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Introduction
As a large country with a rapidly aging population, the incidence of atherosclerotic cardiovascular disease is on the rise in China [1,2]. Atherosclerosis, a significant pathological base of cardiovascular disease [3], has garnered significant attention from the World Health Organization due to the substantial disease burden it imposes [4].
Recently, advocates have highlighted the benefits of intervening at the stage of subclinical atherosclerosis (subAS) due to its significant potential in reducing the risk of clinical cardiovascular disease [5,6]. Indeed, subAS represents the preliminary stage of atherosclerosis, which may result in molecular pathological changes leading to vascular endothelial cell dysfunction [7]. The absence of clear clinical signs of subAS poses a significant challenge for early monitoring of cardiovascular events in a clinical context [7].
Recent studies have highlighted the potential value of subclinical atherosclerotic imaging in reducing cardiovascular risk [8-10]. Ultrasound technology is particularly useful for measuring carotid intima-media thickness (cIMT), which is crucial for early risk management in patients with subAS [11]. Typically, carotid-femoral PWV (cfPWV) is the standard method for assessing aortic stiffness [12,13]. However, the use of cfPWV in clinical practice is limited due to its inability to measure local arterial stiffness differences and its sensitivity to factors such as metabolism and obesity [14,15]. Ultrafast ultrasound imaging, which boasts a very high imaging frame rate, enables rapid reconstruction imaging to measure localized PWV [16]. Ultrafast pulse wave velocity (ufPWV) serves as an effective indicator for assessing localized stiffness in the arterial wall, addressing a limitation of cfPWV [15]. Due to its relatively high stability and reliability in measuring vascular elasticity [17,18], ufPWV has recently been recognized as a valuable tool for reflecting arterial stiffness [19]. However, there is still a relative lack of cutoff values for ultrafast ultrasound imaging in the management of subAS, which hampers effective clinical health monitoring [20].
To overcome the relative lack of ufPWV measurements obtained through ultrafast ultrasound imaging in monitoring subAS, this study explored the variations in ufPWV measurements between patients with subAS and healthy individuals. Furthermore, the present research established cutoff values for ufPWV that aid in the detection of subAS, thereby supporting clinical risk management.
Materials and Methods
Compliance with Ethical Standards
The study received approval from the Ethics Review Committee of the Jiangsu Province Official Hospital (2023ERC No.036-1), and the methods were carried out in accordance with the Declaration of Helsinki. Written informed consent was obtained from all patients.
Study Population
This retrospective study included 231 participants with a cIMT of less than 1.5 mm, as measured using conventional ultrasonography, from January 2022 to December 2023. SubAS was defined as a cIMT between 1.0 and 1.5 mm, according to clinical diagnostic criteria that identify atherosclerosis through the presence of atherosclerotic plaque detected by vascular ultrasonography [21]. The healthy population was characterized as those without subAS, with no plaque evident on carotid ultrasonography, and with a cIMT of less than 1.0 mm. Additionally, individuals in the healthy group had no serious diseases that could affect their prognosis and were not on any anti-atherosclerotic medications for at least one month prior to enrollment. Ultimately, the subgroups were divided into 67 individuals with subAS and 164 individuals without subAS.
The inclusion criteria were as follows: (1) cIMT less than 1.5 mm as measured by conventional sonography; (2) age between 40 and 80 years; (3) routine carotid ultrasound findings showing no significant evidence of plaque, including soft, hard, or mixed types; (4) no use of anti-atherosclerotic drugs, such as antihypertensive and lipid-lowering medications, in the month prior to enrollment; (5) no other serious comorbidities that could affect patient prognosis; and (6) satisfactory compliance and informed consent from patients.
The exclusion criteria were as follows: (1) abnormal liver and kidney function, hemoglobin, C-reactive protein, and other biochemical examination indices prior to the examination; (2) routine carotid ultrasonography showing plaque formation or a cIMT ≥1.5 mm on either side of the carotid artery; (3) a history of active bleeding in January (within the month prior to the start of the study), including upper gastrointestinal bleeding, hemoptysis, or hemorrhoids; (4) pregnancy and lactation; (5) severe cardiac conditions, such as acute coronary syndrome, progressive myocardial infarction, or recent coronary intervention; (6) stroke or cardiovascular event within the past six months; (7) malignant tumors; (8) other serious medical conditions, such as decompensated cirrhosis and severe pulmonary hypertension; (9) cervical ankylosis or vascular malformation that prevented bilateral testing on the carotid or brachial arteries; and (10) mental or legal incapacities.
