Myeloperoxidase and the Risk of CKD Progression, Cardiovascular Disease, and Death in the Chronic Renal Insufficiency Cohort (CRIC) Study

Original Investigation

Myeloperoxidase and the Risk of CKD Progression, Cardiovascular Disease, and Death in the Chronic Renal Insufficiency Cohort (CRIC) Study Simon Correa,* Jessy Korina Pena-Esparragoza,* Katherine M. Scovner, Sushrut S. Waikar, and Finnian R. Mc Causland Background: Myeloperoxidase (MPO) catalyzes the formation of reactive nitrogen species and levels are elevated in patients with chronic kidney disease (CKD). Although increased oxidative stress and inflammation are associated with progression of CKD and cardiovascular disease (CVD), relationships between MPO concentration, CKD progression, CVD, and death remain unclear. Study Design: Prospective cohort. Setting & Participants: 3,872 participants from the Chronic Renal Insufficiency Cohort (CRIC) who had MPO measured at baseline. Exposure: Baseline MPO concentration. Outcomes: CKD progression (kidney transplantation, dialysis initiation, or 50% decline in baseline estimated glomerular filtration rate [eGFR] and eGFR ≤ 15 mL/min/1.73 m2), CVD (heart failure, myocardial infarction, or stroke), and death. Analytical Approach: Cox proportional hazards models. Results: In adjusted analyses, higher MPO level (per 1-SD increase in log-transformed MPO) was associated with 10% higher risk for CKD

C

progression (adjusted HR, 1.10; 95% CI, 1.011.19; P = 0.03), 12% higher risk for CVD (adjusted HR, 1.12; 95% CI, 1.03-1.22; P < 0.01), and 13% increased risk for death (adjusted HR, 1.13; 95% CI, 1.04-1.22; P < 0.01). There was evidence for effect modification of the association of MPO level with CKD progression by baseline eGFR (P interaction = 0.02), but not for CVD (P interaction = 0.2) or death (P interaction = 0.1). In stratified analyses, MPO level (per 1-SD increase in log-transformed MPO) was associated with greater risk for CKD progression among participants with eGFR > 45 mL/min/1.73 m2 (adjusted HR, 1.23; 95% CI, 1.03-1.46; P = 0.02) compared with those with eGFR ≤ 45 mL/min/ 1.73 m2 (adjusted HR, 1.10; 95% CI, 1.02-1.20; P = 0.02). The association of MPO level with CVD and death was no longer significant after adjustment for cardiac biomarkers.

Correspondence to S. Correa (scorreagaviria@ bwh.harvard.edu) *S.C. and J.K.P.-E. contributed equally to this work. Am J Kidney Dis. XX(XX):110. Published online Month XX, XXXX. doi: 10.1053/ j.ajkd.2019.09.006

© 2019 by the National Kidney Foundation, Inc.

Limitations: Potential residual confounding, lack of repeated measurements of MPO. Conclusions: Higher MPO level was associated with increased risk for CKD progression, but not with CVD and death in patients with CKD from CRIC. Whether therapies aimed at reducing MPO activity can result in improved clinical outcomes is yet to be determined.

hronic kidney disease (CKD) is a well-recognized public health problem that affects nearly 34 million (14%) adult Americans and is associated with multiple complications, including cardiovascular (CV) disease (CVD) and mortality.1,2 Oxidative stress, resulting from an imbalance between pro-oxidant and antioxidant defense mechanisms, has been associated with the progression of several diseases, including CKD.3 Patients with kidney failure and earlier stages of CKD are exposed to increased oxidative stress and inflammation, which may be related to multiple derangements, including accumulation of uremic toxins, cachexia, and other metabolic disorders.4 Activated neutrophils produce reactive mediators that contribute to an oxidative imbalance in which myeloperoxidase (MPO) plays a key role. MPO is the main enzyme involved in lipoprotein peroxidation, leading to the formation of reactive nitrogen species with microbicidal activity against parasites, bacteria, viruses, and other agents.5 MPO is a biomarker of oxidative stress that promotes nitric oxide consumption, prompts endothelial dysfunction, and elicits the generation of numerous oxidative reactants. AJKD Vol XX | Iss XX | Month 2019

Complete author and article information provided before references.

