Investigating hepatic steatosis: the MISHTI study (Multicentric cross-sectional Indian Study of Hepatic and Metabolic Trends in India)

Investigating hepatic steatosis: the MISHTI study (Multicentric cross-sectional Indian Study of Hepatic and Metabolic Trends in India) | 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 Article Investigating hepatic steatosis: the MISHTI study (Multicentric cross-sectional Indian Study of Hepatic and Metabolic Trends in India) Debasis Datta, Krishna G Seshadri, Samit Ghosal This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6224000/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Background and Aim: Hepatic fibrosis is a critical complication of metabolic disorders, particularly in patients with Type 2 Diabetes (T2D). This study aimed to evaluate the performance of the Fibrosis-4 index (Fib 4) score in detecting significant fibrosis (transient elastography [TE] ≥ 8 kPa) and identify key predictors of advanced fibrosis using logistic regression analysis in patients with T2D. Methods: This retrospective study included propensity-matched T2D and non-T2D patients. Sensitivity, specificity, and Cohen's Kappa were used to assess agreement between Fib 4 score ≥ 1.3 and TE ≥ 8 kPa. Logistic regression models were used to identify independent predictors of significant fibrosis. The predictive performance of the models was evaluated using ROC curves. Results: The Fib 4 score demonstrated high sensitivity (85.3%) but low specificity (13.7%) in the T2D cohort, with a Cohen’s Kappa of -0.01, indicating no agreement with TE. In the non-T2D cohort, specificity improved to 47.2% with a Cohen’s Kappa of 0.16. Logistic regression identified BMI, hypertension, and HbA1c as significant predictors of hepatic fibrosis in T2D patients, with odds ratios of 1.076, 1.824, and 1.279, respectively. Male sex, BMI, AST, and HbA1c were retained in the refined multivariate model, achieving an AUC of 0.735, indicating good discriminatory ability. Elevated transaminases were weakly associated with fibrosis, while BMI and HbA1c showed stronger associations. Conclusion: While Fib 4 is sensitive for detecting significant fibrosis, its low specificity limits its utility as a standalone diagnostic test, particularly in T2D patients. Logistic regression highlighted BMI, AST, and HbA1c as key predictors of fibrosis, emphasising the need to combine non-invasive tools with clinical variables for more accurate risk stratification and improved management of MASLD. Future research should focus on refining diagnostic algorithms to better address the burden of advanced fibrosis in at-risk populations. Health sciences/Gastroenterology/Gastrointestinal diseases/Liver diseases/Non alcoholic steatohepatitis Health sciences/Endocrinology/Endocrine system and metabolic diseases/Diabetes/Type 2 diabetes mellitus MASLD T2D cross-sectional study Fib 4 transient elastography Figures Figure 1 1.0 Introduction Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most common chronic liver disorder, with Type 2 Diabetes (T2D) accounting for a significant proportion of cases (1,2). Globally, the prevalence of MASLD is estimated at 32.4% (3), while in T2D, it reaches 65.33% (4,5). MASLD is a progressive condition that can lead to cirrhosis, end-stage liver disease, and hepatocellular carcinoma (6). Mortality associated with MASLD has doubled in the past decade, and it is now a leading cause of liver transplantation (7,8). Furthermore, MASLD is an independent risk factor for cardiovascular disease, as highlighted by the American Heart Association's 2022 consensus statement (9). Risk factors for MASLD include male sex, age > over 50, and high BMI (10). Studies from Belgium and India also reported poor glycemic control, elevated transaminases, and systolic blood pressure as key correlates (11,12). On transient elastography, 75.8% of Indian T2D patients had some degree of fibrosis, and 7.4% had advanced fibrosis (13). Given the heterogeneity in existing data and the predominance of underpowered studies, the MISHTI study was conducted as a multicentre analysis. This study compared T2D and non-diabetic cohorts to evaluate differences in clinical correlates. It assessed the agreement between guideline-recommended Fib 4 cut-offs and significant fibrosis measured by transient elastography in the Indian population. 2.0 METHODS 2.1 Study population and aims of the study. Patients more than 18 years of age presenting with altered liver transaminases or evidence of steatosis on routine ultrasound (US) examination were included for analysis. Given two of the three centres being dedicated to endocrinology, a skewed data pattern was expected, skewed heavily towards type 2 diabetes (T2D) mellitus. However, data related to both cohorts (T2D and non-T2D) were collected to obtain a reasonable idea about MASLD baseline characteristics and agreement on Fib 4 & TE values. The logistic regression analysis was planned only in the cohort with T2D. The principal aims of this analysis were: To assess the prevalence of MASLD in the T2D cohort. To assess the baseline characteristics of MASLD patients both with and without T2D. The aim is to assess the Fib 4 cut-off values in patients with T2D who agree with significant hepatic fibrosis, defined as a TE value > 8 kPa. To conduct a logistic regression analysis to identify clinical and laboratory attributes associated with MASLD and T2D. Exclusion criteria included hepatitis B or C, a history of hemochromatosis or Wilson’s disease, an alcohol intake exceeding 21 standard drinks per week for males (≥30 g/day) and ≥14 standard drinks per week for females (≥20 g/day), a history of hepatocellular carcinoma, type 1 or 3C diabetes, gestational diabetes (GDM) or pregnancy, and any acute metabolic emergencies. 2.2 Data collection. The data was collected prospectively over eight weeks (July to August 2024). All methods were carried out in accordance with relevant guidelines and regulations, including the Declaration of Helsinki and Indian Council of Medical Research (ICMR) guidelines for biomedical research. All experimental protocols were approved by the INDIRA IVF Hospital Institutional Ethics Committee (Approval: MSPL-MASLD-001, version-1.0; Registration No. ECR/1627/Inst/WB/2021). Following ethical approval, informed consent was obtained from all participants using a standardized patient consent form, which was prepared and distributed to the investigators along with a pre-specified Excel sheet for data collection. The data collected for analysis included the patient's medical history (T2D duration, presence of HT and dyslipidemia), demographics (age and sex), anthropometric measurements (height, weight, and BMI), clinical measurements (SBP and DBP), laboratory parameters (HBA1c, TG, LDL-C, UACR, creatinine, eGFR, ALT, AST, platelet count, Fib 4 score), and imaging (TE). Significant hepatic fibrosis was diagnosed using a TE cut-off value of ≥8 kPa. All three centres used the same device to measure TE and used a standardised reporting format. 