, and high density lipoprotein cholesterol.
Adjusted for antiretroviral therapy use at baseline, exposure to abacavir prior to baseline, study arm assignment, baseline CD4 count, nadir CD4 count, baseline HIV RNA, history of injection drug use, and hepatitis C.
Adjusted for hs-CRP, IL-6, and D-dimer
Adjusted for factors in footnotes a–c.
Hazard ratios expressed per 1 standard deviation (SD) lower for each GFR estimation method: GFR creatinine (SD=18.3), GFR cystatin C (SD=21.5), and GFR creatinine-cystatin C (SD=19.5).
Adjustment for demographics and cardiovascular risk factors substantially decreased GFR associations with mortality and CVE, while adjusting for these factors had little effect on GFR associations with OD (Table 2). In contrast, adjustment for HIV-related factors had minimal effect on GFR associations with any of the outcomes, including OD. Adjustment for the inflammation markers had only modest effects on GFR associations with the three outcomes. Supplementary Table 1 shows rates of clinical events in four groups according to GFRcr and GFRcys stratification > or ≤ 90 mL/min/1.73m2. Event rates were highest in the group with both GFRcr and GFRcys ≤ 90 mL/min/1.73m2, and next highest in the group with GFRcr > 90 and GFRcys ≤ 90 mL.min/1.73m2, while rates were lowest and similar in the group with both GFRcr and GFRcys > 90 mL/min/1.73m2 and the group with eGFRcr ≤ 90 and GFRcys ≥ 90 mL/min/1.73m2.
We assessed whether cystatin C-based equations improved discriminatory ability for clinical events by calculating IDI statistics, a quantification of the improvement (or worsening) in sensitivity and specificity with an extended versus a simpler nested model. Compared to a model with GFRcr only, the IDIs for models with both GFRcr and GFRcys were statistically significant for mortality (IDI=0.0137, P=0.0007), CVE (IDI=0.0023, P=0.0038), and OD (IDI=0.0034, P=0.0026). In contrast, compared to a model with GFRcys only, a model with both GFRcys and GFRcr-cys did not show improved discrimination for mortality (IDI=0.0026, P=0.43), CVD (IDI=0, P>0.99), or OD (IDI=0.0007, P=0.45).
Sensitivity analyses
There was no evidence that study arm assignment (continuous or episodic antiretroviral therapy) modified the associations of GFRcr (P value range for interactions, 0.21 to 0.65), GFRcys (P value range for interactions, 0.11 to 0.87), or GFRcr-cys (P value range for interactions, 0.12 to 0.76) with the clinical outcomes. Nevertheless, we repeated the analyses in Table 2 in the subset of subjects that was randomized to continuous antiretroviral therapy, as this reflects the standard of care for HIV management (Supplementary Table 2). Although confidence intervals were wider, the findings were qualitatively similar to those from the complete study cohort, with the exception that GFRcr had a stronger association with OD in the continuous antiretroviral subset than in the cohort overall.
We also repeated analyses for CVE after excluding 14 events defined by serial electrocardiogram changes only (silent myocardial infarction). In the sensitivity analysis, which included 79 clinical CVEs, the associations were similar to those from the primary analysis (Table 2); specifically the HR (95% CI) for clinical CVE per standard deviation lower GFR were 1.16 (0.95, 1.41), 1.24 (1.01, 1.53) and 1.21 (1.00, 1.48) for GFRcr, GFRcys, and GFRcr-cys, respectively. Finally, we assessed the associations of GFR calculated by averaging GFRcr and GFRcys with clinical outcomes. The associations were nearly identical to those between GFRcr-cys and clinical outcomes (data not shown).
DISCUSSION
In this secondary analysis of data from the SMART study, we found that baseline kidney function, as measured by any of the three CDK-EPI GFR estimating equations, was independently associated with all-cause mortality during follow-up. However, only GFRcys was statistically significantly associated with CVE and OD in fully adjusted models. GFRcr had the weakest associations with clinical outcomes of the three equations, while GFRcr-cys had associations with outcomes that were close to, but slightly smaller than, those of GFRcys. We also found that inclusion of GFRcys to a base model with GFRcr statistically significantly improved discrimination (IDI) for all three outcomes, whereas inclusion of GFRcr-cys to a base model with GFRcys did not improve discrimination for any outcome.
