Hemodynamic Characteristics of Carotid Plaques as Potential Biomarkers for Predicting Anterior Circulation Acute Cerebral Infarction: A Prospective Study

doi:10.21203/rs.3.rs-4987539/v1

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Hemodynamic Characteristics of Carotid Plaques as Potential Biomarkers for Predicting Anterior Circulation Acute Cerebral Infarction: A Prospective Study

https://doi.org/10.21203/rs.3.rs-4987539/v1

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This study aimed to examine the hemodynamic properties of carotid artery plaques, exploring their potential as indicators for predicting anterior circulation acute cerebral infarction (ACI). A prospective analysis was performed on 113 carotid atherosclerotic plaques from 68 patients, dividing them into two groups: those with ACI (73 plaques) and those without (NACI, 40 plaques). Computational fluid dynamics (CFD) was applied to measure hemodynamic parameters, focusing on wall shear stress (WSS) in the upstream, downstream, and core regions of the plaque, as well as pressure and blood flow velocity. The results indicated that patients with ACI had significantly lower WSS in all regions compared to the NACI group (P < 0.05). The ACI group also showed higher pressures and core blood flow velocities, while downstream velocity was reduced. Among the WSS parameters, WSSdown was the most efficient predictor for ACI, with an area under the receiver operating characteristic curve (AUC) of 0.96, an F1 Score of 0.95, and a diagnostic Odds Ratio significantly higher than other parameters. The findings suggest that reduced WSS is a significant correlate of ACI and may be a reliable biomarker for its early detection.

Hemodynamics

Carotid Artery Plaques

Anterior Circulation Acute Cerebral Infarction (ACI)

Computational Fluid Dynamics (CFD)

Wall Shear Stress (WSS)

Cerebrovascular Disease

Stroke remains a leading cause of mortality worldwide, with cerebral infarction accounting for asignificant proportion of cases, particularly in China where it constitutes up to 80% of all stroke cases[1–4]. Identifying and addressing the high-risk factors associated with cerebral infarction is crucial for preventing its devastating outcomes through more intensive pharmacological and rehabilitative interventions[5, 6].

Atherosclerosis, especially the presence of high-risk plaques, has been established as a critical pathological factor in cerebrovascular events[1, 7–11]. Studies have consistently demonstrated a causal link between plaque development and cerebral infarction[8, 11–13], suggesting that treatment of carotid atherosclerotic stenosis can serve as a preventative measure[9, 14]. The severity of carotid stenosis, induced by atherosclerotic plaques, has been identified as a pivotal imaging biomarker for stroke risk and the efficacy of atherosclerotic disease treatment[15, 16]. However, recent investigations have indicated that the mere assessment of carotid stenosis severity may not be adequate for determining surgical necessity or stroke risk, as some asymptomatic patients with severe stenosis do not derive benefit from surgical intervention[17]. Furthermore, evidence is mounting that not all plaques undergo rapid progression, and result in clinical events[18]. Local hemodynamic parameters, such as wall shear stress (WSS), have been implicated in plaque initiation, progression, vulnerability, and the occurrence of acute clinical events[19–23]. These findings suggest that plaque vulnerability is a complex trait, influenced by more than just morphology[24, 25].

The evaluation of both hemodynamic and morphologic characteristics of plaques is essential for informed clinical decision-making regarding surgical intervention[26]. Computational fluid dynamics (CFD) has emerged as a validated tool in various research domains, demonstrating accuracy in predicting accelerated atherosclerosis[27, 28]. CFD, in conjunction with computed tomography (CT), enables the simulation of blood flow conditions and the non-invasive assessment of in vivo hemodynamic parameters[29, 30]. Despite these advancements, the correlation between hemodynamic parameters of carotid atherosclerotic plaques and cerebral infarction remains understudied.

Advancements in both invasive and non-invasive imaging techniques have facilitated the identification of morphologic features characteristic of high-risk plaques, which are typically non-calcified, expansive, and associated with minimal inflammation, large lipid-rich necrotic cores, and intraplaque hemorrhage[31, 32]. Magnetic resonance imaging (MRI) has proven particularly valuable in identifying and assessing the components of carotid atherosclerotic plaques, offering clinical guidance and serving as a predictive tool for stroke risk[33].

This study aims to harness the power of MRI-based CFD analysis to non-invasively investigate the impact of hemodynamic parameters on cerebral infarction, thereby providing robust clinical evidence for early management and intervention. The primary objective is to compare and analyze differences in hemodynamic parameters between groups with and without ACI. The secondary objectives include the use of receiver operating characteristic (ROC) curves to evaluate the diagnostic potential of hemodynamic parameters, specifically WSS, pressure (P), and velocity (V) in distinguishing between the two groups. Additionally, this study employs Python for comparative analysis to assess the discriminative efficacy of these parameters.

2.1 Study population

This study conducted a comprehensive analysis of patients diagnosed with carotid plaques causing more than 50% luminal stenosis. Participants were selected based on their receipt of Doppler ultrasonography, magnetic resonance imaging (MRI) of the carotid arteries, and brain MRI within a one-week window following the onset of symptoms. The study period spanned from January 2019 to December 2023, and a thorough collection of baseline demographic and clinical data was systematically implemented. This study was performed in line with the principles of the World Medical Association Declaration of Helsinki. The study protocol was reviewed and approved by the Institutional Review Board of Shenzhen University General Hospital (Approval No. SUGHKYLL2022070601).

