Early Hepatic Alterations in Type 2 Diabetes: Diagnostic Value of Liver Viscosity in a Prospective Study

Early Hepatic Alterations in Type 2 Diabetes: Diagnostic Value of Liver Viscosity in a Prospective Study | 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 Early Hepatic Alterations in Type 2 Diabetes: Diagnostic Value of Liver Viscosity in a Prospective Study Bin Ying, Wei Zhu, Xinyue Zhu, Chenke Pan, Jianlian Pan, Yunkai Luo, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7748066/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background Type 2 diabetes mellitus (T2DM) is associated with a spectrum of liver disorders, ranging from nonalcoholic fatty liver disease to fibrosis and hepatocellular carcinoma. Early detection of hepatic changes is critical for risk stratification and management. Sound Touch Viscosity Imaging (STVi) allows noninvasive quantification of both tissue elasticity and viscosity, potentially providing complementary information beyond conventional stiffness measurements. This study aimed to evaluate the diagnostic value of liver viscosity as a biomarker in patients with T2DM. Methods 99 patients with T2DM and 73 healthy controls underwent liver assessment using STVi. Measurements of hepatic elasticity and viscosity were obtained under standardized protocols. Laboratory and demographic data were collected. Statistical analyses included Mann–Whitney U tests, Student’s t-tests, receiver operating characteristic (ROC) curve analysis, and multivariate logistic regression to assess the diagnostic performance of elasticity, viscosity, and their combination. Correlation between viscosity and elasticity was evaluated using Spearman analysis. Results T2DM patients showed significantly elevated liver viscosity (1.54 ± 0.36 Pa·s) and elasticity (7.37 ± 2.07 kPa) compared to controls (P < 0.001). Viscosity and elasticity were positively correlated (r = 0.683, P < 0.001). ROC analysis revealed superior diagnostic performance for viscosity (AUC = 0.836) over elasticity (AUC = 0.774), with the combined model yielding an AUC of 0.89. Multivariate analysis identified viscosity, ALT, and age as independent predictors of T2DM. Conclusions Liver viscosity serves as a sensitive biomarker for early liver alterations in T2DM, outperforming stiffness alone. Ultrasound-based viscosity imaging holds promise for non-invasive screening and risk stratification of liver injury in diabetic populations. Type 2 diabetes mellitus (T2DM) Sound Touch Viscosity imaging (STVi) Shear wave elastography (SWE) Transient Elastography (TE) Figures Figure 1 Figure 2 Figure 3 1 Background Type 2 diabetes mellitus (T2DM) is a rapidly growing global health challenge: in 2019, an estimated 463 million people (9.3% of the world’s population) were affected, and the number is projected to reach 629 million by 2045( 1 , 2 ). In addition to classic macro- and microvascular complications, diabetes is increasingly linked to hepatic disorders across the spectrum from nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH) to fibrosis and hepatocellular carcinoma (HCC)( 3 – 6 ). This expanding burden underscores an urgent need for accurate, scalable tools to characterize liver involvement in diabetes. Current hepatic assessments in patients with diabetes rely largely on liver enzymes and ultrasound-based imaging. Conventional ultrasonography - B-mode, color Doppler, and contrast-enhanced ultrasound (CEUS) - is widely available and effective for detecting structural and perfusion abnormalities( 7 ); however, it provides limited insight into the mechanical properties of the hepatic parenchyma, which reflect key biological processes such as inflammation and fibrosis. Ultrasound elastography partly addresses this gap by estimating tissue stiffness (elasticity)( 8 , 9 ), but most implementations model tissue as a purely elastic medium and therefore do not capture the viscoelastic behavior of living tissues—a critical limitation when assessing inflammatory activity and edema that may precede or accompany fibrotic remodeling. Sound touch viscosity imaging (STVi) has emerged as a promising viscoelastic imaging technique that noninvasively quantifies both the elastic and viscous components of soft tissue within a region of interest, providing intuitive parametric maps( 10 – 13 ). Conceptually, elasticity reflects resistance to deformation and is closely related to collagen deposition and architectural remodeling( 14 ), whereas viscosity captures energy dissipation and is more sensitive to dynamic processes such as inflammation and tissue edema. In shear wave-based methods, the elastic modulus ( E ) can be expressed by the following fundamental relationship: $$\:E=3\rho\:{c}_{s}^{2}$$ where \(\:\rho\:\) represents the tissue density and where \(\:{c}_{s}\) denotes the shear wave velocity. Differences in shear wave propagation speeds between healthy and pathological tissues, arising from variations in tissue stiffness, provide the physical basis for tissue characterization. In biological tissues, viscosity induces shear wave dispersion, causing the propagation speed of shear waves to increase with frequency. Consequently, the extent of shear wave dispersion can serve as a quantitative measure of tissue viscosity. When fitted with a Voigt model, the equation can be expressed as follows( 15 ): $$\:{c}_{\rho\:}=\sqrt{\frac{2({\mu\:}_{1}^{2}\:+{\omega\:}^{2}{\mu\:}_{2}^{2})}{\rho\:({\mu\:}_{1}+\sqrt{({\mu\:}_{1}^{2}+{\omega\:}^{2}{\mu\:}_{2}^{2}))}}}$$ where \(\:\mu\:1\) and \(\:\mu\:2\) represent shear elasticity and viscosity, respectively, \(\:\omega\:\) denotes the frequency in radians/second, and \(\:\rho\:\) indicates the material density. This dual-parameter framework is clinically attractive in the context of diabetes, where metabolic stress, lipotoxicity, and low-grade inflammation can alter the hepatic microenvironment before advanced fibrosis is established( 16 , 17 ). While prior studies suggest that diabetes may increase tissue viscoelasticity and potentially promote oncogenic progression( 18 ), clinical evidence in diabetic cohorts remains limited, especially regarding the added value of viscosity metrics beyond conventional stiffness measures. Accordingly, the present study evaluated the clinical utility of viscoelastic ultrasound imaging in patients with diabetes. Specifically, we (i) quantify hepatic elasticity and viscosity concurrently via the STVi; (ii) examine their individual and combined associations with diabetic liver involvement; and (iii) explore whether viscosity provides complementary, activity-sensitive information that could enhance risk stratification and longitudinal management beyond traditional ultrasound and stiffness alone. By addressing the mechanistic and methodological gaps outlined above, this work aims to inform a more comprehensive, noninvasive approach to the hepatic manifestations of diabetes. 2 Methods 2.1 Participants This study was conducted in accordance with the ethical principles of the Declaration of Helsinki and was approved by the hospital’s ethics review committee (K2025152). Data from patients diagnosed with T2DM at this research center between January 2025 and May 2025 were collected. The collected data included demographic characteristics, anthropometric measurements, liver stiffness measurements via transient elastography and sound touch viscosity imaging (LSM TE , LSM STVi ), fasting plasma glucose (FPG), glycated hemoglobin (HbA1c), and hepatic function indices. The inclusion criterion was patients with a diagnosis of T2DM. The diagnostic criteria are based on certain guidelines( 19 ). Patients aged 18 years or older provided informed consent to participate in the study. The exclusion criteria were pregnant women, patients with implanted pacemakers, patients with focal liver lesions detected by ultrasound, patients who refused blood sampling or elastographic measurements, and patients with significant alcohol intake (greater than 20 g/day). 2.2 TE and STVi measurements A Resona A20 Pro ultrasound system (Mindray Biomedical Electronics Co., Ltd., Shenzhen, China) equipped with an SC7-1U convex array transducer was utilized for both the TE and the STVi. To ensure methodological rigor, all the examinations were independently conducted by two senior sonologists, each with at least 5 years of experience in abdominal ultrasound diagnostics and more than 3 years of experience in elastography operation and diagnostics. The participants needed an 8-hour fasting period prior to imaging. Examinations were performed in the supine position with the bilateral upper limbs elevated to optimize intercostal acoustic window exposure. The transducer was operated in abdominal preset mode (SC7-1U convex array, frequency range: 1–6 MHz). The S5/S6 hepatic segments were then systematically identified via right intercostal scanning planes, followed by the M-Ref Live imaging protocol for simultaneous viscoelastic data acquisition. The measurement ROI depth was controlled within 6 cm, and the sampling ROI size was uniformly set to 4 × 3 cm. The top of the ROI was located 1–2 cm below the liver capsule, avoiding vascular structures and the gallbladder. The patients should hold their breath when the appropriate area is selected (the breath-hold time must be controlled within 3–5 s). Once the quality control criteria were met—specifically, a stability index (M-STB) of ≥ ****, indicating minimal tissue motion relative to the probe and thereby reducing motion-induced measurement errors, and a reliability index (RLB) of ≥ 90%, reflecting high image quality and measurement accuracy—the “update” function was activated to record the measurement. After 3–4 s, the image was generated and automatically frozen. A circular region of interest (ROI) with a diameter of 20 mm was placed centrally within the most homogeneous area of the elastogram. The STVi was used to assess liver stiffness. The image was then unfrozen, and the “update” function was reactivated to acquire a new image and repeat the measurement. Each patient underwent at least five valid measurements, with the interquartile range-to-median ratio (IQR/M) required to be ≤ 30% to ensure data reliability. In addition, all laboratory measurements were collected within 24 hours of ultrasound examination. 