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American Journal of Kidney Diseases

Use of Measures of Inflammation and Kidney Function for Prediction of Atherosclerotic Vascular Disease Events and Death in Patients With CKD: Findings From the CRIC Study

Open AccessPublished:December 10, 2018DOI:https://doi.org/10.1053/j.ajkd.2018.09.012

      Rationale & Objective

      Traditional risk estimates for atherosclerotic vascular disease (ASVD) and death may not perform optimally in the setting of chronic kidney disease (CKD). We sought to determine whether the addition of measures of inflammation and kidney function to traditional estimation tools improves prediction of these events in a diverse cohort of patients with CKD.

      Study Design

      Observational cohort study.

      Setting & Participants

      2,399 Chronic Renal Insufficiency Cohort (CRIC) Study participants without a history of cardiovascular disease at study entry.

      Predictors

      Baseline plasma levels of biomarkers of inflammation (interleukin 1β [IL-1β], IL-1 receptor antagonist, IL-6, tumor necrosis factor α [TNF-α], transforming growth factor β, high-sensitivity C-reactive protein, fibrinogen, and serum albumin), measures of kidney function (estimated glomerular filtration rate [eGFR] and albuminuria), and the Pooled Cohort Equation probability (PCEP) estimate.

      Outcomes

      Composite of ASVD events (incident myocardial infarction, peripheral arterial disease, and stroke) and death.

      Analytical Approach

      Cox proportional hazard models adjusted for PCEP estimates, albuminuria, and eGFR.

      Results

      During a median follow-up of 7.3 years, 86, 61, 48, and 323 participants experienced myocardial infarction, peripheral arterial disease, stroke, or death, respectively. The 1-decile greater levels of IL-6 (adjusted HR [aHR], 1.12; 95% CI, 1.08-1.16; P < 0.001), TNF-α (aHR, 1.09; 95% CI, 1.05-1.13; P < 0.001), fibrinogen (aHR, 1.07; 95% CI, 1.03-1.11; P < 0.001), and serum albumin (aHR, 0.96; 95% CI, 0.93-0.99; P < 0.002) were independently associated with the composite ASVD-death outcome. A composite inflammation score (CIS) incorporating these 4 biomarkers was associated with a graded increase in risk for the composite outcome. The incidence of ASVD-death increased across the quintiles of risk derived from PCEP, kidney function, and CIS. The addition of eGFR, albuminuria, and CIS to PCEP improved (P = 0.003) the area under the receiver operating characteristic curve for the composite outcome from 0.68 (95% CI, 0.66-0.71) to 0.73 (95% CI, 0.71-0.76).

      Limitations

      Data for cardiovascular death were not available.

      Conclusions

      Biomarkers of inflammation and measures of kidney function are independently associated with incident ASVD events and death in patients with CKD. Traditional cardiovascular risk estimates could be improved by adding markers of inflammation and measures of kidney function.

      Index Words

      Atherosclerosis is a chronic immunoinflammatory fibroproliferative disease that is characterized by lipid infiltration.
      • Ross R.
      Atherosclerosis--an inflammatory disease.
      Patients with chronic kidney disease (CKD) have elevated circulating levels of acute-phase proteins and proinflammatory cytokines.
      • Gupta J.
      • Mitra N.
      • Kanetsky P.A.
      • et al.
      Association between albuminuria, kidney function, and inflammatory biomarker profile.
      Considerable evidence indicates that biomarkers of inflammation are associated with adverse cardiovascular outcomes in the general population.
      • Kaptoge S.
      • Seshasai S.R.
      • Gao P.
      • et al.
      Inflammatory cytokines and risk of coronary heart disease: new prospective study and updated meta-analysis.
      The American College of Cardiology (ACC) and American Heart Association (AHA) Task Force on Practice Guidelines opined that assessment of C-reactive protein (CRP) levels is reasonable for patients at an intermediate cardiovascular disease (CVD) risk category.
      • Greenland P.
      • Alpert J.S.
      • Beller G.A.
      • et al.
      2010 ACCF/AHA guideline for assessment of cardiovascular risk in asymptomatic adults: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines.
      In population-based cohorts with participants in early stages of CKD or studies involving small numbers of dialysis patients, measurement of a limited number of biomarkers has suggested an association between inflammation and CVD outcomes.
      • Stenvinkel P.
      • Heimburger O.
      • Paultre F.
      • et al.
      Strong association between malnutrition, inflammation, and atherosclerosis in chronic renal failure.
      • Zoccali C.
      • Tripepi G.
      • Mallamaci F.
      Dissecting inflammation in ESRD: do cytokines and C-reactive protein have a complementary prognostic value for mortality in dialysis patients?.
      • Knight E.L.
      • Rimm E.B.
      • Pai J.K.
      • et al.
      Kidney dysfunction, inflammation, and coronary events: a prospective study.
      However, these results have not been confirmed in larger and more ethnically diverse CKD cohorts with a wide range of glomerular filtration rates (GFRs) and long-term follow-up for clinical outcomes. Recently, the ACC/AHA introduced the new Pooled Cohort Equations to estimate 10-year atherosclerotic vascular disease (ASVD) risk.
      • Goff Jr., D.C.
      • Lloyd-Jones D.M.
      • Bennett G.
      • et al.
      2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines.
      However, to our knowledge, the applicability of this risk estimate to patients with CKD has not been tested.
      In this study, we examine the association of incident ASVD events and death with a panel of circulating inflammatory biomarkers and kidney function measures in participants from the Chronic Renal Insufficiency Cohort (CRIC) Study.
      • Feldman H.I.
      • Appel L.J.
      • Chertow G.M.
      • et al.
      The Chronic Renal Insufficiency Cohort (CRIC) Study: design and methods.
      We also evaluate whether adding measures of kidney function and biomarkers of inflammation improve on traditional risk variables for risk-stratifying patients with CKD for ASVD outcomes and death. One of the purposes of this report is to provide results that can inform the development of future risk prediction equations.

