Abstract
Background
Monocyte chemoattractant protein‐1 (MCP‐1) plays a role in cardiovascular disease (CVD) and renal injury. Recent clinical studies have suggested that circulating levels of MCP‐1 could be a biomarker of atherosclerosis and future cardiovascular events in humans. Because chronic kidney disease (CKD) is one of the risk factors of CVD, it is conceivable that elevated MCP‐1 levels may link the increased risk of CVD in CKD patients. However, as far as we know, in addition to well‐known traditional risk factors for atherosclerosis, whether renal dysfunction could be independently associated with the elevation of MCP‐1 levels in a general population remains unknown. Therefore, we examined here which anthropometric and metabolic variables, including renal function, could be independent correlates of circulating levels of MCP‐1 in a general population.
Hypothesis
We hypothesized that renal function was one of the independent correlates of serum MCP‐1 levels.
Methods
A total of 860 Japanese residents (318 males and 542 females, mean age 65.4 ± 9.8 years) in a small fishing community underwent a complete history and physical examination with determination of blood chemistries, including serum levels of MCP‐1.
Results
Mean MCP‐1 levels were 281.4 pg/mL. Multiple stepwise regression analyses revealed that male sex (P < 0.0001), age (P = 0.03), estimated glomerular filtration rate (eGFR) (P < 0.0001, inversely), and white blood cell count (P = 0.037) were independently associated with MCP‐1 levels.
Conclusions
The present study demonstrated for the first time that other than white blood cell count, eGFR was an independent correlate of serum levels of MCP‐1 in a Japanese general population. Elevated MCP‐1 levels may partly explain the increased risk of CVD in CKD patients. © 2011 Wiley Periodicals, Inc.
The authors have no funding, financial relationships, or conflicts of interest to disclose.
Introduction
There is a growing body of evidence, ranging from in vitro experiments to pathologic analysis to epidemiologic studies, indicating that atherosclerosis is intrinsically an inflammatory disease.1, 2 Upon exposure to proinflammatory cytokines, endothelial cells have been reported to undergo profound functional changes that include expression of adhesion molecules, production of monocyte chemoattractant protein‐1 (MCP‐1), and induction of procoagulant activity.3, 4, 5 MCP‐1 plays an important role in the early phase of atherosclerosis by initiating monocyte recruitment to the vessel wall.6 MCP‐1 levels are increased in human atherosclerotic plaques,7 and the selective targeting of chemokine (C‐C motif) receptor 2, the receptor for MCP‐1, is reported to markedly decrease atheromatous lesion formation in apolipoprotein E knockout mice.8 Further, recent clinical studies have suggested that circulating levels of MCP‐1 could be a biomarker of atherosclerosis and future cardiovascular events in humans.9, 10 Indeed, plasma concentrations of MCP‐1 are associated with traditional cardiovascular risk factors and the prevalence of coronary artery calcification in a general population.11 In addition, circulating levels of MCP‐1 are elevated in patients with acute coronary syndromes (ACS),12, 13, 14 and also are associated with the increased risks for restenosis after percutaneous coronary intervention,15, 16 myocardial infarction, or death in patients with ACS.17
Recent clinical observations have shown that even minor renal dysfunction is associated with the increased risk of future cardiovascular events in a general population.18, 19 Now chronic kidney disease (CKD) is one of the risk factors of cardiovascular disease (CVD) in humans, and the concept of so‐called cardiorenal syndrome has received much attention.20, 21, 22 Although several possible mechanisms that could link to CKD to CVD have been proposed so far,23, 24 given the proatherosclerotic properties of MCP‐1, it is conceivable that MCP‐1 may contribute to the increased risk of CVD in CKD patients. However, as far as we know, in addition to well‐known traditional risk factors for atherosclerosis, whether renal dysfunction could be independently associated with the elevation of MCP‐1 levels in a general population remains unknown. Therefore, in this study, we examined which anthropometric and metabolic variables, including renal function, could be independent correlates of circulating levels of MCP‐1 in a Japanese general population.
Methods
Subjects
A periodic epidemiological survey was performed in a small fishing community in southwestern Japan (Uku town). This town is an isolated island in Nagasaki prefecture, and the total population is about 3700. A health checkup examination for residents aged >40 years was carried out for consecutive years starting in 2004. A total of 860 residents (318 males and 542 females (mean age 65.4 ± 9.8 years) received the examination. Seventeen of them rejected the blood test. A complete dataset was available for 843 subjects (312 men and 531 women) in this study.
