Value of multimodal ultrasound assessment of placental dysfunction in gestational diabetes mellitus: a prospective study

Introduction

Gestational diabetes mellitus (GDM) is one of the most prevalent pregnancy-related metabolic disorders, affecting approximately 5–15% of pregnancies worldwide, depending on diagnostic criteria and population characteristics (1,2). It is associated with an increased risk of adverse outcomes such as fetal overgrowth, neonatal hypoglycemia, preterm delivery, hypertensive disorders, and long-term metabolic syndrome in both mothers and offspring (3-5). The pathogenesis of GDM involves complex hormonal and metabolic changes that impair maternal insulin sensitivity and glucose regulation, ultimately altering the intrauterine environment. As the key organ mediating maternal-fetal exchange, the placenta is particularly susceptible to the effects of maternal hyperglycemia (6,7).

Growing evidence suggests that GDM induces progressive placental structural and functional abnormalities throughout gestation (8). During the first trimester, elevated glucose levels may impair trophoblast invasion and spiral artery remodeling, resulting in reduced placental perfusion capacity (9). In the second trimester, chronic metabolic dysregulation can provoke oxidative stress and endothelial dysfunction, contributing to thickened basement membranes and abnormal vascular formation (10). By late pregnancy, compensatory mechanisms may fail, leading to interstitial edema, decreased vascular density, and impaired glucose transport across the placenta (11). These alterations have been confirmed by histological studies and animal models, yet their comprehensive assessment and in vivo monitoring remain clinical challenges (12).

Traditionally, placental function is assessed indirectly through 2-dimensional (2D) ultrasound and spectral Doppler parameters such as placental thickness, uterine artery (UtA) resistance index (RI), and umbilical artery (UA) pulsatility index (PI) (13). However, these indicators often lack sensitivity and cannot capture microvascular or biomechanical changes in early gestation. Recent studies have explored the potential of advanced ultrasound technologies to address this gap. For instance, three-dimensional (3D) power Doppler imaging allows for volumetric assessment of placental blood flow, with indices such as vascularization index (VI), flow index (FI), and vascularization flow index (VFI) showing promising correlations with pregnancy complications. Shear wave elastography (SWE) provides quantitative measurement of placental stiffness, which has been linked to fibrosis and vascular remodeling in GDM (14). A study by Anuk et al. found that increased placental stiffness in the third trimester was associated with elevated glycated hemoglobin (HbA1c) levels in GDM patients (15). Similarly, research by Yang et al. demonstrated that decreased VI and VFI were predictive of fetal growth restriction, suggesting that perfusion deficits precede clinical symptoms (16). However, few studies have systematically integrated multiple ultrasound modalities to comprehensively assess placental changes in GDM.

Moreover, the diagnostic value of these novel parameters remains insufficiently corroborated. Most existing studies have relied on cross-sectional designs with limited sample sizes, heterogeneous measurement protocols, or lack of gold standard comparisons. The integration of real-time functional imaging with post-delivery anatomical corroboration—such as vascular casting—offers a unique opportunity to bridge this gap (17). There is a clear need for comprehensive, multimodal approaches that combine hemodynamic, microstructural, and mechanical information to improve early risk stratification in pregnancies complicated by GDM.

In this prospective study, we aimed to evaluate the value of multimodal ultrasound in detecting placental structural and functional abnormalities in GDM. Using a combination of spectral Doppler, 3D power Doppler, and SWE, we compared placental parameters between women with GDM and healthy controls. We further explored the diagnostic performance of individual and combined parameters. By identifying reliable sonographic biomarkers of GDM-related placental dysfunction, our study sought to improve multimodal assessment, provide insights into the underlying pathophysiology, and support more precise prenatal care strategies. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-aw-2103/rc).

Methods

Study design and participants

This was a prospective observational study conducted at Xiangyang No. 1 People’s Hospital, Hubei University of Medicine, from September 2022 to December 2024. A total of 177 singleton pregnant women undergoing routine prenatal care were recruited and categorized into two groups based on the presence or absence of GDM: the GDM group (n=82) and the healthy control group (n=95). Participants were enrolled consecutively. The final numbers in the GDM and control groups differed because some eligible participants were excluded after screening or lacked complete data required for the multimodal ultrasound analyses (e.g., missing key clinical variables or incomplete acquisition of one or more ultrasound parameters). The 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. XYYYE20220040), and all participants provided written informed consent.