      Methods

      Study Participants

      The CRIC Study includes 3,939 racially and ethnically diverse men and women with CKD recruited from 7 clinical sites within the United States, aged 21 to 74 years at entry, and with approximately half of all participants having diabetes mellitus.
      • Lash J.P.
      • Go A.S.
      • Appel L.J.
      • et al.
      Chronic Renal Insufficiency Cohort (CRIC) Study: baseline characteristics and associations with kidney function.
      The main exclusion criteria were cirrhosis, class III or IV heart failure, human immunodeficiency virus (HIV) infection, cancer, active or recent immunosuppression, polycystic kidney disease, and pregnancy. The study complies with the Declaration of Helsinki and is approved by the institutional review board at each participating site. All participants provided written informed consent. For this study, we excluded participants who had a history of CVD at baseline and those with missing medication data. The enrollment period was 2003 to 2007 and the administrative censoring date was March 2013.

      CRIC Data Collection

      Demographic characteristics, medical history, and medication use were recorded at baseline. Serum creatinine was measured using a coupled enzymatic method. GFR was estimated using the 4-variable Modification of Diet in Renal Disease (MDRD) Study equation. Albuminuria was assessed using urinary albumin-creatinine ratio (UACR). Diabetes was defined as fasting glucose level ≥ 126 mg/dL (≥7 mmol/L), random glucose level ≥ 200 mg/dL (≥11.1 mmol/L), or use of insulin or antidiabetic medication. Hypertension was defined as systolic blood pressure (SBP) ≥ 140 mm Hg and/or diastolic blood pressure ≥ 90 mm Hg and/or self-reported antihypertensive medication use.

      Event Adjudication

      Study participants were queried twice annually during study visits about possible CVD events and hospitalizations. When International Classification of Diseases, Ninth Revision discharge codes indicated myocardial infarction (MI), cerebrovascular accident (stroke), peripheral arterial disease (PAD), or death, medical records were reviewed using event-specific guidelines by 2 physicians. MI was adjudicated based on a combination of electrocardiogram abnormalities, elevations of cardiac injury enzyme levels, and the presence of symptoms. PAD was ascertained by a trained nurse abstractor and necessitated evidence of amputation or peripheral surgical or percutaneous revascularization procedures. Two neurologists adjudicated stroke based on clinical and radiographic evidence of ischemic stroke or intracranial hemorrhage.

      Measurement of Biomarkers of Inflammation

      Biomarkers were measured in baseline blood samples at the time of initial thawing.
      • Gupta J.
      • Mitra N.
      • Kanetsky P.A.
      • et al.
      Association between albuminuria, kidney function, and inflammatory biomarker profile.
      High-sensitivity enzyme-linked immunosorbent assays (Quantikine HS, R&D Systems) were used to measure plasma interleukin 1β (IL-1β), IL-6, and tumor necrosis factor α (TNF-α) levels. Standard enzyme-linked immunosorbent assays (Quantikine, R&D Systems) were used to quantify IL-1 receptor antagonist and transforming growth factor β (TGFβ) levels. All assays were performed in duplicate and mean values were used in the analysis. The coefficient of variation was <13% for all cytokine assays except for TNF-α and TGFβ, for which the estimated imprecisions were 15.2% and 21.5%, respectively. High-sensitivity (hs)-CRP and fibrinogen were quantified in EDTA plasma samples using specific laser-based immunonephelometric methods on the BNII (Siemens Healthcare Diagnostics). Imprecision for hs-CRP and fibrinogen was <5%. Serum albumin was measured using the bromcresol green method. These biomarkers were chosen based on their potential role in CVD in CKD.
      • Amdur R.L.
      • Feldman H.I.
      • Gupta J.
      • et al.
      Inflammation and progression of CKD: the CRIC Study.
      • Stenvinkel P.
      • Carrero J.J.
      • Axelsson J.
      • Lindholm B.
      • Heimburger O.
      • Massy Z.
      Emerging biomarkers for evaluating cardiovascular risk in the chronic kidney disease patient: how do new pieces fit into the uremic puzzle?.

      Predictors and Outcomes

      Predictors were baseline measurements of IL-1β, IL-1 receptor antagonist, IL-6, TNF-α, TGFβ, hs-CRP, fibrinogen, and serum albumin, as well as measures of kidney function (estimated GFR [eGFR] and albuminuria). The primary outcome was a composite of incident ASVD (MI, PAD, or stroke) and death during the follow-up period. Secondary outcomes were individual components of the primary outcome, as well as a CVD composite (MI/stroke/PAD).

