Contrast-enhanced ultrasound evaluation of plaque vulnerability and the relationship between peripheral blood leukocytes

Abstract

OBJECTIVE:

To evaluate plaque vulnerability by carotid contrast-enhanced ultrasound (CEUS) and to analyze the correlation between plaque vulnerability and peripheral blood leukocyte classification.

MATERIALS AND METHODS:

135 patients with carotid plaque were examined by contrast-enhanced ultrasound. Plaque vulnerability was assessed by semiquantitative visual classification. Baseline clinical data and peripheral leukocyte classification were collected. Ordered logistic regression was used to analyze the correlation between plaque neovascularization grade and peripheral leukocyte classification count.

RESULTS:

There were significant differences in leukocyte, monocyte, neutrophil, mean platelet volume, lymphocyte, and neutrophil counts between different neovascular plaque grades and peripheral blood (P < 0.05). Correlation analysis showed that leukocyte, monocyte, and neutrophil counts were significantly positively correlated.

CONCLUSION:

The increase in plaque neovascularization is associated with an increase in circulating leukocytes, monocytes, and neutrophils. Therefore, CEUS combined with peripheral blood leukocytes may serve as an early warning of plaque vulnerability and provide a theoretical basis for clinical treatment.

Keywords

Contrast-enhanced ultrasound carotid plaque leukocyte inflammation

1 Background

Stroke is a major risk factor affecting human health worldwide, with high rates of mortality and disability [1]. Approximately 18% –25% of strokes are caused by thromboembolism from carotid plaques, and certain vulnerable plaques can predict stroke [2]. Intraplaque neovascularization (IPN) is an important indicator of vulnerable plaque [3 –5]. Studies have shown a direct correlation between the degree of enhancement within CEUS plaques and the degree of neovascularization within histological plaques using quantitative or semi-quantitative measures [6, 7]. CEUS is a good non-invasive diagnostic method that can be used to detect IPN and predict the occurrence of stroke [8]. Leukocytes are the effector arm of the immune system, providing both immune surveillance and a rapid response to tissue damage [9]. Careful analysis of circulating leukocytes may provide a valuable tool for assessing the inflammatory and immune status of patients [10]. The increase of leukocytes might increase the viscosity of blood, what was reported in patients with carotid artery plaque [11]. This increased blood viscosity may worsen the conditions associated with atherosclerosis. Atherosclerosis is a disease characterized by low-grade chronic inflammation of the arterial wall [12]. Chronic atherosclerotic inflammation is characterized by the progressive accumulation of lipids and inflammatory cells in atherosclerotic lesions. The infiltration and accumulation of plasma lipoprotein and leukocyte subsets are the driving forces for atherosclerotic lesions [13]. In patients with atherosclerosis, monocytes and lymphocytes and their subsets are most frequently altered [14]. However, the effect of peripheral leukocytes on plaque characteristics and the relationship between them are not well understood. The aim of this study is to observe neovascularization in plaques by contrast-enhanced ultrasound (CEUS), to evaluate different grades of vulnerable plaques by semi-quantitative visual grading, and to explore their correlation with peripheral leukocyte differential counts in order to elucidate the mechanism of aggravated atherosclerosis and to provide a basis for the development of new treatment strategies.

2 Data and methods

2.1 Patient selection

From March 2021 to August 2022, patients with carotid plaque examined by contrast-enhanced ultrasound in the First Hospital of Qinhuangdao were selected, including 93 males and 42 females, with a mean age of (62.46±8.739) years. This study was approved by the hospital ethics committee. The ethics number is 2023YY100.

Inclusion criteria: 1) hypoechoic plaque (type I-II) detected by conventional ultrasound: 2) plaque > 2 mm; Exclusion criteria: 1) patients with contrast media allergy; 2) previous carotid artery stenting or carotid endarterectomy; 3) patients with hematological diseases, malignant tumors, or severe liver, kidney, or lung diseases; 4) non-atherosclerotic intracranial vascular disease (moyamoya disease, reversible vasoconstriction syndrome, etc.); 5) presence of cardiogenic stroke; 6) acute heart failure, unstable angina, or acute endocarditis; 7) recent history of bleeding; This study was approved by the ethics Committee of our hospital, and all subjects signed informed consent.

