Figures
Abstract
Background
Monocyte chemoattractant protein-1 (MCP-1) is an important chemokine at multiple phases of atherosclerosis in animals, but human studies are few and inconsistent. The aim of this study is to investigate the association of serum MCP-1with all-cause and cardiovascular disease (CVD) mortality among coronary artery disease (CAD) patients and determine whether this biomarker can add secondary prognostic value to standard risk predictors.
Methods
MCP-1 was measured at baseline in 1411 CAD patients who were 40–85 years of age. Cox proportional hazards regression models were used to estimate the association of MCP-1 levels with death risk.
Results
During a median follow-up of 3.3 years, 117 deaths were recorded, 88 of which were due to CVD. The multivariable-adjusted hazard ratios across tertiles of MCP-1 were 1.51 (95% confidence intervals [CI] 0.89–2.58), 1.00, and 2.11 (95% CI 1.31–3.40) for all-cause mortality, and 1.50 (95% CI 0.80–2.81), 1.00, and 2.21 (95% CI 1.27–3.87) for CVD mortality. The addition of serum MCP-1 to the fully adjusted model increased the C-index by 0.009 (p<0.0001) for all-cause mortality and 0.008 (p<0.0001) for CVD mortality and significantly improved the predictive ability by 12.1% (P = 0.006) on all-cause mortality and 12.6% (P = 0.003) on CVD mortality using the net reclassification improvement method.
Citation: Ding D, Su D, Li X, Li Z, Wang Y, Qiu J, et al. (2015) Serum Levels of Monocyte Chemoattractant Protein-1 and All-Cause and Cardiovascular Mortality among Patients with Coronary Artery Disease. PLoS ONE 10(3): e0120633. https://doi.org/10.1371/journal.pone.0120633
Academic Editor: Christian Herder, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Duesseldorf, GERMANY
Received: July 22, 2014; Accepted: January 27, 2015; Published: March 18, 2015
Copyright: © 2015 Ding et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Data Availability: All relevant data are within the paper and its Supporting Information files.
Funding: This work was supported by the Key Project of National Natural Science Fund [grant number 81130052], China. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Cardiovascular disease (CVD) is the leading cause of death and disability in China and worldwide, and it is projected by the World Health Organization to be the greatest cause of death in the world within the next 15 years [1]. Since coronary artery disease (CAD) patients are at high risk of recurrent cardiovascular events and premature deaths, more attention is needed on the risk prediction and secondary prevention of CAD patients.
It is widely accepted that inflammation plays a crucial role in both initiation and progression of CAD, and a lot of immune cells and chemokines are involved in the inflammatory pathway. C-reactive protein (CRP) and IL-6 have been widely studied as effective biomarkers and independent risk factors for CAD [2]. Although CRP is a reliable marker of inflammation, several other inflammatory mediators are critically involved in the pathogenesis of CAD and may serve as potential biomarkers providing additional prognostic information, i.e. monocyte chemoattractant protein-1 (MCP-1) [3, 4]. MCP-1 is a chemokine responsible for the recruitment of monocytes to sites of inflammation, while monocytes play a key role in various phrases of CAD, including initiation of the fatty streak, promotion of plaque instability, as well as remodeling and restenosis after myocardial infarction [5, 6]. Animal experiments have found that the expression level of MCP-1 is directly associated with the extent of atherosclerosis and the infiltration of monocytes into the atherosclerotic lesion [7, 8]. However, population studies on the association of MCP-1 with CVD or death risk among CAD patients are still limited. Several small case-control studies find MCP-1 level is higher among CAD patients than in healthy subjects [9, 10]. Only 4 cohort studies have analyzed the association between MCP-1 levels and death risk, and 3 of them recruited acute coronary syndrome (ACS) patients, and the other one collected patients with ischemic chest pain [3, 4, 11, 12]. None of them recruited stable CAD patients at baseline. Thus, the aim of the present study is to evaluate whether MCP-1 can add prognostic value to traditional risk factors for Chinese patients with ACS and stable CAD.
