Predictive Performance of TyG, TyG-BMI, TyG-WC, METS-IR, and SPISE for incident MASLD in an Iranian prospective Cohort Study

Predictive Performance of TyG, TyG-BMI, TyG-WC, METS-IR, and SPISE for incident MASLD in an Iranian prospective Cohort 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 Article Predictive Performance of TyG, TyG-BMI, TyG-WC, METS-IR, and SPISE for incident MASLD in an Iranian prospective Cohort Study Mohammad Shahmansouri, Bahman Cheraghian, Amir Hooshang Bavarsad, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9415232/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 Insulin resistance is a key contributor to metabolic dysfunction-associated steatotic liver disease (MASLD), yet comparative prospective data on surrogate metabolic indices are scarce. This study followed 1,277 adults aged 35–70 years without MASLD at baseline over five years to evaluate the predictive value of TyG, TyG-BMI, TyG-WC, METS-IR, and SPISE for incident MASLD. MASLD incidence was defined by a Fatty Liver Index ≥ 60 plus at least one cardiometabolic risk factor. Associations between metabolic indices and MASLD risk were assessed using multivariable Poisson regression with robust variance, and predictive performance was evaluated via ROC curves. Over follow-up, 195 participants (15.3%) developed MASLD. In adjusted models, the highest quartile of TyG-BMI (RR 18.57; 95% CI 8.08,42.69), METS-IR (RR 14.25; 6.57,30.87), and TyG-WC (RR 10.70; 5.49,20.85) showed the greatest risk. Each SD increase in TyG-BMI and METS-IR increased risk by 172% and 107%, respectively, while SPISE was inversely associated (RR 0.29; 0.23,0.36). TyG-BMI showed the highest discrimination (AUC 0.75). These findings indicate that composite metabolic indices, especially TyG-BMI and METS-IR, are robust predictors of incident MASLD and could be valuable for early risk stratification in clinical and population-level settings. Health sciences/Biomarkers Health sciences/Diseases Health sciences/Endocrinology Health sciences/Gastroenterology Health sciences/Medical research Health sciences/Risk factors MASLD Insulin resistance Fatty liver Cohort study Southwestern Iran Figures Figure 1 Figure 2 Introduction Metabolic dysfunction associated steatotic liver disease (MASLD) was recently introduced as a replacement for nonalcoholic fatty liver disease (NAFLD). It is recognized as now a major contributor to the global burden of chronic liver disease and is strongly associated with metabolic dysregulation 1 , 2 . The diagnosis of MASLD requires evidence of hepatic steatosis in the presence of at least one cardiometabolic risk factor. It is commonly associated with a constellation of metabolic abnormalities, including obesity, type 2 diabetes mellitus, hypertension, and dyslipidemia 3 . MASLD may progress to metabolic dysfunction-associated steatohepatitis (MASH), a more severe form that significantly increases the risk of cirrhosis and hepatocellular carcinoma 4 . Currently, no specific pharmacological cure is available for MASLD. Management primarily focuses on lifestyle interventions, including dietary modification, structured physical activity, and weight reduction strategies 1 , 5 . The Fatty Liver Index (FLI) is a widely used non-invasive scoring system developed to estimate the probability of hepatic steatosis. It serves as a practical screening tool in epidemiological studies and clinical settings to identify individuals at risk of fatty liver disease, including NAFLD and MASLD 6 , 7 . Insulin resistance is a central mechanistic driver in the pathogenesis of MASLD 8 . It promotes hepatic steatosis through multiple pathways, including enhanced de novo lipogenesis mediated by upregulation of lipogenic enzymes and impaired suppression of adipose tissue lipolysis, which increases the flux of free fatty acids to the liver 9 .The Triglyceride-Glucose (TyG) index and its derivatives, TyG-BMI and TyG-WC, have been proposed as reliable surrogate markers of insulin resistance, a key contributor to metabolic disorders. The TyG index, calculated using fasting triglyceride and glucose levels, is widely recognized as a simple and cost-effective proxy for insulin resistance 10 , 11 . TyG-BMI and TyG-WC incorporate anthropometric measures into the TyG formula, potentially enhancing its ability to capture metabolic risk. The metabolic score for insulin resistance (METS-IR) is a non-insulin-based index that incorporates fasting glucose, triglycerides, HDL cholesterol, and body mass index to estimate insulin resistance, offering a practical tool for large-scale epidemiological studies 12 . Similarly, the single-point insulin sensitivity estimator (SPISE), derived from HDL cholesterol, triglycerides, and BMI, provides a rapid and non-invasive estimate of insulin sensitivity. It has been validated in both pediatric and adult populations and has demonstrated utility in predicting future metabolic risk, including metabolic syndrome and cardiovascular disease 13 – 15 . Although insulin resistance indices have been increasingly investigated, important gaps remain. Most existing evidence is derived from cross-sectional studies, limiting evaluation of their predictive value for incident MASLD 16 , 17 . Direct longitudinal comparisons of TyG-derived indices (TyG-BMI and TyG-WC) with METS-IR and SPISE are limited, and it remains unclear which marker provides the best prediction of future MASLD across diverse metabolic profiles 18 . Furthermore, the generalizability of previous findings is limited, as most studies have focused on specific subgroups, such as non-obese individuals or patients with type 2 diabetes. A prospective, population-based cohort study from Iran could strengthen the global evidence base and help address these gaps 19 .Accordingly, the present study aims to compare the predictive performance of TyG, TyG-BMI, TyG-WC, METS-IR, and SPISE for incidence MASLD in Iranian adults participating in the Hoveyzeh Cohort Study (HCS). Results A total of 1,277 participants free of MASLD at baseline were included in the final analysis. The mean age of the study population was 48.43 ± 9.07 years. During the 5-year reassessment, 195 individuals developed incidence of MASLD, corresponding to a cumulative incidence of 15.3%. (Table 1). Participants were categorized into four predefined age groups, with borderline differences observed between groups (p = 0.050). Although the proportion of males was slightly higher among individuals who developed MASLD (17.17% vs. 13.76%), this difference did not reach statistical significance (p = 0.093). Adiposity related measures showed strong associations with incidence of MASLD. The incidence increased progressively across BMI categories, from 0% in underweight individuals to 39.0% among obese participants (p 90 cm exhibited a markedly higher incidence compared to those with lower values (22.76% vs. 6.10%, p < 0.001). Alcohol consumption was associated with a higher cumulative incidence of MASLD (35.29% vs. 15.0%, p = 0.021), although the absolute number of alcohol users was small. No statistically significant differences were observed across categories of smoking status, physical activity quartiles, socioeconomic status, systolic or diastolic blood pressure, lipid profile components (LDL, total cholesterol), diabetes, or hypertension. Borderline associations were observed for HDL (p = 0.057), ALT levels (p = 0.065), diastolic blood pressure (p = 0.072), and triglycerides (p = 0.085), suggesting potential metabolic gradients warranting further multivariable evaluation. As shown in Table 2, the incidence of MASLD increased significantly across quartiles of TyG (9.35% in Q1 to 18.18% in Q4, p = 0.008). A stronger dose–response pattern was observed for TyG-BMI, TyG-WC, and METS-IR, with markedly higher incidence in the highest quartiles compared with the lowest (all p < 0.001). In contrast, SPISE showed an inverse trend, with MASLD incidence decreasing progressively across increasing quartiles (p < 0.001). (Table 2) In crude Poisson regression analyses, higher quartiles of all insulin resistance related indices were significantly associated with increased risk of MASLD, whereas SPISE showed a significant inverse association. (Table 3) For the TyG index, participants in the highest quartile had a higher risk of MASLD compared with those in the lowest quartile (RR = 1.94, 95% CI: 1.25,3.02). This association remained statistically significant after adjustment for age and sex (RR = 2.01, 95% CI: 1.28,3.15) and after further adjustment for socioeconomic, lifestyle, clinical, and biochemical covariates (RR = 1.83, 95% CI: 1.14,2.95), indicating a modest but independent association. Stronger associations were observed for indices incorporating adiposity measures. For TyG-BMI, the risk of MASLD increased markedly across quartiles, with participants in the highest quartile exhibiting more than an eighteen-fold higher risk compared with the lowest quartile in the fully adjusted model (RR = 18.57, 95% CI: 8.08,42.69). A similar dose response pattern was observed for TyG-WC, where the highest quartile was associated with a substantially increased risk (RR = 10.70, 95% CI: 5.49,20.85) in the fully adjusted model. METS-IR also demonstrated a strong graded relationship with incidence of MASLD. Compared with the lowest quartile, the highest quartile was associated with a markedly elevated risk in the fully adjusted model (RR = 14.25, 95% CI: 6.57,30.87), with consistent estimates across all adjustment models. In contrast, SPISE was inversely associated with MASLD risk. Participants in the highest quartile had a substantially lower risk compared with those in the lowest quartile (RR = 0.06, 95% CI: 0.02,0.14) after full adjustment, indicating a strong protective gradient consistent with higher insulin sensitivity. In the fully adjusted model, each one standard deviation (SD) increases in insulin resistance–related indices were significantly associated with MASLD incidence. Specifically, the risk increased by 19% for the TyG index (RR=1.19, 95% CI: 1.05,1.36, P=0.007), 172% for TyG-BMI (RR=2.72, 95% CI: 2.32,3.19, P<0.001), 132% for TyG-WC (RR=2.32, 95% CI: 1.98,2.73, P<0.001), and 107% for METS-IR (RR=2.07, 95% CI: 1.78,2.40, P<0.001). In contrast, each one-SD increase in SPISE was associated with a significantly lower risk of MASLD (RR=0.29, 95% CI: 0.23,0.36, P<0.001). Overall, the magnitude and direction of associations remained largely consistent across crude and adjusted models, suggesting robust relationships between insulin resistance–related indices and incidence of MASLD. The predictive performance of metabolic indices for incidence of MASLD was evaluated using receiver operating characteristic (ROC) curve analysis (Figure 2). Among the evaluated indices, TyG-BMI demonstrated the highest discriminative ability (AUC = 0.75, 95% CI :0.71, 0.78), followed closely by METS-IR (AUC = 0.74, 95% CI: 0.70,0.77) and TyG-WC (AUC = 0.71, 95% CI: 0.67,0.74). In contrast, the TyG index alone showed relatively poor predictive performance (AUC = 0.57, 95% CI: 0.52,0.61), indicating limited discrimination when not combined with anthropometric measures (Table 4). Overall, indices incorporating adiposity measures (TyG-BMI and TyG-WC) or composite metabolic parameters (METS-IR) showed moderate predictive accuracy, whereas the TyG index alone demonstrated weak predictive capability. These findings suggest that combining triglyceride glucose-based metrics with anthropometric parameters substantially improves risk discrimination for incidence of MASLD. Discussion In this prospective cohort study of adults free of MASLD at baseline, we observed a 5-year cumulative incidence of 15.3%. Higher levels of insulin resistance related indices were consistently associated with an increased risk of incidence of MASLD. While the TyG index alone showed a modest association, indices that combined metabolic and anthropometric measures—particularly TyG-BMI, TyG-WC, and METS-IR—showed stronger and more consistent relationships across quartiles. In contrast, SPISE was inversely associated with MASLD risk ROC analyses also showed that indices incorporating adiposity measures had better discriminative ability than TyG alone. Our study confirms that insulin resistance-related indices are strong predictors of hepatic steatosis and MASLD. Previous cohort studies have reported clear dose response relationships between TyG-related indices and fatty liver risk. Longitudinal analyses indicate that persistently high TyG levels increase the risk of MASLD over time, even after adjusting for other factors. These findings support TyG as a simple marker of metabolic dysfunction and liver fat accumulation 20 . In line with earlier studies, composite indices such as TyG-BMI and TyG-WC performed better than TyG alone in identifying individuals at higher risk of fatty liver disease 21 , 22 . Incorporating adiposity measures likely enhances predictive performance, as excess body fat particularly central adiposity is closely linked to greater insulin resistance and increased hepatic fat accumulation. Comparative analyses of insulin resistance markers have similarly identified TyG-BMI as one of the strongest predictors of MASLD 23 . The positive association observed for TyG-WC is also supported by pooled evidence. A recent meta-analysis of over 38,000 participants reported a significant positive correlation between TyG-WC and NAFLD risk 24 . This suggests that indices combining lipid, glycemic, and central adiposity measures may better capture the underlying pathophysiology than single biomarkers. Our findings on METS-IR are consistent with previous studies. Population-based studies have shown a clear dose response relationship between METS-IR and fatty liver disease. Higher METS-IR values are linked to significantly increased risk and demonstrate stronger predictive performance than simpler metabolic indices 25 . In contrast, the inverse association observed between SPISE and MASLD risk in the present study aligns with the conceptual framework of SPISE as a