Data Collection
The subjects' medical history, smoking habits, and use of alcohol were ascertained by questionnaire. Height and weight were measured, and body mass index (BMI; kg/m2) was calculated as an index of the presence or absence of obesity. Waist circumference was measured at the level of the umbilicus in the standing position. Blood pressure (BP) was measured twice, first with subjects seated and then in a supine position, at a 3‐min interval, using an upright standard sphygmomanometer. Vigorous physical activity and smoking were avoided for ≥30 minutes before BP measurement. The second BP measurement in the supine position with the fifth phase diastolic pressure was used for analysis.
After overnight fasting, blood was drawn from the antecubital vein in the morning to determine white blood cell (WBC) count, γ‐glutamyl transpeptidase, lipids (total cholesterol, high‐density lipoprotein cholesterol [HDL‐C], low‐density lipoprotein cholesterol [LDL‐C], and triglycerides [TG]), fasting plasma glucose, glycosylated hemoglobin A1c, insulin, blood urea nitrogen (BUN), creatinine (Cr), uric acid, high‐sensitivity C‐reactive protein (hs‐CRP), and MCP‐1 levels. Serum levels of MCP‐1 were measured with an enzyme‐linked immunosorbent assay system (ELISA; R&D Systems, Minneapolis, MN) according to the manufacturer's instructions. Other biochemistries were measured at a commercially available laboratory (Kyodo Igaku Laboratory, Fukuoka, Japan). Glomerular filtration rate (GFR) was estimated (eGFR) from the serum Cr values, using the following equation according to the guideline of Japanese Society of Nephrology in 200925: eGFR (mL/min/1.73 m2) = 194 × Cr−1.094 × age−0.287 (×0.739 if female).
The mayor and the welfare section of the city approved this study. The ethical committee of Kurume University also approved this study. All participants gave written informed consent.
Statistical Analysis
Because of skewed distributions, the natural logarithmic transformations were performed for TG, insulin, and hs‐CRP. Mean values with upper and lower 95% confidence intervals (CI) were exponentiated and presented as geometric mean ± SD, where the SD was approximated as the difference of the exponentiated CI/3.92, which is the number of SD in a 95% CI where data are normally distributed. Medications for hypertension, hyperlipidemia, and diabetes were coded as dummy variables. Univariate analysis was performed for correlates of serum MCP‐1 levels. To determine independent correlates of serum MCP‐1 levels, multiple stepwise linear regression analysis was performed. Further, age‐ and sex‐adjusted mean levels of serum MCP‐1 stratified by the independent correlates were compared using analysis of covariance. Statistical significance was defined as P < 0.05. All statistical analyses were performed with SAS software (SAS Institute, Cary, NC).
Results
The demographic data of the 843 subjects are presented in Table 1. Mean serum levels of MCP‐1 were 314.2 ± 8.2 pg/mL in males and 262.4 ± 6.8 pg/mL in females. Serum MCP‐1 levels in males were significantly higher than those in females (P < 0.0001). Table 2 shows the results of univariate analysis for correlates of serum MCP‐1 levels. Parameters statistically and significantly associated with MCP‐1 levels were age (P < 0.0001), male sex (P < 0.0001), WBC count (P = 0.0008), γ‐glutamyltranspeptidase (P < 0.0001), HDL‐C (P = 0.0046, inversely), BUN (P < 0.0001), Cr (P < 0.0001), eGFR (P < 0.0001, inversely), uric acid (P < 0.0001), intima‐media thickness (P = 0.0053), hs‐CRP (P = 0.028), alcohol intake (P = 0.0021), and medication for hypertension (P = 0.035). Because these parameters could be closely correlated with each other, multiple stepwise regression analysis was performed. The analysis showed that male sex (P < 0.0001), eGFR (P < 0.0001, inversely), age (P = 0.03), and WBC count (P = 0.037) were independently correlated to MCP‐1 levels (R 2 = 0.08) (Table 3).
Table 1.
