Nonculprit Coronary Plaque Characteristics of Chronic Kidney Disease
Abstract
Background—
Chronic kidney disease (CKD) promotes the development of atherosclerosis and increases the risk of cardiovascular disease. The aim of the present study was to compare the coronary plaque characteristics of patients with and without CKD using optical coherence tomography.
Methods and Results—
We identified 463 nonculprit plaques from 287 patients from the Massachusetts General Hospital (MGH) optical coherence tomography registry. CKD was defined as estimated glomerular filtration rate <60 mL/min per 1.73 m2. A total of 402 plaques (250 patients) were in the non-CKD group and 61 plaques (37 patients) were in the CKD group. Compared with non-CKD plaques, plaques with CKD had a larger lipid index (mean lipid arc×lipid length, 1248.4±782.8 mm° [non-CKD] versus 1716.1±1116.2 mm° [CKD]; P=0.003). Fibrous cap thickness was not significantly different between the groups. Calcification (34.8% [non-CKD] versus 50.8% [CKD]; P=0.041), cholesterol crystals (11.2% [non-CKD] versus 23.0% [CKD]; P=0.048), and plaque disruption (5.5% [non-CKD] versus 13.1% [CKD]; P=0.049) were more frequently observed in the CKD group. In the multivariate linear regression model, a lower estimated glomerular filtration rate and diabetes mellitus were independent risk factors for a larger lipid index.
Conclusions—
Compared with non-CKD patients, the patients with CKD had a larger lipid index with a higher prevalence of calcium, cholesterol crystals, and plaque disruption. The multivariate linear regression model demonstrated that a lower estimated glomerular filtration rate was an independent risk factor for a larger lipid index.
Clinical Trial Registration—
URL: http://www.clinicaltrials.gov. Unique identifier: NCT01110538.
Introduction
The impact of chronic kidney disease (CKD) on cardiovascular and all-cause mortality has been well established.1–3 It has been reported that renal function deterioration increases the risk of not only the new onset of acute coronary syndrome (ACS)1–3 but also the major adverse clinical events during and after percutaneous coronary intervention.4,5 Although the underlying pathophysiology is probably related to plaque type, the plaque characteristics of CKD patients have not been clearly identified. Pathology studies have demonstrated that coronary atherosclerosis and calcification were more frequent and advanced in patients with CKD compared with non-CKD patients.6,7 A subgroup analysis of the Providing Regional Observation to Study Predictors of Events in the Coronary Tree (PROSPECT) study showed that CKD patients had a larger plaque burden, a greater necrotic core, and dense calcium in their nonculprit lesions.8 The underlying mechanism for poor outcomes and high recurrent ischemic events in CKD patients has not been fully elucidated. In vivo coronary plaque characteristics in CKD patients may help us to understand the underlying pathophysiology. Optical coherence tomography (OCT) is a high-resolution intravascular imaging modality that allows a detailed assessment of coronary plaque morphology. The aim of the present study was to characterize the nonculprit plaques in patients with kidney disease.
Clinical Perspective on p 456
Methods
Study Population
The MGH OCT Registry is an ongoing multicenter registry of patients who have had OCT imaging during their coronary catheterization and includes subjects from 20 sites across 6 countries.9,10 Between August 2010 and September 2011, 912 subjects were enrolled in the registry. Among these, 53 subjects (5.8%) were excluded because of poor image quality. Plaques were identified when the diameter stenosis was >30% by OCT as compared with the reference vessel. We did not identify any native coronary artery plaques in 485 subjects (390 subjects had only poststent images, and 95 subjects had only stent restenosis lesion imaging). An additional 5 subjects were excluded because they were already on hemodialysis. On the basis of angiograms, any plaque treated by percutaneous coronary intervention was defined as a culprit plaque and was excluded. All culprit lesions were identified by the operators using a combination of ECG findings, left ventricular wall motion abnormalities, scintigraphic defects, and angiographic lesion morphology. Eighty-two subjects were excluded because of the acquired OCT images only containing the culprit plaque. Therefore, the final analysis included the remaining 287 subjects with 463 nonculprit plaques (Figure 1).
Figure 1. The patient count breakdown outline. CKD indicates chronic kidney disease.
The exact location of the OCT catheter identified as the OCT pullback was recorded simultaneously with contrast injection. Coregistration of plaques on both OCT images and angiograms was performed using fiduciary landmarks such as side branches or implanted stents. All plaques were divided into coronary artery surgery segment groups for plaque identification. Plaque analysis was performed in each segment. Each plaque was separated at least 5 mm from the edge of another plaque or from an implanted stent edge as seen on the longitudinal OCT pullback. Any plaque continuous to an implanted stent was excluded. The estimated glomerular filtration rate (eGFR) was calculated using the Modification of Diet in Renal Disease equation: eGFR (mL/min per 1.73 m2)=175×(Scr)−1.154×(age)−0.203×0.742 (if female)×1.210 (if black), and CKD was defined as an eGFR <60 mL/min per 1.73 m2.11 The registry was approved by each site’s institutional review board, and all patients provided informed consent.
Acquisition of OCT Images
Images were acquired using a commercially available frequency domain (C7-XR OCT Intravascular Imaging System, St. Jude Medical, St. Paul, MN) or time domain (M2/M3 Cardiology Imaging System, LightLab Imaging, Inc, Westford, MA) OCT system. The intracoronary OCT imaging technique has been described previously.10 In brief, in the frequency domain OCT system, a 2.7F OCT imaging catheter (Dragonfly, LightLab Imaging, Inc) is advanced distal to the lesion, and automated pullback is initiated in concordance with blood clearance by the injection of contrast media or Dextran. In the time domain OCT system, an occlusion balloon (Helios, LightLab Imaging, Inc) is inflated proximal to the lesion at 0.4 to 0.6 atm during image acquisition. The imaging wire is automatically pulled back from a distal to a proximal position at a rate of 1.0 to 3.0 mm/s, and saline is continuously infused from the tip of the occlusion balloon. The frequency domain OCT system was used in 22.6% of non-CKD patients and 29.5% of CKD patients (P=0.258). All images were deidentified and digitally stored.
