The potential of Insulin Resistance Indices to predict Non-alcoholic Fatty Liver Disease in Patients with Type 2 Diabetes

The potential of Insulin Resistance Indices to predict Non-alcoholic Fatty Liver Disease in Patients with Type 2 Diabetes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The potential of Insulin Resistance Indices to predict Non-alcoholic Fatty Liver Disease in Patients with Type 2 Diabetes Jie Tian, Yutian Cao, Wenhui Zhang, Aiyao Wang, Xinyi Yang, Yinfeng Dong, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4482766/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Dec, 2024 Read the published version in BMC Endocrine Disorders → Version 1 posted 18 You are reading this latest preprint version Abstract Background: The triglyceride-glucose (TyG) index and related parameters, and as well as Homeostatic Model Assessment for Insulin Resistance(HOMA-IR), are recently developed as insulin resistance markers, which can identify the individuals with a risk of non-alcoholic fatty liver disease (NAFLD). However, whether it can be used to predict NAFLD among patients with type 2 diabetes mellitus (T2DM) remains unclear. This study aims to observe the performance of insulin resistance indices in diagnosing NAFLD combined with T2DM, and compare the diagnostic values in clinical practice. Patients and Methods: 268 patients with T2DM from the Endocrinology Department of Jiangsu Provincial Hospital of Traditional Chinese Medicine were enrolled in this study, they were divided into two groups: the NAFLD group (T2DM with NAFLD) and the T2DM group (T2DM without NAFLD). General information and blood indicators of the pariticipants were collected, and insulin resistance indices were calculated based on the data. Furthermore, receiver operating characteristic (ROC) analysis was conducted to calculate the area under the curve (AUC) of the insulin resistance-related indices. Results：ROC analysis revealed that among the five insulin resistance-related indices, four parameters (TyG、TyG-BMI、TyG-WC and TyG-WHR) exhibit high predictive performance for identifying NAFLD except for HOMA-IR. Of particular, TyG-BMI demonstrated the superior predictive value, especially in the males and individuals with a BMI less than 23 kg/m². For the male and the lean patients, AUC for TyG-BMI was 0.764 (95% CI 0.691 - 0.827) and 0.817 (95% CI 0.626 - 0.937), respectively. The sensitivity and specificity for the male NAFLD were 90.32% and 47.89%. While for the lean patients, the sensitivity and specificity were 80% and 82.6%, respectively. Moreover, In the fully adjusted models, there were positive associations of TyG, TyG-BMI, TyG-WC, TyG-WHR and HOMA-IR to CAP, with the βs of 21.30, 0.745, 0.247 and 2.549 (all p<0.001), respectively. Conclusion: TyG-BMI is promising to predict NAFLD combined with T2DM, especially for the lean and male T2DM patients. type 2 diabetes mellitus non-alcoholic fatty liver disease TyG index-related parameters BMI ROC curves Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Non-alcoholic fatty liver disease (NAFLD) is one of the most common liver disorders characterized by abnormal lipid deposition in the liver, it is closely associated with the adverse outcomes of diabetes mellitus[ 1 ]. Research indicates that approximately 55.5% of type 2 diabetes mellitus (T2DM) patients globally have concomitant NAFLD, and more than 90% of T2DM patients eventually progress into NAFLD[ 2 ]. There is a vicious cycle between T2DM and NAFLD, causing widespread and severe implications. Early diagnosis and treatment is critical for the worse progress. However, liver biopsy remains as the "gold standard" for diagnosing fatty liver. Due to the invasive procedure, it is limited in the clinical practice. Therefore, development of non-invasive diagnosis has been bringing into notice. Specially, it is vital for early diagnosis and evaluation of NAFLD. Increasing evidence has found that insulin resistance (IR) and obesity are common contributors to the occurrence of T2DM complicated with NAFLD. The triglyceride-glucose (TyG) index has been ranked as a reliable marker for IR[ 3 ]. Mainly, it includes fasting plasma glucose (FPG) and triglycerides (TG). Moreover, studies have demonstrated that TyG combined with obesity indices, such as Triglyceride glucose-body mass index(TyG-BMI) and Triglyceride Glucose-Waist Circumference (TyG-WC), have a potential predictive utility for insulin resistance (IR), and IR is closely correlated to obesity.