Characterization of carotid remodeling in gestational hypertension using radiofrequency data-based vessel stiffness analysis and vector flow imaging

Introduction

Hypertensive disorders of pregnancy (HDP) remain a major global health challenge, affecting approximately 5% to 10% of all pregnancies, and representing a leading cause of maternal and perinatal morbidity (1-3). Among these, gestational hypertension (GH) is increasingly recognized not merely as a transient complication of pregnancy but as a critical “vascular stress test” (4-6). GH may unmask underlying maternal cardiovascular susceptibility, significantly increasing the long-term risk of chronic hypertension, ischemic heart disease, and stroke later in life (7). Early identification of subclinical vascular remodeling during pregnancy is thus important for both immediate obstetric management and long-term cardiovascular risk stratification (8).

Carotid intima-media thickness (CIMT) is widely regarded as a reliable noninvasive surrogate marker of subclinical atherosclerosis and systemic vascular remodeling (9,10). This marker may be particularly relevant in young women, as early-life increased CIMT may indicate premature vascular remodeling rather than age-related arterial change. Evidence from longitudinal vascular studies suggests that CIMT progression is associated with subsequent adverse cardiovascular events, especially when vascular risk factors are not adequately controlled (11). Moreover, recent pregnancy-specific evidence indicates that maternal and cardiovascular factors are associated with CIMT during pregnancy, supporting its relevance as a vascular marker in obstetric populations (12). While numerous studies have documented increased CIMT in women with GH, the clinical utility of conventional gray-scale ultrasound may be limited by several factors (13-15). Morphological changes, such as intimal thickening, typically represent a relatively late stage of the vascular remodeling process, often lagging behind functional and biomechanical impairments. Further, traditional ultrasound measurement of CIMT is based on B-mode pixel analysis, which is inherently limited by image resolution and significant inter-observer variability, potentially overlooking the subtle, early-stage alterations in the arterial wall (16-18).

The emergence of quantitative imaging technologies, including radiofrequency data-based quantitative vessel stiffness (R-QVS) and vector flow imaging (VFI), provides a novel framework for hemodynamic and biomechanical assessment. Unlike conventional pixel-tracking methods, R-QVS uses raw radiofrequency (RF) signals to capture instantaneous vessel wall displacement with superior spatial and temporal resolution (19,20). This enables the high-precision quantification of arterial stiffness metrics, such as pulse wave velocity (PWV) and the hardness coefficient (HC), directly reflecting the elastic properties of the vascular wall. Simultaneously, VFI represents a paradigm shift in hemodynamic evaluation by overcoming the inherent angle-dependency and Nyquist limits of traditional Doppler ultrasound (21,22). Using multi-angle plane-wave transmission, VFI enables the visualization and quantification of complex flow patterns, and provides highly accurate measurements of wall shear stress (WSS) throughout the cardiac cycle (23,24).

Despite the theoretical advantages of these techniques, few studies have integrated vascular mechanical and hemodynamic phenotype and complex hemodynamics to characterize the “vasculomechanical” phenotype of carotid remodeling in GH (25). To date, most research has focused on either structural indicators or functional parameters in isolation (26-28), thereby failing to capture the synergistic interplay between flow-mediated shear forces and the resulting structural adaptation (29,30). Therefore, the present study aimed to use R-QVS analysis and VFI to comprehensively evaluate the carotid artery features in GH patients compared to healthy pregnant controls (31-33). We hypothesized that increased CIMT in GH is preceded or accompanied by a distinct “high-stiffness, low-shear” profile, and that these quantitative parameters provide incremental diagnostic value for identifying high-risk subclinical remodeling. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2026-0643/rc).

Methods

Study design and participants

This prospective observational study was conducted at Xiangyang No. 1 People’s Hospital, Hubei Province, China, from January 2024 to October 2025. Eligible pregnant women undergoing antenatal evaluation and standardized carotid ultrasonography during the study period were consecutively enrolled in the study. No formal individual matching or propensity score matching was performed between the GH and control groups. Group comparability was assessed using key demographic and obstetric variables, including maternal age, gestational age at examination, gravidity, and parity.

