Investigating the early diagnostic value of popliteal artery wall shear stress in lower extremity arterial disease in type 2 diabetes patients using color doppler ultrasound combined with WSS quantitative analysis

Investigating the early diagnostic value of popliteal artery wall shear stress in lower extremity arterial disease in type 2 diabetes patients using color doppler ultrasound combined with WSS quantitative analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Investigating the early diagnostic value of popliteal artery wall shear stress in lower extremity arterial disease in type 2 diabetes patients using color doppler ultrasound combined with WSS quantitative analysis Yuxin Zhao, He Wang, Junyi Gu, YuQing Sun, Bo Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4712099/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Feb, 2026 Read the published version in BioMedical Engineering OnLine → Version 1 posted 14 You are reading this latest preprint version Abstract Background The global rise in type 2 diabetes mellitus (T2DM) has led to an epidemic of lower extremity arterial disease (LEAD), primarily caused by atherosclerosis and compounded by late detection and high treatment costs. Early detection is key for managing LEAD effectively. Color doppler ultrasound (DUS), a non-invasive and cost-effective technique, enhances early diagnosis through high-resolution imaging. Integrating DUS with proprietary MATLAB-based software for quantitative wall shear stress (WSS) analysis offers a non-invasive method to assess WSS. This approach targets the popliteal artery, using WSS as a reliable marker for early LEAD detection in T2DM patients. Methods This study included 202 patients clinically diagnosed with T2DM from March 2019 to November 2023 at Shanghai East Hospital, along with 69 healthy volunteers recruited during the same period. The T2DM group was further divided into three subgroups based on the intima-media thickness (IMT) of the popliteal artery: T2DM IMT normal group (IMT < 0.9 mm), T2DM IMT thickening group (1.0 ≤ IMT < 1.2 mm), and T2DM plaque formation group (IMT ≥ 1.2 mm). Using WSS quantitative analysis software, we calculated the average WSS of the popliteal artery and created two-dimensional WSS distribution maps, three-dimensional WSS spatial distribution maps, and WSS fusion images. Subsequently, we analyzed the WSS and its variation patterns among the control group, the T2DM group, and its various subgroups. Results In a study comparing T2DM patients to controls, T2DM groups showed significantly altered blood pressure, blood lipids, and blood viscosity, along with reduced WSS values, indicating advanced arterial damage. Specifically, WSS was lower in T2DM groups with normal and thickening IMT and those with plaque formation compared to controls. The optimal WSS cutoff for predicting LEAD was 1.82 dyne/cm², with a sensitivity of 68% and specificity of 83%. WSS negatively correlated with factors like age and disease duration, and positively with peak systolic velocity (PSV). Conclusions Non-invasive WSS measurement using DUS provides a valuable diagnostic tool for early LEAD detection in T2DM patients. Reduced WSS in the popliteal artery is a predictive marker of disease onset, offering potential for earlier intervention and better management of LEAD, ultimately improving patient outcomes. Type 2 Diabetes Mellitus Lower Extremity Arterial Disease Wall Shear Stress Color Doppler Ultrasound Popliteal Artery Atherosclerosis Figures Figure 1 Figure 2 Figure 3 Background In recent years, changes in the economy, diet, and social demographics have led to a global increase in the incidence of type 2 diabetes mellitus (T2DM), gradually reaching epidemic proportions[ 1 ]. Lower extremity arterial disease (LEAD) is one of the most common complications of T2DM, characterized by prolonged disease duration, high treatment costs, high rates of amputation, and mortality[ 2 ]. It has become a serious public health issue that severely threatens human health. Therefore, a safe, economical, and effective method for early diagnosis and prediction of LEAD in T2DM patients, enabling prompt clinical prevention and treatment, is crucial for reversing LEAD and reducing amputation and mortality rates among diabetic patients. In T2DM patients, LEAD primarily refers to ischemic diseases of the lower limbs caused by atherosclerosis[ 3 ]. The clinical manifestations of LEAD primarily depend on the degree of obstruction in the lower limb arteries. In the early stages of the disease, symptoms are often concealed, with many patients showing no clinical symptoms or only experiencing cold limbs and abnormal skin sensations[ 4 ]. As the vascular lesions progress, patients gradually develop knee pain, intermittent claudication, limb ulcers, and gangrene. Once LEAD progresses to limb ischemia, reversing it through conventional treatments such as medication and physical rehabilitation becomes difficult. Patients with moderate to severe conditions require vascular reconstruction surgery to restore blood supply, with the cumulative rate of amputation or death within three years post-surgery reaching up to high level[ 5 ]. Once LEAD occurs in T2DM patients, merely assessing the risk associated with existing atherosclerotic plaques or the degree of vascular narrowing is too late. At this point, clinical interventions for atherosclerotic plaques are very limited, and non-surgical treatments can only delay plaque progression. Currently, LEAD diagnosis primarily relies on imaging methods such as digital subtraction angiography (DSA), CT angiography (CTA), magnetic resonance angiography (MRA), and color doppler ultrasound (DUS)[ 6 ]. DSA, CTA, and MRA are invasive procedures associated with issues such as contrast agent allergies, radiation exposure, and high examination costs. Furthermore, DSA, CTA, and MRA can only diagnose LEAD when there is significant narrowing or obvious atherosclerotic plaques in the lower extremity vessels, making them less sensitive for early diagnosis. In contrast, DUS is non-invasive, safe, and inexpensive[ 7 ]. It produces pseudocolor blood flow images, allowing real-time and dynamic visualization of blood flow within the vessels. DUS has high spatial resolution, clear and detailed image content, and can display even small lesions and intima-media thickness (IMT) within the arteries. This provides a solid foundation for the early diagnosis and prediction of LEAD in T2DM patients. Wall shear stress (WSS) is the tangential frictional force exerted by the flow of fluid over the surface of a blood vessel per unit area, acting directly on the endothelial cells within arteries[ 8 ]. Variations in WSS can induce genetic, molecular, and cellular changes in the vessel wall. WSS is considered a primary signaling factor in the initiation of atherosclerosis, playing a crucial role from the onset of the disease process to plaque formation. Extensive experimental research suggests that low WSS is a significant factor in promoting the occurrence and development of atherosclerosis[ 9 ]. Due to its "invisible" and "intangible" nature, measuring WSS presents specific challenges. Currently, MRI and ultrasound are commonly used to measure indirect parameters such as blood flow velocity and vessel diameter within arteries, with WSS being roughly calculated using the Hagen-Poiseuille equation[ 10 ]. This platform has developed proprietary WSS quantitative analysis software using MATLAB, integrating WSS with color Doppler flow imaging (CDFI) technology. This integration enables non-invasive quantitative analysis of WSS, making it "visible," and offers new possibilities for the early diagnosis and prediction of LEAD in T2DM patients. LEAD in T2DM patients primarily refers to atherosclerotic lesions. LEAD is characterized by its staged and selective distribution. While small arteries in the lower limbs are prone to atherosclerosis, narrowing and occlusion of major vessels are the principal causes of adverse outcomes in LEAD [ 11 ]. Angiographic analysis of lower limb arteriosclerotic lesion distribution has shown that LEAD atherosclerotic lesions are more likely to occur at sites of stenosis or curvature in the major arteries of the lower limbs, consistent with the anatomical structure and physiological function of the popliteal artery. Traditionally, plaques also develop early at the bifurcation of the common femoral artery due to its unique physiological structure, but these plaques often exhibit independent characteristics and have a weak correlation with LEAD progression[ 12 ]. Moreover, the popliteal artery, as the "blood engine" of the calf, directly gives rise to the major arteries of the calf—the anterior tibial, posterior tibial, and peroneal arteries—making it highly significant for LEAD in terms of hemodynamics[ 13 ]. Anatomically, the popliteal artery passes through the adductor hiatus and runs behind the knee joint capsule and popliteus muscle, featuring a deep position, regular shape, and fixed, straight course. This makes it closer to the ideal vascular structure assumed in the Hagen-Poiseuille formula used in WSS quantitative analysis software, minimizing interference from other factors on the WSS of the popliteal artery. Therefore, popliteal artery WSS is a stable and continuous indicator for evaluating LEAD. This study selects popliteal artery WSS as a window to explore early diagnosis and prediction of LEAD in T2DM patients. Results Clinical & Laboratory results Clinical and laboratory results for the control group and the T2DM group are summarized in Table 1 . Significant differences were observed in SBP, and BMI, with higher values in the T2DM group, and levels of HCT, Fib, TC, TG, LDL-C, FPG, 2h-PBG, HbA1c, and GA were also significantly higher compared to the control group (P 0.05) Table 1 Clinical and laboratory Characteristics in the Control Group and the T2DM Group Variable Control group T2DM group P-value Age (years) 58.22 ± 10.35 61.56 ± 13.35 0.059 SBP (mmHg) 119.56 ± 12.28 126.13 ± 14.26 < 0.05 DBP(mmHg) 73.01 ± 8.20 79.58 ± 48.46 0.264 MAP(mmHg) 88.53 ± 8.50 95.10 ± 33.37 0.107 BMI (kg/m²) 22.91 ± 3.00 25.16 ± 4.37 < 0.05 HCT(%) 38.79 ± 3.04 40.85 ± 4.66 < 0.05 Fib(g/l) 2.77 ± 0.59 3.11 ± 1.02 < 0.05 TC(mmol/l) 3.45 ± 0.93 3.85 ± 1.17 < 0.05 TG(mmol/l) 1.14 ± 0.43 1.59 ± 1.08 < 0.05 HDL-C(mmol/l) 1.47 ± 0.45 1.23 ± 0.56 < 0.05 LDL-C(mmol/l) 2.13 ± 0.77 2.40 ± 1.05 < 0.05 Cr(mmol/l) 68.86 ± 12.63 72.79 ± 23.89 0.193 FPG(mmol/l) 5.39 ± 0.75 8.63 ± 2.79 < 0.05 2h-PBG(mmol/l) 6.66 ± 0.69 14.40 ± 4.57 < 0.05 HbA1c(%) 5.36 ± 0.57 9.48 ± 2.47 < 0.05 GA(%) 14.76 ± 1.45 25.44 ± 9.88 < 0.05 Clinical and laboratory results for the control group, T2DM IMT normal group, T2DM IMT thickening group, and T2DM plaque formation group are summarized in Table 2 . Compared to the control group, the T2DM IMT normal group showed increased BMI, HCT, TC, TG, FPG, 2h-PBG, HbA1c, and GA, while age was decreased (P < 0.05). The T2DM IMT thickening group, compared to the control group, exhibited increases in BMI, HCT, FPG, 2h-PBG, HbA1c, and GA, with a decrease in HDL-C (P < 0.05). The T2DM plaque formation group, in comparison to the control group, showed increases in age, SBP, BMI, HCT, TG, FPG, 2h-PBG, HbA1c, and GA, with a decrease in HDL-C (P < 0.05). When comparing the T2DM IMT thickening group to the T2DM IMT normal group, there were increases in age and disease duration, with a decrease in TG (P < 0.05). Comparing the T2DM with plaque formation group to the T2DM IMT normal group, there were increases in age and disease duration (P < 0.05). When comparing the T2DM with plaque formation group to the T2DM IMT thickening group, there was an increase in age (P 0.05). Table 2 Clinical and laboratory Characteristics in the Control Group, T2DM IMT normal group, T2DM IMT thickening group, and T2DM plaque formation group Variable Control group T2DM group IMT normal IMT thickening plaque formation Age (years) 58.22 ± 10.35 b 50.04 ± 13.6 c 61.53 ± 8.83 b 67.17 ± 12.75 a Disease duration (years) - 8.27 ± 8.10 b 12.28 ± 7.54 a 14.35 ± 8.75 a SBP (mmHg) 119.56 ± 12.28 b 124.16 ± 13.54 ab 124.45 ± 12.46 ab 127.93 ± 15.06 a DBP(mmHg) 73.01 ± 8.20 a 78.81 ± 8.47 a 75.51 ± 7.29 a 82.70 ± 68.77 a MAP(mmHg) 88.53 ± 8.50 a 93.92 ± 9.39 a 91.82 ± 8.08 a 97.77 ± 46.78 a BMI (kg/m²) 22.91 ± 3.00 b 25.76 ± 5.0 a 24.80 ± 5.07 a 25.06 ± 3.46 a HCT(%) 38.79 ± 3.04 b 41.79 ± 5.26 a 41.12 ± 3.95 a 41.00 ± 4.64 a Fib(g/l) 2.77 ± 0.59 a 3.03 ± 0.91 a 3.13 ± 0.98 a 3.14 ± 1.07 a TC(mmol/l) 3.45 ± 0.93 b 4.06 ± 1.12 a 3.76 ± 1.22 ab 3.81 ± 1.15 ab TG(mmol/l) 1.14 ± 0.43 c 1.86 ± 1.02 a 1.36 ± 0.67 bc 1.59 ± 1.25 ab HDL-C(mmol/l) 1.47 ± 0.45 a 1.25 ± 0.63 ab 1.21 ± 0.44 b 1.23 ± 0.59 b LDL-C(mmol/l) 2.13 ± 0.77 a 2.48 ± 0.98 a 2.41 ± 1.06 a 2.35 ± 1.07 a Cr(mmol/l) 68.86 ± 12.63 a 67.45 ± 21.19 a 69.89 ± 14.74 a 76.97 ± 28.00 a FPG(mmol/l) 5.39 ± 0.75 b 8.82 ± 2.2.79 a 8.49 ± 2.39 a 8.61 ± 2.98 a 2h-PBG(mmol/l) 6.66 ± 0.69 b 14.31 ± 4.38 a 13.74 ± 3.83 a 14.81 ± 4.96 a HbA1c(%) 5.36 ± 0.57 b 8.95 ± 2.57 a 9.53 ± 2.38 a 9.72 ± 2.42 a GA(%) 14.76 ± 1.45 b 24.61 ± 9.21 a 25.09 ± 8.16 a 26.04 ± 10.93 a a, b, c The same letters indicate no significant difference between groups (P > 0.05), while different letters indicate a significant difference between groups (P < 0.05). Popliteal artery ultrasound & WSS quantitative analysis results Popliteal artery ultrasound parameters and quantitative WSS for the control group and the T2DM group are shown in Table 3 . Compared to the control group, the T2DM group showed a significant reduction in PSV and WSS (P 0.05). Table 3 Popliteal artery ultrasound and WSS parameters in the Control Group and the T2DM Group Variable Control group T2DM group P-value R(mm) 4.56 ± 0.79 4.75 ± 0.89 0.114 PSV(cm/s) 65.35 ± 13.94 51.31 ± 14.81 < 0.05 WSS(dyne/cm²) 2.36 ± 0.43 1.81 ± 0.53 < 0.05 As shown in Table 4 , compared to the control group, the T2DM IMT normal group exhibited a