[ 4 ] Subsequently, researchers have evaluated the potential of TyG-related indices in diagnosing NAFLD. However, there are some significant variations in diagnostic performance [ 5 ]. Moreover, the homeostasis model used for the assessment of insulin resistance index (HOMA-IR) was proposed by Matthews et al. in 1985, it serves as an indirect approach for evaluating insulin resistance (IR) in clinical[ 6 ]. Of note, this index utilizes the fasting plasma insulin and glucose concentrations to evaluate IR and β-cell deficiency. However, the value of IR-related indices in diagnosing T2DM complicated with NAFLD needs to be further addressed. This study aims to investigate the diagnostic performance of the TyG index and HOMA-IR index in T2DM complicated with NAFLD, and compare their diagnostic values in clinical practice. PARTICIPANTS AND METHODS Study design and populations A total of 268 T2DM patients complicated with fatty liver disease, admitted to the Endocrinology Department of Jiangsu Provincial Hospital from January 2021 to October 2023, were collected. Our study obtained approval from the Ethics Committee of Jiangsu Provincial Hospital. Recruitment was conducted by two researchers. Inclusion and exclusion criteria Inclusion criteria: (1) Age was from 18 to 75 years old; (2) T2DM patients were selected with the diagnostic criteria outlined in the "Chinese Guidelines for the Prevention and Treatment of Type 2 Diabetes Mellitus (2017 Edition)”[ 7 ] ; (3) The current investigation incorporates a threshold of CAP ≥ 258 dB/m to indicate the presence of substantial hepatic steatosis, which is based on prior research[ 8 ]. Exclusion criteria: (1) Patients with diabetes complicated with acute infection; (2) Individuals with a history of long-term alcohol consumption, equivalent to ethanol intake of > 140 g/week for males and > 70 g/week for females; (3) Patients with acute or chronic hepatitis, autoimmune hepatitis, hepatic steatosis, drug-induced liver injury, liver malignancies, etc.; Data Collection General clinical data, including age, gender, hypertension, diabetes, height, and weight, were obtained from medical records. All eligible participants fasted for at least 8 hours overnight, and blood samples were collected on the next morning between 8:00 and 9:00 a.m. Observed index included high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), triglycerides (TG), glycosylated hemoglobin (HbA1c), fasting blood glucose (FBG), serum gamma-glutamyl transferase (GGT), alanine aminotransferase (ALT), and fasting insulin. Two researchers type in and reviewed all these data. The calculating formulas used for analyzing the indices are listed as follows: BMI = weight (kg)/height2 (m2); TyG = Ln [TG(mg/dL) × FPG (mg/dL) / 2]; TyG-BMI = Ln [TG (mg/dL) × FPG (mg/dL) / 2] × BMI (kg/m2); TyG-WC = Ln[TG (mg/dL) × FPG (mg/dL) / 2] × WC (cm); TyG-WHR = Ln [TG (mg/dL) × FPG (mg/dL) / 2] × [WC (cm)/ Height (cm)]; HOMA-IR = FPG(mmol/L)×FINS(µU/mL༉/22.5. Statistical analysis Statistical analysis was conducted using SPSS V.26.0 (IBM Corp) and MedCalc V.16.2 (MedCalc Software). The categorical data were presented as proportions (%), and the comparisons between two different groups were conducted using the chi-square test. While the continuous data were expressed as the median and interquartile range M (Q1–Q3), and grouped comparisons were made using independent-sample t-tests. Moreover, Logistic regression analysis was utilized for multivariate analysis. Furthermore, targeted parameters were categorized into quartiles to explore the