      Statistical Analysis

      Standard descriptive statistics were used to describe baseline characteristics for the full cohort. Univariate associations of inflammatory biomarkers with the composite outcome were examined using separate Cox proportional hazard models for each inflammatory biomarker. Covariates, including the natural log-transformed Pooled Cohort Equation probability (PCEP; ln[PCEP]), natural log-transformed UACR (ln[UACR]), and eGFR, were then added to these models to determine whether each inflammatory marker had an independent association with the composite outcome after adjusting for traditional risk factors and measures of kidney function. All Cox models were tested to ensure that there was no violation of the proportional hazards assumption and no problem with multicollinearity. In all survival analyses, cases without events were right-censored at last study visit. We tested for linearity of the relationship of markers with the incident ASVD-death composite outcome by examining the significance of the linear regression coefficient between marker quartile and the outcome.
      To assess which of the inflammatory biomarkers had associations with the composite outcome that were independent of each other, a single Cox proportional hazard model was then tested including only the inflammatory biomarkers that had significant multivariable effects in the prior analysis. Those that remained significant in this model were used to create a composite inflammation score (CIS) by summing the deciles of these biomarkers. We also tested a weighted version in which the weights were the parameter estimates in this model. Also, for hazard ratios (HRs) to be used to compare effect sizes between biomarkers, we first divided each marker by its standard deviation.
      To test the improvement in prediction accuracy when kidney function variables and CIS were added to traditional risk predictors, we examined the area under the receiver operating curve (AUC) for each model, calculated from Cox models using the SurvCstd SAS macro,
      • Kremers W.K.
      Concordance for Survival Time Data Including Time-Dependent Covariates Accounting for Ties in Predictor and Time. Technical report.
      and compared them using χ2 test.
      • Gonen M.
      Analyzing Receiver Operating Characteristic Curves With SAS.
      Rather than testing at a single risk cut point, this method tests the overall improvement in discrimination across risk levels.
      As a secondary analysis, we examined independent associations of each inflammatory biomarker separately, with time to first MI, PAD, stroke, or death and composite of MI, stroke, or PAD individually, using Cox proportional hazards regression with biomarkers coded into deciles. We then examined interaction effects for each of the significant inflammatory biomarkers with baseline eGFR (<30 vs ≥30 mL/min/1.73 m2) and albuminuria (UACR ≥ 30 vs <30 mg/g).
      • Sung K.C.
      • Ryu S.
      • Lee J.Y.
      • et al.
      Urine albumin/creatinine ratio below 30 mg/g is a predictor of incident hypertension and cardiovascular mortality.
      This was done using a marker × covariate interaction term in the Cox proportional hazards model, including PCEP and measures of kidney function as covariates.
      We conducted a number of sensitivity analyses. First, we examined associations of the biomarkers with only the ASVD events, using a competing-risk model
      • Fine J.P.
      • Gray R.J.
      A proportional hazards model for the subdistribution of a competing risk.
      to estimate the cumulative incidence function for biomarker quintile with the incident composite ASVD outcome, for which death was considered as the competing risk. Second, we tested a traditional Cox model that included the variables used to compute the PCEP as individual predictors. These included age, race, sex, ln(total cholesterol), ln(high-density lipoprotein) (ln[HDL]), smoking, diabetes, and treated or untreated SBP. Blood pressure–treated was coded yes if the patient had hypertension and received angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, β-blockers, calcium channel blockers, or diuretics. The AUC of this model was computed and then compared with the AUC when kidney function variables and CIS were added as predictors. Third, we compared the AUC for the PCEP-only model versus PCEP plus kidney function and inflammation measures, looking only at participants older than 40 years, and fourth, stratifying these Cox models by statin use.
      We explored whether there was a cut point relevant for clinical decision making using either the model that included CIS, kidney function, and PCEP or PCEP alone. To be clinically useful, at a minimum, the predicted outcome should have sensitivity and specificity both ≥ 0.70 and preferably ≥ 0.80. We used the logistic regression model including CIS, kidney function variables, and PCEP to calculate a predicted probability of reaching ASVD-death during follow-up for each participant based on the following formula: probability = exp(risk)/(1 + exp(risk)), where risk = intercept + x1(CIS) + x2(ln[PCEP]) + x3(ln[UACR]) + x4(eGFR), where each xn is a model parameter estimate.
      All analyses were done using SAS (version 9.3 or 9.4; SAS Institute). P < 0.05 was considered statistically significant.

      Results

      Participant Characteristics

      After excluding 1,316 participants who had a history of CVD and an additional 104 who were missing data for inflammatory markers, 94 with missing kidney function data, and 26 for whom key data needed to calculate PCEP were not available, 2,399 participants were available for multivariable analysis (Fig 1). Participants were followed up for a median of 7.3 (interquartile range [IQR], 5.4-8.4) years. During follow-up, 86, 61, and 48 participants developed MI, PAD, or stroke, respectively, and 323 participants died. There were 471 participants with the primary outcome, a composite of incident ASVD (MI, PAD, or stroke) or death. Characteristics of study participants are shown in Table 1. The mean age of the study participants was 56 years, 48% were women, 38% were black, and 41% had diabetes mellitus at baseline. Mean baseline eGFR was 44.1 mL/min/1.73 m2 and median PCEP was 9.6% (IQR, 3.5%-18.8%; ie, 10-year risk for a first ASVD event), with 58% of the sample having a baseline raw PCEP ≥ 7.5%, and 46% were being treated with statins. IL-1β level was highly skewed, so when this was coded into quintiles, quintiles 1 and 2 were combined.
      Figure thumbnail gr1
      Figure 1Patient flow into the study. Abbreviations: CRIC, Chronic Renal Insufficiency Cohort; CVD, cardiovascular disease; PCEP, Pooled Cohort Equation probability.
      Table 1Baseline Characteristics of Study Participants
      VariableStudy Cohort
      Age, y56.0 ± 11.6
      Female sex1,144 (48%)
      Race
       Black913 (38%)
       White1,192 (50%)
       Other294 (12%)
      Current smoker279 (12%)
      Body mass index, kg/m231.8 ± 7.8
      Diabetes995 (41%)
      Hypertension1,994 (83%)
      SBP, mm Hg126.7 ± 20.7
      DBP, mm Hg72.5 ± 12.3
      HbA1c, %6.4 ± 1.5
      Total cholesterol, mmol/L187.7 ± 44.0
      HDL cholesterol, mmol/L48.7 ± 16.0
      eGFR, mL/min/1.73 m244.1 ± 13.9
      UACR, mg/g39.9 [7.4-395.9]
      Raw PCEP for ASVD9.6% [3.5%-18.7%]
      Raw PCEP ≥ 7.5%1,393 (58%)
      Inflammatory Biomarkers
       hs-CRP, mg/L2.46 [1.00-6.04]
       Fibrinogen, g/L3.95 [3.31-4.66]
       Serum albumin, g/dL4.00 [3.70-4.30]
       IL-1β, pg/mL0.19 [0.06-1.27]
       IL-1RA, pg/mL694.8 [367.6-1,504.2]
       IL-6, pg/mL1.69 [1.03-2.80]
       TNF-α, pg/mL2.10 [1.40-3.20]
       TGFβ, ng/mL11.00 [6.51-17.98]
      Medications
       β-Blocker912 (38%)
       ACEi/ARB1,545 (64%)
       Calcium channel blocker892 (37%)
       Diuretic1,256 (52%)
       Aspirin811 (34%)
       Statin1,110 (46%)
      Note: N = 2,399. Values for continuous data given as mean ± standard deviation or median [interquartile range]; for categorical data, as count (percentage).
      Abbreviations: ACEi/ARB, angiotensin-converting enzyme inhibitor/angiotensin receptor blocker; CVD, cardiovascular disease; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; HbA1c, glycated hemoglobin; HDL, high-density lipoprotein; hs-CRP, high-sensitivity C-reactive protein; IL, interleukin; IL-1RA, interleukin 1 receptor antagonist; PCEP, Pooled Cohort Equation probability; SBP, systolic blood pressure; TGFβ, transforming growth factor β; TNF-α, tumor necrosis factor α; UACR, urinary albumin-creatinine ratio.