2.3 Statistical analysis. The analysis utilized DATAtab and R Studio (v4.2.3) for statistical calculations. Based on an expected sensitivity of 85%, specificity of 90%, prevalence of 38%, and a 10% dropout rate, the required sample size was estimated at 244 (15,16). Descriptive statistics were reported as percentages for categorical variables and as mean ± standard deviation (SD) or median with interquartile range (IQR) for continuous variables, depending on data distribution assessed by the Kolmogorov-Smirnov test. To balance the T2D and non-T2D cohorts, propensity score matching (PSM) was performed using 1:1 nearest-neighbour matching without replacement, adjusting for covariates including age, sex, BMI, SBP, DBP, triglycerides, LDL-C, eGFR, ALT, AST, platelet count, Fib 4 score, and Fibroscan TE categories. Post-matching included 236 T2D and 236 non-T2D patients, with covariate balance assessed using standardised mean differences. Continuous variables were compared using independent t-tests, and categorical variables were compared with chi-square tests. The diagnostic performance of the Fib 4 score (≥ 1.3) in predicting significant fibrosis (TE ≥ 8 kPa) was evaluated using sensitivity, specificity, PPV, NPV, and Cohen’s Kappa for T2D and non-T2D groups. Predictors of MASLD in T2D were identified through logistic regression, with effect sizes reported as odds ratios (OR) with 95% confidence intervals (CI). A significance level of p < 0.05 was used. 3.0 Results 491 T2D patients were screened for MASLD, which was 53.97% prevalent in this cohort. Due to incomplete or missing data, a 12.4% dropout rate resulted in 236 T2D patients with MASLD for the final analysis. Additionally, another 52 MASLD patients without T2D were included in the analysis after 15 patients with incomplete data were removed. The propensity-matched dataset included 236 T2D and 236 non-T2D patients, resulting in balanced groups for comparative analysis. The T2D and non-T2D groups had similar mean ages (58.9 ± 10.2 vs. 59.1 ± 9.8 years, p = 0.854) and male proportions (70% vs. 65%, p = 0.312). BMI was significantly higher in the T2D group (26.4 ± 3.5 vs. 25.1 ± 3.2, p = 0.002), while SBP, DBP, and eGFR were comparable between groups. Triglyceride levels were significantly elevated in T2D patients (168.2 ± 80.3 vs. 150.7 ± 75.1 mg/dL, p = 0.043), but LDL, ALT, AST, and platelet counts showed no significant differences. The Fib 4 score was slightly higher in T2D patients (1.68 ± 0.52 vs. 1.54 ± 0.48, p = 0.087), and the proportion of TE ≥ 8 kPa was similar in both groups (47% vs. 40%, p = 0.304). (Table 1 ) HBA1c data was captured in the T2D cohort only. The median HBA1c was 7.58% (IQR:1.5). Table 1 Comparison of Clinical and Laboratory Parameters Between T2D and Non-T2D Groups After Propensity Matching. *Represents the proportion, while the standard deviation (SD) measures the variability in this proportion. #The difference in means was statistically significant (p < 0.05). Variable T2D Mean (SD) Non-T2D Mean (SD) Difference in Means P-value Age 58.9 (10.2) 59.1 (9.8) -0.2 0.854 Sex (Male) 0.70 (0.46)* 0.65 (0.48)* 0.05 0.312 BMI 26.4 (3.5) 25.1 (3.2) 1.3 0.002# SBP 130.2 (15.4) 128.7 (14.9) 1.5 0.521 DBP 75.6 (9.2) 76.2 (9.5) -0.6 0.605 eGFR 90.5 (15.8) 91.2 (14.7) -0.7 0.772 Triglyceride 168.2 (80.3) 150.7 (75.1) 17.5 0.043# LDL 108.5 (32.4) 104.1 (30.7) 4.4 0.421 ALT 28.3 (10.5) 25.7 (9.8) 2.6 0.215 AST 25.6 (8.4) 24.3 (7.9) 1.3 0.457 Platelet count 220.7 (53.2) 222.9 (51.7) -2.2 0.742 Fib 4 score 1.68 (0.52) 1.54 (0.48) 0.14 0.087 TE ≥ 8 kPa 0.47 (0.20)* 0.40 (0.18)* 0.07 0.304 3.1 Agreement Between Fib 4 Score ≥ 1.3 and Transient Elastography (TE ≥ 8 kPa) in T2D and Non-T2D propensity-matched Cohorts. In the T2D group, the Fib 4 score showed high sensitivity (85.3%) but very low specificity (13.7%), resulting in a low PPV (31.5%) and moderate NPV (66.7%). In the non-T2D group, sensitivity was lower (73.3%), but specificity improved to 47.2%, with a slightly higher PPV (36.7%) and better NPV (80.9%). There was poor agreement between the Fib 4 score and TE in both cohorts, as assessed by Cohen’s D. 3.2 Univariate logistic regression exploring moderators associated with significant hepatic fibrosis (TE ≥8 kPa) in T2D. The variables with the strongest association with significant fibrosis were BMI (OR: 1.076, 95% CI: 1.007–1.150), history of hypertension (OR:1.824, 95% CI 1.047–3.178), and HBA1c (OR: 1.279, 95% CI 1.058–1.547). For one unit increase in BMI, the odds of a TE value ≥8 kPa increased by 7.58%. Both the overweight (23.0-24.9 kg/m 2 ) as well as the obese (≥25 kg/m 2 ) categories were significantly associated with significant hepatic fibrosis. (Table 2 ) The presence of hypertension (HT) was associated with an 82.4% odds of a TE value ≥8 kPa. For one unit increase in HBA1c, the odds of a TE≥8 kPa increased by 27.94%. Elevated transaminases (ALT and AST) were weakly associated with significant hepatic fibrosis. Every one-unit increase in ALT and AST was associated with a 0.84% and a 1.66% increased odds of TE ≥8 kPa, respectively. (Table 2 ) Table 2 Univariate logistic regression analysis for variables associated with significant hepatic fibrosis. OR: Odds ratio. CI: Confidence interval. BMI: Body mass index. HBA1c: glycated hemoglobin. UACR: Urine albumin creatinine ratio. TG. Triglyceride. LDL-C: low-density lipoprotein cholesterol. ALT: alanine aminotransferase. AST: aspartate aminotransferase. Variable Subgroups OR 95% CI P value Age 1.003 0.977–1.028 0.846 Sex Male (Ref: Female) 1.511 0.809–2.821 0.195 T2D duration 1.021 0.981–1.064 0.308 HT Yes (Ref: No) 1.824 1.047–3.178 0.034 Dyslipidemia Yes (Ref: No) 1.717 0.954–3.09 0.071 BMI 1.076 1.007–1.150 0.031 23-24.9 (Ref:<23) 3.094 1.197–7.993 0.020 ≥25 (Ref:<23) 3.171 1.381–7.281 0.007 HBA1c 1.279 1.058–1.547 0.011 UACR 1.0004 0.9998–1.001 0.197 eGFR 1.694 0.866–3.273 0.124 TG 0.999 0.997–1.002 0.597 LDL-C 1.002 0.995–1.011 0.478 ALT 1.001 1.0001–1.016 0.047 AST 1.016 1.006–1.028 0.003 Platelet count 1.000 0.996–1.004 0.996 3.3 Multivariate binary logistic regression exploring moderators associated with significant hepatic fibrosis (TE ≥8 kPa) in T2D. The Chi-square test for the multivariate binary logistic regression model yielded a statistic of 38.51 with 14 degrees of freedom and a p-value < 0.001, indicating a statistically significant overall relationship between the model's predictors and the outcome. The variables associated with significant hepatic fibrosis include male sex (OR: 2.276, 95% CI 1.00-4.842), BMI (1.127, 95% CI 1.037–1.2224), AST (OR: 1.021, 95% CI 1.001–1.041), and HBA1c (OR: 1.361, 95% CI 1.086–1.706). (Table 3 ) The predictive performance of the logistic regression model was evaluated using the Receiver Operating Characteristic (ROC) curve, which yielded an Area Under the Curve (AUC) of 0.735. (Fig. 1 ) This indicates good discrimination between the outcome classes, with the model performing significantly better than chance. The ROC curve demonstrates an acceptable balance between sensitivity and specificity, supporting the robustness of the refined model in predicting the binary outcome. (Fig. 1 ) Table 3 Multivariate logistic regression analysis for variables associated with significant hepatic fibrosis. Variable Subgroups OR 95% CI P value Age 1.008 0.976–1.042 0.606 Sex Male (Ref: Female) 2.276 1.0–4.842 0.033 T2D Duration 0.979 0.934–1.027 0.399 Hypertension Yes (Ref: No) 1.362 0.709–2.615 0.353 Dyslipidemia Yes (Ref: No) 2.019 0.998–4.087 0.051 BMI 1.127 1.037–1.224 0.005 UACR 1.0004 0.99–1.001 0.259 eGFR 0.995 0.98–1.007 0.454 Triglyceride 0.998 0.995–1.001 0.24 LDL 0.999 0.990–1.008 0.924 ALT 0.994 0.977–1.01 0.472 AST 1.021 1.001–1.041 0.031 Platelet count 0.997 0.992–1.003 0.436 HbA1c 1.361 1.086–1.706 0.007 3.4 Sensitivity Analysis and Model Refinement. The initial multivariate model, including 14 predictors, demonstrated overall significance (χ² = 38.51, df = 14, p < 0.001) but did not significantly outperform the null model (LLR p = 0.081). Stepwise backward elimination identified Sex (Male), AST, BMI, and HbA1c as key variables. Although BMI and HbA1c were not statistically significant, they were retained for clinical relevance. The refined model achieved a lower AIC (696.62) and a significant LLR test (p = 0.039), offering improved interpretability while maintaining clinical significance. 