inner relationships. The diagnostic value of TyG-related indices for NAFLD was evaluated using receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC). Subgroup analyses were conducted based on gender and BMI, and non-parametric DeLong tests were used to reveal the differences in AUC between TyG-related indices and HOMA-IR. A two-­tailed p value < 0.05 was considered as a statistical significance. RESULTS General information of the participants A total of 268 patients were included in the final analysis. Baseline characteristics of the participants are presented in Table 1 . Among the 268 participants, there were 145 T2DM cases complicated with NAFLD, the prevalence of NAFLD was 54.10% among the patients with T2DM. The mean age of the complicated NAFLD group was significantly younger than the T2DM group (P < 0.001), with a higher prevalence in the males than the females (64.1% vs. 57.8%). Moreover, compared to the control group, the NAFLD group exhibited elevated levels of ALT, GGT, TG, and HbA1c (all P < 0.001). Notably, participants complicated with NAFLD had significantly higher BMI, WC, WHR, CAP, LSM and TyG-related indices than the T2DM group (all P < 0.001). Table 1. Participant Characteristics Variables NAFLD Group(T2DM with NAFLD) (n=145) T2DMGroup(T2DM without NAFLD) （n=123） P value Demographic parameters Age(years) 49.0(40.0,60.0) 56.0(47.0,61.0) ＜0.001 Sex (%) Female Male 52(35.9%) 93(64.1%) 52(42.2%) 71(57.8%) 0.315 Anthropometric parameters WC(cm) WHR(cm) BMI 97.40(88.7,106.0) 0.96(0.91,1.00) 28.50(25.70,31.07) 89.70(83.0,95.4) 0.91(0.87,0.95) 26.0(23.60,27.90) ＜0.001 ＜0.001 ＜0.001 Serum test ALT(U/L) GGT(U/L) FBG(umol/L) TC(umol/L) TG(umol/L) HDL-C(umol/L) LDL-C(umol/L) FINS(pmol/L) HbA1c (%) 28.00(18.5,49.50) 45.00(27.25,69.75) 7.09(5.47,9.41) 4.62(3.93,5.35) 2.03(1.52,1.85) 1.18(1.00,1.35) 2.79(2.31,3.36) 8.50(7.40,10.20) 12.11(7.3,16.95) 20.00(14.00,27.00) 25.00 (17.75,33.25) 6.82(5.80,7.68) 4.40(3.63,5.02) 0.96(0.96,2.09) 1.30(1.08,1.54) 2.53(2.04,2.98) 9.87(5.55,16.06) 7.70(6.70,8.80) ＜0.001 ＜0.001 0.015 0.029 ＜0.001 ＜0.001 0.003 0.276 ＜0.001 Noninvasive indices TyG TyG-BMI TyG-WC TyG-WHR HOMA-IR 2.05(1.46,2.41) 56.98(43.92,74.61) 195.67(142.16,249.08) 1.89(1.45,2.44) 3.86(2.27,5.91) 1.56(1.04,2.03) 38.41(26.43,56.39) 136.35(88.54,186.62) 1.43(0.93,1.87) 2.71(1.46,5.27) ＜0.001 ＜0.001 ＜0.001 ＜0.001 0.006 VCTE parameters CAP (dB/m) LSM (kPa) 313.00(286.0,336.0) 7.0(5.3,9.82) 242(224.0,253.25) 4.90(4.0,6.0) ＜0.001 ＜0.001 Metabolic diseases Hypertension Yes No 53(36.6%) 92(63.4%) 48(39.0%) 75(61.0%) 0.706 Abbreviations:WC,waist circumference;WHR,Waist-to-Hip Ratio, BMI, body mass index;ALT,alanine aminotransferase;GGT, γ-glutamyltransferase; FBG, fasting blood glucose; TC, cholesterol; TG, triglycerides; HDL, high-density lipoprotein; LDL, low-density lipoprotein; HbA1c, glycosylated hemoglobin; FINS, fasting insulin; TyG, triglyceride glucose; TyG-BMI,Triglyceride glucose-body mass index;TyG-WC,Triglyceride Glucose-Waist Circumference , HOMA-IR,Homeostasis model assessment of insulin resistance index, CAP Controlled attenuation parameter;LSM,Liver Stiffness Measurement. Optimal Cut-off Analysis of Various Insulin Resistance Indices for Diagnosing NAFLD ROC curve analysis was performed to clarify the diagnostic capabilities of TyG, TyG-BMI, TyG-WC, TyG-WHR, and HOMA-IR for NAFLD in the patients with T2DM. The results showed that TyG, TyG-BMI, TyG-WC, and TyG-WHR were significantly associated with the occurrence of NAFLD in patients with T2DM (P < 0.001). The cut-off values of predicting NAFLD in patients with T2DM were 2.04 for TyG, 39.58 for TyG-BMI, 211.12 for TyG-WC, 1.52 for TyG-WHR, and 2.12 for HOMA-IR. Among these indices, TyG-BMI demonstrated an optimal effect, with an AUC of 0.738 (Table 2 ). The AUC value of HOMA-IR was significantly lower than that of the other parameters (P < 0.001) (Fig. 1). Table 2 Diagnostic efficacy of different indicators for NAFLD in patients with type 2 diabetes mellitus combined with fatty liver disease Variables