A total of 619 women were screened, of whom 347 (239 with GH and 108 normotensive healthy pregnant controls) met the eligibility criteria and were included in the final analysis. GH was diagnosed in accordance with the 2021 International Society for the Study of Hypertension in Pregnancy (ISSHP) guidelines, which define new-onset hypertension as systolic blood pressure (SBP) ≥140 mmHg and/or diastolic blood pressure (DBP) ≥90 mmHg on at least two occasions after 20 weeks’ gestation in previously normotensive women (34). The GH participants were further stratified by CIMT into normal-CIMT and increased-CIMT groups. Increased CIMT was defined as a mean CIMT ≥1.0 mm, consistent with established thresholds for identifying subclinical target organ damage (35,36), and participants with carotid plaque were excluded from the analysis.

The study was registered in the medical research registration and filing system of Xiangyang No. 1 People’s Hospital (Registration No. MR-42-26-040051). The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments, and approved by the Ethics Committee of Xiangyang No. 1 People’s Hospital (No. XYYYE20230085). Written informed consent was obtained from all participants before enrollment, and all personal information was anonymized prior to analysis.

Inclusion and exclusion criteria

Pregnant women who volunteered to participate in the study and underwent standardized carotid ultrasonography were eligible for inclusion in the study. Women were assigned to the GH group if GH was newly diagnosed during pregnancy in accordance with guideline-based criteria, and to the control group if they remained normotensive and free of hypertensive disorders throughout pregnancy. After screening 619 candidates, 347 participants met the eligibility criteria and were included in the final analysis.

Participants were excluded from the study if any of the following conditions were present: pre-existing GH, gestational diabetes mellitus, chronic kidney disease, cardiovascular or cerebrovascular disease, autoimmune disease, peripheral vascular disease, or carotid artery plaque. Carotid plaque was defined according to the Mannheim Consensus as a focal structure encroaching into the arterial lumen by at least 0.5 mm or 50% of the surrounding IMT value, or as a focal thickness >1.5 mm measured from the intima-lumen interface to the media-adventitia interface. To ensure the reliability of the imaging-derived indices, participants with failed acquisition of R-QVS and VFI datasets or poor ultrasound image quality were also excluded from the study (Figure 1).

Figure 1 Study flowchart. CIMT, carotid intima-media thickness; Diam, systolic carotid artery diameter; Dist, carotid vessel wall displacement; GH, gestational hypertension; HC, hardness coefficient; PWV, pulse wave velocity; R-QVS, radiofrequency data-based vessel stiffness; VFI, vector flow imaging; Vmax, peak systolic flow velocity; WSS_max, maximum wall shear stress; WSS_mean, mean wall shear stress.

Clinical and baseline data collection

Maternal demographic, obstetric, and clinical information was retrieved from the hospital electronic medical record system. The collected variables included maternal age, gestational age at the time of examination, gravidity, parity, and pre-pregnancy body mass index (BMI). Blood pressure (BP) was measured at the time of the ultrasound examination with a validated automated sphygmomanometer after the participant had rested in a seated position for at least 5 minutes. Two measurements were obtained at an interval of 1–2 minutes, and the mean value was used for analysis. Laboratory data obtained within one week of ultrasound examination included hemoglobin A1c (HbA1c) and lipid parameters, including total cholesterol (TC), triglycerides (TG), high-density lipoprotein (HDL) cholesterol, and low-density lipoprotein (LDL) cholesterol. These variables were collected to explore their associations with carotid structural, stiffness-related changes, and flow-related changes (19).

Equipment and methodology

Two trained sonographers performed all the carotid ultrasound examinations using a Mindray Resona R9S ultrasound system (Shenzhen Mindray Bio-Medical Electronics Co., Ltd., Shenzhen, China) equipped with a high-frequency linear array transducer (L14-3WU, 3.8–11.8 MHz). Participants were examined in the supine position after a 10-min rest, with the head slightly rotated to optimize visualization of the common carotid artery (CCA). Bilateral measurements were acquired on the far wall of the right CCA, 1–2 cm proximal to the carotid bifurcation. CIMT was identified as a double-line pattern visualized by echography on both walls of the CCA on longitudinal images. This pattern consists of two parallel lines representing the leading edges of two anatomical boundaries: the lumen-intima and media-adventitia interfaces.