4.0 Discussion The prevalence of MASLD is very high in the Indian subcontinent. (14) However, there is no guideline for identifying high-risk individuals in the Indian population. A single-centre, cross-sectional study from Mumbai, India, suggested BMI as an independent risk factor for fibrosis. (15) Identifying risk factors for MASLD early in the disease can prevent the progression of liver fibrosis if appropriate interventions are taken Due to the paucity of structured data from India, the MISHTI cross-sectional observational study was conducted. Our analysis revealed significant differences in BMI and triglyceride levels between T2D and non-T2D patients, reflecting higher metabolic risk in T2D patients. Logistic regression, conducted on the T2D cohort, identified male sex, BMI, AST, and HbA1c as key predictors of significant liver fibrosis. Though our study showed significant differences in BMI and triglyceride levels between T2D and non-T2D patients, interestingly, triglyceride levels were not an independent risk factor predicting considerable fibrosis. Although current guidelines recommend using Fib 4 for initial risk assessment, followed by imaging tools like transient elastography for confirmation, our study found poor agreement between the Fib 4 scores and TE (16,17). Graupera I et al. reported a very similar observation: Measurement of waist circumference outperformed Fib 4-based screening in individuals with risk factors for chronic liver disease. (18) In India, ultrasound findings of fatty liver are often overlooked. We recommend that all patients diagnosed with fatty liver undergo estimation of liver function tests, BMI, HBA1C, fasting lipid profiles, and FIB 4 score screenings. These high-risk patients should be identified and evaluated with appropriate imaging techniques (transient elastography or MR elastography of the liver) to detect early liver fibrosis. We should not depend solely on the FIB4 score to diagnose liver fibrosis. The current study's retrospective design and the absence of liver biopsy as the gold standard for fibrosis assessment are limitations. In addition, the data were grossly skewed towards the T2D cohort, making a comparative analysis of the risk factors between the T2D and non-T2D cohorts impossible. Despite these limitations, the strengths of multicentre data, propensity score matching, and robust statistical modelling provide valuable insights into MASLD in T2D patients. Future research should refine diagnostic algorithms and develop targeted strategies to mitigate the burden of MASLD. 5.0 Conclusion This study identified key clinical and laboratory predictors of significant hepatic fibrosis in T2D and non-T2D patients. The T2D group had a higher prevalence of metabolic risk factors, including elevated BMI and triglycerides. While Fib 4 demonstrated high sensitivity, its poor specificity limits its use as a standalone tool, particularly in T2D patients. A refined predictive model incorporating BMI, AST, and HbA1c achieved good discriminatory ability (AUC 0.735). These findings emphasise the need for combining non-invasive tools with clinical variables to improve the diagnosis and management of MASLD in high-risk populations. Future research should focus on developing tailored diagnostic algorithms to reduce the burden of advanced liver disease. Declarations Disclosures: The authors do not have any conflict to disclose for this manuscript. Financial Support and Sponsorship section: No external financial support was received. Conflict of interest: None. Data availability statement: The datasets used and/or analysed during the current study are available from the corresponding author, Dr. Samit Ghosal (Email: [email protected] ), on reasonable request. Ethical Committee Approval: MSPL-MASLD-001 & version-1.0. Approving centre: INDIRA IVF hospital Institutional Ethics Committee. Reg No. ECR/1627/Inst/WB/2021. Acknowledgement: To all the centres who agreed to share the data essential to conducting this analysis. References Lekakis V, Papatheodoridis GV. Natural history of metabolic dysfunction-associated steatotic liver disease. Eur J Intern Med. 2024;122:3-10. doi: 10.1016/j.ejim.2023.11.005. https://pubmed.ncbi.nlm.nih.gov/37940495/ Otero Sanchez L, Chen Y, Lassailly G, Qi X. Exploring the links between types two diabetes and liver-related complications: A comprehensive review. 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Cite Share Download PDF Status: Published Journal Publication published 09 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 30 May, 2025 Reviews received at journal 29 May, 2025 Reviewers agreed at journal 30 Apr, 2025 Reviews received at journal 11 Apr, 2025 Reviewers agreed at journal 11 Apr, 2025 Reviewers agreed at journal 20 Mar, 2025 Reviewers invited by journal 18 Mar, 2025 Editor assigned by journal 18 Mar, 2025 Editor invited by journal 17 Mar, 2025 Submission checks completed at journal 14 Mar, 2025 First submitted to journal 14 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-6224000","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":431545296,"identity":"3e252943-99ce-40a5-bdf6-e5d62c13837a","order_by":0,"name":"Debasis Datta","email":"","orcid":"","institution":"Fortis Hospital","correspondingAuthor":false,"prefix":"","firstName":"Debasis","middleName":"","lastName":"Datta","suffix":""},{"id":431545298,"identity":"42010bcd-ae11-4b9f-b98a-1297f84b45f8","order_by":1,"name":"Krishna G Seshadri","email":"","orcid":"","institution":"Apollo hospitals","correspondingAuthor":false,"prefix":"","firstName":"Krishna","middleName":"G","lastName":"Seshadri","suffix":""},{"id":431545299,"identity":"27791c50-ff65-40af-95d1-15387a85e5d6","order_by":2,"name":"Samit Ghosal","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYJACCSBOAGLGAx+AJBs7CVoYDs4AaWEmRcthHhBJSIt5++nEGz/+1Obx8x9+cNjm1zZ5PmYGxg8fc3BrkTmTu9myt+14seSMNIPDuX23DduYGZglZ27D56jcbRK8DccSN9xgAGrpuc0I1MLGzItPC//bbZJ//gC1nD/+4bBlz217wlokcrdJ87DVJG44kGNwmOHH7UQitLzdbC3bdiBx5oycgoO9DbeT25gZm/H7hT934803f+oS+/mPb3zw489t2/ntzQc/fMSjBQoOQyjGNjDZQFA9ENRB6T/EKB4Fo2AUjIKRBgDNbFiXvX8/oAAAAABJRU5ErkJggg==","orcid":"","institution":"Nightingale Hospital","correspondingAuthor":true,"prefix":"","firstName":"Samit","middleName":"","lastName":"Ghosal","suffix":""}],"badges":[],"createdAt":"2025-03-14 06:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6224000/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6224000/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-10871-9","type":"published","date":"2025-07-09T15:57:33+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79330716,"identity":"aeb0f0ed-5aa4-44ed-a1ad-5038ef5414b6","added_by":"auto","created_at":"2025-03-27 06:35:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":38986,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver Operating Characteristic (ROC) curve for the multivariate binary logistic regression model. The diagonal red line represents the reference line for random chance (AUC = 0.735).