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Relationships between serum MCP-1 and subclinical kidney disease: African American-Diabetes Heart Study



Monocyte chemoattractant protein-1 (MCP-1) plays important roles in kidney disease susceptibility and atherogenesis in experimental models. Relationships between serum MCP-1 concentration and early nephropathy and subclinical cardiovascular disease (CVD) were assessed in African Americans (AAs) with type 2 diabetes (T2D).


Serum MCP-1 concentration, urine albumin:creatinine ratio (ACR), estimated glomerular filtration rate (eGFR), and atherosclerotic calcified plaque (CP) in the coronary and carotid arteries and infrarenal aorta were measured in 479 unrelated AAs with T2D. Generalized linear models were fitted to test for associations between MCP-1 and urine ACR, eGFR, and CP.


Participants were 57% female, with mean ± SD (median) age 55.6±9.5 (55.0) years, diabetes duration 10.3±8.2 (8.0) years, urine ACR 149.7±566.7 (14.0) mg/g, CKD-EPI eGFR 92.4±23.3 (92.0) ml/min/1.73m2, MCP-1 262.9±239.1 (224.4) pg/ml, coronary artery CP 280.1±633.8 (13.5), carotid artery CP 47.1±132.9 (0), and aorta CP 1616.0±2864.0 (319.0). Adjusting for age, sex, smoking, HbA1c, BMI, and LDL, serum MCP-1 was positively associated with albuminuria (parameter estimate 0.0021, P=0.04) and negatively associated with eGFR (parameter estimate −0.0003, P=0.001). MCP-1 remained associated with eGFR after adjustment for urine ACR. MCP-1 levels did not correlate with the extent of CP in any vascular bed, HbA1c or diabetes duration, but were positively associated with BMI. No interaction between BMI and MCP-1 was detected on nephropathy outcomes.


Serum MCP-1 levels are associated with eGFR and albuminuria in AAs with T2D. MCP-1 was not associated with subclinical CVD in this population. Inflammation appears to play important roles in development and/or progression of kidney disease in AAs.

Peer Review reports


Inflammation, influx of circulating inflammatory cells, synthesis and secretion of chemokines and cytokines play important roles in diabetic kidney disease and atherosclerosis [1, 2]. The relationship between serum chemokine monocyte chemoattractant protein-1 (MCP-1, or CCL2) levels with kidney disease and subclinical cardiovascular disease (CVD) has not been evaluated in the African American (AA) population. Macrophages contribute to the pathophysiology of atherosclerosis, albuminuria, diabetic nephropathy (DN), and kidney failure [3, 4]. Macrophage trafficking and influx to the blood vessel wall is driven in part by chemokines, and MCP-1 inhibition delays formation of atherosclerotic plaque [5]. In experimental and human DN, macrophages are the principal infiltrating leukocyte population and the degree of macrophage influx and MCP-1 expression in the glomerular and interstitial compartments correlate with albuminuria and kidney function outcome [4, 68]. Experimentally, MCP-1 suppression ameliorated albuminuria and kidney interstitial disease [7].

Albuminuria and kidney disease are strongly linked with CVD. Presence of a graded association has been demonstrated between estimated glomerular filtration rate (eGFR) and albuminuria, with cardiovascular events, mortality, and presence and severity of coronary artery calcification (CAC) in European-derived populations [911]. Despite presence of more severe conventional CVD risk factors, AAs have markedly lower amounts of CAC, carotid artery CP, and aorta CP than EAs [12, 13], along with significantly reduced rates of myocardial infarction when provided equal access to healthcare [1416]. Relationships between conventional CVD risk factors and subclinical CVD do not appear to differ by race, suggesting that novel risk factors including cytokines and genetic variation may contribute to population-specific risks for CP and CVD [17].

As inflammation has emerged at the core pathophysiology of both diabetic nephropathy and atherosclerosis, we sought to investigate the relationships between serum MCP-1 concentrations with albuminuria, kidney function, and vascular calcification in a well-characterized cohort of AAs with type 2 diabetes (T2D) in the African American-Diabetes Heart Study (AA-DHS). Previous reports indicated that inflammation is a protracted process, occurring from the early stages of nephropathy (eGFR >90ml/min/1.73m2 and microalbuminuria) in patients with type 1 diabetes (T1D) [18, 19]. Presence of inflammation in patients with chronic kidney disease (CKD) has been associated with carotid intimal-medial thickness [20] and increased risk of cardiovascular death [21]. Similarly, vascular endothelial damage begins before it becomes clinically apparent, at early stages of kidney disease (GFR >90 ml/min/1.73m2) [22]. Elucidation of inflammatory markers with impact on early kidney disease and vascular dysfunction may guide innovative therapies to prevent or reverse nephropathy and/or vascular damage. We hypothesized that serum MCP-1 concentration, a surrogate of systemic and vascular inflammation, changes in T2D patients in relation to kidney function and vascular integrity. As such, the relationships between serum MCP-1 concentrations with early diabetic nephropathy and vascular calcified plaque were examined.


