Multiple atherosclerosis-related biomarkers associated with short- and long-term mortality after stroke

Clinical Biochemistry 45 (2012) 1308–1315

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Multiple atherosclerosis-related biomarkers associated with short- and long-term mortality after stroke Cangel Pui-yee Chan a, Hui-lin Jiang b, Ling-yan Leung a, Wai-man Wan c, Nga-man Cheng c, Wai-sze Ip c, Kwan-yee Cheung c, Rebecca Wing-yan Chan d, Lawrence Ka-sing Wong e, Colin Alexander Graham a, Reinhard Renneberg c, Timothy Hudson Rainer a,⁎ a

Accident and Emergency Medicine Academic Unit, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong Special Administrative Region Department of Emergency, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China c Department of Chemistry, The Hong Kong University of Science and Technology, Hong Kong Special Administrative Region d Department of Chemical Pathology, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong Special Administrative Region e Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong Special Administrative Region b

a r t i c l e

i n f o

Article history: Received 2 April 2012 Received in revised form 24 May 2012 Accepted 10 June 2012 Available online 19 June 2012 Keywords: High-sensitivity C-reactive protein (hs-CRP) Lipocalin-2 (LCN2) Matrix metalloproteinase 9 (MMP9) Mortality Myeloperoxidase (MPO) Stroke

a b s t r a c t Objectives: We investigated the relationships of biomarkers of various pathophysiologic pathways including high-sensitivity C-reactive protein (hs-CRP), lipocalin-2 (LCN2), myeloperoxidase (MPO) and matrix metalloproteinases 9 (MMP9) with mortality in stroke patients. Design and methods: hs-CRP, LCN2 and MPO concentrations in 92 patients were determined using enzyme-linked immunosorbent assays. MMP9 mRNA concentrations were determined using real-time quantitative reverse transcription-polymerase chain reaction. Results: Twelve patients (13.0%) died at 6 months and 34 patients (37.0%) died at 5 years. The independent predictors for 6-month mortality were hs-CRP (adjusted OR = 16.0) and LCN2 (adjusted OR = 16.9), while for 5-year mortality was hs-CRP (adjusted OR = 5.56). For patients with hs-CRP > 3.4 mg/L, an increase in LCN2 was associated with 2.5-fold higher 6-month mortality, while an increase in normalized MMP9 mRNA was associated with 5.8-fold higher 6-month and 1.5-fold higher 5-year mortality. Conclusion: hs-CRP was the most significant independent predictor of both short- and long-term mortality after stroke, with LCN2 and MMP9 mRNA each adding further to the risk stratification. © 2012 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

1. Introduction Inflammation plays an important role in atherosclerosis and its clinical sequelae, including myocardial infarction and stroke [1]. Growing evidence suggests that inflammatory biomarkers predict adverse events in coronary artery disease [2]. The role of elevated concentrations of these biomarkers in predicting adverse events after stroke is less clear. Increasing evidence indicates that inflammation is involved in the pathogenesis of stroke [3]. Cessation of cerebral blood flow leads to neuronal cell death which triggers an immune response ultimately leading to inflammatory cell activation and infiltration [3]. Once activated, inflammatory cells release a variety of proteins including high-sensitivity C-reactive protein (hs-CRP), lipocalin-2 (LCN2), matrix metalloproteinase 9 (MMP9), and myeloperoxidase (MPO). hs-CRP is expressed in smooth muscle cells of atherosclerotic arteries and has been implicated in multiple aspects of atherogenesis ⁎ Corresponding author at: Accident and Emergency Medicine Academic Unit, 2/F, Main Clinical Block and Trauma Centre, Prince of Wales Hospital, Shatin, NT, Hong Kong Special Administrative Region. Fax: + 852 2648 1469. E-mail address: [email protected] (T.H. Rainer).

and plaque destabilization [4–6]. It has been found to be an independent predictor of cardiovascular events and of all-cause mortality [7]. MMP9 plays a key role in proteolytic activity in the plaque, thus contributing to collagen degradation, plaque instability and risk of rupture. High MMP9 concentrations are independently associated with adverse cardiovascular outcomes [8]. It also plays a significant role in stroke [9,10] and is one of the highly expressed genes in leucocytes after stroke [11]. We have demonstrated that MMP9 mRNA concentration was associated with disease severity in stroke patients, and was a predictor of poor outcome and mortality [12]. LCN2, also known as neutrophil gelatinase‐associated lipocalin (NGAL), inhibits MMP9 inactivation leading to enhanced proteolytic activity with prolonged effects on collagen degradation [13]. Elevated LCN2 concentrations have been detected in patients suffering from acute cerebral ischemia [14–16]. Its concentration in plasma increased in the acute phase of cerebral ischemia and during follow-up, indicating persistent and increasing leukocyte activation after the acute phase. MPO plays a causal role in plaque destabilization [17]. It is a strong independent predictor of increased risk for subsequent cardiovascular events at 6 months in patients with acute coronary syndrome (ACS) [18]. MPO can generate potent oxidants and contribute to additional

0009-9120/$ – see front matter © 2012 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved. doi:10.1016/j.clinbiochem.2012.06.014