Inclusion Criteria:

1)High-resolution MRI images devoid of motion and flow artifacts, deemed suitable for computational fluid dynamics (CFD) modeling.

2)Comprehensive follow-up data spanning 1 to 3 years post-enrollment.

3)Diagnosis of anterior circulation acute cerebral infarction confirmed by cranial diffusion-weighted imaging (DWI).

Exclusion Criteria:

1)Cardioembolic cerebral infarction attributable to recent myocardial infarction, atrial fibrillation, valvular heart disease, dilated cardiomyopathy, sick sinus syndrome, acute endocarditis, patent foramen ovale, or severe cardiac dysfunction (ejection fraction ≤ 30%).

2)Conditions such as aneurysms, vasculitis, or other pathologies impacting the carotid arteries.

3)Presence of cerebral hemorrhage, transient ischemic attack, cerebral vascular malformations, other intracranial pathologies, peripheral arterial disease, immunological disorders, hematological conditions, or metabolic disorders.

Informed consent

was obtained from all participants or their legally authorized representatives prior to their inclusion in the study. The research was conducted in compliance with the ethical standards of the World Medical Association Declaration of Helsinki.

2.2 Clinical variables

Demographic and clinical data, including essential risk factors for cerebral infarction (hypertension, hyperlipidemia, diabetes, and smoking status), were recorded upon admission. Laboratory parameters such as triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), uric acid (UA), fasting blood glucose (FBG), and homocysteine (HCY) were also collected.

2.3 Collection of follow-up information

Follow-up data spanning 1 to 3 years were collected for each patient. The primary endpoint was the occurrence of new clinical symptoms with continuous neurological impairment, confirmed as anterior circulation acute cerebral infarction by DWI. Follow-up assessments were conducted every six months ± 2 weeks for up to 36 months post-baseline. Data on survival, endpoint events, time of occurrence, paroxysmal atrial fibrillation during follow-up, and other vascular events were collected. Two experienced radiologists independently evaluated endpoint events based on DWI.

2.4 Brain MRI protocol

MRI was performed using a 3.0 Tesla General Electric Company (GE Discovery MR750) with a standard 8-channel head coil (Wk401 Jiangyin Wankang Medical Technology).Before the scanning procedure, patients were instructed to follow specific precautions, including wearing specialized earplugs to protect against the noise associated with the MRI machine. They were positioned supine and advised to keep their heads still to minimize motion artifacts that could affect the image quality.

The MRI protocol included the following sequences:

Diffusion-Weighted Imaging (DWI): Parameters were set with a repetition time (TR) of 7000 ms, echo time (TE) of 75 ms, field of view (FOV) of 22 cm, matrix size of 98×140, slice thickness of 5.0 mm, and interslice spacing of 1.5 mm.

Fluid-Attenuated Inversion Recovery (FLAIR): With TR of 12,000 ms, TE of 87 ms, inversion time of 2800 ms, FOV of 22 cm, matrix size of 226×384, slice thickness of 5.0 mm, and interslice spacing of 1.5 mm.

T2-Weighted Imaging(T2WI): TR of 617 ms, TE of 14 ms, FOV of 22 cm, matrix size of 224×320, slice thickness of 5.0 mm, and interslice spacing of 1.5 mm.

T1-Weighted Imaging(T1WI): TR of 500 ms, TE of 5 ms, FOV of 22 cm, matrix size of 256×256, slice thickness of 5.0 mm, and interslice spacing of 1.5 mm.

Three-Dimensional Time-of-Flight Magnetic Resonance Angiography (3D-TOF-MRA): TR of 22 ms, TE of 3.69 ms, FOV of 200 cm, matrix size of 215×320, slice thickness of 0.50 mm, and a negative interslice spacing of 6.0 mm to ensure contiguous slices.

2.5 Definition of acute cerebral infarction and evaluation of cerebral small vascular disease (CSVD) on Brain MRI.

Acute cerebral infarction was diagnosed based on clinical symptoms within 48 hours of onset and confirmed by characteristic MRI findings: hypointense on T1-weighted imaging (T1WI), hyperintense on T2-weighted imaging (T2WI), high signal on DWI, and low apparent diffusion coefficient (ADC) indicative of restricted diffusion.

Patients were categorized into acute cerebral infarction(ACI) and non- acute cerebral infarction(NACI) groups based on clinical presentations and MRI results.

Imaging features of CSVD were evaluated following the STandards for ReportIng Vascular changes on Euroimaging (STRIVE) criteria[34]. White matter lesions, including periventricular hyperintensity (PVH) and deep subcortical white matter hyperintensity (DSWMH), were graded according to the Fazekas scale (1–3)[35]. Lacunar infarcts were classified by their anatomical location: thalamus, medial capsule, corona radiata, basal ganglia, and brainstem[34]. Cerebral microbleeds (CMBs) were categorized as deep, lobar, or infratentorial[34]. Enlarged perivascular space (EPVS) was quantified in the basal ganglia and centrum semiovale, with categories ranging from 1 (0–10 EPVS) to 3 (26 or more) [36].

The total Cerebral Small Vessel Disease (CSVD) score, ranging from 0 to 4, was calculated to assess CSVD severity on MRI. It encompasses lacunar infarcts, cerebral microbleeds (CMBs) distribution, Fazekas-graded white matter changes (PVH and DSWMH), and the extent of enlarged perivascular space (EPVS).