AUC(95% CI) 95%CI P value for AUROC Cutoff value P Value TyG 0.710 0.652–0.764 0.001 2.04 <0.001 TyG-BMI 0.738 0.617–0.778 <0.001 39.58 <0.001 TyG-WC 0.737 0.680–0.789 <0.001 211.12 <0.001 TyG-WHR 0.730 0.673–0.782 <0.001 1.52 <0.001 HOMA-IR 0.598 0.537–0.657 - 2.12 <0.004 Abbreviations:AUC, area under the receiver operating characteristic curve; BMI, body mass index; WHR,Waist-to-Hip Ratio;WC, waist circumference༛TyG, triglyceride glucose; TyG-BMI,Triglyceride glucose-body mass index;TyG-WC, Triglyceride Glucose-Waist Circum ference;HOMA-IR,Homeostasis model assessment of insulin resistance index. Subgroup analysis of the Predict values of different indices for NAFLD. Subgroup analysis based on gender. As shown in the Fig. 2 , the AUC of TyG-BMI was highest for both males and females (0.702 and 0.764). For the males, the optimal predictive performance was observed when the cut-off value of TyG-BMI was 31.54, with a sensitivity of 90.32% and a specificity of 47.89%. However, among TyG and related parameters, the TyG index performed as the poorest for both males and females (AUCs was 0.744 and 0.658, respectively). It is noteworthy that the AUC of HOMA-IR in females (0.659) was significantly higher than the males (0.559), it was shown in Table 3 . Table 3 Cut-off values and AUCs (95%CI) of each parameter for predicting Non-alcoholic fatty liver disease according to sex AUC(95%CI) Cut-off value Sensitivity(%) Specificity(%) Female (n = 104) TyG 0.658(0.558–0.748) 2.04 48.08 78.85 TyG-BMI 0.702(0.605–0.788) 38.40 90.38 44.23 TyG-WC 0.670(0.571–0.759) 211.12 36.54 90.38 TyG-WHR 0.678(0.579–0.766) 1.43 78.85 48.08 HOMA-IR 0.659(0.560–0.749) 1.88 92.31 38.46 Male (n = 164) TyG 0.744(0.670–0.809) 1.19 93.55 43.66 TyG-BMI 0.764(0.691–0.827) 31.54 90.32 47.89 TyG-WC 0.763(0.701–0.822) 186.62 60.22 78.87 TyG-WHR 0.764(0.691–0.826) 1.09 93.55 45.07 HOMA-IR 0.559(0.479–0.636) 2.282 69.89 43.66 Abbreviations:AUC, area under the receiver operating characteristic curve;BMI, body mass index;WHR,Waist-to-Hip Ratio;WC, waist circumference;TyG, triglyceride glucose; TyG-BMI,Triglyceride glucose-body mass index;TyG-WC, Triglyceride Glucose-Waist Circum ference;HOMA-IR,Homeostasis model assessment of insulin resistance index. Subgroup analysis based on BMI. As shown in Table 4 , In the lean group (BMI < 23 kg/m²), TyG-BMI exhibited particularly strong performance, with an AUC of 0.817, followed closely by TyG-WC and TyG-WHR (AUC of 0.809). When the cut-off value of TyG-BMI was 30.7, the overall performance was optimal, with a sensitivity of 80% and a specificity of 82.61%. HOMA-IR was displayed as the poorest performance among there groups, the AUCs of them were 0.652, 0.533, and 0.552, respectively. Table 4 Cut-off values and AUCs (95%CI) of each parameter for predicting Non-alcoholic fatty liver disease in different BMI subgroups AUC(95%CI) Cut-off value Sensitivity(%) Specificity(%) BMI<23(n=28) TyG 0.800(0.607–0.926) 2.04 60.00 100 TyG-BMI 0.817(0.626–0.937) 30.7 80.00 82.61 TyG-WC 0.809(0.616–0.932) 108.11 80.00 82.61 TyG-WHR 0.809(0.616–0.932) 1.18 80.00 82.61 HOMA-IR 0.652(0.450–0.821) 4.75 60.00 91.30 23 ≤ BMI<25(n=47) TyG 0.607(0.454–0.746) 1.82 52.17 79.17 TyG-BMI 0.620(0.466–0.757) 38.95 60.87 70.83 TyG-WC 0.627(0.474–0.763) 157.00 52.17 79.17 TyG-WHR 0.623(0.470–0.760) 1.71 47.83 83.33 HOMA-IR 0.533(0.381–0.680) 2.35 56.52 62.50 BMI ≥ 25(n=193) TyG 0.671(0.599–0.736) 2.25 39.32 89.47 TyG-BMI 0.701(0.631–0.764) 68.10 39.3 92.10 TyG-WC 0.698(0.628–0.762) 211.12 47.01 86.84 TyG-WHR 0.690(0.620–0.755) 2.20 36.75 93.42 HOMA-IR 0.552(0.479–0.624) 1.90 85.47 30.26 Abbreviations:AUC, area under the receiver operating characteristic curve;BMI,body mass index; WHR,Waist-to-Hip Ratio;WC, waist circumference;TyG, triglyceride glucose; TyG-BMI,Triglyceride glucose-body mass index;TyG-WC, Triglyceride Glucose-Waist Circum ference;HOMA-IR,Homeostasis model assessment of insulin resistance index Relationship between different indices and NAFLD The results showed that even after adjusting for