OCT Data Analysis
Plaques were classified into 2 categories: (1) fibrous (homogeneous, high backscattering region) or (2) lipid (low-signal region with diffuse border).12–14 When lipid was present in ≥90° in any of the cross-sectional images within the plaque, it was considered to be a lipid-rich plaque.14–17 In lipid-rich plaques, the lipid arc was measured on the cross-sectional view at 1-mm intervals over the entire length, and the values were averaged. Lipid length was also measured on a longitudinal view. Lipid index was defined as the mean lipid arc multiplied by lipid length.10 The fibrous cap thickness of a lipid-rich plaque was measured 3 times at its thinnest part, and the average value was calculated (Figure 2).10 Thin-cap fibroatheroma was defined as a lipid-rich plaque with fibrous cap thickness ≤65 μm at the thinnest part on a cross-sectional image.18 Calcification was also recorded when an area showed a signal-poor or heterogeneous region with a sharply delineated border (Figure 3A).12,18 Macrophage accumulations were defined as signal-rich, distinct, or confluent punctuate regions that exceeded the intensity of background speckle noise (Figure 3B).18–20 Cholesterol crystals were defined as thin, linear regions of high intensity within the plaque (Figure 3C).18 Microchannels were defined as signal-poor voids that were sharply delineated in multiple contiguous frames (Figure 3D).12,18,21 Plaque disruption was identified by the presence of fibrous cap discontinuity with a clear cavity formation inside the plaque.12,14 Intracoronary thrombus was defined as a mass (diameter ≥250 μm) attached to the luminal surface or floating within the lumen, including red (red blood cell–rich) thrombus, which showed high backscattering with high attenuation (resembling blood) (Figure 3E), and white (platelet-rich) thrombus, which showed less backscattering, was homogeneous and had low attenuation (Figure 3F).12,14,18,22 Calcification, macrophage accumulations, cholesterol crystals, microchannels, plaque disruption, and thrombus were recorded only for their presence. The OCT data were analyzed using proprietary software (LightLab Imaging, Inc) at an independent MGH OCT core laboratory by 2 experienced investigators who were blinded to the angiographic and clinical findings. When there was discordance between the investigators, a consensus reading was obtained from a third independent reviewer. The intraclass correlation coefficients for interobserver and intraobserver reliabilities of the lipid arc were 0.892 and 0.934, respectively. The estimated interobserver and intraobserver κ coefficients for cholesterol crystals were 0.810 and 0.933, respectively.
Figure 2. Optical coherence tomography measurement. In lipid-rich plaques, the lipid arc was measured at 1-mm intervals over the entire length, and the values were averaged. Lipid length was also measured on a longitudinal view. Lipid index was defined as the mean lipid arc multiplied by lipid length. The fibrous cap thickness of a lipid-rich plaque was measured 3 times at its thinnest part, and the average value was calculated.
Figure 3. Representative optical coherence tomography images. A, Calcification is an area with low backscatter signal and a sharp border. B, Macrophage accumulations are observed as bright spots with high signal variances (arrows). C, Cholesterol crystals were defined as thin, linear regions of high intensity within the plaque (arrows). D, Microchannels are observed as black holes within a plaque. E, Red (red blood cell-rich) thrombus is highly backscattering with high attenuation (arrows). F, White (platelet-rich) thrombus is less backscattering and homogeneous with low signal attenuation (arrows).
Clinical Follow-up
In the MGH OCT Registry, subjects are followed up at 6 months and then annually thereafter for 5 years. When a clinical event is identified, detailed data are collected at the study site and entered into the database. Clinical events include death resulting from any cause, myocardial infarction, any form of revascularization, ischemic cerebrovascular events, and any bleeding complications.
Statistical Analysis
All statistical analyses were performed by an independent statistician at a core laboratory. Categorical variables are presented as counts and proportions, and the comparisons were performed using a χ2 test or Fisher exact test, depending on the data. The continuous variables are presented as mean±SD, and if necessary, median and 25th to 75th percentile are also presented. In the present study, 39 (15.6%) subjects had ≥3 plaques. The generalized estimating equations was used to take into count the within-subject correlation for the analysis of multiple plaques within a single patient. The mean values of the continuous measurements between the 2 groups were compared using generalized estimating equations with identity link and exchangeable correlation. For comparisons of between-group differences in the rates of dichotomous plaque characteristics, the analysis was performed using generalized estimating equations with logit link and exchangeable correlation structure. Univariate and multivariate linear regression models, including all patients and plaques, were fit to assess the relationship between lipid index and other factors for which the inference was carried out using generalized estimating equations with identity link and exchangeable correlation structure. Intraobserver and interobserver reliabilities were estimated by means of a κ coefficient for binary outcomes and an intraclass correlation coefficient for continuous measurements. There were no type 1 error rate controls for multiple statistical comparisons because of the exploratory nature of the analyses, and a value of P<0.05 from 2-sided test was considered to be statistically significant. All analyses were performed using IBM SPSS software version 19.0 (SPSS Inc, Chicago, IL).