Study population

The AA-DHS is an observational study conducted on a cohort of self-reported and unrelated AAs with T2D lacking advanced nephropathy. Participants with advanced nephropathy or end-stage renal disease were excluded. Recruitment was conducted from internal medicine clinics and community advertising, as previously published [23]. Briefly, participant examinations were conducted in the Clinical Research Unit of Wake Forest Baptist Medical Center and included interviews for medical history and health behaviors, anthropometric measures, resting blood pressure (BP), electrocardiography, fasting blood sampling (total cholesterol, low density lipoprotein [LDL] cholesterol, high density lipoprotein [HDL] cholesterol, triglycerides, hemoglobin A1c [HbA1c, glucose and high sensitivity C-reactive protein [hsCRP]), spot urine collection for albumin:creatinine ratio (ACR), and computed tomography (CT).

History of CVD was provided by participant report and medical record review. Individuals with a history of myocardial infarction or stroke were included; however, CP scores in the coronary arteries were excluded in participants who underwent prior coronary artery bypass grafting and in the carotid arteries in participants who underwent carotid endarterectomy. We assessed eGFR using the simplified MDRD study and CKD-EPI equations [24, 25]. Serum creatinine concentration was measured using a modified kinetic Jaffe method and corrected for inter-laboratory differences and calibrated to the Cleveland Clinic [26]. Medications known to influence atherosclerosis (lipid lowering medications) and urine ACR (angiotensin-converting enzyme inhibitors [ACEi] and angiotensin-receptor blockers [ARB]) were recorded. The study was approved by the Institutional Review Board at the Wake Forest School of Medicine and all participants provided written informed consent.

Vascular imaging

CP in the coronary arteries (CAC), carotid arteries (CarCP), and infrarenal aorta (AorCP) were determined using multidetector computed tomography (MDCT4) with cardiac gating and capable of 500-millisecond temporal resolution using the segmented reconstruction algorithm (LightSpeed Qxi; General Electric Medical Systems, Waukesha, WI, USA). Techniques for the coronary and carotid scans have been described in detail [11]. In brief, participants were placed in the supine position on the CT couch over a quality control calibration phantom (Image Analysis, Inc., Columbia, KY, USA) for scans of the heart and abdomen. The abdomen scan series was used to measure AorCP. Technical factors for this series were: 120 kV, 250 mA, 0.8-second gantry rotation helical mode (7.5 mm/s), 2.5-mm slice thickness, and standard reconstruction kernel. The display field of view was 35 cm, resulting in a pixel dimension of 0.68 by 0.68 mm. CT scans of the three vascular territories were analyzed on a G.E. Advantage Windows Workstation with the SmartScores software package (General Electric Medical Systems) using a modified Agatston scoring method, which adjusts for slice thickness and uses the conventional threshold of 130 Hounsfield units.

MCP-1 assay

Serum MCP-1 was measured using an enzyme-linked immunosorbent assay (ELISA) (Quantikine® Human CCL2/MCP-1 ELISA; R&D Systems, Minneapolis) in freshly thawed serum samples which had been stored at -80C since collection. Analyses were performed in batches using ELISA kits from a single lot to minimize variability due to manufacturing variation. Intra- and inter-assay coefficients of variation for MCP-1 were 4.0%/3.4% at 62.5 pg/ml and 1.8%/2.1% at 500 pg/ml.

Statistical methods

Generalized linear models (GLM) were fitted to test for associations between serum MCP-1 concentration and diabetes duration, HbA1C, body mass index (BMI), urine ACR, eGFR, CAC, CarCP and AorCP [27]. MCP-1 values greater than 486.7 pg/ml, corresponding to the 95th percentile in the distribution, were winsorized to 486.7 [28]. The Box-Cox method was applied to identify the appropriate transformation best approximating the distributional assumptions of conditional normality and homogeneity of variance of the residuals [29]. This method suggested taking the natural log of (CAC+1), (CarCP+1) and (AorCP+1), (ACR+1), CRP, MDRD and CKD eGFR, the inverse of HbA1c and the inverse square root of BMI to minimize the influence of extremely large covariate values on parameter estimates in the models. No transformation was required for eGFR. GLM models were fitted using the winsorized values of MCP-1 as the dependent variable. After an unadjusted analysis, adjustments for age, sex, smoking, HbA1c, BMI, and LDL levels were incorporated. Urine ACR was analyzed both as categorical variable and as a continuous variable. The models used to test for association between BMI and HbA1c with MCP-1 contained one less variable than the fully adjusted models between MCP-1 and other variables. Inter-active effects between BMI and MCP-1 on kidney function measures were also performed. Interaction effects were evaluated by testing for the direct interaction effect by including the centered product of BMI by MCP-1 and performing the association analysis between MCP-1 and the kidney function measures stratified by BMI where the sample was stratified into two subgroups (non-obese: BMI <30.0 kg/m2 and obese: BMI ≥ 30.0 kg/m2). Type III sum of squares were also computed to evaluate the effect of eGFR adjusted for ACR (and vice-versa) and all other covariates on the vascular calcification and renal function measures.