C.P. Chan et al. / Clinical Biochemistry 45 (2012) 1308–1315

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2. Materials and methods

50 μL of monoclonal detector antibodies conjugated with horseradish peroxidases (HyTest Ltd., Finland) were added and incubated together with 50 μL of samples or standards containing hs-CRP for 30 min. After removal of the unbound conjugates and proteins by a washing step, 100 μL of substrate mixture (peroxidase EIA substrate kit, Bio-Rad Laboratories, USA) containing 3,3′,5,5′-tetramethylbenzidine (TMB) and hydrogen peroxide (H2O2) was added to each well. The enzyme reaction was stopped at 15 min with 50 μL of 2 M sulfuric acid, and the absorbance at 450 nm was measured using a microtiter plate reader (FLUOstar OPTIMA, BMG Labtech GmbH, Germany). The detection limit of the assay was 2.0 μg/L, and intra- and inter-assay coefficients of variation were 4.3% and 6.9% respectively.

2.1. Participants

2.4. Enzyme-linked immunosorbent assay for LCN2

Eligible patients 18 years of age and older presenting to the Emergency Department of the Prince of Wales Hospital in Hong Kong within 24 h of onset of symptoms and with either hemorrhagic or ischemic stroke confirmed by computed tomography (CT) scan and/or magnetic resonance imaging (MRI) were recruited into the study. Exclusion criteria included trauma, meningitis, encephalitis or other sepsis, hypertensive encephalopathy, intracranial tumor, seizures with persistent neurologic signs (Todd paralysis), Bell palsy, migraine, metabolic disturbances (e.g., hypo- and hyper-glycemia), post-cardiac arrest, endocrine disorders (e.g., myxedema), renal failure, psychiatric syndromes, or shock with hypoperfusion. Patients were also excluded if the time from symptom onset to blood taking was over 24 h. Ethical approval was obtained from the local Institutional Review Board and written consent was obtained from all patients or the closest available relatives. Stroke was defined as the acute occurrence of focal neurologic signs lasting for more than 24 h in a different neuroanatomical location from that of any previous stroke, or worsening of an existing deficit that lasted for more than 1 week or more than 24 h if accompanied by a new lesion on neuroimaging [24].

LCN2 concentrations in plasma were determined using a sandwich ELISA. The monoclonal capture antibodies (Bio-Way Co. Ltd., Guangzhou) were coated on 96-well microtiter plates in 0.1 mol/L carbonate buffer, pH 9.4 at 4 °C overnight. All further steps were performed at room temperature in PBT. Between each step, the plate was washed 5 times with PBT. After coating and washing, 50 μL of monoclonal detector antibody conjugated with HRP (Bio-Way Co. Ltd., Guangzhou) was added and incubated together with 50 μL of samples or standards containing LCN2 for 1 h. After removal of the unbound conjugates and proteins by a washing step, 100 μL of substrate mixture containing TMB and H2O2 was added to each well. The enzyme reaction was stopped at 15 min with 50 μL of 0.5 M sulfuric acid, and the absorbance was measured at 450 nm. The detection limit of the assay was 0.5 μg/L, and intra- and inter-assay coefficients of variation were 5.6% and 6.0% respectively.

damage in cerebral ischemia [19]. Its concentration in plasma is increased in the acute phase of stroke [20,21]. Certain MPO genotypes have been associated with increased brain infarct size, poorer functional outcome and stroke susceptibility [22,23]. Yet little is known about the relationship of hs-CRP, LCN2, MPO, MMP9 mRNA and their combined roles in predicting mortality after stroke. This study was designed to assess the predictive values of multiple atherosclerosis-related biomarkers when used in addition to traditional risk factors for 6-month and 5-year mortality in patients following stroke.

2.2. MMP9 mRNA analysis Venous blood samples were taken into EDTA tubes via a direct venous puncture and were centrifuged at 4 °C. Then the buffy coats were mixed with three times Trizol® LS reagent (Invitrogen Corporation, Carlsbad, CA, USA) according to the manufacturer's recommendation. Samples were stored at −80 °C for further processing. Total RNAs were extracted and purified using an RNeasy Mini Kit (Qiagen, Valencia, CA, USA). Then the MMP9 mRNA concentration was measured by the real-time quantitative reverse transcription-polymerase chain reaction (RT-qPCR) using the EZ rTth RNA PCR reagent kit (Applied Biosystems, USA) and normalized by glyceraldehyde 3-phosphate dehydrogenase (GAPDH) mRNA. The assay for MMP9 mRNA detection was designed to be intron-spanning with the primers (sense: 5′-CCTGAGAACCAATC TCACCG-3′; antisense: 5′-GCCACCCGAGTGTAACCATA-3′) and the duallabeled fluorescent probe [5-(FAM)-AGGCAGCTGGCAGAGGAATACCT GTA-(TAMRA)-3′, where FAM is 6-carboxyfluorescein and TAMRA is 6-carboxytetramethylrhodamine]. A calibration curve for MMP9 mRNA measurement was prepared by serial dilutions of HPLC-purified singlestranded synthetic oligonucleotides specifying a 72-bp amplicon with concentrations ranging from 10 copies to 1 × 107 copies. 2.3. Enzyme-linked immunosorbent assay for hs-CRP hs-CRP concentrations in plasma were determined using a sandwich enzyme-linked immunosorbent assay (ELISA). The monoclonal capture antibodies (HyTest Ltd., Finland) were coated on 96-well microtiter plates in 0.1 mol/L carbonate buffer, pH 9.4 at 4 °C overnight. All further steps were performed at room temperature in PBT (10 mM phosphatebuffered saline, pH 7.4 and 0.05% (vol/vol) Tween-20). Between each step, the plate was washed 5 times with PBT. After coating and washing,