2.6 carotid plaque MRI protocol

Carotid plaque imaging was conducted on all patients using a 3T General Electric Company (GE Discovery MR750) scanner equipped with an 8-channel carotid surface coil (Wk401 Jiangyin Wankang Medical Technology). The protocol included the following sequences:

3D Phase-Contrast Magnetic Resonance Angiography (3D PC MRA): Parameters were set with a repetition time (TR) of 15.0 ms, echo time (TE) of 3.7 ms, field of view (FOV) of 32 cm × 32 cm, and a slice thickness of 1.8 mm.

3D CUBE T1-Weighted Imaging (3D CUBE T1WI): TR/TE of 575/15 ms, an echo train length (ETL) of 24, a slice thickness of 2 mm, and an FOV of 32 cm × 32 cm.

3D CUBE T2-Weighted Imaging (3D CUBE T2WI) : TR/TE of 3500/120 ms, flip angle of 150°, a slice thickness of 2 mm, and an FOV of 32 cm × 32 cm.

Post-acquisition, the raw data were transferred to a workstation and reconstructed into cross-sectional images with a slice thickness of 0.625 mm using Multiple Planar Volume Reconstruction (MPVR) techniques.

2.7 Duplex ultrasonography protocol

Extracranial carotid artery ultrasound was conducted on all patients using a GE LOGIQ E7 ultrasound system. During the examination, patients were positioned supine. The peak systolic velocity (PSV) and peak diastolic velocity (PDV) within the common carotid artery (CCA) were measured in centimeters per second (cm/s) and recorded for subsequent post-processing analysis. These measurements are critical for the assessment of blood flow dynamics in the carotid arteries.

Carotid plaques were identified as focal areas of increased echogenicity with a minimal intima-media thickness (IMT) of 1.2 mm or greater, consistent with the established criteria for carotid atherosclerotic disease[37, 38]. The identification of these plaques is essential for the evaluation of carotid stenosis and the assessment of stroke risk.

2.8 Computational blood flow modeling

We employed computational fluid dynamics (CFD) for the analysis of blood flow in carotid arteries using the commercial software ANSYS ICEM (ANSYS, Inc., USA). The morphological technique of open and close reconstruction was applied to reduce noise inherent in magnetic resonance angiography (MRA) images, facilitating precise extraction of the carotid arterial tree, plaque identification, and segmentation of the lumen boundary [39]. The blood was modeled as an incompressible Newtonian fluid, and the arterial wall was assumed to be rigid with a no-slip condition. Inflow boundary conditions were defined using the peak systolic (PSV) and diastolic velocities (PDV) of the common carotid artery (CCA). The outflow was modeled using an open boundary condition. Hemodynamic parameters within the stenotic lesions were calculated for three distinct regions: upstream, downstream, and core, based on finite element analysis specific to the patient's carotid artery geometry. These parameters included wall shear stress (WSS) in the upstream (WSSup), downstream (WSSdown), and core (WSScore) regions, as well as pressure (Pup, Pdown, Pcore) and blood flow velocity (Vup, Vdown, Vcore).

To elucidate local hemodynamic characteristics within the carotid plaques, each lesion was further divided into segments: upstream, minimal lumen area (MLA) extending 3 mm in length, and downstream[19]. Hemodynamic quantities were computed for these segments and then averaged to provide a comprehensive assessment of the local flow dynamics.

2.9 Analysis of carotid plaque characteristics

Carotid plaques were characterized as intraluminal lesions with an area greater than or equal to 1 mm², clearly demarcated from the vessel lumen and adjacent tissues[40]. Luminal stenosis was quantified on Maximum Intensity Projection (MIP) reconstructed Phase Contrast Magnetic Resonance Angiography (PC MRA) images, while plaque and vascular areas were measured on 3D CUBE T1-Weighted Imaging (3D CUBE T1WI) sequences. Two experienced radiologists, blinded to clinical data and computational fluid dynamics (CFD) results, independently assessed and documented the following characteristics: the presence of high-risk plaques was determined based on signal intensity patterns, overall morphology, plaque burden, and remodeling index, as well as the angle of the vessel at the internal and external carotid artery junction. Vulnerable plaque components, such as lipid-rich necrosis and hemorrhage, were identified by their mild to marked hyperintense signals on MRI, potentially accompanied by ulceration on the plaque surface. In contrast, stable components, including calcification and fibrous tissue, exhibited hypointense to isointense signals[33]. Plaque burden was calculated as the ratio of the plaque area to the total vascular area. The remodeling index was derived from the ratio of the maximal vascular diameter, encompassing both plaque and lumen at the lesion site, to the average normal lumen diameter at the proximal and distal reference segments of the plaque[19]. A remodeling index of 1.10 or higher was indicative of positive remodeling[19]. In cases of discordance between the radiologists' assessments, a consensus was reached through joint discussion.

