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Published ahead of print on June 18, 2008
Clin J Am Soc Nephrol 3: 1289-1295, 2008
© 2008 American Society of Nephrology
doi: 10.2215/CJN.00010108
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Clinical Nephrology

Coronary Artery Calcification, ADMA, and Insulin Resistance in CKD Patients

Shuzo Kobayashi, Machiko Oka, Kyoko Maesato, Ryota Ikee, Tsutomu Mano, Moriya Hidekazu, and Takayasu Ohtake

Department of Nephrology, and Kidney & Dialysis Center, Shonan Kamakura General Hospital, Kamakura, Kanagawa, Japan

Correspondence: Dr. Shuzo Kobayashi, Shonan Kamakura General Hospital, 1202–1 Yamazaki, Kamakura, Kanagawa, 247-8533, Japan. Phone: 81-467-32-9071; Fax: 81-467-32-9148; E-mail: shuzo{at}shonankamakura.or.jp


   Abstract
Top
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 Conclusion
 Disclosures
 References
 
Background and objectives: It is known that coronary artery calcification (CAC) develops in chronic kidney disease (CKD) before initiation of renal replacement therapy, and factors associated with CKD mineral and bone disorders (CKD-MBDs) are involved. However, little information is available about any association between plasma levels of asymmetric dimethylarginine (ADMA), insulin resistance, and CAC.

Design, setting, participants, & measurements: A total of 111 CKD patients (79 men, 32 women; glomerular filtration rate [GFR] median, 33.7 ml/min per 1.73 m2), free of cardiovascular disease, were consecutively recruited along with 30 age-matched healthy subjects. Coronary artery calcification scores (CACS) were measured by multidetector-row CT according to Agatston score.

Results: In CKD patients, CACS was distributed widely from 0 to 2901, while in age-matched, healthy control subjects (n = 30), CACS showed a range from 0 to 307. GFR had a significant negative correlation with CACS. Plasma ADMA levels were negatively correlated with GFR and positively correlated with CACS. When CACS was divided into quartiles (<50, n = 56; 50 to 300, n = 24; 300 to 600, n = 14; >600, n = 17), the patients with CACS >600 had significantly higher values of HOMA-IR, plasma ADMA levels, and fibrinogen along with serum levels of phosphorus, compared with those in patients having CACS <50. Multivariate regression analysis determined HOMA-IR as an independent contributing factor to CACS.

Conclusions: CAC becomes more prevalent and severe with a decline in GFR, and plasma ADMA levels and insulin resistance, independent of factors associated with CKD-MBD, are correlated with CAC.


   Introduction
Top
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 Conclusion
 Disclosures
 References
 
Coronary artery calcification (CAC) is regarded as an index of the severity of atherosclerotic vascular disease and may predict future adverse cardiovascular events in patients on dialysis (1–3). In patients with chronic kidney disease (CKD) before initiation of renal replacement therapy, CAC is already present in the early phase of CKD (4–6) and among diabetic nephropathy (7). Associated factors with CAC besides age, calcium, phosphorus, iPTH, and inflammation have not been fully elucidated.

Insulin resistance is known to play an important role for atherosclerosis (8) and to develop at an early stage of nondiabetic CKD in U.S. general populations (9). We report a similar result using a hyperinsulinemic euglycemic glucose clamp method and also showed that acidemia and dyslipidemia are independently associated with insulin resistance in CKD (10). In progressive renal disease, the relationship between hyperinsulinemia and hypertension is well documented (11). Arad et al. have reported that asymptomatic individuals with insulin resistance have elevated coronary calcification in general populations (12). However, it remains unknown whether insulin resistance is also correlated with CAC in CKD patients. Insulin resistance leading to atherosclerosis may be explained by increased plasma levels of asymmetric dimethylarginine (ADMA), an endogenous nitric oxide synthase inhibitor (13). Concentrations of ADMA are related to endothelial dysfunction (14,15) because increased ADMA may impair blood flow, accelerate atherogenesis, and interfere with angiogenesis by inhibiting the production of nitric oxide (16). Of note, ADMA concentrations are higher in dialysis patients with clinically manifest atherosclerosis than in those without atherosclerotic disease (17), which suggests that accumulation of ADMA might be an important cardiovascular risk factor in end-stage renal disease. Moreover, the clinical importance of ADMA in coronary artery disease is highlighted in a recent trial by Meinitzer et al. (18) in which ADMA predicted cardiovascular events in 3200 patients. In addition, ADMA predicts coronary events in middle-aged white men (19).