Demographics of Study Subjects
| Parameters | M (n = 312) | F (n = 531) | Total (n = 843) |
|---|---|---|---|
| Age (y) | 66.6 ± 8.5 | 64.7 ± 10.2 | 65.4 ± 9.7 |
| BMI (kg/m2) | 23.8 ± 3.0 | 23.6 ± 3.4 | 23.6 ± 3.3 |
| Waist circumference (cm) | 85.7 ± 7.8 | 83.5 ± 10.0 | 83.7 ± 9.6 |
| Systolic BP (mm Hg) | 138.9 ± 19.8 | 140.5 ± 21.1 | 140.0 ± 20.6 |
| Diastolic BP (mm Hg) | 82.0 ± 11.0 | 79.6 ± 11.1 | 80.5 ± 11.2 |
| WBC (/μL) | 5821.7 ± 1353.8 | 5323.9 ± 1292.7 | 5508.2 ± 1336.7 |
| γ‐GTP (U/L)a | 42.3 ± 1.1 | 24.3 ± 0.6 | 29.7 ± 0.8 |
| Total‐C (mg/dL) | 201.6 ± 35.3 | 213.2 ± 35.4 | 208.9 ± 35.8 |
| HDL‐C (mg/dL) | 55.5 ± 14.6 | 61.3 ± 13.9 | 59.1 ± 14.4 |
| TG (mg/dL)a | 101.5 ± 2.6 | 87.4 ± 2.3 | 91.8 ± 2.4 |
| LDL‐C (mg/dL) | 119.5 ± 33.2 | 130.2 ± 32.7 | 126.3 ± 33.3 |
| Uric acid (mg/dL) | 6.1 ± 1.2 | 4.7 ± 1.2 | 5.2 ± 1.17 |
| BUN (mg/dL) | 19.4 ± 5.3 | 17.1 ± 4.7 | 17.9 ± 5.0 |
| Cr (mg/dL) | 0.8 ± 0.2 | 0.6 ± 0.1 | 0.7 ± 0.2 |
| FPG (mg/dL) | 102.7 ± 19.9 | 93.2 ± 11.5 | 96.7 ± 15.8 |
| HbA1c (%) | 5.4 ± 0.7 | 5.3 ± 0.5 | 5.3 ± 0.6 |
| Insulin (μU/dL)a | 4.6 ± 0.1 | 4.3 ± 0.1 | 4.42 ± 0.11 |
| IMT (mm) | 0.76 ± 0.2 | 0.68 ± 0.1 | 0.71 ± 0.19 |
| MCP‐1 (pg/mL)a | 314.2 ± 8.2 | 262.4 ± 6.8 | 281.4 ± 7.32 |
| hs‐CRP (mg/dL)a | 0.059 ± 0.002 | 0.036 ± 0.001 | 0.043 ± 0.001 |
| eGFR (mL/min/1.73 m2) | 74.7 ± 17.02 | 76.6 ± 16.1 | 75.9 ± 16.4 |
| Current smoking (%) | 21.8 | 2.0 | 9.3 |
| Alcohol intake (%) | 58.2 | 7.7 | 26.2 |
| Medication (%) | |||
| HT | 45.5 | 35.5 | 38.8 |
| HL | 9.1 | 10.0 | 9.6 |
| DM | 8.6 | 2.0 | 4.5 |
| CKD ≥ stage 3 (%) | 17.3 | 13.3 | 14.8 |
Abbreviations: BMI, body mass index; BP, blood pressure; BUN, blood urea nitrogen; CKD, chronic kidney disease; Cr, creatinine; DM, diabetes mellitus; eGFR, estimated glomerular filtration rate; F, female; FPG, fasting plasma glucose; γ‐GTP, γ‐glutamyltranspeptidase; HbA1c, glycated hemoglobin A1c; HDL‐C, high‐density lipoprotein cholesterol; HL, hyperlipidemia; hs‐CRP, high‐sensitivity C‐reactive protein; HT, hypertension; IMT, intima‐media thickness; LDL‐C, low‐density lipoprotein cholesterol; M, male; MCP‐1, monocyte chemoattractant protein‐1; SE, standard error; TG, triglycerides; total‐C, total cholesterol; WBC, white blood cell count.
Data are mean ± SD or percentage, unless otherwise indicated.
