Abstract
Background
The role of coronary calcification in cardiovascular events and plaque stabilization is still being debated, and factors involved in the progression of coronary calcification are not fully understood. This study aimed to identify the predictors for rapid progression of coronary calcification.
Methods and Results
Patients with serial optical coherence tomography imaging at baseline and at 6 months were selected. Changes in the calcification index and predictors for progression of calcification were studied. Calcification index was defined as the product of the mean calcification arc and calcification length. Rapid progression of calcification was defined as an increase in the calcification index above the median value. Among 187 patients who had serial optical coherence tomography imaging, 235 calcified plaques were identified in 105 patients (56.1%) at baseline. After 6 months, the calcification index increased in 95.3% of calcified plaques from 132.0 to 178.2 (P<0.001). In multivariable analysis, diabetes mellitus (odds ratio [OR], 3.911; P<0.001), chronic kidney disease (OR, 2.432; P=0.037), lipid‐rich plaque (OR, 2.698; P=0.034), and macrophages (OR, 6.782; P<0.001) were found to be independent predictors for rapid progression of coronary calcification. Interestingly, rapid progression of calcification was associated with a significant reduction of inflammatory features (thin‐cap fibroatheroma; from 21.2% to 11.9%, P=0.003; macrophages; from 74.6% to 61.0%, P=0.001).
Conclusions
Diabetes mellitus, chronic kidney disease, lipid‐rich plaque, and macrophages were independent predictors for rapid progression of coronary calcification. Baseline vascular inflammation and subsequent stabilization may be related to rapid progression of calcification.
Registration
URL: https://www.clinicaltrials.gov; Unique identifier: NCT01110538.
Keywords: calcification, inflammation, lipid‐rich plaque, macrophage, optical coherence tomography
Subject Categories: Optical Coherence Tomography (OCT), Imaging, Catheter-Based Coronary and Valvular Interventions
Nonstandard Abbreviations and Acronyms
- DM
diabetes mellitus
- LRP
lipid‐rich plaque
- TCFA
thin‐cap fibroatheroma
- VSMC
vascular smooth muscle cell
Clinical Perspective
What Is New?
Diabetes mellitus, chronic kidney disease, lipid‐rich plaque, and macrophages were independent predictors for rapid progression of coronary calcification.
Rapid progression of calcification was associated with a significant reduction of inflammatory features of plaques (thin‐cap fibroatheroma and macrophages).
Majority of precursor plaques of de novo calcified plaques are lipid‐rich plaque and have macrophage infiltration.
What Are the Clinical Implications?
The data from the study support the notion that coronary calcification represents an advanced, but stable stage of atherosclerosis.
Coronary artery calcification can be detected by various imaging modalities. Previous studies using computed tomography showed that the degree and the progression of coronary artery calcification were strongly associated with an increased incidence of cardiovascular events. 1 , 2 Some studies reported that spotty calcification was associated with plaque instability. 3 , 4 On the other hand, calcium density is inversely associated with cardiovascular risk. 5 Recent studies reported that statin stabilizes the plaque by promoting coronary atheroma calcification independent of plaque regression effects. 6 , 7 The role of calcification in cardiovascular events and plaque stabilization is still being debated, and factors involved in the progression of coronary calcification are not fully understood.
Some studies using computed tomography or intravascular ultrasound (IVUS) have evaluated the factors associated with progression of calcification. 7 , 8 However, computed tomography is limited in its spatial resolution and ability to evaluate details of plaque calcification. IVUS is also limited in evaluating calcification because of its poor spatial resolution and high reflection. 9 Optical coherence tomography (OCT) is an effective tool for the evaluation and quantification of coronary calcification because, unlike IVUS, light can penetrate calcification. 10 , 11 OCT can also evaluate microstructures such as fibrous cap, macrophages, and microvessels. 12 However, there are limited data on evaluation of coronary calcification using OCT. 13 This study was conducted to identify the predictors for rapid progression of coronary calcification.
Methods
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Study Population
Patients who had serial OCT imaging at baseline and at 6 months (5–7 months) were selected from the “Massachusetts General Hospital OCT registry” (ClinicalTrials.gov: NCT01110538). All patients provided written informed consent, and this study was conducted in compliance with the Declaration of Helsinki. A total of 194 eligible patients who were enrolled between September 2010 and January 2014 were identified. Among these, 5 patients were excluded because of poor imaging quality and 2 patients because of limited imaging of the stented segment only. Consequently, 187 patients with 321 vessels were included in the final analysis. Using serial OCT images, we evaluated the chronological change of calcified plaque and tried to identify baseline predictors for rapid progression of calcification. Moreover, we identified de novo calcified plaques in follow‐up OCT images, and analyzed baseline patient characteristics and precursor plaque characteristics of de novo calcified plaques.
Definitions
The detailed definitions of clinical presentation, diabetes mellitus (DM), and chronic kidney disease are described in Data S1.
OCT Imaging Acquisition and Analysis
OCT imaging was performed using a frequency‐domain (C7/C8, OCT Intravascular Imaging System, St. Jude Medical, St. Paul, Minnesota) or time‐domain (M2/M3 Cardiology Imaging Systems; Light Lab Imaging Inc) OCT system after intracoronary administration of 100 to 200 μg of nitroglycerin. All OCT images were submitted to the core laboratory at Massachusetts General Hospital for offline analysis. Analysis was performed by 2 independent investigators who were masked to the clinical, angiographic, and laboratory data, using an offline review workstation (Ilumien Optis, St. Jude Medical). Previously stented coronary segments and coronary segments undergoing stent placement during the index procedure were excluded from OCT analysis. Several landmarks, including stent edges and anatomical landmarks such as side branches, pericardium, calcified plaque position and configuration, lumen shape, and/or positional or directional relationships among all these landmarks were used to identify the target calcified plaque. 13
Calcification was identified as heterogeneous areas of high and low reflectivity, with low signal attenuation and sharply demarcated borders. 12 , 14 Calcified plaque was defined as a plaque that contained calcium. 12 Cross‐sectional OCT images were quantitatively analyzed at 1‐mm intervals. 15 Calcification arc and calcification thickness were evaluated in each cross‐sectional view. Calcification length was measured in the longitudinal view. The calcification index was defined as the product of the mean calcification arc and calcification length. Minimal calcification depth was defined as the minimum distance from the lumen to the superficial calcification edge. 14 Because of lack of established OCT criteria for superficial calcification, we used calcification depth thresholds of 65 μm and 100 μm to define calcifications as superficial‐65 and superficial‐100, respectively. 14 Microcalcification was defined as maximal calcium length <1 mm and maximal calcium arc <22.5°, spotty calcification was defined as calcium length ranging from 1 mm to 4 mm or maximal calcium arc ranging from 22.5° to 90°, and macrocalcification was defined as maximal calcium length >4 mm or maximal calcium arc >90°. 14 , 16 Additional plaque analysis was performed according to previously established criteria and described in Data S1. 12
Statistical Analysis
Categorical data are presented as counts and percentages and were compared using the Chi‐square test or Fisher exact test, as appropriate. Continuous data are presented as mean±SD or median (25th–75th percentile), as appropriate depending on the normality of distribution tested by the Kolmogorov–Smirnov test. Between‐group comparisons were performed using independent‐sample t‐tests or Mann–Whitney U tests, as appropriate. Tests for the within‐group longitudinal changes were performed using paired‐sample t tests, Wilcoxon signed rank tests, or McNemar tests, as appropriate. A comparison of calcified plaque characteristics among different groups was performed using generalized estimating equations to consider the potential cluster effects of multiple calcified plaques in a single patient. Predictors for rapid progression of calcification were assessed with a multivariable logistic regression model. Independent variables with a P<0.10 in the univariate test were entered into the multivariable modeling. All analyses were performed with SPSS (version 25 for Windows; SPSS, Inc., Chicago Illinois).