With this background in mind, we aimed to study the prevalence and associated factors of CAC in CKD patients before initiation of renal replacement therapy. Particularly, we wanted to know whether insulin resistance and/or plasma levels of ADMA would be correlated with CAC.


   Materials and Methods
Top
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 Conclusion
 Disclosures
 References
 
Study Design and Subjects
We conducted a cross-sectional, observational study in our hospital. A total of 178 CKD patients (121 men, 57 women) were included. The inclusion criteria were as follows: 1) the patients were consecutively recruited between March 2006 and June 2006 from our Renal Regression Clinic; 2) the patients were diagnosed by renal biopsy as idiopathic chronic glomerulonephritis, tubulointerstitial nephritis, or benign nephrosclerosis; and 3) the patients with less than 60 ml/min/1.73 m2 of 24-h creatinine clearance in case of the patients without renal biopsy, or the patients who did not show any urine abnormality (more than positive of dip stick test for urine protein or for occult urine red blood cell). The exclusion criteria were as follows: 1) the patients who did not give informed consent, 2) the patients who were not examined by mutidetector-row computed tomography (MDCT), 3) the patinets who showed congestive heart failure, 4) the patients with malignancy, or systemic disorders, including chronic liver disease, systemic lupus erythematosus, 5) the patients were free of symptomatic cardiovascular disease or any previous history of myocardial infarction, angina, or percutaneous coronary intervention. A total of 111 CKD patients (79 men, 32 women) were finally included in the present study. Of 111 patients, 38 (34%) had type 2 diabetes mellitus (DM). None of the patients with type 2 DM was treated with insulin. The study protocol was approved by the research and ethics committee of the Shonan Kamakura General Hospital. As age-matched control subjects, 30 individuals without having any diseases other than hypertension, who visited to our hospital for their health checkup, were also studied.

Blood Sampling
Blood was drawn in the morning after an overnight fast of at least 12 h. EDTA-plasma was used for glucose, insulin, and lipids, ADMA and serum for other biochemical assays. Glucose was measured by a glucose oxidase method. Insulin was measured by RIA (Insulin RIA-BEAD II, Dinabot, Tokyo, Japan). Total cholesterol and triglycerides were measured enzymatically. HDL-cholesterol was measured after precipitating apolipoprotein B-containing lipoproteins with dextran sulfate and magnesium chloride. High-sensitive C-reactive protein was measured by a nephelometric immunoassay.

Measurement of GFR and Proteinuria
Using an average value of 2 urine samplings, we measured 24-h creatinine clearance as GFR, and 24 h urinary protein excretion, and expressed as ml/min per 1.73 m2, and g/d, respectively.

Assessment of Insulin Resistance Using HOMA-IR
Insulin resistance was assessed using the homeostasis model assessment (HOMA-IR) originally described by Matthew et al. (20). HOMA-IR was calculated using the following formula: HOMA-IR = fasting glucose (mmol/L) x fasting insulin (µU/ml)/22.5.

Measurement of ADMA
According to the method described by Anderstam et al. (21), concentrations of ADMA in plasma were measured by HPLC, by precolumn derivatization with o-phthalaldehyde, after removal of plasma samples with carboxylic acid solid-phase extraction cartrides. The assay was done in the manufacturer outside our hospital (SRL, Tokyo, Japan). The detection limit of this assay was 0.1 µmol/L. The coefficients of variation of the method were 5.2% intra-assay and 5.5% interassay.

Examination by MDCT
According to the method described by Horiguchi et al. (22), we calculated the Agatston score (23) using MDCT (LightSpeed Ultrafast 16, General Electric Medical System, Tokyo, Japan), in which score is well correlated with that measured by electron-beam CT (22). Volumetric data of the entire heart were obtained in helical mode with scanning parameters of 1.25-mm collimation width x16 detectors, a gantry rotation speed of 0.5 s per rotation, 120 kV, and 100 mA. Pitches were variable according to the heart rate. Images of 2.5-mm thickness with the center of the temporal window corresponding to 80% of the R-R interval were reconstructed with 2.5-mm spacing. Calcium score, volume, and mass were determined on a commercially available external workstation (Adventure Windows, version 4.4.1, General Electric Medical System, Tokyo, Japan) using CAC-scoring software (version 3.5, Smartscore, Tokyo, Japan), with MDCT. According to the Agatston method (23), we defined the regions of interest by vessel and slice with the threshold option for pixels greater than 130 Hounsfield units to measure the area and peak density of plaques.