General information and medical history of the subjects were collected. Fasting venous blood samples were collected from all subjects in the morning. Levels of TC (total cholesterol), TG (triglycerides), HDL-C (high-density lipoprotein cholesterol), LDL-C (low-density lipoprotein cholesterol), LP(a) (lipoprotein (a)), SUA (serum uric acid), tHcy(homocysteine), GLU (glucose), leukocyte count, absolute monocyte count, monocyte percentage, absolute lymphocyte count, lymphocyte percentage, absolute neutrophil count, neutrophil percentage, platelet count, and MPV (mean platelet volume) were measured.

2.2 Instruments and methods

The Aixplorer v (SuperSonic Imagine, Aix-en-Provence, France) L9-3 ultrasound probe with a frequency of 5–9 MHz was used to examine the common carotid artery, carotid bifurcation, and internal carotid artery in longitudinal and transverse sections by experienced physicians. According to the Gray-Weale classification system, plaque echogenicity was classified as follows Type I, homogeneously hypoechoic; Type II, predominantly hypoechoic; Type III, predominantly hyperechoic; Type IV, homogeneously hyperechoic; Type V, extensive calcification with acoustic shadow. Hypoechoic (type I to II) was selected.

Carotid contrast-enhanced ultrasound was performed Hypoechoic plaques (type I-II) detected by B-mode ultrasound were evaluated by contrast-enhanced ultrasound. CEUS evaluates neovascularization within the plaque using the same robotic evaluation used for carotid B-mode ultrasound. The contrast-enhanced ultrasound software built into the device was used. The appropriate mechanical index was selected to avoid premature bubble destruction. 1.6 mL of contrast medium (SonoVue; Bracco, Italy) was selected through the median cubital vein and immediately flushed with 5 mL of sodium chloride saline. The injection was simultaneously timed, the dynamic images of the angiography were recorded for 2 minutes, and the images were stored. Visual semi-quantitative analysis was used to grade plaque neovascularization: Grade 0: no bubbles within the plaque or bubbles confined to the adjacent adventitia, Grade 1: moving bubbles confined to the adventitial side, Grade 2: moving bubbles at the plaque shoulder, Grade 3: bubbles moving to the plaque core, Grade 4: extensive intraplaque enhancement [15] (Fig. 1).

Fig. 1

Contrast-enhanced ultrasound grading of carotid plaque: Grade 0, no enhancement within the plaque (A); Grade 1, multiple punctate enhancement at the base of the plaque (B); Grade 2, punctate enhancement at the shoulder and base of the plaque (C); Grade 3, punctate enhancement moving toward the center of the plaque (D); Grade 4, diffuse punctate enhancement in ulcerated plaques and in the core (E).

2.3 Statistical methods

SPSS 26.0 software was used. Normal continuous variables were expressed as mean±standard deviation, while non-normal continuous variables were expressed as median (interquartile range). Analysis of variance or Kruskal-Wallis test was used for comparison between groups, Bonferroni test for pairwise comparison between groups. Categorical variables are presented as counts (and percentages); X² test or Fisher’s exact probability method was used to compare frequencies of occurrence. Ordered logistic regression analysis was used for correlation between plaque grades and risk factors. P < 0.05 was considered statistically significant.

Multivariate ordered logistic regression analysis was used to build a model of the effect of leukocytes and their subtypes on plaque classification (model 1), Independent variables with group differences and correlations in univariate ordinal logistic regression were used to adjust the model as potential confounders (model 2). Confusion matrix and receiver operating characteristic (ROC) curve were calculated to evaluate the performance of the model.

3 Results

The result among the included data, the classification of neovascularization in the plaque was as follows: 7 cases of grade 0, 19 cases of grade 1, 64 cases of grade 2, 30 cases of grade 3, and 15 cases of grade 4.