marker of insulin sensitivity rather than insulin resistance. Individuals with MASLD typically have lower insulin sensitivity and a worse metabolic profile compared with controls, supporting the biological plausibility of this inverse relationship 26 . The observed associations are biologically plausible. Insulin resistance increases the flow of free fatty acids to the liver and stimulates de novo lipogenesis, leading to triglyceride accumulation in hepatocytes 27 , 28 . Furthermore, visceral adiposity contributes to this pathophysiological cascade via enhanced release of free fatty acids and pro-inflammatory cytokines, thereby exacerbating both metabolic dysfunction and hepatic steatosis 29 . Surrogate indices such as TyG, TyG-BMI, TyG-WC, and METS-IR capture key metabolic disturbances, including dyslipidemia, impaired glucose metabolism, and adiposity. In contrast, SPISE reflects insulin sensitivity, consistent with its inverse association with MASLD risk 30 , 31 . This study has several strengths. The prospective cohort design allowed the assessment of incident MASLD and reduced the likelihood of reverse causation. The findings of this cohort study may be generalizable to populations with similar demographic and clinical characteristics. Evaluating multiple insulin resistance related indices within the same population enabled direct comparison of their associations and predictive performance. In addition, adjustment for a wide range of demographic, lifestyle, and clinical variables helped minimize confounding. However, several limitations should be considered. First, MASLD was defined using the Fatty Liver Index rather than imaging or histological methods, which may have resulted in misclassification. Second, MASLD status was assessed only at follow-up, and the exact timing of disease onset could not be determined. Finally, residual confounding such as genetic factors cannot be excluded despite multivariable adjustment. Conclusion In this prospective cohort study, insulin resistance-related indices especially TyG-BMI, TyG-WC, and METS-IR were independently associated with the 5-year incidence of MASLD, whereas SPISE showed a significant inverse relationship. These findings suggest that simple, routinely available metabolic indices could serve as practical tools for early risk stratification and identifying individuals at higher risk of MASLD in population-based settings. Further longitudinal studies in diverse populations are needed to confirm these results and refine clinically relevant cut-off values for risk prediction. Methods Study design and population This prospective cohort study enrolled 10009 adults aged (35–70 years) at baseline (May 2016 and August 2018 until) from Hoveyzeh cohort study 32 . Due to logistical constraints, a subsample of approximately 30% of the original cohort (n = 3,019) was selected for a second-phase reassessment aimed at evaluating changes in lifestyle factors, anthropometry, and socioeconomic variables over time. The subsample was selected using systematic random sampling to ensure representativeness. All participants in this subsample underwent comprehensive clinical, anthropometric, and biochemical evaluations approximately five years after baseline. Participants with evidence of MASLD at baseline were excluded from the present analysis. Additional exclusions were made for those with missing data required to calculate insulin resistance–related indices or outcome variables. After these exclusions, 1,277 participants free of MASLD at baseline were included in the final analytic sample (Fig. 1 ). Figure 1 . flow chart of study Outcome Assessment: metabolic dysfunction associated steatotic liver disease (MASLD) The Fatty Liver Index (FLI) represents a well-validated, non-invasive algorithmic score designed to estimate the probability of hepatic steatosis. This biochemical prediction model employs routinely measured clinical parameters, including triglyceride levels, body mass index (BMI), γ-glutamyl transferase (GGT), and waist circumference to quantify steatosis risk. The FLI was originally developed and validated by Bedogni et al. 33 , and is calculated using the following formula 34 : $$\:FLI=\frac{{\left(e\right[0.953\:\times\:\:ln\left(TG\right)\:+\:0.139\:\times\:\:BMI\:+\:0.718\:\times\:\:ln\left(GGT\right)\:+\:0.053\:\times\:\:WC\:-\:15.745\left)\right]}^{}}{(1\:+\:e[0.953\:\times\:\:ln\left(TG\right)\:+\:0.139\:\times\:\:BMI\:+\:0.718\:\times\:\:ln\left(GGT\right)\:+\:0.053\:\times\:\:WC\:-\:15.745\left]\right)}\times\:100$$ Incident hepatic steatosis was defined as an FLI value ≥ 60 at reassessment among participants with FLI < 60 at baseline. The primary outcome was incident of MASLD, defined as hepatic steatosis (FLI ≥ 60) in the presence of at least one cardiometabolic risk factor, including overweight or obesity, characterized by a body mass index ≥ 25 kg/m² or elevated waist circumference (≥ 94 cm for men, ≥ 80 cm for women of Caucasian descent, with appropriate ethnic-specific adjustments applied where indicated); (2) dysglycemia, encompassing either type 2 diabetes or prediabetes, with the latter defined by impaired fasting glucose (100–125 mg/dL), abnormal glucose tolerance (2-hour post-load glucose 140–199 mg/dL), or elevated HbA1c levels (5.7%-6.4%); (3) hypertension, defined as blood pressure ≥ 130/85 mmHg or current use of antihypertensive pharmacotherapy; and (4) atherogenic dyslipidemia, evidenced by elevated triglycerides (≥ 150 mg/dL), reduced HDL-cholesterol (< 40 mg/dL in men, < 50 mg/dL in women), or ongoing lipid-lowering treatment 34 , 35 . Exposure Assessment: metabolic indicators Five metabolic indices were evaluated as predictors of incidence MASLD: TyG index = ln (TG (mg/dL) × fasting glucose (mg/dL) / 2) 11,36 TyG-BMI = TyG × BMI 37 TyG-WC = TyG × waist circumference 37 METS-IR = (ln ((2 × fasting glucose) + TG) × BMI) / ln (HDL-C) 38 SPISE = 600 × HDL-C^0.185 / (TG^0.2 × BMI^1.338) Covariates Data were collected using standardized questionnaires, clinical examinations, and laboratory measurements at baseline and reassessment. Demographic variables included age (35–44, 45–54, 55–64, ≥ 65 years) and sex. Socioeconomic status was assessed using a household wealth index based on ownership of nine assets (washing machine, motorcycle, personal car, home ownership, computer, internet access, number of persons per room, vacuum cleaner, and freezer). Weighted scores were categorized into quintiles from poorest to reachest 39 . Lifestyle factors included physical activity, smoking status, and alcohol consumption. Physical activity was quantified using metabolic equivalent of task (MET) values and categorized into quartiles (from lowest activity in the first quartile to highest activity in the fourth) 40 . Smoking status was self-reported; individuals who had smoked at least 100 cigarettes during their lifetime were classified as smokers. Anthropometric and clinical assessments included body mass index (BMI), calculated as weight (kg) divided by height squared (m²), waist circumference, and blood pressure. Laboratory measurements included fasting blood glucose, lipid profile (triglycerides, HDL, LDL, and total cholesterol), and liver enzymes. Elevated liver enzymes were defined as alanine aminotransferase > 30 U/L in men and > 20 U/L in women; aspartate aminotransferase > 30 U/L in men and > 20 U/L in women; and alkaline phosphatase ≥ 306 U/L. Type 2 diabetes was defined as a self-reported physician diagnosis, use of glucose-lowering medication, or fasting plasma glucose ≥ 126 mg/dL 41 , Hypertension was defined as physician diagnosis, antihypertensive treatment, or measured systolic/diastolic blood pressure ≥ 140/90 mmHg 32 . Statistical Analysis Baseline characteristics of the study population were summarized using descriptive statistics. The normality of continuous variables was assessed with the Shapiro–Wilk test. Continuous variables, including TyG, TyG-BMI, TyG-WC, METS-IR, and SPISE, were categorized into quartiles for descriptive analyses, while categorical variables were reported as counts and percentages. For the main analyses, metabolic indices were examined both as continuous variables and by quartiles to explore dose–response associations with incident MASLD. Differences across quartiles were tested using the chi-square test, when appropriate. MASLD was evaluated only at the 5-year follow-up, making the 5-year cumulative incidence the primary measure of disease occurrence. Associations between each metabolic index and MASLD were estimated using Poisson regression with robust (sandwich) variance estimators, which provide consistent risk ratio (RR) estimates for binary outcomes in cohort studies. As a supplementary analysis, we estimated the relative risk per 1-standard-deviation increase in each metabolic index, adjusting for relevant covariates, to allow standardized comparisons across variables with different units and to facilitate interpretation of effect sizes. The predictive performance of the metabolic indices was assessed using receiver operating characteristic (ROC) curves. ROC curves were generated separately for TyG, TyG-BMI, TyG-WC, and METS-IR, and the area under the curve (AUC) with 95% confidence intervals was calculated. Optimal cut-off values were determined using Youden’s J statistic to balance sensitivity and specificity. All analyses were conducted in Stata version 14 and R version 4.4.3, and p-values < 0.05 were considered statistically significant. Declarations Ethics statement The Ethics Research Committee at Ahvaz Jundishapur University of Medical Sciences approved this study, which was conducted in accordance with the Helsinki Declaration. The approval number is IR.AJUMS.REC.1404.361. All participants signed a written informed consent form. C ompeting interest The authors declare no conflict of interest. Funding This study was supported by the Vice-Chancellor for Research at Ahvaz Jundishapur University of Medical Sciences with grant number U-04254. Author Contribution BCh: conceptualization, project administration, formal analysis, methodology, and writing- review editing. ZR: conceptualization, formal analysis, methodology, writing-original draft, and writing- review editing. MSh: data curation, and writing-original draft. AHB: project administration, supervision, and writing- review editing. All authors read and approved the final manuscript. Acknowledgement The authors would like to express their sincere gratitude to all participants and the staff of the Hoveyzeh Cohort Study Center for their invaluable contributions to this research. The authors also acknowledge the financial support provided by the Vice-Chancellor for Research at Ahvaz Jundishapur University of Medical Sciences. This article is derived from the MSc thesis of Mohammad Shahmansouri conducted at Ahvaz Jundishapur University of Medical Sciences. Data Availability The datasets from the current study are included in the article. References Younossi, Z. M., Kalligeros, M. & Henry, L. Epidemiology of metabolic dysfunction-associated steatotic liver disease. Clin. Mol. Hepatol. 31 10.3350/cmh.2024.0431 (2024). Chan, W. K. et al. Metabolic dysfunction-associated steatotic liver disease (MASLD): a state-of-the-art review. J. Obes. metabolic syndrome . 32 , 197. 10.7570/jomes23052 (2023). Yanai, H., Adachi, H., Hakoshima, M., Iida, S. & Katsuyama, H. Metabolic-dysfunction-associated steatotic liver disease—its pathophysiology, association with atherosclerosis and cardiovascular disease, and treatments. Int. J. Mol. Sci. 24 , 15473. 10.3390/ijms242015473 (2023). Younossi, Z. M. et al. Addressing the High and Rising Global Burden of Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) and Metabolic Dysfunction‐Associated Steatohepatitis (MASH): From the Growing Prevalence to Payors' Perspective. Aliment. Pharmacol. Ther. 61 , 1467–1478. 10.1111/apt.70020 (2025). Ali, S. M. & Lai, M. Metabolic dysfunction–associated steatotic liver disease. Annals of internal medicine 178, ITC1-ITC16 (2025). 10.7326/ANNALS-24-02933 Koehler, E. M. et al. External validation of the fatty liver index for identifying nonalcoholic fatty liver disease in a population-based study. Clin. Gastroenterol. Hepatol. 11 , 1201–1204. 10.1016/j.cgh.2013.01.012 (2013). Sang, Z. et al. Association of Dietary Inflammatory Index with liver fibrosis and fatty liver index in a population with metabolic dysfunction-associated steatotic liver disease: a cross-sectional study. Front. Nutr. 12 , 1594192. 10.3389/fnut.2025.1594192 (2025). Chen, L. From metabolic dysfunction-associated fatty liver disease to metabolic dysfunction-associated steatotic liver disease: Controversy and consensus. World J. Hepatol. 15 , 1253. 10.4254/wjh. v15.i12.1253 (2023). Pal, S. C. Méndez-Sánchez, N. Insulin resistance and adipose tissue interactions as the cornerstone of metabolic (dysfunction)-associated fatty liver disease pathogenesis. World J. Gastroenterol. 29 , 3999. 10.3748/wjg. v29.i25.3999 (2023). VK, R. N., Satheesh, P., Shenoy, M. T. & Kalra, S. Triglyceride Glucose (TyG) Index: A surrogate biomarker of insulin resistance. J. Pak. Med. Assoc. 72 , 986–988. 10.47391/JPMA.22-63 (2022). Simental-Mendía, L. E., Rodríguez-Morán, M. & Guerrero-Romero, F. The product of fasting glucose and triglycerides as surrogate for identifying insulin resistance in apparently healthy subjects. Metab. Syndr. Relat. Disord. 6 , 299–304. 10.1089/met.2008.0035 (2008). Bello-Chavolla, O. Y. et al. Prediction of incident hypertension and arterial stiffness using the non–insulin‐based metabolic score for insulin resistance (METS‐IR) index. J. Clin. Hypertens. 21 , 1063–1070. 10.1111/jch.13645 (2019). Paulmichl, K. et al. Modification and validation of the Triglyceride-to–HDL cholesterol ratio as a surrogate of insulin sensitivity in White juveniles and adults without diabetes mellitus: the single point insulin sensitivity estimator (SPISE). Clin. Chem. 62 , 1211–1219. 