Results
Baseline Patient Demographics
Table 1 shows baseline patient demographics. The non-CKD group consisted of 250 subjects and the CKD group of 37 subjects. The CKD group was older, had a lower level of hemoglobin, and had a higher use of angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker.
| Non-CKD (n=250) | CKD (n=37) | P Value | |
|---|---|---|---|
| Men | 197 (78.8) | 24 (64.9) | 0.092 |
| Age, y | 58.7±11.0 | 67.4±8.6 | <0.001* |
| ACS | 66 (26.4) | 10 (27.0) | 0.999 |
| Hypertension | 151 (60.4) | 28 (75.7) | 0.101 |
| Hyperlipidemia | 194 (77.6) | 27 (73.0) | 0.534 |
| Smoking | 64 (25.6) | 4 (10.8) | 0.061 |
| Diabetes mellitus | 101 (40.4) | 19 (51.4) | 0.216 |
| HbA1c, %† | 7.5±1.4 | 8.0±1.8 | 0.216 |
| Hb, g/dL | 14.1±1.3 | 12.5±2.1 | <0.001* |
| LDL-C, mg/dL | 90.5±39.3 | 84.2±29.0 | 0.351 |
| HDL-C, mg/dL | 43.3±11.8 | 43.4±12.0 | 0.963 |
| Triglycerides, mg/dL | 153.4±115.6 | 177.0±162.6 | 0.277 |
| Cre, mg/dL | 0.89±0.15 | 1.86±2.51 | 0.025* |
| eGFR, mL/min per 1.73m2 | 85.4±17.7 | 48.2±12.8 | <0.001* |
| Statin | 182 (72.8) | 28 (75.7) | 0.843 |
| ACE-I/ARB | 98 (39.2) | 23 (62.2) | 0.012* |
| β-Blocker | 112 (44.8) | 20 (54.1) | 0.296 |
| LVEF, % | 61.7±9.2 | 58.9±11.3 | 0.114 |
| Imaged CASS segments | 4.2±1.9 | 3.9±1.9 | 0.398 |
Baseline Plaque Characteristics
A total of 463 nonculprit plaques were detected in 287 subjects: 402 plaques in the non-CKD group (1.6 plaques per patient) and 61 plaques in the CKD group (1.6 plaques per patient). The distribution of plaques in the 3 coronary arteries is shown in Table 2: 35.2% of plaques were located in the right coronary artery, 42.1% in the left anterior descending artery, and 22.7% in the left circumflex. This distribution did not differ between the groups. Although the reference diameter and area were not significantly different, the minimum area was smaller and the percentage diameter stenosis was greater in CKD patients than in non-CKD patients.
| Non-CKD (n=402) | CKD (n=61) | P Value | |
|---|---|---|---|
| Plaque location, n (%) | 0.763 | ||
| RCA | 142 (35.3) | 21 (34.4) | |
| CASS 1 | 25 (6.2) | 3 (4.9) | |
| CASS 2 | 58 (14.4) | 8 (13.1) | |
| CASS 3 | 59 (14.7) | 10 (16.4) | |
| LAD | 165 (41.0) | 30 (49.2) | |
| CASS 12 | 45 (11.2) | 6 (9.8) | |
| CASS 13 | 86 (21.4) | 15 (24.6) | |
| CASS 14 | 34 (8.5) | 9 (14.6) | |
| LCX | 95 (23.6) | 10 (16.4) | |
| CASS 18 | 49 (12.2) | 5 (8.2) | |
| CASS 19 | 46 (11.4) | 5 (8.2) | |
| OCT measurement | |||
| Minimum lumen diameter, mm | 2.0±0.4 | 1.9±0.5 | 0.058 |
| Minimum lumen area, mm2 | 3.3±1.5 | 2.9±1.3 | 0.027* |
| Reference lumen diameter, mm | 3.1±0.6 | 3.1±0.6 | 0.642 |
| Reference lumen area, mm2 | 7.9±3.0 | 7.8±2.9 | 0.736 |
| Diameter stenosis, % | 37.2±0.6 | 40.8±8.2 | 0.001* |
OCT Findings
Comparisons of quantitative OCT findings between non-CKD and CKD are shown in Figure 4. Plaques in the CKD patients had a wider lipid arc (156.7±34.6°; median=150.0° [25th–75th percentile, 133.0°–178.0°; non-CKD] versus 172.9±37.9°; 170.0° [144.3–191.0°; CKD]; P=0.006), a longer lipid length (8.0±4.7 mm; 7.0 mm [4.3–10.8 mm; non-CKD] versus 9.5±4.6 mm; 8.0 mm [6.8–11.9 mm; CKD]; P=0.015), and a larger lipid index (1248.4±782.8 mm°; 1069.2 mm° [590.0–1744.4 mm°; non-CKD] versus 1716.1±1116.2 mm°; 1257.8 mm° [1105.2–1978.1 mm°; CKD]; P=0.003). Fibrous cap thickness was not significantly different between the groups (93.4±45.5; 80.0 [63.0–117.0; non-CKD] versus 87.9±33.0; 80.0 [63.0–99.3; CKD]; P=0.268). The prevalence of OCT plaque characteristics is shown in Figure 5. The prevalence of lipid-rich plaques was similar between the groups, as was the prevalence of macrophage accumulations, microchannels, and thin-cap fibroatheroma. However, calcification, cholesterol crystals, and disruption were more frequently observed and thrombus tended to be more frequent in the CKD group. Although the baseline patient characteristics were different between the non-CKD and CKD groups, quantitative and qualitative OCT findings were not adjusted for other factors because the primary purpose was to make a direct comparative description of the plaque characteristics. No correlation between eGFR and lipid index was observed (P=0.102; r=0.061; Figure 6).