CIMT was measured using real-time IMT mode. Increased CIMT was defined as a mean CIMT ≥1.0 mm, consistent with commonly used carotid IMT thresholds (Figure 2). To evaluate arterial elasticity, R-QVS analysis was used to obtain the systolic carotid artery diameter (Diam), carotid vessel wall displacement (Dist), HC, and local PWV. Brachial BP was measured with an automated cuff and incorporated into the stiffness calculation.

Figure 2 Real-time intima-media thickness measurement: (A) healthy control group; (B) GH group with normal intima-media; (C) GH group with increased CIMT. CIMT, carotid intima-media thickness; GH, gestational hypertension.

Dynamic VFI was used to assess carotid hemodynamic features by visualizing and quantifying blood-flow vectors in an angle-independent manner. The recorded VFI-derived parameters included maximum wall shear stress (WSS_max), mean wall shear stress (WSS_mean), and peak systolic flow velocity (V_max). Each measurement was performed three times, and the mean value was used for subsequent analysis. Reproducibility was evaluated in 30 participants who underwent repeated measurements by the same sonographer and by a second blinded sonographer. For intra-observer reproducibility, the same sonographer repeated the measurements while blinded to the initial results. For inter-observer reproducibility, a second sonographer, blinded to the clinical group allocation and the first sonographer’s measurements, independently repeated the measurements. Intraclass correlation coefficients (ICCs) were calculated for each parameter to assess intra-observer and inter-observer agreement (Figures 3-5).

Figure 3 R-QVS-based ultrasound analysis: (A) healthy control group; (B) GH group with normal intima-media; (C) GH group with increased CIMT. CIMT, carotid intima-media thickness; GH, gestational hypertension; R-QVS, radiofrequency data-based quantitative vessel stiffness.

Figure 4 Dynamic VFI ultrasound images: (A) healthy control group; (B) GH group with normal intima-media; (C) GH group with CIMT. CIMT, carotid intima-media thickness; GH, gestational hypertension; VFI, vector flow imaging.

Figure 5 Dynamic VFI ultrasound images: (A) healthy control group; (B) GH group with normal intima-media; (C) GH group with CIMT. CIMT, carotid intima-media thickness; GH, gestational hypertension; VFI, vector flow imaging.

Statistical analysis

All statistical analyses were performed using SPSS version 27.0. The normality of the continuous variables was assessed using the Kolmogorov-Smirnov test. Continuous variables with a normal distribution were summarized as mean ± standard deviation and compared among groups using one-way analysis of variance (ANOVA). When the overall ANOVA was significant, pairwise comparisons were performed with Bonferroni adjustment. Continuous variables with a non-normal distribution were summarized as median (interquartile range) and compared using the Kruskal-Wallis test. When the overall Kruskal-Wallis test was significant, post hoc pairwise comparisons were conducted using Dunn’s test. Categorical variables were presented as frequencies (%) and compared using the chi-square test or Fisher’s exact test, as appropriate.

Receiver operating characteristic (ROC) curve analysis was performed to evaluate the ability of R-QVS- and VFI-derived parameters to detect increased CIMT among women with GH. The area under the curve (AUC) with 95% confidence intervals (CIs) was reported. The optimal cutoff value was determined using the Youden index, and sensitivity and specificity at the optimal threshold were calculated.

To identify the factors associated with increased CIMT in GH, univariate logistic regression analyses were first performed for candidate variables derived from clinical characteristics, laboratory indices, and ultrasound parameters. Variables with P<0.05 in the univariate analyses and/or those deemed clinically relevant were entered into a multivariable logistic regression model. To reduce potential collinearity, clinically overlapping variables were not entered simultaneously; rather, the more clinically interpretable or representative variable was selected. Adjusted odds ratios (ORs) with 95% CIs were reported. All tests were two-sided, and a P value <0.05 was considered statistically significant.