2.3 Data analysis and statistical methods Statistical analysis was performed via SPSS 26.0 and GraphPad Prism 10 software. For statistical comparisons, differences in median values were analyzed via the Mann‒Whitney U test, whereas mean values were analyzed via Student’s t test. ROC curve analysis and the Youden index were used to detect cutoff values for all indices. Logistic regression was employed to evaluate the diagnostic performance of combining LSM STVi and LSM TE . By establishing a logistic regression equation, we could predict the probability of the patient’s disease, the accuracy of which was evaluated by comparing it with the real disease situation, and the ROC curve was used to show the results. A Bland‒Altman plot was used to demonstrate the differences and consistency boundaries between LSM STVi and LSM TE . A P value of less than 0.05 was considered statistically significant. 3 Results 3.1 Demographic, biological, and ultrasonographic characteristics of the study population A portion of participants were excluded from the study because of incomplete data or inability to cooperate with the ultrasound examination. Ultimately, 99 patients with T2DM and 73 participants in the control group were included in the study. The mean age (51.26 ± 13.12) and BMI (25.10 ± 4.27) of the diabetes group were significantly greater than those of the normal group (P < 0.01). The ALT, ALB, ALP and Glu levels were significantly greater than those in the normal group (P < 0.01). (Table 1 ). Table 1 Characteristics information for the entire cohort Parameters T2DM group (n = 99) Control group (n = 73) P value Demographic and anthropometric data Age (years) 51.26 ± 13.12 38.78 ± 12.67 < 0.01 Sex (female), n (%) 33(33.33%) 40(54.79%) < 0.01 Body mass index (kg/m 2 ) 25.10 ± 4.27 21.70 ± 3.18 < 0.01 Ultrasonography LSM STVi (Pa·s) 1.54 ± 0.36 1.07 ± 0.32 < 0.01 LSM TE (KPa) 7.37 ± 2.07 5.71 ± 1.21 < 0.01 ATT 0.70 ± 0.14 0.61 ± 0.08 < 0.01 CS 1.53 ± 0.19 1.36 ± 0.14 < 0.01 Biochemical data ALT (U/L) 33.84 ± 32.62 19.51 ± 14.15 < 0.01 AST (U/L) 26.97 ± 21.09 20.88 ± 6.59 0.06 TBil 11.59 ± 5.48 11.38 ± 5.54 0.05 DBil 3.39 ± 1.97 3.22 ± 1.59 0.71 IBil 8.19 ± 3.89 8.15 ± 4.05 0.70 TP 70.33 ± 5.82 72.14 ± 3.53 0.05 ALB 43.22 ± 4.54 51.16 ± 49.81 < 0.05 GLB 27.11 ± 3.65 27.20 ± 2.56 0.50 ALP 81.87 ± 29.68 70.35 ± 22.33 < 0.05 Glu 38.75 ± 49.23 19.37 ± 12.52 < 0.01 Note: T2DM, Type 2 Diabetes Mellitus; LSM TE , LSM STVi , liver stiffness measurements by Transient Elastography and Sound-Touch Viscoelastography; ATT, Attenuation; CS, Capacitance Score; ALT, Alanine Aminotransferase; AST, Aspartate Aminotransferase; TBil, Total Bilirubin; DBil, Direct Bilirubin; IBil, Indirect Bilirubin; TP, Total Protein; ALB, Albumin; GLB, Globulin; ALP, Alkaline Phosphatase; Glu, Glucose; 3.2 Comparison between the T2DM and control groups and ROC curve analysis Compared with those of patients in the control group, the viscosity value and SWE-based elasticity value were significantly greater in patients with T2DM (P < 0.001). Figure 1 shows representative liver elastography and viscosity images from a healthy control and a T2DM patient. These findings suggest that notable changes in liver biomechanical properties are associated with T2DM. To further evaluate the diagnostic performance of these parameters in distinguishing diabetic individuals from nondiabetic individuals, receiver operating characteristic (ROC) curve analysis was conducted. (Fig. 2 ). The area under the curve (AUC) for LSM STVi was 0.84 (95% CI: 0.78–0.89), with an optimal cutoff value of 1.23 Pa·s, yielding a sensitivity of 80% and a specificity of 75% (P = 0.01). In comparison, the AUC for liver elasticity (LSM TE ) was 0.77 (95% CI: 0.70–0.84), with a cutoff value of 6.27 kPa, a sensitivity of 68%, and a specificity of 77% (P < 0.01). Moreover, a combined diagnostic index incorporating both viscosity and elasticity achieved the highest diagnostic accuracy, with an AUC of 0.89, a sensitivity of 79%, and a specificity of 84%. (Table 2 ) Table 2 Diagnostic parameters with LSM STVi, LSM TE and combined. Parameters Cut-off value Sensitivity, % Specificity, % AUC (95% CI) P value LSM STVi (Pa·s) 1.23 0.80 0.75 0.83 0.01 LSM TE (KPa) 6.27 0.68 0.77 0.78 < 0.01 Combined diagnostic index 0.65 0.79 0.84 0.89 / Note: LSM TE , LSM STVi , liver stiffness measurements by Transient Elastography and Sound-Touch Viscoelastography. 3.3 Correlations between the STVi and TE To investigate the relationship between viscosity and elasticity in terms of liver stiffness, a Spearman correlation analysis was performed across the entire study population. The results revealed a significant positive correlation between liver viscosity (LSM STVi ) and liver elasticity (LSMTE), with a Spearman correlation coefficient of R 2 = 0.91 (p < 0.001), as shown in Fig. 3 . 3.4 Analysis of other clinical and biochemical variables In addition to liver viscoelastic parameters, several demographic and biochemical variables were analyzed to determine their associations with T2DM. (Table 1 ). The results of the Mann–Whitney U tests revealed that age, BMI, ALT, total protein, albumin, alkaline phosphatase, and glutamate transaminase levels were significantly different between the T2DM and control groups (p < 0.05 for all). Among these factors, age and ALT were also identified as significant predictors in the multivariate logistic regression model (p = 0.002 and p = 0.020, respectively). On the other hand, AST, total bilirubin, direct bilirubin, indirect bilirubin, globulin, and γ-glutamyl transpeptidase (GGT) were not significantly different between the two groups (p > 0.05), and none were retained as independent predictors in the logistic regression model. The gender distribution also differed significantly between groups (p = 0.009), but it was not a significant predictor in the regression analysis (p = 0.360). 3.5 Independent Predictors of T2DM Multivariate logistic regression analysis was performed to identify factors independently associated with T2DM. The results are summarized in Table 3 . LSM STVi emerged as a significant independent predictor of T2DM (OR = 20.15, 95% CI: 2.04–198.35, p = 0.01). Age and alanine aminotransferase (ALT) levels were also significantly associated with T2DM (OR = 1.08, 95% CI: 1.03–1.14, p < 0.05 for age; OR = 1.06, 95% CI: 1.01–1.12, p = 0.02 for ALT). Table 3 Logistic regression analysis results Parameters β OR P value 95% CI Age (years) 0.08 1.08 < 0.05 1.03, 1.14 Body mass index (kg/m 2 ) 0.07 1.08 0.38 0.90, 1.28 LSM STVi (Pa·s) 3.00 20.15 0.01 2.04, 198.35 LSM TE (KPa) 0.39 1.48 0.76 0.11, 18.35 ALT (U/L) 0.06 1.06 0.02 1.01, 1.12 AST (U/L) -0.07 0.92 0.07 0.85, 1.00 Note: OR, Odds Ratio; CI: Confidence Interval; LSMT E , LSM STVi , liver stiffness measurements by Transient Elastography and Sound-Touch Viscoelastography; ALT, Alanine Aminotransferase; AST, Aspartate Aminotransferase. In contrast, body mass index (BMI), LSMTE, and aspartate aminotransferase (AST) were not significantly associated with T2DM according to the multivariate model (p > 0.05 for all). Although AST showed a trend toward a negative association (OR = 0.92, 95% CI: 0.85–1.00, p = 0.07), it did not reach statistical significance. 4 Discussion To our knowledge, this is the first study to systematically quantify both hepatic viscosity and stiffness via the same standardized STVi protocol in an unselected adult cohort with T2DM. The results indicate that, compared with healthy controls, patients with T2DM exhibit not only significantly elevated hepatic viscosity but also increased liver stiffness (both p < 0.001). A positive correlation was observed between these two parameters (r = 0.68, p < 0.001). In differentiating T2DM patients from controls, viscosity (AUC = 0.84) outperformed stiffness (AUC = 0.77), and their combined assessment further improved diagnostic performance (AUC = 0.89). These findings suggest that, in the context of diabetes, hepatic viscosity and stiffness reflect interrelated yet distinct pathological processes. Therefore, combined measurement provides added value and supports its incorporation into noninvasive assessment strategies for metabolic-associated liver diseases. Patients with T2DM exhibit alterations in the hepatic microenvironment driven by chronic insulin resistance, lipotoxicity, and glucotoxicity.( 20 ) Advanced glycation end products (AGEs) can induce collagen cross-linking and extracellular matrix (ECM) remodeling, leading to increased tissue stiffness( 18 ). Concurrently, changes in ECM viscoelasticity and its coupling with oncogenic β-catenin signaling not only explain the fibrosis-related increase in stiffness but also provide a rationale for viscosity sensitivity to "active" components (e.g., inflammation, edema, and ECM viscosity). Inhibiting AGE formation or interfering with AGE-collagen cross-linking may reduce tissue viscoelasticity and suppress tumorigenesis, thereby mechanistically supporting the observed parallel changes in viscosity and stiffness. Previous studies have consistently demonstrated that stiffness measurements provide reliable evidence for fibrosis staging( 21 , 22 ), particularly for identifying advanced fibrosis and cirrhosis, whereas viscosity may more closely reflect “active” pathological processes such as inflammation, edema, and necro-regenerative activity. For example, Deffieux et al.