2.5. Enzyme-linked immunosorbent assay for MPO MPO concentrations in plasma were determined using a sandwich ELISA. The monoclonal capture antibodies (HyTest Ltd., Finland) were coated on 96-well microtiter plates in 0.1 mol/L carbonate buffer, pH 9.4 at 4 °C overnight. All further steps were performed at room temperature in PBT. Between each step, the plate was washed 5 times with PBT. After coating and washing, 50 μL of monoclonal detector antibody conjugated with HRP (HyTest Ltd., Finland) was added and incubated together with 50 μL of samples or standards containing MPO for 2 h with 500 rpm orbital shaking. After removal of the unbound conjugates and proteins by a washing step, 100 μL of substrate mixture containing TMB and H2O2 was added to each well. The enzyme reaction was stopped at 15 min with 50 μL of 0.5 M sulfuric acid, and the absorbance was measured at 450 nm. The detection limit of the assay was 0.5 μg/L, and the intra- and inter-assay coefficients of variation were 4.7% and 5.3% respectively. 2.6. Statistical analysis Data were expressed as mean ± SD unless otherwise stated, and frequencies were given as counts and percentages. Categorical variables were compared using Chi-square test and continuous variables were compared using unpaired Student t test or Mann–Whitney U test. Correlations were determined using Spearman Rank test. Multivariate logistic regression analysis was used to determine odd ratios (ORs) and 95% confidence intervals (CIs) of hs-CRP, LCN2, MMP9 mRNA and MPO for predicting 6-month and 5-year mortality rates. For prediction of 6-month mortality, the model was adjusted for neutrophil count, white blood cell count, Glasgow Coma Scale (GCS), National Institutes of Health Stroke Scale (NIHSS), and time from stroke onset to blood taking. For prediction of 5-year mortality, the model was adjusted for age, neutrophil count, white blood cell count, GCS, and NIHSS. On the basis of receiver operating curve analysis, optimal cutoff values for predicting mortality were established

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as follows: 3.4 mg/L for hs-CRP, 63.7 μg/L for LCN2, 30.6 copies/pg for MMP9 mRNA, and 47.2 μg/L for MPO. Cumulative survival curves in relation to hs-CRP, LCN2, MMP9 mRNA, MPO and different combinations were determined according to the Kaplan–Meier method with the use of log-rank tests for statistical assessment. Cox regression analysis was used to calculate unadjusted and adjusted hazard ratios (HRs) and 95% CIs for mortality rates after 5 years in relation to hsCRP, LCN2, MMP9 mRNA, MPO and different combinations. A P value of less than 0.05 was considered as statistically significant and all probabilities were two tailed. All statistical analyses were performed using MedCalc® version 11.5.1 (Mariakerke, Belgium).

Significant correlations were found between hs-CRP concentration and sex (r = 0.274, P = 0.0082), neutrophil count (r = 0.304, P = 0.0081), white blood cell count (r = 0.298, P = 0.0050), LCN2 concentration (r= 0.299, P = 0.0038), MPO concentration (r = 0.337, P = 0.0010), post-stroke modified Rankin Scale (mRS) (r = 0.234, P = 0.0245) and NIHSS (r= 0.216, P = 0.0389). There were significant correlations between LCN2 concentration and age (r = 0.255, P = 0.0143), hs-CRP concentration (r = 0.299, P = 0.0038), normalized MMP9 mRNA concentration (r=0.219, P=0.0049), MPO concentration (r=0.455, Pb 0.0001), neutrophil count (r=0.278, P=0.0156), GCS (r=−0.370, P=0.0003) and NIHSS (r=0.256, P=0.0138). MPO concentration significantly correlated with hs-CRP concentration (r= 0.337, P = 0.0010), LCN2 concentration (r= 0.455, P b 0.0001), normalized MMP9 mRNA concentration (r= 0.335, P = 0.0011), neutrophil count (r = 0.510, P b 0.0001), white blood cell count (r = 0.431, P b 0.0001), post-stroke mRS (r = 0.255, P = 0.0141) and NIHSS (r= 0.299, P = 0.0038). Normalized MMP9 mRNA concentration significantly correlated with LCN2 concentration (r= 0.219, P = 0.0049), MPO concentration (r= 0.335, P = 0.0011), neutrophil count (r= 0.563, P b 0.0001), white blood cell count (r= 0.425, P b 0.0001), GCS (r= −0.312, P = 0.0025), NIHSS (r = 0.332, P = 0.0012) and time from stroke onset (r = −0.261, P = 0.0121). The univariate and multivariate analyses are shown in Table 2. In a multivariate stepwise logistic regression analysis, hs-CRP and LCN2 remained as the main independent predictors of 6-month mortality after the model was adjusted for neutrophil count, white blood cell count, GCS, NIHSS and time from stroke onset to blood taking. hs-