      Association of Inflammatory Markers With the Composite Outcome

      In survival analysis, all 8 inflammatory markers investigated here had significant univariable associations with the composite outcome (Table 2). However, after adjusting for PCEP, eGFR, and albuminuria, only IL-6, TNF-α, hs-CRP, fibrinogen, and serum albumin values remained significantly associated with the outcome. All these markers had significant linear associations with the outcome when tested by marker quartile (all P < 0.001; Table S1). When these 5 markers were combined as predictors in a single Cox proportional hazards model for the composite outcome without other covariates, hs-CRP level became nonsignificant, but others remained significant (Table 3). We summed the deciles for the 4 markers (IL-6, TNF-α, fibrinogen, and reverse-coded serum albumin) to construct the CIS. Kaplan-Meier analysis showed a significant graded positive association between quintiles of the CIS and the composite outcome (log-rank χ2 P < 0.001; Fig 2). Those in the highest CIS quintile had a 3.1-fold higher hazard for the composite event compared with those in the lowest quintile after adjusting for baseline eGFR, albuminuria, and PCEP. Kaplan-Meier survival estimates for time to first ASVD-death event stratified by biomarker quintiles are shown in Fig S1.
      Table 2Associations of Each Decile Greater Level of Inflammatory Marker With the Composite Outcome Using Separate Cox Proportional Hazards Models for Each Marker
      MarkerUnivariable ModelMultivariable Model
      HR (95% CI)PHR (95% CI)P
      IL-1β1.05 (1.02-1.09)0.0021.01 (0.98-1.05)0.5
      IL-1RA1.04 (1.01-1.07)0.021.01 (0.98-1.04)0.5
      IL-61.19 (1.15-1.23)<0.0011.12 (1.08-1.16)<0.001
      TGFβ1.04 (1.01-1.08)0.0081.03 (0.99-1.06)0.1
      TNF-α1.17 (1.13-1.21)<0.0011.09 (1.05-1.13)<0.001
      hs-CRP1.10 (1.06-1.13)<0.0011.08 (1.05-1.12)<0.001
      Fibrinogen1.16 (1.13-1.20)<0.0011.07 (1.03-1.11)<0.001
      Serum albumin0.90 (0.87-0.92)<0.0010.96 (0.93-0.99)0.02
      Note: Multivariable models were adjusted for Pooled Cohort Equation probability, albuminuria, and estimated glomerular filtration rate. The composite outcome is atherosclerotic vascular disease (myocardial infarction, peripheral arterial disease, and stroke) or death.
      Abbreviations: CI, confidence interval; HR, hazard ratio; hs-CRP, high-sensitivity C-reactive protein; IL, interleukin; IL-1RA, interleukin 1 receptor antagonist; TGFβ, transforming growth factor β; TNF-α, tumor necrosis factor α.
      Table 3Cox Proportional Hazard Model for the Composite Outcome Using Significant Inflammatory Markers Together in One Model With No Other Covariates
      MarkerHR (95% CI)PParameter Estimate (SE)
      IL-61.12 (1.08-1.17)<0.0010.112 (0.02)
      TNF-α1.10 (1.06-1.14)<0.0010.097 (0.02)
      hs-CRP0.99 (0.95-1.03)0.5−0.013 (0.02)
      Fibrinogen1.07 (1.03-1.12)<0.0010.069 (0.02)
      Serum albumin0.95 (0.92-0.99)0.004−0.050 (0.02)
      Note: HR is shown for 1-decile greater level of the inflammatory marker. The composite outcome is atherosclerotic vascular disease (myocardial infarction, peripheral arterial disease, or stroke) or death.
      Abbreviations: CI, confidence interval; HR, hazard ratio; hs-CRP, high-sensitivity C-reactive protein; IL, interleukin; SE, standard error; TNF-α, tumor necrosis factor α.
      Figure thumbnail gr2
      Figure 2Kaplan-Meier estimates of freedom from the atherosclerotic vascular disease (ASVD)-death composite outcome stratified by quintile of the composite inflammation score (CIS), which was computed by adding the deciles of interleukin 6, tumor necrosis factor α, fibrinogen, and reverse-coded serum albumin. Log-rank χ2, 162.28 (P < 0.001). Adjusted hazard ratios (HRs) for quintiles 2 through 5 are shown versus quintile 1 based on a multivariable Cox proportional hazard model using CIS quintile coded as a class variable, adjusting for Pooled Cohort Equation probability, estimated glomerular filtration rate, and urine albumin-creatinine ratio. Abbreviation: CI, confidence interval.
      When CIS was included in a Cox proportional hazard model for the composite outcome with PCEP, eGFR, and albuminuria, it remained significant, as did the other 3 covariates (Table 4). After standardizing covariates so that their effect sizes could be compared using z scores and re-running this model, the adjusted HRs (aHRa) for ASVD-death, per 1-SD higher level on the covariates, were 1.63, 1.47, 1.21, and 0.87 for PCEP, CIS, proteinuria, and eGFR, respectively. We found no evidence of violation of the proportionality assumption for Cox models that included the CIS or individual inflammatory markers with any other covariates. We also did not find evidence of significant multicollinearity (ie, variance inflation factors ranging from 1.02-1.4).
      Table 4Cox Proportional Hazard Model for the Composite Incident CVD–Death Outcome Using the CIS, Adjusted for ln(PCEP), ln(UACR), and eGFR
      ParameteraHR (95% CI) per 1-Unit IncreasePaHR per 1-SD Increase
      ln(PCEP)2.81 (2.32-3.39)<0.0011.62 (1.48-1.77)
      ln(UACR)1.10 (1.05-1.15)<0.0011.22 (1.10-1.35)
      eGFR0.99 (0.98-1.00)0.020.88 (0.79-0.98)
      CIS1.05 (1.04-1.07)<0.0011.47 (1.32-1.65)
      Note: The size of the HR cannot be used to indicate the relative strength of these predictors because their variances are not equal. However, the standardized HRs can be compared.
      Abbreviations: aHR, adjusted hazard ratio; CI, confidence interval; CIS, composite inflammation score; CVD, cardiovascular disease; eGFR, estimated glomerular filtration rate; PECP, Pooled Cohort Equation probability; SD, standard deviation; UACR, urinary albumin-creatinine ratio.