significant reduction in WSS (P < 0.05). Both PSV and WSS were significantly reduced in the T2DM IMT thickening group compared to the control group (P < 0.05). Similarly, the T2DM plaque formation group exhibited significant reductions in PSV and WSS compared to the control group (P < 0.05). When comparing the T2DM IMT thickening group to the T2DM IMT normal group, a significant decrease in WSS was observed (P < 0.05). The T2DM plaque formation group showed significant reductions in PSV and WSS compared to the T2DM IMT normal group (P < 0.05). Additionally, PSV was significantly lower in the T2DM plaque formation group compared to the T2DM IMT thickening group (P 0.05). Table 4 Popliteal artery ultrasound and WSS parameters in the Control Group, T2DM IMT normal group, T2DM IMT thickening group, and T2DM plaque formation group Variable Control group T2DM group IMT normal IMT thickening plaque formation R(mm) 4.56 ± 0.79 a 4.70 ± 0.81 a 4.86 ± 0.78 a 4.71 ± 0.97 a PSV(cm/s) 65.35 ± 13.94 a 60.15 ± 12.16 ab 58.53 ± 13.30 b 43.03 ± 11.83 c WSS(dyne/cm²) 2.36 ± 0.43 a 2.11 ± 0.60 b 1.81 ± 0.44 c 1.67 ± 0.48 c a, b, c The same letters indicate no significant difference between groups (P > 0.05), while different letters indicate a significant difference between groups (P < 0.05). Popliteal artery WSS predicts the occurrence of LEAD in T2DM patients In the T2DM group, patients with IMT thickening and plaque formation were defined as positive, while those with normal IMT were defined as negative. A ROC curve for WSS in the popliteal artery was constructed, showing an area under the curve (AUC) of 0.81 (P < 0.05) ( Table 5 and Fig. 2 ) . Based on statistical analysis, the Youden index was identified as the optimal cutoff point, with a calculated popliteal artery WSS Youden index of 1.82 dyne/cm². This value is considered the best cutoff for predicting the occurrence of LEAD in T2DM patients ( Table 6 ) . Table 5 AUC for LEAD prediction in T2DM patients through popliteal artery WSS analysis AUC Standard Error P-value 95% CI for OR Lower Upper Lower Upper 0.81 0.026 < 0.001 0.76 0.86 Table 6 The optimal cutoff value for predicting LEAD in T2DM patients using popliteal artery WSS Popliteal Artery WSS Sensitivity Specificity Youden's Index 1.82 0.68 0.83 0.50 Factors correlated with popliteal artery WSS in T2DM group In the T2DM group, popliteal artery WSS was found to be significantly negatively correlated with age, duration of diabetes, and popliteal artery IMT, and positively correlated with PSV (P < 0.001). WSS showed a negative correlation with GA (P < 0.01). Additionally, WSS exhibited weak positive correlations with TC, TG, HCT, and a weak negative correlation with HbA1c (P 0.05) ( Table 7 and Fig. 3 ) . Table 7 Analysis of the correlation between different parameters and WSS in T2DM group Variable Age Duration SBP DBP MAP (years) (years) (mmHg) (mmHg) (mmHg) WSS r -0.50 *** -0.34 *** -0.10 0.01 0 P^ <0.001 <0.001 0.18 0.84 0.99 Variable BMI TC TG HDL-C LDL-C (kg/m²) (mmol/l) (mmol/l) (mmol/l) (mmol/l) WSS r 0.08 0.14 * 0.17 * -0.1 0.09 P^ 0.26 0.04 0.01 0.17 0.21 Variable Cr HCT Fib FPG PBG (mmol/l) (%) (%) (mmol/l) (mmol/l) WSS r -0.02 0.18 * -0.09 0.09 0.02 P^ 0.75 0.01 0.23 0.21 0.82 Variable HbAlc GA IMT R PSV (%) (%) (mm) (mm) (cm/s) WSS r -0.21 * -0.18 ** -0.53 *** -0.04 0.57 *** P^ 0.01 <0.01 <0.001 0.61 <0.001 r correlation coefficient; ^ denotes Pearson correlation analysis; *P<0.05; **P<0.01; ***P<0.001 Multiple linear regression analysis of popliteal artery WSS in the T2DM group Using WSS as the dependent variable, and age, duration of disease, TC, TG, HCT, HbA1c, GA, popliteal artery IMT, and PSV as independent risk factors, a multiple linear regression analysis was conducted. The positive correlation coefficient R = 0.707 indicates a closely linear relationship and good model fit. WSS is positively correlated with PSV and negatively correlated with age, duration of disease, and popliteal artery IMT, all of which can serve as independent influencing factors affecting WSS (P < 0.05) ( Tables 8 and 9 ) . Table 8 multiple linear regression model R R 2 Adjusted R 2 Se 0.707a 0.499 0.476 0.387 a Predictive variables include age, duration of disease, TC, TG, HCT, HbA1c, GA), popliteal artery IMT, PSV Table 9 multiple linear regression coefficients of variables Variable Standardized Coefficients t P-value Age(years) -0.246 -4.125 <0.05 Duration(years) -0.15 -2.585 <0.05 TC(mmol/l) 0.028 0.51 0.611 TG(mmol/l) 0.094 1.738 0.084 HCT(%) -0.04 -0.731 0.466 HbA1c(%) -0.109 -1.451 0.148 GA(%) -0.004 -0.048 0.962 IMT(mm) -0.23 -3.959 <0.05 PSV(cm/s) 0.312 5.076 <0.05 Discussion Diabetes is a systemic metabolic disorder characterized primarily by chronic hyperglycemia, with complications causing the main damage. T2DM accounts for 90% of all diabetes cases. The risk of LEAD in patients with T2DM is twice that of non-diabetic individuals[ 14 ]. LEAD in T2DM primarily refers to atherosclerotic changes, which, once established, are difficult to reverse. The treatment options available clinically are very limited, generally only capable of slowing the progression of atherosclerotic plaques[ 15 ]. Importantly, the early symptoms of LEAD are mild and insidious, and the disease progression often coincides with the progression of atherosclerosis[ 16 ]. Once the stage of limb ischemia is reached, the patient's condition can deteriorate rapidly, and the means of clinical intervention at this stage are extremely limited. Moderate to severe patients require vascular reconstruction to restore blood supply, and nearly half face amputation or mortality within three years post-surgery[ 17 ]. Currently, early diagnosis and prediction of atherosclerotic changes are the core components in the treatment of LEAD. T2DM affects all body systems, leading to abnormalities in blood glucose, blood pressure, blood lipids, and blood viscosity[ 18 ]. By comparing clinical data of the control group and the T2DM group, we found that T2DM patients had higher FPG, 2h-PBG, HbA1c, GA, SPB, HCT, Fib, TC, TG, LDL-C, and FPG than the normal group, while HDL-C was lower (P < 0.05). Comparing three T2DM subgroups: IMT normal group, IMT thickening group, and plaque formation group, we found no significant statistical difference or linear relationship in FPG, 2h-PBG, HbA1c, GA, SPB, DBP, MAP, HCT, Fib, TC, and LDL-C (P > 0.05). However, HDL-C in the IMT thickening and plaque formation groups was lower than in the IMT normal group (P < 0.05), with no significant linear relationship. Although T2DM patients show significant changes in blood glucose, blood pressure, blood lipids, and blood viscosity compared to normal individuals, these do not fully reflect the progression of LEAD atherosclerosis. Currently, non-invasive imaging techniques like ultrasound can detect atherosclerotic plaques but cannot predict or dynamically assess atherosclerosis. Therefore, we need other sensitive and accurate indicators to predict the occurrence and development of atherosclerosis[ 19 ]. Vascular ultrasound is the most common method for measuring vascular IMT and plaques. Vascular ultrasound was used to record the diameter, IMT, PSV, and RI of the popliteal artery, and WSS was calculated using Hagen-Poiseuille. Compared to the control group, the T2DM group had significantly lower PSV and WSS (P 0.05). All three T2DM subgroups had lower WSS than the control group (P < 0.05), indicating that WSS is a sensitive indicator for early assessment of lower limb atherosclerosis. Among the three T2DM subgroups (IMT normal group, IMT thickening group, and plaque formation group), the plaque formation group had significantly lower PSV compared to the IMT normal group and IMT thickening groups (P < 0.05). A decrease in blood flow velocity in the popliteal artery of the lower limb indicates hemodynamic changes. The center of the vessel has the highest blood flow velocity, which gradually decreases toward the vessel wall, forming a velocity gradient. When PSV decreases, the velocity gradient is affected, and the velocity near the vessel wall also decreases, which mechanically aligns with changes in WSS[ 20 ]. The IMT thickening group and the plaque formation group had lower WSS compared to the IMT normal group (P 0.05). In T2DM patients, WSS decreased even when the popliteal artery IMT was normal, and further decreased when IMT thickened. WSS, the mechanical force exerted by blood flow on the vessel wall, acts parallel to the vessel wall. WSS directly affects endothelial cells, having both physiological and pathological significance for the vessel wall. Hemodynamic changes drive alterations in WSS. This study shows that popliteal artery WSS decreases in T2DM patients before the onset of LEAD. When LEAD progresses to the IMT thickening stage, popliteal artery WSS further decreases, providing a hemodynamic basis for atherosclerotic plaque formation. Thus, a decrease in popliteal artery WSS can serve as an early warning indicator for LEAD before atherosclerosis occurs. Additionally, in this study, although WSS in the T2DM plaque formation group was lower than in the T2DM IMT thickening group, the difference was not significant (P > 0.05). The WSS quantitative analysis framework used in this study was developed using MATLAB. Our software extracts color flow signals from CDFI using digital image processing technology, converts color data of each pixel in CDFI to blood flow velocity data, and then converts it to shear stress data based on the WSS definition formula. Theoretically, it can analyze the WSS distribution in any vascular region of interest. This has been confirmed in previous studies by Wang et al[ 21 ]. However, the presence of plaques creates irregular and uneven surface structures in the vessel, significantly disrupting the ideal straight, regular, and rigid vessel structure required by the Hagen-Poiseuille formula for calculating WSS, causing the WSS in the T2DM plaque formation group to deviate from the ideal state[ 22 ]. In this study, WSS decreased even when the popliteal artery IMT was normal in T2DM patients. The area under the ROC curve (AUC) for predicting LEAD in T2DM patients using popliteal artery WSS was 0.81 (P < 0.05). Based on the Youden index, the optimal cutoff value for popliteal artery WSS was determined to be 1.82 dyne/cm², with a sensitivity of 68% and a specificity of 83%. When popliteal artery WSS is ≤ 1.82 dyne/cm², the likelihood of LEAD in T2DM patients is high, making it a critical indicator for LEAD occurrence. This study analyzed the correlation and multiple regression between popliteal artery WSS and various variables in the diabetic group, finding that WSS is closely related to indicators causing severe endothelial dysfunction, changing blood viscosity, and affecting vascular compliance. In the T2DM group, WSS was negatively correlated with age, disease duration, GA, HbA1c, and popliteal artery IMT, and positively correlated with PSV, TC, TG, and HCT (P < 0.05). PSV, age, disease duration, and popliteal artery IMT were independent risk factors affecting WSS (P < 0.05). WSS, the tangential force directly contacting endothelial cells parallel to the vessel wall, can be influenced by blood flow velocity, blood viscosity, vascular compliance, and endothelial cell function. Age and diabetes duration are independent risk factors for WSS. As age increases, endothelial regulation and vascular compliance tend to show varying degrees of impairment. When blood flows through relatively straight and regular vessels, it can be considered a Newtonian fluid. In this case, WSS generated by adjacent fluid layers is proportional to the velocity gradient in the perpendicular direction. Therefore, PSV, an independent risk factor for WSS, can partly reflect WSS magnitude. When blood flow velocity decreases, WSS decreases, prolonging blood component residence time along the vessel wall, which low WSS more easily promotes atherosclerosis formation[ 23 ]. Identifying independent risk factors for WSS can help clinicians further investigate lipid levels, blood viscosity, and other WSS-related indicators in T2DM patients with reduced popliteal artery WSS for early clinical treatment. Patients with independent risk factors for WSS can be considered high-risk for LEAD and should undergo regular screening. Conclusion Using non-invasive, quantitative analysis of popliteal artery WSS in T2DM patients with color Doppler ultrasound provides imaging methods and evidence for the early diagnosis of LEAD; the reduction in popliteal artery shear stress occurs before the onset of LEAD in T2DM patients, showing good predictive value for the occurrence of such diseases. It is expected to become a new indicator for clinical prevention and early diagnosis and treatment of LEAD in T2DM patients. Methods Study participants and grouping From March 2019 to November 2023, T2DM patients treated at Shanghai East Hospital and healthy volunteers from a health examination center were selected and divided into two groups: the type 2 diabetes mellitus group (T2DM group) and the control group (Control group). This study was approved by the Research Ethics Board of East Hospital, Tongji University (Shanghai, China) [Approval 2017 (No.030)]. (1) T2DM group: Participants in this group met the 1999 WHO criteria for diabetes diagnosis and classification[ 24 ]; those with type 1 diabetes, secondary diabetes, primary hyperlipidemia, hypertension, smoking history, lower limb arterial surgery, severe functional or organic heart disease, renal failure, stroke, or malignant tumors were excluded. Patients who had recently taken lipid-lowering, anticoagulant, or antiplatelet aggregation medications, or had recent adjustments in their diabetes treatment plans were also excluded. A total of 202 T2DM patients were selected for the study, including 126 males and 76 females, with an average age of 61.56 ± 13.35 years. Ultrasound was used to measure the intima-media thickness (IMT) of