3. Results Of the 92 patients recruited with a mean age of 70.7 ± 11.6 years, there were 47 males (51.1%). Hemorrhagic stroke was diagnosed in 25 (27.2%) patients and ischemic stroke in 67 (72.8%) patients. During the 6-month follow-up, there were 12 (13.0%) deaths out of 92 patients. Patients who died had a shorter time from symptom onset to blood taking, higher neutrophil count, higher white blood cell count, higher hsCRP concentration, higher lipocalin-2 concentration, higher normalized MMP9 mRNA concentration, and lower GCS (Table 1). During the 5-year follow-up, the mortality increased to 37.0% (34 out of 92). The mortality rates, as shown in Table 1, were significantly higher in older patients, patients with higher neutrophil count, higher white blood cell count, higher hs-CRP concentration, higher normalized MMP9 mRNA concentration, and lower GCS. Table 1 Patient characteristics at baseline. 6-Month follow-up

Demographic characteristics Age (year), mean ± SD Male, n (%) Time from stroke onset to blood taking (hour), mean ± SD Past medical history Smoker, n (%) Hypertension, n (%) Diabetes mellitus, n (%) Ischemic heart disease, n (%) Atrial fibrillation, n (%) Hyperlipidemia, n (%) Stroke subtypes Ischemic stroke, n (%) Hemorrhagic stroke, n (%) Biochemistry Systolic blood pressure (mm Hg), mean ± SD Diastolic blood pressure (mmHg), mean ± SD Lymphocyte count (x109/L), mean ± SD Monocyte count (×109/L), mean ± SD Neutrophil count (× 109/L), mean ± SD Platelet count (×109/L), mean ± SD White blood cell count (×109/L), mean ± SD High-sensitivity C-reactive protein, mg/L (95%CI) Lipocalin-2, μg/L (95%CI) Normalized MMP9 mRNA, copies/pg (95%CI) Myeloperoxidase, μg/L (95%CI) Clinical outcome Post-stroke-modified-Rankin-Score, n (%) 0–2 3–6 Glasgow Coma Scale, n (%) 3–8 9–12 13–15 National Institutes of Health Stroke Scale (NIHSS), n (%) ≤8 >8

5-Year follow-up

Survivors

Non-survivors

(n = 80)

(n = 12)

69.9 ± 11.1 42 (52.5) 12.1 ± 7.85

75.9 ± 13.7 5 (41.7) 7.12 ± 6.56

21 (26.3) 47 (58.8) 16 (20.0) 4 (5.0) 9 (11.3) 26 (32.5)

0 8 6 0 3 3

61 (76.3) 19 (23.7)

6 (50.0) 6 (50.0)

164 ± 31.0 86.1 ± 18.2 1.83 ± 0.75 0.59 ± 0.39 6.35 ± 3.04 244 ± 88.3 8.64 ± 3.04 2.03 (1.62–3.20) 39.1 (36.2–41.8) 13.8 (10.9–18.7) 46.4 (42.8–51.1)

170 ± 26.9 80.6 ± 18.7 1.59 ± 1.00 0.63 ± 0.39 10.7 ± 7.17 268 ± 89.5 12.3 ± 6.93 7.21 (3.86–8.81) 69.7 (40.4–94.0) 62.0 (28.5–98.5) 62.8 (42.5–71.7)

54 (67.5) 26 (32.5)

0 (0) 12 (100)

2 (2.5) 5 (6.3) 73 (91.2)

3 (25) 3 (25) 6 (50)

45 (56.3) 35 (43.7)

4 (33.3) 8 (66.7)

(0) (66.7) (50.0) (0) (25.0) (25.0)

P value

Survivors

Non-survivors

(n = 58)

(n = 34)

0.0940 0.6962 0.0396

68.1 ± 11.2 30 (51.7) 11.7 ± 8.02

75.2 ± 11.0 17 (50.0) 11.0 ± 7.62

0.0040 0.9551 0.6817

0.0986 0.8369 0.0563 0.9737 0.3902 0.8506 0.1192

16 (27.6) 34 (58.6) 12 (20.7) 2 (3.4) 6 (10.3) 20 (34.5)

5 (14.7) 21 (61.8) 10 (29.4) 2 (5.9) 6 (17.6) 9 (26.5)

0.2446 0.9389 0.4880 0.9816 0.4945 0.5714 0.8992

43 (74.1) 15 (25.9)

24 (70.6) 10 (29.4)

166 ± 29.8 87.7 ± 16.7 1.82 ± 0.69 0.57 ± 0.41 6.25 ± 2.91 248 ± 83.3 8.47 ± 2.86 1.92 (1.50–2.69) 39.3 (36.9–43.7) 12.0 (9.49–18.3) 45.6 (40.6–51.6)

163 ± 31.9 81.4 ± 20.1 1.76 ± 0.95 0.64 ± 0.34 8.15 ± 5.40 246 ± 98.1 10.2 ± 5.13 5.13 (3.41–8.19) 40.6 (32.8–64.1) 24.6 (15.5–36.5) 51.1 (43.7–62.6)

44 (75.9) 14 (24.1)

10 (29.4) 24 (70.6)

0 (0) 3 (5.2) 55 (94.8)

5 (14.7) 5 (14.7) 24 (70.6)

35 (60.3) 23 (39.7)

14 (41.2) 20 (58.8)

0.5271 0.3332 0.3259 0.7412 0.0004 0.3831 0.0021 0.0131 0.0068 0.0023 0.0862 b0.0001

0.6509 0.1091 0.7277 0.4031 0.0307 0.9174 0.0404 0.0061 0.3650 0.0111 0.2408 b 0.0001

0.0003

0.0022

0.2721

Chi-square test for categorical variables; Student t test or Mann–Whitney U test for continuous variables.