      Multivariable Association With Death, MI, PAD, and Stroke

      All inflammatory markers had significant univariable associations with death during follow-up (Table S2). Those that remained significant after adjusting for PCEP and kidney function included IL-6, TGFβ, TNF-α, hs-CRP, fibrinogen, and CIS. Only serum albumin and CIS were independently associated with PAD. Although several inflammatory biomarkers had significant univariable associations with incident MI during follow-up, only CIS had a significant independent association with this outcome. No inflammatory biomarker had an independent association with stroke after adjusting for covariates. hs-CRP and CIS had a significant association with the composite of MI, stroke, and PAD in the fully adjusted model.

      Competing-Risk Analysis

      When death was considered as a competing risk and composite ASVD was the primary outcome in univariable time-to-event analysis, quintiles of hs-CRP, fibrinogen, serum albumin, IL-6, TNF-α, and the CIS remained significantly associated with time to the outcome (Fig S2). However, only CIS retained the step function structure in which increasing level of the marker was associated with increasing risk for reaching the outcome.

      Interaction Analyses

      Interaction analysis, adjusted for measures of kidney function and PCEP, showed that in general, inflammatory biomarkers’ associations with the composite outcome were not altered by either baseline eGFR or albuminuria (Fig S3A and B).

      Discrimination for Cox Models Predicting ASVD Events and Death

      The AUC for the Cox model using just PCEP to predict time to ASVD event or death was 0.68 (95% confidence interval [CI], 0.66-0.71). When eGFR, albuminuria, and CIS were added, the AUC became 0.73 (95% CI, 0.71-0.76), a significant improvement (P = 0.003). We used the adjusted parameter estimates from the Cox model that included just the inflammation variables (shown in Table 3) as weights, so the weighted CIS was defined as weighted CIS = 0.112([IL-6] × 10) + 0.97([TNF-α] × 10) + 0.069([fibrinogen] × 10) − 0.05([serum albumin] × 10). When weighted CIS was used in the estimates, results were unchanged. The addition of kidney function variables and CIS to PCEP improved the AUC in individuals older than 40 years (0.72 [95% CI, 0.70-0.75] vs 0.67 [95% CI 0.64-0.69]; P = 0.001), those using statins (0.71 [95% CI 0.68-0.74] vs 0.66 [95% CI, 0.62-0.69]; P = 0.02), and those not using statins (0.75 [95% CI, 0.72-0.78] vs 0.70 [95% CI, 0.67-0.74]; P = 0.05).

      Clinically Relevant Risk Threshold

      We examined sensitivity, specificity, and positive and negative predictive values at various probability cut points for the kidney model and PCEP (Table 5). At a 20% probability cut point, sensitivity and specificity for the kidney model were close to being clinically useful (0.71 and 0.70, respectively). However, there was no probability cut point at which the PCEP could be used for clinical purposes. The equation for calculating the estimated event probability during follow-up, using the kidney model, was probability = exp(risk)/(1 + exp(risk)), where risk = −0.766 + 0.055(CIS) + 1.171(ln[PCEP]) + 0.09(ln[UACR]) − 0.011(eGFR).
      Table 5Sensitivity, Specificity, and Positive and Negative Predictive Values for the Kidney Model and the PCEP in Predicting the ASVD-Death Composite, at Various Probability Cut Points
      Probability Cut Point for Predicting an Event
      5%7.5%10%15%20%25%
      Kidney model
       Sensitivity0.990.950.890.810.710.59
       Specificity0.100.240.360.560.700.79
       PPV0.210.230.250.310.370.41
       NPV0.980.950.930.920.910.89
       OR for event (95% CI)13.21 (4.88-35.74)5.73 (3.78-8.69)4.52 (3.34-6.12)5.29 (4.13-6.77)5.62 (4.51-7.01)5.40 (4.36-6.68)
      PCEP
       Sensitivity0.870.810.710.560.420.29
       Specificity0.370.480.570.720.820.89
       PPV0.250.270.290.330.370.38
       NPV0.920.910.890.870.850.84
       OR for event (95% CI)4.00 (3.01-5.33)3.89 (3.04-4.99)3.25 (2.61-4.05)3.29 (2.67-4.05)3.40 (2.74-4.23)3.14 (2.46-4.00)
      Note: The kidney model probability estimate is calculated as exp(risk)/(1 + exp(risk)), where risk = −0.766 + 0.055(CIS) + 1.171(ln[PCEP]) + 0.09(ln[UACR]) − 0.011(eGFR). Cut points for calculating inflammatory marker deciles, which are needed to calculate the CIS, are provided in the Supplementary Material.
      Abbreviations: ASVD, atherosclerotic vascular disease; CI, confidence interval; CIS, composite inflammation score; eGFR, estimated glomerular filtration rate; NPV, negative predictive value; OR, odds ratio; PCEP, Pooled Cohort Equation probability; PPV, positive predictive value; UACR, urinary albumin-creatinine ratio.
      Cut points for calculating inflammatory marker deciles, which are needed to calculate the CIS, are provided in Table S3.