2.10 Statistical analysis

The statistical analysis was conducted utilizing IBM SPSS Statistics (version 24.0) and Python (version 3.11.4) software packages. The normality of the data distribution was assessed employing the Kolmogorov-Smirnov test. Continuous variables exhibiting normal distribution were reported as mean ± standard deviation (SD), whereas non-normally distributed continuous variables were depicted using median and interquartile range (IQR). Categorical data were presented in terms of frequency counts and percentages. Group comparisons for continuous variables were performed using the independent samples t-test for normally distributed data and the Mann-Whitney U test for non-normally distributed data. Categorical variables were compared between groups using the chi-square test or Fisher's exact test, as appropriate. To assess the diagnostic efficacy of hemodynamic parameters—wall shear stress (WSS), pressure (P), and velocity (V)—in differentiating between patients with acute cerebral infarction and those without, Receiver Operating Characteristic (ROC) curve analysis was conducted. The Area Under the Curve (AUC) was determined to quantify the overall discriminatory capability of these parameters, with AUC values approaching 1 indicating superior diagnostic accuracy. Furthermore, Python was employed to compute additional diagnostic metrics, including positive and negative likelihood ratios, precision, F1 score, diagnostic odds ratio, and Matthews correlation coefficient for the hemodynamic parameters.

All statistical tests were two-tailed, and a p-value of less than 0.05 was considered to denote statistical significance.

3.1 Clinical characteristics of the patients

Our study cohort comprised 113 target carotid plaques from 68 patients, including 23 patients with a single carotid plaque and 45 patients with two carotid plaques. The demographic mean age was 63.46 ± 10.84 years, ranging from 40 to 85 years. Among these plaques, 73 (64.6%) were associated with unilateral anterior circulation acute cerebral infarction. The majority of the patients were male (51 cases, 69.9%), and the prevalence of comorbidities included diabetes in 61 cases (83.6%), hyperlipidemia in 65 cases (89.0%), and a history of hypertension in 62 cases (84.9%).

3.2 Comparison of clinical and laboratory data between acute cerebral infarction and non-cerebral infarction groups.

Significant differences in the prevalence of diabetes, hypertension, and hyperlipidemia were observed between the ACI and NACI groups (P < 0.05). The levels of triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and homocysteine (HCY) were significantly elevated in the ACI group compared to the N ACI group (TG: 3.22 ± 1.27 vs. 1.13 ± 0.77, TC: 5.01 ± 0.95 vs. 2.88 ± 1.19, LDL-C: 3.84 ± 0.97 vs. 1.72 ± 0.75, HCY: 18.69 ± 8.00 vs. 16.55 ± 4.56, all P < 0.001) (Table 1).

Table 1

Comparison of clinical and laboratory data between acute cerebral infarction(ACI) and non-cerebral infarction groups(NACI) (n%; x¯ ± SD).
	ACI (n = 73)	NACI (n = 40)	P value
Mean age(years)	67.98 ± 8.90	55.22 ± 9.15	0.786
Gender (male%)	51(69.9%)	23(57.5%)	0.186
HT (%)	62 (84.9%)	14 (35.0%)	< 0.001
DM (%)	61 (83.6%)	12 (30.0%)	< 0.001
DL (%)	65 (89.0%)	12 (30.0%)	< 0.001
Smoke	43(58.9%)	19(47.5%)	0.244
Risk factors at admission
TG (mmol/L)	3.22 ± 1.27	1.13 ± 0.77	0.001
TC (mmol/L)	5.01 ± 0.95	2.88 ± 1.19	0.015
HDL-C (mmol/L)	1.74 ± 0.51	2.16 ± 0.75	0.01
LDL-C (mmol/L)	3.84 ± 0.97	1.72 ± 0.75	0.001
FBG (mmol/L)	16.53 ± 7.96	4.91 ± 4.53	0.002
UA(µmol/L)	360.20 ± 37.68	225.72 ± 116.77	0.001
HCY(µmol/L)	18.69 ± 8.00	16.55 ± 4.56	0.005
HT: hypertension; DL: dyslipidemia; DM: Diabetes Mellitus; TG: total triglyceride; TC: total cholesterol; LDL-C: low-density lipoprotein cholesterol; HDL-C: high-density lipoprotein cholesterol; FBG: fasting blood glucose; UA: uric acid; HCY: Homocysteine. *P < 0.05 was considered statistically significant.

3.3 A comparison of WSS with clinical and laboratory data

Wall shear stress (WSS) in all regions (upstream, downstream, core) showed a significant decrease in patients with diabetes, hypertension, and hyperlipidemia compared to controls (P < 0.05). Pearson's correlation coefficient analysis revealed a negative correlation between age and WSS in all regions(WSSup, WSSdown and WSScore) (r = -0.406, -0.512, and − 0.364, P < 0.001). Additionally, all carotid WSS (WSSup, WSSdown and WSScore) showed a negative correlation with TG, TC, LDL-C, and HCY levels (Table 2).