Results
Baseline Patient Characteristics
Baseline characteristics are shown in Table 1. The median follow‐up duration was 184 days. The mean age was 59.2 years, 70.6% of patients were men, and 40.1% of patients presented with acute coronary syndrome (ACS). Among the 187 patients, calcified plaque was identified in 105 patients (56.1%) at baseline. Patients with calcified plaque had significantly higher prevalence of prior percutaneous coronary intervention (PCI) and DM. There was a trend toward higher prevalence of chronic kidney disease (CKD) in patients with calcified plaque. Nobody was on warfarin.
Table 1.
Baseline Patient Characteristics
| All Patients (n=187) | Patients With Calcified Plaque (n=105) | Patients Without Calcified Plaque (n=82) | P Value | |
|---|---|---|---|---|
| Follow‐up duration, d | 184 (175–193) | 187 (179–193) | 185 (174–195) | 0.840 |
| Length of OCT observation, mm | 53.0 (32.6–90.6) | 55.0 (35.9–92.4) | 44.2 (30.0–81.0) | 0.067 |
| No. of observed vessels | 2.0 (1.0–2.0) | 2.0 (1.0–2.0) | 1.0 (1.0–2.0) | 0.111 |
| Age, y | 59.2±10.3 | 61.5±10.1 | 57.7±9.9 | 0.617 |
| Men, n (%) | 132 (70.6) | 77 (73.3) | 55 (67.1) | 0.351 |
| Clinical presentation | 0.505 | |||
| STEMI, n (%) | 13 (6.9) | 8 (7.6) | 5 (6.1) | |
| NSTE‐ACS, n (%) | 62 (33.2) | 38 (36.2) | 24 (29.3) | |
| SAP, n (%) | 112 (59.9) | 59 (56.2) | 53 (64.6) | |
| Prior MI, n (%) | 64 (34.2) | 35 (33.3) | 29 (35.4) | 0.771 |
| Prior PCI, n (%) | 102 (54.5) | 67 (63.8) | 35 (43.7) | 0.004 |
| Hypertension, n (%) | 115 (61.5) | 66 (62.9) | 49 (59.8) | 0.665 |
| Dyslipidemia, n (%) | 134 (71.7) | 78 (74.3) | 56 (68.3) | 0.367 |
| Diabetes mellitus, n (%) | 76 (40.6) | 50 (47.6) | 26 (31.7) | 0.028 |
| Insulin user, n (%) | 47 (61.8) | 28 (56.0) | 19 (73.1) | 0.146 |
| CKD, n (%) | 17 (9.1) | 13 (12.4) | 4 (4.9) | 0.077 |
| Family history of CAD, n (%) | 10 (5.3) | 6 (5.7) | 4 (4.9) | 0.801 |
| Smoking | 0.774 | |||
| Current smoker, n (%) | 45 (24.1) | 25 (23.8) | 20 (24.4) | |
| Past smoker, n (%) | 41 (21.9) | 25 (23.8) | 16 (19.5) | |
| Never smoker, n (%) | 101 (54.0) | 55 (52.4) | 46 (56.1) | |
| Creatinine, mg/dL | 0.90 (0.79–1.03) | 0.91 (0.80–1.04) | 0.89 (0.77–1.01) | 0.450 |
| eGFR, mL/min per 1.73m2 | 85.0 (72.8–97.6) | 84.0 (71.5–100.1) | 85.9 (74.1–96.1) | 0.669 |
| LDL‐C, mg/dL | 90.2±33.5 | 92.7±35.7 | 88.9±31.1 | 0.513 |
| HDL‐C, mg/dL | 49.0±19.9 | 49.1±17.0 | 48.9±24.0 | 0.894 |
| Triglyceride, mg/dL | 139.5±111.5 | 133.5±110.9 | 137.0±73.5 | 0.322 |
| HbA1c, % | 6.0 (6.0–7.1) | 6.0 (6.0–8.0) | 6.0 (5.9–7.0) | 0.859 |
| Medication at baseline | ||||
| Aspirin, n (%) | 140 (74.9) | 82 (78.1) | 58 (70.7) | 0.249 |
| Clopidogrel, n (%) | 111 (59.4) | 69 (65.7) | 42 (51.2) | 0.045 |
| Statin, n (%) | 123 (65.8) | 76 (72.4) | 47 (57.3) | 0.031 |
| ACEI/ARB, n (%) | 56 (29.9) | 36 (34.3) | 20 (24.4) | 0.143 |
| Medication at discharge | ||||
| Aspirin, n (%) | 183 (97.9) | 104 (99.0) | 79 (96.3) | 0.224 |
| Clopidogrel, n (%) | 173 (92.5) | 102 (97.1) | 71 (86.6) | 0.006 |
| Statin, n (%) | 182 (97.3) | 103 (98.1) | 79 (96.3) | 0.461 |
| ACEI/ARB, n (%) | 66 (35.3) | 43 (41.0) | 23 (28.0) | 0.067 |
Values are mean±SD, n (%), or median (interquartile range). ACEI/ARB indicates angiotensin‐converting enzyme inhibitor/angiotensin II receptor blocker; CAD, coronary artery disease; eGFR, estimated glomerular filtration rate; HbA1c, glycated hemoglobin; HDL‐C, high‐density lipoprotein cholesterol; LDL‐C, low‐density lipoprotein cholesterol; MI, myocardial infarction; NSTE‐ACS, non–ST‐segment–elevation acute coronary syndrome; OCT, optical coherence tomography; PCI, percutaneous coronary intervention; SAP, stable angina pectoris; and STEMI, ST‐segment–elevation myocardial infarction.
Serial OCT Findings
The results of serial OCT analysis of calcified plaques are shown in Table 2 and representative images are depicted in Figure 1. A total of 235 calcified plaques were identified in 105 patients at baseline. Among 235 calcified plaques, 112 (47.7%) calcified plaques were identified in patients with ACS and 123 (52.3%) were identified in patients with stable angina pectoris (SAP). Baseline and follow‐up calcification index were not significantly different between patients with ACS and patients with SAP (baseline, 127.6 [58.8–315.7] versus 132.0 [57.7–263.8]; P=0.836, follow‐up; 177.6 [90.0–362.9] versus 178.2 [81.0 versus 432.0]; P=0.908). During the 6‐month period, calcification burden increased in the majority of patients (95.3%). At baseline, 52.3% of calcified plaques also met the criteria for lipid‐rich plaque (LRP). The prevalence of thin‐cap fibroatheroma (TCFA) and macrophages significantly decreased at follow‐up (from 13.6% to 6.8%, P<0.001 and from 46.4% to 40.4%, P=0.022, respectively). In calcified plaques which met the criteria for LRP, lipid index significantly decreased and fibrous cap thickness significantly increased at follow‐up.