The study included 479 unrelated AAs with T2D (57% women), 50.7% with hypertension (HTN), with mean ± SD (median) age 55.6 ± 9.5 (55.0) years, diabetes duration 10.3 ± 8.2 (8.0) years, and BMI 35.5 ± 8.7 (34.0) kg/m2 (Table 1). Participants were stratified by baseline urine ACR into non-albuminuric (urine ACR <30 mg/g; n=300) and albuminuric (urine ACR ≥30 mg/g; n=179). Characteristics of the cohort included serum MCP-1 levels 262.9 ± 239.1 (224.4) pg/ml, hsCRP 1.1 ± 1.8 (0.5) mg/dl, MDRD eGFR 95.2 ± 27.2 (93.3) ml/min/1.73m2, CKD-EPI eGFR 92.4 ± 23.3 (92.0) ml/min/1.73m2, and urine ACR 149.7 ± 566.7 (14.0) mg/g. There were no between gender differences in serum MCP-1 levels (267.8 ± 242.0 (229.3) pg/ml in women, and 256.5 ± 235.4 (212.8) pg/ml in men, P=0.26). CAC was present in 62.7% of participants, 48.5% had detectable CarCP, and 77.9% detectable AorCP. CKD-EPI and MDRD determined eGFRs were highly correlated (Spearman correlation =0.93).

Table 1 Demographic characteristics of study participants by urine albumin: creatinine ratio

Subjects with albuminuria had a longer diabetes duration by mean ± SD 2.1 ± 0.2 years (P=0.0007), higher prevalence of HTN (62% vs. 44%, P=0.0001), higher BP values with mean ± SD difference of 8.9 ± 5.4 mmHg in systolic BP (P<0.0001) and 2.9 ± 0.9 mmHg in diastolic BP (P=0.008), and were more often prescribed ARB and insulin (Table 1). Differences in biochemical parameters were also noted, with the albuminuric group having higher HbA1c, total cholesterol, triglycerides, and serum creatinine; and lower fasting glucose and HDL (Table 2). Modeled as a continuous variable, albuminuria was negatively associated with eGFR (parameter estimates and P-values of −0.0014 and 0.04 for CKD-EPI eGFR, and −0.0015 and 0.06 for MDRD eGFR).

Table 2 Laboratory and imaging data by urine albumin: creatinine ratio

In the univariate analysis, serum MCP-1 levels had negative association with Log (eGFR) and trended towards positive association with Log (urine ACR+1). Adjusted models including demographic characteristics (age, sex, smoking, BMI,) and laboratory values (HbA1c, LDL) maintained significant evidence of negative association between MCP-1 and Log (eGFR) (parameter estimate −0.0003, P=0.001) and detected significant positive association with urine ACR after logarithmic transformation (parameter estimate 0.0021, P=0.04) (Table 3). Since urine ACR is associated with eGFR, we analyzed the relationship between MCP-1 and eGFR based on adjusting for Log (ACR+1), in a fully adjusted model. Compared to ACR alone (parameter estimate −0.0135, P=0.05), MCP-1 had the strongest association with CKD-EPI eGFR (parameter estimate −0.0004, P=0.002) (Table 4).

Table 3 MCP-1 associations in the unadjusted and fully adjusted models
Table 4 Association between MCP-1, eGFR, and ACR in the fully adjusted model

We next assessed whether there is a correspondence between MCP-1, ACR, eGFR, and vascular CP. No association was detected between MCP-1 and CAC, CarCP, or AorCP in either unadjusted or adjusted models (Table 3). However, albuminuria was independently and significantly associated with vascular CP in all three vascular beds, while eGFR did not exhibit an association (Table 5).

Table 5 Type III mean squares and association between CP, ACR, and MCP-1

Relationships between serum MCP-1 with diabetes duration, BMI, and hsCRP were also assessed. No correlations were observed between serum MCP-1 and diabetes duration or hsCRP. Serum MCP-1 levels correlated with BMI, and this remained significant in the adjusted model (P=0.01) (Table 3). To assess whether BMI impacts the relationship between MCP-1 and kidney function, association analyses were run with participants stratified as obese (BMI ≥30.0) and non-obese (BMI <30.0). We found no evidence of an interaction effect between BMI and MCP-1 on either eGFR or urine ACR. As shown in Table 6, MCP-1 association parameters in obese participants were similar to those in the non-obese group (−0.0004 vs. -0.0004, P=0.75, for the interaction effect on Log (eGFR); and 0.0017 vs. 0.0013, P=0.68, for the effect on Log (urine ACR+1)). Evidence of an association between MCP-1 and kidney function remained significant in BMI stratified analyses, with meta-analysis P-value = 0.001 for Log (eGFR) and 0.04 for Log (urine ACR+1).