Methods
Participants
The recruitment of the Guangdong Coronary Artery Disease Cohort was between October 2008 and December 2011 [13, 14]. We enrolled 1980 successive patients admitted to the Cardiology Department of 3 superior specialty hospitals in Guangdong and diagnosed as CAD [International Classification of Diseases (ICD)-10 codes I20-I25] according to World Health Organization 1999/2000 guidelines [15, 16]. Eligible diseases were defined as the occurrence of at least two of the following situations: 1) typical chest pain with the property of oppression, dullness and constriction which occurred in the middle and upper part of sternum before, 2) ST-segment deviation of 0.05 mV or more and/or T wave inversion on first or histological electrocardiograph, 3) increased troponin and/or creatine kinase MB on initial blood tests, and/or at least one of the following situations: 1) measurable stenotic valve lesions of coronary arteries by coronary angiography, and 2) history of percutaneous coronary intervention or coronary artery bypass grafting. Generally, when patients entered the Department of Cardiology, eligibility was reviewed by one pathologist and two cardiologists in the specific hospital. After excluding 569 participants because of missing MCP-1 measurements, the final sample comprised 1411 CAD patients aged 40 to 85 years (S1 Fig.). S1 Table showed the comparison between retained and excluded participants. Compared with the retained participants, those excluded from the present analysis were generally younger (62.4 vs. 64.1 years). There were no significant differences of other relative baseline characteristics between included and excluded participants. The study was approved by the Sun Yat-sen University ethics committee, and all participants signed the informed consent.
Clinical measurements
A standardized questionnaire on general information of examination date, birth date, gender, education level, leisure-time physical activity, smoking habits, alcohol consumption, family history of CAD, medication history, and a validated food frequency questionnaire [17] were conducted through a face-to-face interview. Smoking was defined as at least one cigarette a day and lasting more than six months. Alcohol drinking was defined as drinking any type of alcoholic beverage at least once a week and lasting more than six months. Smoking and drinking status was classified as never, past, or current.
Clinical characteristics, clinical tests’ results and treatment of participants were collected from an electronic case record system. At admission, trained nurses measured height, weight and blood pressure using a standard protocol. BMI was calculated by dividing weight in kilograms by the square of height in meters. Glomerular filtration rate (GFR) was used to assess renal function according to the most recent Modification of Diet in Renal Disease Study equation [18], which is estimated at GFR = 175 × (standardized serum creatinine in mg/dL) −1.154 × Age−0.203 × 0.742 (if female). Treatment information of CAD included percutaneous coronary intervention and coronary artery bypass graft. Venous blood samples were drawn in the next morning after hospital admission with at least 12 hours fasting. Lipids and fasting plasma glucose (FPG) were determined by standard methods immediately after collection. Blood samples were stored at −80° C until thawed and then analyzed. Serum levels of MCP-1 and CRP were measured with a FlowCytomix technique using FlowCytomix Human Basic Kit (BMS8420FF, eBioscience, USA) together with Human MCP-1 FlowCytomix Simplex Kit (BMS8281FF, eBioscience, USA) and Human CRP FlowCytomix Simplex Kit (BMS82288FF, eBioscience, USA) on a BD FACSCalibur instrument (BD Biosciences, USA). Data were obtained from CellQuest software (BD Biosciences, USA) and calculated by the FlowCytomix Program (eBioscience, USA). The limit of detection was 18.2pg/mL for MCP-1 and 0.1mg/L for CRP. The mean intra-assay and inter-assay coefficients of variation were 10%, 13% for MCP-1and 9%, 14% for CRP.
Prospective follow-up
Follow-up data were collected from hospitals’ medical records of readmission, telephone contacts with patients or family members, and death registration of the Guangdong Provincial Center for Disease Control and Prevention which provided detailed causes of death. The surveys were followed to the end of July 2013 or patients’ death, whichever occurred first. The ICD codes were used to code the cause of death, and the ICD codes I00-I99 were classified as CVD deaths.