Sample size determination

The sample size was determined based on the requirements for multivariate logistic regression analysis. According to the principle of 10 events per predictor variable (EPV), and considering the inclusion of up to 6 potential predictors (ultrasound parameters and clinical confounders) in the final model, a minimum of 60 GDM cases was required. Our final sample of 82 GDM cases exceeded this threshold. Additionally, a post-hoc power analysis based on the primary outcome (VFI) indicated that the sample sizes of 82 (GDM) and 95 (Control) provided a statistical power of >99% at a 2-sided significance level of 0.05, confirming the robustness of the study conclusions.

Inclusion and exclusion criteria

Participants were eligible for inclusion if they had a singleton pregnancy between 28 and 40 weeks of gestation, were aged 21 to 39 years, and received standardized prenatal care and postpartum follow-up at the study site. This gestational window was selected to ensure that all participants had completed standard GDM screening (typically at 24–28 weeks) and to capture the third-trimester period when GDM-related placental structural and functional maladaptations are most clinically evident. The diagnosis of GDM was based on the criteria established by the International Association of Diabetes and Pregnancy Study Groups (IADPSG, 2010), which include any of the following during a 75-g oral glucose tolerance test (OGTT): fasting plasma glucose ≥5.1 mmol/L, 1-hour plasma glucose ≥10.0 mmol/L, or 2-hour plasma glucose ≥8.5 mmol/L (18).

Participants were excluded from the study if they had pre-existing diabetes mellitus or any chronic maternal illness such as hypertension, thyroid disease, chronic kidney disease, or autoimmune disorders. Additional exclusion criteria included long-term use of medications known to affect glucose metabolism (e.g., aspirin or corticosteroids for ≥4 weeks), evidence of fetal distress or intrauterine fetal demise, or inability to complete either scheduled prenatal ultrasound evaluation. Poor-quality ultrasound imaging—defined as incomplete placental visualization, signal dropout >50%, or a signal-to-noise ratio (SNR) below 20 dB—also led to exclusion. Placental position (anterior, posterior, or lateral) was not used as an exclusion criterion, provided that the placenta was clearly visualized and met the aforementioned image quality requirements. Cases with incomplete or severely damaged placentas, extensive placental infarction, structural abnormalities such as placenta previa, implantation abnormalities including placenta accreta, increta, or percreta, placental abruption, vasa previa, or unsuccessful placental vascular casting perfusion, were excluded. Pregnant women who were unable to complete the relevant prenatal ultrasound examinations or postnatal vascular casting procedures were also excluded; the final group sizes reflect exclusions and missing/insufficient data after enrollment, including participants who were excluded due to incomplete multimodal ultrasound measurements or missing key clinical data (Figure 1).

Figure 1 Flowchart of the study. GDM, gestational diabetes mellitus.

Clinical and baseline data collection

Gestational age was determined using the last menstrual period or first-trimester crown-rump length (CRL) when menstrual history was unreliable. Pre-pregnancy body mass index (BMI) was obtained from clinical records within 3 months before conception or calculated retrospectively from early pregnancy weight (within 12 weeks). Obstetric history was confirmed by 2 independent investigators.

Fetal biometric measurements, including biparietal diameter (BPD), head circumference (HC), femur length (FL), abdominal circumference (AC), and amniotic fluid depth (AFD), were performed according to International Society of Ultrasound in Obstetrics and Gynecology (ISUOG) 2018 guidelines using a GE Voluson E10 ultrasound machine (C5-1 probe; GE Healthcare, Chicago, IL, USA). All values were averaged from 3 repeated measurements.

Multimodal ultrasound assessment

All participants underwent multimodal ultrasound assessment, including spectral Doppler, 3-dimensional power Doppler (3D-PDU), and SWE. To minimize inter-device variability, a standardized protocol was strictly followed: all hemodynamic and 3D power Doppler datasets were acquired exclusively using the GE Voluson E10 system, whereas all SWE measurements were performed solely on the Mindray Resona 8T system (Mindray, Shenzhen, China). No interchange of devices occurred for any specific parameter throughout the study period.