To further evaluate the direct association between key predictors and CIMT as a continuous outcome, multivariable linear regression analysis was performed among women with GH. Variables were selected based on clinical relevance and univariate analysis results. Unstandardized β coefficients, standardized β coefficients, 95% CIs, and P values were reported. Multicollinearity was assessed using the variance inflation factor (VIF), and model explanatory performance was evaluated using R² and adjusted R².

Results

Baseline clinical characteristics

The final analysis included 347 pregnant women: 125 women with GH and normal CIMT, 114 women with GH and increased CIMT, and 108 normotensive pregnant controls. No significant differences were observed among the three groups in maternal age, gestational age at examination, gravidity, or parity (all P>0.05), indicating comparable baseline demographic and obstetric profiles despite the absence of formal matching. In contrast, pre-pregnancy BMI, SBP, DBP, HbA1c, TC, TG, HDL, and LDL differed significantly across the three groups (all P<0.05). Both GH subgroups had higher pre-pregnancy BMI and higher BP than the control group. The GH increased-CIMT subgroup exhibited the most adverse cardiometabolic profile, with higher HbA1c, TC, and TG, and lower HDL compared with both the GH normal-CIMT subgroup and control group (Table 1).

Comparison of carotid ultrasound parameters among the three groups

A preliminary intra-individual analysis was conducted to compare the bilateral vascular indices. No statistically significant differences were observed between the left and right carotid artery parameters (including CIMT, HC, PWV, Diam, Dist, V_max, and WSS) within the GH subgroups (all P>0.05; Table 2). Consequently, the right CCA was selected as the representative vessel for all subsequent comparative analyses. All the structural and functional ultrasound parameters differed significantly among the three groups (all P<0.01; Table 3).

In the GH normal-CIMT group, CIMT remained below the thickening threshold but was higher than that in the control group (0.78±0.17 vs. 0.70±0.14 mm) and substantially lower than that in the GH increased-CIMT group (1.19±0.18 mm), consistent with predefined stratification. R-QVS indicated biomechanical alterations in GH even before increased CIMT was present. Compared with the control group, the GH normal-CIMT group had a larger systolic diameter (7.72±0.53 vs. 7.29±0.48 mm) and lower wall displacement [0.36 (0.31–0.42) vs. 0.45 (0.34–0.50) mm], accompanied by higher stiffness indices (HC: 4.35±0.65 vs. 3.50±0.56; PWV: 7.05±0.77 vs. 5.87±0.68 m/s). Differences in these parameters were significant across groups (all P<0.001), and the GH normal-CIMT group consistently showed intermediate values between the control group and the GH increased-CIMT subgroup. VFI indicated parallel hemodynamic impairment in the GH normal-CIMT group, with lower WSS and V_max than those in the control group (WSS_max: 2.59±0.66 vs. 3.63±0.72 Pa; WSS_mean: 0.89±0.32 vs. 1.30±0.38 Pa; V_max: 52.76±7.55 vs. 55.81±7.86 cm/s), following a graded pattern toward the GH increased-CIMT group. Collectively, these findings suggest that GH patients without increased CIMT already demonstrate carotid stiffening and reduced shear-related flow characteristics, consistent with an early adverse vasculomechanical profile.