These results are consistent with studies of patient populations not infected with HIV, in which cystatin C-based measures have been found to be more strongly associated with mortality, CVE, and incident heart failure than creatinine-based measures (9, 10). Additionally, an analysis by Choi and colleagues of 922 HIV-infected subjects enrolled in the Fat Redistribution and Metabolic Change in HIV Infection study found that cystatin C-based GFR was significantly and independently associated with 5-year all-cause mortality, whereas creatinine-based GFR was not significantly associated with mortality in unadjusted or adjusted analyses (16).
The mechanisms by which cystatin C-based GFR estimates are more strongly associated with clinical events compared to creatinine-based estimates are not clear. It is unlikely that GFRcys is a stronger predictor of clinical events than GFRcr by virtue of being a more accurate index of GFR. In recent studies evaluating the performance of the CKD-EPI estimating equations in non-HIV-infected (7) and HIV-infected (8) individuals, there was no evidence that GFRcys was more accurate or precise than GFRcr. Additionally, GFRcr-cys - which is the most accurate and precise of the CKD-EPI equations (7, 8) - was a slightly weaker predictor of clinical outcomes than GFRcys in our study, suggesting that improved surrogacy for clinical outcomes is not driven by greater GFR fidelity.
A second possibility is that cystatin C tracks inflammatory processes more closely than creatinine. We found that baseline GFRcys had stronger correlations than GFRcr with HIV RNA, CD4 cell count, hs-CRP, IL-6, and D-dimer, corroborating prior studies of these biomarkers in HIV-infected persons (17–19). In populations without HIV-infection, inflammation is an established risk factor for cardiovascular disease and mortality (20). Emerging data from populations with HIV infection highlight the strong associations between inflammatory markers, notably IL-6 and D-dimer, and clinical outcomes, including all-cause mortality (21) and cardiovascular disease (22, 23). Consequently, inflammation may mediate the stronger association between GFRcys and clinical events, compared to GFRcr. However, we found that adjusting for hs-CPR, IL-6, and D-dimer led to only modest changes in the observed associations with clinical events. Similarly, we found that adjustment for HIV-related factors, including baseline HIV RNA, baseline CD4, and nadir CD4, led to only small changes in the observed associations between GFR estimates and clinical events. Moreover, there was little evidence that adjustment for HIV-related factors or inflammation markers had larger effects on GFRcys-outcome associations than on GFRcr-outcome associations, as might be anticipated from the stronger baseline correlations between GFRcys and these factors. This suggests that cystatin C plasma concentrations have additional and as yet unidentified determinants that are predictive for clinical outcomes.
Our study has strengths, including a large and diverse sample of HIV-infected participants, prospectively defined outcomes that were adjudicated by an endpoint committee, use of standardized plasma creatinine and cystatin C measurements, use of the updated CKD-EPI GFR estimating equations that have validation data from HIV-infected subjects (8), and the ability to adjust for systematically collected and measured inflammation markers.
Our study also has limitations. First, we had to exclude 858 SMART participants from the analysis who lacked baseline measures of plasma creatinine or cystatin C, which reduced our power to assess associations with clinical events. Additionally, excluded subjects were at higher risk for clinical outcomes than included subjects. This would be expected to lead to an underestimation of clinical event rates in our analysis, although, it is unclear why this would bias the comparison of the relative prognostic importance of the studied biomarkers. Second, we did not have measurements of proteinuria, which is a strong and GFR-independent risk factor for clinical events (16, 24). Third, we did not directly measure GFR with exogenous filtration markers. Fourth, half of the participants in our study were managed with episodic antiretroviral therapy, which was found to be inferior to continuous treatment (12), and does not reflect the current standard of HIV care. However, we found no evidence that the observed associations differed by study arm, and a subgroup analysis of subjects assigned to continuous therapy produced qualitatively similar findings to the overall analysis.
Future research should examine the mechanisms by which cystatin C predicts clinical outcomes in studies that include directly measured GFR and biomarkers for alternative pathogenic pathways. Additionally, studies are indicated to determine if patient management and clinical outcomes can be improved with the use of cystatin C-based screening or treatment algorithms.