1. Comparison of plaque grade with general data and peripheral blood leukocytes

There were statistically significant differences between the contrast-enhanced ultrasound grades and the leukocyte count, the monocyte count, the neutrophil count, and the mean platelet volume (P < 0.05). There were no significant differences in sex, age, hypertension, diabetes, TC, TG, HDL-C, LDL-C, SUA, tHcy, GLU, LP(a), platelet count, lymphocyte count, monocyte percentage, lymphocyte percentage, neutrophil percentage (P > 0. 05). Bonferroni correction for pairwise comparison between groups, leukocyte count, monocyte count and neutrophil count were significantly different (P < 0.005) Table 1.

Table 1
Comparison of the general data and peripheral leukocyte of carotid plaque by contrast-enhanced ultrasound of different grades

Grade 0 1 2 3 4 F/x2/H P

Age (year) 63.14±5.11 59.21±11.58 62.54±8.80 64.50±7.52 61.87±7.58 1.098 0.360

Men, n (%) 5 (71.4%) 14 (73.7%) 45 (70.3%) 19 (63.3%) 10 (66.7%) 0.878 0.954

Hypertension, n (%) 4 (57.1%) 10 (52.6%) 30 (46.9%) 19 (63.3%) 10 (66.7%) 3.372 0.513

Diabetes, n (%) 1 (14.3%) 7 (36.8%) 20 (31.3%) 12 (40.0%) 5 (33.3%) 1.973 0.753

TC (mmol/L) 3.76 (1.44) 5.34 (1.62) 5.01 (1.60) 4.78 (1.96) 5.09 (2.24) 5.913 0.206

TG (mmol/L) 1.14 (0.88) 1.60 (0.98) 1.63 (1.07) 2.41 (1.46) 1.88 (1.23) 9.377 0.052

HDL-C (mmol/L) 0.94 (0.36) 0.94 (0.32) 0.98 (0.25) 0.99 (0.23) 0.81 (0.18) 8.137 0.087

LDL-C (mmol/L) 2.27 (0.85) 3.11 (1.30) 2.76 (0.89) 2.76 (1.23) 2.73 (1.54) 4.105 0.392

SUA (μ mmol /L) 289.85 (53.10) 279.11 (126.80) 318.12 (112.30) 344.74 (130.20) 309.86 (180.20) 3.484 0.480

tHcy (μ mmol /L) 15.65 (10.10) 13.47 (5.75) 18.79 (7.11) 18.93 (8.24) 19.64 (11.04) 7.382 0.117

GLU (mmol/L) 5.16 (0.61) 7.67 (7.16) 6.49 (2.28) 6.39 (2.56) 6.82 (1.43) 4.297 0.367

LP (a) (mmol/L) 298.55 (248.02) 345.05 (403.00) 321.72 (271.08) 386.90 (324.45) 434.18 (335.25) 4.991 0.288

Leukocytes (×10⁹/L) 5.68 (1.40) 6.17 (3.20) 7.05 (2.34) 7.71 (2.12) 9.10 (3.35)^*# 17.342 0.002

Monocytes (×10⁹/L) 0.39 (0.25) 0.39 (0.18) 0.47 (0.16) 0.50 (0.17) 0.61 (0.21)^# 15.411 0.004

Monocytes (%) 7.21 (4.20) 6.71 (2.72) 6.91 (2.70) 6.82 (2.75) 7.04 (2.27) 2.158 0.707

Platelets (×10⁹/L) 229.43 (96.00) 202.11 (89.00) 236.24 (66.00) 232.37 (94.25) 221.21 (60.25) 5.527 0.237

MPV (fl) 9.54 (0.20) 9.81 (0.70) 10.11 (1.30) 10.35 (1.20) 10.11 (0.47) 9.968 0.041

Lymphocytes (×10⁹/L) 1.62 (0.64) 1.67 (1.02) 1.83 (0.82) 1.69 (0.80) 1.66 (0.43) 3.322 0.505