Data were collected on variables such as the waist-to-hip ratio (WHR) and blood pressure from clinical examinations. Additionally, metabolic data, such as triglyceride and albuminous aminotransferase levels, were obtained from laboratory tests.
Sample Size Calculation
To ensure the statistical power, the minimum sample size for the current study was calculated using the R package pwr through R version 4.3.2 (R Foundation for Statistical Computing, Vienna, Austria). Setting a two-sided test with the type I error rate (α) and statistical power (1-β) at 0.05 and 0.80, respectively, the minimum sample size was calculated as 128 individuals. After propensity score matching (PSM), there were still 134 individuals in this study, which was adequate.
ufPWV Imaging Protocol
ufPWV was measured in all participants using an ultrasound device (Supersonic Imagine, Aixplorer, France) equipped with an SL10-2 linear array probe (frequency: 2-10 MHz; center frequency: 8.0 MHz). The patients were positioned supine with their heads turned to one side, fully exposing their necks in a relaxed position. Participants were instructed to hold their breath, after which the "Pulse Wave Velocity" button was pressed, initiating an ultrafast image acquisition that was completed in 5 seconds. The resulting stable ufPWV image was analyzed by selecting and adjusting a region of interest (ROI) that covered part of the common carotid artery. The software measured PWV at the beginning (PWV-BS) and end (PWV-ES) of systole, along with their corresponding standard deviations (SD), indicated as "Δ±" (Fig. 1). For quality control purposes, a ufPWV value was considered valid if it met the following two criteria: (1) SD ≤1.0 m/s and (2) SD <20% of the measurements for PWV-BS and PWV-ES. Measurements were deemed invalid for three reasons: first, if there was a failure to calculate PWV-BS or PWV-ES; second, if the SD exceeded 1.0 m/s; third, if the ROI was incorrectly positioned with the tracing line outside the arterial wall. To correct this, the final values were obtained by averaging the three valid PWV-BS and PWV-ES values from the bilateral common carotid arteries.
Statistical Analysis
The Shapiro-Wilk test was used to determine the normality of the data distribution for the variables. Normally distributed continuous variables are shown as the mean and SD, and skewed continuous variables are shown as the median and interquartile range. Categorical variables are shown as numbers and percentages. To compare differences between two groups, the two-sample Student t-test was employed for normally distributed continuous variables, and the Mann-Whitney U test was used for skewed continuous variables. The Pearson chi-square test was utilized to compare differences between groups for unordered categorical variables.
To control for potential confounders, participants were evenly distributed between subAS and subAS-free subgroups using PSM at a 1:1 ratio. Additionally, the covariates of age, sex, and WHR were carefully selected to align the characteristics of patients with subAS with those of the healthy population, as supported by previous studies [19]. Logistic regression analyses were conducted to explore the association between ufPWV measurements and subAS. Furthermore, the receiver operating characteristic curve was utilized to assess the performance of the ufPWV measurements in distinguishing subAS and to determine the appropriate cutoff values. Statistical analyses were carried out using R version 4.3.2 (R Foundation for Statistical Computing, Vienna, Austria). A twotailed P-value of less than 0.05 was considered to indicate statistical significance.
Results
Characteristics of Participants before and after PSM
This study included 231 individuals with a median age of 48.0 years, 54.5% of whom were women. The participants were divided into two groups: 164 in the subAS-free subgroup (55.5% female) and 67 in the subAS subgroup (52.2% female). The proportion of individuals aged 60 years or older was lower in both the subAS and healthy populations (28.4% vs. 17.7%) (Table 1). Participants in the subAS-free subgroup exhibited a higher WHR than those in the subAS subgroup (P=0.007) (Table 1). Regarding metabolic indicators, participants in the subAS-free subgroup had significantly higher levels of alanine aminotransferase, aspartate transaminase, γ-glutamyl transferase than those in the subAS subgroup (all P<0.05) (Table 1).