In summary, we found that GFR estimates that used plasma cystatin C had stronger associations with mortality, CVE, and OD than GFR based on plasma creatinine in a secondary data analysis of HIV-infected participants in the SMART study. GFRcys had stronger correlations than GFRcr with a variety of factors that might mediate associations with clinical outcomes, including CD4 cell count, HIV RNA, and inflammation markers. However, adjustment for these factors only modestly reduced the observed associations between GFRcys and clinical events, suggesting that these covariates are only partial mediators.
Supplementary Material
Acknowledgements
This work was supported by the National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH) (U01-AI068641, U01-AI042170 and U01-AI046362). The funding source had no role in data collection, data analysis, or the decision to publish the results. Dr. Lucas is supported by the National Institute on Drug Abuse (R01 DA026770). The views expressed in this manuscript are those of the authors and do not necessarily represent the views of National Institutes of Health. We would like to acknowledge the SMART participants and SMART study team (see (12) for list of investigators) and the INSIGHT Executive Committee.
REFERENCES
- 1.Wyatt CM, Winston JA, Malvestutto CD, Fishbein DA, Barash I, Cohen AJ, et al. Chronic kidney disease in HIV infection: an urban epidemic. AIDS. 2007;21(15):2101–2103. doi: 10.1097/QAD.0b013e3282ef1bb4. [DOI] [PubMed] [Google Scholar]
- 2.Stevens LA, Coresh J, Greene T, Levey AS. Assessing kidney function--measured and estimated glomerular filtration rate. N Engl J Med. 2006;354(23):2473–2483. doi: 10.1056/NEJMra054415. [DOI] [PubMed] [Google Scholar]
- 3.Madero M, Sarnak MJ, Stevens LA. Serum cystatin C as a marker of glomerular filtration rate. Curr. Opin. Nephrol Hypertens. 2006;15(6):610–611. doi: 10.1097/01.mnh.0000247505.71915.05. [DOI] [PubMed] [Google Scholar]
- 4.Knight EL, Verhave JC, Spiegelman D, Hillege HL, de Zeeuw D, Curhan GC, et al. Factors influencing serum cystatin C levels other than renal function and the impact on renal function measurement. Kidney Int. 2004;65(4):1416–1421. doi: 10.1111/j.1523-1755.2004.00517.x. [DOI] [PubMed] [Google Scholar]
- 5.Wiesli P, Schwegler B, Spinas GA, Schmid C. Serum cystatin C is sensitive to small changes in thyroid function. Clin Chim. Acta. 2003;338(1–2):87–90. doi: 10.1016/j.cccn.2003.07.022. [DOI] [PubMed] [Google Scholar]
- 6.Luc G, Bard JM, Lesueur C, Arveiler D, Evans A, Amouyel P, et al. Plasma cystatin-C and development of coronary heart disease: The PRIME Study. Atherosclerosis. 2006;185(2):375–380. doi: 10.1016/j.atherosclerosis.2005.06.017. [DOI] [PubMed] [Google Scholar]
- 7.Inker LA, Schmid CH, Tighiouart H, Eckfeldt JH, Feldman HI, Greene T, et al. Estimating glomerular filtration rate from serum creatinine and cystatin C. N Engl J Med. 2012;367(1):20–29. doi: 10.1056/NEJMoa1114248. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Inker LA, Wyatt C, Creamer R, Hellinger J, Hotta M, Leppo M, et al. Performance of Creatinine and Cystatin C GFR Estimating Equations in an HIV-Positive Population on Antiretrovirals. J Acquir Immune Defic Syndr. 2012;61(3):302–309. doi: 10.1097/QAI.0b013e31826a6c4f. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Shlipak MG, Sarnak MJ, Katz R, Fried LF, Seliger SL, Newman AB, et al. Cystatin C and the risk of death and cardiovascular events among elderly persons. New England Journal of Medicine. 2005;352(20):2049–2060. doi: 10.1056/NEJMoa043161. [DOI] [PubMed] [Google Scholar]