Lymphocytes (%) 28.51±12.10 28.05±7.97 27.03±9.27 22.99±9.00 23.07±6.70 1.757 0.142

Neutrophils (×10⁹/L) 3.50 (0.59) 3.96 (1.82) 4.59 (2.05) 5.41 (2.18) 6.68 (2.89)^# 14.735 0.005

Neutrophils (%) 61.61 (24.20) 60.32 (19.35 64.19 (12.40) 68.33 (17.15) 69.68 (12.75) 6.494 0.165

Grade	0	1	2	3	4	F/x2/H	P
Age (year)	63.14±5.11	59.21±11.58	62.54±8.80	64.50±7.52	61.87±7.58	1.098	0.360
Men, n (%)	5 (71.4%)	14 (73.7%)	45 (70.3%)	19 (63.3%)	10 (66.7%)	0.878	0.954
Hypertension, n (%)	4 (57.1%)	10 (52.6%)	30 (46.9%)	19 (63.3%)	10 (66.7%)	3.372	0.513
Diabetes, n (%)	1 (14.3%)	7 (36.8%)	20 (31.3%)	12 (40.0%)	5 (33.3%)	1.973	0.753
TC (mmol/L)	3.76 (1.44)	5.34 (1.62)	5.01 (1.60)	4.78 (1.96)	5.09 (2.24)	5.913	0.206
TG (mmol/L)	1.14 (0.88)	1.60 (0.98)	1.63 (1.07)	2.41 (1.46)	1.88 (1.23)	9.377	0.052
HDL-C (mmol/L)	0.94 (0.36)	0.94 (0.32)	0.98 (0.25)	0.99 (0.23)	0.81 (0.18)	8.137	0.087
LDL-C (mmol/L)	2.27 (0.85)	3.11 (1.30)	2.76 (0.89)	2.76 (1.23)	2.73 (1.54)	4.105	0.392
SUA (μ mmol /L)	289.85 (53.10)	279.11 (126.80)	318.12 (112.30)	344.74 (130.20)	309.86 (180.20)	3.484	0.480
tHcy (μ mmol /L)	15.65 (10.10)	13.47 (5.75)	18.79 (7.11)	18.93 (8.24)	19.64 (11.04)	7.382	0.117
GLU (mmol/L)	5.16 (0.61)	7.67 (7.16)	6.49 (2.28)	6.39 (2.56)	6.82 (1.43)	4.297	0.367
LP (a) (mmol/L)	298.55 (248.02)	345.05 (403.00)	321.72 (271.08)	386.90 (324.45)	434.18 (335.25)	4.991	0.288
Leukocytes (×10⁹/L)	5.68 (1.40)	6.17 (3.20)	7.05 (2.34)	7.71 (2.12)	9.10 (3.35)^*#	17.342	0.002
Monocytes (×10⁹/L)	0.39 (0.25)	0.39 (0.18)	0.47 (0.16)	0.50 (0.17)	0.61 (0.21)^#	15.411	0.004
Monocytes (%)	7.21 (4.20)	6.71 (2.72)	6.91 (2.70)	6.82 (2.75)	7.04 (2.27)	2.158	0.707
Platelets (×10⁹/L)	229.43 (96.00)	202.11 (89.00)	236.24 (66.00)	232.37 (94.25)	221.21 (60.25)	5.527	0.237
MPV (fl)	9.54 (0.20)	9.81 (0.70)	10.11 (1.30)	10.35 (1.20)	10.11 (0.47)	9.968	0.041
Lymphocytes (×10⁹/L)	1.62 (0.64)	1.67 (1.02)	1.83 (0.82)	1.69 (0.80)	1.66 (0.43)	3.322	0.505
Lymphocytes (%)	28.51±12.10	28.05±7.97	27.03±9.27	22.99±9.00	23.07±6.70	1.757	0.142
Neutrophils (×10⁹/L)	3.50 (0.59)	3.96 (1.82)	4.59 (2.05)	5.41 (2.18)	6.68 (2.89)^#	14.735	0.005
Neutrophils (%)	61.61 (24.20)	60.32 (19.35	64.19 (12.40)	68.33 (17.15)	69.68 (12.75)	6.494	0.165