Statistical analysis
Serum MCP-1 levels were log-transformed and then classified into tertiles(<33.3%, 33.3 to <66.7% [reference group], and ≥66.7%). Differences in normally distributed continuous variables among three groups were analyzed by the general linear model after adjustment for age. A chi-square test was used for categorical variables and a Kruskal-Wallis one-way ANOVA was used for continuous variables that were not normally distributed. The associations between baseline serum MCP-1 levels and the risks of all-cause and CVD mortality were analyzed by using Cox proportional hazards models. All analyses were adjusted for age and gender, and further for education, leisure-time physical activity, smoking, alcohol drinking, BMI, systolic blood pressure, FPG, low-density lipoprotein cholesterol, GFR, history of heart failure, and use of antihypertensive, anti-diabetic, cholesterol-lowering, and anti-platelet drugs. CRP (log transformed) was included in the final models. We used restricted cubic splines in Cox models to test whether there was a dose-response or non-linear association of MCP-1 as a continuous variable with all-cause and CVD mortality risk. The C-index associated with the risk-estimation model was calculated based on all the classic risk factors listed above, and the likelihood ratio test was used to compare the discrimination of the models including and excluding MCP-1 [19, 20]. Net reclassification improvement (NRI) was further used to assess the contribution of MCP-1 [21]. We stratified patients into four risk categories (<5%, 5 to <10%, 10 to <15%, and ≥15%) based on the clinical variables. Statistical significance was considered to be P<0.05. All statistical analyses were performed with IBM SPSS Statistics 20.0 (IBM SPSS Inc, Chicago, III), SAS for Windows, version 9.3 (SAS Institute, Cary, NC), and R for Windows, version 3.0.1.
Results
At baseline, only age, FPG, CRP, CAD types, GFR, history of heart failure, and treatment with percutaneous coronary intervention were different among MCP-1 tertiles (Table 1).
During a median follow-up of 3.3 years, 117 deaths were recorded, 88 of which were due to CVD. The 29 non-CVD deaths comprised 14 deaths of cancer, 10 deaths of asphyxia due to lung disease, and 5 deaths of other reasons. Since the interactions between gender and MCP-1 levels on the risk of all-cause and CVD mortality were not statistically significant, data for males and females were combined in the analyses to maximize the statistical power. After adjustment for major conventional CVD risk factors, serum MCP-1 showed non-linear associations with all-cause and CVD mortality (Table 2). The multivariable-adjusted hazard ratios (HRs) across three MCP-1 categories were 1.61 (95% confidence intervals [CI] 0.95–2.72), 1.00, and 2.12 (95% CI 1.31–3.42) for all-cause mortality (Ptrend = 0.009), and 1.56 (95% CI 0.84–2.92), 1.00, and 2.23 (95% CI 1.27–3.89) for CVD mortality (Ptrend = 0.02). After further adjustment for serum CRP levels, MCP-1 remained related to risks of all-cause and CVD mortality with this non-linear association. The addition of serum MCP-1 to the fully adjusted model increased the C-index from 0.811 to 0.820 (P<0.0001) for all-cause mortality, and 0.827 to 0.835 (P<0.0001) for CVD mortality. When we added MCP-1 to the clinical variables to predict all-cause and CVD mortality risk, 17.9% and 14.8% of dead patients were correctly reclassified to higher risk category and 6.0% and 2.3% incorrectly reclassified to lower risk category. Similarly, 2.3% and 2.6% of survive patients were correctly reclassified to lower risk category and 2.1% and 2.5% incorrectly reclassified to higher risk category. So the estimated NRI was 12.1% (95% CI 3.5–20.9%, P = 0.006) for all-cause mortality and 12.6% (95% CI 4.3–20.9%, P = 0.003) for CVD mortality by including MCP-1 (Table 3).
We did additional analyses on subpopulations stratified by BMI or CRP, the non-linear association of MCP-1 with the risk of all-cause and CVD mortality was only present in patients with BMI below 24 kg/m2 or CRP above 3.0 mg/L (data not shown). We further checked the interactions between CAD subtypes and MCP-1 levels on the risks of all-cause and CVD mortality. Although the interactions were statistically significant, we didn’t stratify the data by CAD subtypes due to the small number of events in the subgroups.