Figure 4. Quantitative optical coherence tomography findings. Compared with non–chronic kidney disease (CKD) plaques, plaques with CKD had a wider lipid arc and a larger lipid index. However, fibrous cap thickness was not significantly different between the groups.
Figure 5. Qualitative optical coherence tomography findings. Compared to non–chronic kidney disease (CKD) plaques, plaques with CKD had a higher prevalence of calcification, cholesterol crystals, and disruption. Thrombus tended to be more frequently observed in the CKD group. TCFA indicates thin-cap fibroatheroma.
Figure 6. Scatterplot of estimated glomerular filtration rate (eGFR; x axis) and lipid index (y axis). No correlation between eGFR and lipid index was observed (P=0.102; r=0.061).
Univariate and Multivariate Linear Regression Models for Lipid Index
Table 3 shows the univariate and multivariate linear regression models. In the univariate model, the presence of ACS, diabetes mellitus, and nonuse of statin were all associated with a larger lipid index. The multivariate linear regression model demonstrated that only lower eGFR and diabetes mellitus were independent risk factors for a larger lipid index after adjustment for other factors.
| Univariate Model | Multivariate Model | |||
|---|---|---|---|---|
| β Coefficient | P Value | β Coefficient | P Value | |
| eGFR | −4.2 | 0.102 | −6.0 | 0.034* |
| Age | −3.1 | 0.510 | −4.5 | 0.465 |
| ACS | 389.2 | 0.002* | 272.9 | 0.050 |
| Hypertension | 113.3 | 0.334 | 204.3 | 0.059 |
| Hyperlipidemia | 40.5 | 0.800 | 141.1 | 0.273 |
| Smoking | 155.9 | 0.212 | 63.1 | 0.604 |
| Diabetes mellitus | 480.5 | <0.001* | 461.7 | <0.001* |
| LVEF | −9.0 | 0.203 | −9.9 | 0.093 |
| Hb | −17.2 | 0.707 | −23.8 | 0.617 |
| LDL-C | −0.4 | 0.756 | −0.9 | 0.446 |
| HDL-C | −2.3 | 0.541 | −0.2 | 0.951 |
| Triglycerides | 0.4 | 0.411 | 0.2 | 0.699 |
| Statin | −367.2 | 0.008* | −240.9 | 0.173 |
| ACE-I/ARB | −159.1 | 0.127 | −121.9 | 0.252 |
| β-Blocker | −161.0 | 0.145 | −148.9 | 0.284 |
| % DS | 5.6 | 0.452 | 3.6 | 0.560 |
Clinical Follow-up Data
The 1-year follow-up rate was 96.5%. Thirteen patients (4.5%) had clinical events: 1 (0.3%) noncardiac death, 10 (3.5%) percutaneous coronary interventions, and 2 coronary artery bypass grafts (0.7%). Only 1 patient with CKD underwent percutaneous coronary intervention during the follow-up period.
Discussion
The present study demonstrated that nonculprit plaques in patients with CKD had a larger lipid index as compared with non-CKD patients. The presence of calcification, cholesterol crystals, and disruption was more frequently observed in CKD patients. The multivariate linear regression model demonstrated that a lower eGFR and diabetes mellitus were independently associated with a larger lipid index.
Plaque Characteristics in CKD Patients
CKD is recognized as an independent risk factor for cardiovascular events, including ACS.1–3 A previous postmortem study demonstrated that a lower eGFR level is associated with a greater percentage of advanced coronary atherosclerosis, defined as American Heart Association types IV to VI.7 Moreover, integrated backscatter intravascular ultrasound studies demonstrated that a lower eGFR level is related to a higher percentage of lipid volume and a lower percentage of fibrous volume.23,24 In the present study, plaques in CKD patients had a wider lipid arc, a longer lipid length, and a larger lipid index. Although a correlation between eGFR and lipid index was not observed, the multivariate linear regression model demonstrated that a lower eGFR was an independent risk factor for a larger lipid index after adjustment for other factors.
In the present study, the prevalence of thin-cap fibroatheroma and macrophage accumulations did not differ between the groups. The subgroup analysis of the PROSPECT study also demonstrated that the prevalence of Virtual Histology-derived thin-cap fibroatheroma was not significantly different, although plaques in CKD patients had a higher plaque burden with a greater necrotic core and a higher frequency of dense calcium.8
Nonculprit plaques were selected because it is often challenging to acquire optimal-quality images in the presence of luminal thrombus and severe stenosis. In that case, operators may be more likely to perform OCT after predilatation or stenting. In the registry database, culprit lesion images of optimal quality were acquired in 36.8% of ACS patients and in 59.7% of non-ACS patients. However, several studies have shown that nonculprit lesions have morphological characteristics similar to culprit lesions as detected by angiography,25 intravascular ultrasound,26 angioscopy,27 and coronary thermography.28,29
Calcification and Cholesterol Crystals
We identified several differences in coronary atherosclerotic plaque composition between patients with and without CKD. The prevalence of calcification and cholesterol crystals was higher in CKD patients as compared with non-CKD patients. In previous autopsy,6,7 intravascular ultrasound,8 and computed tomography angiography30,31 studies, calcification was more frequently observed in plaques of CKD patients compared with those in non-CKD. It has been reported that the uremic state is associated with numerous metabolic abnormalities and endocrine disturbances, including abnormalities in calcium, phosphate metabolism, and inflammatory syndrome. These dysfunctions occur early in the course of CKD and contribute to the development and progression of vascular calcification and atherosclerosis.32
Cholesterol crystals play an important role in the atherosclerotic process33 and serve as a marker for advanced atherosclerotic lesions.34 On OCT images, cholesterol crystals are observed as thin, linear lesions of high intensity, usually associated with lipid-rich plaque. A previous autopsy study demonstrated that there were strong associations between cholesterol crystals and plaque disruption, thrombus, and plaque size; moreover, cholesterol crystals were significantly associated with clinical events.35 In the present study, cholesterol crystals were more frequently observed in plaques of CKD patients compared with those of non-CKD. The present results also suggest that CKD patients have advanced plaques in their coronary trees.