Table 6 Fully adjusted MCP-1 associations stratified by BMI

Discussion and conclusion

This large cross-sectional study characterized relationships between serum MCP-1, albuminuria, eGFR and CP in the understudied AA population with T2D. After adjusting for covariates, higher serum MCP-1 levels associated positively with albuminuria and negatively with eGFR. In contrast, serum MCP-1 did not independently associate with atherosclerosis and subclinical CVD measured as CP, suggesting differential molecular relationships between inflammation, risk for kidney disease, and CVD in AAs with T2D.

The pathophysiologic connection between atherosclerosis, CAC, albuminuria and kidney dysfunction is poorly understood at the molecular level. Previous studies demonstrated that MCP-1 is involved in the pathophysiology of atherosclerosis and DN in T1D and T2D [5, 7]. MCP-1 is synthesized and secreted by a myriad of cells (monocytes, macrophages, endothelial cells, renal mesangial and tubular cells); and both tissue and systemic cells can contribute to detectable serum MCP-1 levels. In the hyperglycemic milieu, MCP-1 is produced by resident renal endothelial cells, mesangial cells, podocytes, and tubular epithelial cells; as well as by circulating or infiltrating monocytes/macrophages [30]. Several reports attest to the positive correlation between tissue MCP-1 expression and urine levels with albuminuria, mesangial proliferation, and interstitial fibrosis in a wide range of kidney diseases in humans [8, 3136]. In small studies comprised of European-derived participants with T1D or T2D, ELISA-based measurements of serum MCP-1 did not correlate with albuminuria [30, 34]. It has been proposed that while the histopathology in diabetic kidney disease has remarkable similarity between type 1 and type 2 diabetes, and between population groups, the pathogenetic background may differ between AAs and EAs, and T2D or T1D [37]. Other longitudinal studies comprised of EAs with T1D, found that urine MCP-1 levels were significantly higher in patients with early nephropathy (GFR<90 ml/min and microalbuminuria) relative to those without nephropathy, with no difference in serum MCP-1 levels. [18, 19] Relative to EAs, it is possible that inflammatory pathways are upregulated in AAs. Previous studies have shown that AAs have higher serum CRP and interleukin-6 (IL-6) concentrations and display heightened oxidative stress and inflammation based on in vitro human umbilical vein endothelial cells (HUVECs) studies [38, 39]. It is biologically plausible that MCP-1 may play differential roles in the pathophysiology of DN based on the type of diabetes and ethnic background.

We originally postulated that inflammation is a common mediator for both subclinical kidney disease and CVD in AAs with T2D and that systemic MCP-1 levels would correlate with markers of kidney disease and atherosclerosis. We found that a higher burden of vascular calcification was present in those with albuminuria, but CP did not associate with serum MCP-1 levels. Other studies demonstrated that serum MCP-1 levels correlate with CVD outcomes following acute coronary events, independent of traditional CVD risk factors [40]. Nevertheless, these studies did not examine the effect of serum MCP-1 on CV events based on kidney function or independent of the association with urine albumin excretion and eGFR. As in the present report, a large population-based sample from the Dallas Heart Study did not observe an association between serum MCP-1 and CAC after adjusting for age and other covariates [41].

This is the first report of which we are aware detecting associations between serum MCP-1 with albuminuria and eGFR in AA patients with T2D and early nephropathy. Study participants were AAs without advanced kidney disease and no differences in serum MCP-1 levels were seen across genders. The nature of the factors determining elevated concentrations of serum MCP-1 in patients with T2D and early DN remains unknown. It is possible that high MCP-1 expression in the interstitial kidney macrophages leads to elevated systemic levels of MCP-1 proportional to the inflammatory and nephropathy stage. Another possibility, not mutually exclusive, is that serum MCP-1 levels are elevated in patients with early nephropathy due to dysregulated activation of systemic leukocytes. Indeed, several studies confirm an aberrant production of inflammatory cytokines and chemokines by circulating lymphocytes and monocytes in T2D patients with nephropathy [42]. Decreased filtration of extra-renally synthesized MCP-1 is less likely, since a minority of participants had an eGFR below 60 ml/min/1.73m2.

In addition to roles of MCP-1 in atherosclerosis and kidney disease, several studies implicated MCP-1 in the pathophysiology of obesity and insulin resistance [43, 44]. In our sample of AAs with T2D, significant correlations were observed between MCP-1 and BMI, but not with diabetes duration or HbA1c. The association between adipose tissue and MCP-1 raised the question whether the link between serum MCP-1 and renal function parameters could have been driven by the high prevalence of obesity in this cohort. Adjustment for BMI and cholesterol failed to modify the association and BMI-stratified effect sizes were not statistically different between obese and non-obese strata. As such, relationships between serum MCP-1 and kidney function were not impacted by obesity.