Discussion
In this large and hospital-based population of Chinese CAD patients, we found a non-linear association between MCP-1 levels and the risks of all-cause and CVD mortality, independent of conventional CVD risk factors and CRP. CAD patients with higher MCP-1 levels were significantly associated with increased risks for all-cause and CVD mortality.
Previous research found that MCP-1 levels were associated with several traditional risk factors of CAD and subclinical atherosclerosis [4, 22]. Several case-control studies with small sample sizes (from 50 to 76 subjects) also found the MCP-1 level was higher in CAD patients than in healthy subjects, especially among patients with ACS [10, 23, 24]. An Italian study of 50 CAD patients who underwent percutaneous transluminal coronary angioplasty (PTCA) found that MCP-1 levels significantly increased among restenotic patients compared with nonrestenotic patients, and the increase was more significant in the samples collected after 15 and 180 days after PTCA compared with that in the samples collected as early as 24 hours after the procedure [24]. This suggested that the induction and high expression of MCP-1 might be sustained to the late phase of acute coronary event compared with CRP which increased markedly in the acute phase only.
Four cohort studies have evaluated the association between baseline MCP-1 levels and death risk among CAD patients. Two posthoc analyses of clinical trials gave inconsistent findings [3, 4]. MCP-1 level was positively associated with the risk of all-cause mortality among ACS patients in the A to Z trial, but there was no significant association between MCP-1 levels and death risk in the Oral Glycoprotein IIb/IIIa Inhibition with Orbofiban in Patients with Unstable Coronary Syndromes (OPUS-TIMI 16) trial. Other two cohort studies with small sample sizes also found a positive association between baseline MCP-1 level and death risk [11, 12]. However, all these four studies only recruited ACS patients and excluded CAD patients in the stable phase. In the A to Z trial, they further measured MCP-1 levels at 4 months after ACS onset to reflex chronic and stable phase of CAD, and still found a positive association between MCP-1 levels and death risk [3]. The result from the A to Z trial suggested that the positive association of MCP-1 levels with death risk was the same among CAD patients at both acute and stable phases. However, we found a non-linear association between MCP-1 levels and death risk among CAD patients, which was different from previous findings. Several differences between our study and above studies limit a direct comparison of all the results. First, study subjects in two trials were treated with particular drugs which had an influence on both MCP-1 levels and the subsequent events; second, the two previous trials had short follow-up time (10–18 months) compared with our study (mean 3.3 years); third, two cohorts had smaller study sample sizes (183 and 216 subjects) and lower death rates, which limit their applicability to find the association.
Previous basic and animal studies have confirmed that MCP-1 plays an important role in the initiation, development, and progression of CAD. Produced mainly by monocytes, smooth muscle cells, and endothelial cells within atherosclerotic plaques, MCP-1recruits monocytes or macrophages and induces them to migrate to the sites of inflammation [8, 25]. Besides the inflammation reaction, MCP-1 also regulates tissue factor expression and interferes the nitric oxide synthesis, proliferates and migrates the smooth muscle cells, neovascularizes the atheromatous plaque and makes it instable, and participates in the oxidative stress reaction [5–7, 26, 27]. Through all these possible mechanism pathways, MCP-1 is involved in the pathogenesis from the early stage of atherosclerosis development to the reperfusion injury and ventricular remodeling after myocardial infarction. However, the non-linear association from the present study reflected that inflammation was not entirely deleterious or entirely beneficial. This can be explained by the finding of Nahrendorf M et al [28]. The monocyte response after myocardial infarction is temporally biphasic, and a well-coordinated biphasic monocyte response is necessary for proper healing. MCP-1 is released during phase 1(days 1 to 4) after myocardial infarction and recruits proinflammatory monocytes to promote digestion of infracted tissue and removal of necrotic debris, which is followed by active resolution of inflammation and tissue repair in phase 2. Thus, insufficient numbers of MCP-1 and proinflammatory monocytes may delay wound healing. However, if inflammatory monocytes persist too long, the reparative functions of monocytes may be impaired. Therefore, there exists an optimum amount of inflammatory monocytes recruitment due to MCP-1to sites of injury for better prognosis.