Disruption and Thrombus
In the present study, the prevalence of disruption in nonculprit plaques was higher in the CKD group as compared with the non-CKD group, and thrombus tended to be more frequent in the CKD group. Plaque disruption exposes thrombogenic substrates to circulating blood, leading to local thrombus formation. When thrombus is not occlusive, the disruption can be silent without causing ischemia or myocardial necrosis. The thrombus will then be organized, resulting in sudden plaque growth. A pathological study showed evidence of multiple plaque ruptures before a clinical event, indicating that the majority of plaque disruptions are silent.36 Several intracoronary imaging studies have reported that plaque rupture was observed not only in the culprit lesion but also in nonculprit sites. A 3-vessel OCT study demonstrated that 35.3% of ACS patients and 12.6% of non-ACS patients had disruption in nonculprit lesions10; a 3-vessel intravascular ultrasound study reported 8% of ACS plaques and 4% non-ACS plaques.37 Tanaka et al38 reported that multiple plaque disruption was associated with systemic inflammation and that patients with multiple plaque rupture had poor prognoses. Although plaque disruption with thrombus formation is thought to be the main underlying mechanism of ACS, the significance of disruption and thrombus on OCT in CKD patients is still unknown. To the best of our knowledge, our study is the first to demonstrate the prevalence of plaque disruption and thrombus in CKD plaques; further investigation, including the clinical significance of plaque disruption and thrombus, is warranted.
Patients with CKD are more likely to die of cardiovascular disease than to require dialysis.39 Therefore, reduction of morbidity and mortality in patients with CKD requires strict management of these patients’ cardiovascular disease risk factors. The National Kidney Foundation’s Kidney Disease Outcome Quality Initiative guidelines recommend an A1c level of <7% with diabetes mellitus,40 a blood pressure goal of <130/80 mm Hg,41 and low-density lipoprotein-cholesterol levels <100 mg/dL.42 All of these recommendations are related to antiatherosclerosis effects. In the present study, plaques in CKD patients had a larger lipid index. Moreover, the multivariate linear regression model demonstrated that a lower eGFR was an independent risk factor for a larger lipid index. We could not find any relationship between lipid data and follow-up clinical events; it is possible that this lack of relationship is related to the small number of subjects and short follow-up period. However, aggressive lipid management would probably have an additional value in CKD patients.
Limitations
First, patients in this registry were selected for OCT by the operator, introducing the possibility of selection bias. Second, exact measurements of necrotic core and plaque burden by OCT are not possible because of the relatively shallow axial penetration. Third, calcification, macrophage accumulations, cholesterol crystals, microchannel, disruption, and thrombus were not quantified or rigorously validated. Fourth, although the baseline patient characteristics were different between patients with and without CKD, quantitative and qualitative OCT findings were not adjusted for other factors because the primary purpose was to make a direct comparative description of the plaque characteristics. Fifth, the sample size for this study was limited, especially with regard to the small number of CKD patients. Sixth, OCT imaging did not include whole coronary artery segments. Seventh, the duration of CKD has been linked with poor outcomes and affected plaque morphology; however, the patient-reported duration of CKD was not collected in the registry. Eighth, the very proximal segment was not visualized because of the proximal occlusion balloon when the time domain OCT system was used. Although 2 OCT systems were used (frequency domain and time domain OCT system) in the present study, the percentage of imaged plaques by each modality and the plaque distribution were not significantly different between the CKD and non-CKD groups. Ninth, 5 patients on hemodialysis (8 plaques) were not included because of the small number. Tenth, the culprit lesions were identified by the operators at each site. Finally, we collected data only from nonculprit plaques.
Conclusions
Compared with non-CKD patients, patients with CKD had a larger lipid index and a higher prevalence of calcium, cholesterol crystals, and disruption. Thrombus tended to be more frequently observed in the CKD group. Multivariate linear regression model demonstrated that a lower eGFR was an independent risk factor for a larger lipid index.
Sources of Funding
This study was supported by research grants from
Disclosures
Dr Jang received a research grant and consulting fees from
Footnotes
References
- 1.
Go AS, Chertow GM, Fan D, McCulloch CE, Hsu CY . Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization.N Engl J Med. 2004; 351:1296–1305.CrossrefMedlineGoogle Scholar - 2.
Manjunath G, Tighiouart H, Ibrahim H, MacLeod B, Salem DN, Griffith JL, Coresh J, Levey AS, Sarnak MJ . Level of kidney function as a risk factor for atherosclerotic cardiovascular outcomes in the community.J Am Coll Cardiol. 2003; 41:47–55.CrossrefMedlineGoogle Scholar - 3.
Sarnak MJ, Levey AS, Schoolwerth AC, Coresh J, Culleton B, Hamm LL, McCullough PA, Kasiske BL, Kelepouris E, Klag MJ, Parfrey P, Pfeffer M, Raij L, Spinosa DJ, Wilson PW ; American Heart Association Councils on Kidney in Cardiovascular Disease, High Blood Pressure Research, Clinical Cardiology, and Epidemiology and Prevention. 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.LinkGoogle Scholar - 4.
Rubenstein MH, Harrell LC, Sheynberg BV, Schunkert H, Bazari H, Palacios IF . Are patients with renal failure good candidates for percutaneous coronary revascularization in the new device era?Circulation. 2000; 102:2966–2972.CrossrefMedlineGoogle Scholar - 5.