All these may contribute to the development of CVD and worsening of kidney function in patients with CKD. Prior studies have suggested an association of markers of oxidative stress with CKD progression6 and have described higher MPO activity as kidney function decreases.7 Additionally, reports in the general population have demonstrated that oxidants formed through MPO influence changes in the low-density lipoprotein cholesterol structure, stimulating progression of atherosclerosis.8,9 However, the association of MPO with CVD and death is unclear because prior reports have provided mixed results.10-12 Limitations to prior investigations assessing the relationship between MPO level and clinical events include small sample size and short follow-up time. Additionally, most studies evaluating MPO have been conducted in patients undergoing or soon to initiative maintenance dialysis, without including those with earlier stages of CKD. Therefore, our aim was to investigate the association of MPO level with CKD progression, CVD, and death in the Chronic Renal Insufficiency Cohort (CRIC) Study. 1

Original Investigation Methods Study Design and Population The CRIC Study is a large prospective multicenter cohort that enrolled ethnically and racially diverse individuals with CKD in 7 clinical centers across the United States between 2003 and 2008. It was designed to examine risk factors for CKD progression, CVD, and death among individuals with CKD and to identify high-risk subgroups. The primary eligibility criteria were age 21 to 74 years and estimated glomerular filtration rate (eGFR) of 20 to 70 mL/min/1.73 m2. Approximately half the patients had diabetes mellitus.13 Participants with polycystic kidney disease and those receiving active immunosuppression, institutionalized, or with New York Heart Association class III or IV heart failure (HF), known cirrhosis, human immunodeficiency virus (HIV) infection/AIDS, multiple myeloma, renal cancer, recent chemotherapy or immunosuppressive therapy, organ transplantation, pregnancy, or dialysis in the month before screening were excluded. Participants received clinical evaluations at baseline and at annual clinic visits and by telephone at 6-month intervals. The design and main results of CRIC have been published elsewhere.13,14 Laboratory Analysis In CRIC, blood samples were obtained at baseline, were centrifuged, and aliquots were handled as follows: 1 aliquot was shipped to the local laboratory and a second aliquot was frozen at −20oC or −80oC and sent to the CRIC Central Laboratory. Biomarker analyses were carried out at the Central Laboratory, including serum MPO, highsensitivity C-reactive protein (hsCRP), B-type natriuretic peptide (BNP), high-sensitivity troponin T (hsTnT), fibroblast growth factor 23 (FGF-23), plasma fibrinogen, and 24-hour urine total protein. eGFR was calculated using the CKD Epidemiology Collaboration (CKD-EPI) equation for the primary analysis in this study.15 Exposure The principal exposure of the present study was baseline MPO concentration (as determined using the Abbott Diagnostics Architect ci8200). Given its right-skewed distribution, MPO level was modeled continuously as a natural log–transformed variable, with effect estimates reported per standard deviation (SD) change. MPO level was also modeled categorically by quartiles and with a spline function to assess for nonlinear relationships with outcomes of interest. Study Outcomes The prespecified primary end point of the present analysis was CKD progression, defined as either: (1) initiation of kidney replacement therapy (KRT; kidney transplantation or dialysis) or (2) a 50% decline in baseline eGFR and eGFR ≤ 15 mL/min/1.73 m2. In addition, we prespecified secondary end points as follows: (1) CVD: a composite end point of congestive heart failure, myocardial infarction, or stroke, and (2) all-cause mortality. Two sensitivity analyses 2

were conducted: one that defined the primary renal end point using the CRIC eGFR formula and one more that defined CKD progression as the need for KRT or 50% decline in baseline eGFR (based on the CKD-EPI equation). Follow-up continued until the occurrence of death, withdrawal of consent, loss to follow-up, or March 2013. All clinical end points were previously adjudicated by an independent clinical events committee. Outcomes definitions are presented in Table S1. Statistical Analyses Continuous variables were described as mean ± SD or median with interquartile range (IQR), and categorical variables as proportion with percentage. Baseline characteristics according to quartiles of baseline MPO level were compared using tests for trend based on linear regression, χ 2 trend test, and the Cuzick nonparametric trend test. The correlation between MPO level and biomarkers of interest was determined through Spearman coefficients. Cumulative risk for clinical outcomes is reported at 5 years. Unadjusted and adjusted Cox proportional hazards models were fit to assess the independent association between log-transformed MPO and outcomes of interest. Model 1 included age, sex, and race/ethnicity. Model 2 (main model) included variables in model 1 plus body mass index, diabetes mellitus, hypertension, coronary artery disease (defined as prior myocardial infarction or prior coronary revascularization), peripheral vascular disease, chronic HF, hematocrit level, baseline eGFR, serum albumin level, 24-hour urine protein excretion, statin use, and number of apolipoprotein 1 (APOL1) risk variants (0 [white, not measured], 1 [0 or 1 copy risk variants], or 2 [2 copies risk variants]), as previously described.16 All models were stratified by clinic center and site. Log-rank test was used to compare the survival distribution of outcomes by MPO quartiles and test for trend was reported for categorical models of MPO. Given prior reports demonstrating the association of traditional cardiac biomarker and FGF-23 levels with adverse CV and renal outcomes,17-21 exploratory models were fit with further adjustment for biomarkers associated with CKD progression and CVD, including hsCRP (per 1 SD), BNP (per 1 SD), hsTnT (per 1 SD), and FGF-23 levels (per 1 SD). Initially, each biomarker level was individually added to model 2 to determine changes to the effect estimate for MPO with outcomes. Subsequently, all 4 biomarkers were added in a combined model. We tested for effect modification of the association between MPO level and outcomes according to baseline eGFR through inclusion of cross-product terms in the main adjusted Cox proportional hazards model (model 2) and conducted further subgroup analyses that stratified eGFR at the mean value (45 mL/min/1.73 m2). The proportional hazard assumption was checked in all models through a test of a nonzero slope based on Schoenfeld residuals, visualization of Schoenfeld residuals, and log-log plots. AJKD Vol XX | Iss XX | Month 2019