^*Compared with plaque grade 0, P < 0.005; ^#compared with plaque grade 1, P < 0.005. TC (total cholesterol), TG (triglycerides), HDL-C (high-density lipoprotein cholesterol), LDL-C (low-density lipoprotein cholesterol), SUA (serum uric acid), tHcy(homocysteine), GLU (glucose), LP(a) (lipoprotein (a)), MPV (mean platelet volume).

2. Correlation analysis of plaque grade with general data and peripheral leukocyte differential

Univariate ordinal logistic regression analysis was used to analyze the correlation between plaque grade and general data and peripheral leukocyte differential count. Plaque grade was significantly positively correlated with leukocyte count (OR = 1.442, 95% CI 0.198∼0.534, P < 0.001), monocyte count (OR = 54.982, 95% CI 1.968∼6.046, P < 0.001), and neutrophil count (OR = 1.394, 95% CI 1.968∼6.046, P < 0.001). 95% CI 0.167∼0.497, P < 0.001). In addition, plaque grade was statistically significantly associated with mean platelet volume (OR = 1.550, 95% CI 0.042–0.834, P = 0.030), lymphocyte percentage (OR = 0.956,95% CI (–0.81)∼(–0.008), P = 0.016), neutrophil percentage (OR = 1.039, 95% CI 0.010∼0.065, P = 0.007), and TG (OR = 1.383, 95% CI 0.045∼0.603, P = 0.045) Table 2.

Table 2

Correlation analysis of plaque grade with general data and peripheral leukocyte differential

	B	95% CI	SE	Wald	OR	P
Age (year)	0.022	(–0.014,0.058)	0.018	1.416	1.022	0.234
Men, n (%)	0.260	(–0.414, 0.934)	0.344	0.573	1.297	0.449
Hypertension, n (%)	–0.396	(–1.026, 0.234)	0.322	1.517	0.673	0.218
Diabetes, n (%)	–0.233	(–0.895, 0.429)	0.338	0.475	0.792	0.491
TC (mmol/L)	0.023	(–0.200, 0.246)	0.114	0.041	1.023	0.840
TG (mmol/L)	0.324	(0.045, 0.603)	0.142	5.193	1.383	0.045
HDL-C (mmol/L)	–0.271	(–1.149, 0.607)	0.448	0.366	0.763	0.545
LDL-C (mmol/L)	–0.046	(–0.377, 0.286)	0.169	0.073	0.955	0.787
SUA (μ mmol /L)	0.002	(–0.001, 0.005)	0.002	2.137	1.002	0.144
tHcy (μ mmol /L)	0.022	(–0.009, 0.053)	0.016	1.977	1.022	0.160
Glu (mmol/L)	–0.016	(–0.143, 0.110)	0.065	0.063	0.984	0.802
LP(a) (mmol/L)	0.001	(<0.001, 0.002)	0.001	1.549	1.001	0.213
Leukocytes (×10⁹/L)	0.366	(0.198, 0.534)	0.086	18.167	1.442	<0.001
Monocytes (×10⁹/L)	4.007	(1.968, 6.046)	1.040	14.836	54.982	<0.001
Monocytes (%)	0.003	(–0.122, 0.129)	0.064	0.003	1.003	0.957
Platelets (×10⁹/L)	0.002	(–0.003, 0.008)	0.003	0.608	1.002	0.436
MPV (fl)	0.438	(0.042, 0.834)	0.202	4.701	1.550	0.030
Lymphocytes (×10⁹/L)	0.002	(–0.506, 0.511)	0.26	<0.001	1.002	0.993
Lymphocytes (%)	–0.045	(–0.81, 0.008)	0.19	5.794	0.956	0.016
Neutrophils (×10⁹/L)	0.332	(0.167, 0.497)	0.084	15.586	1.394	<0.001
Neutrophils (%)	0.038	(0.010, 0.065)	0.014	7.351	1.039	0.007

TC (total cholesterol), TG (triglycerides), HDL-C (high-density lipoprotein cholesterol), LDL-C (low-density lipoprotein cholesterol), SUA (serum uric acid), tHcy(homocysteine), GLU (glucose), LP(a) (lipoprotein (a)), MPV (mean platelet volume).