Table 2

A comparison of WSS with clinical and laboratory data
laboratory data	WSS(Pa)		P value	Correlation coefficient (95% CI)
Male (N = 74) vs female (N = 39)	WSSup	2.99 ± 1.06 vs 2.95 ± 0.89	0.84	-
	WSSdown	2.45 ± 1.10 vs 2.47 ± 0.98	0.92	-
	WSScore	3.02 ± 0.93 vs 3.23 ± 0.77	0.22	-
HT (N = 76) vs non-HT (N = 37)	WSSup	2.74 ± 0.972 vs 3.46 ± 0.89	0.001	-
	WSSdown	2.15 ± 0.97 vs 3.07 ± 0.95	0.001	-
	WSScore	2.88 ± 0.83 vs 3.53 ± 0.81	0.001	-
DL (N = 77) vs non-DL (N = 36)	WSSup	2.70 ± 0.93 vs 3.56 ± 0.89	0.001	-
	WSSdown	2.11 ± 0.93 vs 3.20 ± 0.92	0.001	-
	WSScore	2.83 ± 0.83 vs 3.66 ± 0.70	0.001	-
DM (N = 73) vs non-DM (N = 40)	WSSup	2.70 ± 0.96 vs 3.48 ± 0.87	0.001	-
	WSSdown	2.11 ± 0.97 vs 3.08 ± 0.91	0.001	-
	WSScore	2.810 ± 0.83 vs 3.62 ± 0.70	0.001	-
Smoke (N = 62) vs non-smoke (N = 51)	WSSup	2.93 ± 0.90vs 3.01 ± 1.08	0.66	-
	WSSdown	2.48 ± 0.94 vs2.44 ± 1.14	0.83	-
	WSScore	3.17 ± 0.82vs3.03 ± 0.92	0.39	-
Mean age(years)	WSSup	-	0.001	-0.406(-0.543 – -0.261)
	WSSdown	-	0.001	-0.512(-0.633 – -0.374)
	WSScore	-	0.001	-0.364(-0.525 – -0.194)
TG (mmol/L)	WSSup	-	0.001	-0.474 (-0.608 – -0.318)
	WSSdown	-	0.001	-0.558 (-0.672 – -0.426)
	WSScore	-	0.001	-0.569 (-0.672 – -0.456)
TC (mmol/L)	WSSup	-	0.001	-0.678 (-0.767 – -0.579)
	WSSdown	-	0.001	-0.696 (-0.784 – -0.593)
	WSScore	-	0.001	-0.579 (-0.687 – -0.458)
LDL-C (mmol/L)	WSSup	-	0.001	-0.645 (-0.744 – -0.540)
	WSSdown	-	0.001	-0.713 (-0.782 – -0.637)
	WSScore	-	0.001	-0.654 (-0.729 – -0.550)
HCY(µmol/L)	WSSup	-	0.001	-0.495 (-0.606 – -0.370)
	WSSdown	-	0.001	-0.567 (-0.656 – -0.461)
	WSScore	-	0.001	-0.596 (-0.683 – -0.488)
FBG	WSSup	-	0.001	-0.476 (-0.604 – -0.326)
	WSSdown	-	0.001	-0.542 (-0.668 – -0.399)
	WSScore	-	0.001	-0.553 (-0.662 – -0.437)
WSS indicates wall shear stress; HT: hypertension; DL: dyslipidemia; DM: Diabetes Mellitus; TG: total triglyceride; TC: total cholesterol; LDL-C: low-density lipoprotein cholesterol; FBG: fasting blood glucose; UA: uric acid; HCY: Homocysteine. *P < 0.05 was considered statistically significant. Pearson’s correlation coefficient (r) were used to analyze correlations.

3.4 Relationship between carotid plaque and ACI

In the ACI group, the number of vulnerable plaques (n = 60) was significantly higher than that of stable plaques (n = 13) (P < 0.05). Among vulnerable plaques, the incidence of cerebral infarction was also significantly higher compared to non-cerebral infarction (n = 60 vs. n = 6, P < 0.05). These differences were statistically significant (Table 3).

Table 3

Relationship between carotid plaque and acute cerebral infarction(ACI).
	ACI (n = 73)	NACI (n = 40)	χ²	P value
stable plaque(n = 47)	13 (17.8%)	34(85.0%)	48.02	0.001
vulnerable plaque (n = 66)	60 (82.2%)	6(15%)

3.5 Comparison of morphology and hemodynamic distribution of carotid plaque between ACI and NACI groups.

Analysis of the stenotic lesion regions revealed distinct distributions of hemodynamic parameters. The WSSup(2.42 ± 0.58Pa), WSSdown(1.81 ± 0.53Pa) and WSScore(2.68 ± 0.66Pa)were significantly lower in the ACI group compared to NACI group(3.99 ± 0.78Pa, 3.62 ± 0.72Pa, 3.84 ± 0.72Pa, respectively) (P < 0.05) (Fig. 1a, 1b, 1c,). Patients with ACI exhibited higher Pup(102.49 ± 10.79mmhg), Pdown(113.26 ± 15.21mmhg), Pcore(109.15 ± 13.75mmhg), Vcore(86.28 ± 12.14cm/s) and lower Vdown(47.57 ± 8.68cm/s) than those without ACI (P < 0.05). Additionally, plaque burden(0.65 ± 0.11Pa vs 0.31 ± 0.05Pa), degree of angle(55.52 ± 6.78^° vs 20.52 ± 4.61^°) were significantly higher in ACI group (P < 0.05) (Table 4). The number of positive remodeling cases was significantly higher in ACI group (n = 63 vs. n = 10, χ2 = 69.90, P = 0.001).