P value

0.1670

C.P. Chan et al. / Clinical Biochemistry 45 (2012) 1308–1315

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Table 2 Odd ratios of mortality at 6-month and 5-year follow-up period in relation to the baseline levels of different biomarkers. Prognostic variables

6-Month mortality

hs-CRP LCN2 MPO Normalized MMP9 mRNA

5-Year mortality

Univariate analysis

Logistic regression

Logistic regression

LogLR

OR (95% CI)

OR (95% CI)a

OR (95% CI)a

P value OR (95% CI)

OR (95% CI)b

OR (95% CI)b

P value

16.0 (1.23–210) 16.9 (1.81–158) – –

0.0343 0.0132 – –

6.98 2.04 0.39 4.41

5.56 (1.79–17.3) – – –

0.0030 – – –

8.79 (1.80–42.9) 374 (0.56–251413) 8.78 (2.36–32.6) 32.9 (0.49–2223) 3.86 (0.97–15.3) 1.58 (0.09–26.9) 12.0 (2.91–49.5) 40.3 (0.60–2725)

Univariate analysis Logistic regression Stepwise logistic regression LogLR

5.49 4.73 2.02 3.79

(2.19–13.8) (1.57–14.2) (0.86–4.78) (1.45–9.90)

(1.93–25.2) (0.32–12.8) (0.09–1.60) (1.02–19.1)

Logistic regression analysis; for the stepwise logistic regression, variables were entered into the model if P b 0.05. MMP-9 mRNA: matrix metalloproteinase 9 messenger ribonucleic acid; hs-CRP: high-sensitivity C-reactive protein; LCN2: lipocalin-2; MPO: myeloperoxidase; OR: odd ratio. a Adjusted for neutrophil count, white blood cell count, Glasgow Coma Scale (GCS), time from stroke onset to blood taking and National Institutes of Health Stroke Scale (NIHSS). b Adjusted for age, neutrophil count, white blood cell count, GCS and NIHSS.

CRP was the main independent predictor of 5-year mortality after the model was adjusted with age, neutrophil count, white blood cell count, GCS and NIHSS. Fig. 1 shows the corresponding Kaplan–Meier event-free survival curves for hs-CRP, LCN2, normalized MMP9 mRNA and MPO. Kaplan– Meier analysis demonstrated a significant increased probability of death during 5-year follow-up with hs-CRP >3.4 mg/L, LCN2 >63.7 μg/L and normalized MMP9 mRNA >30.6 copies/pg. In Cox regression analysis, both unadjusted and adjusted HRs of 6-month and 5-year mortality

A

increased significantly with hs-CRP >3.4 mg/L (Table 3). Only unadjusted HRs of 6-month and 5-year mortality increased significantly with LCN2 >63.7 μg/L and normalized MMP9 mRNA >30.6 copies/pg. Of the biomarkers, hs-CRP best stratified patients by risk, with LCN2 and normalized MMP9 mRNA each adding further to the risk stratification (Fig. 2 and Table 3). Fig. 2 shows the corresponding Kaplan–Meier event-free survival curves during 5-year follow-up for hs-CRP combined with LCN2, normalized MMP9 mRNA or MPO. For patients with hs-CRP >3.4 mg/L, increased LCN2 was associated with more than

B hs-CRP ≤3.4 mg/L (G1) LCN2 ≤63.7 μg/L (G1)

hs-CRP >3.4 mg/L (G2) LCN2 >63.7 μg/L (G2)

Log Rank P = 0.0002

Number at risk: G1 G2

Log Rank P = 0.0013

Follow-Up (Years) 53 39

49 22

47 20

43 18

43 17

42 16

C

Number at risk: G1 G2

Follow-Up (Years) 74 18

62 9

58 9

54 9

53 7

52 6

D MPO ≤47.2 μg/L (G1)

MMP9 mRNA ≤30.6 copies/pg (G1)

MPO >47.2 μg/L (G2) MMP9 mRNA >30.6 copies/pg (G2) Log Rank P = 0.0895

Log Rank P = 0.0065

Number at risk: G1 G2

67 25

56 15

Follow-Up (Years) 52 49 15 12

49 11

48 10

Number at risk: G1 G2

Follow-Up (Years) 48 44

42 29

39 28

36 25

35 25

34 24

Fig. 1. Kaplan–Meier all-cause mortality curves according to the optimal cutoff values of hs-CRP, LCN2, MMP9 mRNA and MPO.