Repeatability assessment

Overall measurement repeatability was good to excellent across CIMT and the key R-QVS/VFI parameters. Inter-observer ICC values ranged from 0.802 to 0.913, and intra-observer ICC values ranged from 0.838 to 0.930, indicating robust reproducibility for both the stiffness and hemodynamic indices. Specifically, IMT showed excellent agreement, with an intra-observer ICC of 0.930 (95% CI: 0.879–0.961) and an inter-observer ICC of 0.913 (95% CI: 0.851–0.954). The intra-observer ICC values for the R-QVS parameters Diam, Dist, HC, and PWV were 0.889 (95% CI: 0.830–0.932), 0.873 (95% CI: 0.812–0.925), 0.904 (95% CI: 0.847–0.945), and 0.890 (95% CI: 0.828–0.932), respectively; the corresponding inter-observer ICC values were 0.865 (95% CI: 0.791–0.914), 0.844 (95% CI: 0.775–0.890), 0.886 (95% CI: 0.814–0.920), and 0.874 (95% CI: 0.805–0.921), respectively. For VFI, the intra-observer ICC values for WSS_max, WSS_mean, and V_max were 0.851 (95% CI: 0.780–0.907), 0.838 (95% CI: 0.769–0.884), and 0.867 (95% CI: 0.794–0.911), respectively, while the inter-observer ICC values were 0.825 (95% CI: 0.742–0.885), 0.802 (95% CI: 0.721–0.866), and 0.828 (95% CI: 0.749–0.890), respectively. Bland-Altman plots further supported agreement by demonstrating small mean biases and narrow 95% limits of agreement (LOA), with no obvious proportional bias across the measurement ranges. For IMT, the mean inter-observer bias was 0.002 mm (LOA –0.019 to 0.024 mm), and the mean intra-observer bias was –0.001 mm (LOA –0.021 to 0.019 mm). For PWV, agreement was similarly stable (inter-observer bias –0.015 m/s, LOA –0.243 to 0.212 m/s; intra-observer bias –0.001 m/s, LOA –0.163 to 0.161 m/s). For WSS_max, the mean bias remained close to zero (inter-observer 0.017 Pa, LOA –0.262 to 0.295 Pa; intra-observer 0.002 Pa, LOA –0.223 to 0.230 Pa), with most points falling within the LOA. Only three representative parameters (IMT, PWV, and WSS_max) were plotted in the Bland-Altman analysis to provide an interpretable visual summary spanning the study’s three measurement domains—structure (IMT), biomechanics (PWV), and flow-related shear (WSS_max). The remaining parameters were comprehensively assessed using the ICC with 95% CIs (Table 4 and Figure 6).

Figure 6 Bland-Altman plots for inter- and intra-observer repeatability of IMT, PWV, and WSS_max. Solid line indicates mean bias; dashed lines show 95% limits of agreement (±1.96 SD). IMT, intima-media thickness; PWV, pulse wave velocity; SD, standard deviation; WSS_max, maximum wall shear stress.

ROC curve analysis

A ROC curve analysis was performed to evaluate the ability of the R-QVS- and VFI-derived carotid parameters to detect increased CIMT in women with GH. Overall, all tested indices demonstrated statistically significant diagnostic performance (all P<0.0001). Among the R-QVS parameters, HC showed the best discrimination, yielding an AUC of 0.909 (95% CI: 0.835–0.942) with an optimal cutoff of 4.850, providing 76.67% sensitivity and 95.00% specificity. PWV also performed strongly, with an AUC of 0.885 (95% CI: 0.823–0.946); at a cutoff of 8.072 m/s, the sensitivity and specificity were 91.67% and 71.67%, respectively. In comparison, Diam and Dist showed moderate discriminatory ability (Diam AUC: 0.782, 95% CI: 0.696–0.868, cutoff 7.445 mm; Dist AUC: 0.766, 95% CI: 0.681–0.850, cutoff 0.352 mm), with Diam showing higher sensitivity (84.58%) and Dist showing higher specificity (86.67%). For the VFI-derived metrics, WSS_max and WSS_mean demonstrated good diagnostic performance (WSS_max AUC: 0.850, 95% CI: 0.784–0.915, cutoff 2.365 Pa; WSS_mean AUC 0.837, 95% CI 0.767–0.907, cutoff 0.795 Pa), with sensitivities of 88.66% and 84.36%, respectively. V_max showed comparatively lower diagnostic performance (AUC: 0.728, 95% CI: 0.634–0.819, cutoff 54.473 cm/s). Collectively, these results indicate that stiffness-related indices, particularly HC and PWV, provide the highest diagnostic accuracy for the detection of increased CIMT in GH, while WSS-derived metrics add complementary discriminatory value (Table 5 and Figure 7).