10.1373/clinchem.2015.251965 (2016). Wang, Z., Hui, X., Huang, X., Li, J. & Liu, N. Relationship between a novel non– insulin-based metabolic score for insulin resistance (METS-IR) and coronary artery calcification. BMC Endocr. disorders . 22 , 274. 10.1186/s12902-022-01186-6 (2022). Khamseh, M. E. et al. Insulin resistance/sensitivity measures as screening indicators of metabolic-associated fatty liver disease and liver fibrosis. Dig. Dis. Sci. 69 , 1430–1443. 10.1007/s10620-024-08309-9 (2024). Zhang, F., Wang, Z., Zhang, H. & Zuo, Y. Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) Attenuates the Predictive Value of the Triglyceride–Glucose Index for Carotid Plaque: Evidence of Insulin Resistance-Independent Pathways. Front. Endocrinol. 16 , 1696652. doi.org/10.3389/fendo.2025.1696652 (2025). Song, K. et al. Identification of pediatric MASLD using insulin resistance indices. JHEP Rep. 101419 10.1016/j.jhepr.2025.101419 (2025). Cai, X. et al. Dose-response associations of metabolic score for insulin resistance index with nonalcoholic fatty liver disease among a nonobese Chinese population: retrospective evidence from a population‐based cohort study. Disease markers. 4930355 (2022). (2022). 10.1155/2022/4930355 Boushehri, Y. G. et al. Association of triglyceride glucose-body mass index (TyG-BMI) with metabolic dysfunction-associated steatotic liver disease: A systematic review and meta-analysis. PloS one . 20 , e0324483. doi.org/10.1371/journal.pone.0324483 (2025). Wu, X., Chen, Q., Zhang, C. & Hong, X. The effect of longitudinal trajectories of triglyceride-glucose index on the incident metabolic dysfunction-associated steatotic liver disease. BMC Gastroenterol. 25 , 689. 10.1186/s12876-025-0689-0 (2025). Huang, X. et al. TyG-BMI as a superior predictor of MAFLD and pre-MAFLD in Chinese adults: a cross-sectional study. BMC Gastroenterol. 25 , 495. 10.1186/s12876-025-04087-4 (2025). Chen, Y. et al. Triglyceride-glucose index and related parameters in the diagnosis and predictive efficacy for older adults with NAFLD/MASLD: a prospective cohort study. Front. Med. 12 , 1626672. doi.org/10.3389/fmed.2025.1626672 (2025). Yilmaz, Y. et al. Novel surrogate markers of insulin resistance in patients with metabolic dysfunction-associated steatosis liver disease: Associations with dietary and lifestyle inflammatory scores in the large cross-sectional study. Clin. Nutr. ESPEN . doi.org/10.1016/j.clnesp.2025.09.021 (2025). Xin, Z. et al. Triglyceride glucose waist circumference and non alcoholic fatty liver disease: a systematic review and meta analysis. BMC Gastroenterol. 25 , 328. 10.1186/s12876-025-03888-x (2025). Peng, H. et al. METS-IR/HOMA-IR and MAFLD in US adults: dose–response correlation and the effect mediated by physical activity. BMC Endocr. Disorders . 24 , 132. 10.1186/s12902-024-01646-w (2024). Furthner, D. et al. Single point insulin sensitivity estimator in pediatric non-alcoholic fatty liver disease. Front. Endocrinol. 13 , 830012. doi.org/10.3389/fendo.2022.830012 (2022). Matsuzaka, T. & Shimano, H. Molecular mechanisms involved in hepatic steatosis and insulin resistance. J. diabetes Invest. 2 , 170–175. 10.1111/j.2040-1124.2011 (2011). 00083.x. Bo, T. et al. Hepatic selective insulin resistance at the intersection of insulin signaling and metabolic dysfunction-associated steatotic liver disease. Cell Metabol. 36 , 947–968. 10.1016/j.cmet.2024.04.006 (2024). Bansal, S., Vachher, M., Arora, T., Kumar, B. & Burman, A. Visceral fat: A key mediator of NAFLD development and progression. Hum. Nutr. Metabolism . 33 , 200210. doi.org/10.1016/j.hnm.2023.200210 (2023). Zhou, H., Shi, Y. & Zhou, X. Comparison of nine insulin resistance surrogates for predicting cardiovascular disease: a cohort study. Diabetol. Metab. Syndr. 17 , 365. 10.1186/s13098-025-01933-z (2025). Zhou, T. & Xie, C. Association between Triglyceride-Glucose index and onset of type 2 diabetes in metabolic Dysfunction-Associated steatotic liver disease population. Sci. Rep. 15 , 18044. doi.org/10.1038/s41598-025-03453-2 (2025). Cheraghian, B. et al. Cohort profile: The Hoveyzeh Cohort Study (HCS): A prospective population-based study on non-communicable diseases in an Arab community of Southwest Iran. Med. J. Islamic Repub. Iran. 34 , 141 (2020). Bedogni, G. et al. The Fatty Liver Index: a simple and accurate predictor of hepatic steatosis in the general population. BMC Gastroenterol. 6 , 33. 10.1186/1471-230X-6-33 (2006). Rinella, M. E. et al. A multisociety Delphi consensus statement on new fatty liver disease nomenclature. Hepatology 78 , 1966–1986. 10.1097/HEP.0000000000000520 (2023). Liver, E. A. & Diabetes, E. A. f. t. S. o. t. f. t. S. o. EASL-EASD-EASO Clinical Practice Guidelines on the management of metabolic dysfunction-associated steatotic liver disease (MASLD). Obesity Facts 17, 374–444 (2024). 10.1159/000539371 Guerrero-Romero, F. et al. The product of triglycerides and glucose, a simple measure of insulin sensitivity. Comparison with the euglycemic-hyperinsulinemic clamp. J. Clin. Endocrinol. Metabolism . 95 , 3347–3351. 10.1210/jc.2009-2067 (2010). Qiao, Y., Wang, Y., Chen, C., Huang, Y. & Zhao, C. Association between triglyceride-glucose (TyG) related indices and cardiovascular diseases and mortality among individuals with metabolic dysfunction-associated steatotic liver disease: a cohort study of UK Biobank. Cardiovasc. Diabetol. 24 (1), 12. 10.1186/s12933-024-02572-w (2025). Bello-Chavolla, O. Y. et al. METS-IR, a novel score to evaluate insulin sensitivity, is predictive of visceral adiposity and incident type 2 diabetes. Eur. J. Endocrinol. 178 , 533–544. 10.1530/EJE-17-0813 (2018). Davila, R. L., McCarthy, A. S., Gondwe, D., Kirdruang, P. & Sharma, U. Water, Walls, and Bicycles: Wealth Index Composition Using Census Microdata. J. Demogr Econ. 88 , 79–120. 10.1017/dem.2020.27 (2022). Mendes, M. A. et al. Metabolic equivalent of task (METs) thresholds as an indicator of physical activity intensity. PloS one . 13 , e0200701. 10.1371/journal.pone.0200701 (2018). Khamseh, M. E. et al. Nationwide prevalence of diabetes and prediabetes and associated risk factors among Iranian adults: analysis of data from PERSIAN cohort study. Diabetes Therapy . 12 , 2921–2938. 10.1007/s13300-021-01124-0 (2021). Tables Table 1. Characteristics of participants at baseline phase (May 2016 and August 2018) until stratified by MASLD status at reassessment phase in southwest Iran. characteristic Overall(n=1277) Incidence of MASLD p-value Sex Female Male 712 565 98(13.76%) 97(17.17%) 0.093 Age 35-44 45-54 55-64 >=65 513 428 256 80 84(16.37%) 75(17.52%) 26(10.16%) 10(12.50%) 0.050 BMI Underweight Normal Overweight Obese 33 586 576 82 0(0%) 42(7.17%) 121(21.01%) 32(39.02%) 90 574 703 35(6.10%) 160(22.76%) <0.001 Wealth score Poorest Poor Moderate Rich Richest 269 295 235 238 240 38(14.13%) 40(13.56%) 31(13.19%) 45(18.91%) 41(17.08%) 0.312 Smoking Yes No 293 984 44(15.02%) 151(15.35%) 0.891 Use Alcohol Yes No 17 1260 6(35.29%) 189(15%) 0.021 Physical activity (MET) Q1 Q2 Q3 Q4 272 291 324 390 37(13.60%) 41(14.09%) 59(18.21%) 58(14.87%) 0.375 SPB <130 ≥130 1149 128 174(15.14%) 21(16.41%) 0.706 DPB <85 ≥85 1196 81 177(14.80%) 18(22.22%) 0.072 Triglycerides Normal <150 Abnormal ≥150 972 305 139(14.30%) 56(18.36%) 0.085 HDL Normal ≤40 Abnormal<40 1120 157 163(14.55%) 32(20.38%) 0.057 LDL Normal <100 Abnormal ≥100 587 690 89(15.21%) 106(15.36%) 0.941 Total cholesterol Normal <200 Abnormal ≥200 908 369 144(15.86%) 51(13.82%) 0.359 ALT no yes 1,072 205 155(14.46%) 40(19.51%) 0.065 AST no yes 1121 156 171(15.25%) 24(15.38%) 0.966 ALK no yes 1208 156 180(14.90%) 15(21.74%) 0.125 Diabetes no yes 1097 180 164(14.95%) 31(17.22%) 0.432 Hypertension no yes 1044 233 157(15.04%) 38(16.31%) 0.626 P<0.05 significant for chi-square test Table 2. Levels of TyG indices, METS-IR, and SPISE among participants in the baseline phase, stratified by MASLD status at reassessment phase in southwest Iran . Overall(n=1277) (Mean ± SD) No MASL D Incidence of MASLD p-value Tyg index Q1 Q2 Q3 Q4 (8.61±0.52) 321 318 319 319 291(90.65%) 265(83.33%) 265(83.07%) 261(81.82%) 30(9.35%) 53(16.67%) 54(16.93%) 58(18.18%) 0.008 Tyg BMI Q1 Q2 Q3 Q4 (215.45±30.25) 320 319 319 319 314(98.13%) 290(90.91%) 259(81.19%) 219(68.65%) 6(1.88%) 29(9.09%) 60(18.81%) 100(31.35%) <0.001 Tyg WC Q1 Q2 Q3 Q4 (780.88±84.48) 320 319 319 319 310(96.88%) 280(87.77%) 261(81.82%) 231(72.41%) 10(3.13%) 39(12.23%) 58(18.18%) 88(27.59%) <0.001 METS_IR Q1 Q2 Q3 Q4 (36.76±5.61) 320 319 319 319 313(97.81%) 286(89.66%) 263(82.45%) 220(68.97%) 7(2.19%) 33(10.34%) 56(17.55%) 99(31.03%) <0.001 SPISE Q1 Q2 Q3 Q4 (6.73±1.52) 320 319 319 319 223(69.69%) 255(79.94%) 291(91.22%) 313(98.12%) 97(30.31%) 64(20.06%) 28(8.78%) 6(1.88%) <0.001 P<0.05 significant for chi-square test Table 3. Risk ratios (RRs) and 95% confidence intervals for the association between insulin resistance–related indices and incidence of MASLD based on Poisson regression models. Model 1 P-value Model 2 P-value Model 3 P-value Tyg index Q1 Q2 Q3 Q4 P-Trend RR per 1-SD increase 1.42(1.10,1.82) Ref 1.78(1.13,2.79) 1.81(1.15,2.83) 1.94(1.25,3.02) 1.20(1.07,1.34) 0.006 0.011 0.009 0.003 0.006 0.001 1.45(1.12,1.88) Ref 1.83(1.16,2.87) 1.82(1.16,2.86) 2.01(1.28,3.15) 1.21(1.09,1.36) 0.004 0.008 0.009 0.002 0.005 <0.001 1.40(1.05,1.89) Ref 1.84(1.17,2.88) 1.82(1.15,2.87) 1.83(1.14,2.95) 1.19(1.05,1.36) 0.022 0.008 0.010 0.012 0.022 0.007 Tyg BMI Q1 Q2 Q3 Q4 P-Trend RR per 1-SD increase 1.02(1.02,1.03) Ref 4.84(2.01,11.67) 10.03(4.33,23.21) 16.71(7.33,38.10) 2.41(2.10,2.76) <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 1.03(1.02,1.03) Ref 4.72(1.96,11.37) 10.39(4.48,24.06) 18.18(7.95,41.53) 2.60(2.25,3.00) <0.001 0.001 <0.001 <0.001 <0.001 <0.001 1.03(1.02,1.04) Ref 4.68(1.94,11.31) 10.26(4.41,23.82) 18.57(8.08,42.69) 2.72(2.32,3.19) <0.001 0.001 <0.001 <0.001 <0.001 <0.001 Tyg WC Q1 Q2 Q3 Q4 P-Trend RR per 1-SD increase 1.00(1.00,1.01) Ref 3.91(1.95,7.83) 5.81(2.97,11.38) 8.82(4.58,16.97) 1.90(1.68,2.14) <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 1.00(1.0,1.01) Ref 4.05(2.02,8.12) 6.20(3.17,12.15) 10.42(5.39,20.14) 2.09(1.84,2.37) <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 1.01(1.00,1.01) Ref 4.06(2.02,8.16) 5.88(2.99,11.58) 10.70(5.49,20.85) 2.32(1.98,2.73) <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 METS_IR Q1 Q2 Q3 Q4 P-Trend RR per 1-SD increase 1.12(1.10,1.15) Ref 4.72(2.09,10.69) 8.02(3.65,17.60) 14.18(6.59,30.53) 1.97(1.67,2.33) <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 1.13(1.10,1.15) Ref 4.70(2.08,10.64) 8.15(3.71,17.91) 14.13(6.56,30.45) 1.99(1.69,2.34) <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 1.13(1.10,1.16) Ref 4.64(2.04,10.51) 8.11(3.68,17.86) 14.25(6.57,30.87) 2.07(1.78,2.40) <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 SPISE Q1 Q2 Q3 Q4 P-Trend RR per 1-SD increase 0.44(0.37,0.53) Ref 0.66(0.48,0.90) 0.28(0.19,0.44) 0.06(0.02,0.14) 0.29(0.23,0.36) <0.001 0.010 <0.001 <0.001 <0.001 <0.001 0.44(0.37,0.53) Ref 0.67(0.49,0.92) 0.28(0.18,0.43) 0.06(0.02,0.14) 0.29(0.23,0.36) <0.001 0.014 <0.001 <0.001 <0.001 <0.001 0.44(0.37,0.53) Ref 0.65(0.47,0.89) 0.27(0.18,0.42) 0.06(0.02,0.14) 0.29(0.23,0.36) <0.001 0.009 <0.001 <0.001 <0.001 <0.001 Model 1: crude; Model 2: adjusted for age and sex; Model 3: additionally adjusted for socioeconomic status, smoking, alcohol use, physical activity, liver enzymes, hypertension, and diabetes P<0.05 significant for Poisson regression. Table 4. Discriminatory ability of insulin resistance indices for predicting MASLD ROC area (95% CI) P-value Tyg BMI 0.75(0.71, 0.78) Ref METS_IR 0.74(0.70,0.77) 0.421 Tyg WC 0.71(0.67,0.74) 0.003 Tyg index 0.57(0.52,0.61) <0.001 The areas under the ROC curves were compared using the DeLong test. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 06 May, 2026 Reviews received at journal 04 May, 2026 Reviewers agreed at journal 04 May, 2026 Reviewers invited by journal 04 May, 2026 Editor assigned by journal 04 May, 2026 Editor invited by journal 21 Apr, 2026 Submission checks completed at journal 17 Apr, 2026 First submitted to journal 17 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9415232","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":637756001,"identity":"94ffc628-7feb-418d-9f4f-0832b3de7690","order_by":0,"name":"Mohammad Shahmansouri","email":"","orcid":"","institution":"Ahvaz Jundishapur University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"","lastName":"Shahmansouri","suffix":""},{"id":637756002,"identity":"4228558a-9a3d-4a9e-b7dd-c15832d3b0d1","order_by":1,"name":"Bahman Cheraghian","email":"","orcid":"","institution":"Ahvaz Jundishapur University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Bahman","middleName":"","lastName":"Cheraghian","suffix":""},{"id":637756003,"identity":"8ab220b1-d575-49af-8079-5ee09442f18d","order_by":2,"name":"Amir Hooshang Bavarsad","email":"","orcid":"","institution":"Ahvaz Jundishapur University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Amir","middleName":"Hooshang","lastName":"Bavarsad","suffix":""},{"id":637756004,"identity":"c707342c-583f-4408-929c-350a9cdba9bb","order_by":3,"name":"Zahra Rahimi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYBAC+wYGNoYEBol6++MNQK6BBWEtjFAtCQxnDoC0SBCpBQgSGG4kgGgitDBLH3724GGORR7jzOdXN/wokGDgb+9OwKuFjS/N3CBxm0Qxs3RO2c0eoMMkzpzdgFcLDw+DmQRQC2ObdE7aDR6gFgOJXPxaJHjYv4G19EieSbv5hxgtBjw8YFsSZ0iwH7tNlC1ALWUgLcYGPDlst2UMJHgI+sW+h32b5M9tdXIG7Mef3Xzzx0aOv70XvxYkwGMAJolVDgLsD0hRPQpGwSgYBSMIAADL10FZDa42/AAAAABJRU5ErkJggg==","orcid":"","institution":"Ahvaz Jundishapur University of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Zahra","middleName":"","lastName":"Rahimi","suffix":""}],"badges":[],"createdAt":"2026-04-14 12:08:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9415232/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9415232/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109118864,"identity":"4ee2b4fb-254a-464c-bc70-3672e427507e","added_by":"auto","created_at":"2026-05-12 16:55:29","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":345806,"visible":true,"origin":"","legend":"\u003cp\u003eflowchart of study\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9415232/v1/28cd9d1ec3c8a68c1796d8cc.jpeg"},{"id":109118927,"identity":"6bafc4bb-b974-4e4b-8c4b-d629955cc219","added_by":"auto","created_at":"2026-05-12 16:55:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":121961,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves for incidence of MASLD by metabolic indices.