( 23 ) reported that viscosity is less accurate than stiffness in general fibrosis staging but is valuable for detecting inflammatory activity and steatosis. A systematic comparison by Chen et al.( 24 ) indicated that stiffness generally outperforms viscosity in fibrosis grading. Furthermore, Sugimoto et al.( 25 ) confirmed that shear wave speed (SWS) strongly correlates with fibrosis, whereas dispersion parameters more accurately reflect inflammatory activity. Our study, however, demonstrated that in a T2DM population, the increase in viscosity and its diagnostic performance (AUC) are comparable to—or may even surpass—those of stiffness. Furthermore, the combined assessment of both parameters further improved the diagnostic accuracy. These findings underscore the importance of a dual-parameter approach that captures both fibrosis (reflected by stiffness) and inflammatory/steatotic activity (reflected by viscosity/dispersion). We also observed a strong correlation between viscosity and stiffness (r = 0.68), which may reflect the fact that most participants in our cohort were already at clinically confirmed stages of disease, where fibrotic remodeling and inflammatory processes progress concurrently, leading to parallel elevations at the population level. A considerable proportion of individuals with T2DM exhibit “subclinical” liver damage even when liver enzymes are within the normal range( 26 ). Incorporating both viscosity and elasticity into a stratified assessment algorithm enables multiparametric, quantitative evaluation of “steatotic activity and fibrotic burden” within a single clinical visit, thereby aiding in the identification of high-risk patients requiring referral to hepatology specialists or close monitoring. Compared with relying solely on stiffness measurements, incorporating viscosity/dispersion enhances sensitivity to active liver injury (e.g., inflammation or edema), thereby reducing the risk of misclassifying potentially reversible damage as irreversible structural scarring. The responsiveness of viscosity to inflammatory and necrotic changes also positions it as a promising early indicator of treatment response( 27 ), including lifestyle interventions, weight management, or intensification of glucose-lowering therapies (e.g., GLP-1 receptor agonists or SGLT2 inhibitors). In contrast, stiffness remains better suited for evaluating medium- to long-term structural changes, such as fibrosis regression. When used in combination, these parameters provide noninvasive quantitative support for an evidence-based management strategy in which short-term changes are monitored via viscosity, whereas long-term outcomes are assessed via elasticity. In T2DM patients exhibiting markedly elevated viscosity or stiffness, early screening for cirrhosis-related complications, such as esophageal varices or HCC, is clinically justified. This approach is consistent with the overall risk profile for cardiorenal and vascular complications, thereby facilitating the seamless integration of liver disease management into the comprehensive multidisciplinary care framework for diabetes patients. The present study utilized a single imaging platform to concurrently assess both viscosity and elasticity within the same patient cohort, thereby minimizing interdevice variability. We proposed and validated the added diagnostic value of combining viscosity and elasticity measurements demonstrating an improvement in the area under the curve (AUC) from 0.84/0.77 to 0.89. This finding supports the feasibility of acquiring multidimensional liver tissue characteristics in a single examination. However, several limitations of this study should be acknowledged. First, as a single-center investigation, the generalizability of our findings requires further validation in external populations. Second, although the biological plausibility of viscosity’s sensitivity to inflammation is consistent with the literature, this emerging parameter still lacks a histological “gold standard” for validation. Third, the cross-sectional design limits our ability to evaluate longitudinal outcomes, such as response to interventions or natural disease progression. Finally, the absence of systematic test‒retest reliability assessments and threshold optimization may affect the translatability of the combined model across diverse patient groups. Future studies should stratify participants on the basis of factors such as BMI, liver enzyme levels, diabetes duration, and glycemic control to evaluate the stability and reproducibility of the combined parameters. Moreover, prospective longitudinal research is warranted to examine their predictive value for liver-related outcomes (e.g., decompensation, hepatocellular carcinoma) as well as systemic metabolic and cardiorenal events. 5 Conclusions In the T2DM population, hepatic viscosity and elasticity are concurrently elevated and closely correlated, reflecting the parallel progression of inflammatory activity, tissue viscosity changes, and fibrotic remodeling. Viscosity demonstrates diagnostic performance comparable to that of elasticity in distinguishing high-risk individuals, and their combination significantly enhances accuracy. Integrating both viscosity and elasticity into a noninvasive stratification strategy for diabetes-related liver disease may improve the early identification of high-risk patients, guide personalized intervention and follow-up, and facilitate a shift in clinical decision-making. Abbreviations T2DM Type 2 diabetes mellitus STVi Sound Touch Viscosity Imaging TE Transient Elastography ROC Receiver operating characteristic AUC Area under the curve NAFLD Nonalcoholic fatty liver disease NASH Nonalcoholic steatohepatitis HCC Hepatocellular Carcinoma CEUS Contrast-enhanced ultrasound FPG Fasting Plasma Glucose HbA1c Glycated hemoglobin ROI Region of interest LSM TE Liver stiffness measurements by Transient Elastography LSM STVi Liver stiffness measurements by Sound Touch Viscosity ATT Attenuation CS Capacitance Score ALT Alanine Aminotransferase AST Aspartate Aminotransferase TBil Total Bilirubin DBil Direct Bilirubin IBil Indirect Bilirubin TP Total Protein ALB Albumin GLB Globulin ALP Alkaline Phosphatase Glu Glucose OR Odds Ratio CI Confidence Interval Declarations Ethics approval and consent to participate This study was performed in accordance with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of the Fourth Affiliated Hospital of School of Medicine (K2025152). All patients received a thorough explanation of the TE and STVi technique and provided signed written informed consent. Data availability The datasets supporting the results of this study are available from the corresponding author upon reasonable request. Competing interests The researchers acknowledge that they have no conflicts of interest. Funding This work was supported by the Key Project of Zhejiang Provincial Natural Science Foundation Joint Fun (LKLZ25H180001); the Zhejiang University horizontal science and technology project (2021-KYY-518053-0036); the National Medical Equipment Promotion Program funded by MIIT (2024TGYY11) and the Education Department Project of Zhejiang Province, China (No. Y202353553); and the Zhejiang Clinovation Pride (CXTD202501028). Author Contributions Bin Ying: Writing-original draft, Funding acquisition; Zhu Wei: Data curation. Xinyue Zhu and Chenke Pan: Formal analysis. Jianlian Pan, Yunkai Luo, Rumei Li; Qianyun Shan; Xiaojin Wu, Shouxing Xu : Data curation, Resources. Jian Chen : Supervision, Writing – review & editing. All the authors have read and approved the final version of the manuscript. Acknowledgment The authors would like to thank the medical staff of the Department of Ultrasound, the Fourth Affiliated Hospital of School of Medicine, for performing the ultrasound data collection. The authors also thank the people who came to the clinic for their trust in the hospital and this study. References Aschner P, Karuranga S, James S, Simmons D, Basit A, Shaw JE, et al. The International Diabetes Federation's guide for diabetes epidemiological studies. Diabetes Res Clin Pract. 2021;172:108630. Forouhi NG, Wareham NJ. Epidemiology of diabetes. Medicine. 2019;47(1):22–7. Tomic D, Shaw JE, Magliano DJ. The burden and risks of emerging complications of diabetes mellitus. 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Investigating liver stiffness and viscosity for fibrosis, steatosis and activity staging using shear wave elastography. J Hepatol. 2015;62(2):317–24. Chen S, Sanchez W, Callstrom MR, Gorman B, Lewis JT, Sanderson SO, et al. Assessment of Liver Viscoelasticity by Using Shear Waves Induced by Ultrasound Radiation Force. Radiology. 2013;266(3):964–70. Sugimoto K, Moriyasu F, Oshiro H, Takeuchi H, Yoshimasu Y, Kasai Y, et al. Clinical utilization of shear wave dispersion imaging in diffuse liver disease. Ultrasonography. 2020;39(1):3–10. Makker J, Tariq H, Kumar K, Ravi M, Shaikh DH, Leung V, et al. Prevalence of advanced liver fibrosis and steatosis in type-2 diabetics with normal transaminases: A prospective cohort study. World J Gastroenterol. 2021;27(6):523–33. Henry CJ, Kaur B, Quek RYC. Chrononutrition in the management of diabetes. Nutr Diabetes. 2020;10(1):6. Additional Declarations No competing interests reported. 