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Table 3 Unadjusted and adjusted hazard ratios of all-cause mortality at 6-month and 5-year follow-up periods stratified by hs-CRP, LCN2, MMP9 mRNA, MPO and different combinations of biomarkers. Prognostic variables

hs-CRP ≤3.4 mg/L >3.4 mg/L LCN2 ≤63.7 μg/L >63.7 μg/L MPO ≤47.2 μg/L >47.2 μg/L Normalized MMP9 mRNA ≤30.6 copies/pg >30.6 copies/pg hs-CRP ≤ 3.4 mg/L LCN2 ≤63.7 μg/L LCN2 >63.7 μg/L hs-CRP > 3.4 mg/L LCN2 ≤63.7 μg/L LCN2 >63.7 μg/L hs-CRP ≤ 3.4 mg/L Normalized MMP9 mRNA ≤30.6 copies/pg Normalized MMP9 mRNA >30.6 copies/pg hs-CRP > 3.4 mg/L Normalized MMP9 mRNA ≤30.6 copies/pg Normalized MMP9 mRNA >30.6 copies/pg

Patients, n (%)

6-Month follow-upa

5-Year follow-upb

Mortality, n (%)

HR (95% CI)

P

Adjusted HR (95% CI)

P

Mortality, n (%)

HR (95% CI)

P

Adjusted

P

53 (57.6) 39 (42.4)

2 (3.8) 10 (25.6)

1.00 6.79 (1.50–30.8)

– 0.0134

1.00 11.4 (1.66–78.9)

– 0.0139

11 (20.8) 23 (59.0)

1.00 3.54 (1.72–7.28)

– 0.0006

1.00 3.11 (1.34–7.22)

– 0.0086

74 (80.4) 18 (19.6)

5 (6.8) 7 (38.9)

1.00 5.76 (1.84–18.0)

– 0.0028

1.00 4.83 (0.92–25.4)

– 0.0643

22 (29.7) 12 (66.7)

1.00 2.70 (1.33–5.46)

– 0.0060

1.00 1.05 (0.38–2.92)

– 0.9207

48 (52.2) 44 (47.8)

3 (6.3) 9 (20.5)

1.00 3.27 (0.89–12.0)

– 0.0753

1.00 1.04 (0.19–5.59)

– 0.9649

14 (29.2) 20 (45.5)

1.00 1.72 (0.87–3.40)

– 0.1190

1.00 0.90 (0.35–2.30)

– 0.8241

67 (72.8) 25 (27.2)

3 (4.5) 9 (36.0)

1.00 8.04 (2.19–29.5)

– 0.0018

1.00 3.53 (0.67–18.7)

– 0.1396

19 (28.4) 15 (60.0)

1.00 2.40 (1.22–4.71)

– 0.0117

1.00 1.88 (0.73–4.81)

– 0.1901

47 (51.1) 6 (6.5)

1 (2.1) 1 (16.7)

1.00 7.83 (0.50–123)

– 0.1455

1.00 1.47 (0.02–86.2)

– 0.8550

9 (19.1) 2 (33.3)

1.00 1.79 (0.39–8.22)

– 0.4563

1.00 1.01 (0.17–5.79)

– 0.9952

27 (29.3) 12 (13.1)

4 (14.8) 6 (50.0)

6.96 (0.79–61.6) 23.5 (2.86–193)

0.0826 0.0035

6.96 (0.62–78.0) 17.6 (1.49–207)

0.1175 0.0235

13 (48.1) 10 (83.3)

2.97 (1.27–6.94) 6.51 (2.61–16.2)

0.0123 0.0001

3.26 (1.31–8.13) 2.65 (0.68–10.3)

0.0118 0.1617

40 (43.5)

1 (2.5)

1.00

–

1.00

–

6 (15.0)

1.00

–

1.00

–

13 (14.1)

1 (7.7)

3.08 (0.20–48.5)

0.4268

0.55 (0.01–34.6)

0.7770

5 (38.5)

2.75 (0.84–8.96)

0.0952

2.29 (0.56–9.33)

0.2492

27 (29.3)

2 (7.4)

2.96 (0.27–32.3)

0.3751

3.14 (0.26–37.9)

0.3699

13 (48.1)

3.88 (1.48–10.2)

0.0062

3.62 (1.18–11.1)

0.0251

12 (13.1)

8 (66.7)

26.7 (3.37–211)

0.0020

18.2 (0.95–349)

0.0552

10 (83.3)

8.21 (2.96–22.8)

0.0001

5.54 (1.50–20.5)

0.0107

Cox regression analysis. a Adjusted for neutrophil count, white blood cell count, Glasgow Coma Scale (GCS), time from stroke onset to blood taking and National Institutes of Health Stroke Scale (NIHSS). b Adjusted for age, neutrophil count, white blood cell count, GCS and NIHSS.