Shaw JA, Andrianopoulos N, Duffy S, Walton AS, Clark D, Lew R, Sebastian M, New G, Brennan A, Reid C, Ajani AE ; Melbourne Interventional Group. Renal impairment is an independent predictor of adverse events post coronary intervention in patients with and without drug-eluting stents.Cardiovasc Revasc Med. 2008; 9:218–223.CrossrefMedlineGoogle Scholar - 6.
Nakamura S, Ishibashi-Ueda H, Niizuma S, Yoshihara F, Horio T, Kawano Y . Coronary calcification in patients with chronic kidney disease and coronary artery disease.Clin J Am Soc Nephrol. 2009; 4:1892–1900.CrossrefMedlineGoogle Scholar - 7.
Nakano T, Ninomiya T, Sumiyoshi S, Fujii H, Doi Y, Hirakata H, Tsuruya K, Iida M, Kiyohara Y, Sueishi K . Association of kidney function with coronary atherosclerosis and calcification in autopsy samples from Japanese elders: the Hisayama study.Am J Kidney Dis. 2010; 55:21–30.CrossrefMedlineGoogle Scholar - 8.
Baber U, Stone GW, Weisz G, Moreno P, Dangas G, Maehara A, Mintz GS, Cristea E, Fahy M, Xu K, Lansky AJ, Wennerblom B, Mathey DG, Templin B, Zhang Z, Serruys PW, Mehran R . Coronary plaque composition, morphology, and outcomes in patients with and without chronic kidney disease presenting with acute coronary syndromes.JACC Cardiovasc Imaging. 2012; 5(suppl):S53–S61.CrossrefMedlineGoogle Scholar - 9.
Yonetsu T, Kato K, Kim SJ, Xing L, Jia H, McNulty I, Lee H, Zhang S, Uemura S, Jang Y, Kang SJ, Park SJ, Lee S, Yu B, Kakuta T, Jang IK . Predictors for neoatherosclerosis: a retrospective observational study from the optical coherence tomography registry.Circ Cardiovasc Imaging. 2012; 5:660–666.LinkGoogle Scholar - 10.
Kato K, Yonetsu T, Kim SJ, Xing L, Lee H, McNulty I, Yeh RW, Sakhuja R, Zhang S, Uemura S, Yu B, Mizuno K, Jang IK . Nonculprit plaques in patients with acute coronary syndromes have more vulnerable features compared with those with non-acute coronary syndromes: a 3-vessel optical coherence tomography study.Circ Cardiovasc Imaging. 2012; 5:433–440.LinkGoogle Scholar - 11.
Levey AS, Coresh J, Greene T, Stevens LA, Zhang YL, Hendriksen S, Kusek JW, Van Lente F ; Chronic Kidney Disease Epidemiology Collaboration. Using standardized serum creatinine values in the modification of diet in renal disease study equation for estimating glomerular filtration rate.Ann Intern Med. 2006; 145:247–254.CrossrefMedlineGoogle Scholar - 12.
Prati F, Regar E, Mintz GS, Arbustini E, Di Mario C, Jang IK, Akasaka T, Costa M, Guagliumi G, Grube E, Ozaki Y, Pinto F, Serruys PW ; Expert’s OCT Review Document. Expert review document on methodology, terminology, and clinical applications of optical coherence tomography: physical principles, methodology of image acquisition, and clinical application for assessment of coronary arteries and atherosclerosis.Eur Heart J. 2010; 31:401–415.CrossrefMedlineGoogle Scholar - 13.
Yabushita H, Bouma BE, Houser SL, Aretz HT, Jang IK, Schlendorf KH, Kauffman CR, Shishkov M, Kang DH, Halpern EF, Tearney GJ . Characterization of human atherosclerosis by optical coherence tomography.Circulation. 2002; 106:1640–1645.LinkGoogle Scholar - 14.
Jang IK, Tearney GJ, MacNeill B, Takano M, Moselewski F, Iftima N, Shishkov M, Houser S, Aretz HT, Halpern EF, Bouma BE . In vivo characterization of coronary atherosclerotic plaque by use of optical coherence tomography.Circulation. 2005; 111:1551–1555.LinkGoogle Scholar - 15.
Kubo T, Imanishi T, Takarada S, Kuroi A, Ueno S, Yamano T, Tanimoto T, Matsuo Y, Masho T, Kitabata H, Tsuda K, Tomobuchi Y, Akasaka T . Assessment of culprit lesion morphology in acute myocardial infarction: ability of optical coherence tomography compared with intravascular ultrasound and coronary angioscopy.J Am Coll Cardiol. 2007; 50:933–939.CrossrefMedlineGoogle Scholar - 16.
Fujii K, Kawasaki D, Masutani M, Okumura T, Akagami T, Sakoda T, Tsujino T, Ohyanagi M, Masuyama T . OCT assessment of thin-cap fibroatheroma distribution in native coronary arteries.J Am Coll Cardiol Cardiovasc Imaging. 2010; 3:168–175.CrossrefMedlineGoogle Scholar - 17.
Lee T, Yonetsu T, Koura K, Hishikari K, Murai T, Iwai T, Takagi T, Iesaka Y, Fujiwara H, Isobe M, Kakuta T . Impact of coronary plaque morphology assessed by optical coherence tomography on cardiac troponin elevation in patients with elective stent implantation.Circ Cardiovasc Interv. 2011; 4:378–386.LinkGoogle Scholar - 18.