Log‐transformed values were used for the calculation and reconverted to antilogarithm forms
Table 2.
Univariate Analysis for Correlates of MCP‐1 Levels
| Characteristics | β | SE | P Value |
|---|---|---|---|
| Age (y) | 0.0057 | 0.001 | <0.0001 |
| Sex | −0.181 | 0.02 | <0.0001 |
| BMI (kg/m2) | 0.0008 | 0.004 | 0.836 |
| Waist circumference (cm) | 0.0025 | 0.001 | 0.077 |
| Systolic BP (mm Hg) | −0.0008 | 0.0006 | 0.240 |
| Diastolic BP (mm Hg) | −0.002 | 0.001 | 0.058 |
| WBC (/μL)a | 0.184 | 0.05 | 0.0008 |
| γ‐GTP (U/L)a | 0.078 | 0.02 | <0.0001 |
| Total‐C (mg/dL) | −0.0006 | 0.0004 | 0.113 |
| HDL‐C (mg/dL) | −0.003 | 0.0009 | 0.0046 |
| TG (mg/dL)a | 0.025 | 0.03 | 0.366 |
| LDL‐C (mg/dL) | −0.0001 | 0.0004 | 0.726 |
| BUN (mg/dL) | 0.015 | 0.002 | <0.0001 |
| Cr (mg/dL) | 0.398 | 0.069 | <0.0001 |
| eGFR (mL/min/1.73 m2)a | −0.0034 | 0.0008 | <0.0001 |
| Uric acid (mg/dL) | 0.055 | 0.009 | <0.0001 |
| FPG (mg/dL) | 0.0005 | 0.001 | 0.584 |
| HbA1c (%) | 0.046 | 0.024 | 0.058 |
| Insulin (μU/mL)a | −0.021 | 0.019 | 0.289 |
| IMT (mm) | 0.205 | 0.073 | 0.0053 |
| hs‐CRP (mg/dL)a | 0.024 | 0.011 | 0.028 |
| Smoking | 0.054 | 0.046 | 0.232 |
| Alcohol | 0.093 | 0.030 | 0.002 |
| Medications | |||
| HT | 0.057 | 0.027 | 0.035 |
| HL | 0.005 | 0.04 | 0.910 |
| DM | 0.061 | 0.062 | 0.323 |
Abbreviations: BMI, body mass index; BP, blood pressure; BUN, blood urea nitrogen; Cr, creatinine; DM, diabetes mellitus; eGFR, estimated glomerular filtration rate; FPG, fasting plasma glucose; γ‐GTP, γ‐glutamyltranspeptidase; HbA1c, glycated hemoglobin A1c; HDL‐C, high‐density lipoprotein cholesterol; HL, hyperlipidemia; hs‐CRP, high‐sensitivity C‐reactive protein; HT, hypertension; IMT, intima‐media thickness; LDL‐C, low‐density lipoprotein cholesterol; MCP‐1, monocyte chemoattractant protein‐1; SE, standard error; TG, triglycerides; total‐C, total cholesterol; WBC, white blood cell count.
β indicates standardized regression coefficients: male = 0, female = 1.
Log‐transformed values were used for the analysis
Table 3.
Multiple Stepwise Regression Analysis for Correlates of Serum MCP‐1 Levels
| Characteristics | β | SE | P Value |
|---|---|---|---|
| Sex | −0.18 | 0.03 | <0.0001 |
| eGFRa | −0.003 | 0.0007 | <0.0001 |
| Age | 0.003 | 0.0001 | 0.033 |
| WBCa | 0.114 | 0.005 | 0.037 |
Abbreviations: eGFR, estimated glomerular filtration rate; SE, standard error; WBC, white blood cell count.
R 2 = 0.08.
β indicates standardized regression coefficients: male = 0, female = 1.
Log‐transformed values were used for the analysis
When age‐ and sex‐adjusted mean serum MCP‐1 levels stratified by eGFR or WBC count were compared using analysis of covariance, a significant trend was demonstrated (Figure 1 and Figure 2).
Figure 1.
Age‐ and sex‐adjusted mean serum MCP‐1 levels stratified by eGFR levels tertiles. Abbreviations: eGFR, estimated glomerular filtration rate; MCP‐1, monocyte chemoattractant protein‐1
Figure 2.