Table 2.
Serial Optical Coherence Tomography Analysis of Calcified Plaque
| Baseline | Follow‐Up | Change | P Value | |
|---|---|---|---|---|
| Quantitative analysis of calcification | ||||
| Maximal calcification arc, degree | 61.0 (43.0–101.0) | 68.0 (48.0–106.0) | 4.0 (1.0–10.0) | <0.001 |
| Mean calcification arc, degree | 46.0 (35.0–70.0) | 54.0 (38.0–80.0) | 5.0 (1.0–12.0) | <0.001 |
| Calcification length, mm | 2.6 (1.6–4.4) | 3.2 (2.0–5.5) | 0.4 (0.1–1.0) | <0.001 |
| Calcification index | 132.0 (58.5–281.2) | 178.2 (86.4–402.8) | 40.6 (12.2–107.1) | <0.001 |
| Minimal calcium depth, μm | 90 (40–180) | 70 (40–170) | −10 (−30 to 0) | <0.001 |
| Calcium classification | <0.001 | |||
| Microcalcification, n (%) | 11 (4.7) | 5 (2.1) | … | |
| Spotty calcification, n (%) | 129 (54.9) | 120 (51.1) | … | |
| Macrocalcification, n (%) | 95 (40.4) | 110 (46.8) | … | |
| Superficial–65, n (%) | 90 (38.3) | 105 (44.7) | … | 0.007 |
| Superficial–100, n (%) | 122 (51.9) | 136 (57.9) | … | 0.008 |
| Calcified plaque characteristics | ||||
| Lipid‐rich plaque, n (%) | 123 (52.3) | 121 (51.5) | … | 0.500 |
| Maximal lipid arc, degree | 147.0 (123.0–186.0) | 141.0 (116.0–181.3) | −7.0 (−16.8 to −1.0) | <0.001 |
| Mean lipid arc, degree | 116.0 (98.0–135.8) | 110.0 (95.0–154.0) | −6.0 (−10.8 to −0.3) | <0.001 |
| Lipid length, mm | 5.7 (4.2–7.1) | 5.5 (3.6–7.1) | −0.4 (−0.9 to 0.0) | <0.001 |
| Thinnest FCT, μm | 80 (60–120) | 115 (90–150) | 30 (10–50) | <0.001 |
| Lipid index | 636.4 (475.7–918.3) | 571.2 (393.2–854.9) | −90.4 (−133.9 to −13.1) | <0.001 |
| TCFA, n (%) | 32 (13.6) | 16 (6.8) | … | <0.001 |
| Macrophage, n (%) | 109 (46.4) | 95 (40.4) | … | 0.022 |
| Cholesterol crystal, n (%) | 30 (12.8) | 25 (10.6) | … | 0.267 |
| Microvessel, n (%) | 76 (32.3) | 76 (32.3) | … | 1.000 |
| Minimal lumen area, mm2 | 4.42 (3.20–6.21) | 4.30 (2.76–5.90) | −0.25 (−1.09 to 1.41) | 0.001 |
| Area stenosis, % | 45.6 (38.2–56.2) | 49.1 (38.6–60.7) | 1.8 (−3.6 to 7.3) | 0.002 |
Values are mean±SD, n (%), or median (interquartile range). FCT indicates fibrous cap thickness; and TCFA, thin‐cap fibroatheroma.
Figure 1. Representative images of calcified plaque.
Representative images of progression of coronary calcification with a reduction of inflammatory features. Calcification (white arrow) progressed from baseline (A, C, and E) to follow‐up (B, D, and F). Images display a calcified plaque with lipid pool (dashed half circle) with macrophages (arrowhead) (A and B), calcified plaque with macrophages but without lipid pool (C and D), and the progression of macrocalcification (maximal calcification arc >90°) from baseline to 6‐month follow‐up (E and F).
Excellent intraobserver and interobserver agreement was noted in the OCT identification of calcified plaque (κ, 0.956 and 0.911, respectively), LRP (κ, 0.957 and 0.911), and macrophages (κ, 0.919 and 0.876).
Predictors for Rapid Progression of Calcification
To find the predictors for rapid progression of calcification, calcified plaques were divided into 2 groups based on the degree of calcification index change from baseline to follow‐up. Rapid progression of calcification was defined as an increase in calcification index above the median value (median calcification index change was 40.6). Characteristics of calcified plaques with rapid progression are shown in Figure 2. Calcified plaques with rapid progression of calcification, compared with those without rapid progression, had significantly higher prevalence of LRP, TCFA, macrophages, cholesterol crystals, and microvessels, both at baseline and follow‐up. TCFA and macrophages significantly decreased with rapid progression.
Figure 2. Optical coherence tomography findings of calcified plaque with or without rapid progression of calcification.
Calcified plaque with rapid progression of calcification had significantly higher rates of lipid‐rich plaque, thin‐cap fibroatheroma, macrophages, cholesterol crystals and microvessels, both at baseline and follow‐up. TCFA and macrophages significantly decreased in calcified plaques with rapid progression of calcification from baseline to follow‐up. OCT indicates optical coherence tomography; and TCFA, thin‐cap fibroatheroma.
Table 3 shows the results of univariable and multivariable analysis. In multivariable analysis, DM and CKD were found to be independent clinical predictors for rapid progression of calcification, while LRP and macrophages were found to be independent morphologic predictors for rapid progression of coronary calcification (additional data are shown in Tables S1–S3 and Figure S1).
Table 3.