risk factors, elevations in TyG, TyG-BMI, TyG-WC and TyG-WHR remained independent predictors of NAFLD in patients with T2DM (P < 0.05), as shown in Table 5 . Furthermore, after stratifying the parameters into quartiles, we observed a dose-response relationship between TyG-related parameters or HOMA-IR and occurrence of NAFLD (P < 0.05). The odds ratio (OR) of TyG-BMI increased as the highest quartile of the parameters. The ORs and 95% confidence intervals (CIs) of NAFLD for the TyG-BMI were followed as 0.082 (0.036–0.186), 0.280 (0.129–0.606), and 0.315 (0.145–0.685), respectively (Fig. 3). Table 5 Logistic regression modelling of risk factors for NAFLD in patients with type 2 diabetes mellitus combined with fatty liver disease Variable Unadjusted Model 1 Model2 0R(95%CI) P value 0R(95%CI) P value OR(95%CI) P value TyG 3.415(2.223–5.245) <0.001 3.317(2.138–5.145) <0.001 2.875(1.521–5.434) 0.001 TyG-BMI 1.047(1.032–1.063) <0.001 1.047(1.031–1.062) <0.001 1.038(1.014–1.062) 0.002 TyG-WC 1.014(1.010–1.018) <0.001 1.014(1.009–1.018) <0.001 1.011(1.004–1.017) 0.002 TyG-WHR 4.512(2.631–6.553) <0.001 4.041(2.532,6.447) <0.001 3.478(1.764,6.861) <0.001 HOMA-IR 1.086(1.010–1.168) 0.027 1.080(1.003–1.163) 0.040 0.985(0.899,1.080) 0.75 Abbreviations: Model 1: adjusted for age and sex; TyG, triglyceride glucose; TyG-BMI,Triglyceride glucose-body mass index;TyG-WC, Triglyceride Glucose-Waist Circum ference;HOMA-IR,Homeostasis model assessment of insulin resistance index.Model 2:adjusted for age, sex, blood pressure,BMI,fasting glucose, blood lipids and liver and kidney function. The relationship between TyG, TyG-BMI, TyG-WC, TyG-WHR and CAP, LSM Generalized linear regression models were performed to evaluate the relationship between CAP, LSM and the above indexes. The results showed that TyG and its combination indexes TyG-BMI,TyG-WC,TyG-WHR were positively correlated with the βs(95%CI) of 21.30(95%CI14.292,28.323), 0.745(95%CI0.623,1.047), 0.247(95%CI 0.184,0.310)and24.40(95%CI 17.305–31.505),(P<0.001) respectively. HOMA-IR also had a positive relationship with CAP [β = 2.549(95%CI 1.081,4.017), P = 0.001]. Additionally, we observed a positive association between TyG and its combination indexes with LSM. See details in Table 6 . Table 6 The relationship between TyG, TyG-BMI, TyG-WC, TyG-WHR and CAP, LSM Exposure CAP (dB/m) [β(95%CI)] p-value LSM (kpa) [β(95%CI)] p-value TyG 21.30(14.292–28.323) <0.001 0.872(0.237,1.507) 0.007 TyG-BMI 0.745(0.623,1.047) <0.001 0.042(0.022,0.061) <0.001 TyG-WC 0.247(0.184,0.310) <0.001 0.012(0.006,0.018) <0.001 TyG-WHR 24.40(17.305–31.505) <0.001 1.122(0.474,1.769) 0.001 HOMA-IR 2.549(1.081,4.017) 0.001 0.330(0.208,0.452) <0.001 Abbreviations:BMI,body mass index; WHR,Waist-to-Hip Ratio;WC, waist circumference;TyG, triglyceride glucose; TyG-BMI,Triglyceride glucose-body mass index;TyG-WC, Triglyceride Glucose-Waist Circum ference;HOMA-IR,Homeostasis model assessment of insulin resistance index;CAP,Controlled attenuation parameter;LSM,Liver Stiffness Measurement. DISCUSSION To our knowledge, it is the first time to explore the promising of insulin-based insulin resistance markers (HOMA-IR) and non-insulin-based insulin resistance markers (TyG, TyG-BMI, TyG-WC, TyG-WHR) to identify NAFLD in patients with type 2 diabetes. In this study, we ovserved the performance of TyG-related indices and HOMA-IR for diagnosing NAFLD in patients with type 2 diabetes. We found that TyG-WC, TyG-BMI, and TyG-WHR had better diagnostic value for NAFLD than TyG index and HOMA-IR, especially the TyG-BMI, these results were consistent in subgroup analysis. Although the results showed that TyG-WC and TyG-WHR indices had some predictive potential, they were not as stable as TyG-BMI. To investigate the risk factors for NAFLD, we conducted binary logistic regression analysis. Findings showed that TyG, TyG-BMI, TyG-WC, TyG-WHR and HOMA-IR were independent risk factors for NAFLD in T2DM patients. Additionally, we found that TyG and its related indices, as well as HOMA-IR, were positively