PSM significantly increased the comparability of the subAS and subAS-free groups (Table 2). Specifically, the potential confounders of age, sex, and WHR did not show statistical significance between the subAS and subAS-free subgroups (all P>0.05) (Table 2). Additionally, there were no statistically significant differences in any metabolic indicators between the subAS and subAS-free subgroups after PSM (all P>0.05) (Table 2).
Distribution of ufPWV in Populations with subAS
As shown in Table 3, there was no statistically significant difference in PWV-BS between the subAS and subAS-free subgroups for the left (P=0.827), right (P=0.298), or bilateral averages (P=0.637). However, the PWV-ES values were significantly higher in the subAS subgroup than in the subAS-free subgroup for measurements in the left (8.31 vs. 7.38 m/s, P=0.013), right (7.92 vs. 6.95 m/s, P=0.007), and bilateral averages (8.11 vs. 7.16 m/s, P=0.002).
Association between ufPWV and subAS
No significant associations were observed between PWV-BS measurements and subAS in the left (odds ratio [OR], 0.97; 95% confidence interval [CI], 0.77 to 1.22; P=0.799), right (OR, 1.23; 95% CI, 1.00 to 1.61; P=0.099), or bilateral averages (OR, 1.17; 95% CI, 0.91 to 1.54; P=0.250) (Table 4). The association between PWV-ES measurements and the risk of subAS following PSM is presented in Table 4. On the left side, there was a 23% increase (95% CI, 1.04 to 1.46; P=0.016) in the risk of subAS for each 1 m/s increase in PWV-ES. On the right side, a 26% increase (95% CI, 1.07 to 1.52; P=0.009) was observed in the risk of subAS for each 1 m/s increase in PWV-ES. For bilateral averages, the risk of subAS increased by 38% (95% CI, 1.12 to 1.72; P=0.003) with each 1 m/s increase in PWV-ES.
Performance of ufPWV to Monitor subAS
After PSM, the cutoff values for the left-side, right-side, and bilateral-averaged measurements of PWV-ES to predict subAS were 7.910 m/s (P=0.002), 6.615 m/s (P=0.003), and 7.415 m/s (P<0.001), respectively (Table 5). Additionally, the area under the curves (AUCs) for the left-side, right-side, and bilateral-averaged measurements of PWV-ES to monitor subAS were 0.644, 0.637, and 0.665, respectively (Table 5). The sensitivity of the left-side, right-side, and bilateral-averaged measurements of PWV-ES were 65.7%, 74.6%, and 67.2%, respectively (Table 5). However, the AUCs for the left, right, and bilateral mean measures of PWV-BS to manage the risk of subAS were not statistically significant (all P>0.05) (Table 5).
Discussion
This study investigated the relationship between subAS risk and ufPWV measurements, and higher PWV-ES values were found in individuals with subAS. Additionally, cutoff values for ufPWV were established to monitor subAS and manage populations at high risk for atherosclerosis, a focus that has been seldom studied due to limitations in imaging technology.
cfPWV is the reference standard for assessing aortic segmental stiffness, measuring the arterial pulse velocity as it travels along the vessel wall [12,13]. The evaluation of cfPWV requires specialized equipment and is very time-consuming; additionally, its accuracy is more susceptible to measurement errors, which can lead to unstable performance [18,22]. As a novel imaging technique, ultrafast ultrasonography can capture ultrafast transient photography (2,000 frames/s) to record the displacement of the carotid intima-media line in real time at the moment the pulse passes [23]. It has been suggested that ufPWV can be up to 100 times faster than current conventional ultrasound imaging devices [16]. With its ability to accurately determine the conduction velocity of the pulse wave, ultrafast ultrasound imaging facilitates a quantitative assessment of arterial stiffness [23]. Furthermore, ufPWV has demonstrated excellent stability and reliability in another study [17].