Figure 7 Receiver operating characteristic curves of the diagnostic value of Diam, Dist, HC, PWV, WSS_max, WSS_mean, and V_max in the carotid arteries of patients with GH. Diam, systolic carotid artery diameter; Dist, carotid vessel wall displacement; GH, gestational hypertension; HC, hardness coefficient; PWV, pulse wave velocity; WSS_max, maximum wall shear stress; WSS_mean, mean wall shear stress; V_max, peak systolic flow velocity.

Univariate and multivariable logistic regression analyses of factors associated with CIMT in GH patients

Univariate logistic regression identified several clinical and ultrasound-derived variables significantly associated with increased CIMT in women with GH (Figure 8). Specifically, higher pre-pregnancy BMI (OR: 1.52, 95% CI: 1.20–1.92; P<0.001), SBP (OR: 1.15, 95% CI: 1.07–1.24; P<0.001), and DBP (OR: 1.18, 95% CI: 1.09–1.27; P<0.001) were associated with higher odds of increased CIMT. Adverse lipid indices were also significant, including TC (OR: 3.94, 95% CI: 1.89–8.91; P<0.001) and TG (OR: 5.86, 95% CI: 2.31–14.86; P<0.001). For the imaging parameters, higher stiffness metrics were associated with increased CIMT (HC: OR 3.47, 95% CI: 1.38–8.72; P=0.008; PWV: OR =2.95, 95% CI: 1.44–6.06; P=0.003), whereas flow-related indices showed inverse associations (WSS_max: OR =0.34, 95% CI: 0.15–0.75; P=0.008; WSS_mean: OR =0.06, 95% CI: 0.02–0.26; P=0.002; V_max: OR =0.89, 95% CI: 0.83–0.95; P=0.001).

Figure 8 Forest plot of univariate and multivariable logistic regression analyses of factors associated with increased CIMT in women with GH. BMI, body mass index; CI, confidence interval; CIMT, carotid intima-media thickness; DBP, diastolic blood pressure; Diam, systolic carotid artery diameter; Dist, carotid vessel wall displacement; GH, gestational hypertension; HbA1c, hemoglobin A1c; HC, hardness coefficient; HDL, high-density lipoprotein; LDL, low-density lipoprotein; OR, odds ratio; PWV, pulse wave velocity; SBP, systolic blood pressure; TC, total cholesterol; TG, triglycerides; WSS_max, maximum wall shear stress; WSS_mean, mean wall shear stress; V_max, peak systolic flow velocity.

In the multivariable analysis, pre-pregnancy BMI (OR: 1.48, 95% CI: 1.04–2.09; P=0.028), SBP (OR: 1.09, 95% CI: 1.01–1.18; P=0.023), TG (OR: 5.40, 95% CI: 1.28–15.69; P=0.039), and PWV (OR: 3.61, 95% CI: 1.63–8.02; P=0.002) remained independent risk factors for increased CIMT. In contrast, higher WSS_mean remained independently associated with lower odds of increased CIMT (OR: 0.29, 95% CI: 0.13–0.67; P=0.004). DBP, TC, HC, WSS_max, and V_max were not retained as independent predictors after adjustment (all P>0.05), suggesting that increased CIMT in GH is most strongly associated with systemic metabolic burden, pressure load, and local arterial stiffening, with concurrent reductions in mean shear stress. LDL cholesterol was also included in the regression analysis. Although LDL cholesterol differed among the three groups in the baseline comparison, it was not significantly associated with increased CIMT in either the univariate or multivariable logistic regression analyses. These associations and their 95% CIs were visualized in a corresponding forest plot (Figure 8), facilitating comparison of effect sizes across predictors.