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9415232/v1/163c0ae27b60fca494e1536b.png"},{"id":109119117,"identity":"03e05ab3-5278-40ca-a322-acc3a94e145a","added_by":"auto","created_at":"2026-05-12 16:56:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":991178,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9415232/v1/117c06ca-1f2e-42d0-a632-9a118b4fbc04.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predictive Performance of TyG, TyG-BMI, TyG-WC, METS-IR, and SPISE for incident MASLD in an Iranian prospective Cohort Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMetabolic dysfunction associated steatotic liver disease (MASLD) was recently introduced as a replacement for nonalcoholic fatty liver disease (NAFLD). It is recognized as now a major contributor to the global burden of chronic liver disease and is strongly associated with metabolic dysregulation \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The diagnosis of MASLD requires evidence of hepatic steatosis in the presence of at least one cardiometabolic risk factor. It is commonly associated with a constellation of metabolic abnormalities, including obesity, type 2 diabetes mellitus, hypertension, and dyslipidemia \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. MASLD may progress to metabolic dysfunction-associated steatohepatitis (MASH), a more severe form that significantly increases the risk of cirrhosis and hepatocellular carcinoma \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Currently, no specific pharmacological cure is available for MASLD. Management primarily focuses on lifestyle interventions, including dietary modification, structured physical activity, and weight reduction strategies \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. The Fatty Liver Index (FLI) is a widely used non-invasive scoring system developed to estimate the probability of hepatic steatosis. It serves as a practical screening tool in epidemiological studies and clinical settings to identify individuals at risk of fatty liver disease, including NAFLD and MASLD \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eInsulin resistance is a central mechanistic driver in the pathogenesis of MASLD \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. It promotes hepatic steatosis through multiple pathways, including enhanced de novo lipogenesis mediated by upregulation of lipogenic enzymes and impaired suppression of adipose tissue lipolysis, which increases the flux of free fatty acids to the liver \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.The Triglyceride-Glucose (TyG) index and its derivatives, TyG-BMI and TyG-WC, have been proposed as reliable surrogate markers of insulin resistance, a key contributor to metabolic disorders. The TyG index, calculated using fasting triglyceride and glucose levels, is widely recognized as a simple and cost-effective proxy for insulin resistance \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. TyG-BMI and TyG-WC incorporate anthropometric measures into the TyG formula, potentially enhancing its ability to capture metabolic risk. The metabolic score for insulin resistance (METS-IR) is a non-insulin-based index that incorporates fasting glucose, triglycerides, HDL cholesterol, and body mass index to estimate insulin resistance, offering a practical tool for large-scale epidemiological studies \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Similarly, the single-point insulin sensitivity estimator (SPISE), derived from HDL cholesterol, triglycerides, and BMI, provides a rapid and non-invasive estimate of insulin sensitivity. It has been validated in both pediatric and adult populations and has demonstrated utility in predicting future metabolic risk, including metabolic syndrome and cardiovascular disease \u003csup\u003e\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAlthough insulin resistance indices have been increasingly investigated, important gaps remain. Most existing evidence is derived from cross-sectional studies, limiting evaluation of their predictive value for incident MASLD \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Direct longitudinal comparisons of TyG-derived indices (TyG-BMI and TyG-WC) with METS-IR and SPISE are limited, and it remains unclear which marker provides the best prediction of future MASLD across diverse metabolic profiles \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Furthermore, the generalizability of previous findings is limited, as most studies have focused on specific subgroups, such as non-obese individuals or patients with type 2 diabetes. A prospective, population-based cohort study from Iran could strengthen the global evidence base and help address these gaps \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.Accordingly, the present study aims to compare the predictive performance of TyG, TyG-BMI, TyG-WC, METS-IR, and SPISE for incidence MASLD in Iranian adults participating in the Hoveyzeh Cohort Study (HCS).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 1,277 participants free of MASLD at baseline were included in the final analysis. The mean age of the study population was 48.43 \u0026plusmn; 9.07 years. During the 5-year reassessment, 195 individuals developed incidence of MASLD, corresponding to a cumulative incidence of 15.3%. (Table 1). Participants were categorized into four predefined age groups, with borderline differences observed between groups (p = 0.050). Although the proportion of males was slightly higher among individuals who developed MASLD (17.17% vs. 13.76%), this difference did not reach statistical significance (p = 0.093).\u003c/p\u003e\n\u003cp\u003eAdiposity related measures showed strong associations with incidence of MASLD. The incidence increased progressively across BMI categories, from 0% in underweight individuals to 39.0% among obese participants (p \u0026lt; 0.001). Similarly, participants with waist circumference \u0026gt;90 cm exhibited a markedly higher incidence compared to those with lower values (22.76% vs. 6.10%, p \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003eAlcohol consumption was associated with a higher cumulative incidence of MASLD (35.29% vs. 15.0%, p = 0.021), although the absolute number of alcohol users was small. No statistically significant differences were observed across categories of smoking status, physical activity quartiles, socioeconomic status, systolic or diastolic blood pressure, lipid profile components (LDL, total cholesterol), diabetes, or hypertension.\u003c/p\u003e\n\u003cp\u003eBorderline associations were observed for HDL (p = 0.057), ALT levels (p = 0.065), diastolic blood pressure (p = 0.072), and triglycerides (p = 0.085), suggesting potential metabolic gradients warranting further multivariable evaluation.\u003c/p\u003e\n\u003cp\u003eAs shown in Table 2, the incidence of MASLD increased significantly across quartiles of TyG (9.35% in Q1 to 18.18% in Q4, p = 0.008). A stronger dose\u0026ndash;response pattern was observed for TyG-BMI, TyG-WC, and METS-IR, with markedly higher incidence in the highest quartiles compared with the lowest (all p \u0026lt; 0.001). In contrast, SPISE showed an inverse trend, with MASLD incidence decreasing progressively across increasing quartiles (p \u0026lt; 0.001). (Table 2)\u003c/p\u003e\n\u003cp\u003eIn crude Poisson regression analyses, higher quartiles of all insulin resistance related indices were significantly associated with increased risk of MASLD, whereas SPISE showed a significant inverse association. (Table 3)\u003c/p\u003e\n\u003cp\u003eFor the TyG index, participants in the highest quartile had a higher risk of MASLD compared with those in the lowest quartile (RR = 1.94, 95% CI: 1.25,3.02). This association remained statistically significant after adjustment for age and sex (RR = 2.01, 95% CI: 1.28,3.15) and after further adjustment for socioeconomic, lifestyle, clinical, and biochemical covariates (RR = 1.83, 95% CI: 1.14,2.95), indicating a modest but independent association.\u003c/p\u003e\n\u003cp\u003eStronger associations were observed for indices incorporating adiposity measures. For TyG-BMI, the risk of MASLD increased markedly across quartiles, with participants in the highest quartile exhibiting more than an eighteen-fold higher risk compared with the lowest quartile in the fully adjusted model (RR = 18.57, 95% CI: 8.08,42.69). A similar dose response pattern was observed for TyG-WC, where the highest quartile was associated with a substantially increased risk (RR = 10.70, 95% CI: 5.49,20.85) in the fully adjusted model.\u003c/p\u003e\n\u003cp\u003eMETS-IR also demonstrated a strong graded relationship with incidence of MASLD. Compared with the lowest quartile, the highest quartile was associated with a markedly elevated risk in the fully adjusted model (RR = 14.25, 95% CI: 6.57,30.87), with consistent estimates across all adjustment models.\u003c/p\u003e\n\u003cp\u003eIn contrast, SPISE was inversely associated with MASLD risk. Participants in the highest quartile had a substantially lower risk compared with those in the lowest quartile (RR = 0.06, 95% CI: 0.02,0.14) after full adjustment, indicating a strong protective gradient consistent with higher insulin sensitivity.\u003c/p\u003e\n\u003cp\u003eIn the fully adjusted model, each one standard deviation (SD) increases in insulin resistance\u0026ndash;related indices were significantly associated with MASLD incidence. Specifically, the risk increased by 19% for the TyG index (RR=1.19, 95% CI: 1.05,1.36, P=0.007), 172% for TyG-BMI (RR=2.72, 95% CI: 2.32,3.19, P\u0026lt;0.001), 132% for TyG-WC (RR=2.32, 95% CI: 1.98,2.73, P\u0026lt;0.001), and 107% for METS-IR (RR=2.07, 95% CI: 1.78,2.40, P\u0026lt;0.001). In contrast, each one-SD increase in SPISE was associated with a significantly lower risk of MASLD (RR=0.29, 95% CI: 0.23,0.36, P\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003eOverall, the magnitude and direction of associations remained largely consistent across crude and adjusted models, suggesting robust relationships between insulin resistance\u0026ndash;related indices and incidence of MASLD.\u003c/p\u003e\n\u003cp\u003eThe predictive performance of metabolic indices for incidence of MASLD was evaluated using receiver operating characteristic (ROC) curve analysis (Figure 2).\u003c/p\u003e\n\u003cp\u003eAmong the evaluated indices, TyG-BMI demonstrated the highest discriminative ability (AUC = 0.75, 95% CI :0.71, 0.78), followed closely by METS-IR (AUC = 0.74, 95% CI: 0.70,0.77) and TyG-WC (AUC = 0.71, 95% CI: 0.67,0.74). In contrast, the TyG index alone showed relatively poor predictive performance (AUC = 0.57, 95% CI: 0.52,0.61), indicating limited discrimination when not combined with anthropometric measures (Table 4).\u003c/p\u003e\n\u003cp\u003eOverall, indices incorporating adiposity measures (TyG-BMI and TyG-WC) or composite metabolic parameters (METS-IR) showed moderate predictive accuracy, whereas the TyG index alone demonstrated weak predictive capability.\u003c/p\u003e\n\u003cp\u003eThese findings suggest that combining triglyceride glucose-based metrics with anthropometric parameters substantially improves risk discrimination for incidence of MASLD.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this prospective cohort study of adults free of MASLD at baseline, we observed a 5-year cumulative incidence of 15.3%. Higher levels of insulin resistance related indices were consistently associated with an increased risk of incidence of MASLD. While the TyG index alone showed a modest association, indices that combined metabolic and anthropometric measures\u0026mdash;particularly TyG-BMI, TyG-WC, and METS-IR\u0026mdash;showed stronger and more consistent relationships across quartiles. In contrast, SPISE was inversely associated with MASLD risk ROC analyses also showed that indices incorporating adiposity measures had better discriminative ability than TyG alone.\u003c/p\u003e \u003cp\u003eOur study confirms that insulin resistance-related indices are strong predictors of hepatic steatosis and MASLD. Previous cohort studies have reported clear dose response relationships between TyG-related indices and fatty liver risk. Longitudinal analyses indicate that persistently high TyG levels increase the risk of MASLD over time, even after adjusting for other factors. These findings support TyG as a simple marker of metabolic dysfunction and liver fat accumulation\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn line with earlier studies, composite indices such as TyG-BMI and TyG-WC performed better than TyG alone in identifying individuals at higher risk of fatty liver disease \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Incorporating adiposity measures likely enhances predictive performance, as excess body fat particularly central adiposity is closely linked to greater insulin resistance and increased hepatic fat accumulation. Comparative analyses of insulin resistance markers have similarly identified TyG-BMI as one of the strongest predictors of MASLD \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe positive association observed for TyG-WC is also supported by pooled evidence. A recent meta-analysis of over 38,000 participants reported a significant positive correlation between TyG-WC and NAFLD risk \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. This suggests that indices combining lipid, glycemic, and central adiposity measures may better capture the underlying pathophysiology than single biomarkers.