Original Investigation Table 1. Baseline Characteristics by MPO Quartiles MPO Quartile, pmol/L Characteristic Age, y Male sex Race/ethnicity White Black Hispanic BMI, kg/m2 Diabetes Hypertension CAD PVD Heart failure Anemiaa Hematocrit, % eGFR,b mL/min/1.73 m2 Serum albumin, g/dL Urine protein, mg/24 h hsTnT, pg/mL hsCRP, mg/L BNP, pg/mL FGF-23, RU/mL APOL1 risk status Not assessedc 0 or 1 copy risk variants 2 copies risk variants Statin use

Q1 (<79) (n = 968) 57.7 ± 11.3 578 (59.7%)

Q2 (79-109) (n = 968) 58.6 ± 10.4 551 (56.9%)

Q3 (109-155) (n = 968) 58.2 ± 10.9 504 (52.1%)

Q4 (>155) (n = 968) 58.3 ± 11.3 491 (50.7%)

496 (51.2%) 315 (32.5%) 157 (16.2%) 30.9 ± 6.9 416 (43.0%) 775 (80.1%) 178 (18.4%) 45 (4.6%) 55 (5.7%) 417 (43.1%) 37.8 ± 4.8 46.2 [35.8-57.5] 4.0 ± 0.5 126 [67-625] 9.8 [4.4-19.6] 1.7 [0.8-4.3] 32.7 [14.3-76.5] 125.6 [86.0-202.4]

419 (43.3%) 388 (40.1%) 161 (16.6%) 31.3 ± 7.3 445 (46.0%) 827 (85.4%) 192 (19.8%) 62 (6.4%) 80 (8.3%) 458 (47.4%) 37.7 ± 4.9 44.1 [33.9-53.9] 4.0 ± 0.4 176 [72-748] 11.2 [5.4-22.7] 2.0 [1.0-4.7] 38.5 [15.9-88.1] 136.9 [94.1-207.6]

345 (35.6%) 451 (46.6%) 172 (17.8%) 33.3 ± 8.4 533 (55.1%) 865 (89.4%) 224 (23.1%) 71 (7.3%) 111 (11.5%) 485 (50.3%) 37.2 ± 5.1 41.8 [32.6-52.7] 3.9 ± 0.5 237 [78-1,146] 13.6 [6.7-27.4] 3.3 [1.3-7.8] 49.0 [18.8-106.3] 151.7 [99.2-255.0]

355 (36.7%) 457 (47.2%) 156 (16.1%) 32.9 ± 8.3 489 (50.5%) 865 (89.4%) 248 (25.6%) 78 (8.1%) 131 (13.5%) 472 (49.0%) 37.5 ± 5.5 40.4 [31.1-51.0] 3.9 ± 0.5 228 [76-1,331] 14.4 [7.2-27.0] 3.6 [1.3-8.6] 45.0 [19.1-107.8] 167.9 [107.7-298.4]

459 (61.1%) 229 (30.5%) 63 (8.4%) 488 (50.7%)

396 (53.0%) 282 (37.8%) 69 (9.2%) 556 (57.7%)

331 (45.3%) 328 (44.9%) 71 (9.7%) 566 (59.0%)

338 (45.5%) 329 (44.3%) 76 (10.2%) 507 (52.9%)

P-Trend 0.4 <0.001 <0.001

<0.001 <0.001 <0.001 <0.001 <0.01 <0.001 <0.01 0.07 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001

0.3

Note: Values for continuous variables given as mean ± standard deviation or median [interquartile range]; for categorical variables, as count (percentage). Abbreviations. APOL1, apolipoprotein L1; BMI, body mass index; BNP, brain natriuretic peptide; CAD, coronary artery disease; eGFR, estimated glomerular filtration rate; FGF-23, fibroblast growth factor 23; hsCRP, high-sensitivity C-reactive protein; hsTnT, high-sensitivity troponin T; MPO, myeloperoxidase; PVD, peripheral vascular disease; Q, quartile. a Anemia defined as hemoglobin < 12 g/dL in women and <13 g/dL in men. b Calculated using the Chronic Kidney Disease Epidemiology Collaboration equation. c Not assessed because patients were white.

For covariates that violated the proportionality assumption, the corresponding time interaction term was included in the model. All analyses were conducted using the statistical software package Stata IC, version 14.2 (StataCorp LP), using a data set obtained from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) Data Repository. A 2-sided P < 0.05 was considered statistically significant. Ethics The CRIC Study was approved by institutional review boards at each participating center, and all participants provided written informed consent before the start of the study.