Predictors for Rapid Progression of Calcification
| Univariable | Multivariable | |||
|---|---|---|---|---|
| Odds Ratio (95% CI) | P Value | Odds Ratio (95% CI) | P Value | |
| Patient clinical characteristics | ||||
| Follow‐up duration, d | 1.003 (0.983‒1.024) | 0.755 | ||
| Age, y | 1.031 (0.999‒1.064) | 0.055 | 1.022 (0.991‒1.055) | 0.173 |
| Men | 0.654 (0.317‒1.348) | 0.250 | ||
| ACS | 0.748 (0.394‒1.422) | 0.376 | ||
| Hypertension | 0.943 (0.480‒1.851) | 0.864 | ||
| Dyslipidemia | 1.400 (0.681‒2.877) | 0.360 | ||
| Diabetes mellitus | 4.217 (2.314‒7.685) | <0.001 | 3.911 (2.177‒7.072) | <0.001 |
| Chronic kidney disease | 3.560 (1.550‒8.176) | 0.003 | 2.432 (1.054‒5.615) | 0.037 |
| Family history of CAD | 0.701 (0.196‒2.501) | 0.584 | ||
| Current smoker | 0.764 (0.368‒1.586) | 0.470 | ||
| Prior MI | 1.697 (0.902‒3.192) | 0.101 | ||
| Prior PCI | 1.436 (0.743‒2.774) | 0.282 | ||
| Aspirin at discharge | … | 1.000 | ||
| Clopidogrel at discharge | 1.272 (0.181‒8.924) | 0.809 | ||
| Statin at discharge | 3.135 (0.304‒32.285) | 0.335 | ||
| ACEI/ARB at discharge | 0.745 (0.389‒1.427) | 0.375 | ||
| Plaque characteristics | ||||
| TCFA | 4.224 (1.852‒9.634) | 0.001 | 0.843 (0.295‒2.413) | 0.751 |
| Lipid‐rich plaque | 8.182 (4.098‒16.334) | <0.001 | 2.698 (1.076‒6.762) | 0.034 |
| Macrophage | 13.410 (6.843‒26.312) | <0.001 | 6.782 (3.142‒14.637) | <0.001 |
| Cholesterol crystal | 6.022 (1.902‒19.012) | 0.002 | 2.890 (0.881‒9.477) | 0.080 |
| Microvessel | 3.053 (1.777‒5.245) | <0.001 | 1.571 (0.760‒3.249) | 0.223 |
| Minimal lumen area, mm2 | 0.909 (0.824‒1.003) | 0.058 | 0.993 (0.867‒1.138) | 0.993 |
| Area stenosis, % | 1.015 (0.993‒1.038) | 0.186 | ||
| Baseline calcium index | 1.005 (1.002‒1.009) | 0.006 | 1.004 (1.000‒1.007) | 0.074 |
| Minimal calcium depth, μm | 0.994 (0.991‒0.998) | 0.001 | 0.998 (0.994‒1.001) | 0.229 |
ACEI/ARB indicates angiotensin‐converting enzyme inhibitor/angiotensin II receptor blocker; ACS, acute coronary syndrome; CAD, coronary artery disease; MI, myocardial infarction; PCI, percutaneous coronary intervention; and TCFA, thin‐cap fibroatheroma.
Change in Calcification at 1‐Year Follow‐Up
To evaluate the predictors for rapid progression of calcification over a longer period, we analyzed a subgroup of patients who had 1‐year follow‐up. OCT images of 46 calcified plaques in 18 patients were available with a median follow‐up period of 373 (365–383) days. Median calcification index at baseline was 105.8 (58.2–272.8) and median change in calcification index from baseline to 6‐month follow‐up was 44.4 (11.9–84.1) and that from baseline to 1‐year follow‐up was 68.1 (24.5–165.5). Calcified plaques were divided into 2 groups according to the change in calcification index from baseline to 1‐year follow‐up. One‐year rapid progression of calcification was defined as an increase in calcification index above the median value (median calcification index change was 68.1). Table S4 shows the results of univariable and multivariable analysis. Although male, DM, CKD, current smoker, and prior history of myocardial infarction were associated with 1‐year rapid progression of calcification in univariable analysis, no factor was found to be an independent clinical predictor for 1‐year rapid progression of calcification in the multivariable analysis. On the other hand, macrophages were found to be an independent morphologic predictor for 1‐year rapid progression of coronary calcification in multivariable analysis.
De Novo Calcified Plaque and OCT Findings
At follow‐up, 14 de novo calcified plaques were identified in 14 patients. The baseline characteristics of patients with or without de novo calcified plaque are shown in Table S5. Patients with de novo calcified plaque had significantly higher rates of prior PCI and DM. Six of 14 (42.9%) de novo calcified plaques were identified in the previously stented arteries. Table S6 shows the serial OCT findings of de novo calcified plaque and its precursor plaques. Among 14 de novo calcified plaques, microcalcifications were observed in 7 (50.0%) and spotty calcifications were observed in 7 (50.0%). Among precursor plaques of de novo calcified plaques, 85.7% were LRP and 71.4% had macrophage infiltration.
Univariable and multivariable analysis were performed to find the predictors for de novo calcified plaque (Table S7). Multivariable analysis showed that DM and prior history of PCI were the independent clinical predictors for de novo calcified plaque. There was no significant morphologic predictor for de novo calcification in the multivariable analysis, although LRP tended to be associated with de novo calcified plaque.
Progression of Calcification, De Novo Calcification, and High‐Sensitivity C‐Reactive Protein
The relationships between progression of calcification, de novo calcified plaque, and hs‐CRP (high‐sensitivity C‐reactive protein) are shown in Figure S2 and S3. Neither hs‐CRP levels at baseline and follow‐up, nor the change in hs‐CRP levels were significantly different between patients with or without rapidly progressed calcified plaque or between patients with or without de novo calcified plaque.
Discussion
To the best of our knowledge, this is the largest study to investigate predictors for progression of coronary calcification using serial OCT imaging. Our study demonstrated that (1) calcification was detected in 56.1% of patients; (2) patients with calcified plaques had significantly higher prevalence of DM and prior PCI, and a trend toward higher prevalence of CKD; (3) the calcification index increased in 95.3% of the patients, while lipid burden and macrophage decreased over 6 months; (4) DM, CKD, LRP, and macrophages were independent predictors for rapid progression of calcification (Figure 3).
Figure 3. Predictors for rapid progression of coronary calcification.
Diabetes mellitus, chronic kidney disease, baseline lipid‐rich plaque, and macrophages were the independent predictors for rapid progression of calcification. Those plaques with rapid progression of calcification showed a significant reduction of inflammatory features such as thin‐cap fibroatheroma and macrophages. Baseline vascular inflammation and subsequent stabilization of vascular inflammation may be related to rapid progression of calcification.
Natural History of Calcification
Previous pathology studies have reported the natural history of plaque calcification. 17 , 18 Early microcalcification which originates from vascular smooth muscle cell (VSMC) apoptosis and matrix vesicles is present in lipid pools. Microcalcifications coalesce into larger masses and involve both the necrotic core and surrounding collagen‐rich extracellular matrix to form speckled and fragmented calcification. Further progressed calcified plaque forms calcified sheets or calcified plates. In some cases, calcified plates may fracture, resulting in calcified nodules. Hence, natural history studies show how the lipid component of plaques and formation of calcification inside plaques are intimately related during the progression of atherosclerosis.