\u003c/p\u003e \u003cp\u003eOur findings on METS-IR are consistent with previous studies. Population-based studies have shown a clear dose response relationship between METS-IR and fatty liver disease. Higher METS-IR values are linked to significantly increased risk and demonstrate stronger predictive performance than simpler metabolic indices \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. In contrast, the inverse association observed between SPISE and MASLD risk in the present study aligns with the conceptual framework of SPISE as a marker of insulin sensitivity rather than insulin resistance. Individuals with MASLD typically have lower insulin sensitivity and a worse metabolic profile compared with controls, supporting the biological plausibility of this inverse relationship \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe observed associations are biologically plausible. Insulin resistance increases the flow of free fatty acids to the liver and stimulates de novo lipogenesis, leading to triglyceride accumulation in hepatocytes \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Furthermore, visceral adiposity contributes to this pathophysiological cascade via enhanced release of free fatty acids and pro-inflammatory cytokines, thereby exacerbating both metabolic dysfunction and hepatic steatosis \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Surrogate indices such as TyG, TyG-BMI, TyG-WC, and METS-IR capture key metabolic disturbances, including dyslipidemia, impaired glucose metabolism, and adiposity. In contrast, SPISE reflects insulin sensitivity, consistent with its inverse association with MASLD risk\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study has several strengths. The prospective cohort design allowed the assessment of incident MASLD and reduced the likelihood of reverse causation. The findings of this cohort study may be generalizable to populations with similar demographic and clinical characteristics. Evaluating multiple insulin resistance related indices within the same population enabled direct comparison of their associations and predictive performance. In addition, adjustment for a wide range of demographic, lifestyle, and clinical variables helped minimize confounding. However, several limitations should be considered. First, MASLD was defined using the Fatty Liver Index rather than imaging or histological methods, which may have resulted in misclassification. Second, MASLD status was assessed only at follow-up, and the exact timing of disease onset could not be determined. Finally, residual confounding such as genetic factors cannot be excluded despite multivariable adjustment.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this prospective cohort study, insulin resistance-related indices especially TyG-BMI, TyG-WC, and METS-IR were independently associated with the 5-year incidence of MASLD, whereas SPISE showed a significant inverse relationship. These findings suggest that simple, routinely available metabolic indices could serve as practical tools for early risk stratification and identifying individuals at higher risk of MASLD in population-based settings. Further longitudinal studies in diverse populations are needed to confirm these results and refine clinically relevant cut-off values for risk prediction.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and population\u003c/h2\u003e \u003cp\u003eThis prospective cohort study enrolled 10009 adults aged (35\u0026ndash;70 years) at baseline (May 2016 and August 2018 until) from Hoveyzeh cohort study \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Due to logistical constraints, a subsample of approximately 30% of the original cohort (n\u0026thinsp;=\u0026thinsp;3,019) was selected for a second-phase reassessment aimed at evaluating changes in lifestyle factors, anthropometry, and socioeconomic variables over time. The subsample was selected using systematic random sampling to ensure representativeness. All participants in this subsample underwent comprehensive clinical, anthropometric, and biochemical evaluations approximately five years after baseline. Participants with evidence of MASLD at baseline were excluded from the present analysis. Additional exclusions were made for those with missing data required to calculate insulin resistance\u0026ndash;related indices or outcome variables. After these exclusions, 1,277 participants free of MASLD at baseline were included in the final analytic sample (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e. flow chart of study\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eOutcome Assessment: metabolic dysfunction associated steatotic liver disease (MASLD)\u003c/h3\u003e\n\u003cp\u003eThe Fatty Liver Index (FLI) represents a well-validated, non-invasive algorithmic score designed to estimate the probability of hepatic steatosis. This biochemical prediction model employs routinely measured clinical parameters, including triglyceride levels, body mass index (BMI), γ-glutamyl transferase (GGT), and waist circumference to quantify steatosis risk. The FLI was originally developed and validated by Bedogni et al. \u003csup\u003e33\u003c/sup\u003e, and is calculated using the following formula \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:FLI=\\frac{{\\left(e\\right[0.953\\:\\times\\:\\:ln\\left(TG\\right)\\:+\\:0.139\\:\\times\\:\\:BMI\\:+\\:0.718\\:\\times\\:\\:ln\\left(GGT\\right)\\:+\\:0.053\\:\\times\\:\\:WC\\:-\\:15.745\\left)\\right]}^{}}{(1\\:+\\:e[0.953\\:\\times\\:\\:ln\\left(TG\\right)\\:+\\:0.139\\:\\times\\:\\:BMI\\:+\\:0.718\\:\\times\\:\\:ln\\left(GGT\\right)\\:+\\:0.053\\:\\times\\:\\:WC\\:-\\:15.745\\left]\\right)}\\times\\:100$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIncident hepatic steatosis was defined as an FLI value\u0026thinsp;\u0026ge;\u0026thinsp;60 at reassessment among participants with FLI\u0026thinsp;\u0026lt;\u0026thinsp;60 at baseline.\u003c/p\u003e \u003cp\u003eThe primary outcome was incident of MASLD, defined as hepatic steatosis (FLI\u0026thinsp;\u0026ge;\u0026thinsp;60) in the presence of at least one cardiometabolic risk factor, including overweight or obesity, characterized by a body mass index\u0026thinsp;\u0026ge;\u0026thinsp;25 kg/m\u0026sup2; or elevated waist circumference (\u0026ge;\u0026thinsp;94 cm for men, \u0026ge;\u0026thinsp;80 cm for women of Caucasian descent, with appropriate ethnic-specific adjustments applied where indicated); (2) dysglycemia, encompassing either type 2 diabetes or prediabetes, with the latter defined by impaired fasting glucose (100\u0026ndash;125 mg/dL), abnormal glucose tolerance (2-hour post-load glucose 140\u0026ndash;199 mg/dL), or elevated HbA1c levels (5.7%-6.4%); (3) hypertension, defined as blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;130/85 mmHg or current use of antihypertensive pharmacotherapy; and (4) atherogenic dyslipidemia, evidenced by elevated triglycerides (\u0026ge;\u0026thinsp;150 mg/dL), reduced HDL-cholesterol (\u0026lt;\u0026thinsp;40 mg/dL in men, \u0026lt;\u0026thinsp;50 mg/dL in women), or ongoing lipid-lowering treatment\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eExposure Assessment: metabolic indicators\u003c/h2\u003e \u003cp\u003eFive metabolic indices were evaluated as predictors of incidence MASLD:\u003c/p\u003e \u003cp\u003eTyG index\u0026thinsp;=\u0026thinsp;ln (TG (mg/dL) \u0026times; fasting glucose (mg/dL) / 2)\u003csup\u003e11,36\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eTyG-BMI\u0026thinsp;=\u0026thinsp;TyG \u0026times; BMI\u003csup\u003e37\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eTyG-WC\u0026thinsp;=\u0026thinsp;TyG \u0026times; waist circumference\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eMETS-IR = (ln ((2 \u0026times; fasting glucose)\u0026thinsp;+\u0026thinsp;TG) \u0026times; BMI) / ln (HDL-C)\u003csup\u003e38\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSPISE = 600 × HDL-C^0.185 / (TG^0.2 × BMI^1.338)\u003c/h3\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCovariates\u003c/h2\u003e \u003cp\u003eData were collected using standardized questionnaires, clinical examinations, and laboratory measurements at baseline and reassessment. Demographic variables included age (35\u0026ndash;44, 45\u0026ndash;54, 55\u0026ndash;64, \u0026ge;\u0026thinsp;65 years) and sex. Socioeconomic status was assessed using a household wealth index based on ownership of nine assets (washing machine, motorcycle, personal car, home ownership, computer, internet access, number of persons per room, vacuum cleaner, and freezer). Weighted scores were categorized into quintiles from poorest to reachest \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eLifestyle factors included physical activity, smoking status, and alcohol consumption. Physical activity was quantified using metabolic equivalent of task (MET) values and categorized into quartiles (from lowest activity in the first quartile to highest activity in the fourth) \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Smoking status was self-reported; individuals who had smoked at least 100 cigarettes during their lifetime were classified as smokers.\u003c/p\u003e \u003cp\u003eAnthropometric and clinical assessments included body mass index (BMI), calculated as weight (kg) divided by height squared (m\u0026sup2;), waist circumference, and blood pressure. Laboratory measurements included fasting blood glucose, lipid profile (triglycerides, HDL, LDL, and total cholesterol), and liver enzymes. Elevated liver enzymes were defined as alanine aminotransferase\u0026thinsp;\u0026gt;\u0026thinsp;30 U/L in men and \u0026gt;\u0026thinsp;20 U/L in women; aspartate aminotransferase\u0026thinsp;\u0026gt;\u0026thinsp;30 U/L in men and \u0026gt;\u0026thinsp;20 U/L in women; and alkaline phosphatase\u0026thinsp;\u0026ge;\u0026thinsp;306 U/L. Type 2 diabetes was defined as a self-reported physician diagnosis, use of glucose-lowering medication, or fasting plasma glucose\u0026thinsp;\u0026ge;\u0026thinsp;126 mg/dL \u003csup\u003e41\u003c/sup\u003e, Hypertension was defined as physician diagnosis, antihypertensive treatment, or measured systolic/diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140/90 mmHg \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eBaseline characteristics of the study population were summarized using descriptive statistics. The normality of continuous variables was assessed with the Shapiro\u0026ndash;Wilk test. Continuous variables, including TyG, TyG-BMI, TyG-WC, METS-IR, and SPISE, were categorized into quartiles for descriptive analyses, while categorical variables were reported as counts and percentages. For the main analyses, metabolic indices were examined both as continuous variables and by quartiles to explore dose\u0026ndash;response associations with incident MASLD. Differences across quartiles were tested using the chi-square test, when appropriate. MASLD was evaluated only at the 5-year follow-up, making the 5-year cumulative incidence the primary measure of disease occurrence. Associations between each metabolic index and MASLD were estimated using Poisson regression with robust (sandwich) variance estimators, which provide consistent risk ratio (RR) estimates for binary outcomes in cohort studies. As a supplementary analysis, we estimated the relative risk per 1-standard-deviation increase in each metabolic index, adjusting for relevant covariates, to allow standardized comparisons across variables with different units and to facilitate interpretation of effect sizes.\u003c/p\u003e \u003cp\u003eThe predictive performance of the metabolic indices was assessed using receiver operating characteristic (ROC) curves. ROC curves were generated separately for TyG, TyG-BMI, TyG-WC, and METS-IR, and the area under the curve (AUC) with 95% confidence intervals was calculated. Optimal cut-off values were determined using Youden\u0026rsquo;s J statistic to balance sensitivity and specificity. All analyses were conducted in Stata version 14 and R version 4.4.3, and p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics statement\u003c/h2\u003e \u003cp\u003e The Ethics Research Committee at Ahvaz Jundishapur University of Medical Sciences approved this study, which was conducted in accordance with the Helsinki Declaration. The approval number is IR.AJUMS.REC.1404.361. All participants signed a written informed consent form.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eC\u003cb\u003eompeting interest\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was supported by the Vice-Chancellor for Research at Ahvaz Jundishapur University of Medical Sciences with grant number U-04254.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eBCh: conceptualization, project administration, formal analysis, methodology, and writing- review editing. ZR: conceptualization, formal analysis, methodology, writing-original draft, and writing- review editing. MSh: data curation, and writing-original draft. AHB: project administration, supervision, and writing- review editing. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors would like to express their sincere gratitude to all participants and the staff of the Hoveyzeh Cohort Study Center for their invaluable contributions to this research. The authors also acknowledge the financial support provided by the Vice-Chancellor for Research at Ahvaz Jundishapur University of Medical Sciences. This article is derived from the MSc thesis of Mohammad Shahmansouri conducted at Ahvaz Jundishapur University of Medical Sciences.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets from the current study are included in the article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eYounossi, Z. M., Kalligeros, M. \u0026amp; Henry, L. Epidemiology of metabolic dysfunction-associated steatotic liver disease. \u003cem\u003eClin. Mol. Hepatol.\u003c/em\u003e \u003cb\u003e31\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3350/cmh.2024.0431\u003c/span\u003e\u003cspan address=\"10.3350/cmh.2024.0431\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChan, W. K. et al. Metabolic dysfunction-associated steatotic liver disease (MASLD): a state-of-the-art review. \u003cem\u003eJ. Obes. metabolic syndrome\u003c/em\u003e. \u003cb\u003e32\u003c/b\u003e, 197. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.7570/jomes23052\u003c/span\u003e\u003cspan address=\"10.7570/jomes23052\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYanai, H., Adachi, H., Hakoshima, M., Iida, S. \u0026amp; Katsuyama, H. Metabolic-dysfunction-associated steatotic liver disease\u0026mdash;its pathophysiology, association with atherosclerosis and cardiovascular disease, and treatments. \u003cem\u003eInt. J. Mol. Sci.\u003c/em\u003e \u003cb\u003e24\u003c/b\u003e, 15473. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms242015473\u003c/span\u003e\u003cspan address=\"10.3390/ijms242015473\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYounossi, Z. M. et al. Addressing the High and Rising Global Burden of Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) and Metabolic Dysfunction‐Associated Steatohepatitis (MASH): From the Growing Prevalence to Payors' Perspective. \u003cem\u003eAliment. Pharmacol. Ther.\u003c/em\u003e \u003cb\u003e61\u003c/b\u003e, 1467\u0026ndash;1478. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/apt.70020\u003c/span\u003e\u003cspan address=\"10.1111/apt.70020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAli, S. M. \u0026amp; Lai, M. Metabolic dysfunction\u0026ndash;associated steatotic liver disease. Annals of internal medicine 178, ITC1-ITC16 (2025). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.7326/ANNALS-24-02933\u003c/span\u003e\u003cspan address=\"10.7326/ANNALS-24-02933\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoehler, E. M. et al. External validation of the fatty liver index for identifying nonalcoholic fatty liver disease in a population-based study. \u003cem\u003eClin. Gastroenterol. Hepatol.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 1201\u0026ndash;1204. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cgh.2013.01.012\u003c/span\u003e\u003cspan address=\"10.1016/j.cgh.2013.01.012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSang, Z. et al. Association of Dietary Inflammatory Index with liver fibrosis and fatty liver index in a population with metabolic dysfunction-associated steatotic liver disease: a cross-sectional study. \u003cem\u003eFront. Nutr.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 1594192. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fnut.2025.1594192\u003c/span\u003e\u003cspan address=\"10.3389/fnut.2025.1594192\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, L. From metabolic dysfunction-associated fatty liver disease to metabolic dysfunction-associated steatotic liver disease: Controversy and consensus. \u003cem\u003eWorld J. Hepatol.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, 1253. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4254/wjh. v15.i12.1253\u003c/span\u003e\u003cspan address=\"10.4254/wjh. v15.i12.1253\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePal, S. C. M\u0026eacute;ndez-S\u0026aacute;nchez, N. Insulin resistance and adipose tissue interactions as the cornerstone of metabolic (dysfunction)-associated fatty liver disease pathogenesis. \u003cem\u003eWorld J. Gastroenterol.\u003c/em\u003e \u003cb\u003e29\u003c/b\u003e, 3999. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3748/wjg. v29.i25.3999\u003c/span\u003e\u003cspan address=\"10.3748/wjg. v29.i25.3999\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVK, R. N., Satheesh, P., Shenoy, M. T. \u0026amp; Kalra, S. Triglyceride Glucose (TyG) Index: A surrogate biomarker of insulin resistance. \u003cem\u003eJ. Pak. Med. Assoc.\u003c/em\u003e \u003cb\u003e72\u003c/b\u003e, 986\u0026ndash;988. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.47391/JPMA.22-63\u003c/span\u003e\u003cspan address=\"10.47391/JPMA.22-63\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSimental-Mend\u0026iacute;a, L. E., Rodr\u0026iacute;guez-Mor\u0026aacute;n, M. \u0026amp; Guerrero-Romero, F. The product of fasting glucose and triglycerides as surrogate for identifying insulin resistance in apparently healthy subjects. \u003cem\u003eMetab. Syndr. Relat. Disord.\u003c/em\u003e \u003cb\u003e6\u003c/b\u003e, 299\u0026ndash;304. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1089/met.2008.0035\u003c/span\u003e\u003cspan address=\"10.1089/met.2008.0035\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBello-Chavolla, O. Y. et al. Prediction of incident hypertension and arterial stiffness using the non\u0026ndash;insulin‐based metabolic score for insulin resistance (METS‐IR) index. \u003cem\u003eJ. Clin. Hypertens.\u003c/em\u003e \u003cb\u003e21\u003c/b\u003e, 1063\u0026ndash;1070. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/jch.13645\u003c/span\u003e\u003cspan address=\"10.1111/jch.13645\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaulmichl, K. et al. Modification and validation of the Triglyceride-to\u0026ndash;HDL cholesterol ratio as a surrogate of insulin sensitivity in White juveniles and adults without diabetes mellitus: the single point insulin sensitivity estimator (SPISE). \u003cem\u003eClin. Chem.\u003c/em\u003e \u003cb\u003e62\u003c/b\u003e, 1211\u0026ndash;1219. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1373/clinchem.2015.251965\u003c/span\u003e\u003cspan address=\"10.1373/clinchem.2015.251965\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, Z., Hui, X., Huang, X., Li, J. \u0026amp; Liu, N. Relationship between a novel non\u0026ndash; insulin-based metabolic score for insulin resistance (METS-IR) and coronary artery calcification. \u003cem\u003eBMC Endocr. disorders\u003c/em\u003e. \u003cb\u003e22\u003c/b\u003e, 274. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12902-022-01186-6\u003c/span\u003e\u003cspan address=\"10.1186/s12902-022-01186-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhamseh, M. E. et al. Insulin resistance/sensitivity measures as screening indicators of metabolic-associated fatty liver disease and liver fibrosis. \u003cem\u003eDig. Dis. Sci.\u003c/em\u003e \u003cb\u003e69\u003c/b\u003e, 1430\u0026ndash;1443. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10620-024-08309-9\u003c/span\u003e\u003cspan address=\"10.1007/s10620-024-08309-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, F., Wang, Z., Zhang, H. \u0026amp; Zuo, Y. Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) Attenuates the Predictive Value of the Triglyceride\u0026ndash;Glucose Index for Carotid Plaque: Evidence of Insulin Resistance-Independent Pathways. \u003cem\u003eFront. Endocrinol.\u003c/em\u003e \u003cb\u003e16\u003c/b\u003e, 1696652. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.3389/fendo.2025.1696652\u003c/span\u003e\u003cspan address=\"10.3389/fendo.2025.1696652\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong, K. et al. Identification of pediatric MASLD using insulin resistance indices. \u003cem\u003eJHEP Rep.\u003c/em\u003e \u003cb\u003e101419\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jhepr.2025.101419\u003c/span\u003e\u003cspan address=\"10.1016/j.jhepr.2025.101419\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCai, X. et al. Dose-response associations of metabolic score for insulin resistance index with nonalcoholic fatty liver disease among a nonobese Chinese population: retrospective evidence from a population‐based cohort study. Disease markers. 4930355 (2022). (2022). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1155/2022/4930355\u003c/span\u003e\u003cspan address=\"10.1155/2022/4930355\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoushehri, Y. G. et al. Association of triglyceride glucose-body mass index (TyG-BMI) with metabolic dysfunction-associated steatotic liver disease: A systematic review and meta-analysis. \u003cem\u003ePloS one\u003c/em\u003e. \u003cb\u003e20\u003c/b\u003e, e0324483. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1371/journal.pone.0324483\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0324483\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu, X., Chen, Q., Zhang, C. \u0026amp; Hong, X. The effect of longitudinal trajectories of triglyceride-glucose index on the incident metabolic dysfunction-associated steatotic liver disease. \u003cem\u003eBMC Gastroenterol.\u003c/em\u003e \u003cb\u003e25\u003c/b\u003e, 689. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12876-025-0689-0\u003c/span\u003e\u003cspan address=\"10.1186/s12876-025-0689-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang, X. et al. TyG-BMI as a superior predictor of MAFLD and pre-MAFLD in Chinese adults: a cross-sectional study. \u003cem\u003eBMC Gastroenterol.\u003c/em\u003e \u003cb\u003e25\u003c/b\u003e, 495. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12876-025-04087-4\u003c/span\u003e\u003cspan address=\"10.1186/s12876-025-04087-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, Y. et al. Triglyceride-glucose index and related parameters in the diagnosis and predictive efficacy for older adults with NAFLD/MASLD: a prospective cohort study. \u003cem\u003eFront. Med.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 1626672. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.3389/fmed.2025.1626672\u003c/span\u003e\u003cspan address=\"10.3389/fmed.2025.1626672\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYilmaz, Y. et al. Novel surrogate markers of insulin resistance in patients with metabolic dysfunction-associated steatosis liver disease: Associations with dietary and lifestyle inflammatory scores in the large cross-sectional study. \u003cem\u003eClin. Nutr. ESPEN\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1016/j.clnesp.2025.09.021\u003c/span\u003e\u003cspan address=\"10.1016/j.clnesp.2025.09.021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXin, Z. et al. Triglyceride glucose waist circumference and non alcoholic fatty liver disease: a systematic review and meta analysis. \u003cem\u003eBMC Gastroenterol.\u003c/em\u003e \u003cb\u003e25\u003c/b\u003e, 328. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12876-025-03888-x\u003c/span\u003e\u003cspan address=\"10.1186/s12876-025-03888-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeng, H. et al. METS-IR/HOMA-IR and MAFLD in US adults: dose\u0026ndash;response correlation and the effect mediated by physical activity. \u003cem\u003eBMC Endocr. Disorders\u003c/em\u003e. \u003cb\u003e24\u003c/b\u003e, 132. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12902-024-01646-w\u003c/span\u003e\u003cspan address=\"10.1186/s12902-024-01646-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFurthner, D. et al. Single point insulin sensitivity estimator in pediatric non-alcoholic fatty liver disease. \u003cem\u003eFront. Endocrinol.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e, 830012. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.3389/fendo.2022.830012\u003c/span\u003e\u003cspan address=\"10.3389/fendo.2022.830012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatsuzaka, T. \u0026amp; Shimano, H. Molecular mechanisms involved in hepatic steatosis and insulin resistance. \u003cem\u003eJ. diabetes Invest.\u003c/em\u003e \u003cb\u003e2\u003c/b\u003e, 170\u0026ndash;175. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/j.2040-1124.2011\u003c/span\u003e\u003cspan address=\"10.1111/j.2040-1124.2011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2011). 00083.x.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBo, T. et al. Hepatic selective insulin resistance at the intersection of insulin signaling and metabolic dysfunction-associated steatotic liver disease. \u003cem\u003eCell Metabol.\u003c/em\u003e \u003cb\u003e36\u003c/b\u003e, 947\u0026ndash;968. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cmet.2024.04.006\u003c/span\u003e\u003cspan address=\"10.1016/j.cmet.2024.04.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBansal, S., Vachher, M., Arora, T., Kumar, B. \u0026amp; Burman, A. Visceral fat: A key mediator of NAFLD development and progression. \u003cem\u003eHum. Nutr. Metabolism\u003c/em\u003e. \u003cb\u003e33\u003c/b\u003e, 200210. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1016/j.hnm.2023.200210\u003c/span\u003e\u003cspan address=\"10.1016/j.hnm.2023.200210\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou, H., Shi, Y. \u0026amp; Zhou, X. Comparison of nine insulin resistance surrogates for predicting cardiovascular disease: a cohort study. \u003cem\u003eDiabetol. Metab. Syndr.\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e, 365. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13098-025-01933-z\u003c/span\u003e\u003cspan address=\"10.1186/s13098-025-01933-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou, T. \u0026amp; Xie, C. Association between Triglyceride-Glucose index and onset of type 2 diabetes in metabolic Dysfunction-Associated steatotic liver disease population. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, 18044. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1038/s41598-025-03453-2\u003c/span\u003e\u003cspan address=\"10.1038/s41598-025-03453-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheraghian, B. et al. Cohort profile: The Hoveyzeh Cohort Study (HCS): A prospective population-based study on non-communicable diseases in an Arab community of Southwest Iran. \u003cem\u003eMed. J. Islamic Repub. Iran.\u003c/em\u003e \u003cb\u003e34\u003c/b\u003e, 141 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBedogni, G. et al. The Fatty Liver Index: a simple and accurate predictor of hepatic steatosis in the general population. \u003cem\u003eBMC Gastroenterol.\u003c/em\u003e \u003cb\u003e6\u003c/b\u003e, 33. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/1471-230X-6-33\u003c/span\u003e\u003cspan address=\"10.1186/1471-230X-6-33\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2006).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRinella, M. E. et al. A multisociety Delphi consensus statement on new fatty liver disease nomenclature. \u003cem\u003eHepatology\u003c/em\u003e \u003cb\u003e78\u003c/b\u003e, 1966\u0026ndash;1986. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/HEP.0000000000000520\u003c/span\u003e\u003cspan address=\"10.1097/HEP.0000000000000520\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiver, E. A. \u0026amp; Diabetes, E. A. f. t. S. o. t. f. t. S. o. EASL-EASD-EASO Clinical Practice Guidelines on the management of metabolic dysfunction-associated steatotic liver disease (MASLD). Obesity Facts 17, 374\u0026ndash;444 (2024). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1159/000539371\u003c/span\u003e\u003cspan address=\"10.1159/000539371\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuerrero-Romero, F. et al. The product of triglycerides and glucose, a simple measure of insulin sensitivity. Comparison with the euglycemic-hyperinsulinemic clamp. \u003cem\u003eJ. Clin. Endocrinol. Metabolism\u003c/em\u003e. \u003cb\u003e95\u003c/b\u003e, 3347\u0026ndash;3351. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1210/jc.2009-2067\u003c/span\u003e\u003cspan address=\"10.1210/jc.2009-2067\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQiao, Y., Wang, Y., Chen, C., Huang, Y. \u0026amp; Zhao, C. Association between triglyceride-glucose (TyG) related indices and cardiovascular diseases and mortality among individuals with metabolic dysfunction-associated steatotic liver disease: a cohort study of UK Biobank. \u003cem\u003eCardiovasc. Diabetol.\u003c/em\u003e \u003cb\u003e24\u003c/b\u003e (1), 12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12933-024-02572-w\u003c/span\u003e\u003cspan address=\"10.1186/s12933-024-02572-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBello-Chavolla, O. Y. et al. METS-IR, a novel score to evaluate insulin sensitivity, is predictive of visceral adiposity and incident type 2 diabetes. \u003cem\u003eEur. J. Endocrinol.\u003c/em\u003e \u003cb\u003e178\u003c/b\u003e, 533\u0026ndash;544. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1530/EJE-17-0813\u003c/span\u003e\u003cspan address=\"10.1530/EJE-17-0813\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavila, R. L., McCarthy, A. S., Gondwe, D., Kirdruang, P. \u0026amp; Sharma, U. Water, Walls, and Bicycles: Wealth Index Composition Using Census Microdata. \u003cem\u003eJ. Demogr Econ.\u003c/em\u003e \u003cb\u003e88\u003c/b\u003e, 79\u0026ndash;120. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1017/dem.2020.27\u003c/span\u003e\u003cspan address=\"10.1017/dem.2020.27\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMendes, M. A. et al. Metabolic equivalent of task (METs) thresholds as an indicator of physical activity intensity. \u003cem\u003ePloS one\u003c/em\u003e. \u003cb\u003e13\u003c/b\u003e, e0200701. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0200701\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0200701\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhamseh, M. E. et al. Nationwide prevalence of diabetes and prediabetes and associated risk factors among Iranian adults: analysis of data from PERSIAN cohort study. \u003cem\u003eDiabetes Therapy\u003c/em\u003e. \u003cb\u003e12\u003c/b\u003e, 2921\u0026ndash;2938. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s13300-021-01124-0\u003c/span\u003e\u003cspan address=\"10.1007/s13300-021-01124-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable\u0026nbsp;1. Characteristics of participants at baseline phase (May 2016 and August 2018) until stratified by MASLD status at reassessment phase in southwest Iran.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003echaracteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eOverall(n=1277)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003eIncidence of MASLD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Female\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Male\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e712\u003c/p\u003e\n \u003cp\u003e565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e98(13.76%)\u003c/p\u003e\n \u003cp\u003e97(17.17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 35-44\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 45-54\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 55-64\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026gt;=65\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e513\u003c/p\u003e\n \u003cp\u003e428\u003c/p\u003e\n \u003cp\u003e256\u003c/p\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e84(16.37%)\u003c/p\u003e\n \u003cp\u003e75(17.52%)\u003c/p\u003e\n \u003cp\u003e26(10.16%)\u003c/p\u003e\n \u003cp\u003e10(12.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Underweight\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Normal\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Overweight\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Obese\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003cp\u003e586\u003c/p\u003e\n \u003cp\u003e576\u003c/p\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0(0%)\u003c/p\u003e\n \u003cp\u003e42(7.17%)\u003c/p\u003e\n \u003cp\u003e121(21.01%)\u003c/p\u003e\n \u003cp\u003e32(39.02%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eWaist circumference\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026le;\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003e90\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026gt; 90\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e574\u003c/p\u003e\n \u003cp\u003e703\u003c/p\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e35(6.10%)\u003c/p\u003e\n \u003cp\u003e160(22.76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eWealth score\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Poorest\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Poor\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Moderate\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Rich\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Richest\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e269\u003c/p\u003e\n \u003cp\u003e295\u003c/p\u003e\n \u003cp\u003e235\u003c/p\u003e\n \u003cp\u003e238\u003c/p\u003e\n \u003cp\u003e240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e38(14.13%)\u003c/p\u003e\n \u003cp\u003e40(13.56%)\u003c/p\u003e\n \u003cp\u003e31(13.19%)\u003c/p\u003e\n \u003cp\u003e45(18.91%)\u003c/p\u003e\n \u003cp\u003e41(17.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.312\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Yes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; No\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e293\u003c/p\u003e\n \u003cp\u003e984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e44(15.02%)\u003c/p\u003e\n \u003cp\u003e151(15.35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.891\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eUse Alcohol\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Yes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; No\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003cp\u003e1260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e6(35.29%)\u003c/p\u003e\n \u003cp\u003e189(15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003ePhysical activity (MET)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Q1\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Q2\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Q3\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Q4\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e272\u003c/p\u003e\n \u003cp\u003e291\u003c/p\u003e\n \u003cp\u003e324\u003c/p\u003e\n \u003cp\u003e390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e37(13.60%)\u003c/p\u003e\n \u003cp\u003e41(14.09%)\u003c/p\u003e\n \u003cp\u003e59(18.21%)\u003c/p\u003e\n \u003cp\u003e58(14.87%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.375\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eSPB\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026lt;130\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026ge;130\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1149 \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e174(15.14%)\u003c/p\u003e\n \u003cp\u003e21(16.41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.706\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eDPB\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026lt;85\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026ge;85\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1196\u003c/p\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e177(14.80%)\u003c/p\u003e\n \u003cp\u003e18(22.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eTriglycerides\u003c/p\u003e\n \u003cp\u003eNormal \u0026lt;150\u003c/p\u003e\n \u003cp\u003eAbnormal \u0026ge;150\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e972\u003c/p\u003e\n \u003cp\u003e305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e139(14.30%)\u003c/p\u003e\n \u003cp\u003e56(18.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eHDL\u003c/p\u003e\n \u003cp\u003eNormal \u0026le;40\u003c/p\u003e\n \u003cp\u003eAbnormal\u0026lt;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1120\u003c/p\u003e\n \u003cp\u003e157\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e163(14.55%)\u003c/p\u003e\n \u003cp\u003e32(20.38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eLDL\u003c/p\u003e\n \u003cp\u003eNormal \u0026lt;100\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eAbnormal \u0026ge;100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e587\u003c/p\u003e\n \u003cp\u003e690\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e89(15.21%)\u003c/p\u003e\n \u003cp\u003e106(15.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.941\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eTotal cholesterol\u003c/p\u003e\n \u003cp\u003eNormal \u0026lt;200\u003c/p\u003e\n \u003cp\u003eAbnormal \u0026ge;200\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e908\u003c/p\u003e\n \u003cp\u003e369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e144(15.86%)\u003c/p\u003e\n \u003cp\u003e51(13.82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.359\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eALT\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; no\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; yes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1,072\u003c/p\u003e\n \u003cp\u003e205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e155(14.46%)\u003c/p\u003e\n \u003cp\u003e40(19.51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eAST\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; no\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1121\u003c/p\u003e\n \u003cp\u003e156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e171(15.25%)\u003c/p\u003e\n \u003cp\u003e24(15.38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.966\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eALK\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; no\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1208\u003c/p\u003e\n \u003cp\u003e156\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e180(14.90%)\u003c/p\u003e\n \u003cp\u003e15(21.74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; no\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; yes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1097\u003c/p\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e164(14.95%)\u003c/p\u003e\n \u003cp\u003e31(17.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.432\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; no\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; yes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1044\u003c/p\u003e\n \u003cp\u003e233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e157(15.04%)\u003c/p\u003e\n \u003cp\u003e38(16.31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.626\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eP\u0026lt;0.05 significant for chi-square test\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;2. Levels of TyG indices, METS-IR, and SPISE among participants in the baseline phase, stratified by MASLD status at reassessment phase in southwest Iran\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eOverall(n=1277)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Mean \u0026plusmn; SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u0026nbsp;\u003c/strong\u003eMASL\u003cstrong\u003eD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eIncidence of MASLD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eTyg index\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Q1\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Q2\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Q3\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Q4\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e(8.61\u0026plusmn;0.52)\u003c/p\u003e\n \u003cp\u003e321\u003c/p\u003e\n \u003cp\u003e318\u003c/p\u003e\n \u003cp\u003e319\u003c/p\u003e\n \u003cp\u003e319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e291(90.65%)\u003c/p\u003e\n \u003cp\u003e265(83.33%)\u003c/p\u003e\n \u003cp\u003e265(83.07%)\u003c/p\u003e\n \u003cp\u003e261(81.82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e30(9.35%)\u003c/p\u003e\n \u003cp\u003e53(16.67%)\u003c/p\u003e\n \u003cp\u003e54(16.93%)\u003c/p\u003e\n \u003cp\u003e58(18.