correlated with CAP values and LSM. For every unit increase in TyG, TyG-BMI, TyG-WC, TyG-WHR, and HOMA-IR, CAP increased by 21.30, 0.745, 0.747, 24.40, and 2.549 dB/m, respectively. NAFLD is presently among the most widespread chronic liver diseases worldwide, with rising burden of disease. Nonetheless, the pathogenesis and early diagnosis of NAFLD remains incompletely understood. Insulin resistance (IR) plays a critical role in the development of NAFLD. The hyperinsulinemia-euglycemic clamp (HEC) method is widely accepted as the gold standard for measuring human insulin resistance/sensitivity[ 9 ]. However, it is time-consuming, labor-intensive, and expensive. All these characteristics limit its widespread application. HOMA-IR, a non-invasive measure based on fasting insulin (FINS), is extensively employed in clinical assessment of IR. Some scholars have considered other fasting insulin-independent indices, such as TyG-related indices. The TyG index was initially proposed in 2008 as a substitute for HOMA-IR, perhaps due to the close association between the primary indices (triglycerides, fasting blood glucose) and "glucotoxicity" or "lipotoxicity". Furthermore, the TyG index is derived from fasting measurements, cost-effective, and can be measured in all clinical laboratories without the need for quantifying serum insulin levels. Therefore, the TyG index can serve as a convenient and reliable method for detecting insulin resistance. The parameters related to the TyG index, combining TyG index with WC, BMI, and WHR, were first reported by Leay-Kiaw Er et al[ 10 ]. Since these parameters are essential components of glucose and fat metabolism, they are closely associated with the occurrence of NAFLD and can serve as reliable predictive indicators. Sheng et al. conducted an epidemiological survey in the general population to assess the optimal obesity and lipid-related indicators for predicting NAFLD[ 11 ]. They found that compared to visceral obesity indicators, lipid parameters, lipid ratios, and fat factors, TyG index-related parameters were good predictors of NAFLD. Subsequently, Li et al. used them to measure the diagnostic rate of NAFLD in non-obese populations in the United States, findings demonstrated that TyG-WC had better discriminatory power for NAFLD than other indicators, with an AUC of 0.806[ 12 ]. In contrast to previous studies focused on either obese populations or the general populace, our research concentrated on the ability of TyG index, TyG-BMI, TyG-WC, TyG-WHR, and HOMA-IR to diagnose NAFLD in the individuals with type 2 diabetes. ROC curve analysis demonstrated that the AUC of TyG and related parameters were higher than that of HOMA-IR, with TyG-BMI exhibiting the largest AUC (0.738), which indicated the ability of TyG index in identifying NAFLD were better than the others. This finding was consistent with Chang et al.'s study[ 13 ]. It was worth noting that TyG-BMI performs well in both males and females. But our data showed that it was much better for predicting NAFLD in male T2DM patients. Furthermore, Wang et al. found that the TyG index performed better in females than in males[ 14 ]. Research indicates that sex hormones play a protective role in body composition and metabolic risk. The predictive ability of the TyG index may decrease in postmenopausal women[ 15 ]. In our study, the average age of the women was 53.6 years, compared to 38.3 years in Wang et al.'s study. This age difference may explain the variation in results. Moreover, BMI is easily measurable and widely regarded as a key indicator for evaluating overall obesity and various metabolic irregularities. In this study, TyG-BMI displayed as more efficient in identifying NAFLD than other TyG-related indices. This might be attributed to the comprehensive assessment of individuals' metabolic status and insulin resistance risk, and BMI was combined with the TyG index in our study. This is particularly crucial for predicting NAFLD, given its close association with systemic metabolic abnormalities[ 16 ]. WC and WHR primarily assess the abdominal fat-to-lower body fat ratio, offering unique insights into specific obesity types and cardiovascular risk assessment.