Significant relationships between MCP-1 with eGFR and albuminuria, coupled with lack of association with CP, imply that MCP-1 does not mediate joint pathways implicated in co-existing kidney and CVD. However, the lack of a cross-sectional association between MCP-1 and burden of CP in AAs does not exclude a role for this molecule in the inflammatory component of atherosclerosis. Previous studies have shown that serum MCP-1 levels are higher in patients with active angina (compared to those with stable coronary disease), and higher levels predicted future coronary events and mortality following an acute coronary event [45, 46]. In addition, serum MCP-1 levels have been associated with immunohistochemical indices of inflammation and matrix remodeling in the coronary atherosclerotic plaques of non-human primates [47]. The role of MCP-1 in CP should also be explored in EAs, a population with higher burden of vascular calcification than AAs [12, 13].

This study has important strengths and some limitations. AAs are known to display different patterns of nephropathy and CVD morbidity relative to EAs. The large and well phenotyped AA sample enabled simultaneous evaluation of a molecular biomarker potentially impacting albuminuria, eGFR, and subclinical atherosclerosis. Preserved kidney function in AA-DHS participants lessens concern that altered serum MCP-1 levels were due to kidney failure, whether mediated by poor renal excretion or inflammation-driven overproduction. Limitations include the cross-sectional nature of study measurements, rendering inability to secure a causal relationship between MCP-1 and early DN. Longitudinal studies characterizing relationships between MCP-1 and albuminuria and eGFR are warranted and could provide support for pharmacological MCP-1 inhibition during the incipient stages of DN [48]. Recent studies in mouse models suggest such treatment has promise [49].

In conclusion, MCP-1 serum concentrations manifest positive association with albuminuria and negative association with eGFR in AAs with T2D; without association with subclinical atherosclerosis. Relationships between MCP-1, albuminuria, eGFR, and vascular CP need to be evaluated in EAs and non-diabetic AAs. MCP-1 inhibition could provide a novel therapeutic strategy to prevent diabetic kidney disease in AAs with T2D.


HbA1c :

Hemoglobin A1c


African American(s)


African American-Diabetes Heart Study


Angiotensin-converting enzyme inhibitor


Urine albumin: creatinine ratio


Infrarenal aorta calcified plaque


Angiotensin receptor blocker


Body mass index


Blood pressure


Coronary artery calcified plaque


Carotid artery calcified plaque


Chemokine (C-C motif) ligand 2


Chronic kidney disease


Chronic Kidney Disease Epidemiology


Calcified plaque


Computed tomography


Cardiovascular disease


Diabetic nephropathy


European American(s)


Estimated glomerular filtration rate


High density lipoprotein


High sensitivity C-reactive protein




Low density lipoprotein


Monocyte chemoattractant protein-1


Modification of Diet in Renal Disease Study


Type 1 diabetes


Type 2 diabetes




  1. 1.

    Navarro-Gonzalez JF, Mora-Fernandez C, de Muros FM, Garcia-Perez J: Inflammatory molecules and pathways in the pathogenesis of diabetic nephropathy. Nat Rev Nephrol. 2011, 7: 327-340. 10.1038/nrneph.2011.51.

    CAS  Article  PubMed  Google Scholar 

  2. 2.

    Galkina E, Ley K: Immune and inflammatory mechanisms of atherosclerosis (*). Annu Rev Immunol. 2009, 27: 165-197. 10.1146/annurev.immunol.021908.132620.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  3. 3.

    McNeill E, Channon KM, Greaves DR: Inflammatory cell recruitment in cardiovascular disease: murine models and potential clinical applications. Clin Sci (Lond). 2010, 118: 641-655. 10.1042/CS20090488.

    CAS  Article  Google Scholar 

  4. 4.

    Nguyen D, Ping F, Mu W, Hill P, Atkins RC, Chadban SJ: Macrophage accumulation in human progressive diabetic nephropathy. Nephrology (Carlton). 2006, 11: 226-231. 10.1111/j.1440-1797.2006.00576.x.

    Article  Google Scholar 

  5. 5.

    Gosling J, Slaymaker S, Gu L, Tseng S, Zlot CH, Young SG, et al: MCP-1 deficiency reduces susceptibility to atherosclerosis in mice that overexpress human apolipoprotein B. J Clin Invest. 1999, 103: 773-778. 10.1172/JCI5624.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Young BA, Johnson RJ, Alpers CE, Eng E, Gordon K, Floege J, et al: Cellular events in the evolution of experimental diabetic nephropathy. Kidney Int. 1995, 47: 935-944. 10.1038/ki.1995.139.

    CAS  Article  PubMed  Google Scholar 

  7. 7.