      Traditional Model Using Components of PCEP as Separate Variables

      In the Cox model including the component variables of the Pooled Cohort Equation as separate variables, the significant independent predictors of time to ASVD-death included ln(age) (aHR, 5.82; 95% CI, 3.41-9.93; P < 0.001), ln(HDL) (aHR, 0.61; 95% CI, 0.43-0.87; P = 0.005), smoking (aHR, 1.77; 95% CI, 1.38-2.29; P < 0.001), diabetes (aHR, 1.86; 95% CI, 1.53-2.27; P < 0.001), blood pressure–treated (aHR, 1.47; 95% CI, 1.06-2.05; P = 0.02), and SBP (aHR per 1 mm Hg greater, 1.01; 95% CI, 1.01-1.02; P < 0.001). Female sex (P = 0.11), race (P = 0.72), and lnTC (P = 0.68) did not have significant independent associations with the outcome.
      The AUC for this model was 0.70 (95% CI, 0.68-0.72). When eGFR, proteinuria, and CIS were added to this model, the AUC was 0.74 (95% CI, 0.72-0.77), a significant improvement (P = 0.008). In the model that included Pooled Cohort Equation variables, kidney function variables, and CIS, the significant independent predictors of ASVD-death included female sex (aHR, 0.81; 95% CI, 0.66-0.99; P = 0.04), ln(age) (aHR, 8.88; 95% CI, 5.04-15.62; P < 0.001), smoking (aHR, 1.51; 95% CI, 1.17-1.95; P = 0.002), diabetes (aHR, 1.46; 95% CI, 1.19-1.79; P < 0.001), SBP (aHR per 1 mm Hg greater, 1.01; 95% CI, 1.00-1.01; P = 0.008), ln(UACR) (aHR, 1.13; 95% CI, 1.07-1.19; P < 0.001), eGFR (aHR per 1 mL/min/1.73 m2 greater, 0.99; 95% CI, 0.98-1.00; P = 0.04), and CIS (aHR per 1 point greater, 1.05; 95% CI, 1.03-1.06; P < 0.001).

      Weighted CIS

      We used the adjusted parameter estimates from the Cox model that included just the inflammation variables (shown in Table 3) as weights, so the weighted CIS was defined as weighted CIS = 0.112([IL-6] × 10) + 0.97([TNF-α] × 10) + 0.069([fibrinogen] × 10) − 0.05([serum albumin] × 10). In univariable Cox models with the ASVD-death outcome, both the nonweighted CIS and weighted CIS had identical AUCs of 0.68 (95% CI, 0.65-0.70). When weighted CIS was used with individual risk variables that were used to calculate PCEP in a multivariable Cox model for ASVD-death, the AUC and its CI were identical to the model using CIS, and aHRs for the other covariates were nearly identical.

      Test of Cox Models in Participants Older Than 40 Years

      About 89% of participants were 40 years or older. Among participants 40 years or older, the AUC was 0.72 (95% CI, 0.70-0.75) using the model with covariates PCEP, CIS, and kidney function variables versus an AUC of 0.67 (95% CI, 0.64-0.69) using PCEP as the sole covariate, a significant difference (P = 0.001).

      Test of Cox Models Stratified by Statin Use

      Statins were used by 46% of participants. In the sample without statin use, the AUC for Cox models examining time to first ASVD-death event were 0.75 (95% CI, 0.72-0.78) in the model using CIS, kidney function variables, and PCEP and 0.70 (95% CI, 0.67-0.74) in the model using just PCEP. This difference remained significant (P < 0.05). In the sample with statin use, AUC for the Cox model using inflammation score, kidney function variables, and PCEP was 0.71 (95% CI, 0.68-0.74) versus 0.66 (95% CI, 0.62-0.69) in the model using just PCEP, a significant difference (P = 0.02).