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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-7748066","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":566515927,"identity":"22fac2aa-1692-4a66-a02a-010abc43903b","order_by":0,"name":"Bin Ying","email":"","orcid":"","institution":"International Institutes of Medicine, Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Bin","middleName":"","lastName":"Ying","suffix":""},{"id":566515932,"identity":"a9a77773-cf4b-4d4c-8d18-124dd1021b0b","order_by":1,"name":"Wei Zhu","email":"","orcid":"","institution":"International Institutes of Medicine, Zhejiang 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01:15:59","extension":"xml","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":93378,"visible":true,"origin":"","legend":"","description":"","filename":"7c03cf862ff64f4d8e53dd82160f46391structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7748066/v1/81a35dff4c259bdce2b62545.xml"},{"id":99260046,"identity":"20e8e716-76e2-4090-9d41-446ef77016c2","added_by":"auto","created_at":"2025-12-31 01:15:59","extension":"html","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":103298,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7748066/v1/c50530793852d76e6f4b211f.html"},{"id":99260035,"identity":"662335ef-812c-406b-ab51-50d2aeee6d12","added_by":"auto","created_at":"2025-12-31 01:15:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":853049,"visible":true,"origin":"","legend":"\u003cp\u003eThe results of ultrasound liver elasticity and viscosity imaging in one control subject and one patient with T2DM. (A)Liver elastography (left) and viscosity imaging (right) in a healthy control subject; (B) Liver elastography (left) and viscosity imaging (right) in a patient with T2DM. E, Transient elastography; Vi, Sound Touch Viscoelastography.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7748066/v1/9927c89b67e49efdd387d3c5.png"},{"id":99260033,"identity":"a5f156e6-8108-4546-8afb-382c7694b79e","added_by":"auto","created_at":"2025-12-31 01:15:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":37861,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves of different parameters for patients with T2DM. LSM\u003csub\u003eTE\u003c/sub\u003e, LSM\u003csub\u003eSTVi\u003c/sub\u003e, liver stiffness measurements by Transient Elastography and Sound-Touch Viscoelastography.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7748066/v1/9754bf24a9962154d56f1f36.png"},{"id":99318788,"identity":"63394562-eb7c-410c-9cc4-6d3cd9dba5a1","added_by":"auto","created_at":"2025-12-31 16:34:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":249115,"visible":true,"origin":"","legend":"\u003cp\u003eConsistency between STVi and TE in measuring liver stiffness in patients with T2DM. LSM\u003csub\u003eTE\u003c/sub\u003e, LSM\u003csub\u003eSTVi\u003c/sub\u003e, liver stiffness measurements by Transient Elastography and Sound-Touch Viscosity.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7748066/v1/d793fc3dfd723df92603d16c.png"},{"id":99324156,"identity":"d71c2d44-023c-4ea3-a01f-7ac6f1097582","added_by":"auto","created_at":"2025-12-31 16:47:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2123032,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7748066/v1/59e290c3-d622-4afe-8d84-814c942202b6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Early Hepatic Alterations in Type 2 Diabetes: Diagnostic Value of Liver Viscosity in a Prospective Study","fulltext":[{"header":"1 Background","content":"\u003cp\u003eType 2 diabetes mellitus (T2DM) is a rapidly growing global health challenge: in 2019, an estimated 463\u0026nbsp;million people (9.3% of the world\u0026rsquo;s population) were affected, and the number is projected to reach 629\u0026nbsp;million by 2045(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In addition to classic macro- and microvascular complications, diabetes is increasingly linked to hepatic disorders across the spectrum from nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH) to fibrosis and hepatocellular carcinoma (HCC)(\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). This expanding burden underscores an urgent need for accurate, scalable tools to characterize liver involvement in diabetes.\u003c/p\u003e \u003cp\u003eCurrent hepatic assessments in patients with diabetes rely largely on liver enzymes and ultrasound-based imaging. Conventional ultrasonography - B-mode, color Doppler, and contrast-enhanced ultrasound (CEUS) - is widely available and effective for detecting structural and perfusion abnormalities(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e); however, it provides limited insight into the mechanical properties of the hepatic parenchyma, which reflect key biological processes such as inflammation and fibrosis. Ultrasound elastography partly addresses this gap by estimating tissue stiffness (elasticity)(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), but most implementations model tissue as a purely elastic medium and therefore do not capture the viscoelastic behavior of living tissues\u0026mdash;a critical limitation when assessing inflammatory activity and edema that may precede or accompany fibrotic remodeling.\u003c/p\u003e \u003cp\u003eSound touch viscosity imaging (STVi) has emerged as a promising viscoelastic imaging technique that noninvasively quantifies both the elastic and viscous components of soft tissue within a region of interest, providing intuitive parametric maps(\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Conceptually, elasticity reflects resistance to deformation and is closely related to collagen deposition and architectural remodeling(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), whereas viscosity captures energy dissipation and is more sensitive to dynamic processes such as inflammation and tissue edema. In shear wave-based methods, the elastic modulus (\u003cem\u003eE\u003c/em\u003e) can be expressed by the following fundamental relationship:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:E=3\\rho\\:{c}_{s}^{2}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\rho\\:\\)\u003c/span\u003e\u003c/span\u003e represents the tissue density and where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{c}_{s}\\)\u003c/span\u003e\u003c/span\u003e denotes the shear wave velocity.\u003c/p\u003e \u003cp\u003eDifferences in shear wave propagation speeds between healthy and pathological tissues, arising from variations in tissue stiffness, provide the physical basis for tissue characterization. In biological tissues, viscosity induces shear wave dispersion, causing the propagation speed of shear waves to increase with frequency. Consequently, the extent of shear wave dispersion can serve as a quantitative measure of tissue viscosity. When fitted with a Voigt model, the equation can be expressed as follows(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e):\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{c}_{\\rho\\:}=\\sqrt{\\frac{2({\\mu\\:}_{1}^{2}\\:+{\\omega\\:}^{2}{\\mu\\:}_{2}^{2})}{\\rho\\:({\\mu\\:}_{1}+\\sqrt{({\\mu\\:}_{1}^{2}+{\\omega\\:}^{2}{\\mu\\:}_{2}^{2}))}}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\mu\\:1\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\mu\\:2\\)\u003c/span\u003e\u003c/span\u003e represent shear elasticity and viscosity, respectively, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\omega\\:\\)\u003c/span\u003e\u003c/span\u003e denotes the frequency in radians/second, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\rho\\:\\)\u003c/span\u003e\u003c/span\u003e indicates the material density.\u003c/p\u003e \u003cp\u003eThis dual-parameter framework is clinically attractive in the context of diabetes, where metabolic stress, lipotoxicity, and low-grade inflammation can alter the hepatic microenvironment before advanced fibrosis is established(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). While prior studies suggest that diabetes may increase tissue viscoelasticity and potentially promote oncogenic progression(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), clinical evidence in diabetic cohorts remains limited, especially regarding the added value of viscosity metrics beyond conventional stiffness measures.\u003c/p\u003e \u003cp\u003eAccordingly, the present study evaluated the clinical utility of viscoelastic ultrasound imaging in patients with diabetes. Specifically, we (i) quantify hepatic elasticity and viscosity concurrently via the STVi; (ii) examine their individual and combined associations with diabetic liver involvement; and (iii) explore whether viscosity provides complementary, activity-sensitive information that could enhance risk stratification and longitudinal management beyond traditional ultrasound and stiffness alone. By addressing the mechanistic and methodological gaps outlined above, this work aims to inform a more comprehensive, noninvasive approach to the hepatic manifestations of diabetes.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Participants\u003c/h2\u003e \u003cp\u003e This study was conducted in accordance with the ethical principles of the Declaration of Helsinki and was approved by the hospital\u0026rsquo;s ethics review committee (K2025152). Data from patients diagnosed with T2DM at this research center between January 2025 and May 2025 were collected. The collected data included demographic characteristics, anthropometric measurements, liver stiffness measurements via transient elastography and sound touch viscosity imaging (LSM\u003csub\u003eTE\u003c/sub\u003e, LSM\u003csub\u003eSTVi\u003c/sub\u003e), fasting plasma glucose (FPG), glycated hemoglobin (HbA1c), and hepatic function indices.\u003c/p\u003e \u003cp\u003eThe inclusion criterion was patients with a diagnosis of T2DM. The diagnostic criteria are based on certain guidelines(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Patients aged 18 years or older provided informed consent to participate in the study.\u003c/p\u003e \u003cp\u003eThe exclusion criteria were pregnant women, patients with implanted pacemakers, patients with focal liver lesions detected by ultrasound, patients who refused blood sampling or elastographic measurements, and patients with significant alcohol intake (greater than 20 g/day).