Statistical Analysis
Continuous variables were expressed as mean ± SD. Because GFR and CACS were not normally distributed, these were expressed as median (med) and interquartile range (IQR). In comparison between 2 groups, t test or Mann-Whitney U test was used as appropriate. HOMA-IR, serum levels of hsCRP, and CACS underwent log transformation before statistical analysis. For comparisons among groups more than three groups, one-way analysis of variance was used followed by Dunnett test. Univariate or multivariate regression analysis was also applied for the determinants of CAC. Only when we study an independent contribution factor for plasma levels of ADMA was stepwise multiple regression analysis was used. The F value for a candidate's entrance or removal from the discriminant function test was automatically set at 4.0. A P value of less than 0.05 was considered statistically significant. These were analyzed using statistical software (StatView 5, SAS Institute, Cary, NC) for Windows personal computer.


   Results
Top
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 Conclusion
 Disclosures
 References
 
The present study included CKD patients with a range of 8.9 to 120 ml/min/1.73 m2 (33.7, 30.5 ml/min/1.73 m2) (med, IQR) of GFR expressed as 24-h creatinine clearance. Basic characteristics in CKD patients are shown in Table 1. CACS were distributed widely from 0 to 2901 (45, 338) (med, IQR), while in age-matched, control subjects (Table 2, n = 30), CACS showed a range from 0 to 307 (2.8, 61.9) (med, IQR). GFR was negatively correlated with CACS (r = 0.218, P < 0.05; Figure 1). Particularly, <60 ml/min/1.73 m2 of GFR, CACS more significantly increased (88, 394 in GFR <60 ml/min/1.73 m2 versus 25, 156 in GFR >60 ml/min/1.73 m2, P < 0.05) (med, IQR). There was no difference in CACS between diabetic and nondiabetic patients although there was a tendency of higher prevalence of CAC (log CACS; 2.11 ± 1.12 versus 1.93 ± 0.93 in DM, non-DM, respectively). As shown in Table 3, CACS also had positive significant associations with plasma levels of ADMA (P = 0.016, r = 0.259), fibrinogen (P = 0.002, r = 0.294), and serum levels of phosphate, Ca x P, log HOMA-IR, and marginally significant correlations with age (P = 0.065), serum creatinine levels (P = 0.063), and log CRP (P = 0.09). Multivariate regression analysis, when significant factors chosen by univariate regression analysis were entered, determined HOMA-IR (95% CI, 4.15 to 32.3; β = 0.257, P = 0.011) as an independent contributing factor for CACS (R2 = 0.168; Table 4). When CACS was divided into quartiles (<50, n = 56; 50 to 300, n = 24; 300 to 600, n = 14; >600, n = 17), the patients with CACS >600 had significantly higher values of log HOMA-IR, plasma ADMA and fibrinogen levels, serum creatinine, phosphorus, Ca x P, and significantly lower values of 24-h Ccr, compared with those in patients <50 of CACS (Table 5; Figure 2). Plasma ADMA levels were negatively correlated with GFR (r = –0.551, P < 0.001) and positively correlated with CACS (r = 0.259, P < 0.05). When we study the independent contribution factors for plasma levels of ADMA, stepwise multiple regression analysis chose serum levels of creatinine (β = 0.384, F = 10.46), 24-h creatinine clearance (β = –0.272, F = 5.24) and urinary protein excretion (β = 0.241, F = 4.24). There was also a significant positive correlation between tHcy and Ccr (r = 0.370, P = 0.0004).


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  		Table 1. Basic characteristics of patients

 

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  		Table 2. Basic characteristics of control subjects (n = 30)

 

Figure 1
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  		Figure 1. The significant relationship between CACS and GFR is shown (r = 0.218, P < 0.05). When GFR decreases particularly below 60 ml/min per 1.73 m2, CACS rapidly increases.

 

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  		Table 3. Univariate regression analysis associated with CACS

 

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  		Table 4. Multivariate regression analysis associated with CACS (R2 = 0.168)

 

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  		Table 5. When CACS was divided into quartiles (<50, n = 56; 50-300, n = 24; 300-600, n = 14; >600, n = 17), patients with CACS > 600 had significantly higher values of log HOMA-IR, lower Ccr, higher serum or plasma levels of phosphorus, fibrinogen, and ADMA, compared with patients with CACS < 50

 

Figure 2
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  		Figure 2. When CACS was divided into quartiles, the patients with CACS >600 had significantly higher values of log HOMA-IR compared with those in patients with CACS <50 (*P < 0.05). y-axis shows log HOMA values.