Although the present study indicates that MCP-1 levels may be valuable for prediction of secondary prognosis on CAD patients, these findings require further confirmation from other populations. Since the association was non-linear, future studies are needed to find out the proper cut point for risk prediction in the clinical practice. Recently, a number of animal intervention studies including lipid-lowering, anti-diabetic drugs, hormone replacement, and red wine have been shown to effectively reduce the MCP-1 levels [29–32]. Thus, more studies are needed to confirm this effectiveness in human clinical trials.
There are several limitations in our study. First, our subjects were enrolled from hospitals which may bring selection bias. In general, in-patients are considered having severer disease status than non-hospitalized people. However, we included both acute CAD patients and those with stable manifestation, and some of them were electively admitted patients with mild status. Thus we can reduce the bias. Besides, since our participants were all CAD patients, this limited the generalizability of our findings. Second, we cannot completely exclude the effects of residual confounding resulting from measurement error in the assessment of confounding factors or some unmeasured factors. Third, the present study was a cohort study based on patients already with CAD. We could not get causal inference from the present data. Fourth, age was significantly different between excluded and retained participants (62.4 vs. 64.1 years), which may bring selection bias.
In conclusion, there was a non-linear association between MCP-1 and death risk among CAD patients. The addition of MCP-1 to the fully multivariable-adjusted models modestly but significantly improved the discrimination for all-cause and CVD mortality. Thus, MCP-1 may be considered as a biomarker for risk prediction for CAD patients and more investigations of MCP-1 as a therapeutic target are needed.
Supporting Information
S1 Fig. Flow chart illustrating the recruitment of the patients for the study.
https://doi.org/10.1371/journal.pone.0120633.s001
(DOCX)
S2 Fig. Spline plots displaying the risk cardiovascular mortality over the range of MCP-1 (log transformed).
https://doi.org/10.1371/journal.pone.0120633.s002
(TIF)
S1 Table. Characteristics of included and excluded participants.
https://doi.org/10.1371/journal.pone.0120633.s003
(DOCX)
Author Contributions
Conceived and designed the experiments: DD JQ GH WL. Performed the experiments: DD DS XL ZL YZ WL. Analyzed the data: DD YW PG GH. Contributed reagents/materials/analysis tools: DS PL MX DL YY. Wrote the paper: DD GH WL.
References
- 1.
World Health Organization. Global Health Observatory Data Repository: Mortality and global health estimates. Available at: http://apps.who.int/gho/data/node.main.686?lang=en.
- 2. Hansson GK. Inflammation, atherosclerosis, and coronary artery disease. N Engl J Med. 2005;352(16):1685–95. Epub 2005/04/22. pmid:15843671.
- 3. de Lemos JA, Morrow DA, Blazing MA, Jarolim P, Wiviott SD, Sabatine MS, et al. Serial measurement of monocyte chemoattractant protein-1 after acute coronary syndromes: results from the A to Z trial. J Am Coll Cardiol. 2007;50(22):2117–24. Epub 2007/11/27. pmid:18036447.
- 4. de Lemos JA, Morrow DA, Sabatine MS, Murphy SA, Gibson CM, Antman EM, et al. Association between plasma levels of monocyte chemoattractant protein-1 and long-term clinical outcomes in patients with acute coronary syndromes. Circulation. 2003;107(5):690–5. Epub 2003/02/13. pmid:12578870.
- 5. Birdsall HH, Green DM, Trial J, Youker KA, Burns AR, MacKay CR, et al. Complement C5a, TGF-beta 1, and MCP-1, in sequence, induce migration of monocytes into ischemic canine myocardium within the first one to five hours after reperfusion. Circulation. 1997;95(3):684–92. Epub 1997/02/04. pmid:9024158.
- 6. Libby P. Molecular bases of the acute coronary syndromes. Circulation. 1995;91(11):2844–50. Epub 1995/06/01. pmid:7758192.
- 7. Gu L, Okada Y, Clinton SK, Gerard C, Sukhova GK, Libby P, et al. Absence of monocyte chemoattractant protein-1 reduces atherosclerosis in low density lipoprotein receptor-deficient mice. Mol Cell. 1998;2(2):275–81. Epub 1998/09/12. pmid:9734366.