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 TE and STVi measurements\u003c/h2\u003e \u003cp\u003eA Resona A20 Pro ultrasound system (Mindray Biomedical Electronics Co., Ltd., Shenzhen, China) equipped with an SC7-1U convex array transducer was utilized for both the TE and the STVi. To ensure methodological rigor, all the examinations were independently conducted by two senior sonologists, each with at least 5 years of experience in abdominal ultrasound diagnostics and more than 3 years of experience in elastography operation and diagnostics.\u003c/p\u003e \u003cp\u003eThe participants needed an 8-hour fasting period prior to imaging. Examinations were performed in the supine position with the bilateral upper limbs elevated to optimize intercostal acoustic window exposure. The transducer was operated in abdominal preset mode (SC7-1U convex array, frequency range: 1\u0026ndash;6 MHz). The S5/S6 hepatic segments were then systematically identified via right intercostal scanning planes, followed by the M-Ref Live imaging protocol for simultaneous viscoelastic data acquisition. The measurement ROI depth was controlled within 6 cm, and the sampling ROI size was uniformly set to 4 \u0026times; 3 cm. The top of the ROI was located 1\u0026ndash;2 cm below the liver capsule, avoiding vascular structures and the gallbladder. The patients should hold their breath when the appropriate area is selected (the breath-hold time must be controlled within 3\u0026ndash;5 s). Once the quality control criteria were met\u0026mdash;specifically, a stability index (M-STB) of \u0026ge; ****, indicating minimal tissue motion relative to the probe and thereby reducing motion-induced measurement errors, and a reliability index (RLB) of \u0026ge;\u0026thinsp;90%, reflecting high image quality and measurement accuracy\u0026mdash;the \u0026ldquo;update\u0026rdquo; function was activated to record the measurement. After 3\u0026ndash;4 s, the image was generated and automatically frozen. A circular region of interest (ROI) with a diameter of 20 mm was placed centrally within the most homogeneous area of the elastogram. The STVi was used to assess liver stiffness. The image was then unfrozen, and the \u0026ldquo;update\u0026rdquo; function was reactivated to acquire a new image and repeat the measurement. Each patient underwent at least five valid measurements, with the interquartile range-to-median ratio (IQR/M) required to be \u0026le;\u0026thinsp;30% to ensure data reliability.\u003c/p\u003e \u003cp\u003eIn addition, all laboratory measurements were collected within 24 hours of ultrasound examination.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data analysis and statistical methods\u003c/h2\u003e \u003cp\u003eStatistical analysis was performed via SPSS 26.0 and GraphPad Prism 10 software. For statistical comparisons, differences in median values were analyzed via the Mann‒Whitney U test, whereas mean values were analyzed via Student\u0026rsquo;s t test. ROC curve analysis and the Youden index were used to detect cutoff values for all indices. Logistic regression was employed to evaluate the diagnostic performance of combining LSM\u003csub\u003eSTVi\u003c/sub\u003e and LSM\u003csub\u003eTE\u003c/sub\u003e. By establishing a logistic regression equation, we could predict the probability of the patient\u0026rsquo;s disease, the accuracy of which was evaluated by comparing it with the real disease situation, and the ROC curve was used to show the results. A Bland‒Altman plot was used to demonstrate the differences and consistency boundaries between LSM\u003csub\u003eSTVi\u003c/sub\u003e and LSM\u003csub\u003eTE\u003c/sub\u003e. A P value of less than 0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Demographic, biological, and ultrasonographic characteristics of the study population\u003c/h2\u003e \u003cp\u003eA portion of participants were excluded from the study because of incomplete data or inability to cooperate with the ultrasound examination. Ultimately, 99 patients with T2DM and 73 participants in the control group were included in the study. The mean age (51.26\u0026thinsp;\u0026plusmn;\u0026thinsp;13.12) and BMI (25.10\u0026thinsp;\u0026plusmn;\u0026thinsp;4.27) of the diabetes group were significantly greater than those of the normal group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The ALT, ALB, ALP and Glu levels were significantly greater than those in the normal group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics information for the entire cohort\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2DM group (n\u0026thinsp;=\u0026thinsp;99)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl group (n\u0026thinsp;=\u0026thinsp;73)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eDemographic and anthropometric data\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.26\u0026thinsp;\u0026plusmn;\u0026thinsp;13.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.78\u0026thinsp;\u0026plusmn;\u0026thinsp;12.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (female), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33(33.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40(54.79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.10\u0026thinsp;\u0026plusmn;\u0026thinsp;4.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.70\u0026thinsp;\u0026plusmn;\u0026thinsp;3.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eUltrasonography\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLSM\u003csub\u003eSTVi\u003c/sub\u003e(Pa\u0026middot;s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLSM\u003csub\u003eTE\u003c/sub\u003e(KPa)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.37\u0026thinsp;\u0026plusmn;\u0026thinsp;2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.71\u0026thinsp;\u0026plusmn;\u0026thinsp;1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eATT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eBiochemical data\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.84\u0026thinsp;\u0026plusmn;\u0026thinsp;32.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.51\u0026thinsp;\u0026plusmn;\u0026thinsp;14.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.97\u0026thinsp;\u0026plusmn;\u0026thinsp;21.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.88\u0026thinsp;\u0026plusmn;\u0026thinsp;6.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.59\u0026thinsp;\u0026plusmn;\u0026thinsp;5.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.38\u0026thinsp;\u0026plusmn;\u0026thinsp;5.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.39\u0026thinsp;\u0026plusmn;\u0026thinsp;1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.22\u0026thinsp;\u0026plusmn;\u0026thinsp;1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.19\u0026thinsp;\u0026plusmn;\u0026thinsp;3.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.15\u0026thinsp;\u0026plusmn;\u0026thinsp;4.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.33\u0026thinsp;\u0026plusmn;\u0026thinsp;5.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.14\u0026thinsp;\u0026plusmn;\u0026thinsp;3.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.22\u0026thinsp;\u0026plusmn;\u0026thinsp;4.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.16\u0026thinsp;\u0026plusmn;\u0026thinsp;49.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGLB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.11\u0026thinsp;\u0026plusmn;\u0026thinsp;3.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.20\u0026thinsp;\u0026plusmn;\u0026thinsp;2.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81.87\u0026thinsp;\u0026plusmn;\u0026thinsp;29.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.35\u0026thinsp;\u0026plusmn;\u0026thinsp;22.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.75\u0026thinsp;\u0026plusmn;\u0026thinsp;49.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.37\u0026thinsp;\u0026plusmn;\u0026thinsp;12.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: T2DM, Type 2 Diabetes Mellitus; LSM\u003csub\u003eTE\u003c/sub\u003e, LSM\u003csub\u003eSTVi\u003c/sub\u003e, liver stiffness measurements by Transient Elastography and Sound-Touch Viscoelastography; ATT, Attenuation; CS, Capacitance Score; ALT, Alanine Aminotransferase; AST, Aspartate Aminotransferase; TBil, Total Bilirubin; DBil, Direct Bilirubin; IBil, Indirect Bilirubin; TP, Total Protein; ALB, Albumin; GLB, Globulin; ALP, Alkaline Phosphatase; Glu, Glucose;\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Comparison between the T2DM and control groups and ROC curve analysis\u003c/h2\u003e \u003cp\u003eCompared with those of patients in the control group, the viscosity value and SWE-based elasticity value were significantly greater in patients with T2DM (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows representative liver elastography and viscosity images from a healthy control and a T2DM patient. These findings suggest that notable changes in liver biomechanical properties are associated with T2DM. To further evaluate the diagnostic performance of these parameters in distinguishing diabetic individuals from nondiabetic individuals, receiver operating characteristic (ROC) curve analysis was conducted. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe area under the curve (AUC) for LSM\u003csub\u003eSTVi\u003c/sub\u003e was 0.84 (95% CI: 0.78\u0026ndash;0.89), with an optimal cutoff value of 1.23 Pa\u0026middot;s, yielding a sensitivity of 80% and a specificity of 75% (P\u0026thinsp;=\u0026thinsp;0.01). In comparison, the AUC for liver elasticity (LSM\u003csub\u003eTE\u003c/sub\u003e) was 0.77 (95% CI: 0.70\u0026ndash;0.84), with a cutoff value of 6.27 kPa, a sensitivity of 68%, and a specificity of 77% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003eMoreover, a combined diagnostic index incorporating both viscosity and elasticity achieved the highest diagnostic accuracy, with an AUC of 0.89, a sensitivity of 79%, and a specificity of 84%. (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiagnostic parameters with LSM\u003csub\u003eSTVi,\u003c/sub\u003e LSM\u003csub\u003eTE\u003c/sub\u003e and combined.