Age‐ and sex‐adjusted mean serum MCP‐1 levels stratified by WBC count tertiles. Abbreviations: MCP‐1, monocyte chemoattractant protein‐1; WBC, white blood cell
Discussion
The salient finding of the present study is that in addition to WBC count, eGFR is an independent correlate of serum levels of MCP‐1 in a Japanese general population. MCP‐1 not only plays a central role in atherosclerosis, but also contributes to renal damage by promoting the recruitment of monocytes and subsequently evoking inflammatory reactions in the vessel wall and mesangial and tubulointerstitial areas.6, 26, 27, 28 Given the facts that circulating levels of MCP‐1 are associated with the prevalence of atherosclerosis11 and that CKD is one of the risk factors for atherosclerotic heart disease,18, 19 our present observations suggest that elevation of MCP‐1 levels could link to CKD to the increased risk of CVD in a general population.
The present study did not clarify how decreased eGFR levels were independently correlated with serum MCP‐1 levels. However, Stinghen et alpreviously reported that MCP‐1 levels were inversely correlated with eGFR in CKD patients with mean eGFR of 37 ± 2 mL/min.29 They found in their study that uremic plasma stimulated endothelial MCP‐1 expression in a CKD stage–dependent manner.29 Therefore, endothelial‐cell activation may be involved in the elevation of MCP‐1 levels in patients with even minor renal dysfunction. Further, impaired clearance of MCP‐1 may partly explain the inverse association between eGFR and MCP‐1 levels in our subjects.
In this study, WBC counts were independently correlated with MCP‐1 levels. These observations suggest that WBC count is a potential biomarker for reflecting subclinical inflammation in a general population. Several clinical studies have shown that leukocyte counts are an independent risk factor for future cardiovascular events in not only individuals without CVD, but also subjects with CVD.30, 31, 32 However, in the present study, hs‐CRP, another well‐known biomarker of inflammation,33, 34, 35 was positively correlated with MCP‐1 levels in univariate analysis, but not in multiple stepwise regression analysis. These findings suggest the close interrelationship between hs‐CRP and WBC counts in our subjects and could support the notion that WBCs release MCP‐1 into the circulation.36
In the present study, male sex and age were independently associated with circulating MCP‐1 levels. The results were consistent with the previous observations of Inadera et al,37 who showed that circulating MCP‐1 levels were positively associated with age and tended to be higher in males than females in healthy Japanese subjects.37 However, there was still controversy about the relationship between sex and MCP‐1 levels. In the Orbofiban in Patients With Unstable Coronary Syndromes–Thrombolysis In Myocardial Infarction (OPUS‐TIMI) study,17 female gender was positively associated with MCP‐1 levels.17 The difference of subject population and ethnicity may account for the discrepancies.
Study Limitations
Our study was a cross‐sectional one, and therefore did not elucidate the causal relationships of eGFR values or WBC counts with circulating MCP‐1 levels. Accordingly, we did not know whether MCP‐1 levels could be mechanistically involved in the increased risk of CVD in patients with CKD. Further, we cannot say for sure that WBC count is a potential biomarker for reflecting subclinical inflammation and predicting future cardiovascular events in a general population. A longitudinal study is needed to clarify the issues.