Tearney GJ, Regar E, Akasaka T, Adriaenssens T, Barlis P, Bezerra HG, Bouma B, Bruining N, Cho JM, Chowdhary S, Costa MA, de Silva R, Dijkstra J, Di Mario C, Dudek D, Dudeck D, Falk E, Falk E, Feldman MD, Fitzgerald P, Garcia-Garcia HM, Garcia H, Gonzalo N, Granada JF, Guagliumi G, Holm NR, Honda Y, Ikeno F, Kawasaki M, Kochman J, Koltowski L, Kubo T, Kume T, Kyono H, Lam CC, Lamouche G, Lee DP, Leon MB, Maehara A, Manfrini O, Mintz GS, Mizuno K, Morel MA, Nadkarni S, Okura H, Otake H, Pietrasik A, Prati F, Räber L, Radu MD, Rieber J, Riga M, Rollins A, Rosenberg M, Sirbu V, Serruys PW, Shimada K, Shinke T, Shite J, Siegel E, Sonoda S, Sonada S, Suter M, Takarada S, Tanaka A, Terashima M, Thim T, Troels T, Uemura S, Ughi GJ, van Beusekom HM, van der Steen AF, van Es GA, van Es GA, van Soest G, Virmani R, Waxman S, Weissman NJ, Weisz G ; International Working Group for Intravascular Optical Coherence Tomography (IWG-IVOCT). Consensus standards for acquisition, measurement, and reporting of intravascular optical coherence tomography studies: a report from the International Working Group for Intravascular Optical Coherence Tomography Standardization and Validation.J Am Coll Cardiol. 2012; 59:1058–1072.CrossrefMedlineGoogle Scholar - 19.
Tearney GJ, Yabushita H, Houser SL, Aretz HT, Jang IK, Schlendorf KH, Kauffman CR, Shishkov M, Halpern EF, Bouma BE . Quantification of macrophage content in atherosclerotic plaques by optical coherence tomography.Circulation. 2003; 107:113–119.LinkGoogle Scholar - 20.
MacNeill BD, Jang IK, Bouma BE, Iftimia N, Takano M, Yabushita H, Shishkov M, Kauffman CR, Houser SL, Aretz HT, DeJoseph D, Halpern EF, Tearney GJ . Focal and multi-focal plaque macrophage distributions in patients with acute and stable presentations of coronary artery disease.J Am Coll Cardiol. 2004; 44:972–979.CrossrefMedlineGoogle Scholar - 21.
Kitabata H, Tanaka A, Kubo T, Takarada S, Kashiwagi M, Tsujioka H, Ikejima H, Kuroi A, Kataiwa H, Ishibashi K, Komukai K, Tanimoto T, Ino Y, Hirata K, Nakamura N, Mizukoshi M, Imanishi T, Akasaka T . Relation of microchannel structure identified by optical coherence tomography to plaque vulnerability in patients with coronary artery disease.Am J Cardiol. 2010; 105:1673–1678.CrossrefMedlineGoogle Scholar - 22.
Kume T, Akasaka T, Kawamoto T, Ogasawara Y, Watanabe N, Toyota E, Neishi Y, Sukmawan R, Sadahira Y, Yoshida K . Assessment of coronary arterial thrombus by optical coherence tomography.Am J Cardiol. 2006; 97:1713–1717.CrossrefMedlineGoogle Scholar - 23.
Hayano S, Ichimiya S, Ishii H, Kanashiro M, Watanabe J, Kurebayashi N, Yoshikawa D, Amano T, Matsubara T, Murohara T . Relation between estimated glomerular filtration rate and composition of coronary arterial atherosclerotic plaques.Am J Cardiol. 2012; 109:1131–1136.CrossrefMedlineGoogle Scholar - 24.
Miyagi M, Ishii H, Murakami R, Isobe S, Hayashi M, Amano T, Arai K, Yoshikawa D, Ohashi T, Uetani T, Yasuda Y, Matsuo S, Matsubara T, Murohara T . Impact of renal function on coronary plaque composition.Nephrol Dial Transplant. 2010; 25:175–181.CrossrefMedlineGoogle Scholar - 25.
Goldstein JA, Demetriou D, Grines CL, Pica M, Shoukfeh M, O’Neill WW . Multiple complex coronary plaques in patients with acute myocardial infarction.N Engl J Med. 2000; 343:915–922.CrossrefMedlineGoogle Scholar - 26.
Maehara A, Mintz GS, Bui AB, Walter OR, Castagna MT, Canos D, Pichard AD, Satler LF, Waksman R, Suddath WO, Laird JR, Kent KM, Weissman NJ . Morphologic and angiographic features of coronary plaque rupture detected by intravascular ultrasound.J Am Coll Cardiol. 2002; 40:904–910.CrossrefMedlineGoogle Scholar - 27.
Asakura M, Ueda Y, Yamaguchi O, Adachi T, Hirayama A, Hori M, Kodama K . Extensive development of vulnerable plaques as a pan-coronary process in patients with myocardial infarction: an angioscopic study.J Am Coll Cardiol. 2001; 37:1284–1288.CrossrefMedlineGoogle Scholar - 28.
Toutouzas K, Drakopoulou M, Mitropoulos J, Tsiamis E, Vaina S, Vavuranakis M, Markou V, Bosinakou E, Stefanadis C . Elevated plaque temperature in non-culprit de novo atheromatous lesions of patients with acute coronary syndromes.J Am Coll Cardiol. 2006; 47:301–306.CrossrefMedlineGoogle Scholar - 29.
Toutouzas K, Tsiamis E, Drakopoulou M, Synetos A, Karampelas J, Riga M, Tsioufis C, Tousoulis D, Stefanadi E, Vlasis C, Vlassis C, Stefanadis C . Impact of type 2 diabetes mellitus on diffuse inflammatory activation of de novo atheromatous lesions: implications for systemic inflammation.Diabetes Metab. 2009; 35:299–304.CrossrefMedlineGoogle Scholar - 30.