DM and CKD for Progression of Coronary Calcification
Previous studies have reported the relationship between DM and CKD, and coronary calcification. 19 , 20 , 21 In patients with DM, insulin resistance and hyperglycemia increase oxidative stress by increasing glucose oxidation in the citric acid cycle. Oxidative stress upregulates Runt‐related transcription factor‐2 and promotes VSMC calcification. Furthermore, driven by hyperglycemia and oxidative stress, apoptosis of endothelial cells and their overall dysfunction promote endothelial permeability, exposing VSMCs to hyperglycemia and other inflammatory circulation factors known to promote calcification such as tumor necrosis factor‐α and receptor activator of the nuclear factor‐κB ligand. 20 Moreover, the production of tumor necrosis factor‐α from both endothelial and VSMCs induces the production of bone morphogenetic protein‐2, a potent osteoblastic differentiation factor, which promotes osteogenesis and calcification of VSMCs. 22 One previous study reported that type 2 DM did not have an impact on localization, size, shape, or extension of calcification using OCT. 16 However, this study evaluated only culprit lesions before coronary intervention at one time point. Thus, the prevalence of calcified plaque including non‐culprit plaques and the progression of calcification over time could not be evaluated. In addition, patients with ACS or CKD were excluded in this study. Moreover, the rate of statin therapy was relatively low in this study. Other studies showed the association between statin therapy and calcification. 6 , 7 It is important to study the relationship between DM and calcification in patients treated with statin because most patients with coronary artery disease are treated with statin. Furthermore, the number of plaques evaluated in this study was small (n=105). In contrast, our study evaluated changes in calcification over time using serial OCT imaging. In addition, our study included the patients with ACS or CKD.
In patients with CKD, dysregulation of calcium and phosphate metabolism, and inflammation are the most common factors causing vascular calcification. 23 Elevated calcium and phosphate have direct effects on VSMCs that promote vascular calcification, including stimulation of osteogenic/chondrogenic differentiation, vesicle release, apoptosis, loss of inhibitors (such as matrix Gla protein and osteopontin), and extracellular matrix degeneration. 23 Furthermore, increased systemic and local inflammation and oxidative stress cause the development of calcifying particles by multiple mechanisms. 24 , 25 Inflammation triggers and precedes osteogenic conversion of VSMC through an endoplasmic reticulum stress pathway activation, and release of calcifying extracellular vesicles, which promotes the calcification process. 26 Moreover, activated macrophages produce high levels of metalloproteinases, cysteine endoproteases, and cytokines. They will lead to elastin and collagen degeneration, and remodeling of structural changes of extracellular matrix, which will contribute to create a nidus for calcium‐phosphate (CaP) crystal growth. 26
Prevalence of Calcified Plaque in ACS and SAP
ACS is usually associated with higher inflammation, compared with SAP. Some studies reported that the number of calcium deposits was higher in ACS than in patients with SAP. 4 , 14 However, prevalence of calcified plaque was not different between ACS and SAP in our study. In addition, the rate of ACS was not significantly different between those with or without rapid progression of calcification (43.3% versus 44.7%, P=0.885). These results might be attributable to a small number of ST‐segment–elevation myocardial infarction (STEMI) in the ACS cohort (STEMI is usually associated with high inflammation). 27 To better understand the relationship between the progression of calcification and ACS, large studies are warranted.
LRP and Macrophages for Progression of Coronary Calcification
In our study, LRP and macrophages were associated with progression of vascular calcification. Previous studies reported that there are 2 phases of calcium progression: early‐stage inflammation‐related calcification (microcalcification and spotty calcification) and later‐stage stabilized calcification (macrocalcification). 28 , 29 Histological studies have suggested that early microcalcification occurs within the lipid pool and that VSMCs and macrophages play important roles in the progression of calcification. 17 , 18 , 30 , 31 Macrophages are classified into 2 groups according to their role: the inflammatory macrophage and resolving macrophage. 28 , 32 , 33 , 34 , 35 In plaques with necrotic lipid cores, inflammatory macrophages promote the initiation of the early phase of calcification through vesicle‐mediated mineralization as the result of the apoptosis of macrophages and VSMCs. 28 Furthermore, the progression of early‐stage calcification proceeds through osteogenic trans‐differentiation of VSMCs by the action of proinflammatory cytokines such as tumor necrosis factor‐α and oncostatin M produced from inflammatory macrophages (tumor necrosis factor‐α and oncostatin M increase the activity of alkaline phosphate, which is an ectoenzyme indispensable for bone mineralization in vascular smooth muscle cells). 28 , 36 On the other hand, calcification is also regarded as a healing and stabilizing process in the late stage. After the initial inflammation process, the main component of macrophages switch from inflammatory macrophages to resolving macrophages. This is fundamental for the resolution of inflammation and the healing response. 28 , 34 , 35 In this phase, the progression of calcification from early‐stage inflammation‐related calcification (microcalcification and spotty calcification) proceeds to later‐stage stabilized calcification (macrocalcification) through the resolving macrophage facilitation of osteogenic differentiation and maturation of VSMCs. 28 Thus, macrophages play an important role in calcium progression in both the early inflammatory phase and the late stabilizing phase. In our study, baseline LRP and macrophages were independent predictors for rapid progression of calcification. On the other hand, significant improvement of inflammatory features (TCFA and macrophages) over time was observed, as well as improvement of the lipid index in the calcified plaque with rapid progression of calcification. Progression and healing of atherosclerotic plaque are dynamic processes. Thus, there is a possibility that both increased macrophage apoptosis in an active inflammation phase and reduced macrophage infiltration in a resolving phase may be associated with a decrease in macrophages. This study demonstrated that not only macrophages, but also the lipid index decreased from baseline to 6‐month follow‐up. This result may imply that reduced macrophage infiltration (with resolving inflammation and regression of lipid content) is more strongly associated with rapid progression of calcification. Hence, baseline LRP and high inflammatory status in the early calcification stage and late‐stage recovery from inflammation may both be important for the rapid progression of calcification. On the other hand, although macrophages significantly decreased from baseline to follow‐up, they were still frequently observed in calcified plaque with rapid progression of calcification at follow‐up. This may not only reflect the ongoing recovery from inflammation, but also the fact that main component of macrophages switched from inflammatory macrophages to resolving macrophages and facilitated plaque stabilization and further progression of calcification in the late stabilizing phase. Thus, the major content of macrophages in rapidly progressed calcified plaques at baseline might be inflammatory macrophages which are usually abundant in active inflammatory plaques and promote the progression of calcification through inflammatory cytokines. On the other hand, major content of macrophages which were still observed at follow‐up might be resolving macrophages which also promote the calcification through proliferation and osteogenic differentiation of VSMCs. 28 , 37 Unfortunately, OCT cannot differentiate macrophage content, including the distinction between inflammatory macrophages and resolving macrophages.
To better understand the association between calcification and macrophages, we analyzed the relationship between change in calcification index and change in macrophages (Figure S4). Calcified plaques were divided into 4 groups based on changes in macrophage; Group 1: no macrophage both at baseline and at follow‐up; Group 2: macrophages detected only at follow‐up, but not present at baseline; Group 3: macrophages present at baseline but not detected at follow‐up; Group 4: macrophages detected both at baseline and at follow‐up. Groups 3 and 4 (groups with macrophages present at baseline), compared with Group 1, showed significant increases in the calcification index. This analysis suggests that baseline vascular inflammation (evidenced by macrophage infiltration) is particularly important for rapid progression of calcification.
Almost all patients were prescribed statin at discharge. Previous studies reported that statin facilitates the healing process against plaque inflammation by enhancing the resolving macrophages, resulting in progression of calcification. 28 , 38 , 39 A previous study reported the possibility of a favorable response to statin in baseline inflammatory and vulnerable features. 40 Thus, in our study, statins may have facilitated the rapid progression of calcification under baseline high inflammatory status.