References
- 1. Mantovani A, Bussolino F, Dejana E. Cytokine regulation of endothelial cell function. FASEB J 1992. 6 2591–2599. [DOI] [PubMed] [Google Scholar]
- 2. Yamagishi S, Matsui T, Nakamura K. Atheroprotective properties of pigment epithelium‐derived factor (PEDF) in cardiometabolic disorders. Curr Pharm Des 2009. 15 1027–1033. [DOI] [PubMed] [Google Scholar]
- 3. Vanden Berghe W, Vermeulen L, De Wilde G, et al. Signal transduction by tumor necrosis factor and gene regulation of the inflammatory cytokine interleukin‐6. Biochem Pharmacol 2000. 60 1185–1195. [DOI] [PubMed] [Google Scholar]
- 4. Nawroth PP, Stern DM. Modulation of endothelial cell hemostatic properties by tumor necrosis factor. J Exp Med 1986. 163 740–745. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Mackay F, Loetscher H, Stueber D, et al. Tumor necrosis factor alpha (TNF‐alpha)‐induced cell adhesion to human endothelial cells is under dominant control of one TNF receptor type, TNF‐R55. J Exp Med 1993. 177 1277–1286. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Rollins BJ, Yoshimura T, Leonard EJ, et al. Cytokine‐activated human endothelial cells synthesize and secrete a monocyte chemoattractant, MCP‐1/JE. Am J Pathol 1990. 136 1229–1233. [PMC free article] [PubMed] [Google Scholar]
- 7. Nelken NA, Coughlin SR, Gordon D, et al. Monocyte chemoattractant protein‐1 in human atheromatous plaques. J Clin Invest 1991. 88 1121–1127. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Boring L, Gosling J, Cleary M, et al. Decreased lesion formation in CCR2−/−mice reveals a role for chemokines in the initiation of atherosclerosis. Nature 1998. 394 894–897. [DOI] [PubMed] [Google Scholar]
- 9. De Lemos JA, Morrow DA, Blazing MA, 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 2117–2124. [DOI] [PubMed] [Google Scholar]
- 10. Piemonti L, Calori G, Lattuada G, et al. Association between plasma monocyte chemoattractant protein‐1 concentration and cardiovascular disease mortality in middle‐aged diabetic and nondiabetic individuals. Diabetes Care 2009. 32 2105–2110. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Deo R, Khera A, McGuire DK, et al. Association among plasma levels of monocyte chemoattractant protein‐1, traditional cardiovascular risk factors, and subclinical atherosclerosis. J Am Coll Cardiol 2004. 44 1812–1818. [DOI] [PubMed] [Google Scholar]
- 12. Matsumori A, Furukawa Y, Hashimoto T, et al. Plasma levels of the monocyte chemotactic and activating factor/monocyte chemoattractant protein‐1 are elevated in patients with acute myocardial infarction. J Mol Cell Cardiol 1997. 29 419–423. [DOI] [PubMed] [Google Scholar]
- 13. Hojo Y, Ikeda U, Takahashi M, et al. Increased levels of monocyte‐related cytokines in patients with unstable angina. Atherosclerosis 2002. 161 403–408. [DOI] [PubMed] [Google Scholar]
- 14. Nishiyama K, Ogawa H, Yasue H, et al. Simultaneous elevation of the levels of circulating monocyte chemoattractant protein‐1 and tissue factor in acute coronary syndromes. Jpn Circ J 1998. 62 710–712. [DOI] [PubMed] [Google Scholar]
- 15. Oshima S, Ogawa H, Hokimoto S, et al. Plasma monocyte chemoattractant protein‐1 antigen levels and the risk of restenosis after coronary stent implantation. Jpn Circ J 2001. 65 261–264. [DOI] [PubMed] [Google Scholar]
- 16. Hokimoto S, Ogawa H, Saito T, et al. Increased plasma antigen levels of monocyte chemoattractant protein‐1 in patients with restenosis after percutaneous transluminal coronary angioplasty. Jpn Circ J 2000. 64 831–834. [DOI] [PubMed] [Google Scholar]
- 17. De Lemos JA, Morrow DA, Sabatine MS, et al. Association between plasma levels of monocyte chemoattractant protein‐1 and long‐term clinical outcomes in patients with acute coronary syndromes. Circulation 2003. 107 690–695. [DOI] [PubMed] [Google Scholar]
- 18. Go AS, Chertow GM, Fan D, et al. Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization [published correction appears in N Engl J Med 2008;18:4]. N Engl J Med 2004. 351 1296–1305. [DOI] [PubMed] [Google Scholar]
- 19. Amann K, Wanner C, Ritz E. Cross‐talk between the kidney and the cardiovascular system. J Am Soc Nephrol 2006. 17 2112–2119. [DOI] [PubMed] [Google Scholar]