Table 4

Comparison of morphology and hemodynamic distribution of carotid plaque between ACI and NACI groups (n%; x¯ ± SD).
	ACI (n = 73)	NACI (n = 40)	P value
WSSup(Pa)	2.42 ± 0.58	3.99 ± 0.78	<0.001
WSSdown (Pa)	1.81 ± 0.53	3.62 ± 0.72	<0.001
WSScore (Pa)	2.68 ± 0.66	3.84 ± 0.72	<0.001
Pup(mmhg)	102.49 ± 10.79	87.51 ± 7.74	<0.001
Pdown(mmhg)	113.26 ± 15.21	88.70 ± 7.69	<0.001
Pcore(mmhg)	109.15 ± 13.75	89.72 ± 5.21	<0.001
Vup(cm/s)	75.81 ± 8.18	63.37 ± 7.32	<0.001
Vdown(cm/s)	47.57 ± 8.68	53.62 ± 13.99	0.015
Vcore(cm/s)	86.28 ± 12.14	66.97 ± 6.85	<0.001
plaque burden (Pa)	0.65 ± 0.11	0.31 ± 0.05	<0.001
degree of angle(°)	55.52 ± 6.78	20.52 ± 4.61	<0.001

3.6 Relationship between morphology and hemodynamic distribution of carotid plaque and total CSVD load score.

Significantly lower WSSup(2.61 ± 0.83Pa), WSSdown(2.02 ± 0.84Pa) and WSScore(2.81 ± 0.81Pa) were observed in patients with a total CSVD load score ≥ 3 compared to those with a score < 3 (3.71 ± 0.91Pa, 3.32 ± 0.89 Pa, 3.66 ± 0.74Pa, respectively) (P < 0.05). Higher pressures (Pup, Pdown, Pcore) were observed in patients with a higher CSVD load score, as well as increased plaque burden (0.60 ± 0.16Pa vs 0.39 ± 0.17Pa) and vessel angle(50.42 ± 12.19° vs 28.73 ± 18.69°) (P < 0.05). The number of positive remodeling cases was also significantly higher in this group (n = 57 vs. n = 18, χ2 = 60.61, P = 0.001) (Table 5).

Table 5

Relationship between morphology and hemodynamic distribution of carotid plaque and total CSVD load score (n%; x¯ ± SD).
	Total CSVD load score<3 (n = 38)	Total CSVD load score ≥3 (n = 75)	P value
WSSup(Pa)	3.71 ± 0.91	2.61 ± 0.83	0.005
WSSdown (Pa)	3.32 ± 0.89	2.02 ± 0.84	0.001
WSScore (Pa)	3.66 ± 0.74	2.81 ± 0.81	0.007
Pup(mmhg)	89.14 ± 10.01	101.27 ± 11.09	0.009
Pdown(mmhg)	92.75 ± 14.11	110.55 ± 16.11	0.008
Pcore(mmhg)	91.71 ± 11.34	107.62 ± 13.39	0.009
Vup(cm/s)	66.94 ± 7.40	73.66 ± 10.22	0.01
Vdown(cm/s)	52.52 ± 13.34	48.29 ± 9.70	0.006
Vcore(cm/s)	71.26 ± 11.50	83.60 ± 13.43	0.008
plaque burden (Pa)	0.39 ± 0.17	0.60 ± 0.16	0.007
degree of angle(°)	28.73 ± 18.69	50.42 ± 12.19	0.009

3.7 The diagnostic efficiency of WSS

The area under the ROC curve (AUC) for WSSup, WSSdown, and WSScore in predicting unilateral anterior circulation acute cerebral infarction were 0.93 (95% CI, 0.84–0.98), 0.96 (95% CI, 0.87–0.99), and 0.86 (95% CI, 0.77–0.95) (Fig. 1d), respectively. WSSdown showed the highest diagnostic efficiency with sensitivity, specificity, and accuracy values of 94.5%, 92.5%, and 95.8%, respectively. The corresponding values for WSSup and WSScore were 90.4%, 90%, 94.2% and 89.0%, 77.5%, 87.8%, respectively.

3.8 Diagnostic Test Comparisons

The parameter WSSdown exhibited the highest diagnostic performance, with an F1 Score of 0.95, signifying its accuracy in classification tasks. This score was superior to that of WSSup, which had an F1 Score of 0.92, and WSScore, with an F1 Score of 0.88. The Diagnostic Odds Ratio, which integrates both sensitivity and specificity, was notably higher for WSSdown at 212.75, compared to 84.85 for WSSup and 27.98 for WSScore. The Matthews Correlation Coefficient, a measure of the quality of binary classification, was highest for WSSdown at 0.86, followed by WSSup at 0.79, and WSScore at 0.66. Furthermore, the positive and negative likelihood ratios for WSSdown were 12.60 and 0.06, respectively, indicating strong discriminatory power. For WSSup, these ratios were 9.04 and 0.11, and for WSScore, they were 3.96 and 0.14, respectively.

There is a paucity of research focusing on the correlation between hemodynamics of carotid plaques and acute cerebral infarction. By utilizing advanced imaging techniques and computational blood flow modeling, we have comprehensively assessed hemodynamic parameters in carotid plaques and their correlation with acute cerebral infarction.

A key finding of our research is the significant difference in traditional risk factors such as diabetes, hypertension, and hyperlipidemia between patients with and without acute cerebral infarction, reinforcing their role as pivotal contributors to the condition, which is consistent with the previous research results[1, 41], indicating that these chronic diseases are important risk factors for cerebral infarction[1, 41]. Additionally, elevated levels of triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and homocysteine (HCY) in the infarction group suggest a mechanistic link between dyslipidemia and plaque instability, potentially leading to cerebral infarction[42–45]. These biochemical derangements may underlie endothelial dysfunction and plaque progression, highlighting the importance of lipid management in stroke prevention.