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6224000/v1/0f200a618149679f1a8762ed.png"},{"id":86699385,"identity":"927e5f31-cd7b-465e-8ccc-f71a74e75464","added_by":"auto","created_at":"2025-07-14 16:08:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":894696,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6224000/v1/1190b8b9-da1d-47d2-8864-45d480523a6a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Investigating hepatic steatosis: the MISHTI study (Multicentric cross-sectional Indian Study of Hepatic and Metabolic Trends in India)","fulltext":[{"header":"1.0 Introduction","content":"\u003cp\u003eMetabolic dysfunction-associated steatotic liver disease (MASLD) is the most common chronic liver disorder, with Type 2 Diabetes (T2D) accounting for a significant proportion of cases (1,2). Globally, the prevalence of MASLD is estimated at 32.4% (3), while in T2D, it reaches 65.33% (4,5). MASLD is a progressive condition that can lead to cirrhosis, end-stage liver disease, and hepatocellular carcinoma (6). Mortality associated with MASLD has doubled in the past decade, and it is now a leading cause of liver transplantation (7,8). Furthermore, MASLD is an independent risk factor for cardiovascular disease, as highlighted by the American Heart Association's 2022 consensus statement (9).\u003c/p\u003e \u003cp\u003eRisk factors for MASLD include male sex, age\u0026thinsp;\u0026gt;\u0026thinsp;over 50, and high BMI (10). Studies from Belgium and India also reported poor glycemic control, elevated transaminases, and systolic blood pressure as key correlates (11,12). On transient elastography, 75.8% of Indian T2D patients had some degree of fibrosis, and 7.4% had advanced fibrosis (13).\u003c/p\u003e \u003cp\u003eGiven the heterogeneity in existing data and the predominance of underpowered studies, the MISHTI study was conducted as a multicentre analysis. This study compared T2D and non-diabetic cohorts to evaluate differences in clinical correlates. It assessed the agreement between guideline-recommended Fib 4 cut-offs and significant fibrosis measured by transient elastography in the Indian population.\u003c/p\u003e"},{"header":"2.0 METHODS","content":" \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study population and aims of the study.\u003c/h2\u003e \u003cp\u003ePatients more than 18 years of age presenting with altered liver transaminases or evidence of steatosis on routine ultrasound (US) examination were included for analysis. Given two of the three centres being dedicated to endocrinology, a skewed data pattern was expected, skewed heavily towards type 2 diabetes (T2D) mellitus. However, data related to both cohorts (T2D and non-T2D) were collected to obtain a reasonable idea about MASLD baseline characteristics and agreement on Fib 4 \u0026amp; TE values. The logistic regression analysis was planned only in the cohort with T2D.\u003c/p\u003e \u003cp\u003eThe principal aims of this analysis were:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eTo assess the prevalence of MASLD in the T2D cohort.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTo assess the baseline characteristics of MASLD patients both with and without T2D.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe aim is to assess the Fib 4 cut-off values in patients with T2D who agree with significant hepatic fibrosis, defined as a TE value\u0026thinsp;\u0026gt;\u0026thinsp;8 kPa.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTo conduct a logistic regression analysis to identify clinical and laboratory attributes associated with MASLD and T2D.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eExclusion criteria included hepatitis B or C, a history of hemochromatosis or Wilson\u0026rsquo;s disease, an alcohol intake exceeding 21 standard drinks per week for males (\u0026ge;30 g/day) and \u0026ge;14 standard drinks per week for females (\u0026ge;20 g/day), a history of hepatocellular carcinoma, type 1 or 3C diabetes, gestational diabetes (GDM) or pregnancy, and any acute metabolic emergencies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data collection.\u003c/h2\u003e \u003cp\u003eThe data was collected prospectively over eight weeks (July to August 2024). All methods were carried out in accordance with relevant guidelines and regulations, including the Declaration of Helsinki and Indian Council of Medical Research (ICMR) guidelines for biomedical research. All experimental protocols were approved by the INDIRA IVF Hospital Institutional Ethics Committee (Approval: MSPL-MASLD-001, version-1.0; Registration No. ECR/1627/Inst/WB/2021). Following ethical approval, informed consent was obtained from all participants using a standardized patient consent form, which was prepared and distributed to the investigators along with a pre-specified Excel sheet for data collection. The data collected for analysis included the patient's medical history (T2D duration, presence of HT and dyslipidemia), demographics (age and sex), anthropometric measurements (height, weight, and BMI), clinical measurements (SBP and DBP), laboratory parameters (HBA1c, TG, LDL-C, UACR, creatinine, eGFR, ALT, AST, platelet count, Fib 4 score), and imaging (TE).\u003c/p\u003e \u003cp\u003eSignificant hepatic fibrosis was diagnosed using a TE cut-off value of \u0026ge;8 kPa. All three centres used the same device to measure TE and used a standardised reporting format.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Statistical analysis.\u003c/h2\u003e \u003cp\u003eThe analysis utilized DATAtab and R Studio (v4.2.3) for statistical calculations. Based on an expected sensitivity of 85%, specificity of 90%, prevalence of 38%, and a 10% dropout rate, the required sample size was estimated at 244 (15,16). Descriptive statistics were reported as percentages for categorical variables and as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or median with interquartile range (IQR) for continuous variables, depending on data distribution assessed by the Kolmogorov-Smirnov test.\u003c/p\u003e \u003cp\u003eTo balance the T2D and non-T2D cohorts, propensity score matching (PSM) was performed using 1:1 nearest-neighbour matching without replacement, adjusting for covariates including age, sex, BMI, SBP, DBP, triglycerides, LDL-C, eGFR, ALT, AST, platelet count, Fib 4 score, and Fibroscan TE categories. Post-matching included 236 T2D and 236 non-T2D patients, with covariate balance assessed using standardised mean differences. Continuous variables were compared using independent t-tests, and categorical variables were compared with chi-square tests.\u003c/p\u003e \u003cp\u003eThe diagnostic performance of the Fib 4 score (\u0026ge;\u0026thinsp;1.3) in predicting significant fibrosis (TE\u0026thinsp;\u0026ge;\u0026thinsp;8 kPa) was evaluated using sensitivity, specificity, PPV, NPV, and Cohen\u0026rsquo;s Kappa for T2D and non-T2D groups. Predictors of MASLD in T2D were identified through logistic regression, with effect sizes reported as odds ratios (OR) with 95% confidence intervals (CI). A significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was used.\u003c/p\u003e \u003c/div\u003e"},{"header":"3.0 Results","content":"\u003cp\u003e491 T2D patients were screened for MASLD, which was 53.97% prevalent in this cohort. Due to incomplete or missing data, a 12.4% dropout rate resulted in 236 T2D patients with MASLD for the final analysis. Additionally, another 52 MASLD patients without T2D were included in the analysis after 15 patients with incomplete data were removed. The propensity-matched dataset included 236 T2D and 236 non-T2D patients, resulting in balanced groups for comparative analysis.