      Discussion

      In this study, we sought to identify associations of a select panel of inflammatory biomarkers with risk for incident ASVD events and death in patients with CKD. Plasma levels of fibrinogen, IL-6, and TNF-α measured at baseline had positive independent associations with a composite of incident MI, PAD, stroke, and death, whereas serum albumin level had a negative association with the outcome. Study participants in the highest quintile of CIS had a 3.1-fold higher adjusted hazard for the composite event compared with those in the lowest quintile. The proportion of participants with incident ASVD-death showed a monotonic increase across quintiles of risk score computed using PCEP, kidney function, and CIS. Discrimination for Cox models predicting the ASVD-death outcome assessed using AUC improved significantly when kidney function and CIS were added to PCEP.
      In a systematic review of 39 studies that followed up a total of 1,371,990 participants, Tonelli et al
      • Tonelli M.
      • Wiebe N.
      • Culleton B.
      • et al.
      Chronic kidney disease and mortality risk: a systematic review.
      noted that CKD is consistently associated with increased all-cause and cardiovascular mortality. A 10–mL/min/1.73 m2 lower GFR is associated with a 19% increase in risk for ASVD.
      • Manjunath G.
      • Tighiouart H.
      • Ibrahim H.
      • et al.
      Level of kidney function as a risk factor for atherosclerotic cardiovascular outcomes in the community.
      Findings from the PROSPECT study showed that patients with CKD have higher atherosclerotic plaque burden, greater luminal encroachment, more necrotic core, and higher calcium content compared with those without CKD.
      • Baber U.
      • Stone G.W.
      • Weisz G.
      • et al.
      Coronary plaque composition, morphology, and outcomes in patients with and without chronic kidney disease presenting with acute coronary syndromes.
      Experimental studies suggest that CKD increases the potential for foam cell formation by enhancing macrophage influx into the vascular wall and inhibiting the efflux of cholesterol.
      • Ponda M.P.
      • Barash I.
      • Feig J.E.
      • Fisher E.A.
      • Skolnik E.Y.
      Moderate kidney disease inhibits atherosclerosis regression.
      • Suganuma E.
      • Zuo Y.
      • Ayabe N.
      • et al.
      Antiatherogenic effects of angiotensin receptor antagonism in mild renal dysfunction.
      In our study, after excluding the 1,316 participants with a history of CVD, 20% of participants experienced incident ASVD events during follow-up or died.
      In a prospective nested case-control study involving 244 Nurses’ Health Study participants, increased hs-CRP, IL-6, and soluble TNF receptor I and II levels were found to be significantly associated with coronary events only in those with reduced kidney function.
      • Knight E.L.
      • Rimm E.B.
      • Pai J.K.
      • et al.
      Kidney dysfunction, inflammation, and coronary events: a prospective study.
      High CRP level was also an independent risk factor for cardiovascular death in MDRD Study participants.
      • Menon V.
      • Greene T.
      • Wang X.
      • et al.
      C-Reactive protein and albumin as predictors of all-cause and cardiovascular mortality in chronic kidney disease.
      An inverse association between serum albumin level and ASVD is reported in the general population, as well as in patients with kidney disease.
      • Dhu S.
      • Kaysen G.A.
      • Yan G.
      • et al.
      Association of serum albumin and atherosclerosis in chronic hemodialysis patients.
      • Nelson J.J.
      • Liao D.
      • Sharrett A.R.
      • et al.
      Serum albumin level as a predictor of incident coronary heart disease: the Atherosclerosis Risk in Communities (ARIC) study.
      Zocalli et al
      • Zoccali C.
      • Tripepi G.
      • Mallamaci F.
      Dissecting inflammation in ESRD: do cytokines and C-reactive protein have a complementary prognostic value for mortality in dialysis patients?.
      measured CRP, IL-1β, IL-6, IL-18, and TNF-α in 217 patients with end-stage kidney disease and noted that IL-6 level captured almost entirely the death prediction power of the inflammation burden in these patients. In our study, each 1-decile greater IL-6 level was associated with a 12% increase in adjusted hazard for reaching the composite outcome. We also found that TNF-α, fibrinogen, and serum albumin levels also were associated with incident ASVD independently of IL-6 level.
      Fibrinogen, an acute-phase protein, promotes atherogenesis and thrombogenesis.
      • Kaptoge S.
      • White I.R.
      • Thompson S.G.
      • et al.
      Associations of plasma fibrinogen levels with established cardiovascular disease risk factors, inflammatory markers, and other characteristics: individual participant meta-analysis of 154,211 adults in 31 prospective studies: the fibrinogen studies collaboration.
      Experimental studies have shown that vascular endothelium and smooth muscle cells produce IL-6, which may have a procoagulant effect.
      • Stouthard J.M.
      • Levi M.
      • Hack C.E.
      • et al.
      Interleukin-6 stimulates coagulation, not fibrinolysis, in humans.
      IL-6 gene transcripts are expressed in human atherosclerotic lesions, and recombinant IL-6 injection has been reported to exacerbate atherosclerosis in mice.
      • Seino Y.
      • Ikeda U.
      • Ikeda M.
      • et al.
      Interleukin 6 gene transcripts are expressed in human atherosclerotic lesions.
      TNF-α enhances in vitro vascular calcification by promoting osteoblastic differentiation of vascular cells.
      • Tintut Y.
      • Patel J.
      • Parhami F.
      • Demer L.L.
      Tumor necrosis factor-alpha promotes in vitro calcification of vascular cells via the cAMP pathway.
      The ACC/AHA guidelines suggested that measurement of CRP may be useful in improving risk stratification of those in an intermediate CVD risk category.
      • Greenland P.
      • Alpert J.S.
      • Beller G.A.
      • et al.
      2010 ACCF/AHA guideline for assessment of cardiovascular risk in asymptomatic adults: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines.
      When IL-6, TNF-α, hs-CRP, fibrinogen, and serum albumin levels were combined as predictors in a single Cox model for the composite outcome, only hs-CRP level became nonsignificant (Table 3). Also, when hs-CRP decile is added to the Cox regression model that included ln(PCEP), ln(UACR), eGFR, and CIS, hs-CRP did not have a significant independent contribution (aHR, 1.03; 95% CI, 1.00-1.07; P = 0.08). This could be because we adjusted for other inflammatory biomarkers and the risk relationship is different in patients with CKD.
      • Sarnak M.J.
      • Levey A.S.
      • Schoolwerth A.C.
      • et al.
      Kidney disease as a risk factor for development of cardiovascular disease: a statement from the American Heart Association Councils on Kidney in Cardiovascular Disease, High Blood Pressure Research, Clinical Cardiology, and Epidemiology and Prevention.
      • Chobanian A.V.
      • Bakris G.L.
      • Black H.R.
      • et al.
      The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report.
      There is emerging interest in integrating multiple biomarkers to generate a single score to predict clinical outcomes.
      • Duncan B.B.
      • Schmidt M.I.
      • Pankow J.S.
      • et al.
      Low-grade systemic inflammation and the development of type 2 diabetes: the Atherosclerosis Risk in Communities Study.
      Cytokines act as a highly complex and coordinated network and hence it is logical to examine their combined effect on outcome measures.
      • Conen D.
      • Ridker P.M.
      • Everett B.M.
      • et al.
      A multimarker approach to assess the influence of inflammation on the incidence of atrial fibrillation in women.
      In this study, we combined deciles of 4 biomarkers that exhibited significant independent associations with the composite outcome to construct a CIS. The CIS was an independent predictor of the composite outcome after adjusting for PCEP, eGFR, and albuminuria. CIS was associated with death, PAD, and MI, but not with stroke, in the fully adjusted model. No significant association between inflammatory biomarkers and stroke was discernable in our study, perhaps due to the small number of events or the definition of stroke used that combines both ischemic stroke and intracranial hemorrhage.
      The CKD-CVD model in SHARP predicted long-term cardiovascular event risk, kidney disease progression, and survival in patients with CKD.
      • Schlackow I.
      • Kent S.
      • Herrington W.
      • et al.
      A policy model of cardiovascular disease in moderate-to-advanced chronic kidney disease.
      The Pooled Cohort Equation was derived from a more diverse population when compared to the Framingham risk score.
      • Goff Jr., D.C.
      • Lloyd-Jones D.M.
      • Bennett G.
      • et al.
      2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines.
      In adults enrolled in the REGARDS study, Muntner et al
      • Muntner P.
      • Colantonio L.D.
      • Cushman M.
      • et al.
      Validation of the atherosclerotic cardiovascular disease pooled cohort risk equations.
      noted that using the Pooled Cohort Equation, the observed and predicted 5-year atherosclerotic CVD events were similar. However, other studies have shown that the Pooled Cohort Equation overestimates CVD risk, and there is call for recalibration.
      • Pencina M.J.
      • Navar-Boggan A.M.
      • D'Agostino Sr., R.B.
      • et al.
      Application of new cholesterol guidelines to a population-based sample.
      In our study, a risk score incorporating PCEP, kidney function, and CIS showed that the proportion of individuals with the composite event increased across risk score quintiles. Discrimination is the ability of a prediction model to separate those who had outcome events from those who did not have events by assigning higher risk scores to those with events. The addition of kidney function and CIS significantly improved the AUC in our full sample and also after stratifying by age or statin use.
      Our study is important in that we show that increased levels of inflammatory biomarkers are associated with incident ASVD events and death in patients with CKD after adjusting for PCEP and measures of kidney function and when considering death as a competing risk. Other strengths of the study include analysis of a large cohort of patients with CKD with a broad range of decreased kidney function, the extent of long-term follow-up, measurement of multiple biomarkers, examining the utility of an inflammation score, and a significant number of outcome events. Limitations include the measurement of biomarkers at only one time point and lack of data about CVD mortality. Inflammation markers may be associated with higher risk for all-cause mortality other than that related to ASVD. Preliminary evidence suggests that a single baseline measurement accurately reflects an individuals' inflammatory status over time
      • Snaedal S.
      • Heimburger O.
      • Qureshi A.R.
      • et al.
      Comorbidity and acute clinical events as determinants of C-reactive protein variation in hemodialysis patients: implications for patient survival.
      and predicts mortality in patients with end-stage kidney disease and earlier stages of CKD.
      • Kimmel P.L.
      • Phillips T.M.
      • Simmens S.J.
      • et al.
      Immunologic function and survival in hemodialysis patients.
      Our findings need external validation because the deciles of inflammation biomarker in CRIC Study participants might not reflect deciles of the same biomarker in other CKD populations.
      To summarize, select inflammatory biomarkers are independently associated with a composite of incident MI, PAD, stroke, and death in patients with CKD. A composite inflammation score based on deciles of IL-6, TNF-α, fibrinogen, and reverse-coded serum albumin showed a positive and graded association with incident ASVD events and death. A risk score that incorporates PCEP, kidney function, and CIS was associated with the ASVD-death outcome. We found that inclusion of eGFR, albuminuria, and CIS improved the model discrimination beyond that achieved by PCEP. These observations should be confirmed in an independent cohort of patients with CKD.