Linear regression analysis of key factors associated with CIMT as a continuous variable

To further evaluate the direct influence of key predictors on CIMT as a continuous outcome, multivariable linear regression analysis was performed among women with GH. Higher pre-pregnancy BMI, SBP, TG, and PWV were positively associated with CIMT, whereas WSS_mean was inversely associated with CIMT. Specifically, each 1-kg/m² increase in pre-pregnancy BMI was associated with a 0.026-mm increase in CIMT, each 10-mmHg increase in SBP with a 0.040-mm increase in CIMT, each 1-mmol/L increase in TG with a 0.074-mm increase in CIMT, and each 1-m/s increase in PWV with a 0.081-mm increase in CIMT. In contrast, each 1-Pa increase in WSS_mean was associated with a 0.158-mm decrease in CIMT. No severe multicollinearity was observed among the retained predictors, with all VIF values below 2.5 and a maximum VIF of 2.18. The model explained 56.8% of the variance in CIMT, with an adjusted R² of 0.553 (Figure 9).

Figure 9 Multivariable linear regression coefficient plot for CIMT in women with GH. BMI, body mass index; CI, confidence interval; CIMT, carotid intima-media thickness; GH, gestational hypertension; PWV, pulse wave velocity; SBP, systolic blood pressure; TG, triglycerides; WSS_mean, mean wall shear stress; VIF, variance inflation factor.

Discussion

This study demonstrated a progressive transition toward a “high-stiffness, low-shear” phenotype from healthy controls to patients with GH. These findings suggest that functional changes in vascular mechanics and hemodynamics are integral to the vascular remodeling process in GH.

First, we observed that GH was accompanied by a less favorable cardiometabolic profile, the GH subgroup with increased CIMT exhibited the greatest overall burden. Although maternal age, gestational age, and parity were comparable across groups, significant differences in pre-pregnancy BMI, BP, and lipid parameters suggest an underlying increase in systemic vascular risk in GH. Pewowaruk et al. emphasized that hypertensive individuals exhibit greater load-dependent carotid stiffness even after adjusting for BP, suggesting a complex interaction between vascular structure and hemodynamic load (37).

A potential limitation of this study is the use of a 1.0-mm CIMT threshold to define thickening. We acknowledge that this threshold is conservative for a young pregnant population with a median age of 30 years. Brik et al. recently reported that CIMT during pregnancy is associated with maternal and cardiovascular factors, indicating that even modest CIMT increases may reflect underlying vascular susceptibility rather than benign physiological variation (12). Further, an age-specific formula [(0.009 × age in years) + 0.116 mm] suggests that CIMT values exceeding 0.7–0.8 mm in this age group may already be abnormal (38). In the present study, 1.0 mm was used as a pragmatic clinical cutoff to identify a subgroup with clear, pronounced structural remodeling for comparative biomechanical analysis. However, it should be noted that even the “normal CIMT” GH group exhibited significantly higher CIMT values (0.78±0.17 mm) compared to the healthy control group (0.70±0.14 mm). Future studies should incorporate age-specific reference ranges to enhance the sensitivity of ultrasound-based criteria for detecting early subclinical vascular changes in young hypertensive pregnancies. To maintain the focus on diffuse intima-media thickening rather than localized atherosclerotic plaque, we excluded focal structures exceeding 1.5 mm or significantly encroaching into the lumen according to the Mannheim Carotid Consensus (35).

Second, carotid biomechanical and hemodynamic indices demonstrated a coherent, stepwise deterioration. R-QVS analysis captured larger carotid diameter and higher stiffness indices (PWV and HC), while VFI revealed progressively lower WSS. As emphasized by de Korte et al., while conventional echography measures distensibility, it often fails to capture the detailed mechanical behavior of the arterial wall (15). By using R-QVS analysis, we addressed this limitation, providing a more granular view of the vascular stress in GH. He et al. confirmed that hemodynamic shear alterations often precede structural remodeling, which is consistent with our observation of impaired WSS even in GH patients with normal CIMT (39). Moreover, a longitudinal study by by Strecker et al. demonstrated that low WSS and unfavorable geometry independently predict intima-media thickening progression, strengthening the evidence for shear-driven mechanisms (40).