\u003c/p\u003e \u003cp\u003eThe T2D and non-T2D groups had similar mean ages (58.9\u0026thinsp;\u0026plusmn;\u0026thinsp;10.2 vs. 59.1\u0026thinsp;\u0026plusmn;\u0026thinsp;9.8 years, p\u0026thinsp;=\u0026thinsp;0.854) and male proportions (70% vs. 65%, p\u0026thinsp;=\u0026thinsp;0.312). BMI was significantly higher in the T2D group (26.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5 vs. 25.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2, p\u0026thinsp;=\u0026thinsp;0.002), while SBP, DBP, and eGFR were comparable between groups. Triglyceride levels were significantly elevated in T2D patients (168.2\u0026thinsp;\u0026plusmn;\u0026thinsp;80.3 vs. 150.7\u0026thinsp;\u0026plusmn;\u0026thinsp;75.1 mg/dL, p\u0026thinsp;=\u0026thinsp;0.043), but LDL, ALT, AST, and platelet counts showed no significant differences. The Fib 4 score was slightly higher in T2D patients (1.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52 vs. 1.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48, p\u0026thinsp;=\u0026thinsp;0.087), and the proportion of TE\u0026thinsp;\u0026ge;\u0026thinsp;8 kPa was similar in both groups (47% vs. 40%, p\u0026thinsp;=\u0026thinsp;0.304). (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) HBA1c data was captured in the T2D cohort only. The median HBA1c was 7.58% (IQR:1.5).\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\u003eComparison of Clinical and Laboratory Parameters Between T2D and Non-T2D Groups After Propensity Matching. *Represents the proportion, while the standard deviation (SD) measures the variability in this proportion. #The difference in means was statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\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\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2D Mean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-T2D Mean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDifference in Means\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58.9 (10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59.1 (9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.854\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex (Male)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.70 (0.46)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.65 (0.48)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26.4 (3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.1 (3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002#\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSBP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e130.2 (15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e128.7 (14.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.521\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDBP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75.6 (9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76.2 (9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.605\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eeGFR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e90.5 (15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91.2 (14.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.772\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTriglyceride\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e168.2 (80.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150.7 (75.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.043#\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLDL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e108.5 (32.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e104.1 (30.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.421\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eALT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.3 (10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.7 (9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAST\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25.6 (8.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.3 (7.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.457\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlatelet count\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e220.7 (53.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e222.9 (51.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.742\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFib 4 score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.68 (0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.54 (0.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTE\u0026thinsp;\u0026ge;\u0026thinsp;8 kPa\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.47 (0.20)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.40 (0.18)*\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\u003e0.304\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3.1 Agreement Between Fib 4 Score\u0026thinsp;\u0026ge;\u0026thinsp;1.3 and Transient Elastography (TE\u0026thinsp;\u0026ge;\u0026thinsp;8 kPa) in T2D and Non-T2D propensity-matched Cohorts.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn the T2D group, the Fib 4 score showed high sensitivity (85.3%) but very low specificity (13.7%), resulting in a low PPV (31.5%) and moderate NPV (66.7%). In the non-T2D group, sensitivity was lower (73.3%), but specificity improved to 47.2%, with a slightly higher PPV (36.7%) and better NPV (80.9%). There was poor agreement between the Fib 4 score and TE in both cohorts, as assessed by Cohen\u0026rsquo;s D.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Univariate logistic regression exploring moderators associated with significant hepatic fibrosis (TE \u0026ge;8 kPa) in T2D.\u003c/h2\u003e \u003cp\u003eThe variables with the strongest association with significant fibrosis were BMI (OR: 1.076, 95% CI: 1.007\u0026ndash;1.150), history of hypertension (OR:1.824, 95% CI 1.047\u0026ndash;3.178), and HBA1c (OR: 1.279, 95% CI 1.058\u0026ndash;1.547). For one unit increase in BMI, the odds of a TE value \u0026ge;8 kPa increased by 7.58%. Both the overweight (23.0-24.9 kg/m\u003csup\u003e2\u003c/sup\u003e) as well as the obese (\u0026ge;25 kg/m\u003csup\u003e2\u003c/sup\u003e) categories were significantly associated with significant hepatic fibrosis. (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) The presence of hypertension (HT) was associated with an 82.4% odds of a TE value \u0026ge;8 kPa. For one unit increase in HBA1c, the odds of a TE\u0026ge;8 kPa increased by 27.94%. Elevated transaminases (ALT and AST) were weakly associated with significant hepatic fibrosis. Every one-unit increase in ALT and AST was associated with a 0.84% and a 1.66% increased odds of TE \u0026ge;8 kPa, respectively. (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\u003eUnivariate logistic regression analysis for variables associated with significant hepatic fibrosis. OR: Odds ratio. CI: Confidence interval. BMI: Body mass index. HBA1c: glycated hemoglobin. UACR: Urine albumin creatinine ratio. TG. Triglyceride. LDL-C: low-density lipoprotein cholesterol. ALT: alanine aminotransferase. AST: aspartate aminotransferase.\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=\"left\" 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\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubgroups\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\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.977\u0026ndash;1.