2.5-fold higher 6-month mortality rate (adjusted HRs: 17.6 vs. 6.96) compared to those with LCN2 ≤63.7 μg/L (Table 3). For patients with hs-CRP >3.4 mg/L, increased normalized MMP9 mRNA was associated with 5.8-fold higher 6-month mortality rate (adjusted HRs: 18.2 vs. R= 3.14) and 1.5-fold higher 5-year mortality (adjusted HRs: 5.54 vs. 3.62) compared to those with normalized MMP9 mRNA ≤30.6 copies/pg. 4. Discussion The 5-year mortality rate in the present study is 37.0%, similar to that reported by Hartmann et al. (41.0%), lower than those reported by Whiting et al. (60.0%) and Kammersgaard et al. (58.4%), but higher than that reported by Putaala et al. (10.7%) [25–28]. The differences are likely to be due to different study designs and settings (e.g. differences in mean age, first-ever stroke vs. all strokes, and a community vs. a hospital setting). As expected, several baseline variables were important predictors of mortality during the 6-month and 5-year follow-up in this study. Prognostic factors, such as age, GCS at presentation, white blood cell count and neutrophil count have been shown in various studies to predict outcome in stroke patients. Similar findings were observed in the present study. On the other hand, there was a significant correlation between the time from symptom onset to blood taking and GCS at presentation. This might explain why those patients who died during the 6-month follow-up had a shorter time from symptom onset to blood taking. hs-CRP, LCN2 and MMP9 mRNA were also found to be significant predictors of mortality after stroke. However, only hs-CRP was independent of other clinical and laboratory prognostic factors in predicting both short- and long-term mortality. hs-CRP is not only an inflammatory biomarker but also performs complex modulatory functions. It participates in maintaining and enhancing inflammation in

cerebral vessels and brain injury through activation of complement cascade, initiation of leukocyte chemotaxis and expression of adhesion molecules through a positive feedback mechanism [29–31]. Besides, it induces apoptosis through a caspase-dependent mechanism [32]. The American Heart Association and the Centers for Disease Control and Prevention published a joint scientific statement in 2003 on the use of inflammatory biomarkers in clinical practice. This statement was developed after systematically reviewing the evidence of association between inflammatory biomarkers (mainly hs-CRP) and coronary artery disease. A growing number of studies have examined whether hs-CRP can predict recurrent cardiovascular disease and death in different settings. The role of hs-CRP as a biomarker during and after stroke is less extensively studied in comparison to coronary artery disease. In keeping with our results, several prospective studies have demonstrated that high hs-CRP concentration was an independent predictor of mortality in patients with stroke. Elevated hs-CRP concentration was an independent predictor of long-term mortality over a period of 2.5 years after ischemic stroke [33]. hs-CRP concentration was also an independent predictor of short-term mortality after supratentorial intracerebral hemorrhage [34]. The present data demonstrate that measurements of LCN2 and MMP9 mRNA were additive to hs-CRP for the identification of patients who were at high-risk of death after stroke. A high MMP9 concentration in stroke patients was an independent predictor of subsequent hemorrhagic complications and also an important mediator of brain edema in intracerebral hemorrhage [35,36]. By degrading the integrity of extracellular matrix, especially within the neurovascular basal lamina, MMP9 may disrupt the function of the blood–brain barrier and thus cause edema and hemorrhagic transformation. When blood MMP9 diffuses into the brain after intracerebral hemorrhage, neurovascular proteolysis may lead to the leakage of the blood–brain barrier and spread of edema beyond the

C.P. Chan et al. / Clinical Biochemistry 45 (2012) 1308–1315

A

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B hs-CRP ≤3.4 mg/L; MMP9 mRNA ≤30.6 copies/pg (G1)

hs-CRP ≤3.4 mg/L; LCN2 ≤63.7 μg/L (G1) hs-CRP ≤3.4 mg/L; LCN2 >63.7 μg/L (G2)

hs-CRP ≤3.4 mg/L; MMP9 mRNA >30.6 copies/pg (G2)

hs-CRP >3.4 mg/L; LCN2 ≤63.7 μg/L (G3)

P < 0.0001 for trend

Number at risk: G1 G2 G3 G4

47 6 27 12

44 5 18 4

Follow-Up (Years) 42 38 5 5 16 16 4 2

hs-CRP >3.4 mg/L; MMP9 mRNA ≤30.6 copies/pg (G3)

P < 0.0001 for trend

hs-CRP >3.4 mg/L; LCN2 >63.7 μg/L (G4)

38 5 15 2

38 4 14 2

Number at risk: G1 G2 G3 G4

40 13 27 12

38 11 18 4

Follow-Up (Years) 36 35 11 8 16 14 4 4

hs-CRP >3.4 mg/L; MMP9 mRNA >30.6 copies/pg (G4)

35 8 14 3

34 8 14 2

C hs-CRP ≤3.4 mg/L; MPO ≤47.2 μg/L (G1) hs-CRP ≤3.4 mg/L; MPO >47.2 μg/L (G2)

hs-CRP >3.4 mg/L; MPO >47.2 μg/L (G4) hs-CRP >3.4 mg/L; MPO ≤47.2 μg/L (G3) P = 0.0003 for trend

Number at risk: G1 G2 G3 G4

33 20 15 24

33 16 9 13

Follow-Up (Years) 31 28 16 15 8 8 12 10

28 15 7 10

28 14 6 10

Fig. 2. Overall survival stratified by different combinations of hs-CRP, LCN2, MMP9 mRNA and MPO.