the popliteal artery. Based on the IMT, the T2DM group was divided into: T2DM IMT normal group: IMT < 0.9 mm (48 cases, 24 males and 24 females, average age 50.04 ± 13.6 years), T2DM IMT thickening group: 0.9 ≤ IMT < 1.2 mm (55 cases, 38 males and 17 females, average age 61.53 ± 8.83 years), and T2DM Plaque formation group: IMT ≥ 1.2 mm (99 cases, 64 males and 35 females, average age 67.17 ± 12.75 years). (2) Control group: Participants with a history of diabetes, hypertension, primary hyperlipidemia, lower limb vascular disease, lower limb surgery, heart failure, organic heart disease, renal failure, stroke, or malignant tumors were excluded, as well as those who had recently taken any medications. A total of 69 healthy volunteers were selected for the study, including 32 males and 37 females, with an average age of 58.22 ± 10.35 years. Clinical Data & Laboratory Findings Clinical data collected included participant records of gender, age, medical history, and duration of diabetes for the T2DM group. Mean arterial pressure (MAP), body mass index (BMI), systolic blood pressure (SBP), and diastolic blood pressure (DBP) were measured for all subjects in both the control group and the T2DM group. The formula for calculating MAP is: MAP = (SBP + 2×DBP) / 3. SBP and DBP were manually measured using a mercury sphygmomanometer after a 10-minute rest period. All participants fasted for 12 hours and had venous blood drawn the following morning to test for glycated hemoglobin (HbA1c), fasting plasma glucose (FPG), two-hour postprandial blood glucose (2h-PBG), glycated albumin (GA), fibrinogen (Fib), hematocrit (HCT), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and creatinine (Cr). Inspection methods The Philips EPIQ7 system (Philips Medical Systems, Andover, MA, USA) with an L11-3 linear array transducer was used in our study. Before the examination, subjects were required to rest for 10 minutes, maintain a constant room temperature of 24°C, and fully expose their lower limbs. During the examination, subjects were instructed to lie in a prone position, breathe calmly, and relax their knee joints. Continuous scanning of the popliteal artery was performed from the adductor hiatus to the origin of the anterior tibial artery, ensuring an image frame rate of ≥ 30 frames/s. To ensure accuracy, all data were collected at least three times. Parameters of popliteal artery ultrasound The conventional data of popliteal artery ultrasound was collected. The transducer was positioned transversely, clearly displaying the anterior and posterior walls of the popliteal artery. At the thickest part of the IMT, the vascular image was locally magnified, and measurements were taken at the thickest part of the popliteal artery's posterior wall IMT and its maximum diameter. The transducer was then rotated to the longitudinal plane at the thickest part of the IMT, ensuring the vessel course was straight. The ultrasound plane passed through the vessel's central axis, clearly displaying the anterior and posterior walls of the popliteal artery, with the thickest part of the IMT centered in the image. Color Doppler flow was initiated, with the beam angle to the popliteal artery kept below 60°. The velocity range and sample box size were adjusted to 2 cm, and the blood flow gain was regulated to ensure the blood flow filled the lumen without aliasing or overflow. The peak systolic velocity (PSV) of the popliteal artery during systole, along with a 5-second dynamic color Doppler flow image of the lower limb popliteal artery, were collected, with the dynamic images stored in DICOM format. WSS quantitative analysis by CDFI Long-axis dynamic images of the popliteal artery spanning at least three cardiac cycles were obtained, and all CDFI images were saved in DICOM format (image matrix: 600×800; pixel spacing: 0.085×0.085 mm) and imported into a WSS (Wall Shear Stress) quantitative analysis software on the MATLAB platform (The Mathworks Inc., Natick, MA, USA). The software determined the coordinates of the color Doppler velocity scale from the image information to define the color blood flow pixels and the wall grayscale pixels. It converted the color pixel gradient into a velocity gradient based on the velocity scale. This allowed for the calculation of WSS values for each pixel on the image. The calculation formula for WSS is \(\:{\tau\:}_{w}=\mu\:{\gamma\:}_{w}=\mu\:\frac{du}{dr}|r=wall\) .In this equation, \(\:{\tau\:}_{w}\) is the wall shear stress, which is the shear stress near the vessel wall. \(\:\mu\:\) is the viscosity of the blood, and \(\:\frac{du}{dr}\) represents the velocity gradient (or shear rate). \(\:r=wall\) means that \(\:r\) is near the boundary of tube. It was important to note that this formula assumed that blood is a Newtonian fluid. The WSS analysis software generated fusion images of WSS Doppler blood flow distribution, segmentation maps, and two-dimensional WSS distribution maps, further enabling the creation of three-dimensional blood flow velocity profiles and three-dimensional spatial distribution maps of WSS.As shown in Fig. 1 . This ultimately facilitated the quantitative analysis and visualization of WSS. Statistical analysis Data statistical analysis was conducted using SPSS 24.0. (SPSS Inc., Chi cago, IL, USA). Quantitative and count data were expressed as mean ± standard deviation (x̄ ± s) and number of cases, respectively. The Shapiro-Wilk test was used to assess the distribution of continuous variables, while group comparisons were made using t-tests, non-parametric tests, chi-square (χ²) tests, and LSD multiple comparison methods. The predictive value of popliteal artery WSS for LEAD in patients with T2DM was analyzed using ROC curve analysis. The correlation between variables was assessed using Pearson correlation analysis. Multivariate linear regression was employed to analyze the relationship between popliteal artery WSS and various variables. A p-value < 0.05 was considered statistically significant. Declarations Ethics approval and consent to participate The study was performed in accordance with the Declaration of Helsinki. The Ethical approval of this study was provided by The East Hospital, Tongji University (Shanghai, China) [Approval 2017 (No.030)]. Written informed consent for publication was obtained from all participants Consent for publication All authors approved the final manuscript and the submission to this journal Availability of data and materials The datasets used during the current study are available from the corresponding author on reasonable request. Competing interests Competing interests the authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article Funding This study was funded by Important Weak Subject Construction Project of the Pudong Health and Family Planning Commission of Shanghai (Grant No. PWZbr2017-09) Authors’ contributions Performed the literature review: YXZ; Carried out Ultrasound measurements: JYG, YQS; Checked the validity of data: BZ, YXZ, HW; Data analysis: YXZ, HW; Supported the experiments financially: BZ; Wrote and revised the manuscript: BZ, YXZ, HW. All authors read and approved the final manuscript. Competing Interests The authors declared that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported. Acknowledgements Not applicable. References Tinajero MG, Malik VS: An Update on the Epidemiology of Type 2 Diabetes: A Global Perspective. Endocrinol Metab Clin North Am 2021, 50(3):337-355. Nativel M, Potier L, Alexandre L, Baillet-Blanco L, Ducasse E, Velho G, Marre M, Roussel R, Rigalleau V, Mohammedi K: Lower extremity arterial disease in patients with diabetes: a contemporary narrative review. Cardiovasc Diabetol 2018, 17(1):138. Schneider C, Stratman S, Kirsner RS: Lower Extremity Ulcers. Med Clin North Am 2021, 105(4):663-679. Behrendt CA, Lareyre F, Raffort J: Impact of Diabetes on Outcomes of Patients with Lower Extremity Artery Disease. Angiology 2022, 73(6):493-494. Sen P, Demirdal T, Emir B: Meta-analysis of risk factors for amputation in diabetic foot infections. Diabetes Metab Res Rev 2019, 35(7):e3165. Pomposelli F: Arterial imaging in patients with lower extremity ischemia and diabetes mellitus. J Vasc Surg 2010, 52(3 Suppl):81s-91s. Andersen CA: Noninvasive assessment of lower extremity hemodynamics in individuals with diabetes mellitus. J Vasc Surg 2010, 52(3 Suppl):76s-80s. Hogan B, Shen Z, Zhang H, Misbah C, Barakat AI: Shear stress in the microvasculature: influence of red blood cell morphology and endothelial wall undulation. Biomech Model Mechanobiol 2019, 18(4):1095-1109. Papaioannou TG, Karatzis EN, Vavuranakis M, Lekakis JP, Stefanadis C: Assessment of vascular wall shear stress and implications for atherosclerotic disease. Int J Cardiol 2006, 113(1):12-18. Zhang B, Sun Y, Xia L, Gu J: Time-dependent flow velocity measurement using two-dimensional color Doppler flow imaging and evaluation by Hagen-Poiseuille equation. Australas Phys Eng Sci Med 2015, 38(4):755-766. Chen Q, Shi Y, Wang Y, Li X: Patterns of disease distribution of lower extremity peripheral arterial disease. Angiology 2015, 66(3):211-218. Zemaitis MR, Boll JM, Dreyer MA: Peripheral Arterial Disease. In StatPearls. Treasure Island (FL) ineligible companies. Disclosure: Julia Boll declares no relevant financial relationships with ineligible companies. Disclosure: Mark Dreyer declares no relevant financial relationships with ineligible companies.: StatPearls Publishing Copyright © 2024, StatPearls Publishing LLC.; 2024. Kropman RH, Kiela G, Moll FL, de Vries JP: Variations in anatomy of the popliteal artery and its side branches. Vasc Endovascular Surg 2011, 45(6):536-540. Zheng Y, Ley SH, Hu FB: Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat Rev Endocrinol 2018, 14(2):88-98. Conte MS, Pomposelli FB, Clair DG, Geraghty PJ, McKinsey JF, Mills JL, Moneta GL, Murad MH, Powell RJ, Reed AB, et al: Society for Vascular Surgery practice guidelines for atherosclerotic occlusive disease of the lower extremities: management of asymptomatic disease and claudication. J Vasc Surg 2015, 61(3 Suppl):2s-41s. Criqui MH, Matsushita K, Aboyans V, Hess CN, Hicks CW, Kwan TW, McDermott MM, Misra S, Ujueta F: Lower Extremity Peripheral Artery Disease: Contemporary Epidemiology, Management Gaps, and Future Directions: A Scientific Statement From the American Heart Association. Circulation 2021, 144(9):e171-e191. Whicher CA, O'Neill S, Holt RIG: Diabetes in the UK: 2019. Diabet Med 2020, 37(2):242-247. Ma CX, Ma XN, Guan CH, Li YD, Mauricio D, Fu SB: Cardiovascular disease in type 2 diabetes mellitus: progress toward personalized management. Cardiovasc Diabetol 2022, 21(1):74. Ibanez B, Fernández-Ortiz A, Fernández-Friera L, García-Lunar I, Andrés V, Fuster V: Progression of Early Subclinical Atherosclerosis (PESA) Study: JACC Focus Seminar 7/8. J Am Coll Cardiol 2021, 78(2):156-179. Peiffer V, Sherwin SJ, Weinberg PD: Does low and oscillatory wall shear stress correlate spatially with early atherosclerosis? A systematic review. Cardiovasc Res 2013, 99(2):242-250. Wang C, Chen M, Liu SL, Liu Y, Jin JM, Zhang YH: Spatial distribution of wall shear stress in common carotid artery by color Doppler flow imaging. J Digit Imaging 2013, 26(3):466-471. Rouleau L, Farcas M, Tardif JC, Mongrain R, Leask RL: Endothelial cell morphologic response to asymmetric stenosis hemodynamics: effects of spatial wall shear stress gradients. J Biomech Eng 2010, 132(8):081013. Glagov S, Zarins C, Giddens DP, Ku DN: Hemodynamics and atherosclerosis. Insights and perspectives gained from studies of human arteries. Arch Pathol Lab Med 1988, 112(10):1018-1031. Alberti KG, Zimmet PZ: Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. Diabet Med 1998, 15(7):539-553. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 26 Feb, 2026 Read the published version in BioMedical Engineering OnLine → Version 1 posted Editorial decision: Revision requested 06 Aug, 2025 Reviews received at journal 29 Mar, 2025 Reviews received at journal 26 Mar, 2025 Reviews received at journal 25 Mar, 2025 Reviewers agreed at journal 19 Mar, 2025 Reviewers agreed at journal 18 Mar, 2025 Reviewers agreed at journal 17 Mar, 2025 Reviewers agreed at journal 17 Mar, 2025 Reviews received at journal 16 Mar, 2025 Reviewers agreed at journal 16 Mar, 2025 Reviewers invited by journal 16 Mar, 2025 Editor assigned by journal 16 Jul, 2024 Submission checks completed at journal 16 Jul, 2024 First submitted to journal 09 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4712099","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":337365406,"identity":"1d42986b-4a79-451f-84d8-d1b368b2d6b6","order_by":0,"name":"Yuxin Zhao","email":"","orcid":"","institution":"Shanghai East Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yuxin","middleName":"","lastName":"Zhao","suffix":""},{"id":337365407,"identity":"a79d2009-9431-4700-ad65-b3a47f428ce2","order_by":1,"name":"He Wang","email":"","orcid":"","institution":"Shanghai East Hospital","correspondingAuthor":false,"prefix":"","firstName":"He","middleName":"","lastName":"Wang","suffix":""},{"id":337365408,"identity":"1f925ac9-79fe-499f-a19e-706f47095a03","order_by":2,"name":"Junyi Gu","email":"","orcid":"","institution":"Shanghai East Hospital","correspondingAuthor":false,"prefix":"","firstName":"Junyi","middleName":"","lastName":"Gu","suffix":""},{"id":337365409,"identity":"bd87e8d0-738b-41ab-bd6b-7abf9bd95689","order_by":3,"name":"YuQing Sun","email":"","orcid":"","institution":"Shanghai East Hospital","correspondingAuthor":false,"prefix":"","firstName":"YuQing","middleName":"","lastName":"Sun","suffix":""},{"id":337365410,"identity":"74df7c38-c559-4661-bffa-30d5a4e9695d","order_by":4,"name":"Bo Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYDACCSBmbAAhBoYPQCxHkhbGGUC2MelaEhsI6ZCf3fzs4dcdNrL90u0XG37uqE3vO57A+OFjDm4tjHOOmRvLnkkznjnnTGFj75njuTPPPGCWnLkNtxZmiQQzacm2w4kbbuSkP+BtO5a74UYCGzMvHi1sEunfYFoSG/+2HUs3IKSFRyLHTPIjWEv6wWbetpoEglokJHLKpBlBfpmRw9gs23bAcOaZh814/SI/I32b5E9QiEmkP2x821Ynz3c8+eCHj3i0gIOAB+JGAyBxmIHhAOGoYWD8AabYHwCJOqCWBII6RsEoGAWjYGQBAFCKXpqTvKY6AAAAAElFTkSuQmCC","orcid":"","institution":"Shanghai