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale (Ref: Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.809\u0026ndash;2.821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2D duration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.981\u0026ndash;1.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes (Ref: No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.047\u0026ndash;3.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.034\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyslipidemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes (Ref: No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.954\u0026ndash;3.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.007\u0026ndash;1.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.031\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23-24.9 (Ref:\u0026lt;23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.197\u0026ndash;7.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.020\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;25 (Ref:\u0026lt;23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.381\u0026ndash;7.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHBA1c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.058\u0026ndash;1.547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUACR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9998\u0026ndash;1.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.866\u0026ndash;3.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.997\u0026ndash;1.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.597\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.995\u0026ndash;1.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.478\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0001\u0026ndash;1.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.047\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.006\u0026ndash;1.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.996\u0026ndash;1.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3.3 Multivariate binary logistic regression exploring moderators associated with significant hepatic fibrosis (TE \u0026ge;8 kPa) in T2D.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe Chi-square test for the multivariate binary logistic regression model yielded a statistic of 38.51 with 14 degrees of freedom and a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001, indicating a statistically significant overall relationship between the model's predictors and the outcome.\u003c/p\u003e \u003cp\u003eThe variables associated with significant hepatic fibrosis include male sex (OR: 2.276, 95% CI 1.00-4.842), BMI (1.127, 95% CI 1.037\u0026ndash;1.2224), AST (OR: 1.021, 95% CI 1.001\u0026ndash;1.041), and HBA1c (OR: 1.361, 95% CI 1.086\u0026ndash;1.706). (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThe predictive performance of the logistic regression model was evaluated using the Receiver Operating Characteristic (ROC) curve, which yielded an Area Under the Curve (AUC) of 0.735. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) This indicates good discrimination between the outcome classes, with the model performing significantly better than chance. The ROC curve demonstrates an acceptable balance between sensitivity and specificity, supporting the robustness of the refined model in predicting the binary outcome. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\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\u003eMultivariate logistic regression analysis for variables associated with significant hepatic fibrosis.\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=\"left\" 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\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubgroups\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\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.976\u0026ndash;1.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.606\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale (Ref: Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0\u0026ndash;4.842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.033\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2D Duration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.934\u0026ndash;1.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.399\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes (Ref: No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.709\u0026ndash;2.615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.353\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyslipidemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes (Ref: No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.998\u0026ndash;4.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.037\u0026ndash;1.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUACR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99\u0026ndash;1.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.259\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.98\u0026ndash;1.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.454\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglyceride\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.995\u0026ndash;1.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.990\u0026ndash;1.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.924\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.977\u0026ndash;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.472\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.001\u0026ndash;1.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.031\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.992\u0026ndash;1.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.436\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.086\u0026ndash;1.706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Sensitivity Analysis and Model Refinement.\u003c/h2\u003e \u003cp\u003eThe initial multivariate model, including 14 predictors, demonstrated overall significance (χ\u0026sup2; = 38.51, df\u0026thinsp;=\u0026thinsp;14, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) but did not significantly outperform the null model (LLR p\u0026thinsp;=\u0026thinsp;0.081). Stepwise backward elimination identified Sex (Male), AST, BMI, and HbA1c as key variables. Although BMI and HbA1c were not statistically significant, they were retained for clinical relevance. The refined model achieved a lower AIC (696.62) and a significant LLR test (p\u0026thinsp;=\u0026thinsp;0.039), offering improved interpretability while maintaining clinical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"4.0 Discussion","content":"\u003cp\u003eThe prevalence of MASLD is very high in the Indian subcontinent. (14) However, there is no guideline for identifying high-risk individuals in the Indian population. A single-centre, cross-sectional study from Mumbai, India, suggested BMI as an independent risk factor for fibrosis. (15) Identifying risk factors for MASLD early in the disease can prevent the progression of liver fibrosis if appropriate interventions are taken\u003c/p\u003e \u003cp\u003eDue to the paucity of structured data from India, the MISHTI cross-sectional observational study was conducted. Our analysis revealed significant differences in BMI and triglyceride levels between T2D and non-T2D patients, reflecting higher metabolic risk in T2D patients.