- 10.Ix JH, Shlipak MG, Chertow GM, Whooley MA. Association of cystatin C with mortality, cardiovascular events, and incident heart failure among persons with coronary heart disease: data from the Heart and Soul Study. Circulation. 2007;115(2):173–179. doi: 10.1161/CIRCULATIONAHA.106.644286. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.El Sadr WM, Grund B, Neuhaus J, Babiker A, Cohen CJ, Darbyshire J, et al. Risk for opportunistic disease and death after reinitiating continuous antiretroviral therapy in patients with HIV previously receiving episodic therapy: a randomized trial. Ann. Intern Med. 2008;149(5):289–299. doi: 10.7326/0003-4819-149-5-200809020-00003. [DOI] [PubMed] [Google Scholar]
- 12.El Sadr WM, Lundgren JD, Neaton JD, Gordin F, Abrams D, Arduino RC, et al. CD4+ count-guided interruption of antiretroviral treatment. N. Engl. J Med. 2006;355(22):2283–2296. doi: 10.1056/NEJMoa062360. [DOI] [PubMed] [Google Scholar]
- 13.Myers GL, Miller WG, Coresh J, Fleming J, Greenberg N, Greene T, et al. Recommendations for improving serum creatinine measurement: a report from the Laboratory Working Group of the National Kidney Disease Education Program. Clin Chem. 2006;52(1):5–18. doi: 10.1373/clinchem.2005.0525144. [DOI] [PubMed] [Google Scholar]
- 14.Grubb A, Blirup-Jensen S, Lindstrom V, Schmidt C, Althaus H, Zegers I, et al. First certified reference material for cystatin C in human serum ERM-DA471/IFCC. Clin Chem Lab Med. 2010;48(11):1619–1621. doi: 10.1515/CCLM.2010.318. [DOI] [PubMed] [Google Scholar]
- 15.Pencina MJ, D'Agostino RB, Sr, D'Agostino RB, Jr, Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med. 2008;27(2):157–172. doi: 10.1002/sim.2929. discussion 207–12. [DOI] [PubMed] [Google Scholar]
- 16.Choi A, Scherzer R, Bacchetti P, Tien PC, Saag MS, Gibert CL, et al. Cystatin C, albuminuria, and 5-year all-cause mortality in HIV-infected persons. Am J Kidney Dis. 2010;56(5):872–882. doi: 10.1053/j.ajkd.2010.05.019. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Neuhaus J, Jacobs DR, Jr, Baker JV, Calmy A, Duprez D, La RA, et al. Markers of inflammation, coagulation, and renal function are elevated in adults with HIV infection. J. Infect. Dis. 2010;201(12):1788–1795. doi: 10.1086/652749. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Mocroft A, Wyatt C, Szczech L, Neuhaus J, El-Sadr W, Tracy R, et al. Interruption of antiretroviral therapy is associated with increased plasma cystatin C. AIDS. 2009;23(1):71–82. doi: 10.1097/QAD.0b013e32831cc129. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Mauss S, Berger F, Kuschak D, Henke J, Hegener P, Wolf E, et al. Cystatin C as a marker of renal function is affected by HIV replication leading to an underestimation of kidney function in HIV patients. Antivir. Ther. 2008;13(8):1091–1095. [PubMed] [Google Scholar]
- 20.Pai JK, Pischon T, Ma J, Manson JE, Hankinson SE, Joshipura K, et al. Inflammatory markers and the risk of coronary heart disease in men and women. N. Engl. J Med. 2004;351(25):2599–2610. doi: 10.1056/NEJMoa040967. [DOI] [PubMed] [Google Scholar]
- 21.Kuller LH, Tracy R, Belloso W, De Wit S, Drummond F, Lane HC, et al. Inflammatory and coagulation biomarkers and mortality in patients with HIV infection. PLoS Med. 2008;5(10):e203. doi: 10.1371/journal.pmed.0050203. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Hsue PY, Deeks SG, Hunt PW. Immunologic basis of cardiovascular disease in HIV-infected adults. J Infect Dis. 2012;205(Suppl 3):S375–S382. doi: 10.1093/infdis/jis200. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Duprez DA, Neuhaus J, Kuller LH, Tracy R, Belloso W, De Wit S, et al. Inflammation, coagulation and cardiovascular disease in HIV-infected individuals. PLoS One. 2012;7(9):e44454. doi: 10.1371/journal.pone.0044454. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.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):2073–2081. doi: 10.1016/S0140-6736(10)60674-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.