3. Multivariate ordinal logistic regression analysis of the parameters of plaque grade and the differential count of leukocytes

To further investigate the effect of measurement data on plaque grade, the multivariate ordinal logistic regression method was used to analyze the measurement data and construct a model. The quantitative data of leukocytes and their subspecies were included in the model in model-1, and the results showed that plaque grade was positively correlated with the absolute value of monocytes (OR = 26.924, 95% CI 0.487–6.099, P = 0.021). The potential confounders TG, S UA, HDL and tHcy were then used as correction factors to construct model 2. The results showed that plaque grade was positively correlated with the absolute value of monocytes (OR = 31.469, 95% CI 0.241–6.656, P = 0.035). ROC curves were constructed by micro-averaging each element of the plaque grade index as a binary predictor (Fig. 2). The area under the curve (AUC) was 0.701. The adjusted multivariate ordinal logistic regression model predicted the confusion matrix. We were able to correctly identify 100 plaque grades with an overall accuracy of 65.0%.

Fig. 2

ROC curves were constructed by micro-averaging each element of the plaque grade index as a binary predictor (Fig. 2). The area under the curve (AUC) was 0.701.

4 Discussion

This study found that as plaque grade on contrast-enhanced ultrasound increased, peripheral blood leukocyte, monocyte and neutrophil counts also increased, suggesting an increase in inflammatory cells in the circulation of patients with vulnerable plaque. There was a significant positive correlation between plaque grade and monocyte count. Contrast-enhanced ultrasound is a readily available imaging modality for the evaluation of patients with known or suspected carotid atherosclerosis that can provide information on neovascularization within the plaque [16]. An increase in the intensity of plaque enhancement is significantly associated with an increase in histopathologic microvessels and a corresponding increase in the risk of plaque vulnerability [17]. Semi-quantitative visual scoring can be used to assess enhancement intensity and neovascularization within the plaque. It is a simple and accurate clinical examination method to assess plaque vulnerability [18, 19].

A large body of previous data has shown that chronic inflammation is a key mechanism in the pathogenesis of cardiovascular disease [20]. Peripheral leukocyte count is a simple, inexpensive, and readily available method to assess systemic inflammation. The number of leukocytes and their subtypes (neutrophils, lymphocytes, and monocytes) is associated with the risk of cardiovascular complications such as stroke and myocardial infarction [21]. The presence and severity of carotid plaque were significantly associated with increased leukocyte and neutrophil counts [22]. Consistent with this, our experimental results showed that leukocyte, monocyte, and neutrophil counts were significantly correlated with plaque grade. Atherosclerotic plaque rupture is the main concern of carotid plaque rupture. The more neovascularization in the plaque, the higher the risk of plaque rupture, and the formation of neovascularization in the plaque is closely related to inflammation in the plaque [23]. Atherosclerosis is pathologically characterized by multifocal structural changes in the vessel walls of the middle and large arteries, characterized by localized cholesterol accumulation and persistent inflammation [24]. The conditions of relative hypoxia and local inflammation within the plaque may trigger the release of pro-angiogenic and pro-inflammatory factors, thereby stimulating the occurrence of neovascularization [25]. In addition, circulating leukocytes may also colonize the plaque, induce the inflammatory response within the plaque, and stimulate the formation of neovascularization within the plaque [26].