Our study delves into the underexplored territory of how hemodynamic factors, particularly wall shear stress (WSS), relate to the development of acute cerebral infarction. WSS, a critical determinant of endothelial function, has emerged as a significant marker that could potentially illuminate the pathophysiological processes underlying atherosclerotic plaque formation[46]. Before exploring the relationship between WSS and cerebral infarction, we first analyzed the relationship between WSS and traditional risk factors. Our findings underscore the role of traditional risk factors—diabetes, hypertension, and hyperlipidemia—in modulating WSS, with significantly lower values observed in affected groups. This association aligns with the notion that these comorbidities could precipitate endothelial dysfunction and plaque vulnerability[46]. Intriguingly, WSS exhibited a negative correlation with age and lipid profiles ( TG, TC and LDL-C) suggesting that age-related vascular degeneration and dyslipidemia may promote the reduction of WSS, thus promoting atherosclerotic changes[47]. Therefore, It can be inferred from the above that actively controlling traditional risk factors will help to control WSS, which plays a huge role in the formation and progress of atherosclerosis.

The relationship between total CSVD load score and the morphology and hemodynamic distribution of carotid plaque is a novel contribution of our study. Lower WSS in patients with higher CSVD scores indicates a potential link between disease severity and hemodynamic status, which could have implications for early identification of at-risk patients.This finding is consistent with previous research[47], which suggests a decrease WSS is considered the maker of the artery atherosclerosis (AA), may also be relevant for CSVD[47]. Elevated pressures (Pup, Pdown, Pcore) in this cohort may reflect the hemodynamic stress exacerbating vascular wall damage, further implicating the role of pulse pressure in vascular pathology[47]. Moreover, our study highlights the impact of morphological features, such as positive remodeling, increased plaque burden and vascular angle in higher CSVD scores group, which indicate that pathological changes in vascular wall structure are related to the formation and progress of atherosclerotic plaque[32]and may could predispose to CSVD[32]. These results may provide new perspectives for the management and treatment strategies of CSVD in the future. In addition to focusing on hemodynamic changes in the vascular wall, pathological changes in vascular structure should also be taken into consideration.

By analyzing the morphology and hemodynamic distribution of carotid artery plaques, we identified significant differences between patients with and without cerebral infarction(Fig. 2), enriching the understanding of the pathophysiological mechanisms leading to stroke. A pivotal finding was the lower blood flow velocity (Vdown) in the cerebral infarction group, suggesting impaired cerebral perfusion that could precipitate neuronal dysfunction and stroke once the metabolic reserve of the brain is exceeded[48]. This aligns with the hypothesis that hypoperfusion can lead to downstream embolism risk due to intravascular flushing impairment[48]. The observed low and turbulent flow velocities downstream of the stenosis[49] likely contribute to plaque formation and exacerbate perfusion disorders, underscoring the importance of hemodynamic factors in atherosclerotic processes. Our results also highlight the role of wall shear stress (WSS) in cerebral infarction. The significantly lower WSS values in patients with acute cerebral infarction compared to the non-infarction group support the notion that reduced WSS can increase low-density lipoprotein content in the vessel wall, leading to endothelial changes at molecular and cellular levels, and promoting atherosclerosis and indirectly led to the occurrence of stroke[48–50]. This finding is consistent with previous studies that identified low WSS as a risk factor for plaque development and stroke[48, 49]. Furthermore, our study extends current knowledge by demonstrating the association between carotid plaque pressure gradients and the risk of ischemic events. The higher pressures (Pup, Pdown, Pcore) in patients with cerebral infarction suggest that increased pressure may contribute to plaque progression and stroke occurrence[51, 52].This research result further confirms the promoting effect of pressure on carotid artery plaques on cerebral infarction. Morphological features, including increased plaque load and vascular angle, were more pronounced in the cerebral infarction group, indicating severe atherosclerotic changes that could alter hemodynamic modes and increase stroke risk[32]. Notably, the higher prevalence of positive remodeling in patients with cerebral infarction implies that these plaques, characterized by larger lipid cores and thinner fibrous caps, are more susceptible to rupture, thereby posing a higher risk of stroke[32]. The implications of these findings are profound for clinical practice. They suggest that aggressive pharmacological or interventional approaches may be warranted for patients with high plaque burden and positive remodeling to mitigate the risk of stroke. Moreover, our findings emphasize the need for a comprehensive assessment of both hemodynamic alterations and pathological vascular changes in the management of cerebrovascular disease.

In terms of diagnostic efficiency, our results show that the WSSup, WSSdown, and WSScore have high diagnostic efficiency in predicting acute cerebral infarction. Especially WSSdown, it performs excellently in diagnostic efficiency with high sensitivity of 94.5%, effectively identifying the majority of stroke patients,and high specificity of 92.5%, ensures a commendable precision in differentiating between affected and unaffected individuals. Collectively, these attributes culminate in an accuracy rate of 95.8%, substantiating WSSdown's reliability as a diagnostic indicator. The robustness of WSSdown is further corroborated by its area under the receiver operating characteristic curve (AUC) value of 0.96, a metric that highlights its discriminatory power in clinical diagnostics. The F1 score of 0.95 positions WSSdown as the most accurate parameter in our comparative analysis, while its Diagnostic Odds Ratio (DOR) of 212.75 surpasses those of WSSup and WSScore, indicative of a more efficacious amalgamation of sensitivity and specificity.The Matthews Correlation Coefficient (MCC) further accentuates WSSdown's superiority in binary classification, augmenting its diagnostic value. Notably, the positive and negative likelihood ratios for WSSdown are 12.60 and 0.06, respectively, signifying its potential to substantially augment diagnostic assurance and to rule out cerebral infarction. These findings suggest that WSS parameters, especially WSSdown, may serve as potential biomarkers for clinical doctors in assessing the risk of cerebral infarction.