\u003c/p\u003e \u003cp\u003eLogistic regression, conducted on the T2D cohort, identified male sex, BMI, AST, and HbA1c as key predictors of significant liver fibrosis. Though our study showed significant differences in BMI and triglyceride levels between T2D and non-T2D patients, interestingly, triglyceride levels were not an independent risk factor predicting considerable fibrosis.\u003c/p\u003e \u003cp\u003e Although current guidelines recommend using Fib 4 for initial risk assessment, followed by imaging tools like transient elastography for confirmation, our study found poor agreement between the Fib 4 scores and TE (16,17). Graupera I et al. reported a very similar observation: Measurement of waist circumference outperformed Fib 4-based screening in individuals with risk factors for chronic liver disease. (18)\u003c/p\u003e \u003cp\u003eIn India, ultrasound findings of fatty liver are often overlooked. We recommend that all patients diagnosed with fatty liver undergo estimation of liver function tests, BMI, HBA1C, fasting lipid profiles, and FIB 4 score screenings. These high-risk patients should be identified and evaluated with appropriate imaging techniques (transient elastography or MR elastography of the liver) to detect early liver fibrosis. We should not depend solely on the FIB4 score to diagnose liver fibrosis.\u003c/p\u003e \u003cp\u003eThe current study's retrospective design and the absence of liver biopsy as the gold standard for fibrosis assessment are limitations. In addition, the data were grossly skewed towards the T2D cohort, making a comparative analysis of the risk factors between the T2D and non-T2D cohorts impossible. Despite these limitations, the strengths of multicentre data, propensity score matching, and robust statistical modelling provide valuable insights into MASLD in T2D patients. Future research should refine diagnostic algorithms and develop targeted strategies to mitigate the burden of MASLD.\u003c/p\u003e"},{"header":"5.0 Conclusion","content":"\u003cp\u003eThis study identified key clinical and laboratory predictors of significant hepatic fibrosis in T2D and non-T2D patients. The T2D group had a higher prevalence of metabolic risk factors, including elevated BMI and triglycerides. While Fib 4 demonstrated high sensitivity, its poor specificity limits its use as a standalone tool, particularly in T2D patients. A refined predictive model incorporating BMI, AST, and HbA1c achieved good discriminatory ability (AUC 0.735). These findings emphasise the need for combining non-invasive tools with clinical variables to improve the diagnosis and management of MASLD in high-risk populations. Future research should focus on developing tailored diagnostic algorithms to reduce the burden of advanced liver disease.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDisclosures:\u0026nbsp;\u003c/strong\u003eThe authors do not have any conflict to disclose for this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial Support and Sponsorship section:\u0026nbsp;\u003c/strong\u003eNo external financial support was received.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u0026nbsp;\u003c/strong\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement:\u0026nbsp;\u003c/strong\u003eThe datasets used and/or analysed during the current study are available from the corresponding author, Dr. Samit Ghosal (Email: [email protected]), on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Committee Approval:\u0026nbsp;\u003c/strong\u003eMSPL-MASLD-001 \u0026amp; version-1.0. Approving centre: INDIRA IVF hospital Institutional Ethics Committee. Reg No. ECR/1627/Inst/WB/2021.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement:\u0026nbsp;\u003c/strong\u003eTo all the centres who agreed to share the data essential to conducting this analysis.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLekakis V, Papatheodoridis GV. Natural history of metabolic dysfunction-associated steatotic liver disease. Eur J Intern Med. 2024;122:3-10. doi: 10.1016/j.ejim.2023.11.005. https://pubmed.ncbi.nlm.nih.gov/37940495/\u003c/li\u003e\n\u003cli\u003eOtero Sanchez L, Chen Y, Lassailly G, Qi X. Exploring the links between types two diabetes and liver-related complications: A comprehensive review. United European Gastroenterol J. 2024;12(2):240-251. doi: 10.1002/ueg2.12508. https://pubmed.ncbi.nlm.nih.gov/38103189/\u003c/li\u003e\n\u003cli\u003eRiazi K, Azhari H, Charette JH, Underwood FE, King JA, Afshar EE, Swain MG, Congly SE, Kaplan GG, Shaheen AA. The prevalence and incidence of NAFLD worldwide: a systematic review and meta-analysis. Lancet Gastroenterol Hepatol. 2022;7(9):851-861. doi: 10.1016/S2468-1253(22)00165-0. https://pubmed.ncbi.nlm.nih.gov/35798021/\u003c/li\u003e\n\u003cli\u003eYang Z, Li A, Jiang Y, Maidaiti X, Wu Y, Jin Y. Global burden of metabolic dysfunction-associated steatotic liver disease attributable to high fasting plasma glucose in 204 countries and territories from 1990 to 2021. Sci Rep. 2024;14(1):22232. doi: 10.1038/s41598-024-72795-0. https://pmc.ncbi.nlm.nih.gov/articles/PMC11437073/\u003c/li\u003e\n\u003cli\u003eYounossi ZM, Golabi P, Price JK, Owrangi S, Gundu-Rao N, Satchi R, Paik JM. The Global Epidemiology of Nonalcoholic Fatty Liver Disease and Nonalcoholic Steatohepatitis Among Patients With Type 2 Diabetes. Clin Gastroenterol Hepatol. 2024;22(10):1999-2010.e8. doi: 10.1016/j.cgh.2024.03.006. https://pubmed.ncbi.nlm.nih.gov/38521116/\u003c/li\u003e\n\u003cli\u003eRiley DR, Hydes T, Hernadez G, Zhao SS, Alam U, Cuthbertson DJ. The synergistic impact of type 2 diabetes and MASLD on cardiovascular, liver, diabetes-related and cancer outcomes. Liver Int. 2024;44(10):2538-2550. doi: 10.1111/liv.16016. https://pubmed.ncbi.nlm.nih.gov/38949295/\u003c/li\u003e\n\u003cli\u003eChen H, Zhan Y, Zhang J, Cheng S, Zhou Y, Chen L, Zeng Z. The Global, Regional, and National Burden and Trends of NAFLD in 204 Countries and Territories: An Analysis From Global Burden of Disease 2019. JMIR Public Health Surveill. 2022;8(12):e34809. doi: 10.2196/34809. https://pubmed.ncbi.nlm.nih.gov/36508249/\u003c/li\u003e\n\u003cli\u003eTerrault NA, Pageaux GP. A changing landscape of liver transplantation: King HCV is dethroned, ALD and NAFLD take over! J Hepatol. 2018;69(4):767-768. doi: 10.1016/j.jhep.2018.07.020. https://pubmed.ncbi.nlm.nih.gov/30104027/\u003c/li\u003e\n\u003cli\u003eDuell PB, Welty FK, Miller M, Chait A, Hammond G, Ahmad Z, Cohen DE, Horton JD, Pressman GS, Toth PP; American Heart Association Council on Arteriosclerosis, Thrombosis and Vascular Biology; Council on Hypertension; Council on the Kidney in Cardiovascular Disease; Council on Lifestyle and Cardiometabolic Health; and Council on Peripheral Vascular Disease. Nonalcoholic Fatty Liver Disease and Cardiovascular Risk: A Scientific Statement From the American Heart Association. Arterioscler Thromb Vasc Biol. 2022;42(6):e168-e185. doi: 10.1161/ATV.0000000000000153. https://pubmed.ncbi.nlm.nih.gov/35418240/\u003c/li\u003e\n\u003cli\u003eShen TH, Wu CH, Lee YW, Chang CC. Prevalence, trends, and characteristics of metabolic dysfunction-associated steatotic liver disease among the US population aged 12-79 years. Eur J Gastroenterol Hepatol. 2024;36(5):636-645. doi: 10.1097/MEG.0000000000002741. https://pubmed.ncbi.nlm.nih.gov/38477858/\u003c/li\u003e\n\u003cli\u003eBinet Q, Loumaye A, Hermans MP, Lanthier N. A Cross-sectional Real-life Study of the Prevalence, Severity, and Determinants of Metabolic Dysfunction-associated Fatty Liver Disease in Type 2 Diabetes Patients. J Clin Transl Hepatol. 