Table 3
Multiple ordinal logistic regression analysis and clinical prediction model of plaque grade and leukocyte and its subtypes

B 95% CI SE Wald OR P

Leukocytes (×10⁹/L) 0.226 (–0.722, 1.173) 0.484 0.217 1.254 0.641

Monocytes (×10⁹/L) 3.293 (0.487, 6.099) 1.432 5.291 26.924 0.021

MPV (fl) 0.362 (–0.047, 0.771) 0.209 3.009 1.436 0.083

Lymphocytes (%) 0.033 (–0.076, 0.141) 0.055 0.352 1.034 0.553

Neutrophils (×10⁹/L⁾ 0.025 (–1.136, 1.185) 0.592 0.002 1.025 0.966

Neutrophils (%) 0.043 (–0.025, 0.110) 0.034 1.532 1.044 0.216

Threshold [Grade 0] 7.314 (–0.407, 15.036) 3.94 3.447 1501.170 0.063

[Grade 1] 8.915 (1.162, 16.668) 3.956 5.079 7442.782 0.024

[Grade 2] 11.385 (3.534, 19.235) 4.005 8.079 87991.896 0.004

[Grade 3] 13.049 (5.137, 20.960) 4.037 10.450 464631.548 0.001

	B	95% CI	SE	Wald	OR	P
Leukocytes (×10⁹/L)	0.226	(–0.722, 1.173)	0.484	0.217	1.254	0.641
Monocytes (×10⁹/L)	3.293	(0.487, 6.099)	1.432	5.291	26.924	0.021
MPV (fl)	0.362	(–0.047, 0.771)	0.209	3.009	1.436	0.083
Lymphocytes (%)	0.033	(–0.076, 0.141)	0.055	0.352	1.034	0.553
Neutrophils (×10⁹/L⁾	0.025	(–1.136, 1.185)	0.592	0.002	1.025	0.966
Neutrophils (%)	0.043	(–0.025, 0.110)	0.034	1.532	1.044	0.216
Threshold [Grade 0]	7.314	(–0.407, 15.036)	3.94	3.447	1501.170	0.063
[Grade 1]	8.915	(1.162, 16.668)	3.956	5.079	7442.782	0.024
[Grade 2]	11.385	(3.534, 19.235)	4.005	8.079	87991.896	0.004
[Grade 3]	13.049	(5.137, 20.960)	4.037	10.450	464631.548	0.001

MPV (mean platelet volume).

Table 4

Potential confounders TG, UA, HDL and tHcy were adjusted in the clinical prediction model

	B	95% CI	SE	Wald	OR	P
Leukocytes (×10⁹/L)	0.141	(–0.948, 1.231)	0.556	0.065	1.151	0.799
Monocytes (×10⁹/L)	3.449	(0.241, 6.656)	1.636	4.441	31.469	0.035
MPV (fl)	0.389	(–0.078, 0.855)	0.238	2.662	1.476	0.103
Lymphocytes (%)	0.069	(–0.051, 0.190)	0.061	1.271	1.071	0.260
Neutrophils (×10⁹/L⁾	0.166	(–1.160, 1.493)	0.677	0.060	1.181	0.806
Neutrophils (%)	0.053	(–0.017, 0.123)	0.036	2.176	1.054	0.140
TG (mmol/L)	0.276	(–0.078, 0.630)	0.181	2.338	1.318	0.126
SUA (μ mmol /L)	0.002	(–0.002, 0.005)	0.002	0.995	1.002	0.319
tHcy (μ mmol /L)	–0.006	(–0.042, 0.030)	0.018	0.102	0.994	0.750
HDL-C (mmol/L)	–0.883	(–2.901, 1.135)	1.030	0.736	0.414	0.391
Threshold [Grade 0]	9.29	(0.791, 17.789)	4.336	4.590	10829.184	0.032
[Grade 1]	10.924	(2.378, 19.470)	4.360	6.277	55492.325	0.012
[Grade 2]	13.603	(4.913, 22.292)	4.434	9.413	808551.780	0.002
[Grade 3]	15.188	(6.431, 23.946)	4.468	11.555	3945159.727	0.001

TG (triglycerides), HDL-C (high-density lipoprotein cholesterol), SUA (serum uric acid), tHcy(homocysteine), MPV (mean platelet volume).