Results Baseline Characteristics MPO was measured in 3,872 participants from CRIC who were followed up for a median of 5.1 years.

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Baseline characteristics of the study population by MPO quartiles are shown in Table 1. Participants with higher baseline MPO concentrations were more likely to be women; be black; have a history of diabetes, hypertension, coronary artery disease, peripheral vascular disease, chronic HF, and anemia; and tended to have higher body mass index, troponin, hsCRP, BNP, and FGF-23 levels, 24-hour proteinuria, and greater frequency of APOL1 risk alleles. Participants with higher baseline MPO levels were more likely to have lower eGFRs and serum albumin levels (Table 1). Correlation With Other Biomarkers MPO level was weakly correlated with levels of all biomarkers, including hsCRP (r = 0.21; P < 0.001), fibrinogen (r = 0.23; P < 0.001), and FGF-23 (r = 0.16; P < 0.001). MPO level was also weakly correlated with white blood cell count (r = 0.22; P < 0.001) and neutrophil count (r = 0.26; P < 0.001; Table 2).

3

Original Investigation Association With Outcomes CKD Progression MPO level was weakly correlated with eGFR (r = −0.14; P < 0.001; Table 2). In unadjusted models, increasing MPO concentration (per every 1-SD increase in logtransformed measurement) was associated with 21% higher hazard of CKD progression (hazard ratio [HR], 1.21; 95% confidence interval [CI], 1.14-1.28; P < 0.001). Median MPO concentration among progressors was 120.8 (IQR, 87.5-174) pmol/L and was 99.2 (IQR, 73.4-141.8) pmol/L among nonprogressors. In model 1, each 1-SD greater log-transformed MPO value was associated with 20% higher hazard of CKD progression (HR, 1.20; 95% CI, 1.13-1.27; P < 0.001). After further adjustment (model 2, main model), the association remained, albeit with 10% higher hazard of CKD progression (HR, 1.10; 95% CI, 1.01-1.19; P = 0.03; Table 3). CV Disease

Median MPO level among participants who experienced a CV event was 120.8 (IQR, 86.3-177) pmol/L, and 106.1 (IQR, 77.3-149.8) pmol/L among those who did not. The hazard of congestive heart failure, myocardial infarction, or stroke increased by 21% for each 1-SD greater logtransformed MPO value (HR, 1.21; 95% CI, 1.13-1.29; P < 0.001) in unadjusted models. After controlling for demographics (model 1), each 1-SD greater logtransformed MPO value was associated with 20% higher hazard of the CV composite (HR, 1.20; 95% CI, 1.12-1.28; P < 0.001). In model 2, this association persisted (HR, 1.12; 95% CI, 1.03-1.22; P < 0.01; Table 3).

Table 2. Correlation Between Myeloperoxidase and Other Biomarkers Biomarker Estimated glomerular filtration ratea Cystatin C High-sensitivity troponin T Brain natriuretic peptide High-sensitivity C-reactive protein Fibroblast growth factor 23 Fibrinogen White blood cell count Neutrophil count

ρ −0.14 0.21 0.14 0.11 0.21 0.16 0.23 0.22 0.26

P <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001

a Calculated using the Chronic Kidney Disease Epidemiology Collaboration equation.

1.38; 95% CI, 1.08-1.76; P = 0.01), 50% increased hazard of CVD (HR, 1.50; 95% CI, 1.18-1.89; P = 0.001), and 69% higher hazard of death (HR, 1.69; 95% CI, 1.31-2.19; P < 0.001; Table 4). Monotonic relationships between MPO level, CKD progression, and CVD were confirmed on spline analysis (Fig 3). Event counts by MPO quartiles are presented in Table S2. Subgroup Analyses There was evidence of effect modification on the relationship of MPO level with CKD progression by baseline eGFR (P interaction = 0.02). Each 1-SD greater logtransformed MPO value was associated with greater risk for CKD progression among participants with higher baseline eGFR: HRs of 1.23 (95% CI, 1.03-1.46; P = 0.02) and 1.10 (95% CI, 1.02-1.20; P = 0.02) in those with eGFR > 45 versus ≤45 mL/min/1.73 m2 (Table S3). We

Death

Median MPO concentration among participants who died during follow-up was 122.3 (IQR, 88.1-178.1) pmol/L, and was 104.9 (IQR, 76.7-149.1) pmol/L among those who did not. Each 1-SD greater log-transformed MPO value was associated with 21% higher hazard of death (HR, 1.21; 95% CI, 1.14-1.29; P < 0.001) in unadjusted models. In the model adjusted for demographic covariates (model 1), each 1-SD greater log-transformed MPO value was associated with 21% higher hazard of death (HR, 1.21; 95% CI, 1.13-1.28; P < 0.001), and in model 2, the association persisted, albeit with 13% higher hazard of death (HR, 1.13; 95% CI, 1.04-1.22; P < 0.01; Table 3). Categorical Models and Splines A stepwise increase in unadjusted risk for CKD progression, death, and CVD was observed for increasing quartiles of MPO (Figs 1 and 2). In the main multivariable model (model 2), we found evidence for a linear association of increasing MPO concentration quartile with CKD progression (P trend = 0.01), CVD (P trend < 0.01), and death (P trend < 0.001). Compared with MPO in the lowestconcentration quartile, MPO in the top quartile was associated with 38% increased hazard of CKD progression (HR, 4