    Chow FY, Nikolic-Paterson DJ, Ozols E, Atkins RC, Rollin BJ, Tesch GH: Monocyte chemoattractant protein-1 promotes the development of diabetic renal injury in streptozotocin-treated mice. Kidney Int. 2006, 69: 73-80. 10.1038/

    CAS  Article  PubMed  Google Scholar 

  8. 8.

    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. 10.1152/ajprenal.00016.2008.

    CAS  Article  PubMed  Google Scholar 

  9. 9.

    Gerstein HC, Mann JF, Yi Q, Zinman B, Dinneen SF, Hoogwerf B, et al: Albuminuria and risk of cardiovascular events, death, and heart failure in diabetic and nondiabetic individuals. JAMA. 2001, 286 (4): 421-426. 10.1001/jama.286.4.421.

    CAS  Article  PubMed  Google Scholar 

  10. 10.

    Arnlov J, Evans JC, Meigs JB, Wang TJ, Fox CS, Levy D, et al: Low-grade albuminuria and incidence of cardiovascular disease events in nonhypertensive and nondiabetic individuals: the Framingham Heart Study. Circulation. 2005, 112 (7): 969-975. 10.1161/CIRCULATIONAHA.105.538132.

    Article  PubMed  Google Scholar 

  11. 11.

    Freedman BI, Langefeld CD, Lohman KK, Bowden DW, Carr JJ, Rich SS, et al: Relationship between Albuminuria and Cardiovascular Disease in Type 2 Diabetes. J Am Soc Nephrol. 2005, 16: 2156-2161. 10.1681/ASN.2004100884.

    CAS  Article  PubMed  Google Scholar 

  12. 12.

    Bild DE, Detrano R, Peterson D, Guerci A, Liu K, Shahar E, et al: Ethnic differences in coronary calcification: the Multi-Ethnic Study of Atherosclerosis (MESA). Circulation. 2005, 111 (10): 1313-1320. 10.1161/01.CIR.0000157730.94423.4B.

    Article  PubMed  Google Scholar 

  13. 13.

    Freedman BI, Hsu FC, Langefeld CD, Rich SS, Herrington DM, Carr JJ, et al: The impact of ethnicity and sex on subclinical cardiovascular disease: the Diabetes Heart Study. Diabetologia. 2005, 48 (12): 2511-2518. 10.1007/s00125-005-0017-2.

    CAS  Article  PubMed  Google Scholar 

  14. 14.

    Hozawa A, Folsom AR, Sharrett AR, Chambless LE: Absolute and attributable risks of cardiovascular disease incidence in relation to optimal and borderline risk factors: comparison of African American with white subjects–Atherosclerosis Risk in Communities Study. Arch Intern Med. 2007, 167: 573-579. 10.1001/archinte.167.6.573.

    Article  PubMed  Google Scholar 

  15. 15.

    Young BA, Rudser K, Kestenbaum B, Seliger SL, Andress D, Boyko EJ: Racial and ethnic differences in incident myocardial infarction in end-stage renal disease patients: the USRDS. Kidney Int. 2006, 69: 1691-1698. 10.1038/

    CAS  Article  PubMed  Google Scholar 

  16. 16.

    Karter AJ, Ferrara A, Liu JY, Moffet HH, Ackerson LM, Selby JV: Ethnic disparities in diabetic complications in an insured population. JAMA. 2002, 287: 2519-2527. 10.1001/jama.287.19.2519.

    Article  PubMed  Google Scholar 

  17. 17.

    Wagenknecht LE, Divers J, Bertoni AG, Langefeld CD, Carr JJ, Bowden DW, et al: Correlates of coronary artery calcified plaque in blacks and whites with type 2 diabetes. Ann Epidemiol. 2011, 21: 34-41. 10.1016/j.annepidem.2010.10.007.

    Article  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Niewczas MA, Ficociello LH, Johnson AC, Walker W, Rosolowsky ET, Roshan B, et al: Serum concentrations of markers of TNFalpha and Fas-mediated pathways and renal function in nonproteinuric patients with type 1 diabetes. Clin J Am Soc Nephrol. 2009, 4: 62-70. 10.2215/CJN.03010608.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Wolkow PP, Niewczas MA, Perkins B, Ficociello LH, Lipinski B, Warram JH, et al: Association of urinary inflammatory markers and renal decline in microalbuminuric type 1 diabetics. J Am Soc Nephrol. 2008, 19: 789-797. 10.1681/ASN.2007050556.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Stenvinkel P, Heimburger O, Paultre F, Diczfalusy U, Wang T, Berglund L, et al: Strong association between malnutrition, inflammation, and atherosclerosis in chronic renal failure. Kidney Int. 1999, 55: 1899-1911. 10.1046/j.1523-1755.1999.00422.x.

    CAS  Article  PubMed  Google Scholar 

  21. 21.