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCut-off value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSensitivity, %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpecificity, %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAUC (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLSM\u003csub\u003eSTVi\u003c/sub\u003e(Pa\u0026middot;s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLSM\u003csub\u003eTE\u003c/sub\u003e(KPa)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCombined diagnostic index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: LSM\u003csub\u003eTE\u003c/sub\u003e, LSM\u003csub\u003eSTVi\u003c/sub\u003e, liver stiffness measurements by Transient Elastography and Sound-Touch Viscoelastography.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Correlations between the STVi and TE\u003c/h2\u003e \u003cp\u003eTo investigate the relationship between viscosity and elasticity in terms of liver stiffness, a Spearman correlation analysis was performed across the entire study population. The results revealed a significant positive correlation between liver viscosity (LSM\u003csub\u003eSTVi\u003c/sub\u003e) and liver elasticity (LSMTE), with a Spearman correlation coefficient of R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.91 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Analysis of other clinical and biochemical variables\u003c/h2\u003e \u003cp\u003eIn addition to liver viscoelastic parameters, several demographic and biochemical variables were analyzed to determine their associations with T2DM. (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The results of the Mann\u0026ndash;Whitney U tests revealed that age, BMI, ALT, total protein, albumin, alkaline phosphatase, and glutamate transaminase levels were significantly different between the T2DM and control groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all). Among these factors, age and ALT were also identified as significant predictors in the multivariate logistic regression model (p\u0026thinsp;=\u0026thinsp;0.002 and p\u0026thinsp;=\u0026thinsp;0.020, respectively).\u003c/p\u003e \u003cp\u003eOn the other hand, AST, total bilirubin, direct bilirubin, indirect bilirubin, globulin, and γ-glutamyl transpeptidase (GGT) were not significantly different between the two groups (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), and none were retained as independent predictors in the logistic regression model.\u003c/p\u003e \u003cp\u003eThe gender distribution also differed significantly between groups (p\u0026thinsp;=\u0026thinsp;0.009), but it was not a significant predictor in the regression analysis (p\u0026thinsp;=\u0026thinsp;0.360).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Independent Predictors of T2DM\u003c/h2\u003e \u003cp\u003eMultivariate logistic regression analysis was performed to identify factors independently associated with T2DM. The results are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. LSM\u003csub\u003eSTVi\u003c/sub\u003e emerged as a significant independent predictor of T2DM (OR\u0026thinsp;=\u0026thinsp;20.15, 95% CI: 2.04\u0026ndash;198.35, p\u0026thinsp;=\u0026thinsp;0.01). Age and alanine aminotransferase (ALT) levels were also significantly associated with T2DM (OR\u0026thinsp;=\u0026thinsp;1.08, 95% CI: 1.03\u0026ndash;1.14, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for age; OR\u0026thinsp;=\u0026thinsp;1.06, 95% CI: 1.01\u0026ndash;1.12, p\u0026thinsp;=\u0026thinsp;0.02 for ALT).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic regression analysis results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.03, 1.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.90, 1.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLSM\u003csub\u003eSTVi\u003c/sub\u003e(Pa\u0026middot;s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.04, 198.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLSM\u003csub\u003eTE\u003c/sub\u003e(KPa)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.11, 18.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.01, 1.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.85, 1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: OR, Odds Ratio; CI: Confidence Interval; LSMT\u003csub\u003eE\u003c/sub\u003e, LSM\u003csub\u003eSTVi\u003c/sub\u003e, liver stiffness measurements by Transient Elastography and Sound-Touch Viscoelastography; ALT, Alanine Aminotransferase; AST, Aspartate Aminotransferase.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn contrast, body mass index (BMI), LSMTE, and aspartate aminotransferase (AST) were not significantly associated with T2DM according to the multivariate model (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 for all). Although AST showed a trend toward a negative association (OR\u0026thinsp;=\u0026thinsp;0.92, 95% CI: 0.85\u0026ndash;1.00, p\u0026thinsp;=\u0026thinsp;0.07), it did not reach statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eTo our knowledge, this is the first study to systematically quantify both hepatic viscosity and stiffness via the same standardized STVi protocol in an unselected adult cohort with T2DM. The results indicate that, compared with healthy controls, patients with T2DM exhibit not only significantly elevated hepatic viscosity but also increased liver stiffness (both p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A positive correlation was observed between these two parameters (r\u0026thinsp;=\u0026thinsp;0.68, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In differentiating T2DM patients from controls, viscosity (AUC\u0026thinsp;=\u0026thinsp;0.84) outperformed stiffness (AUC\u0026thinsp;=\u0026thinsp;0.77), and their combined assessment further improved diagnostic performance (AUC\u0026thinsp;=\u0026thinsp;0.89). These findings suggest that, in the context of diabetes, hepatic viscosity and stiffness reflect interrelated yet distinct pathological processes. Therefore, combined measurement provides added value and supports its incorporation into noninvasive assessment strategies for metabolic-associated liver diseases.\u003c/p\u003e \u003cp\u003ePatients with T2DM exhibit alterations in the hepatic microenvironment driven by chronic insulin resistance, lipotoxicity, and glucotoxicity.(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) Advanced glycation end products (AGEs) can induce collagen cross-linking and extracellular matrix (ECM) remodeling, leading to increased tissue stiffness(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Concurrently, changes in ECM viscoelasticity and its coupling with oncogenic β-catenin signaling not only explain the fibrosis-related increase in stiffness but also provide a rationale for viscosity sensitivity to \"active\" components (e.g., inflammation, edema, and ECM viscosity). Inhibiting AGE formation or interfering with AGE-collagen cross-linking may reduce tissue viscoelasticity and suppress tumorigenesis, thereby mechanistically supporting the observed parallel changes in viscosity and stiffness.\u003c/p\u003e \u003cp\u003ePrevious studies have consistently demonstrated that stiffness measurements provide reliable evidence for fibrosis staging(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), particularly for identifying advanced fibrosis and cirrhosis, whereas viscosity may more closely reflect \u0026ldquo;active\u0026rdquo; pathological processes such as inflammation, edema, and necro-regenerative activity. For example, Deffieux et al.(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) reported that viscosity is less accurate than stiffness in general fibrosis staging but is valuable for detecting inflammatory activity and steatosis. A systematic comparison by Chen et al.(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) indicated that stiffness generally outperforms viscosity in fibrosis grading. Furthermore, Sugimoto et al.(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) confirmed that shear wave speed (SWS) strongly correlates with fibrosis, whereas dispersion parameters more accurately reflect inflammatory activity.\u003c/p\u003e \u003cp\u003eOur study, however, demonstrated that in a T2DM population, the increase in viscosity and its diagnostic performance (AUC) are comparable to\u0026mdash;or may even surpass\u0026mdash;those of stiffness. Furthermore, the combined assessment of both parameters further improved the diagnostic accuracy. These findings underscore the importance of a dual-parameter approach that captures both fibrosis (reflected by stiffness) and inflammatory/steatotic activity (reflected by viscosity/dispersion).\u003c/p\u003e \u003cp\u003eWe also observed a strong correlation between viscosity and stiffness (r\u0026thinsp;=\u0026thinsp;0.68), which may reflect the fact that most participants in our cohort were already at clinically confirmed stages of disease, where fibrotic remodeling and inflammatory processes progress concurrently, leading to parallel elevations at the population level. A considerable proportion of individuals with T2DM exhibit \u0026ldquo;subclinical\u0026rdquo; liver damage even when liver enzymes are within the normal range(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Incorporating both viscosity and elasticity into a stratified assessment algorithm enables multiparametric, quantitative evaluation of \u0026ldquo;steatotic activity and fibrotic burden\u0026rdquo; within a single clinical visit, thereby aiding in the identification of high-risk patients requiring referral to hepatology specialists or close monitoring.