- 8. Namiki M, Kawashima S, Yamashita T, Ozaki M, Hirase T, Ishida T, et al. Local overexpression of monocyte chemoattractant protein-1 at vessel wall induces infiltration of macrophages and formation of atherosclerotic lesion: synergism with hypercholesterolemia. Arterioscler Thromb Vasc Biol. 2002;22(1):115–20. Epub 2002/01/15. pmid:11788470.
- 9. Economou E, Tousoulis D, Katinioti A, Stefanadis C, Trikas A, Pitsavos C, et al. Chemokines in patients with ischaemic heart disease and the effect of coronary angioplasty. Int J Cardiol. 2001;80(1):55–60. Epub 2001/09/05. pmid:11532547.
- 10. Nishiyama K, Ogawa H, Yasue H, Soejima H, Misumi K, Takazoe K, et al. Simultaneous elevation of the levels of circulating monocyte chemoattractant protein-1 and tissue factor in acute coronary syndromes. Jpn Circ J. 1998;62(9):710–2. Epub 1998/10/10. pmid:9766714.
- 11. Kavsak PA, Ko DT, Newman AM, Palomaki GE, Lustig V, MacRae AR, et al. Risk stratification for heart failure and death in an acute coronary syndrome population using inflammatory cytokines and N-terminal pro-brain natriuretic peptide. Clin Chem. 2007;53(12):2112–8. Epub 2007/10/13. pmid:17932131.
- 12. Kervinen H, Manttari M, Kaartinen M, Makynen H, Palosuo T, Pulkki K, et al. Prognostic usefulness of plasma monocyte/macrophage and T-lymphocyte activation markers in patients with acute coronary syndromes. Am J Cardiol. 2004;94(8):993–6. Epub 2004/10/13. pmid:15476610.
- 13. Ding D, Qiu J, Li X, Li D, Xia M, Li Z, et al. Hyperglycemia and mortality among patients with coronary artery disease. Diabetes Care. 2014;37(2):546–54. Epub 2013/10/04. pmid:24089546.
- 14. Li X, Zhang Y, Wang M, Lv X, Su D, Li Z, et al. The prevalence and awareness of cardiometabolic risk factors in Southern Chinese population with coronary artery disease. ScientificWorldJournal. 2013;2013:416192. Epub 2013/11/14. pmid:24222736; PubMed Central PMCID: PMC3810187.
- 15. Gibbons RJ, Chatterjee K, Daley J, Douglas JS, Fihn SD, Gardin JM, et al. ACC/AHA/ACP-ASIM guidelines for the management of patients with chronic stable angina: executive summary and recommendations. A Report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (Committee on Management of Patients with Chronic Stable Angina). Circulation. 1999;99(21):2829–48. Epub 1999/06/03. pmid:10351980.
- 16. Braunwald E, Antman EM, Beasley JW, Califf RM, Cheitlin MD, Hochman JS, et al. ACC/AHA guidelines for the management of patients with unstable angina and non-ST-segment elevation myocardial infarction: executive summary and recommendations. A report of the American College of Cardiology/American Heart Association task force on practice guidelines (committee on the management of patients with unstable angina). Circulation. 2000;102(10):1193–209. Epub 2000/09/07. pmid:10973852.
- 17. Zhang B, Wang P, Chen CG, He QQ, Zhuo SY, Chen YM, et al. Validation of an FFQ to estimate the intake of fatty acids using erythrocyte membrane fatty acids and multiple 3d dietary records. Public Health Nutr. 2010;13(10):1546–52. Epub 2009/12/19. S1368980009992849 [pii] pmid:20018122.
- 18. Levey AS, Coresh J, Greene T, Stevens LA, Zhang YL, Hendriksen S, et al. Using standardized serum creatinine values in the modification of diet in renal disease study equation for estimating glomerular filtration rate. Ann Intern Med. 2006;145(4):247–54. Epub 2006/08/16. pmid:16908915.
- 19. Harrell FE Jr., Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med. 1996;15(4):361–87. pmid:8668867.