Spectral Doppler ultrasound

UtA, UA, and middle cerebral artery (MCA) waveforms were acquired using GE Voluson E10 (C5-1 probe) in obstetric mode. PI, RI, and systolic/diastolic ratio (S/D) were measured. Cerebroplacental ratio (CPR) was calculated as MCA-PI/UA-PI. Each vessel was sampled with a 2 mm volume gate at an insonation angle <30°, recording at least 5 steady waveforms. All values were averaged over three measurements (Figure 2).

Figure 2 Blood flow parameters in the UA, MCA, and UtA. (A) UA; (B) MCA; (C) UtA. ED, end-diastolic velocity; HR, heart rate; MD, mean diastolic velocity; PI, pulsatility index; PS, peak systolic velocity; RI, resistance index; Rt MCA, right middle cerebral artery; Rt Ut, right uterine artery; S/D, systolic/diastolic ratio; TA_max, time-averaged maximum velocity; UA, uterine artery; Umb, umbilical artery; UtA, uterine artery.

3D power Doppler imaging

Using a GE Voluson E10 with RM7C probe and Slow Flow HD mode, placental vascularization was assessed at the umbilical cord insertion site with a scanning angle of 45–65°. After optimizing image quality, VI, FI, and VFI were extracted using Virtual Organ Computer-aided AnaLysis (VOCAL) software (Samsung, Seoul, Korea) after manual contouring of regions of interest (ROI) (Figure 3).

Figure 3 Placental ultramicro blood flow measurements. (A) Two-dimensional ultrasound showing the intact placenta. (B) Slow Flow HD showing the intact placenta. (C) ROI delineation. (D) Extraction of blood flow parameters. ROI, region of interest.

SWE

SWE was performed using a Mindray Resona 8T ultrasound system (SC5-1U probe). Two placental regions were selected: the central third (excluding calcification and large vessels) and the peripheral 1 cm ring (4 quadrants). Emean (mean elasticity in kPa) was measured in both regions under minimal probe pressure (real-time pressure index <3). Quality control was ensured using the system’s Reliability (RLB) Map; only ROIs showing high reliability (green coding with RLB Index >95%) were accepted for analysis. A total of 3 valid measurements were taken for each region and averaged (Figure 4).

Figure 4 Evaluation of placental stiffness using ultrasound elastography. (A,B) Control group; (C,D) GDM group. GDM, gestational diabetes mellitus; RLB, reliability; SD, standard deviation; TSM, tissue stiffness measurement.

Statistical analysis

All analyses were performed using the software SPSS 26.0 (IBM Corp., Armonk, NY, USA). Data normality was assessed using the Kolmogorov-Smirnov test. Normally distributed variables were expressed as mean ± SD and compared using independent t-tests; non-normally distributed variables were expressed as median (P25, P75) and analyzed using the Mann-Whitney U test. Pearson or Spearman correlation coefficients were used as appropriate.

Univariate logistic regression was performed to screen for potential indicators. Variables that demonstrated statistical significance (P<0.05) in the univariate analysis were entered into the multivariate logistic regression model. To control for potential clinical confounding, maternal metabolic characteristics (pre-pregnancy BMI and HbA1c) were included as covariates in the final model alongside the ultrasound parameters. Receiver operating characteristic (ROC) curves were plotted to evaluate the diagnostic performance of individual and combined ultrasound parameters. The Youden index was used to determine optimal cut-off values. Inter- and intra-observer reproducibility was assessed using the intraclass correlation coefficient (ICC), with values ≥0.75 considered acceptable. A 2-tailed P value <0.05 was considered statistically significant.

Results

Baseline clinical characteristics

A total of 177 pregnant women were included in the final analysis, comprising 82 in the GDM group and 95 in the healthy control group. There were no statistically significant differences between the 2 groups in terms of maternal age, gestational age at examination, gravidity, parity, or fetal biometric parameters, including BPD, HC, FL, AC, and AFD (all P>0.05). However, the GDM group exhibited significantly higher pre-pregnancy BMI and HbA1c levels compared to the control group (P<0.001), indicating a higher metabolic burden (Table 1).

Comparison of placental ultrasound parameters

Multimodal ultrasound evaluation revealed distinct differences in placental function between the 2 groups. There were no significant intergroup differences in the Doppler parameters of the UA, MCA, or CPR (P>0.05). However, the UtA indices—including S/D ratio, PI, and RI—were significantly elevated in the GDM group (P<0.05), suggesting increased placental vascular resistance.