Table 3. Unadjusted and Adjusted Hazard of Outcomes by MPO Concentration Model CKD progression Unadjusted Model 1a Model 2b CHF, MI, stroke composite Unadjusted Model 1 Model 2 Death Unadjusted Model 1 Model 2

HR (95% CI)

P

1.21 (1.14-1.28) 1.20 (1.13-1.27) 1.10 (1.01-1.19)

<0.001 <0.001 0.03

1.21 (1.13-1.29) 1.20 (1.12-1.28) 1.12 (1.03-1.22)

<0.001 <0.001 <0.01

1.21 (1.14-1.29) 1.21 (1.13-1.28) 1.13 (1.04-1.22)

<0.001 <0.001 <0.01

Note: HR expressed per 1–standard deviation greater log-transformed MPO level. Model 1 adjusted for age, sex, and race/ethnicity. Model 2 (main model) adjusted for age, sex, race/ethnicity, body mass index, diabetes mellitus, hypertension, coronary artery disease, peripheral vascular disease, chronic heart failure, hematocrit, eGFR (calculated using the Chronic Kidney Disease Epidemiology Collaboration equation), 24-hour urine protein excretion, serum albumin level, apolipoprotein L1 risk status, and statin use. Abbreviations: CHF, congestive heart failure; CI, confidence interval; CKD, chronic kidney disease; eGFR, estimated glomerular filtration rate; HR, hazard ratio; MI, myocardial infarction; MPO, myeloperoxidase.; a Age and race included as time-varying covariates. b eGFR and 24-hour urine protein excretion included as time-varying covariates.

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Original Investigation 35%

5-year risk of clinical event

30% 25% Quarle 1

20%

Quarle 2 Quarle 3

15%

Quarle 4

10% 5% 0% CKD progression

Death

CHF, MI or stroke

CKD Progression

Death

CHF, MI or Stroke

Quarle 1

17.2%

7.4%

13.6%

Quarle 2

21.0%

10.6%

15.8%

Quarle 3

26.3%

13.9%

19.2%

Quarle 4

31.1%

17.8%

24.2%

Figure 1. Five-year cumulative risk for clinical outcomes by myeloperoxidase (MPO) quartile. Abbreviations: CKD, chronic kidney disease; CHF, congestive heart failure; MI, myocardial infarction.

found no evidence for interaction between eGFR and MPO level for the hazard of CVD (P interaction = 0.2) and death (P interaction = 0.1). Effect estimates for subgroups are reported in Table S3. Sensitivity Analysis With the CRIC Formula for the Renal Outcome In sensitivity analysis that used the CRIC eGFR formula for the renal end point, associations between MPO level and CKD progression were consistent with results from models using the CKD-EPI equation (Table S4). Similarly, the direction of the effect was consistent in sensitivity analysis that defined CKD progression as the need for KRT or 50% decline in baseline eGFR (Table S5). Exploratory Models The addition of each biomarker separately to model 2 attenuated the association of MPO level with all outcomes to a similar extent, though generally the associations remained statistically significant. However, when all biomarkers were included in the same model, the resultant attenuation of the association of MPO level with outcomes was greater. Although a similar direction of the effect estimates remained (ie, higher risk per unit increase in MPO), only the association with CKD progression was of borderline statistical significance (Table 5).

Discussion In this analysis of 3,872 patients with CKD from CRIC, we found that MPO level was significantly associated with CKD progression, independent of traditional risk factors AJKD Vol XX | Iss XX | Month 2019