    Stenvinkel P, Lindholm B, Heimburger M, Heimburger O: Elevated serum levels of soluble adhesion molecules predict death in pre-dialysis patients: association with malnutrition, inflammation, and cardiovascular disease. Nephrol Dial Transplant. 2000, 15: 1624-1630. 10.1093/ndt/15.10.1624.

    CAS  Article  PubMed  Google Scholar 

  22. 22.

    Mourad JJ, Pannier B, Blacher J, Rudnichi A, Benetos A, London GM, et al: Creatinine clearance, pulse wave velocity, carotid compliance and essential hypertension. Kidney Int. 2001, 59: 1834-1841. 10.1046/j.1523-1755.2001.0590051834.x.

    CAS  Article  PubMed  Google Scholar 

  23. 23.

    Wagenknecht LE, Bowden DW, Carr JJ, Langefeld CD, Freedman BI, Rich SS: Familial aggregation of coronary artery calcium in families with type 2 diabetes. Diabetes. 2001, 50: 861-866. 10.2337/diabetes.50.4.861.

    CAS  Article  PubMed  Google Scholar 

  24. 24.

    Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D: A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. modification of diet in renal disease study group. Ann Intern Med. 1999, 130 (6): 461-470.

    CAS  Article  PubMed  Google Scholar 

  25. 25.

    Levey AS, Stevens LA, Schmid CH, Zhang Y, Castro AF, Feldman HI, et al: A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009, 150: 604-612.

    Article  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Coresh J, Astor BC, McQuillan G, Kusek J, Greene T, Van LF, et al: Calibration and random variation of the serum creatinine assay as critical elements of using equations to estimate glomerular filtration rate. Am J Kidney Dis. 2002, 39: 920-929. 10.1053/ajkd.2002.32765.

    CAS  Article  PubMed  Google Scholar 

  27. 27.

    McCullagh P, Nelder J: Generalized Linear Models. 1989, Boca Raton, Florida: Chapman and Hall/CRC Publishers, Second

    Book  Google Scholar 

  28. 28.

    Hastings C, Mosteller F, Tukey JW, Winsor CP: Low moments for small samples: a comparative study of order statistics. Ann Math Stat. 1947, 18: 413-426. 10.1214/aoms/1177730388.

    Article  Google Scholar 

  29. 29.

    Box GEP, Cox DR: An analysis of tranformations. J R StatSoc, Series B. 1964, 26: 211-246.

    Google Scholar 

  30. 30.

    Banba N, Nakamura T, Matsumura M, Kuroda H, Hattori Y, Kasai K: Possible relationship of monocyte chemoattractant protein-1 with diabetic nephropathy. Kidney Int. 2000, 58: 684-690. 10.1046/j.1523-1755.2000.00214.x.

    CAS  Article  PubMed  Google Scholar 

  31. 31.

    Rovin BH, Doe N, Tan LC: Monocyte chemoattractant protein-1 levels in patients with glomerular disease. Am J Kidney Dis. 1996, 27: 640-646. 10.1016/S0272-6386(96)90097-9.

    CAS  Article  PubMed  Google Scholar 

  32. 32.

    Wada T, Yokoyama H, Su SB, Mukaida N, Iwano M, Dohi K, et al: Monitoring urinary levels of monocyte chemotactic and activating factor reflects disease activity of lupus nephritis. Kidney Int. 1996, 49: 761-767. 10.1038/ki.1996.105.

    CAS  Article  PubMed  Google Scholar 

  33. 33.

    Yokoyama H, Wada T, Furuichi K, Segawa C, Shimizu M, Kobayashi K, et al: Urinary levels of chemokines (MCAF/MCP-1, IL-8) reflect distinct disease activities and phases of human IgA nephropathy. J Leukoc Biol. 1998, 63: 493-499.

    CAS  PubMed  Google Scholar 

  34. 34.

    Wada T, Furuichi K, Sakai N, Iwata Y, Yoshimoto K, Shimizu M, et al: Up-regulation of monocyte chemoattractant protein-1 in tubulointerstitial lesions of human diabetic nephropathy. Kidney Int. 2000, 58: 1492-1499. 10.1046/j.1523-1755.2000.00311.x.

    CAS  Article  PubMed  Google Scholar 

  35. 35.

    Morii T, Fujita H, Narita T, Shimotomai T, Fujishima H, Yoshioka N, et al: Association of monocyte chemoattractant protein-1 with renal tubular damage in diabetic nephropathy. J Diabetes Complications. 2003, 17 (1): 11-15. 10.1016/S1056-8727(02)00176-9.

    Article  PubMed  Google Scholar 

  36. 36.

    Munshi R, Johnson A, Siew ED, Ikizler TA, Ware LB, Wurfel MM, et al: MCP-1 Gene Activation Marks Acute Kidney Injury. J Am Soc Nephrol. 2011, 22: 165-175. 10.1681/ASN.2010060641.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Ruggenenti P, Remuzzi G: Nephropathy of type 1 and type 2 diabetes: diverse pathophysiology, same treatment?. Nephrol Dial Transplant. 2000, 15: 1900-1902. 10.1093/ndt/15.12.1900.