\u003c/p\u003e \u003cp\u003eCompared with relying solely on stiffness measurements, incorporating viscosity/dispersion enhances sensitivity to active liver injury (e.g., inflammation or edema), thereby reducing the risk of misclassifying potentially reversible damage as irreversible structural scarring. The responsiveness of viscosity to inflammatory and necrotic changes also positions it as a promising early indicator of treatment response(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), including lifestyle interventions, weight management, or intensification of glucose-lowering therapies (e.g., GLP-1 receptor agonists or SGLT2 inhibitors). In contrast, stiffness remains better suited for evaluating medium- to long-term structural changes, such as fibrosis regression.\u003c/p\u003e \u003cp\u003eWhen used in combination, these parameters provide noninvasive quantitative support for an evidence-based management strategy in which short-term changes are monitored via viscosity, whereas long-term outcomes are assessed via elasticity. In T2DM patients exhibiting markedly elevated viscosity or stiffness, early screening for cirrhosis-related complications, such as esophageal varices or HCC, is clinically justified. This approach is consistent with the overall risk profile for cardiorenal and vascular complications, thereby facilitating the seamless integration of liver disease management into the comprehensive multidisciplinary care framework for diabetes patients.\u003c/p\u003e \u003cp\u003eThe present study utilized a single imaging platform to concurrently assess both viscosity and elasticity within the same patient cohort, thereby minimizing interdevice variability. We proposed and validated the added diagnostic value of combining viscosity and elasticity measurements demonstrating an improvement in the area under the curve (AUC) from 0.84/0.77 to 0.89. This finding supports the feasibility of acquiring multidimensional liver tissue characteristics in a single examination.\u003c/p\u003e \u003cp\u003eHowever, several limitations of this study should be acknowledged. First, as a single-center investigation, the generalizability of our findings requires further validation in external populations. Second, although the biological plausibility of viscosity\u0026rsquo;s sensitivity to inflammation is consistent with the literature, this emerging parameter still lacks a histological \u0026ldquo;gold standard\u0026rdquo; for validation. Third, the cross-sectional design limits our ability to evaluate longitudinal outcomes, such as response to interventions or natural disease progression. Finally, the absence of systematic test‒retest reliability assessments and threshold optimization may affect the translatability of the combined model across diverse patient groups.\u003c/p\u003e \u003cp\u003eFuture studies should stratify participants on the basis of factors such as BMI, liver enzyme levels, diabetes duration, and glycemic control to evaluate the stability and reproducibility of the combined parameters. Moreover, prospective longitudinal research is warranted to examine their predictive value for liver-related outcomes (e.g., decompensation, hepatocellular carcinoma) as well as systemic metabolic and cardiorenal events.\u003c/p\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eIn the T2DM population, hepatic viscosity and elasticity are concurrently elevated and closely correlated, reflecting the parallel progression of inflammatory activity, tissue viscosity changes, and fibrotic remodeling. Viscosity demonstrates diagnostic performance comparable to that of elasticity in distinguishing high-risk individuals, and their combination significantly enhances accuracy. Integrating both viscosity and elasticity into a noninvasive stratification strategy for diabetes-related liver disease may improve the early identification of high-risk patients, guide personalized intervention and follow-up, and facilitate a shift in clinical decision-making.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eT2DM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eType 2 diabetes mellitus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSTVi\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSound Touch Viscosity Imaging\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTransient Elastography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceiver operating characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArea under the curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNAFLD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNonalcoholic fatty liver disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNASH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNonalcoholic steatohepatitis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHCC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHepatocellular Carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCEUS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eContrast-enhanced ultrasound\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFPG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFasting Plasma Glucose\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHbA1c\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlycated hemoglobin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRegion of interest\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLSM\u003csub\u003eTE\u003c/sub\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLiver stiffness measurements by Transient Elastography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLSM\u003csub\u003eSTVi\u003c/sub\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLiver stiffness measurements by Sound Touch Viscosity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eATT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAttenuation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCapacitance Score\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eALT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAlanine Aminotransferase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAST\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAspartate Aminotransferase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTBil\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTotal Bilirubin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDBil\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDirect Bilirubin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIBil\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIndirect Bilirubin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTotal Protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eALB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAlbumin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGLB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlobulin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eALP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAlkaline Phosphatase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGlu\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlucose\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was performed in\u0026nbsp;accordance with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of the Fourth Affiliated Hospital of School of Medicine (K2025152). All patients received a thorough explanation of the TE and STVi\u0026nbsp;technique and provided signed written informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets supporting the results of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe researchers acknowledge that they have no\u0026nbsp;conflicts of interest.\u003c/p\u003e\n\u003cp skip=\"true\"\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp skip=\"true\"\u003eThis work was supported by the Key Project of Zhejiang Provincial Natural Science Foundation Joint Fun (LKLZ25H180001); the Zhejiang University horizontal science and technology project (2021-KYY-518053-0036); the National Medical Equipment Promotion Program funded by MIIT (2024TGYY11) and the Education Department Project of Zhejiang Province, China (No. Y202353553); and the Zhejiang Clinovation Pride (CXTD202501028).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBin\u0026nbsp;\u003c/strong\u003eYing: Writing-original draft, Funding acquisition; Zhu Wei: Data curation.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eXinyue Zhu and Chenke Pan:\u0026nbsp;\u003c/strong\u003eFormal analysis. \u003cstrong\u003eJianlian Pan, Yunkai Luo, Rumei Li; Qianyun Shan; Xiaojin Wu, Shouxing Xu\u003c/strong\u003e: Data curation, Resources. \u003cstrong\u003eJian Chen\u003c/strong\u003e: Supervision, Writing \u0026ndash; review \u0026amp; editing. All\u0026nbsp;the\u0026nbsp;authors have read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the medical staff of the Department of Ultrasound, the Fourth Affiliated Hospital of School of Medicine, for performing the ultrasound data collection. The authors also thank the people who came to the clinic for their trust in the hospital and this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAschner P, Karuranga S, James S, Simmons D, Basit A, Shaw JE, et al. The International Diabetes Federation's guide for diabetes epidemiological studies. Diabetes Res Clin Pract. 2021;172:108630.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eForouhi NG, Wareham NJ. Epidemiology of diabetes. Medicine. 2019;47(1):22\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTomic D, Shaw JE, Magliano DJ. The burden and risks of emerging complications of diabetes mellitus. Nat Rev Endocrinol. 