and other inflammatory biomarker levels. Also, we have demonstrated that MPO level is associated with higher risk for adverse CV outcomes and death, but these associations were attenuated after adjustment for established CV biomarkers. These findings add strength to previous smaller studies in which the relationship between level of MPO, a biomarker of oxidative stress, and progression of CKD has been suggested.6,22 When the production of pro-oxidants increases or there is an inadequate elimination of reactive oxygen species, the redox homeostasis of the body may decompensate, predisposing to oxidative stress.6 First, innate immune system cells recognize pathogen- or damage-associated molecular patterns, which serve as a stimulus for neutrophil activation, adhesion, and migration. Activated neutrophils release granular proteins, including MPO, an enzyme that catalyzes the reaction of hydrogen peroxide and chloride, precipitating the production of redox-active compounds.23,24 These end products have microbicidal properties and participate in immune responses and alter the bioavailability of nitric oxide, disrupt vascular tone, and promote apoptosis and necrosis, which may predispose to accelerated atherosclerosis and CVD in patients with CKD25-27 and may lead to progression of kidney disease. Prior studies have demonstrated an association of biomarkers reflecting oxidative stress with CKD progression, including superoxide dismutase, glutathione peroxidase, and catalase.6,22,28 Also, animal models have shown that MPO-deficient mice develop less albuminuria, glomerular injury, and renal inflammation than wild-type mice.29 However, the relationship between MPO level and CKD progression in humans seems less clear. It is known that patients with CKD are exposed to both pro-oxidants and a proinflammatory state, which contributes to frequent complications of CKD, such as CVD,30 and may contribute to CKD progression. However, detailed studies with longitudinal assessment of renal outcomes are limited. Our current study adds to the knowledge base in this area. We report a negative correlation between MPO level and eGFR, which is consistent with smaller studies that have found MPO activity to increase with worsening CKD stage7 and serum MPO level to be increased in patients undergoing or soon to initiate maintenance dialysis compared with healthy volunteers.31 More importantly, we found MPO level to be associated with risk for CKD progression independent of baseline eGFR, suggesting that MPO level may not simply be a marker of decreased kidney function, but also an independent predictor of adverse outcomes. Importantly, these findings were consistent in a sensitivity analysis that used the CRIC eGFR formula. Moreover, there was evidence for effect modification according to baseline eGFR, providing further support that MPO level is not just a marker of kidney function that parallels eGFR. Specifically, our finding that MPO level is more predictive of CKD progression in patients with higher baseline eGFRs (>45 mL/min/1.73 m2) suggests that the role of MPO and oxidative stress in CKD progression may be more 5

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Figure 2. Event-free survival for: (A) chronic kidney disease (CKD) progression; (B) congestive heart failure, myocardial infarction, or stroke; and (C) death, by myeloperoxidase quartile (Q). Abbreviation: CVD, cardiovascular disease.

significant at earlier CKD stages. Although it is likely that the accumulation of other risk factors with progressive CKD outweighs the association of MPO level with adverse outcomes, it is also possible that preventive measures in these earlier stages may be more efficacious than at later stages of CKD. Smaller studies have evaluated the relationship between MPO level, CV outcomes, and death. For instance, in patients with systolic HF, MPO levels were elevated in comparison to healthy controls, and MPO level was an independent predictor of 1-year mortality. In the SPARCL trial, MPO level was associated with higher risk for recurrent stroke10 and with higher risk for death in a cohort of 363 frail elders.11 Despite these associations, an analysis from the ARIC Study failed to demonstrate an association between intracellular MPO level, incident coronary heart disease, HF, and all-cause mortality.12 We found increased risk for CV events and death with higher MPO levels in our analyses of CRIC participants. However, these associations lost significance after adjustment for

traditional cardiac biomarkers, perhaps suggesting that other risk factors outweigh the association of MPO level or oxidative stress in CV outcomes in such individuals. MPO is a marker that reflects both oxidative stress and inflammation, which seem to contribute to kidney disease progression. Although the relationships between oxidative stress and inflammation are interconnected, the mechanisms leading to CKD progression are likely complex. A clear relationship between inflammation and CKD progression has been described,32-35 yet the independent contribution of oxidative stress remains questionable.36 Interestingly, we found MPO concentration to be only weakly correlated with traditional markers of inflammation, such as hsCRP and fibrinogen levels. These findings, along with the independent association of MPO level and CKD progression, even in a model that included hsCRP level alone and all cardiac biomarker levels, may indicate that oxidative stress and MPO play a role in the progression of kidney disease that is independent of inflammation. Perhaps there is a common trigger for inflammation and

Table 4. Adjusted Hazard of Outcomes by MPO Concentration Quartiles HR (95% CI) by MPO Quartile Outcome CKD progressiona CHF, MI, or strokeb Death

Q1 1.00 (reference) 1.00 (reference) 1.00 (reference)

Q2 1.17 (0.91-1.49) 1.11 (0.87-1.41) 1.23 (0.95-1.60)

Q3 1.16 (0.91-1.48) 0.86 (0.68-1.10) 1.25 (0.96-1.62)

Q4 1.38 (1.08-1.76) 1.50 (1.18-1.89) 1.70 (1.31-2.19)

P-Trend 0.01 <0.01 <0.001

Note: Models adjusted for age, sex, race/ethnicity, body mass index, diabetes mellitus, hypertension, coronary artery disease, peripheral vascular disease, chronic heart failure, hematocrit, eGFR (calculated using the Chronic Kidney Disease Epidemiology Collaboration equation), 24-hour urine protein excretion, serum albumin level, apolipoprotein L1 risk status, and statin use. Abbreviations: CHF, congestive heart failure; CI, confidence interval; CKD, chronic kidney disease; eGFR, estimated glomerular filtration rate; HR, hazard ratio; MI, myocardial infarction; MPO, myeloperoxidase; Q, quartile. a eGFR and 24-hour urine protein excretion included as time-varying covariates. b Coronary artery disease included as a time-varying covariate.