    CAS  Article  PubMed  Google Scholar 

  38. 38.

    Paalani M, Lee JW, Haddad E, Tonstad S: Determinants of inflammatory markers in a bi-ethnic population. Ethn Dis. 2011, 21: 142-149.

    PubMed  PubMed Central  Google Scholar 

  39. 39.

    Feairheller DL, Park JY, Sturgeon KM, Williamson ST, Diaz KM, Veerabhadrappa P, et al: Racial differences in oxidative stress and inflammation: in vitro and in vivo. Clin Transl Sci. 2011, 4: 32-37. 10.1111/j.1752-8062.2011.00264.x.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Gonzalez-Quesada C, Frangogiannis NG: Monocyte chemoattractant protein-1/CCL2 as a biomarker in acute coronary syndromes. Curr Atheroscler Rep. 2009, 11: 131-138. 10.1007/s11883-009-0021-y.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Deo R, Khera A, McGuire DK, Murphy SA, Meo Neto JP, 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: 1812-1818. 10.1016/j.jacc.2004.07.047.

    CAS  Article  PubMed  Google Scholar 

  42. 42.

    Wong CK, Ho AW, Tong PC, Yeung CY, Kong AP, Lun SW, et al: Aberrant activation profile of cytokines and mitogen-activated protein kinases in type 2 diabetic patients with nephropathy. Clin Exp Immunol. 2007, 149: 123-131. 10.1111/j.1365-2249.2007.03389.x.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Sartipy P, Loskutoff DJ: Monocyte chemoattractant protein 1 in obesity and insulin resistance. Proc Natl Acad Sci USA. 2003, 100: 7265-7270. 10.1073/pnas.1133870100.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Sell H, Eckel J: Monocyte chemotactic protein-1 and its role in insulin resistance. Curr Opin Lipidol. 2007, 18: 258-262. 10.1097/MOL.0b013e3281338546.

    CAS  Article  PubMed  Google Scholar 

  45. 45.

    Matsumori A, Furukawa Y, Hashimoto T, Yoshida A, Ono K, Shioi 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. 10.1006/jmcc.1996.0285.

    CAS  Article  PubMed  Google Scholar 

  46. 46.

    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: 690-695. 10.1161/01.CIR.0000049742.68848.99.

    CAS  Article  PubMed  Google Scholar 

  47. 47.

    Register TC, Cann JA, Kaplan JR, Williams JK, Adams MR, Morgan TM, et al: Effects of soy isoflavones and conjugated equine estrogens on inflammatory markers in atherosclerotic, ovariectomized monkeys. J Clin Endocrinol Metab. 2005, 90: 1734-1740.

    CAS  Article  PubMed  Google Scholar 

  48. 48.

    Xia M, Sui Z: Recent developments in CCR2 antagonists. Expert Opin Ther Pat. 2009, 19: 295-303. 10.1517/13543770902755129.

    CAS  Article  PubMed  Google Scholar 

  49. 49.

    Kang YS, Lee MH, Song HK, Ko GJ, Kwon OS, Lim TK, et al: CCR2 antagonism improves insulin resistance, lipid metabolism, and diabetic nephropathy in type 2 diabetic mice. Kidney Int. 2010, 78: 883-894. 10.1038/ki.2010.263.

    CAS  Article  PubMed  Google Scholar 

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This study was supported in part by the General Clinical Research Center of the Wake Forest University School of Medicine grant M01 RR07122; and NIDDK grant RO1 DK071891 (BIF). The investigators acknowledge the cooperation of our participants and study recruiter Cassandra Bethea. The authors report no conflicts of interest.

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Corresponding authors

Correspondence to Mariana Murea or Barry I Freedman.

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The authors report no conflicts of interest.

Authors’ contributions

MM – study design, manuscript preparation; TCR – study design, MCP-1 assays, manuscript preparation; JD – statistical analysis, manuscript editing; DWB – manuscript editing; JJC – radiologic imaging and interpretation, manuscript editing; CRH – radiologic imaging interpretation; JX – database management; CSS – participant recruitment, manuscript editing; CDL – statistical analysis, manuscript editing; KAH – study design, manuscript editing; BIF – study design, participant recruitment, supervision of data analyses, manuscript preparation. All authors read and approved the final manuscript.

Mariana Murea, Thomas C Register contributed equally to this work.

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Murea, M., Register, T.C., Divers, J. et al. Relationships between serum MCP-1 and subclinical kidney disease: African American-Diabetes Heart Study. BMC Nephrol 13, 148 (2012).

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  • African Americans
  • Albuminuria
  • Atherosclerotic calcified plaque
  • Diabetes
  • GFR
  • MCP-1