2023;11(6):1377-1386. doi: 10.14218/JCTH.2023.00117. https://pmc.ncbi.nlm.nih.gov/articles/PMC10500296/\u003c/li\u003e\n\u003cli\u003eDe A, Mehta M, Duseja A; ICOM-D study group. Substantial overlap between NAFLD and MASLD with comparable disease severity and non-invasive test performance: An analysis of the Indian Consortium on MASLD (ICOM-D) cohort. J Hepatol. 2024;81(4):e162-e164. doi: 10.1016/j.jhep.2024.05.027. https://pubmed.ncbi.nlm.nih.gov/38801919/\u003c/li\u003e\n\u003cli\u003eNaskar A, Mondal A, Chatterjee R, De RD, Roy S. Assessing Liver Fibrosis in Type 2 Diabetes Mellitus Patients With Metabolic Dysfunction-Associated Steatotic Liver Disease: The Role of Non-invasive Scoring Systems and Associated Factors. Cureus. 2024;16(6):e62405. doi: 10.7759/cureus.62405. https://pubmed.ncbi.nlm.nih.gov/39011198/\u003c/li\u003e\n\u003cli\u003eShalimar, Elhence A, Bansal B, Gupta H, Anand A, Singh TP, Goel A. Prevalence of Non-alcoholic Fatty Liver Disease in India: A Systematic Review and Meta-analysis. J Clin Exp Hepatol. 2022;12(3):818-829. doi: 10.1016/j.jceh.2021.11.010. https://pubmed.ncbi.nlm.nih.gov/35677499/\u003c/li\u003e\n\u003cli\u003ePanikar V, Gupta A, Nasikkar N, Joshi S, Walwalkar S, Sachdev I, Tiwaskar M, Panikar K, Mahajan A, Deogaonkar N, Vadgama J, Tuteja H, Khan M, Kader P. Prevalence and Association of Risk Factors According to Liver Steatosis and Fibrosis Stages among Nonalcoholic Fatty Liver Disease Patients with Type 2 Diabetes Mellitus in India: A Cross-sectional Study. J Assoc Physicians India. 2024;72(7):29-33. doi: 10.59556/japi.72.0582. https://www.japi.org/article/japi-72-7-29\u003c/li\u003e\n\u003cli\u003eEuropean Association for the Study of the Liver (EASL); European Association for the Study of Diabetes (EASD); European Association for the Study of Obesity (EASO). EASL-EASD-EASO Clinical Practice Guidelines on the management of metabolic dysfunction-associated steatotic liver disease (MASLD). J Hepatol. 2024;81(3):492-542. doi: 10.1016/j.jhep.2024.04.031. https://pubmed.ncbi.nlm.nih.gov/38851997/\u003c/li\u003e\n\u003cli\u003eAmerican Diabetes Association Professional Practice Committee. 9. Pharmacologic Approaches to Glycemic Treatment: Standards of Care in Diabetes-2025. Diabetes Care. 2025;48(Supplement_1):S181-S206. doi: 10.2337/dc25-S009. https://pubmed.ncbi.nlm.nih.gov/39651989/\u003c/li\u003e\n\u003cli\u003eGraupera I, Thiele M, Serra-Burriel M, Caballeria L, Roulot D, Wong GL, Fabrellas N, Guha IN, Arslanow A, Exp\u0026oacute;sito C, Hern\u0026aacute;ndez R, Aithal GP, Galle PR, Pera G, Wong VW, Lammert F, Gin\u0026egrave;s P, Castera L, Krag A; Investigators of the LiverScreen Consortium. Low Accuracy of FIB-4 and NAFLD Fibrosis Scores for Screening for Liver Fibrosis in the Population. Clin Gastroenterol Hepatol. 2022;20(11):2567-2576.e6. doi: 10.1016/j.cgh.2021. https://pubmed.ncbi.nlm.nih.gov/34971806/\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"MASLD, T2D, cross-sectional study, Fib 4, transient elastography","lastPublishedDoi":"10.21203/rs.3.rs-6224000/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6224000/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground and Aim:\u003c/h2\u003e \u003cp\u003eHepatic fibrosis is a critical complication of metabolic disorders, particularly in patients with Type 2 Diabetes (T2D). This study aimed to evaluate the performance of the Fibrosis-4 index (Fib 4) score in detecting significant fibrosis (transient elastography [TE]\u0026thinsp;\u0026ge;\u0026thinsp;8 kPa) and identify key predictors of advanced fibrosis using logistic regression analysis in patients with T2D.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eThis retrospective study included propensity-matched T2D and non-T2D patients. Sensitivity, specificity, and Cohen's Kappa were used to assess agreement between Fib 4 score\u0026thinsp;\u0026ge;\u0026thinsp;1.3 and TE\u0026thinsp;\u0026ge;\u0026thinsp;8 kPa. Logistic regression models were used to identify independent predictors of significant fibrosis. The predictive performance of the models was evaluated using ROC curves.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eThe Fib 4 score demonstrated high sensitivity (85.3%) but low specificity (13.7%) in the T2D cohort, with a Cohen\u0026rsquo;s Kappa of -0.01, indicating no agreement with TE. In the non-T2D cohort, specificity improved to 47.2% with a Cohen\u0026rsquo;s Kappa of 0.16. Logistic regression identified BMI, hypertension, and HbA1c as significant predictors of hepatic fibrosis in T2D patients, with odds ratios of 1.076, 1.824, and 1.279, respectively. Male sex, BMI, AST, and HbA1c were retained in the refined multivariate model, achieving an AUC of 0.735, indicating good discriminatory ability. Elevated transaminases were weakly associated with fibrosis, while BMI and HbA1c showed stronger associations.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e \u003cp\u003eWhile Fib 4 is sensitive for detecting significant fibrosis, its low specificity limits its utility as a standalone diagnostic test, particularly in T2D patients. Logistic regression highlighted BMI, AST, and HbA1c as key predictors of fibrosis, emphasising the need to combine non-invasive tools with clinical variables for more accurate risk stratification and improved management of MASLD. Future research should focus on refining diagnostic algorithms to better address the burden of advanced fibrosis in at-risk populations.\u003c/p\u003e","manuscriptTitle":"Investigating hepatic steatosis: the MISHTI study (Multicentric cross-sectional Indian Study of Hepatic and Metabolic Trends in India)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-27 06:35:07","doi":"10.21203/rs.3.rs-6224000/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-30T19:17:19+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-29T12:11:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"142230395970826410281857724271621719236","date":"2025-05-01T03:24:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-11T12:21:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"291007472367688275391895778374089478672","date":"2025-04-11T07:54:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"102887597285697109249982220975308730646","date":"2025-03-20T11:21:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-18T07:27:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-18T07:26:31+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-03-18T02:27:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-14T12:31:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-14T06:24:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1f24bd6f-b710-4659-b32b-887036058aca","owner":[],"postedDate":"March 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":45964136,"name":"Health sciences/Gastroenterology/Gastrointestinal diseases/Liver diseases/Non alcoholic steatohepatitis"},{"id":45964137,"name":"Health sciences/Endocrinology/Endocrine system and metabolic diseases/Diabetes/Type 2 diabetes mellitus"}],"tags":[],"updatedAt":"2025-07-14T16:01:45+00:00","versionOfRecord":{"articleIdentity":"rs-6224000","link":"https://doi.org/10.1038/s41598-025-10871-9","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-07-09 15:57:33","publishedOnDateReadable":"July 9th, 2025"},"versionCreatedAt":"2025-03-27 06:35:07","video":"","vorDoi":"10.1038/s41598-025-10871-9","vorDoiUrl":"https://doi.org/10.1038/s41598-025-10871-9","workflowStages":[]},"version":"v1","identity":"rs-6224000","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6224000","identity":"rs-6224000","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}