Third, the measurement reliability of these advanced indices was high, with inter- and intra-observer ICCs ranging from good to excellent. This minimizes the likelihood that the observed group differences were attributable to measurement variability. Marais et al. previously showed that ultrafast pulse wave imaging provides reproducible and load-sensitive measures of carotid stiffness in hypertensive adults (41). From a mechanistic perspective, the significant reduction in WSS_mean identifies low shear stress as a key driver of remodeling. As noted by Davies, WSS modulates endothelial signaling and governs intimal homeostasis (23). In line with this concept, Zhao et al. recently demonstrated that carotid WSS measured by vector flow mapping was significantly associated with CIMT and atherosclerotic cardiovascular disease risk stratification in a Chinese population, further supporting the clinical relevance of WSS assessment for vascular risk evaluation (42). Further, disturbed flow characterized by low WSS may promote stiffening through molecular pathways such as thrombospondin-1, as suggested by Kim et al., underscoring the bidirectional interplay between hemodynamics and wall structure (24).

Fourth, the ROC curve analysis confirmed that stiffness and shear-related metrics significantly improved the discrimination of increased CIMT. In particular, R-QVS stiffness indices (HC and PWV) achieved strong diagnostic performance (AUC 0.909 and 0.885, respectively). Wang et al. established that arterial stiffness increased with age and risk exposure, emphasizing the diagnostic potential of these ultrasound-based indices in clinical practice (43).

Finally, multivariable regression identified higher pre-pregnancy BMI, SBP, TG, and PWV as independent risk factors, while higher WSS_mean remained higher WSSmean was independently associated with lower odds of increased CIMT. These findings suggest that increased CIMT in GH is not explained by BP alone; rather, it reflects the combined impact of metabolic burden and the local mechanical environment. Castleman et al. further reported that women with a history of HDP may have persistently increased vascular stiffness in subsequent pregnancies. This persistent vascular alteration supports the concept of early vascular imprinting after HDP and may contribute to long-term cardiovascular risk in affected women (44).

Clinical implications

From a clinical standpoint, these results support a more granular vascular phenotype for GH beyond conventional CIMT alone. A multiparametric assessment may help identify GH patients with an adverse remodeling signature—characterized by elevated stiffness and depressed WSS—who may warrant closer prenatal surveillance and more structured postpartum cardiovascular risk follow-up. Moreover, because stiffness and WSS capture potentially modifiable dimensions of vascular dysfunction, these indices may serve as quantitative endpoints for future interventional studies focused on BP control and metabolic optimization during and after pregnancy.

Strengths and limitations

The major strengths of this study include its prospective design, standardized acquisition of carotid stiffness and flow metrics, and robust reproducibility testing. However, several limitations should be acknowledged. The study was conducted at a single center, which may limit the generalizability of the study findings. The analysis was cross-sectional, precluding causal inference. Because formal individual matching or propensity score matching was not performed, residual confounding from unmeasured or incompletely measured variables cannot be excluded. Finally, while the 1.0-mm CIMT threshold is a pragmatic clinical cutoff, we recognize it is conservative for a young population; the incorporation of age-specific reference ranges and newer normative data during pregnancy, as highlighted by the Mannheim Consensus and recent obstetric cohorts, remains a critical area for future refinement.

Future directions

Longitudinal studies tracking postpartum vascular recovery are needed to determine whether the “high-stiffness, low-shear” profile represents a transient adaptation or persistent vascular vulnerability. Future work should focus on external validation across diverse ultrasound platforms and the development of integrated risk models that combine these high-precision R-QVS and VFI parameters with clinical variables to enhance prenatal risk stratification.

Conclusions

An integrated ultrasound strategy combining R-QVS analysis and VFI enables the reproducible characterization of carotid remodeling in GH. Increased CIMT in GH is independently associated with higher pre-pregnancy BMI, SBP, TG, and PWV, while higher WSS_mean serves as a protective factor. This distinct “high-stiffness, low-shear” phenotype, identified through multimodal quantitative imaging, adds significant value to vascular risk stratification and targeted follow-up in hypertensive pregnancies.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2026-0643/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2026-0643/dss

Funding: This study was supported by the Natural Science Foundation of Hubei Province (No. 2025AFB845).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2026-0643/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of Xiangyang No. 1 People’s Hospital (No. XYYYE20230085). Written informed consent was obtained before enrollment, and all personal information was anonymized prior to analysis.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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