East Hospital","correspondingAuthor":true,"prefix":"","firstName":"Bo","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-07-09 12:36:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4712099/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4712099/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12938-026-01539-0","type":"published","date":"2026-02-26T15:59:11+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":62647470,"identity":"3a84a06f-a84c-4997-989a-0b9eaf0cbe10","added_by":"auto","created_at":"2024-08-16 21:29:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":91240,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAUC for predicting LEAD in T2DM patients using popliteal artery WSS\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4712099/v1/df096faa88e285209a358cc5.png"},{"id":62647472,"identity":"3cd82f60-6683-4c1c-badf-4ef76f306bc1","added_by":"auto","created_at":"2024-08-16 21:29:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":140133,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScatter plot of popliteal artery WSS and related factors in the T2DM group\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4712099/v1/d952b8997278eabd00d755f8.png"},{"id":62647471,"identity":"aca897cc-936c-4bfa-ac13-3c44ff0bee5c","added_by":"auto","created_at":"2024-08-16 21:29:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":276813,"visible":true,"origin":"","legend":"\u003cp\u003e(\u003cstrong\u003ea\u003c/strong\u003e) Doppler flow image of the popliteal artery; (\u003cstrong\u003eb\u003c/strong\u003e) WSS distribution map fused with the Doppler flow image of the popliteal artery; (\u003cstrong\u003ec\u003c/strong\u003e) Segmentation map of the popliteal artery Doppler flow; (\u003cstrong\u003ed\u003c/strong\u003e)\u003cstrong\u003e \u003c/strong\u003eTwo-dimensional distribution map of WSS in the popliteal artery; (\u003cstrong\u003ee\u003c/strong\u003e) Three-dimensional spatial map of Doppler blood flow velocity in the popliteal artery; (\u003cstrong\u003ef\u003c/strong\u003e) Three-dimensional spatial distribution map of WSS in the popliteal artery.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4712099/v1/5ffb40cf7ea2c838fadfe30e.png"},{"id":103765841,"identity":"b1e0c75f-0f4f-4096-b0cf-d9e0c5abaa85","added_by":"auto","created_at":"2026-03-02 16:10:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1866484,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4712099/v1/fdd8d3ca-99a4-4aa7-b403-1ad362f71133.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Investigating the early diagnostic value of popliteal artery wall shear stress in lower extremity arterial disease in type 2 diabetes patients using color doppler ultrasound combined with WSS quantitative analysis","fulltext":[{"header":"Background","content":"\u003cp\u003eIn recent years, changes in the economy, diet, and social demographics have led to a global increase in the incidence of type 2 diabetes mellitus (T2DM), gradually reaching epidemic proportions[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Lower extremity arterial disease (LEAD) is one of the most common complications of T2DM, characterized by prolonged disease duration, high treatment costs, high rates of amputation, and mortality[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. It has become a serious public health issue that severely threatens human health. Therefore, a safe, economical, and effective method for early diagnosis and prediction of LEAD in T2DM patients, enabling prompt clinical prevention and treatment, is crucial for reversing LEAD and reducing amputation and mortality rates among diabetic patients.\u003c/p\u003e \u003cp\u003eIn T2DM patients, LEAD primarily refers to ischemic diseases of the lower limbs caused by atherosclerosis[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The clinical manifestations of LEAD primarily depend on the degree of obstruction in the lower limb arteries. In the early stages of the disease, symptoms are often concealed, with many patients showing no clinical symptoms or only experiencing cold limbs and abnormal skin sensations[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. As the vascular lesions progress, patients gradually develop knee pain, intermittent claudication, limb ulcers, and gangrene. Once LEAD progresses to limb ischemia, reversing it through conventional treatments such as medication and physical rehabilitation becomes difficult. Patients with moderate to severe conditions require vascular reconstruction surgery to restore blood supply, with the cumulative rate of amputation or death within three years post-surgery reaching up to high level[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Once LEAD occurs in T2DM patients, merely assessing the risk associated with existing atherosclerotic plaques or the degree of vascular narrowing is too late. At this point, clinical interventions for atherosclerotic plaques are very limited, and non-surgical treatments can only delay plaque progression.\u003c/p\u003e \u003cp\u003eCurrently, LEAD diagnosis primarily relies on imaging methods such as digital subtraction angiography (DSA), CT angiography (CTA), magnetic resonance angiography (MRA), and color doppler ultrasound (DUS)[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. DSA, CTA, and MRA are invasive procedures associated with issues such as contrast agent allergies, radiation exposure, and high examination costs. Furthermore, DSA, CTA, and MRA can only diagnose LEAD when there is significant narrowing or obvious atherosclerotic plaques in the lower extremity vessels, making them less sensitive for early diagnosis. In contrast, DUS is non-invasive, safe, and inexpensive[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. It produces pseudocolor blood flow images, allowing real-time and dynamic visualization of blood flow within the vessels. DUS has high spatial resolution, clear and detailed image content, and can display even small lesions and intima-media thickness (IMT) within the arteries. This provides a solid foundation for the early diagnosis and prediction of LEAD in T2DM patients.\u003c/p\u003e \u003cp\u003eWall shear stress (WSS) is the tangential frictional force exerted by the flow of fluid over the surface of a blood vessel per unit area, acting directly on the endothelial cells within arteries[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Variations in WSS can induce genetic, molecular, and cellular changes in the vessel wall. WSS is considered a primary signaling factor in the initiation of atherosclerosis, playing a crucial role from the onset of the disease process to plaque formation. Extensive experimental research suggests that low WSS is a significant factor in promoting the occurrence and development of atherosclerosis[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Due to its \"invisible\" and \"intangible\" nature, measuring WSS presents specific challenges. Currently, MRI and ultrasound are commonly used to measure indirect parameters such as blood flow velocity and vessel diameter within arteries, with WSS being roughly calculated using the Hagen-Poiseuille equation[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This platform has developed proprietary WSS quantitative analysis software using MATLAB, integrating WSS with color Doppler flow imaging (CDFI) technology. This integration enables non-invasive quantitative analysis of WSS, making it \"visible,\" and offers new possibilities for the early diagnosis and prediction of LEAD in T2DM patients.\u003c/p\u003e \u003cp\u003eLEAD in T2DM patients primarily refers to atherosclerotic lesions. LEAD is characterized by its staged and selective distribution. While small arteries in the lower limbs are prone to atherosclerosis, narrowing and occlusion of major vessels are the principal causes of adverse outcomes in LEAD [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Angiographic analysis of lower limb arteriosclerotic lesion distribution has shown that LEAD atherosclerotic lesions are more likely to occur at sites of stenosis or curvature in the major arteries of the lower limbs, consistent with the anatomical structure and physiological function of the popliteal artery. Traditionally, plaques also develop early at the bifurcation of the common femoral artery due to its unique physiological structure, but these plaques often exhibit independent characteristics and have a weak correlation with LEAD progression[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Moreover, the popliteal artery, as the \"blood engine\" of the calf, directly gives rise to the major arteries of the calf\u0026mdash;the anterior tibial, posterior tibial, and peroneal arteries\u0026mdash;making it highly significant for LEAD in terms of hemodynamics[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Anatomically, the popliteal artery passes through the adductor hiatus and runs behind the knee joint capsule and popliteus muscle, featuring a deep position, regular shape, and fixed, straight course. This makes it closer to the ideal vascular structure assumed in the Hagen-Poiseuille formula used in WSS quantitative analysis software, minimizing interference from other factors on the WSS of the popliteal artery. Therefore, popliteal artery WSS is a stable and continuous indicator for evaluating LEAD. This study selects popliteal artery WSS as a window to explore early diagnosis and prediction of LEAD in T2DM patients.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eClinical \u0026amp; Laboratory results\u003c/h2\u003e\n \u003cp\u003eClinical and laboratory results for the control group and the T2DM group are summarized in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Significant differences were observed in SBP, and BMI, with higher values in the T2DM group, and levels of HCT, Fib, TC, TG, LDL-C, FPG, 2h-PBG, HbA1c, and GA were also significantly higher compared to the control group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). There were no significant differences between the groups in terms of age, DBP, MAP and Cr (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05)\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eClinical and laboratory Characteristics in the Control Group and the T2DM Group\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eControl group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eT2DM group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58.22\u0026thinsp;\u0026plusmn;\u0026thinsp;10.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61.56\u0026thinsp;\u0026plusmn;\u0026thinsp;13.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e119.56\u0026thinsp;\u0026plusmn;\u0026thinsp;12.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e126.13\u0026thinsp;\u0026plusmn;\u0026thinsp;14.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDBP(mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73.01\u0026thinsp;\u0026plusmn;\u0026thinsp;8.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79.58\u0026thinsp;\u0026plusmn;\u0026thinsp;48.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.264\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMAP(mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e88.53\u0026thinsp;\u0026plusmn;\u0026thinsp;8.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e95.10\u0026thinsp;\u0026plusmn;\u0026thinsp;33.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.91\u0026thinsp;\u0026plusmn;\u0026thinsp;3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.16\u0026thinsp;\u0026plusmn;\u0026thinsp;4.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHCT(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.79\u0026thinsp;\u0026plusmn;\u0026thinsp;3.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40.85\u0026thinsp;\u0026plusmn;\u0026thinsp;4.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFib(g/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.11\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC(mmol/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.85\u0026thinsp;\u0026plusmn;\u0026thinsp;1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTG(mmol/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.59\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHDL-C(mmol/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDL-C(mmol/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.40\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCr(mmol/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68.86\u0026thinsp;\u0026plusmn;\u0026thinsp;12.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72.79\u0026thinsp;\u0026plusmn;\u0026thinsp;23.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.193\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFPG(mmol/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.63\u0026thinsp;\u0026plusmn;\u0026thinsp;2.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2h-PBG(mmol/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.40\u0026thinsp;\u0026plusmn;\u0026thinsp;4.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHbA1c(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.48\u0026thinsp;\u0026plusmn;\u0026thinsp;2.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGA(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.76\u0026thinsp;\u0026plusmn;\u0026thinsp;1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.44\u0026thinsp;\u0026plusmn;\u0026thinsp;9.