Change in Calcification Over a Longer Period
A consistent result on macrophages was obtained at 6‐month and 1‐year follow‐ups. On the other hand, no clinical factor was found to be an independent predictor for 1‐year rapid progression of calcification in the multivariable analysis. However, these results should be interpreted with caution because of the small sample size.
Change in Laboratory Data and Progression of Calcification
We also evaluated the relationship between the change in laboratory data and progression of calcification. Laboratory data at follow‐up and changes are shown in Table S8. The levels of low‐density lipoprotein, triglyceride, and hs‐CRP significantly improved only in patients with rapid progression of calcification. In addition, the low‐density lipoprotein level at follow‐up was significantly lower in patients with rapid progression of coronary calcification, compared with those without. Furthermore, we analyzed the correlation between changes in each laboratory parameters with changes in calcification index. However, changes in lipid profiles or in inflammatory marker did not correlate with changes in calcification index (low‐density lipoprotein; r=−0.090, P=0.202, high‐density lipoprotein; r=0.028, P=0.687, triglyceride; r=0.105, P=0.134, hs‐CRP; r=−0.038, P=0.695). The lack of direct correlation may be related to the small sample size.
De Novo Calcified Plaque
In our study, patients with de novo calcified plaque at follow‐up had significantly higher rates of DM and prior history of PCI. Multivariable analysis also showed that DM and prior history of PCI were the independent predictors for de novo calcified plaque, although this analysis was limited by the small number of cases with de novo calcification. It has been previously described that DM is associated with progression of calcification, and patients with a prior history of PCI usually have more extensive atherosclerotic features than patients without a history of PCI. Our result showed that 78.6% of de novo calcified plaques were LRP. Furthermore, TCFA and macrophages were frequently observed both in precursor plaques at baseline and de novo calcified plaques at follow‐up. From these results, early‐stage calcium deposition may occur in a continuously active inflammatory state. This is consistent with previous pathological studies. 17 , 18 In fact, all de novo calcified plaques presented as microcalcification or spotty calcification, which is usually observed in the early‐stage inflammatory calcification phase. 28
Clinical Implications
Current study reveals that baseline high vascular inflammation, evidenced by lipid‐rich plaque and macrophage accumulation, and subsequent stabilization of vascular inflammation, evidenced by reduction of TCFA and macrophage accumulation, may be related to coronary calcification. Thus, the data from this study support the notion that coronary calcification represents an advanced, but stable stage of atherosclerosis.
Study Limitations
This study has several limitations. First, the Massachusetts General Hospital OCT Registry is a multicenter, prospective registry for all comers, which does not have strict inclusion criteria or guidelines for OCT image acquisition. Follow‐up OCT was not mandated in this registry. Patients who had a follow‐up OCT were retrospectively selected in this study. Therefore, selection bias cannot be excluded. Second, we did not have detailed information on medications, such as type, dose, and duration of medications including statin. Third, the number of de novo calcified plaque was small. Although we were able to evaluate characteristics of de novo calcified plaque to some extent, larger studies are warranted to analyze de novo calcified plaques in more detail. Fourth, we did not collect the data of serum calcium and serum blood urea nitrogen, although they might affect the progression of calcification, especially in patients with CKD. Finally, although this is the largest study on this topic, the sample size is still small for evaluating clinical significance.
Conclusions
This study demonstrated that DM, CKD, LRP, and macrophages were independent predictors for rapid progression of coronary calcification. Moreover, rapid progression of calcification was associated with a significant reduction of inflammatory features of plaques (TCFA and macrophages). Baseline vascular inflammation and subsequent stabilization of vascular inflammation may stimulate rapid progression of calcification.
Sources of Funding
Dr Jang’s research was supported by Mr and Mrs Allan and Gill Gray, and Mr and Mrs Michael and Kathryn Park. They had no role in the design or conduct of this research. Dr Malhotra was supported by the National Heart, Lung, and Blood Institute (NHLBI) (R01 HL 142809).
Disclosures
Dr Jang has received educational grant support from Abbott Vascular and a consulting fee from Svelte Medical Systems. The remaining authors have no disclosures to report.
Supporting information
(J Am Heart Assoc. 2021;10:e019235. DOI: 10.1161/JAHA.120.019235.)
For Sources of Funding and Disclosures, see page 12.
Contributor Information
Yoshiyasu Minami, Email: nrg12391@yahoo.co.jp.
Ik‐Kyung Jang, Email: ijang@mgh.harvard.edu.
References
- 1. Detrano R, Guerci AD, Carr JJ, Bild DE, Burke G, Folsom AR, Liu K, Shea S, Szklo M, Bluemke DA, et al. Coronary calcium as a predictor of coronary events in four racial or ethnic groups. N Engl J Med. 2008;358:1336–1345. DOI: 10.1056/NEJMoa072100. [DOI] [PubMed] [Google Scholar]
- 2. Budoff MJ, Hokanson JE, Nasir K, Shaw LJ, Kinney GL, Chow D, DeMoss D, Nuguri V, Nabavi V, Ratakonda R, et al. Progression of coronary artery calcium predicts all‐cause mortality. JACC Cardiovasc Imaging. 2010;3:1229–1236. DOI: 10.1016/j.jcmg.2010.08.018. [DOI] [PubMed] [Google Scholar]
- 3. Kataoka Y, Puri R, Hammadah M, Duggal B, Uno K, Kapadia SR, Tuzcu EM, Nissen SE, Nicholls SJ. Spotty calcification and plaque vulnerability in vivo: frequency‐domain optical coherence tomography analysis. Cardiovasc Diagn Ther. 2014;4:460–469. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Mizukoshi M, Kubo T, Takarada S, Kitabata H, Ino Y, Tanimoto T, Komukai K, Tanaka A, Imanishi T, Akasaka T. Coronary superficial and spotty calcium deposits in culprit coronary lesions of acute coronary syndrome as determined by optical coherence tomography. Am J Cardiol. 2013;112:34–40. DOI: 10.1016/j.amjcard.2013.02.048. [DOI] [PubMed] [Google Scholar]