Moreover, 3D power Doppler parameters showed significantly lower values in the GDM group: VI, FI, and VFI were all reduced (P<0.05), indicating impaired placental perfusion. SWE demonstrated significantly increased placental stiffness in GDM patients, with both central and marginal Emean values higher than those in controls (P<0.01) (Table 2, Figure 5). To validate the ultrasound perfusion findings, placental vascular casting was performed. The morphological analysis of the casts demonstrated a strong correspondence with the 3D power Doppler indices. Specifically, vascular casts from the GDM group exhibited sparse capillary networks with reduced terminal villous branching and decreased vascular density compared to the dense, well-arborized networks observed in the control group. This structural evidence supports the functional reduction in VI, FI, and VFI detected by ultrasound.

Figure 5 Comparison of hemodynamic and elastography parameters between the 2 groups. (A-E) Between-group comparisons of ultrasound parameters for UA, MCA, UtA, 3D-PDU, and SWE. *, P<0.05; **, P<0.01; ***, P<0.001. 3D-PDU, 3-dimensional power Doppler ultrasound; GDM, gestational diabetes mellitus; MCA, middle cerebral artery; PI, pulsatility index; RI, resistance index; S/D, systolic/diastolic ratio; SWE, shear wave elastography; UA, uterine artery; UtA, uterine artery.

Logistic regression analysis of clinical and ultrasound variables

Univariate logistic regression analysis identified several parameters positively associated with GDM, including pre-pregnancy BMI, UtA-S/D, UtA-PI, UtA-RI, and both central and marginal placental elasticity (Emean). In contrast, VI, FI, and VFI were negatively associated with GDM risk (P<0.05).

Multivariate logistic regression analysis was subsequently performed to adjust for potential confounders (pre-pregnancy BMI and HbA1c). The results revealed that VFI, central Emean, and marginal Emean remained variables independently associated with GDM (P<0.05). Although UtA-S/D, UtA-PI, and UtA-RI were significant in the univariate model, they were not statistically significant after adjusting for clinical confounders in the multivariate analysis (P>0.05) (Table 3).

Diagnostic performance of ultrasound parameters for GDM

ROC curve analysis was used to evaluate the diagnostic value of individual and combined ultrasound parameters. Among the Doppler indices, UtA-PI showed the best diagnostic performance [area under the curve (AUC) =0.704], followed by UtA-S/D (AUC =0.671) and UtA-RI (AUC =0.655). Among perfusion indicators, VFI had the highest AUC [0.849], with a sensitivity of 78.18% and specificity of 81.25%.

Elasticity parameters also showed strong diagnostic performance. Central Emean yielded an AUC of 0.859 (sensitivity: 83.64%, specificity: 75.00%), whereas marginal Emean had an AUC of 0.845 (sensitivity: 87.27%, specificity: 72.92%). Importantly, a combined model incorporating VFI, central Emean, and marginal Emean achieved the highest diagnostic accuracy, with an AUC of 0.898, sensitivity of 80.00%, and specificity of 89.58%, indicating superior performance compared to any single parameter (Table 4, Figure 6).

Figure 6 ROC curve analysis of different ultrasound parameters for diagnosing GDM. (A-D) ROC curves for UtA (S/D, PI, RI), 3D-PDU (VI, FI, VFI), and SWE (placental center Emean; placental margin Emean), each tested alone and in combination for GDM diagnosis. 3D-PDU, 3-dimensional power Doppler ultrasound; FI, flow index; GDM, gestational diabetes mellitus; PI, pulsatility index; RI, resistance index; ROC, receiver operating characteristic; S/D, systolic/diastolic ratio; SWE, shear wave elastography; UtA, uterine artery; VFI, vascular flow index; VI, vascularization index.

Discussion

This study systematically evaluated placental structural and functional changes in pregnancies complicated by GDM using a multimodal ultrasound approach, including spectral Doppler, 3D-PDU, and SWE. The findings demonstrate that GDM is associated with increased UtA resistance, impaired microvascular perfusion, and increased placental stiffness, all of which reflect underlying pathophysiological disturbances such as trophoblast dysfunction, vascular remodeling impairment, and extracellular matrix (ECM) remodeling.