- 20. Sarnak MJ, Levey AS, Schoolwerth AC, et al. Kidney disease as a risk factor for development of cardiovascular disease: a statement from the American Heart Association Councils on Kidney in Cardiovascular Disease, High Blood Pressure Research, Clinical Cardiology, and Epidemiology and Prevention. Circulation 2003. 108 2154–2169. [DOI] [PubMed] [Google Scholar]
- 21. Ronco C. Cardiorenal syndromes: definition and classification. Contrib Nephrol 2010. 164 33–38. [DOI] [PubMed] [Google Scholar]
- 22. McCullough PA, Verrill TA. Cardiorenal interaction: appropriate treatment of cardiovascular risk factors to improve outcomes in chronic kidney disease. Postgrad Med 2010. 122 25–34. [DOI] [PubMed] [Google Scholar]
- 23. Aso Y. Cardiovascular disease in patients with diabetic nephropathy. Curr Mol Med 2008. 8 533–543. [DOI] [PubMed] [Google Scholar]
- 24. Ueda S, Yamagishi S, Kaida Y, et al. Asymmetric dimethylarginine may be a missing link between cardiovascular disease and chronic kidney disease. Nephrology (Carlton) 2007. 12 582–590. [DOI] [PubMed] [Google Scholar]
- 25. Matsuo S, Imai E, Horio M, et al. Revised equations for estimated GFR from serum creatinine in Japan. Am J Kidney Dis 2009;53: 982–992. [DOI] [PubMed] [Google Scholar]
- 26. Charo IF, Taubman MB. Chemokines in the pathogenesis of vascular disease. Circ Res 2004. 95 858–866. [DOI] [PubMed] [Google Scholar]
- 27. Matsui T, Yamagishi S, Takeuchi M, et al. Nifedipine, a calcium channel blocker, inhibits advanced glycation end product (AGE)‐elicited mesangial cell damage by suppressing AGE receptor (RAGE) expression via peroxisome proliferator‐activated receptor‐gamma activation. Biochem Biophys Res Commun 2009. 385 269–272. [DOI] [PubMed] [Google Scholar]
- 28. Tesch GH. MCP‐1/CCL2: a new diagnostic marker and therapeutic target for progressive renal injury in diabetic nephropathy. Am J Physiol Renal Physiol 2008. 294 F697–F701. [DOI] [PubMed] [Google Scholar]
- 29. Stinghen AE, Gonçalves SM, Martines EG, et al. Increased plasma and endothelial cell expression of chemokines and adhesion molecules in chronic kidney disease. Nephron Clin Pract 2009. 111 c117–c26. [DOI] [PubMed] [Google Scholar]
- 30. Lee CD, Folsom AR, Nieto FJ, et al. White blood cell count and incidence of coronary heart disease and ischemic stroke and mortality from cardiovascular disease in African‐American and White men and women: atherosclerosis risk in communities study. Am J Epidemiol 2001. 154 758–764. [DOI] [PubMed] [Google Scholar]
- 31. Brown DW, Giles WH, Croft JB. White blood cell count: an independent predictor of coronary heart disease mortality among a national cohort. J Clin Epidemiol 2001. 54 316–322. [DOI] [PubMed] [Google Scholar]
- 32. Danesh J, Collins R, Appleby P, et al. Association of fibrinogen, C‐reactive protein, albumin, or leukocyte count with coronary heart disease: meta‐analyses of prospective studies. JAMA 1998. 279 1477–1482. [DOI] [PubMed] [Google Scholar]
- 33. Ridker PM, Cushman M, Stampfer MJ, et al. Inflammation, aspirin, and the risk of cardiovascular disease in apparently healthy men [published correction appears in N Engl J Med 1997;337:356]. N Engl J Med 1997. 336 973–979. [DOI] [PubMed] [Google Scholar]
- 34. Danesh J, Whincup P, Walker M, et al. Low grade inflammation and coronary heart disease: prospective study and updated meta‐analyses. BMJ 2000. 321 199–204. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35. Ridker PM, Rifai N, Rose L, et al. Comparison of C‐reactive protein and low‐density lipoprotein cholesterol levels in the prediction of first cardiovascular events. N Engl J Med 2002. 347: 1557–1565. [DOI] [PubMed] [Google Scholar]
- 36. Song C, Hsu K, Yamen E, et al. Serum amyloid A induction of cytokines in monocytes/macrophages and lymphocytes. Atherosclerosis 2009. 207 374–383. [DOI] [PubMed] [Google Scholar]
- 37. Inadera H, Egashira K, Takemoto M, et al. Increase in circulating levels of monocyte chemoattractant protein‐1 with aging. J Interferon Cytokine Res 1999. 19 1179–1182. [DOI] [PubMed] [Google Scholar]