6

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Original Investigation B

C

0

50

100 150 200 Myeloperoxidase (pmol/L)

250

2

Death HRadj 1 0

0

0

CHF, MI or stroke HRadj 1

CKD progression HRadj 1

2

2

A

0

50

100 150 200 Myeloperoxidase (pmol/L)

250

0

50

100 150 200 Myeloperoxidase (pmol/L)

250

Figure 3. Restricted cubic spline for the relationship between myeloperoxidase and (A) chronic kidney disease (CKD) progression; (B) congestive heart failure (CHF), myocardial infarction (MI), or stroke; and (C) death. Adjusted for age, sex, race/ethnicity, body mass index, diabetes mellitus, hypertension, coronary artery disease, peripheral vascular disease, chronic heart failure, hematocrit, estimated glomerular filtration rate (calculated using the CKD Epidemiology Collaboration equation), 24-hour urine protein, serum albumin level, apolipoprotein L1 risk status, and statin use.

oxidation, but at some point, these pathways diverge, contributing independently to CKD progression. In this regard, therapies targeting the inflammatory response that also have an upstream effect in oxidative stress may provide benefits in patients with CKD. For instance, interleukin 1β inhibition with canakinumab provided a significant reduction in risk for recurrent adverse CV events in patients with a persistent inflammatory response (defined as hsCRP ≥ 2 mg/L)37 and in patients with CKD38 in CANTOS. However, the effects of these therapies on progression of kidney disease remain unknown. Also, the nuclear factor-erythroid-2-related factor 2 (Nrf-2)-Kelch-like ECH-associated protein 1 (Keap1) system is an important pathway in regulating antioxidant mechanisms.39 To this end, therapies that target the Nrf-2 pathway may represent therapeutic targets for select individuals, although CV concerns have been raised in some reports.40,41 Recent studies have pointed out that cardiac biomarkers may be useful in prognosticating adverse renal outcomes. In an analysis from the PREVEND study, hsTnT level was associated with incident moderately increased albuminuria,17 and in a nested case-control study from the ADVANCE trial, NT-pro BNP (N-terminal pro–BNP) and hsTnT levels were significantly associated with diabetic nephropathy and retinopathy.20 More recent data in patients with type 2 diabetes mellitus comes from an analysis by Zelniker et al21 in the SAVOR-TIMI 53 trial. In this analysis hsTnT, NT-pro–BNP, and hsCRP levels were associated with several renal end points, including a >40% decrease in eGFR, worsening urinary albumin-creatinine ratio category, and the composite of need for KRT or serum creatinine level > 6.0 mg/dL.21 Similarly, FGF-23 level has been associated with adverse cardiac and renal outcomes, including prior analyses from CRIC.18,19 Interactions between the kidney and heart are complex and bidirectional.42 The pathophysiologic pathways underlying these associations AJKD Vol XX | Iss XX | Month 2019

may include dysfunction in neurohormonal axes, hemodynamic alterations, pump and endothelial dysfunction, and inflammation.21,43 Based on the current evidence, we fit models that adjusted for cardiac biomarkers and demonstrated consistency with our main findings for CKD progression. Importantly, the associations of MPO level with adverse outcomes were independent of hsCRP level, supporting the notion that oxidative stress may play a role in such outcomes that is additional to that of inflammation alone. The present study explores the association between MPO level, CKD progression, CVD, and all-cause mortality in patients with CKD and adds to prior reports that have suggested the contribution of oxidative stress to CKD progression. The present work builds upon CRIC, a multicenter observational cohort study of CKD. Strengths of our study include a large sample size, adjudicated end points, a diverse CKD sample of varying levels of kidney function with racial and ethnic diversity, collection of contemporary biomarkers, and median follow-up of 5.1 years. Limitations to our analyses should be acknowledged, including the potential for residual confounding, lack of repeated measurements of MPO and neutrophil MPO activity, and limited representation of earlier stages of CKD. Last, there is not a validated threshold for MPO level; therefore, we conducted our analyses based on a statistically driven approach. In conclusion, we found that MPO level is independently associated with risk for CKD progression in individuals with varying degrees of baseline CKD. Although there is a similar direction of association of MPO level with adverse CV outcomes and death, this magnitude is attenuated on adjustment for an array of cardiac biomarkers. Further research to elucidate the causal pathways underlying such associations are necessary and may pave the way for interventions targeting reduction of inflammation and oxidative stress, particularly at earlier stages of CKD. 7