There are some limitations in this study. First, our sample size is relatively limited, follow-up studies are warranted large sample to assess the prognostic value of plaque composition and hemodynamic parameter with regard to the cerebral infarction. Second, this study is a cross-section study, which generally does not analysis causal relationships, future research should consider conducting longitudinal studies to further explore the dynamic relationship between the progression of the cerebrovascular disease and changes in morphology and hemodynamic distribution of carotid plaque. However, this study can find high-risk etiology clues and provide basis for disease prevention and treatment. In order to further confirm the correlation between cerebral infarction and carotid atherosclerotic plaque, a prospective follow-up study may demonstrate the facts more clearly.

The results of this study emphasize the importance of the morphology and hemodynamic characteristics of carotid plaques in the occurrence of acute cerebral infarction and provide a new perspective for future future research and clinical practice. Through further research, we hope to develop more effective prevention and treatment strategies to reduce the occurrence and impact of cerebral infarction.

Data Availability

Our Data is provided within the supplementary information files or and can be obtained from the corresponding author.

Author contributions

Yumeng Liu¹, Bin Ji¹, Bokai Wu², Yajing Xu¹, Rui Mi¹, Panying Wang¹, Yungang Lv¹, Ruodai Wu¹, Zhengkun Peng¹, Hai Ye¹, Songxiong Wu¹, Guangyao Li¹, Guangyao Wu^1,*, Jia Liu^2,*

1Department of Radiology, Shenzhen University General Hospital, Shenzhen University Clinical Medical Academy, Shenzhen, 518055, China.

²Laboratory for Engineering and Scientiﬁc Computing, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.

* Corresponding author(s):

^1,*Guangyao Wu, Department of Radiology, Shenzhen University General Hospital, Shenzhen University Clinical Medical Academy, Shenzhen, 518055, China. Email: [email protected] or [email protected].

^2,*Jia Liu, Laboratory for Engineering and Scientiﬁc Computing, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China. Email: [email protected].

All authors contributed to the study conception and design. Conceptualization, Methodology, Formal analysis were performed by JiaLiu and GuangyaoWu. The first draft of the manuscript was written by YumengLiu. Computational blood flow modeling and hemodynamic parameters implementation, Phantom data acquisition, Analyzed and interpreted the data were performed by BokaiWu and SongxiongWu. The Writing- Reviewing and Editing of the manuscript were performed by BinJi, YajingXu, RuiMi, PanyingWang and YungangLv. Data acquisition, analysis, curation, Statistical analysis were performed by RuodaiWu, ZhengkunPeng, HaiYe and GuangyaoLi. And consent is given by all contributing authors. All authors commented on previous versions of the manuscript, read and approved the version to be published, take public responsibility for appropriate portions of the content.

Additional Information

Declaration of competing interests

The authors have no competing interests to declare that are relevant to the content of this article.

Declaration of fundings

The authors did not receive support from any organization for the submitted work.

Research ethics and patient consent

Our research were conducted according to the World Medical Association Declaration of Helsinki. Informed consent was obtained from all individual participants included in the study.

Compliance with Ethical Standards

This study was performed in line with the principles of the World Medical Association Declaration of Helsinki. The study protocol was reviewed and approved by the Institutional Review Board of Shenzhen University General Hospital (Approval No. SUGHKYLL2022070601).

Consent to publish

The authors affirm that participants provided written informed consent for patient information and images to be published in Figures 1, 2.

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Hemodynamic Characteristics of Carotid Plaques as Potential Biomarkers for Predicting Anterior Circulation Acute Cerebral Infarction: A Prospective Study

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Abstract

Figures

1. Introduction

2. Materials and methods

2.1 Study population

2)Comprehensive follow-up data spanning 1 to 3 years post-enrollment.

3)Diagnosis of anterior circulation acute cerebral infarction confirmed by cranial diffusion-weighted imaging (DWI).

2)Conditions such as aneurysms, vasculitis, or other pathologies impacting the carotid arteries.

2.2 Clinical variables

2.3 Collection of follow-up information

2.4 Brain MRI protocol

2.6 carotid plaque MRI protocol

2.7 Duplex ultrasonography protocol

2.8 Computational blood flow modeling

2.9 Analysis of carotid plaque characteristics

2.10 Statistical analysis

3. Results

3.1 Clinical characteristics of the patients

3.2 Comparison of clinical and laboratory data between acute cerebral infarction and non-cerebral infarction groups.

3.3 A comparison of WSS with clinical and laboratory data

3.4 Relationship between carotid plaque and ACI

3.5 Comparison of morphology and hemodynamic distribution of carotid plaque between ACI and NACI groups.

3.6 Relationship between morphology and hemodynamic distribution of carotid plaque and total CSVD load score.

3.7 The diagnostic efficiency of WSS

3.8 Diagnostic Test Comparisons

4. Discussion

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

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