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eClinical and laboratory results for the control group, T2DM IMT normal group, T2DM IMT thickening group, and T2DM plaque formation group are summarized in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Compared to the control group, the T2DM IMT normal group showed increased BMI, HCT, TC, TG, FPG, 2h-PBG, HbA1c, and GA, while age was decreased (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The T2DM IMT thickening group, compared to the control group, exhibited increases in BMI, HCT, FPG, 2h-PBG, HbA1c, and GA, with a decrease in HDL-C (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The T2DM plaque formation group, in comparison to the control group, showed increases in age, SBP, BMI, HCT, TG, FPG, 2h-PBG, HbA1c, and GA, with a decrease in HDL-C (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). When comparing the T2DM IMT thickening group to the T2DM IMT normal group, there were increases in age and disease duration, with a decrease in TG (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Comparing the T2DM with plaque formation group to the T2DM IMT normal group, there were increases in age and disease duration (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). When comparing the T2DM with plaque formation group to the T2DM IMT thickening group, there was an increase in age (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Between the control group, T2DM normal IMT, T2DM increased IMT, and T2DM plaque formation groups, there were no significant differences in DBP, MAP, Fib, LDL-C, Cr across any of the groups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eClinical and laboratory Characteristics in the Control Group, T2DM IMT normal group, T2DM IMT thickening group, and T2DM plaque formation group\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eControl group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eT2DM group\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIMT normal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIMT thickening\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eplaque formation\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.22\u0026thinsp;\u0026plusmn;\u0026thinsp;10.35\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.04\u0026thinsp;\u0026plusmn;\u0026thinsp;13.6\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.53\u0026thinsp;\u0026plusmn;\u0026thinsp;8.83\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.17\u0026thinsp;\u0026plusmn;\u0026thinsp;12.75\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisease duration (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.27\u0026thinsp;\u0026plusmn;\u0026thinsp;8.10\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.28\u0026thinsp;\u0026plusmn;\u0026thinsp;7.54\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.35\u0026thinsp;\u0026plusmn;\u0026thinsp;8.75\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e119.56\u0026thinsp;\u0026plusmn;\u0026thinsp;12.28\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e124.16\u0026thinsp;\u0026plusmn;\u0026thinsp;13.54\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e124.45\u0026thinsp;\u0026plusmn;\u0026thinsp;12.46\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e127.93\u0026thinsp;\u0026plusmn;\u0026thinsp;15.06\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDBP(mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.01\u0026thinsp;\u0026plusmn;\u0026thinsp;8.20\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.81\u0026thinsp;\u0026plusmn;\u0026thinsp;8.47\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.51\u0026thinsp;\u0026plusmn;\u0026thinsp;7.29\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82.70\u0026thinsp;\u0026plusmn;\u0026thinsp;68.77\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMAP(mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88.53\u0026thinsp;\u0026plusmn;\u0026thinsp;8.50\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93.92\u0026thinsp;\u0026plusmn;\u0026thinsp;9.39\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.82\u0026thinsp;\u0026plusmn;\u0026thinsp;8.08\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.77\u0026thinsp;\u0026plusmn;\u0026thinsp;46.78\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.91\u0026thinsp;\u0026plusmn;\u0026thinsp;3.00\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.76\u0026thinsp;\u0026plusmn;\u0026thinsp;5.0\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.80\u0026thinsp;\u0026plusmn;\u0026thinsp;5.07\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.06\u0026thinsp;\u0026plusmn;\u0026thinsp;3.46\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHCT(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.79\u0026thinsp;\u0026plusmn;\u0026thinsp;3.04\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.79\u0026thinsp;\u0026plusmn;\u0026thinsp;5.26\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.12\u0026thinsp;\u0026plusmn;\u0026thinsp;3.95\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.00\u0026thinsp;\u0026plusmn;\u0026thinsp;4.64\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFib(g/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.14\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC(mmol/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.06\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.76\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.81\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTG(mmol/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.86\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.59\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHDL-C(mmol/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDL-C(mmol/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.41\u0026thinsp;\u0026plusmn;\u0026thinsp;1.06\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCr(mmol/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68.86\u0026thinsp;\u0026plusmn;\u0026thinsp;12.63\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.45\u0026thinsp;\u0026plusmn;\u0026thinsp;21.19\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.89\u0026thinsp;\u0026plusmn;\u0026thinsp;14.74\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.97\u0026thinsp;\u0026plusmn;\u0026thinsp;28.00\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFPG(mmol/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.82\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2.79\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.49\u0026thinsp;\u0026plusmn;\u0026thinsp;2.39\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.61\u0026thinsp;\u0026plusmn;\u0026thinsp;2.98\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2h-PBG(mmol/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.31\u0026thinsp;\u0026plusmn;\u0026thinsp;4.38\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.74\u0026thinsp;\u0026plusmn;\u0026thinsp;3.83\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.81\u0026thinsp;\u0026plusmn;\u0026thinsp;4.96\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHbA1c(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.95\u0026thinsp;\u0026plusmn;\u0026thinsp;2.57\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.53\u0026thinsp;\u0026plusmn;\u0026thinsp;2.38\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.72\u0026thinsp;\u0026plusmn;\u0026thinsp;2.42\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGA(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.76\u0026thinsp;\u0026plusmn;\u0026thinsp;1.45\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.61\u0026thinsp;\u0026plusmn;\u0026thinsp;9.21\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.09\u0026thinsp;\u0026plusmn;\u0026thinsp;8.16\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.04\u0026thinsp;\u0026plusmn;\u0026thinsp;10.93\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003ea, b, c\u003c/strong\u003e \u003cstrong\u003eThe same letters indicate no significant difference between groups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), while different letters indicate a significant difference between groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003ePopliteal artery ultrasound \u0026amp; WSS quantitative analysis results\u003c/h2\u003e\n \u003cp\u003ePopliteal artery ultrasound parameters and quantitative WSS for the control group and the T2DM group are shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. Compared to the control group, the T2DM group showed a significant reduction in PSV and WSS (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). There were no significant differences in R between the two groups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePopliteal artery ultrasound and WSS parameters in the Control Group and the T2DM Group\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eControl group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eT2DM group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR(mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePSV(cm/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65.35\u0026thinsp;\u0026plusmn;\u0026thinsp;13.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51.31\u0026thinsp;\u0026plusmn;\u0026thinsp;14.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWSS(dyne/cm\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, compared to the control group, the T2DM IMT normal group exhibited a significant reduction in WSS (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Both PSV and WSS were significantly reduced in the T2DM IMT thickening group compared to the control group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Similarly, the T2DM plaque formation group exhibited significant reductions in PSV and WSS compared to the control group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). When comparing the T2DM IMT thickening group to the T2DM IMT normal group, a significant decrease in WSS was observed (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The T2DM plaque formation group showed significant reductions in PSV and WSS compared to the T2DM IMT normal group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Additionally, PSV was significantly lower in the T2DM plaque formation group compared to the T2DM IMT thickening group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). There were no significant differences in R among the four groups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePopliteal artery ultrasound and WSS parameters in the Control Group, T2DM IMT normal group, T2DM IMT thickening group, and T2DM plaque formation group\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eControl group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eT2DM group\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIMT normal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIMT thickening\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eplaque formation\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR(mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePSV(cm/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.35\u0026thinsp;\u0026plusmn;\u0026thinsp;13.94\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.15\u0026thinsp;\u0026plusmn;\u0026thinsp;12.16\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.53\u0026thinsp;\u0026plusmn;\u0026thinsp;13.30\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.03\u0026thinsp;\u0026plusmn;\u0026thinsp;11.83\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWSS(dyne/cm\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003ea, b, c\u003c/strong\u003e \u003cstrong\u003eThe same letters indicate no significant difference between groups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), while different letters indicate a significant difference between groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003ePopliteal artery WSS predicts the occurrence of LEAD in T2DM patients\u003c/h2\u003e\n \u003cp\u003eIn the T2DM group, patients with IMT thickening and plaque formation were defined as positive, while those with normal IMT were defined as negative. A ROC curve for WSS in the popliteal artery was constructed, showing an area under the curve (AUC) of 0.81 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) \u003cem\u003e(\u003c/em\u003eTable \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e \u003cem\u003eand\u003c/em\u003e Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cem\u003e)\u003c/em\u003e.\u003c/p\u003e\n \u003cp\u003eBased on statistical analysis, the Youden index was identified as the optimal cutoff point, with a calculated popliteal artery WSS Youden index of 1.82 dyne/cm\u0026sup2;. This value is considered the best cutoff for predicting the occurrence of LEAD in T2DM patients \u003cem\u003e(\u003c/em\u003eTable \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e\u003cem\u003e)\u003c/em\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAUC for LEAD prediction in T2DM patients through popliteal artery WSS analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eStandard Error\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e95% CI for OR\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLower Upper\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLower Upper\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe optimal cutoff value for predicting LEAD in T2DM patients using popliteal artery WSS\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePopliteal Artery WSS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYouden\u0026apos;s Index\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003eFactors correlated with popliteal artery WSS in T2DM group\u003c/h2\u003e\n \u003cp\u003eIn the T2DM group, popliteal artery WSS was found to be significantly negatively correlated with age, duration of diabetes, and popliteal artery IMT, and positively correlated with PSV (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). WSS showed a negative correlation with GA (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Additionally, WSS exhibited weak positive correlations with TC, TG, HCT, and a weak negative correlation with HbA1c (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). There were no significant correlations between WSS and SBP, DBP, MAP, BMI, HDL-C, LDL-C, Cr, Fib, FPG, 2h-PBG and radius R (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) \u003cem\u003e(\u003c/em\u003eTable \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e \u003cem\u003eand\u003c/em\u003e Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cem\u003e)\u003c/em\u003e.