      Article Information

      CRIC Study Investigators

      Jeffrey Fink, MD, Lawrence J. Appel, James P. Lash.

      Authors’ Full Names and Academic Degrees

      Richard L. Amdur, PhD, Harold I. Feldman, MD, MSCE, Elizabeth A. Dominic, MD, MS, Amanda H. Anderson, PhD, MPH, Srinivasan Beddhu, MD, Mahboob Rahman, MD, Myles Wolf, MD, MMSc, Muredach Reilly, MBBCH, MSCE, Akinlolu Ojo, MD, PhD, Raymond R. Townsend, MD, Alan S. Go, MD, Jiang He, MD, PhD, Dawei Xie, PhD, Sally Thompson, MA, Matthew Budoff, MD, Scott Kasner, MD, MSCE, Paul L. Kimmel, MD, John W. Kusek, PhD, and Dominic S. Raj, MD.

      Authors’ Contributions

      Research idea and study design: DSR, RLA; data acquisition: HIF, AHA, MR, MR, AO, RRT, ASG, JH, DX, ST, MB, SK, JWK; data analysis/interpretation: RLA, DSR, PLK, EAD, MW, SB; statistical analysis: RLA; supervision or mentorship: DSR. Each author contributed important intellectual content during manuscript drafting or revision and accepts accountability for the overall work by ensuring that questions pertaining to the accuracy or integrity of any portion of the work are appropriately investigated and resolved.

      Support

      Dr Raj is supported by R01 DK073665-01A1 , 1U01DK099914-01 and 1U01DK099924-01 from the National Institutes of Health (NIH). Funding for the CRIC Study was obtained under a cooperative agreement from the National Institute of Diabetes and Digestive and Kidney Diseases ( U01DK060990 , U01DK060984 , U01DK061022 , U01DK061021 , U01DK061028 , U01DK060980 , U01DK060963 , and U01DK060902 ). In addition, this work was supported in part by the Perelman School of Medicine at the University of Pennsylvania Clinical and Translational Science Award (CTSA) NIH/National Center for Advancing Translational Sciences (NCATS) UL1TR000003 , Johns Hopkins University UL1 TR-000424 , University of Maryland General Clinical Research Center M01 RR-16500 , Clinical and Translational Science Collaborative of Cleveland , UL1TR000439 from the NCATS component of the NIH and NIH Roadmap for Medical Research, Michigan Institute for Clinical and Health Research UL1TR000433 , University of Illinois at Chicago CTSA UL1RR029879 , Tulane COBRE for Clinical and Translational Research in Cardiometabolic Diseases P20 GM109036 , Kaiser Permanente NIH / National Center for Research Resources UCSF-CTSI UL1 RR-024131 . The funders did not have a role in study design, data collection, analysis, reporting, or the decision to submit for publication.

      Financial Disclosure

      The authors declare that they have no other relevant financial interests.

      Peer Review

      Received April 2, 2018. Evaluated by 4 external peer reviewers and a statistician, with editorial input from an Acting Editor-in-Chief (Editorial Board Member Kerri Cavanaugh, MD, MHS). Accepted in revised form September 18, 2018. The involvement of an Acting Editor-in-Chief to handle the peer-review and decision-making processes was to comply with AJKD’s procedures for potential conflicts of interest for editors, described in the Information for Authors & Journal Policies.

      Supplementary Material

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