Our results showed significantly elevated UtA-PI, UtA-RI, and S/D ratio in the GDM group, consistent with previous studies linking GDM to impaired spiral artery remodeling and increased placental vascular resistance. Normally, physiological trophoblast invasion reduces UtA resistance as gestation progresses. However, in GDM, hyperglycemia and inflammatory responses (e.g., elevated cytokines, endothelial dysfunction) contribute to inadequate vascular transformation, leading to persistently high impedance flow patterns and suboptimal uteroplacental perfusion (19). These hemodynamic alterations have been associated with adverse outcomes, including fetal growth restriction, preeclampsia, and stillbirth (20,21).

In terms of placental microcirculation, our study found that GDM patients exhibited significantly reduced VI, FI, and VFI on 3D-PDU imaging. These findings indicate compromised microvascular density and perfusion, potentially attributable to imbalances in angiogenic factors such as vascular endothelial growth factor (VEGF) and placental growth factor (PLGF), or increased levels of anti-angiogenic molecules such as soluble fms-like tyrosine kinase 1 (sFlt-1). Impaired angiogenesis in GDM has been previously linked to reduced villous branching, increased infarction, and placental hypoxia, which in turn disrupt fetal oxygen and nutrient transport (22-24).

Additionally, SWE analysis revealed increased placental stiffness in both central and marginal zones among GDM patients, suggesting altered mechanical properties of the placental parenchyma. This may reflect excessive collagen deposition and ECM remodeling, which are driven by chronic inflammation and oxidative stress in hyperglycemic environments. Previous studies have reported positive correlations between placental Emean values and maternal HbA1c, reinforcing the hypothesis that metabolic dysregulation contributes to structural placental pathology (25-27).

Multivariate logistic regression in our study identified VFI, central and marginal Emean as independent variables associated with GDM, and ROC analysis demonstrated high diagnostic performance when these markers were combined (AUC =0.898). This suggests that integrating multimodal ultrasound parameters offers a sensitive and noninvasive approach to detecting early placental changes in GDM. Notably, VFI represents a composite metric reflecting both vascular density and perfusion intensity, whereas elasticity indices offer biomechanical insight into placental tissue integrity (28-30). In line with the concept of microvascular ‘vessel density’ assessment, a recent study reported high performance of the diffusion magnetic resonance imaging (MRI)-derived biomarker diffusion-derived ‘vessel density’ in discriminating placentas associated with pre-eclampsia from normal pregnancy, highlighting the potential complementary value of cross-modality placental vascular biomarkers (31). Together, they may serve as surrogate markers for early placental maladaptation in response to metabolic insult.

Our study has several strengths, including its prospective design, comprehensive imaging protocol, and incorporation of postnatal vascular casting as supporting qualitative evidence. However, several limitations should be acknowledged. First, the sample size, although adequate for initial analysis, may limit generalizability across different populations. Second, we acknowledge that as the ultrasound assessments were performed after GDM diagnosis, the statistical models presented reflect an association with the disease state and diagnostic discrimination of placental dysfunction, rather than a prospective prediction of GDM onset. Second, the study focused on the third trimester, and future work is needed to explore these markers in earlier gestation for true early prediction. Finally, no long-term neonatal outcomes were included, which may further support the clinical utility of these parameters. Finally, although vascular casting provided visual evidence of vascular impairment, we did not perform quantitative analysis of the casts in the current study due to the complexity of stereological measurements. Future studies will specifically focus on the quantitative correlation between ultrasound perfusion indices (VI, FI, VFI) and histological metrics, such as microvessel density and vascular branching indices, to further validate the biological basis of these sonographic biomarkers.

Conclusions

Our findings demonstrate that multimodal ultrasound—particularly the integration of spectral Doppler, 3D power Doppler, and SWE—offers a powerful, noninvasive means of assessing placental dysfunction in GDM. Parameters such as UtA resistance, VFI, and placental elasticity provide valuable insights into hemodynamic, microvascular, and biomechanical alterations in the GDM placenta. VFI and Emean values, individually and in combination, exhibited high diagnostic performance for GDM, with a combined AUC approaching 0.90. These results highlight the potential of multimodal sonographic assessment as a screening tool for accurate assessment of placental impairment in GDM pregnancies. Future studies with larger cohorts and earlier gestational evaluations are warranted to validate these findings and guide timely clinical interventions.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-aw-2103/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-aw-2103/dss

Funding: This study was supported by Nature 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-2025-aw-2103/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. The 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. XYYYE20220040), and all participants provided written informed consent.

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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