[ 17 ]Nevertheless, BMI offers more comprehensive insights into assessing the risk of NAFLD, especially for type 2 diabetes mellitus. Additionally, due to differences in the location, the data of WC measurements may vary.[ 18 ]Therefore, precise measurement of body fat and its distribution is critical in epidemiological investigations. Interestingly, this study revealed that the predictive potential of TyG and its related indicators varies across different BMI subgroups. Stratification by BMI showed that TyG and its related parameters have better predictive value for the lean patients, especially for TyG-BMI. Lean NAFLD was first reported in Asian populations, with a prevalence rate of 25.2%[ 19 ]. Unlike previous research, an increasing number of studies suggest that compared to obese NAFLD patients, lean NAFLD patients have a higher risk of developing diabetes, cardiovascular diseases, and all-cause mortality, and lean NAFLD is challenged to indentify and therapy[ 20 ]. Due to lifestyle and genetic factors, unlike Western populations, East Asians are more prone to lean NAFLD, they are also more susceptible to insulin resistance (IR). In our study, the positive correlation between TyG-BMI and NAFLD in the leans suggests that the increase in TyG may outweigh the effect of BMI reduction in individuals with NAFLD. This is consistent with previous findings[ 21 ]. This implies that insulin resistance caused by excessive visceral fat accumulation may play a more significant role in the development of NAFLD for the lean patients. Therefore, relying on reduced BMI or increased TyG may not be sufficient to accurately predict lean NAFLD. Combinding TyG with BMI is crucial for better diagnozing lean NAFLD. In addition, the diagnostic capability of HOMA-IR for NAFLD was limited in this study. However, Zeng et al[ 22 ].demonstrated an AUC of 0.724 for HOMA-IR, which was much higher than other insulin markers. We speculate that this discrepancy may be related to variations in the diagnostic cut-off points, study populations, dietary habits, ultrasound diagnostic expertise, and regional differences. Furthermore, in the female subgroup, HOMA-IR outperformed the males, possibly due to differences in body fat, muscle mass, hormone levels, and fat distribution[ 23 ]. It was consistent with the findings of Xue's study[ 24 ]. Research indicates that estrogen reduces the risk of fatty liver and diabetes by promoting insulin secretion and regulating energy allocation. The decline in estrogen levels in late menopause is associated with various metabolic disorders, including lipid abnormalities and insulin resistance (IR), making it a significant risk factor for female NAFLD. In this study, the average age of females was over 53 years, emphasizing the role of IR in the progression of NAFLD. However, the underlying mechanisms require to be further elucidated[ 25 ]. Our study has several limitations. Firstly, the cross-sectional design of the study precludes establishing causal relationships, only the correlations were observed. Secondly, the data was based on the participants from one institution. Considering variations in TG levels among different ethnic groups, further research is need to evaluate the applicability of TyG-BMI across different populations. In the future, we shall enlarge the sample size and aquire more comprehensive and reliable findings. CONCLUSION This study demonstrates that TyG-BMI is promising to predict NAFLD, especially for male and lean T2D patients. Furthermore, individuals with the normal BMI should undergo a more comprehensive assessment for NAFLD. These parameters also have a potential to evaluate the risk of NAFLD in the general population. Declarations Ethics approval and consent to participate: the study was performed in accordance with the Declaration of Helsinki. All experimental