Kawai H, Sarai M, Motoyama S, Harigaya H, Ito H, Sanda Y, Biswas S, Anno H, Ishii J, Murohara T, Ozaki Y . Coronary plaque characteristics in patients with mild chronic kidney disease.Circ J. 2012; 76:1436–1441.CrossrefMedlineGoogle Scholar - 31.
Budoff MJ, Rader DJ, Reilly MP, Mohler ER, Lash J, Yang W, Rosen L, Glenn M, Teal V, Feldman HI ; CRIC Study Investigators. Relationship of estimated GFR and coronary artery calcification in the CRIC (Chronic Renal Insufficiency Cohort) Study.Am J Kidney Dis. 2011; 58:519–526.CrossrefMedlineGoogle Scholar - 32.
Massy ZA, Ivanovski O, Nguyen-Khoa T, Angulo J, Szumilak D, Mothu N, Phan O, Daudon M, Lacour B, Drüeke TB, Muntzel MS . Uremia accelerates both atherosclerosis and arterial calcification in apolipoprotein E knockout mice.J Am Soc Nephrol. 2005; 16:109–116.CrossrefMedlineGoogle Scholar - 33.
Duewell P, Kono H, Rayner KJ, Sirois CM, Vladimer G, Bauernfeind FG, Abela GS, Franchi L, Nuñez G, Schnurr M, Espevik T, Lien E, Fitzgerald KA, Rock KL, Moore KJ, Wright SD, Hornung V, Latz E . NLRP3 inflammasomes are required for atherogenesis and activated by cholesterol crystals.Nature. 2010; 464:1357–1361.CrossrefMedlineGoogle Scholar - 34.
Stary HC, Chandler AB, Dinsmore RE, Fuster V, Glagov S, Insull W, Rosenfeld ME, Schwartz CJ, Wagner WD, Wissler RW . A definition of advanced types of atherosclerotic lesions and a histological classification of atherosclerosis: a report from the Committee on Vascular Lesions of the Council on Arteriosclerosis, American Heart Association.Circulation. 1995; 92:1355–1374.LinkGoogle Scholar - 35.
Abela GS, Aziz K, Vedre A, Pathak DR, Talbott JD, Dejong J . Effect of cholesterol crystals on plaques and intima in arteries of patients with acute coronary and cerebrovascular syndromes.Am J Cardiol. 2009; 103:959–968.CrossrefMedlineGoogle Scholar - 36.
Virmani R, Kolodgie FD, Burke AP, Farb A, Schwartz SM . Lessons from sudden coronary death: a comprehensive morphological classification scheme for atherosclerotic lesions.Arterioscler Thromb Vasc Biol. 2000; 20:1262–1275.LinkGoogle Scholar - 37.
Hong MK, Mintz GS, Lee CW, Lee JW, Park JH, Park DW, Lee SW, Kim YH, Cheong SS, Kim JJ, Park SW, Park SJ . A three-vessel virtual histology intravascular ultrasound analysis of frequency and distribution of thin-cap fibroatheromas in patients with acute coronary syndrome or stable angina pectoris.Am J Cardiol. 2008; 101:568–572.CrossrefMedlineGoogle Scholar - 38.
Tanaka A, Shimada K, Sano T, Namba M, Sakamoto T, Nishida Y, Kawarabayashi T, Fukuda D, Yoshikawa J . Multiple plaque rupture and C-reactive protein in acute myocardial infarction.J Am Coll Cardiol. 2005; 45:1594–1599.CrossrefMedlineGoogle Scholar - 39.
Foley RN, Murray AM, Li S, Herzog CA, McBean AM, Eggers PW, Collins AJ . Chronic kidney disease and the risk for cardiovascular disease, renal replacement, and death in the United States Medicare population, 1998 to 1999.J Am Soc Nephrol. 2005; 16:489–495.CrossrefMedlineGoogle Scholar - 40. Kdoqi. Kdoqi clinical practice guidelines and clinical practice recommendations for diabetes and chronic kidney disease.Am J Kidney Dis. 2007; 49:S12–154.CrossrefMedlineGoogle Scholar
- 41. Kidney Disease Outcomes Quality I. KDOQI clinical practice guidelines on hypertension and antihypertensive agents in chronic kidney disease.Am J Kidney Dis. 2004; 43:S1–290.MedlineGoogle Scholar
- 42. Kidney Disease Outcomes Quality Initiative G. K/doqi clinical practice guidelines for management of dyslipidemias in patients with kidney disease.Am J Kidney Dis. 2003; 41:I–IV, S1-S91.CrossrefMedlineGoogle Scholar
Clinical Perspective
Chronic kidney disease (CKD) promotes the development of atherosclerosis and increases the risk of cardiovascular disease. Although pathology studies have demonstrated that coronary atherosclerosis and calcification are more frequent and severe in patients with CKD, the underlying mechanism for poor outcomes and higher recurrent ischemic events in CKD patients has not been fully elucidated. In the current study, we attempted to clarify these underlying mechanisms using a high-resolution intravascular imaging modality: optical coherence tomography. Indeed, compared with non-CKD patients, patients with CKD had a wider lipid arc, a larger lipid index, and a higher prevalence of calcium, cholesterol crystals, and disruption. Thrombus tended to also be more frequent in the CKD group. Multivariate linear regression model demonstrated that a lower estimated glomerular filtration rate was an independent risk factor for a larger lipid index. The present study demonstrated that coronary atherosclerosis was more complex in patients with CKD compared with non-CKD.