2022;18(9):525\u0026ndash;39.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen J, Han Y, Xu C, Xiao T, Wang B. Effect of type 2 diabetes mellitus on the risk for hepatocellular carcinoma in chronic liver diseases: a meta-analysis of cohort studies. Eur J Cancer Prev. 2015;24(2):89\u0026ndash;99.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDonadon V, Balbi M, Casarin P, Vario A, Alberti A. Association between hepatocellular carcinoma and type 2 diabetes mellitus in Italy: potential role of insulin. World J Gastroenterol. 2008;14(37):5695\u0026ndash;700.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMantovani A, Targher G. Type 2 diabetes mellitus and risk of hepatocellular carcinoma: spotlight on nonalcoholic fatty liver disease. Ann Transl Med. 2017;5(13):270.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGerstenmaier JF, Gibson RN. Ultrasound in chronic liver disease. Insights Imaging. 2014;5(4):441\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarr RG, Wilson SR, Rubens D, Garcia-Tsao G, Ferraioli G. Update to the Society of Radiologists in Ultrasound Liver Elastography Consensus Statement. Radiology. 2020;296(2):263\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSporea I, Mare R, Lupusoru R, Sima A, Sirli R, Popescu A, et al. Liver Stiffness Evaluation by Transient Elastography in Type 2 Diabetes Mellitus Patients with Ultrasound-proven Steatosis. J Gastrointestin Liver Dis. 2016;25(2):167\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRus G, Faris IH, Torres J, Callejas A, Melchor J. Why Are Viscosity and Nonlinearity Bound to Make an Impact in Clinical Elastographic Diagnosis? Sens (Basel). 2020;20(8).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJung EM, Wiesinger I, Kaiser U, Jung F. Initial experiences with dynamic, quality indicator-based multimodal tissue analysis (M-Ref) with parallel assessment of viscosity and shear wave elastography in liver parenchyma alterations1. Clin Hemorheol Microcirc. 2024;88(4):419\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang X, Zhang Y, Zhou Q, Deng Q. Value of Ultrasound Super-Resolution Imaging for the Assessment of Renal Microcirculation in Patients with Acute Kidney Injury: A Preliminary Study. Diagnostics (Basel). 2024;14(11).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi W, Jiang J, Cao J, Wei L, Cheng R, Lu X, et al. The value of ultrasound viscosity imaging in preoperative differential diagnosis between malignant and benign breast lesions: Preliminary clinical applications. Clin Hemorheol Microcirc. 2025;89(1):111\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarr R, editor. editor Clinical applications of a real time elastography technique in breast imaging. Proc 5th Int Conf on Ultrasonic Measurement and Imaging of Tissue Elasticity. ITEC Austin, TX; 2006.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan H, Huang Q, Wen J, Gao Y. Ultrasound viscoelastic imaging in the noninvasive quantitative assessment of chronic kidney disease. Ren Fail. 2024;46(2):2407882.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHossain IA, Akter S, Bhuiyan FR, Shah MR, Rahman MK, Ali L. Subclinical inflammation in relation to insulin resistance in prediabetic subjects with nonalcoholic fatty liver disease. BMC Res Notes. 2016;9:266.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNoureddin M, Rinella ME. Nonalcoholic Fatty liver disease, diabetes, obesity, and hepatocellular carcinoma. Clin Liver Dis. 2015;19(2):361\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFan W, Adebowale K, Vancza L, Li Y, Rabbi MF, Kunimoto K, et al. Matrix viscoelasticity promotes liver cancer progression in the pre-cirrhotic liver. Nature. 2024;626(7999):635\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeFronzo RA, Ferrannini E, Groop L, Henry RR, Herman WH, Holst JJ, et al. Type 2 diabetes mellitus. Nat Rev Dis Primers. 2015;1:15019.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMlynarska E, Czarnik W, Dzieza N, Jedraszak W, Majchrowicz G, Prusinowski F et al. Type 2 Diabetes Mellitus: New Pathogenetic Mechanisms, Treatment and the Most Important Complications. Int J Mol Sci. 2025;26(3).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerzigotti A, Castera L. Update on ultrasound imaging of liver fibrosis. J Hepatol. 2013;59(1):180\u0026ndash;2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang K, Li Q, Zeng W, Chen X, Liu L, Wan X, et al. Ultrasound score combined with liver stiffness measurement by sound touch elastography for staging liver fibrosis in patients with chronic hepatitis B: a clinical prospective study. Ann Transl Med. 2022;10(6):271.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeffieux T, Gennisson JL, Bousquet L, Corouge M, Cosconea S, Amroun D, et al. Investigating liver stiffness and viscosity for fibrosis, steatosis and activity staging using shear wave elastography. J Hepatol. 2015;62(2):317\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen S, Sanchez W, Callstrom MR, Gorman B, Lewis JT, Sanderson SO, et al. Assessment of Liver Viscoelasticity by Using Shear Waves Induced by Ultrasound Radiation Force. Radiology. 2013;266(3):964\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSugimoto K, Moriyasu F, Oshiro H, Takeuchi H, Yoshimasu Y, Kasai Y, et al. Clinical utilization of shear wave dispersion imaging in diffuse liver disease. Ultrasonography. 2020;39(1):3\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMakker J, Tariq H, Kumar K, Ravi M, Shaikh DH, Leung V, et al. Prevalence of advanced liver fibrosis and steatosis in type-2 diabetics with normal transaminases: A prospective cohort study. World J Gastroenterol. 2021;27(6):523\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHenry CJ, Kaur B, Quek RYC. Chrononutrition in the management of diabetes. Nutr Diabetes. 2020;10(1):6.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Type 2 diabetes mellitus (T2DM), Sound Touch Viscosity imaging (STVi), Shear wave elastography (SWE), Transient Elastography (TE)","lastPublishedDoi":"10.21203/rs.3.rs-7748066/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7748066/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eType 2 diabetes mellitus (T2DM) is associated with a spectrum of liver disorders, ranging from nonalcoholic fatty liver disease to fibrosis and hepatocellular carcinoma. Early detection of hepatic changes is critical for risk stratification and management. Sound Touch Viscosity Imaging (STVi) allows noninvasive quantification of both tissue elasticity and viscosity, potentially providing complementary information beyond conventional stiffness measurements. This study aimed to evaluate the diagnostic value of liver viscosity as a biomarker in patients with T2DM.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e99 patients with T2DM and 73 healthy controls underwent liver assessment using STVi. Measurements of hepatic elasticity and viscosity were obtained under standardized protocols. Laboratory and demographic data were collected. Statistical analyses included Mann\u0026ndash;Whitney U tests, Student\u0026rsquo;s t-tests, receiver operating characteristic (ROC) curve analysis, and multivariate logistic regression to assess the diagnostic performance of elasticity, viscosity, and their combination. Correlation between viscosity and elasticity was evaluated using Spearman analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eT2DM patients showed significantly elevated liver viscosity (1.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36 Pa\u0026middot;s) and elasticity (7.37\u0026thinsp;\u0026plusmn;\u0026thinsp;2.07 kPa) compared to controls (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Viscosity and elasticity were positively correlated (r\u0026thinsp;=\u0026thinsp;0.683, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). ROC analysis revealed superior diagnostic performance for viscosity (AUC\u0026thinsp;=\u0026thinsp;0.836) over elasticity (AUC\u0026thinsp;=\u0026thinsp;0.774), with the combined model yielding an AUC of 0.89. Multivariate analysis identified viscosity, ALT, and age as independent predictors of T2DM.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eLiver viscosity serves as a sensitive biomarker for early liver alterations in T2DM, outperforming stiffness alone. Ultrasound-based viscosity imaging holds promise for non-invasive screening and risk stratification of liver injury in diabetic populations.\u003c/p\u003e","manuscriptTitle":"Early Hepatic Alterations in Type 2 Diabetes: Diagnostic Value of Liver Viscosity in a Prospective Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-31 01:15:54","doi":"10.21203/rs.3.rs-7748066/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-12-29T07:46:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"129718228498059877468054158201814449908","date":"2025-12-27T11:24:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"71469403021234766838897087256568077615","date":"2025-12-25T14:41:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-25T13:56:47+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-28T13:54:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-07T12:47:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-07T12:47:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Imaging","date":"2025-09-30T06:46:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3d7a3b10-7d4f-48fd-a960-4b6cb01207d4","owner":[],"postedDate":"December 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-31T01:15:54+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-31 01:15:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7748066","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7748066","identity":"rs-7748066","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}