- 5. Criqui MH, Denenberg JO, Ix JH, McClelland RL, Wassel CL, Rifkin DE, Carr JJ, Budoff MJ, Allison MA. Calcium density of coronary artery plaque and risk of incident cardiovascular events. JAMA. 2014;311:271–278. DOI: 10.1001/jama.2013.282535. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Puri R, Nicholls SJ, Shao M, Kataoka Y, Uno K, Kapadia SR, Tuzcu EM, Nissen SE. Impact of statins on serial coronary calcification during atheroma progression and regression. J Am Coll Cardiol. 2015;65:1273–1282. [DOI] [PubMed] [Google Scholar]
- 7. Andrews J, Psaltis PJ, Bayturan O, Shao M, Stegman B, Elshazly M, Kapadia SR, Tuzcu EM, Nissen SE, Nicholls SJ, et al. Warfarin use is associated with progressive coronary arterial calcification: insights from serial intravascular ultrasound. JACC Cardiovasc Imaging. 2018;11:1315–1323. DOI: 10.1016/j.jcmg.2017.04.010. [DOI] [PubMed] [Google Scholar]
- 8. Diederichsen SZ, Gronhoj MH, Mickley H, Gerke O, Steffensen FH, Lambrechtsen J, Ronnow Sand NP, Rasmussen LM, Olsen MH, Diederichsen A. CT‐detected growth of coronary artery calcification in asymptomatic middle‐aged subjects and association with 15 biomarkers. JACC Cardiovasc Imaging. 2017;10:858–866. [DOI] [PubMed] [Google Scholar]
- 9. Krishnamoorthy P, Vengrenyuk Y, Ueda H, Yoshimura T, Pena J, Motoyama S, Baber U, Hasan C, Kesanakurthy S, Sweeny J, et al. Three‐dimensional volumetric assessment of coronary artery calcification in patients with stable coronary artery disease by OCT. EuroIntervention. 2017;13:312–319. DOI: 10.4244/EIJ-D-16-00139. [DOI] [PubMed] [Google Scholar]
- 10. Kume T, Okura H, Kawamoto T, Yamada R, Miyamoto Y, Hayashida A, Watanabe N, Neishi Y, Sadahira Y, Akasaka T, et al. Assessment of the coronary calcification by optical coherence tomography. EuroIntervention. 2011;6:768–772. DOI: 10.4244/EIJV6I6A130. [DOI] [PubMed] [Google Scholar]
- 11. Wang X, Matsumura M, Mintz GS, Lee T, Zhang W, Cao Y, Fujino A, Lin Y, Usui E, Kanaji Y, et al. In vivo calcium detection by comparing optical coherence tomography, intravascular ultrasound, and angiography. JACC Cardiovasc Imaging. 2017;10:869–879. DOI: 10.1016/j.jcmg.2017.05.014. [DOI] [PubMed] [Google Scholar]
- 12. Tearney GJ, Regar E, Akasaka T, Adriaenssens T, Barlis P, Bezerra HG, Bouma B, Bruining N, Cho J‐M, Chowdhary S, et al. 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. DOI: 10.1016/j.jacc.2011.09.079. [DOI] [PubMed] [Google Scholar]
- 13. Zeng Y, Tateishi H, Cavalcante R, Tenekecioglu E, Suwannasom P, Sotomi Y, Collet C, Nie S, Jonker H, Dijkstra J, et al. Serial assessment of tissue precursors and progression of coronary calcification analyzed by fusion of IVUS and OCT: 5‐year follow‐up of scaffolded and nonscaffolded arteries. JACC Cardiovasc Imaging. 2017;10:1151–1161. [DOI] [PubMed] [Google Scholar]
- 14. Ong DS, Lee JS, Soeda T, Higuma T, Minami Y, Wang Z, Lee H, Yokoyama H, Yokota T, Okumura K, et al. Coronary calcification and plaque vulnerability: an optical coherence tomographic study. Circ Cardiovasc Imaging. 2016;9:e003929. DOI: 10.1161/CIRCIMAGING.115.003929. [DOI] [PubMed] [Google Scholar]
- 15. Sugiyama T, Yamamoto E, Fracassi F, Lee H, Yonetsu T, Kakuta T, Soeda T, Saito Y, Yan BP, Kurihara O, et al. Calcified plaques in patients with acute coronary syndromes. JACC Cardiovasc Interv. 2019;12:531–540. [DOI] [PubMed] [Google Scholar]
- 16. Milzi A, Burgmaier M, Burgmaier K, Hellmich M, Marx N, Reith S. Type 2 diabetes mellitus is associated with a lower fibrous cap thickness but has no impact on calcification morphology: an intracoronary optical coherence tomography study. Cardiovasc Diabetol. 2017;16:152. DOI: 10.1186/s12933-017-0635-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Otsuka F, Sakakura K, Yahagi K, Joner M, Virmani R. Has our understanding of calcification in human coronary atherosclerosis progressed? Arterioscler Thromb Vasc Biol. 2014;34:724–736. DOI: 10.1161/ATVBAHA.113.302642. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Mori H, Torii S, Kutyna M, Sakamoto A, Finn AV, Virmani R. Coronary artery calcification and its progression: what does it really mean? JACC Cardiovasc Imaging. 2018;11:127–142. [DOI] [PubMed] [Google Scholar]
- 19. Anand DV, Lim E, Darko D, Bassett P, Hopkins D, Lipkin D, Corder R, Lahiri A. Determinants of progression of coronary artery calcification in Type 2 diabetes role of glycemic control and inflammatory/vascular calcification markers. J Am Coll Cardiol. 2007;50:2218–2225. DOI: 10.1016/j.jacc.2007.08.032. [DOI] [PubMed] [Google Scholar]
- 20. Yahagi K, Kolodgie FD, Lutter C, Mori H, Romero ME, Finn AV, Virmani R. Pathology of human coronary and carotid artery atherosclerosis and vascular calcification in diabetes mellitus. Arterioscler Thromb Vasc Biol. 2017;37:191–204. DOI: 10.1161/ATVBAHA.116.306256. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Bundy JD, Chen J, Yang W, Budoff M, Go AS, Grunwald JE, Kallem RR, Post WS, Reilly MP, Ricardo AC, et al. Risk factors for progression of coronary artery calcification in patients with chronic kidney disease: the CRIC study. Atherosclerosis. 2018;271:53–60. DOI: 10.1016/j.atherosclerosis.2018.02.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Shao JS, Cheng SL, Pingsterhaus JM, Charlton‐Kachigian N, Loewy AP, Towler DA. Msx2 promotes cardiovascular calcification by activating paracrine Wnt signals. J Clin Invest. 2005;115:1210–1220. DOI: 10.1172/JCI24140. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Shanahan CM, Crouthamel MH, Kapustin A, Giachelli CM. Arterial calcification in chronic kidney disease: key roles for calcium and phosphate. Circ Res. 2011;109:697–711. DOI: 10.1161/CIRCRESAHA.110.234914. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Benz K, Hilgers KF, Daniel C, Amann K. Vascular calcification in chronic kidney disease: the role of inflammation. Int J Nephrol. 2018;2018:4310379. DOI: 10.1155/2018/4310379. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Vervloet M, Cozzolino M. Vascular calcification in chronic kidney disease: different bricks in the wall? Kidney Int. 2017;91:808–817. DOI: 10.1016/j.kint.2016.09.024. [DOI] [PubMed] [Google Scholar]