Inflammation plays an important role in the development and progression of atherosclerosis. The accumulation of oxidized LDL-C in the walls of blood vessels triggers the expression of adhesion molecules that facilitate the migration of monocytes into the vessel wall [27]. These monocytes differentiate into macrophages and engulf oxLDL (oxidized low-density lipoprotein), becoming lipid-filled foam cells. This activates the production of cytokines that promote the influx and activation of other inflammatory cells, leading to their accumulation of these cells in the plaque. Macrophages are highly active metabolic cells that consume oxygen, leading to oxygen depletion in the plaque [28 –30]. They also release pro-angiogenic factors that induce an imbalance in the synthesis of the extracellular matrix and promote neovascularization [31]. Mast cells located near newly formed vessels contain pro-angiogenic factors, such as FGF, which increase vasa vasorum vessel density in atherosclerotic lesions [32]. In advanced lesions, neovascular leakage allows entry of inflammatory cells, including neutrophils and mast cells, which release proteases that digest components of elastic fibers and the basement membrane [33]. This can eventually lead to a thinning of the fibrous cap and erosion of the plaque [34]. The accumulation of RBC (red blood cells) remnants and apoptotic cells further contributes to inflammation and necrotic core expansion [35]. Ultimately, this leads to plaque rupture and thrombosis, resulting in a cerebrovascular accident. This is the traditional “inside-out” hypothesis of vascular inflammation [36].

In addition, there is increasing evidence that vascular inflammation may originate in the adventitia, a new “outside-in” hypothesis. Circulating exogenous cell types (monocytes, macrophages, lymphocytes, etc.) accumulate in the adventitia and invade the intima [37]. In the early stages of atherosclerosis, activated adventitial fibroblasts significantly increase adventitial inflammation and produce reactive oxygen species, which in turn promote adventitial neovascularization by increasing the expression of adhesion molecules, ultimately allowing leukocytes to further infiltrate the vessel wall [38]. In turn, local or systemic inflammation triggers neovascularization and increased vascular permeability of the adventitia, allowing more inflammatory cells to enter the plaque through the leaky and newly formed blood vessels [39]. The inflammatory mechanism of atherosclerosis may not be a single “inside-out” or “outside-in” process, but rather a complex pathophysiological process that synergistically interacts between the inside and the outside. Although there is a paucity of data from prospective studies to elucidate this mechanism, the use of advanced methods such as intravascular ultrasound (IVUS), virtual histology IVUS, and optical coherence tomography (OCT) to better delineate the microstructure may lead to major advances in understanding the role of inflammation in plaque vulnerability.

5 Conclusions

In conclusion, this study shows that the increase of circulating leukocyte in patients with carotid plaque is closely related to the increase of neovascularization in the plaque, suggesting that the combination of peripheral inflammatory cell detection and ultrasound plaque CEUS can more accurately assess the vulnerability of carotid plaque in high-risk population, which is conducive to early screening of high-risk population, and provides new ideas for anti-inflammatory and clinical prevention and treatment of various patients. The limitations of this study are as follows: 1. The sample size is small and there may be data bias. A larger multicenter sample size is needed to obtain more accurate conclusions; 2. This study does not discuss whether the echo characteristics of plaques under gray-scale ultrasound and the morphological characteristics of plaques are different from those of peripheral blood leukocytes, and the sample size needs to be further expanded for discussion. 3. CEUS for carotid plaque still has certain limitations due to the influence of human operation and patients’ own physiological activities.

Footnotes

Acknowledgments

All authors contributed to the design and conduct of the data collection, analysis and interpretation of the results, and writing and drafting of the article. Dr. Qiao had the primary responsibility for data collection, conduct of the research, and drafting of the article with input from all authors. All authors read and approved the final article.

Sources of funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Disclosures

None.

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