confines of the initial hemorrhagic lesion. It may play an important role in the early pathogenesis of stroke to cause blood–brain barrier breakdown secondary to microvascular basal lamina proteolysis. This may ultimately contribute to neuronal injury after stroke. Smooth muscle cell-produced LCN2 is present as mono- and homomeric forms in the cytosol and in a complex with MMP9. It has a protective effect on MMP9 and thus enhances its proteolytic activity. LCN2/MMP9 over-expression was found in atherosclerotic plaques, particularly those with intramural hemorrhagic debris and central necrosis [37,38]. LCN2 was also markedly expressed in the necrotic areas and the surrounding areas of the infarcted tissues. It may be an active mediator in post-ischemic inflammation and remodeling responses [38,39]. Following acute cerebrovascular events, LCN2 concentration progressively increased and remained high for up to one year [14,15]. Its concentration measured 1 to 3 days after the acute event stratified patients according to their mortality risk over the 4-year period [16]. In this study, the concentrations of the inflammatory biomarkers showed significant correlations. These correlations indicate that there may be a link between these biomarkers of acute phase response, leukocyte activation and plaque destabilization in stroke patients, which may be associated with an unstable atherosclerotic condition that can increase mortality rates. Inflammation in patients with stroke is not only an acute phenomenon but also a chronic condition and seems to have an important contribution to disease progression and outcome. Patients who respond to stroke with marked activation of the inflammatory system may be at risk for more intense activation of pathological events. MPO, a key inflammatory enzyme secreted by activated neutrophils and macrophages, catalyzes the formation of MPO-derived

reactive species and thus may contribute to progression of atherosclerosis and destabilization of atherosclerotic plaques. However, there are very limited studies about the clinical role of MPO in patients with stroke. Elevated MPO concentrations have been found in the acute phase of stroke [20,21] and certain MPO genotypes were associated with infarct size, poorer functional outcome and stroke susceptibility [22,23]. Nevertheless, no significant change in MPO concentration was observed after recanalization in patients with occlusion of middle cerebral artery [20]. In this study, its plasma concentration was not significantly associated with either short- or long-term mortality after stroke. In contrast, high MPO concentrations significantly correlated with poor functional outcome assessed by post-stroke mRS and great severity of disease as assessed by NIHSS. There are several limitations in this study. Firstly, the number of participants was relatively small. The possibility that the lack of significant association between MPO and mortality was due to low power to detect these associations cannot be excluded. Secondly, the causes of death were not available for most of the cases. We cannot be certain that the high mortality in patients with elevated concentrations of inflammatory biomarkers was due to vascular events. The results from a previous study showed that hs-CRP concentration was associated with the risk of death but not with vascular death or future vascular events [40]. High hs-CRP concentration may be considered as an “alert” biomarker for high mortality, but the therapeutic implications of this finding remain uncertain. Thirdly, only a single biomarker measurement at admission was obtained and could be influenced by many factors. Serial measurements would be of great interest to allow better understanding

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of the differences in the kinetics of the biomarkers, as well as for effective therapeutic strategies. Measurements of these atherosclerosis-related biomarkers in stroke patients may enhance risk assessment and help to identify a high-risk group who might benefit from aggressive secondary prevention. Further studies are required to assess how the concentrations of these multiple biomarkers change across time after stroke. These studies should have larger sample sizes, involve multiple centers, and provide statistical confirmation of incremental values of these biomarkers beyond that provided by potential confounding risk factors before inflammatory biomarkers could be considered as part of the routine clinical assessment of patients with stroke. 5. Conclusions hs-CRP was the most significant independent predictor of both shortand long-term mortality in stroke patients regardless of other risk factors, with LCN2 and MMP9 mRNA each adding further to the risk stratification. Moving from basic research to clinical application requires a great deal of experimental evidence and a number of interventional clinical trials in multiple settings to support the pathophysiological role and clinical importance of a biomarker. Our findings demonstrate an initial proof-of-principle for the crucial involvement of atherosclerosis-related biomarkers in the progression of stroke, and thus may enhance risk assessment and help to identify high-risk groups for appropriate treatment in order to improve their clinical outcome. Source of funding This work was supported by a CUHK Direct Grant for research (reference number 2041314). Disclosures None. Authors' contributions CPY Chan participated in the design of the study, performed the statistical analysis, drafted the manuscript and participated in critical revision of the manuscript. HL Jiang participated in its design and collected patients' data. LY Leung participated in its design and sample preparation. WM Wan, NM Cheng, WS Ip and KY Cheung carried out the immunoassays and participated in critical revision of the manuscript. RWY Chan performed mRNA analysis. LKS Wong, CA Graham, R Renneberg and TH Rainer participated in its design, coordination and critical revision of the manuscript. All authors read and approved the final version of the manuscript. Acknowledgment We thank Miss Paulina Mak and Miss Nicole Lam for their kind assistance on patient recruitment, data entry and sample processing. References [1] Ross R, Bu DX, Hemdahl AL, Gabrielsen A, Fuxe J, Zhu C, et al. Atherosclerosis — an inflammatory disease. N Engl J Med 1999;340(2):115–26. [2] Pearson TA, Mensah GA, Alexander RW, Anderson JL, Cannon 3rd RO, Criqui M, et al. Markers of inflammation and cardiovascular disease: application to clinical and public health practice: a statement for healthcare professionals from the Centers for Disease Control and Prevention and the American Heart Association. Circulation 2003;107(3):499–511. [3] Wang Q, Tang XN, Yenari MA. The inflammatory response in stroke. J Neuroimmunol 2006;184:53–68. [4] Calabró P, Willerson JT, Yeh ET. Inflammatory cytokines stimulated C-reactive protein production by human coronary artery smooth muscle cells. Circulation 2003;108(16):1930–2.

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