- 26. Viegas C, Araujo N, Marreiros C, Simes D. The interplay between mineral metabolism, vascular calcification and inflammation in Chronic Kidney Disease (CKD): challenging old concepts with new facts. Aging (Albany NY). 2019;11:4274–4299. DOI: 10.18632/aging.102046. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Di Stefano R, Di Bello V, Barsotti MC, Grigoratos C, Armani C, Dell'Omodarme M, Carpi A, Balbarini A. Inflammatory markers and cardiac function in acute coronary syndrome: difference in ST‐segment elevation myocardial infarction (STEMI) and in non‐STEMI models. Biomed Pharmacother. 2009;63:773–780. DOI: 10.1016/j.biopha.2009.06.004. [DOI] [PubMed] [Google Scholar]
- 28. Shioi A, Ikari Y. Plaque calcification during atherosclerosis progression and regression. J Atheroscler Thromb. 2018;25:294–303. DOI: 10.5551/jat.RV17020. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. Akers EJ, Nicholls SJ, Di Bartolo BA. Plaque calcification: do lipoproteins have a role? Arterioscler Thromb Vasc Biol. 2019;39:1902–1910. DOI: 10.1161/ATVBAHA.119.311574. [DOI] [PubMed] [Google Scholar]
- 30. Sakamoto A, Virmani R, Finn AV. Coronary artery calcification: recent developments in our understanding of its pathologic and clinical significance. Curr Opin Cardiol. 2018;33:645–652. DOI: 10.1097/HCO.0000000000000558. [DOI] [PubMed] [Google Scholar]
- 31. Nakahara T, Dweck MR, Narula N, Pisapia D, Narula J, Strauss HW. Coronary artery calcification: from mechanism to molecular imaging. JACC Cardiovasc Imaging. 2017;10:582–593. DOI: 10.1016/j.jcmg.2017.03.005. [DOI] [PubMed] [Google Scholar]
- 32. Geeraerts X, Bolli E, Fendt SM, Van Ginderachter JA. Macrophage metabolism as therapeutic target for cancer, atherosclerosis, and obesity. Front Immunol. 2017;8:289. DOI: 10.3389/fimmu.2017.00289. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33. Williams JW, Giannarelli C, Rahman A, Randolph GJ, Kovacic JC. Macrophage biology, classification, and phenotype in cardiovascular disease: JACC macrophage in CVD series (part 1). J Am Coll Cardiol. 2018;72:2166–2180. DOI: 10.1016/j.jacc.2018.08.2148. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34. Moore KJ, Koplev S, Fisher EA, Tabas I, Björkegren JLM, Doran AC, Kovacic JC. Macrophage trafficking, inflammatory resolution, and genomics in atherosclerosis: JACC macrophage in CVD series (part 2). J Am Coll Cardiol. 2018;72:2181–2197. DOI: 10.1016/j.jacc.2018.08.2147. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35. Lavine KJ, Pinto AR, Epelman S, Kopecky BJ, Clemente‐Casares X, Godwin J, Rosenthal N, Kovacic JC. The macrophage in cardiac homeostasis and disease: JACC macrophage in CVD series (part 4). J Am Coll Cardiol. 2018;72:2213–2230. DOI: 10.1016/j.jacc.2018.08.2149. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36. Shioi A, Katagi M, Okuno Y, Mori K, Jono S, Koyama H, Nishizawa Y. Induction of bone‐type alkaline phosphatase in human vascular smooth muscle cells: roles of tumor necrosis factor‐alpha and oncostatin M derived from macrophages. Circ Res. 2002;91:9–16. DOI: 10.1161/01.RES.0000026421.61398.F2. [DOI] [PubMed] [Google Scholar]
- 37. Zhang Y, Böse T, Unger RE, Jansen JA, Kirkpatrick CJ, van den Beucken J. Macrophage type modulates osteogenic differentiation of adipose tissue MSCs. Cell Tissue Res. 2017;369:273–286. DOI: 10.1007/s00441-017-2598-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38. Zhang O, Zhang J. Atorvastatin promotes human monocyte differentiation toward alternative M2 macrophages through p38 mitogen‐activated protein kinase‐dependent peroxisome proliferator‐activated receptor gamma activation. Int Immunopharmacol. 2015;26:58–64. [DOI] [PubMed] [Google Scholar]
- 39. Asai J, Takenaka H, Hirakawa S, Sakabe J‐I, Hagura A, Kishimoto S, Maruyama K, Kajiya K, Kinoshita S, Tokura Y, et al. Topical simvastatin accelerates wound healing in diabetes by enhancing angiogenesis and lymphangiogenesis. Am J Pathol. 2012;181:2217–2224. DOI: 10.1016/j.ajpath.2012.08.023. [DOI] [PubMed] [Google Scholar]
- 40. Minami Y, Wang Z, Aguirre AD, Ong DS, Kim CJ, Uemura S, Soeda T, Lee H, Fujimoto J, Jang IK. Clinical predictors for lack of favorable vascular response to statin therapy in patients with coronary artery disease: a serial optical coherence tomography study. J Am Heart Assoc. 2017;6:e006241. DOI: 10.1161/JAHA.117.006241. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41. O'Gara PT, Kushner FG, Ascheim DD, Casey DE Jr, Chung MK, de Lemos JA, Ettinger SM, Fang JC, Fesmire FM, Franklin BA, et al. 2013 ACCF/AHA guideline for the management of ST‐elevation myocardial infarction: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2013;61:e78–e140. [DOI] [PubMed] [Google Scholar]
- 42. Amsterdam EA, Wenger NK, Brindis RG, Casey DE, Ganiats TG, Holmes DR, Jaffe AS, Jneid H, Kelly RF, Kontos MC, et al. 2014 AHA/ACC guideline for the management of patients with non‐ST‐elevation acute coronary syndromes: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2014;64:e139–e228. DOI: 10.1161/CIR.0000000000000134. [DOI] [PubMed] [Google Scholar]
- 43. Diagnosis and classification of diabetes mellitus . Diabetes Care. 2010;33:S62–S69. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44. Levey AS, Coresh J, Greene T, Stevens LA, Zhang YL, Hendriksen S, Kusek JW, Van Lente F. 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. DOI: 10.7326/0003-4819-145-4-200608150-00004. [DOI] [PubMed] [Google Scholar]
- 45. Prati F, Regar E, Mintz GS, Arbustini E, Di Mario C, Jang I‐K, Akasaka T, Costa M, Guagliumi G, Grube E, et al. 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. DOI: 10.1093/eurheartj/ehp433. [DOI] [PubMed] [Google Scholar]
- 46. Vergallo R, Uemura S, Soeda T, Minami Y, Cho J‐M, Ong DS, Aguirre AD, Gao L, Biasucci LM, Crea F, et al. Prevalence and predictors of multiple coronary plaque ruptures. In vivo 3‐vessel optical coherence tomography imaging study. Arterioscler Thromb Vasc Biol. 2016;36:2229–2238. DOI: 10.1161/ATVBAHA.116.307891. [DOI] [PubMed] [Google Scholar]
- 47. Kato K, Yonetsu T, Kim S‐J, Xing L, Lee H, McNulty I, Yeh RW, Sakhuja R, Zhang S, Uemura S, et al. 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. DOI: 10.1161/CIRCIMAGING.112.973701. [DOI] [PubMed] [Google Scholar]
- 48. 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. DOI: 10.1161/01.ATV.20.5.1262. [DOI] [PubMed] [Google Scholar]
- 49. 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. DOI: 10.1161/01.CIR.0000044384.41037.43. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.