A key finding of this study was the observation of significantly higher PWV-ES values in populations with subAS. This suggests a potential link between PWV-ES measurements and the risk of subAS. However, this association was not evident in PWV-BS measurements, indicating that PWV-ES may be more sensitive than PWV-BS in assessing the risk of subAS. Several reasons may explain this difference. First, as an indicator of systole, PWV-ES might be technically more suitable than PWV-BS for measuring atherosclerosis [24,25]. Atherosclerosis is very closely related to age [26,27], and this association has been shown to be stable in multiple studies [24,28]. Additionally, the accuracy of PWV-BS measurements may be more prone to interference from reflected waves in older individuals [24,29]. Third, the systolic diastole of left ventricular ejection could subtly influence the detection of arterial stiffness [25].
As previous studies have reported, age and sex are crucial risk factors for cardiovascular events [19,30]. Historically, obesity has been recognized as a significant factor in the development of cardiovascular disease [31]. Body mass index (BMI) was commonly used to categorize obesity phenotypes in the past, but its accuracy is limited [31,32]. Studies have shown that BMI is an inaccurate measure of metabolic health and the body's visceral fat status, making it imprecise for determining cardiovascular risk [31,33]. It has been proposed that WHR is a better indicator of obesity status than BMI [31,34]. Based on these considerations, participants were matched for potential confounders such as age, sex, and WHR using PSM. After PSM, no statistically significant differences were found in metabolic indicators between the subAS and subAS-free subgroups, indicating a substantial increase in comparability. Notably, in the authors’ previous study, the technique of ufPWV measurements was validated, demonstrating good intra-operator and inter-operator reproducibility [28]. To ensure adequate statistical power, the minimum sample size was calculated for the current study.
The present study also established cutoff values for PWV-ES in unilateral carotid arteries. To the best of the authors’ knowledge, most previous studies have focused on providing cutoff values for the combined bilateral carotid arteries, which may not be sufficient for precise risk management. In another study, the cutoff value of PWV-ES for determining the severity of coronary artery disease was established at 8.0 m/s [35]. SubAS, which has not yet manifested obvious clinical symptoms, is significantly less severe than coronary artery disease. Furthermore, taking into account the potential for detection errors across different laboratories, the cutoff value of 7.415 m/s for PWV-ES observed in this study is reasonable.
In addition, the cutoff value of PWV-ES was found to be higher on the left side than on the right side. This difference may be attributed to variations in vascular dynamics between the two sides of the carotid arteries, as well as effects at the biochemical level [36,37]. This was demonstrated in a prospective study with an extraordinarily large sample size [37]. As a possible explanation, it should be noted first that the origin of the carotid vasculature is different on the right and left sides, and the left carotid artery, which is influenced by the aortic arch, has the potential to be exposed to higher vascular pressures [37]. Additionally, the interaction of blood with the carotid intima-media and blood hormone levels are likely influential factors [38-40]. To improve the precision of cardiovascular risk prediction, it is recommended that clinicians consider these differences between the two carotid arteries when utilizing ultrafast ultrasound imaging in clinical practice.
Nonetheless, this study had several limitations. First, due to the nature of this small-sample cross-sectional study, it was not possible to draw causal inferences. Therefore, further longitudinal studies with larger sample sizes are necessary for additional validation in the future. Second, PWV-ES and PWV-BS are measurements taken at different time points of the cardiac cycle and may be underestimated compared to cfPWV values [25]. Accordingly, it is important to be aware of the differences between different PWV measurements when comparing imaging data on arterial stiffness. Third, the measurements of ufPWV in this study were obtained with a single probe (SL10-2). Although some studies suggest that ufPWV measurements are not influenced by probes with different ultrasound frequencies [37], it is important to consider potential differences in testing equipment when comparing results across populations. Finally, the study did not fully account for some confounding factors that may influence outcomes, such as diet, exercise, and lifestyle.
In conclusion, this study demonstrates that PWV-ES, measured using ultrafast ultrasonography, is significantly higher in individuals with subAS compared to those without. Additionally, the study identified specific PWV-ES cutoff values that predict an increased risk, supporting their use in cardiovascular risk assessments. Further research is necessary to validate these findings for routine clinical practice.
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