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 7 Analysis of the correlation between different parameters and WSS in T2DM group\u003c/strong\u003e\u003c/p\u003e\n \u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"579\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.805555555555555%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.805555555555555%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\"\u003e\n \u003cp\u003eDuration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\"\u003e\n \u003cp\u003eSBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\"\u003e\n \u003cp\u003eDBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\"\u003e\n \u003cp\u003eMAP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.385826771653543%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.322834645669293%\"\u003e\n \u003cp\u003e(years) \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.322834645669293%\"\u003e\n \u003cp\u003e(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.322834645669293%\"\u003e\n \u003cp\u003e\u0026nbsp;(mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.322834645669293%\"\u003e\n \u003cp\u003e\u0026nbsp;(mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.322834645669293%\"\u003e\n \u003cp\u003e\u0026nbsp;(mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.805555555555555%\" rowspan=\"2\"\u003e\n \u003cp\u003eWSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.805555555555555%\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\"\u003e\n \u003cp\u003e\u0026nbsp; -0.50\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\"\u003e\n \u003cp\u003e\u0026nbsp;-0.34\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\"\u003e\n \u003cp\u003e-0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.385826771653543%\"\u003e\n \u003cp\u003e\u0026nbsp;P^\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.322834645669293%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.322834645669293%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.322834645669293%\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.322834645669293%\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.322834645669293%\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.805555555555555%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.805555555555555%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.805555555555555%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.805555555555555%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\"\u003e\n \u003cp\u003eTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\"\u003e\n \u003cp\u003eHDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\"\u003e\n \u003cp\u003eLDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.385826771653543%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.322834645669293%\"\u003e\n \u003cp\u003e\u0026nbsp;(kg/m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.322834645669293%\"\u003e\n \u003cp\u003e\u0026nbsp;(mmol/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.322834645669293%\"\u003e\n \u003cp\u003e\u0026nbsp;(mmol/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.322834645669293%\"\u003e\n \u003cp\u003e\u0026nbsp;(mmol/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.322834645669293%\"\u003e\n \u003cp\u003e\u0026nbsp;(mmol/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.805555555555555%\" rowspan=\"2\"\u003e\n \u003cp\u003eWSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.805555555555555%\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\"\u003e\n \u003cp\u003e0.14\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\"\u003e\n \u003cp\u003e0.17\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\"\u003e\n \u003cp\u003e-0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.385826771653543%\"\u003e\n \u003cp\u003e\u0026nbsp;P^\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.322834645669293%\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.322834645669293%\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.322834645669293%\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.322834645669293%\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.322834645669293%\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.805555555555555%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.805555555555555%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.805555555555555%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.805555555555555%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\"\u003e\n \u003cp\u003eCr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\"\u003e\n \u003cp\u003eHCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\"\u003e\n \u003cp\u003eFib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\"\u003e\n \u003cp\u003eFPG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\"\u003e\n \u003cp\u003ePBG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.385826771653543%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.322834645669293%\"\u003e\n \u003cp\u003e\u0026nbsp;(mmol/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.322834645669293%\"\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.322834645669293%\"\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.322834645669293%\"\u003e\n \u003cp\u003e\u0026nbsp;(mmol/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.322834645669293%\"\u003e\n \u003cp\u003e(mmol/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.805555555555555%\" rowspan=\"2\"\u003e\n \u003cp\u003eWSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.805555555555555%\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\"\u003e\n \u003cp\u003e0.18\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.385826771653543%\"\u003e\n \u003cp\u003e\u0026nbsp;P^\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.322834645669293%\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.322834645669293%\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.322834645669293%\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.322834645669293%\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.322834645669293%\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.805555555555555%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.805555555555555%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.805555555555555%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.805555555555555%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\"\u003e\n \u003cp\u003eHbAlc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\"\u003e\n \u003cp\u003eGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\"\u003e\n \u003cp\u003eIMT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\"\u003e\n \u003cp\u003ePSV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.385826771653543%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.322834645669293%\"\u003e\n \u003cp\u003e \u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.322834645669293%\"\u003e\n \u003cp\u003e \u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.322834645669293%\"\u003e\n \u003cp\u003e \u0026nbsp;(mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.322834645669293%\"\u003e\n \u003cp\u003e \u0026nbsp;(mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.322834645669293%\"\u003e\n \u003cp\u003e \u0026nbsp;(cm/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.805555555555555%\" rowspan=\"2\"\u003e\n \u003cp\u003eWSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.805555555555555%\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\"\u003e\n \u003cp\u003e-0.21\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\"\u003e\n \u003cp\u003e-0.18\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\"\u003e\n \u003cp\u003e-0.53\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\"\u003e\n \u003cp\u003e0.57\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.385826771653543%\"\u003e\n \u003cp\u003e\u0026nbsp;P^\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.322834645669293%\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.322834645669293%\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.322834645669293%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.322834645669293%\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.322834645669293%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003er\u003c/em\u003e\u003c/strong\u003e correlation coefficient; \u003cstrong\u003e\u003cem\u003e^\u003c/em\u003e\u003c/strong\u003e denotes Pearson correlation analysis; \u003cstrong\u003e\u003cem\u003e*P\u0026lt;0.05; **P\u0026lt;0.01; ***P\u0026lt;0.001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eMultiple linear regression analysis of popliteal artery WSS in the T2DM group\u003c/h2\u003e\n \u003cp\u003eUsing WSS as the dependent variable, and age, duration of disease, TC, TG, HCT, HbA1c, GA, popliteal artery IMT, and PSV as independent risk factors, a multiple linear regression analysis was conducted. The positive correlation coefficient R\u0026thinsp;=\u0026thinsp;0.707 indicates a closely linear relationship and good model fit. WSS is positively correlated with PSV and negatively correlated with age, duration of disease, and popliteal artery IMT, all of which can serve as independent influencing factors affecting WSS (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) \u003cem\u003e(\u003c/em\u003eTables \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e\u003cem\u003e)\u003c/em\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab9\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003emultiple linear regression model\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAdjusted R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSe\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.707a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.387\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cem\u003ea\u003c/em\u003e Predictive variables include age, duration of disease, TC, TG, HCT, HbA1c, GA), popliteal artery IMT, PSV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab10\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003emultiple linear regression coefficients of variables\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStandardized Coefficients\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDuration(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC(mmol/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.611\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTG(mmol/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.738\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHCT(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.466\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHbA1c(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGA(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.962\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIMT(mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePSV(cm/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eDiabetes is a systemic metabolic disorder characterized primarily by chronic hyperglycemia, with complications causing the main damage. T2DM accounts for 90% of all diabetes cases. The risk of LEAD in patients with T2DM is twice that of non-diabetic individuals[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. LEAD in T2DM primarily refers to atherosclerotic changes, which, once established, are difficult to reverse. The treatment options available clinically are very limited, generally only capable of slowing the progression of atherosclerotic plaques[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Importantly, the early symptoms of LEAD are mild and insidious, and the disease progression often coincides with the progression of atherosclerosis[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Once the stage of limb ischemia is reached, the patient's condition can deteriorate rapidly, and the means of clinical intervention at this stage are extremely limited. Moderate to severe patients require vascular reconstruction to restore blood supply, and nearly half face amputation or mortality within three years post-surgery[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Currently, early diagnosis and prediction of atherosclerotic changes are the core components in the treatment of LEAD.