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eTyg BMI\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Q1\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Q2\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Q3\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Q4\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e(215.45\u0026plusmn;30.25)\u003c/p\u003e\n \u003cp\u003e320\u003c/p\u003e\n \u003cp\u003e319\u003c/p\u003e\n \u003cp\u003e319\u003c/p\u003e\n \u003cp\u003e319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e314(98.13%)\u003c/p\u003e\n \u003cp\u003e290(90.91%)\u003c/p\u003e\n \u003cp\u003e259(81.19%)\u003c/p\u003e\n \u003cp\u003e219(68.65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e6(1.88%)\u003c/p\u003e\n \u003cp\u003e29(9.09%)\u003c/p\u003e\n \u003cp\u003e60(18.81%)\u003c/p\u003e\n \u003cp\u003e100(31.35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eTyg WC\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Q1\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Q2\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Q3\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Q4\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e(780.88\u0026plusmn;84.48)\u003c/p\u003e\n \u003cp\u003e320\u003c/p\u003e\n \u003cp\u003e319\u003c/p\u003e\n \u003cp\u003e319\u003c/p\u003e\n \u003cp\u003e319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e310(96.88%)\u003c/p\u003e\n \u003cp\u003e280(87.77%)\u003c/p\u003e\n \u003cp\u003e261(81.82%)\u003c/p\u003e\n \u003cp\u003e231(72.41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e10(3.13%)\u003c/p\u003e\n \u003cp\u003e39(12.23%)\u003c/p\u003e\n \u003cp\u003e58(18.18%)\u003c/p\u003e\n \u003cp\u003e88(27.59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eMETS_IR\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Q1\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Q2\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Q3\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Q4\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e(36.76\u0026plusmn;5.61)\u003c/p\u003e\n \u003cp\u003e320\u003c/p\u003e\n \u003cp\u003e319\u003c/p\u003e\n \u003cp\u003e319\u003c/p\u003e\n \u003cp\u003e319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e313(97.81%)\u003c/p\u003e\n \u003cp\u003e286(89.66%)\u003c/p\u003e\n \u003cp\u003e263(82.45%)\u003c/p\u003e\n \u003cp\u003e220(68.97%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7(2.19%)\u003c/p\u003e\n \u003cp\u003e33(10.34%)\u003c/p\u003e\n \u003cp\u003e56(17.55%)\u003c/p\u003e\n \u003cp\u003e99(31.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003eSPISE\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Q1\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Q2\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Q3\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Q4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e(6.73\u0026plusmn;1.52)\u003c/p\u003e\n \u003cp\u003e320\u003c/p\u003e\n \u003cp\u003e319\u003c/p\u003e\n \u003cp\u003e319\u003c/p\u003e\n \u003cp\u003e319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e223(69.69%)\u003c/p\u003e\n \u003cp\u003e255(79.94%)\u003c/p\u003e\n \u003cp\u003e291(91.22%)\u003c/p\u003e\n \u003cp\u003e313(98.12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e97(30.31%)\u003c/p\u003e\n \u003cp\u003e64(20.06%)\u003c/p\u003e\n \u003cp\u003e28(8.78%)\u003c/p\u003e\n \u003cp\u003e6(1.88%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eP\u0026lt;0.05 significant for chi-square test\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 3. Risk ratios (RRs) and 95% confidence intervals for the association between insulin resistance\u0026ndash;related indices and incidence of MASLD based on Poisson regression models. \u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"718\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eTyg index\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eQ1 \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003cp\u003eQ2 \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003cp\u003eP-Trend\u003c/p\u003e\n \u003cp\u003eRR per 1-SD increase\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e1.42(1.10,1.82)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e1.78(1.13,2.79)\u003c/p\u003e\n \u003cp\u003e1.81(1.15,2.83)\u003c/p\u003e\n \u003cp\u003e1.94(1.25,3.02)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.20(1.07,1.34)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e1.45(1.12,1.88)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e1.83(1.16,2.87)\u003c/p\u003e\n \u003cp\u003e1.82(1.16,2.86)\u003c/p\u003e\n \u003cp\u003e2.01(1.28,3.15)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.21(1.09,1.36)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e1.40(1.05,1.89)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e1.84(1.17,2.88)\u003c/p\u003e\n \u003cp\u003e1.82(1.15,2.87)\u003c/p\u003e\n \u003cp\u003e1.83(1.14,2.95)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.19(1.05,1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eTyg BMI\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eQ1 \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003cp\u003eQ2 \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003cp\u003eP-Trend\u003c/p\u003e\n \u003cp\u003eRR per 1-SD increase\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e1.02(1.02,1.03)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e4.84(2.01,11.67)\u003c/p\u003e\n \u003cp\u003e10.03(4.33,23.21)\u003c/p\u003e\n \u003cp\u003e16.71(7.33,38.10)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.41(2.10,2.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e1.03(1.02,1.03)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e4.72(1.96,11.37)\u003c/p\u003e\n \u003cp\u003e10.39(4.48,24.06)\u003c/p\u003e\n \u003cp\u003e18.18(7.95,41.53)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.60(2.25,3.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e1.03(1.02,1.04)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e4.68(1.94,11.31)\u003c/p\u003e\n \u003cp\u003e10.26(4.41,23.82)\u003c/p\u003e\n \u003cp\u003e18.57(8.08,42.69)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.72(2.32,3.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eTyg WC\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eQ1 \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003cp\u003eQ2 \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003cp\u003eP-Trend\u003c/p\u003e\n \u003cp\u003eRR per 1-SD increase\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e1.00(1.00,1.01)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e3.91(1.95,7.83)\u003c/p\u003e\n \u003cp\u003e5.81(2.97,11.38)\u003c/p\u003e\n \u003cp\u003e8.82(4.58,16.97)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.90(1.68,2.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e1.00(1.0,1.01)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e4.05(2.02,8.12)\u003c/p\u003e\n \u003cp\u003e6.20(3.17,12.15)\u003c/p\u003e\n \u003cp\u003e10.42(5.39,20.14)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.09(1.84,2.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e1.01(1.00,1.01)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e4.06(2.02,8.16)\u003c/p\u003e\n \u003cp\u003e5.88(2.99,11.58)\u003c/p\u003e\n \u003cp\u003e10.70(5.49,20.85)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.32(1.98,2.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eMETS_IR\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eQ1 \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003cp\u003eQ2 \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003cp\u003eP-Trend\u003c/p\u003e\n \u003cp\u003eRR per 1-SD increase\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e1.12(1.10,1.15)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e4.72(2.09,10.69)\u003c/p\u003e\n \u003cp\u003e8.02(3.65,17.60)\u003c/p\u003e\n \u003cp\u003e14.18(6.59,30.53)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.97(1.67,2.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e1.13(1.10,1.15)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e4.70(2.08,10.64)\u003c/p\u003e\n \u003cp\u003e8.15(3.71,17.91)\u003c/p\u003e\n \u003cp\u003e14.13(6.56,30.45)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.99(1.69,2.34)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e1.13(1.10,1.16)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e4.64(2.04,10.51)\u003c/p\u003e\n \u003cp\u003e8.11(3.68,17.86)\u003c/p\u003e\n \u003cp\u003e14.25(6.57,30.87)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.07(1.78,2.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eSPISE\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eQ1 \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003cp\u003eQ2 \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003cp\u003eP-Trend\u003c/p\u003e\n \u003cp\u003eRR per 1-SD increase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e0.44(0.37,0.53)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e0.66(0.48,0.90)\u003c/p\u003e\n \u003cp\u003e0.28(0.19,0.44)\u003c/p\u003e\n \u003cp\u003e0.06(0.02,0.14)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.29(0.23,0.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e0.44(0.37,0.53)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e0.67(0.49,0.92)\u003c/p\u003e\n \u003cp\u003e0.28(0.18,0.43)\u003c/p\u003e\n \u003cp\u003e0.06(0.02,0.14)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.29(0.23,0.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e0.44(0.37,0.53)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003cp\u003e0.65(0.47,0.89)\u003c/p\u003e\n \u003cp\u003e0.27(0.18,0.42)\u003c/p\u003e\n \u003cp\u003e0.06(0.02,0.14)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.29(0.23,0.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eModel 1: crude; Model 2: adjusted for age and sex; Model 3: additionally adjusted for socioeconomic status, smoking, alcohol use, physical activity, liver enzymes, hypertension, and diabetes\u003c/p\u003e\n\u003cp\u003eP\u0026lt;0.05 significant for Poisson regression.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;4. Discriminatory ability of insulin resistance indices for predicting MASLD\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eROC area (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eTyg BMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e0.75(0.71, 0.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eMETS_IR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e0.74(0.70,0.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e0.421\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eTyg WC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e0.71(0.67,0.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eTyg index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e0.57(0.52,0.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe areas under the ROC curves were compared using the DeLong test.\u003c/p\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":"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, Insulin resistance, Fatty liver, Cohort study, Southwestern Iran","lastPublishedDoi":"10.21203/rs.3.rs-9415232/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9415232/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eInsulin resistance is a key contributor to metabolic dysfunction-associated steatotic liver disease (MASLD), yet comparative prospective data on surrogate metabolic indices are scarce. This study followed 1,277 adults aged 35\u0026ndash;70 years without MASLD at baseline over five years to evaluate the predictive value of TyG, TyG-BMI, TyG-WC, METS-IR, and SPISE for incident MASLD. MASLD incidence was defined by a Fatty Liver Index\u0026thinsp;\u0026ge;\u0026thinsp;60 plus at least one cardiometabolic risk factor. Associations between metabolic indices and MASLD risk were assessed using multivariable Poisson regression with robust variance, and predictive performance was evaluated via ROC curves. Over follow-up, 195 participants (15.3%) developed MASLD. In adjusted models, the highest quartile of TyG-BMI (RR 18.57; 95% CI 8.08,42.69), METS-IR (RR 14.25; 6.57,30.87), and TyG-WC (RR 10.70; 5.49,20.85) showed the greatest risk. Each SD increase in TyG-BMI and METS-IR increased risk by 172% and 107%, respectively, while SPISE was inversely associated (RR 0.29; 0.23,0.36). TyG-BMI showed the highest discrimination (AUC 0.75). These findings indicate that composite metabolic indices, especially TyG-BMI and METS-IR, are robust predictors of incident MASLD and could be valuable for early risk stratification in clinical and population-level settings.\u003c/p\u003e","manuscriptTitle":"Predictive Performance of TyG, TyG-BMI, TyG-WC, METS-IR, and SPISE for incident MASLD in an Iranian prospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-12 16:53:14","doi":"10.21203/rs.3.rs-9415232/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"137103231871005808701552642810077824100","date":"2026-05-06T11:56:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-04T13:34:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"182362574701557309562254046555313754991","date":"2026-05-04T12:30:08+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-04T11:51:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-04T11:48:44+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-21T08:53:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-17T10:38:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-04-17T09:05:54+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":"a1d11280-f26f-4eb0-a7ab-6d8a70da6580","owner":[],"postedDate":"May 12th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"137103231871005808701552642810077824100","date":"2026-05-06T11:56:30+00:00","index":69,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-04T13:34:47+00:00","index":68,"fulltext":""},{"type":"reviewerAgreed","content":"182362574701557309562254046555313754991","date":"2026-05-04T12:30:08+00:00","index":67,"fulltext":""},{"type":"reviewersInvited","content":"10","date":"2026-05-04T11:51:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-04T11:48:44+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":67877437,"name":"Health sciences/Biomarkers"},{"id":67877438,"name":"Health sciences/Diseases"},{"id":67877439,"name":"Health sciences/Endocrinology"},{"id":67877440,"name":"Health sciences/Gastroenterology"},{"id":67877441,"name":"Health sciences/Medical research"},{"id":67877442,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-05-12T16:53:16+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-12 16:53:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9415232","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9415232","identity":"rs-9415232","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}