 
When all data including control subjects and CKD patients were analyzed together, univariate regression analysis showed that ADMA levels (r = 0.294, P = 0.0013), log HOMA-IR (r = 0.266, P = 0.0019), and serum creatinine levels (r = 0.250, P = 0.0028) still demonstrated significant positive associations with CACS.


   Discussion
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
Conclusion
 Disclosures
 References
 
Although CAC is known to equate to occlusive coronary artery disease (24), it is also reported that CAC is not an accurate marker of the degree of vessel stenosis (25). However, CAC is a generally well-accepted surrogate marker of coronary artery atherosclerosis (1).

Our results showed that coronary artery calcification became more prevalent and severe with a decline in GFR, particularly <60 ml/min/1.73 m2. In CKD patients, it is known that renal function is inversely associated with CAC (6). Our results support this evidence.

Several potential causative factors have been suggested to be responsible for CAC, including calcium, phosphorus, inflammation, and age. In the present study, we first revealed that insulin resistance contributed to CAC in CKD patients independent of factors associated with CKD-MBD, which might be caused by endothelial dysfunction evidenced by increased plasma levels of ADMA. In advanced renal failure, there is an accumulation of a naturally occurring inhibitor of NO synthase (26). This endogenous inhibitor ADMA was found to be elevated in patients with CKD and atherosclerotic disease (17). It is also shown that, even in mild incipient renal disease, the level of ADMA in plasma is already threefold higher (27). In hemodialysis patients, plasma ADMA is a strong and independent predictor of overall mortality and cardiovascular outcome (28). Plasma levels of ADMA in our study were lower than those in the study reported by Kielstein et al. (27). However, in the report by Anderstam et al., plasma levels of ADMA in control subjects were comparable to ours (21). The main reason of the difference in plasma levels of ADMA would be the protein precipitation steps, performed by most but not all laboratories. Protein precipitation results in lower ADMA levels, probably because only the non–protein-bound portion of circulating ADMA is assessed (29).

Regarding the correlation between GFR and ADMA, Kielstein et al. (27) reported that plasma levels of ADMA are not correlated with GFR, whereas our results were correlated with GFR. Fliser et al. (30) have reported that plasma levels of ADMA were negatively correlated with GFR. The reason why there is a discrepancy in association of GFR with CACS between these studies remains unknown.

The present study suggests the link between CACS and ADMA, which has been already reported by Iribarren et al. (31). Regarding one of the possible mechanisms, Suda et al. describe that ADMA infusion leads to coronary microvascular lesions in mice (32). Likewise, It has been recently shown by Kielstein et al. (33) that ADMA infusion decreased renal (microvascular) blood flow in men in a dose-dependent manner and leads to arterial stiffness.

Increased plasma levels of ADMA, however, did not remain significant after an adjustment of HOMA-IR. Therefore, insulin resistance may play an important and predominant role for CAC, being upstream of endothelial dysfunction evidenced by increased plasma levels of ADMA. Interestingly, Sydow et al. (34) recently reported that ADMA contributes to the insulin resistance, providing a new link between insulin resistance and ADMA.

It has been shown among individuals with type II diabetes that the presence of nephropathy is associated with a significant higher probability and rate of progression of CAC compared with diabetes-duration matched subjects with normoalbuminuria (7). In the present study, there was no difference in the prevalence of CAC between diabetic and nondiabetic patients, although there was a tendency of higher prevalence of CAC in diabetic patients.

Recently, it has been reported that the patients with proteinuria have higher ADMA levels along with impaired insulin sensitivity (35). Increased ADMA appears to be a potential link between proteinuria and endothelial dysfunction and atherosclerotic complications. Our study confirmed that, as independent risk factors for ADMA, proteinuria along with a decline in GFR was chosen.

This study, however, has limitations. The cross-sectional nature of our observations precludes cause-effect inferences about the links between CAC, ADMA, and insulin resistance. Yet the internal consistency and the strength of the associations of CAC with insulin resistance and ADMA that emerged from our study form a convincing basis for conducting a cohort and intervention studies.


   Conclusion
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Conclusion
Disclosures
 References
 
Coronary artery calcification becomes more prevalent and severe with a decline in GFR, particularly when <60 ml/min/1.73 m2 independent of factors associated with CKD-MBD. Insulin resistance, probably through endothelial dysfunction evidenced by increased plasma levels of ADMA, plays an important role for CAC in CKD patients.


   Disclosures
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Conclusion
 Disclosures
References
 
None.


   Footnotes
 
Published online ahead of print. Publication date available at www.cjasn.org.

Received January 1, 2008. Accepted April 24, 2008.


   References
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Conclusion
 Disclosures
 References
 

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