\u003c/p\u003e \u003cp\u003eT2DM affects all body systems, leading to abnormalities in blood glucose, blood pressure, blood lipids, and blood viscosity[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. By comparing clinical data of the control group and the T2DM group, we found that T2DM patients had higher FPG, 2h-PBG, HbA1c, GA, SPB, HCT, Fib, TC, TG, LDL-C, and FPG than the normal group, while HDL-C was lower (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Comparing three T2DM subgroups: IMT normal group, IMT thickening group, and plaque formation group, we found no significant statistical difference or linear relationship in FPG, 2h-PBG, HbA1c, GA, SPB, DBP, MAP, HCT, Fib, TC, and LDL-C (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, HDL-C in the IMT thickening and plaque formation groups was lower than in the IMT normal group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with no significant linear relationship. Although T2DM patients show significant changes in blood glucose, blood pressure, blood lipids, and blood viscosity compared to normal individuals, these do not fully reflect the progression of LEAD atherosclerosis. Currently, non-invasive imaging techniques like ultrasound can detect atherosclerotic plaques but cannot predict or dynamically assess atherosclerosis. Therefore, we need other sensitive and accurate indicators to predict the occurrence and development of atherosclerosis[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eVascular ultrasound is the most common method for measuring vascular IMT and plaques. Vascular ultrasound was used to record the diameter, IMT, PSV, and RI of the popliteal artery, and WSS was calculated using Hagen-Poiseuille. Compared to the control group, the T2DM group had significantly lower PSV and WSS (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). There was no significant difference in R between the two groups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). All three T2DM subgroups had lower WSS than the control group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that WSS is a sensitive indicator for early assessment of lower limb atherosclerosis. Among the three T2DM subgroups (IMT normal group, IMT thickening group, and plaque formation group), the plaque formation group had significantly lower PSV compared to the IMT normal group and IMT thickening groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). A decrease in blood flow velocity in the popliteal artery of the lower limb indicates hemodynamic changes. The center of the vessel has the highest blood flow velocity, which gradually decreases toward the vessel wall, forming a velocity gradient. When PSV decreases, the velocity gradient is affected, and the velocity near the vessel wall also decreases, which mechanically aligns with changes in WSS[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe IMT thickening group and the plaque formation group had lower WSS compared to the IMT normal group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with no significant differences in R among the three subgroups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). In T2DM patients, WSS decreased even when the popliteal artery IMT was normal, and further decreased when IMT thickened. WSS, the mechanical force exerted by blood flow on the vessel wall, acts parallel to the vessel wall. WSS directly affects endothelial cells, having both physiological and pathological significance for the vessel wall. Hemodynamic changes drive alterations in WSS. This study shows that popliteal artery WSS decreases in T2DM patients before the onset of LEAD. When LEAD progresses to the IMT thickening stage, popliteal artery WSS further decreases, providing a hemodynamic basis for atherosclerotic plaque formation. Thus, a decrease in popliteal artery WSS can serve as an early warning indicator for LEAD before atherosclerosis occurs. Additionally, in this study, although WSS in the T2DM plaque formation group was lower than in the T2DM IMT thickening group, the difference was not significant (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The WSS quantitative analysis framework used in this study was developed using MATLAB. Our software extracts color flow signals from CDFI using digital image processing technology, converts color data of each pixel in CDFI to blood flow velocity data, and then converts it to shear stress data based on the WSS definition formula. Theoretically, it can analyze the WSS distribution in any vascular region of interest. This has been confirmed in previous studies by Wang et al[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, the presence of plaques creates irregular and uneven surface structures in the vessel, significantly disrupting the ideal straight, regular, and rigid vessel structure required by the Hagen-Poiseuille formula for calculating WSS, causing the WSS in the T2DM plaque formation group to deviate from the ideal state[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, WSS decreased even when the popliteal artery IMT was normal in T2DM patients. The area under the ROC curve (AUC) for predicting LEAD in T2DM patients using popliteal artery WSS was 0.81 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Based on the Youden index, the optimal cutoff value for popliteal artery WSS was determined to be 1.82 dyne/cm\u0026sup2;, with a sensitivity of 68% and a specificity of 83%. When popliteal artery WSS is \u0026le;\u0026thinsp;1.82 dyne/cm\u0026sup2;, the likelihood of LEAD in T2DM patients is high, making it a critical indicator for LEAD occurrence.\u003c/p\u003e \u003cp\u003eThis study analyzed the correlation and multiple regression between popliteal artery WSS and various variables in the diabetic group, finding that WSS is closely related to indicators causing severe endothelial dysfunction, changing blood viscosity, and affecting vascular compliance. In the T2DM group, WSS was negatively correlated with age, disease duration, GA, HbA1c, and popliteal artery IMT, and positively correlated with PSV, TC, TG, and HCT (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). PSV, age, disease duration, and popliteal artery IMT were independent risk factors affecting WSS (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). WSS, the tangential force directly contacting endothelial cells parallel to the vessel wall, can be influenced by blood flow velocity, blood viscosity, vascular compliance, and endothelial cell function. Age and diabetes duration are independent risk factors for WSS. As age increases, endothelial regulation and vascular compliance tend to show varying degrees of impairment. When blood flows through relatively straight and regular vessels, it can be considered a Newtonian fluid. In this case, WSS generated by adjacent fluid layers is proportional to the velocity gradient in the perpendicular direction. Therefore, PSV, an independent risk factor for WSS, can partly reflect WSS magnitude. When blood flow velocity decreases, WSS decreases, prolonging blood component residence time along the vessel wall, which low WSS more easily promotes atherosclerosis formation[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Identifying independent risk factors for WSS can help clinicians further investigate lipid levels, blood viscosity, and other WSS-related indicators in T2DM patients with reduced popliteal artery WSS for early clinical treatment. Patients with independent risk factors for WSS can be considered high-risk for LEAD and should undergo regular screening.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eUsing non-invasive, quantitative analysis of popliteal artery WSS in T2DM patients with color Doppler ultrasound provides imaging methods and evidence for the early diagnosis of LEAD; the reduction in popliteal artery shear stress occurs before the onset of LEAD in T2DM patients, showing good predictive value for the occurrence of such diseases. It is expected to become a new indicator for clinical prevention and early diagnosis and treatment of LEAD in T2DM patients.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStudy participants and grouping\u003c/h2\u003e \u003cp\u003eFrom March 2019 to November 2023, T2DM patients treated at Shanghai East Hospital and healthy volunteers from a health examination center were selected and divided into two groups: the type 2 diabetes mellitus group (T2DM group) and the control group (Control group). This study was approved by the Research Ethics Board of East Hospital, Tongji University (Shanghai, China) [Approval 2017 (No.030)].\u003c/p\u003e \u003cp\u003e(1) T2DM group: Participants in this group met the 1999 WHO criteria for diabetes diagnosis and classification[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]; those with type 1 diabetes, secondary diabetes, primary hyperlipidemia, hypertension, smoking history, lower limb arterial surgery, severe functional or organic heart disease, renal failure, stroke, or malignant tumors were excluded. Patients who had recently taken lipid-lowering, anticoagulant, or antiplatelet aggregation medications, or had recent adjustments in their diabetes treatment plans were also excluded. A total of 202 T2DM patients were selected for the study, including 126 males and 76 females, with an average age of 61.56\u0026thinsp;\u0026plusmn;\u0026thinsp;13.35 years. Ultrasound was used to measure the intima-media thickness (IMT) of the popliteal artery. Based on the IMT, the T2DM group was divided into: T2DM IMT normal group: IMT\u0026thinsp;\u0026lt;\u0026thinsp;0.9 mm (48 cases, 24 males and 24 females, average age 50.04\u0026thinsp;\u0026plusmn;\u0026thinsp;13.6 years), T2DM IMT thickening group: 0.9\u0026thinsp;\u0026le;\u0026thinsp;IMT\u0026thinsp;\u0026lt;\u0026thinsp;1.2 mm (55 cases, 38 males and 17 females, average age 61.53\u0026thinsp;\u0026plusmn;\u0026thinsp;8.83 years), and T2DM Plaque formation group: IMT\u0026thinsp;\u0026ge;\u0026thinsp;1.2 mm (99 cases, 64 males and 35 females, average age 67.17\u0026thinsp;\u0026plusmn;\u0026thinsp;12.75 years).\u003c/p\u003e \u003cp\u003e(2) Control group: Participants with a history of diabetes, hypertension, primary hyperlipidemia, lower limb vascular disease, lower limb surgery, heart failure, organic heart disease, renal failure, stroke, or malignant tumors were excluded, as well as those who had recently taken any medications. A total of 69 healthy volunteers were selected for the study, including 32 males and 37 females, with an average age of 58.22\u0026thinsp;\u0026plusmn;\u0026thinsp;10.35 years.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eClinical Data \u0026amp; Laboratory Findings\u003c/h2\u003e \u003cp\u003eClinical data collected included participant records of gender, age, medical history, and duration of diabetes for the T2DM group. Mean arterial pressure (MAP), body mass index (BMI), systolic blood pressure (SBP), and diastolic blood pressure (DBP) were measured for all subjects in both the control group and the T2DM group. The formula for calculating MAP is: MAP = (SBP\u0026thinsp;+\u0026thinsp;2\u0026times;DBP) / 3. SBP and DBP were manually measured using a mercury sphygmomanometer after a 10-minute rest period. All participants fasted for 12 hours and had venous blood drawn the following morning to test for glycated hemoglobin (HbA1c), fasting plasma glucose (FPG), two-hour postprandial blood glucose (2h-PBG), glycated albumin (GA), fibrinogen (Fib), hematocrit (HCT), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and creatinine (Cr).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eInspection methods\u003c/h2\u003e \u003cp\u003eThe Philips EPIQ7 system (Philips Medical Systems, Andover, MA, USA) with an L11-3 linear array transducer was used in our study. Before the examination, subjects were required to rest for 10 minutes, maintain a constant room temperature of 24\u0026deg;C, and fully expose their lower limbs. During the examination, subjects were instructed to lie in a prone position, breathe calmly, and relax their knee joints. Continuous scanning of the popliteal artery was performed from the adductor hiatus to the origin of the anterior tibial artery, ensuring an image frame rate of \u0026ge;\u0026thinsp;30 frames/s. To ensure accuracy, all data were collected at least three times.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eParameters of popliteal artery ultrasound\u003c/h2\u003e \u003cp\u003eThe conventional data of popliteal artery ultrasound was collected. The transducer was positioned transversely, clearly displaying the anterior and posterior walls of the popliteal artery. At the thickest part of the IMT, the vascular image was locally magnified, and measurements were taken at the thickest part of the popliteal artery's posterior wall IMT and its maximum diameter. The transducer was then rotated to the longitudinal plane at the thickest part of the IMT, ensuring the vessel course was straight. The ultrasound plane passed through the vessel's central axis, clearly displaying the anterior and posterior walls of the popliteal artery, with the thickest part of the IMT centered in the image. Color Doppler flow was initiated, with the beam angle to the popliteal artery kept below 60\u0026deg;. The velocity range and sample box size were adjusted to 2 cm, and the blood flow gain was regulated to ensure the blood flow filled the lumen without aliasing or overflow. The peak systolic velocity (PSV) of the popliteal artery during systole, along with a 5-second dynamic color Doppler flow image of the lower limb popliteal artery, were collected, with the dynamic images stored in DICOM format.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eWSS quantitative analysis by CDFI\u003c/h2\u003e \u003cp\u003eLong-axis dynamic images of the popliteal artery spanning at least three cardiac cycles were obtained, and all CDFI images were saved in DICOM format (image matrix: 600\u0026times;800; pixel spacing: 0.085\u0026times;0.085 mm) and imported into a WSS (Wall Shear Stress) quantitative analysis software on the MATLAB platform (The Mathworks Inc., Natick, MA, USA). The software determined the coordinates of the color Doppler velocity scale from the image information to define the color blood flow pixels and the wall grayscale pixels. It converted the color pixel gradient into a velocity gradient based on the velocity scale. This allowed for the calculation of WSS values for each pixel on the image. The calculation formula for WSS is \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\tau\\:}_{w}=\\mu\\:{\\gamma\\:}_{w}=\\mu\\:\\frac{du}{dr}|r=wall\\)\u003c/span\u003e\u003c/span\u003e.In this equation, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\tau\\:}_{w}\\)\u003c/span\u003e\u003c/span\u003e is the wall shear stress, which is the shear stress near the vessel wall. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\mu\\:\\)\u003c/span\u003e\u003c/span\u003e is the viscosity of the blood, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{du}{dr}\\)\u003c/span\u003e\u003c/span\u003e represents the velocity gradient (or shear rate). \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:r=wall\\)\u003c/span\u003e\u003c/span\u003e means that \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:r\\)\u003c/span\u003e\u003c/span\u003e is near the boundary of tube. It was important to note that this formula assumed that blood is a Newtonian fluid. The WSS analysis software generated fusion images of WSS Doppler blood flow distribution, segmentation maps, and two-dimensional WSS distribution maps, further enabling the creation of three-dimensional blood flow velocity profiles and three-dimensional spatial distribution maps of WSS.As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. This ultimately facilitated the quantitative analysis and visualization of WSS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData statistical analysis was conducted using SPSS 24.0. (SPSS Inc., Chi cago, IL, USA). Quantitative and count data were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (x̄ \u0026plusmn; s) and number of cases, respectively. The Shapiro-Wilk test was used to assess the distribution of continuous variables, while group comparisons were made using t-tests, non-parametric tests, chi-square (χ\u0026sup2;) tests, and LSD multiple comparison methods. The predictive value of popliteal artery WSS for LEAD in patients with T2DM was analyzed using ROC curve analysis. The correlation between variables was assessed using Pearson correlation analysis. Multivariate linear regression was employed to analyze the relationship between popliteal artery WSS and various variables. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was performed in accordance with the Declaration of Helsinki. The Ethical approval of this study was provided by The East Hospital, Tongji University (Shanghai, China) [Approval 2017 (No.030)]. Written informed consent for publication was obtained from all participants\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors approved the final manuscript and the submission to this journal\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCompeting interests the authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by Important Weak Subject Construction Project of the Pudong Health and Family Planning Commission of Shanghai (Grant No. PWZbr2017-09)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePerformed the literature review: YXZ; Carried out Ultrasound measurements: JYG, YQS; Checked the validity of data: BZ, YXZ, HW; Data analysis: YXZ, HW; Supported the experiments financially: BZ; Wrote and revised the manuscript: BZ, YXZ, HW. All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declared that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTinajero MG, Malik VS: An Update on the Epidemiology of Type 2 Diabetes: A Global Perspective. Endocrinol Metab Clin North Am 2021, 50(3):337-355.\u003c/li\u003e\n\u003cli\u003eNativel M, Potier L, Alexandre L, Baillet-Blanco L, Ducasse E, Velho G, Marre M, Roussel R, Rigalleau V, Mohammedi K: Lower extremity arterial disease in patients with diabetes: a contemporary narrative review. Cardiovasc Diabetol 2018, 17(1):138.\u003c/li\u003e\n\u003cli\u003eSchneider C, Stratman S, Kirsner RS: Lower Extremity Ulcers. Med Clin North Am 2021, 105(4):663-679.\u003c/li\u003e\n\u003cli\u003eBehrendt CA, Lareyre F, Raffort J: Impact of Diabetes on Outcomes of Patients with Lower Extremity Artery Disease. Angiology 2022, 73(6):493-494.\u003c/li\u003e\n\u003cli\u003eSen P, Demirdal T, Emir B: Meta-analysis of risk factors for amputation in diabetic foot infections. Diabetes Metab Res Rev 2019, 35(7):e3165.\u003c/li\u003e\n\u003cli\u003ePomposelli F: Arterial imaging in patients with lower extremity ischemia and diabetes mellitus. J Vasc Surg 2010, 52(3 Suppl):81s-91s.\u003c/li\u003e\n\u003cli\u003eAndersen CA: Noninvasive assessment of lower extremity hemodynamics in individuals with diabetes mellitus. J Vasc Surg 2010, 52(3 Suppl):76s-80s.\u003c/li\u003e\n\u003cli\u003eHogan B, Shen Z, Zhang H, Misbah C, Barakat AI: Shear stress in the microvasculature: influence of red blood cell morphology and endothelial wall undulation. Biomech Model Mechanobiol 2019, 18(4):1095-1109.\u003c/li\u003e\n\u003cli\u003ePapaioannou TG, Karatzis EN, Vavuranakis M, Lekakis JP, Stefanadis C: Assessment of vascular wall shear stress and implications for atherosclerotic disease. Int J Cardiol 2006, 113(1):12-18.\u003c/li\u003e\n\u003cli\u003eZhang B, Sun Y, Xia L, Gu J: Time-dependent flow velocity measurement using two-dimensional color Doppler flow imaging and evaluation by Hagen-Poiseuille equation. Australas Phys Eng Sci Med 2015, 38(4):755-766.\u003c/li\u003e\n\u003cli\u003eChen Q, Shi Y, Wang Y, Li X: Patterns of disease distribution of lower extremity peripheral arterial disease. Angiology 2015, 66(3):211-218.\u003c/li\u003e\n\u003cli\u003eZemaitis MR, Boll JM, Dreyer MA: Peripheral Arterial Disease. In StatPearls. Treasure Island (FL) ineligible companies. Disclosure: Julia Boll declares no relevant financial relationships with ineligible companies. Disclosure: Mark Dreyer declares no relevant financial relationships with ineligible companies.: StatPearls Publishing Copyright \u0026copy; 2024, StatPearls Publishing LLC.; 2024.\u003c/li\u003e\n\u003cli\u003eKropman RH, Kiela G, Moll FL, de Vries JP: Variations in anatomy of the popliteal artery and its side branches. Vasc Endovascular Surg 2011, 45(6):536-540.\u003c/li\u003e\n\u003cli\u003eZheng Y, Ley SH, Hu FB: Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat Rev Endocrinol 2018, 14(2):88-98.\u003c/li\u003e\n\u003cli\u003eConte MS, Pomposelli FB, Clair DG, Geraghty PJ, McKinsey JF, Mills JL, Moneta GL, Murad MH, Powell RJ, Reed AB, et al: Society for Vascular Surgery practice guidelines for atherosclerotic occlusive disease of the lower extremities: management of asymptomatic disease and claudication. J Vasc Surg 2015, 61(3 Suppl):2s-41s.\u003c/li\u003e\n\u003cli\u003eCriqui MH, Matsushita K, Aboyans V, Hess CN, Hicks CW, Kwan TW, McDermott MM, Misra S, Ujueta F: Lower Extremity Peripheral Artery Disease: Contemporary Epidemiology, Management Gaps, and Future Directions: A Scientific Statement From the American Heart Association. Circulation 2021, 144(9):e171-e191.\u003c/li\u003e\n\u003cli\u003eWhicher CA, O\u0026apos;Neill S, Holt RIG: Diabetes in the UK: 2019. Diabet Med 2020, 37(2):242-247.\u003c/li\u003e\n\u003cli\u003eMa CX, Ma XN, Guan CH, Li YD, Mauricio D, Fu SB: Cardiovascular disease in type 2 diabetes mellitus: progress toward personalized management. Cardiovasc Diabetol 2022, 21(1):74.\u003c/li\u003e\n\u003cli\u003eIbanez B, Fern\u0026aacute;ndez-Ortiz A, Fern\u0026aacute;ndez-Friera L, Garc\u0026iacute;a-Lunar I, Andr\u0026eacute;s V, Fuster V: Progression of Early Subclinical Atherosclerosis (PESA) Study: JACC Focus Seminar 7/8. J Am Coll Cardiol 2021, 78(2):156-179.\u003c/li\u003e\n\u003cli\u003ePeiffer V, Sherwin SJ, Weinberg PD: Does low and oscillatory wall shear stress correlate spatially with early atherosclerosis? A systematic review. Cardiovasc Res 2013, 99(2):242-250.\u003c/li\u003e\n\u003cli\u003eWang C, Chen M, Liu SL, Liu Y, Jin JM, Zhang YH: Spatial distribution of wall shear stress in common carotid artery by color Doppler flow imaging. J Digit Imaging 2013, 26(3):466-471.\u003c/li\u003e\n\u003cli\u003eRouleau L, Farcas M, Tardif JC, Mongrain R, Leask RL: Endothelial cell morphologic response to asymmetric stenosis hemodynamics: effects of spatial wall shear stress gradients. J Biomech Eng 2010, 132(8):081013.\u003c/li\u003e\n\u003cli\u003eGlagov S, Zarins C, Giddens DP, Ku DN: Hemodynamics and atherosclerosis. Insights and perspectives gained from studies of human arteries. Arch Pathol Lab Med 1988, 112(10):1018-1031.\u003c/li\u003e\n\u003cli\u003eAlberti KG, Zimmet PZ: Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. Diabet Med 1998, 15(7):539-553.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"biomedical-engineering-online","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmeo","sideBox":"Learn more about [BioMedical Engineering OnLine](http://biomedical-engineering-online.biomedcentral.com/)","snPcode":"12938","submissionUrl":"https://submission.nature.com/new-submission/12938/3","title":"BioMedical Engineering OnLine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Type 2 Diabetes Mellitus, Lower Extremity Arterial Disease, Wall Shear Stress, Color Doppler Ultrasound, Popliteal Artery, Atherosclerosis","lastPublishedDoi":"10.21203/rs.3.rs-4712099/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4712099/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe global rise in type 2 diabetes mellitus (T2DM) has led to an epidemic of lower extremity arterial disease (LEAD), primarily caused by atherosclerosis and compounded by late detection and high treatment costs. Early detection is key for managing LEAD effectively. Color doppler ultrasound (DUS), a non-invasive and cost-effective technique, enhances early diagnosis through high-resolution imaging. Integrating DUS with proprietary MATLAB-based software for quantitative wall shear stress (WSS) analysis offers a non-invasive method to assess WSS. This approach targets the popliteal artery, using WSS as a reliable marker for early LEAD detection in T2DM patients.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis study included 202 patients clinically diagnosed with T2DM from March 2019 to November 2023 at Shanghai East Hospital, along with 69 healthy volunteers recruited during the same period. The T2DM group was further divided into three subgroups based on the intima-media thickness (IMT) of the popliteal artery: T2DM IMT normal group (IMT\u0026thinsp;\u0026lt;\u0026thinsp;0.9 mm), T2DM IMT thickening group (1.0\u0026thinsp;\u0026le;\u0026thinsp;IMT\u0026thinsp;\u0026lt;\u0026thinsp;1.2 mm), and T2DM plaque formation group (IMT\u0026thinsp;\u0026ge;\u0026thinsp;1.2 mm). Using WSS quantitative analysis software, we calculated the average WSS of the popliteal artery and created two-dimensional WSS distribution maps, three-dimensional WSS spatial distribution maps, and WSS fusion images. Subsequently, we analyzed the WSS and its variation patterns among the control group, the T2DM group, and its various subgroups.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn a study comparing T2DM patients to controls, T2DM groups showed significantly altered blood pressure, blood lipids, and blood viscosity, along with reduced WSS values, indicating advanced arterial damage. Specifically, WSS was lower in T2DM groups with normal and thickening IMT and those with plaque formation compared to controls. The optimal WSS cutoff for predicting LEAD was 1.82 dyne/cm\u0026sup2;, with a sensitivity of 68% and specificity of 83%. WSS negatively correlated with factors like age and disease duration, and positively with peak systolic velocity (PSV).\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eNon-invasive WSS measurement using DUS provides a valuable diagnostic tool for early LEAD detection in T2DM patients. Reduced WSS in the popliteal artery is a predictive marker of disease onset, offering potential for earlier intervention and better management of LEAD, ultimately improving patient outcomes.\u003c/p\u003e","manuscriptTitle":"Investigating the early diagnostic value of popliteal artery wall shear stress in lower extremity arterial disease in type 2 diabetes patients using color doppler ultrasound combined with WSS quantitative analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-16 21:29:40","doi":"10.21203/rs.3.rs-4712099/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-06T13:50:25+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-29T14:57:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-26T10:40:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-25T19:15:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"116829533061091475648539504486352002705","date":"2025-03-19T06:37:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"84629482575688051598041421396497177896","date":"2025-03-18T06:43:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"322886113926309682097948987393070111423","date":"2025-03-17T13:24:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"116059807310869920846160199980268300135","date":"2025-03-17T04:31:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-16T12:22:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"156599035873651539999444642571116526579","date":"2025-03-16T12:01:48+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-16T11:13:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-16T22:16:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-16T22:14:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"BioMedical Engineering OnLine","date":"2024-07-09T12:35:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"biomedical-engineering-online","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmeo","sideBox":"Learn more about [BioMedical Engineering OnLine](http://biomedical-engineering-online.biomedcentral.com/)","snPcode":"12938","submissionUrl":"https://submission.nature.com/new-submission/12938/3","title":"BioMedical Engineering OnLine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7d9fb7b0-d151-46eb-af8c-9677b747f2a0","owner":[],"postedDate":"August 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-02T16:07:42+00:00","versionOfRecord":{"articleIdentity":"rs-4712099","link":"https://doi.org/10.1186/s12938-026-01539-0","journal":{"identity":"biomedical-engineering-online","isVorOnly":false,"title":"BioMedical Engineering OnLine"},"publishedOn":"2026-02-26 15:59:11","publishedOnDateReadable":"February 26th, 2026"},"versionCreatedAt":"2024-08-16 21:29:40","video":"","vorDoi":"10.1186/s12938-026-01539-0","vorDoiUrl":"https://doi.org/10.1186/s12938-026-01539-0","workflowStages":[]},"version":"v1","identity":"rs-4712099","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4712099","identity":"rs-4712099","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}