8

Note: HR expressed per 1-SD greater log-transformed MPO; values in parentheses are 95% confidence intervals. Model 2 adjusted for age, sex, race/ethnicity, body mass index, diabetes mellitus, hypertension, coronary artery disease, peripheral vascular disease, chronic heart failure, hematocrit, eGFR (calculated using the Chronic Kidney Disease Epidemiology Collaboration equation), 24-hour urine protein excretion, serum albumin level, apolipoprotein L1 risk status, and statin use plus the biomarker of interest noted in the column heading (log-transformed MPO [per 1 SD], log-transformed hsCRP [per 1 SD], log-transformed BNP [per 1 SD], log-transformed hsTnT [per 1 SD], or logtransformed FGF-23 [per 1 SD] or all biomarkers). Abbreviations: BNP, brain natriuretic peptide; CHF, congestive heart failure; CKD, chronic kidney disease; eGFR, estimated glomerular filtration rate; FGF-23, fibroblast growth factor 23; HR, hazard ratio; hsCRP, high-sensitivity C-reactive protein; hsTnT, high-sensitivity troponin T; MI, myocardial infarction; MPO, myeloperoxidase; SD, standard deviation. a hsCRP, hsTnT, BNP, and FGF-23. b eGFR and 24-hour urine protein excretion included as time-varying covariates. c eGFR, 24-hour urine protein, and log-transformed BNP included as time-varying covariates.

P 0.02 0.03 0.02 + BNP 1.10 (1.02-1.20)c 1.09 (1.01-1.19) 1.10 (1.01-1.19) P 0.06 0.03 0.01 + hsTnT 1.09 (1.00-1.19)b 1.10 (1.01-1.19) 1.11 (1.02-1.21) P 0.02 0.02 0.02 + hsCRP 1.10 (1.01-1.20)b 1.11 (1.02-1.20) 1.10 (1.02-1.20) Outcome CKD progression CHF, MI, or stroke Death

HRs and P for Addition of Biomarkers to Model 2

Table 5. Effect Estimates for the Association of MPO With Clinical Events After the Addition of Cardiac Biomarkers

+ FGF-23 1.09 (1.00-1.18)b 1.11 (1.02-1.21) 1.09 (1.01-1.19)

P 0.05 0.02 0.03

+ All Biomarkersa 1.09 (1.00-1.19)c 1.06 (0.97-1.15) 1.05 (0.97-1.15)

P 0.05 0.2 0.2

Original Investigation Supplementary Material Supplementary File (PDF) Table S1: End point definitions. Table S2: Event counts by MPO quartile. Table S3: Adjusted hazard of adverse outcomes by MPO concentration stratified by baseline eGFR (subgroup analyses). Table S4: Adjusted hazard of CKD progression by MPO concentration using the CRIC eGFR formula for ascertainment of the outcome (sensitivity analysis). Table S5: Adjusted hazard of CKD progression defined as need for KRT or eGFR fall of >50% by MPO concentration (sensitivity analysis).

Article Information Authors’ Full Names and Academic Degrees: Simon Correa, MD, MMSc, Jessy Korina Pena-Esparragoza, MD, Katherine M. Scovner, MD, Sushrut S. Waikar, MD, MPH, and Finnian R. Mc Causland, MBBCh, MMSc Authors’ Affiliations: Division of Renal Medicine, Brigham and Women’s Hospital (SC, KMS, SSW, FMC); Harvard Medical School, Boston, MA (SC, KMS, SSW, FMC); Nephrology Section, Hospital Universitario Príncipe de Asturias, Alcal a de Henares, Madrid, Spain (JKP-E); and Renal Section, Department of Medicine, Boston University Medical Center, Boston, MA (SSW). Address for Correspondence: Simon Correa, MD, MMSc, Division of Renal Medicine, Department of Medicine, Brigham and Women’s Hospital, 75 Francis St, Medical Research Bldg, Ste 416, Boston, MA 02115. E-mail: [email protected] Authors’ Contributions: Research idea and study design: FMC, SC; data acquisition: FMC; data analysis/interpretation: all authors; statistical analysis: SC, FMC, JKP-E; supervision or mentorship: FMC. SC and JKP-E contributed equally to this work. 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 Mc Causland is supported by NIDDK grant K23DK102511. Financial Disclosure: The authors declare that they have no relevant financial interests. Disclaimer: The CRIC Study was conducted by the CRIC Investigators and supported by the NIDDK. The data from the CRIC Study reported here were supplied by the NIDDK Central Repositories. This manuscript was not prepared in collaboration with investigators of the CRIC Study and does not necessarily reflect the opinions or views of the CRIC Investigators, the NIDDK Central Repositories, or the NIDDK. Prior Presentation: Aspects of this work were presented at the 56th ERA-EDTA Congress; June 13-16, 2019; Budapest, Hungary. Peer Review: Received May 2, 2019. Evaluated by 2 external peer reviewers, with direct editorial input from a Statistics/Methods Editor and an Associate Editor, who served as Acting Editor-in-Chief. Accepted in revised form September 7, 2019. The involvement of an Acting Editor-in-Chief was to comply with AJKD’s procedures for potential conflicts of interest for editors, described in the Information for Authors & Journal Policies.

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