- 20. Pencina MJ, D'Agostino RB. Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation. Stat Med. 2004;23(13):2109–23. pmid:15211606.
- 21. Pencina MJ, D'Agostino RB Sr., D'Agostino RB Jr., Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med. 2008;27(2):157–72; discussion 207–12. pmid:17569110.
- 22. Deo R, Khera A, McGuire DK, Murphy SA, Meo Neto Jde P, Morrow DA, et al. Association among plasma levels of monocyte chemoattractant protein-1, traditional cardiovascular risk factors, and subclinical atherosclerosis. J Am Coll Cardiol. 2004;44(9):1812–8. Epub 2004/11/03. pmid:15519012.
- 23. Hokimoto S, Ogawa H, Saito T, Oshima S, Noda K, Soejima H, et al. Increased plasma antigen levels of monocyte chemoattractant protein-1 in patients with restenosis after percutaneous transluminal coronary angioplasty. Jpn Circ J. 2000;64(11):831–4. Epub 2000/12/08. pmid:11110426.
- 24. Cipollone F, Marini M, Fazia M, Pini B, Iezzi A, Reale M, et al. Elevated circulating levels of monocyte chemoattractant protein-1 in patients with restenosis after coronary angioplasty. Arterioscler Thromb Vasc Biol. 2001;21(3):327–34. Epub 2001/03/07. pmid:11231910.
- 25. Nelken NA, Coughlin SR, Gordon D, Wilcox JN. Monocyte chemoattractant protein-1 in human atheromatous plaques. J Clin Invest. 1991;88(4):1121–7. Epub 1991/10/01. pmid:1843454; PubMed Central PMCID: PMC295565.
- 26. Sakamoto T, Ishibashi T, Sakamoto N, Sugimoto K, Egashira K, Ohkawara H, et al. Endogenous NO blockade enhances tissue factor expression via increased Ca2+ influx through MCP-1 in endothelial cells by monocyte adhesion. Arterioscler Thromb Vasc Biol. 2005;25(9):2005–11. Epub 2005/07/16. pmid:16020745.
- 27. Lee PC, Ho IC, Lee TC. Oxidative stress mediates sodium arsenite-induced expression of heme oxygenase-1, monocyte chemoattractant protein-1, and interleukin-6 in vascular smooth muscle cells. Toxicol Sci. 2005;85(1):541–50. Epub 2005/02/04. pmid:15689417.
- 28. Nahrendorf M, Pittet MJ, Swirski FK. Monocytes: protagonists of infarct inflammation and repair after myocardial infarction. Circulation. 2010;121(22):2437–45. pmid:20530020; PubMed Central PMCID: PMC2892474.
- 29. Martinez-Gonzalez J, Alfon J, Berrozpe M, Badimon L. HMG-CoA reductase inhibitors reduce vascular monocyte chemotactic protein-1 expression in early lesions from hypercholesterolemic swine independently of their effect on plasma cholesterol levels. Atherosclerosis. 2001;159(1):27–33. Epub 2001/11/02. pmid:11689203.
- 30. Ghanim H, Garg R, Aljada A, Mohanty P, Kumbkarni Y, Assian E, et al. Suppression of nuclear factor-kappaB and stimulation of inhibitor kappaB by troglitazone: evidence for an anti-inflammatory effect and a potential antiatherosclerotic effect in the obese. J Clin Endocrinol Metab. 2001;86(3):1306–12. Epub 2001/03/10. pmid:11238525.
- 31. Stork S, Baumann K, von Schacky C, Angerer P. The effect of 17 beta-estradiol on MCP-1 serum levels in postmenopausal women. Cardiovasc Res. 2002;53(3):642–9. Epub 2002/02/28. pmid:11861035.
- 32. Feng AN, Chen YL, Chen YT, Ding YZ, Lin SJ. Red wine inhibits monocyte chemotactic protein-1 expression and modestly reduces neointimal hyperplasia after balloon injury in cholesterol-Fed rabbits. Circulation. 1999;100(22):2254–9. Epub 1999/12/01. pmid:10578000.