protocols were approved and carried out following the guidelines of the Ethics Committee of Jiangsu Provincial Hospital (Approval Number: 2022NL-071-02). All participants in this research were completely voluntary. Conflict of Interest Disclosures: No potential conflict of interest was reported by the authors. Funding: The study was funded by Administration of Traditional Chinese Medicine of Jiangsu National Science Foundation of China (No.82004286) and Jiangsu Province Postgraduate Innovation Project (No. SJCX23-0730). The funding source was only used for article processing charges. Data Availability statement: The data that support the findings of this study are available from the corresponding author upon reasonable request. Consent to Publication: Not applicable. Acknowledgment: We would like to thank the participants in this study. References L.J. H.KT, W.ZS,S.QY, andG.J, Correlation between degree of steatosis and insulin resistance in type 2 diabetes mellitus （T2DM） with nonalcoholic fatty liver disease. . Chinese Imaging Journal of Integrated Traditional and Western Medicine 21 (2023) 319-323. Y. ZM, G. P, d.A. L, P. JM, S. M, F. N, Q. Y, B. L, A. A, and N. F, - The global epidemiology of NAFLD and NASH in patients with type 2 diabetes: A. D - 8503886 - 793-801. H. Zou, X. Ma, F. Zhang, and Y. Xie, Comparison of the diagnostic performance of twelve noninvasive scores of metabolic dysfunction-associated fatty liver disease. Lipids in Health and Disease 22 (2023). L. J, O. Id, K. J, K. SH, K. GC, and O. Id, - Comparison of triglyceride glucose index, and related parameters to predict. D - 101285081 (2019) - e0212963. L. J, O. Id, K. J, K. SH, K. GC, and O. Id, - Comparison of triglyceride glucose index, and related parameters to predict. D - 101285081 (2019) - e0212963. M. DR, H. JP, R. AS, N. BA, T. DF, and T. RC, - Homeostasis model assessment: insulin resistance and beta-cell function from. C.D. Society, Chinese guideline for the prevention and treatment of type 2 diabetes mellitus(2017 edition). Chinese Journal of Diabetes Mellitus (2018) 4-67. K. T, P. D, S. M, F. JG, M. YQ, d.L. V, K. M, L.-P. M, H. KH, C. AC, F. G, C. WK, W. VW, M. RP, C. K, F.-R. M, B. M, S. F, H. JB, S. SK, B. R, J. KS, M. P, F. C, M. S, W. GL, C. P, M. K, B. J, B. P, K. V, and W. J, - Individual patient data meta-analysis of controlled attenuation parameter (CAP). - J Hepatol. 2017 May;66(5):1022-1030. doi: 10.1016/j.jhep.2016.12.022. Epub 2016 - 1022-1030. P. SY, G. JF, and C. S, - Assessment of Insulin Secretion and Insulin Resistance in Human. D - 101556588 - 641-654. E. LK, W. S, C. HH, H. LA, T. MS, S. YC, and K. YL, - Triglyceride Glucose-Body Mass Index Is a Simple and Clinically Useful Surrogate. - PLoS One. 2016 Mar 1;11(3):e0149731. doi: 10.1371/journal.pone.0149731. - e0149731. S. G, L. S, X. Q, P. N, K. M, Z. Y, and X. Id- Orcid, - The usefulness of obesity and lipid-related indices to predict the presence of. D - 101147696 - 134. L. S, F. L, D. J, Z. W, Y. T, and M. J, - Triglyceride glucose-waist circumference: the optimum index to screen. D - 100968547 - 376. C. M, O. Id, S. Z, and S. G, - Association between triglyceride glucose-related markers and the risk of. D - 101552874 - e070189. W. Y, W. J, L. L, Y. P, D. S, L. X, Z. L, W. C, and L. Y, - Baseline level and change trajectory of the triglyceride-glucose index in. - Front Endocrinol (Lausanne). 2023 May 8;14:1137098. doi: - 1137098. G. EB, and S. W, - Gender differences in insulin resistance, body composition, and energy balance. D - 101225178 - 60-75. F. DH, F. JM, A. PW, and H. CB, - Development and Progression of Non-Alcoholic Fatty Liver Disease: The Role of. D - 101092791 T - epublish. K. JL, L. S, H. SB, and R. R, - Waist circumference and abdominal adipose tissue distribution: influence of age. G. EB, and S. W, - Gender differences in insulin resistance, body composition, and energy balance. D - 101225178 - 60-75. X. R, P. J, Z. W, J. G, and D. Y, - Recent advances in lean NAFLD. D - 8213295 - 113331. K. JL, L. S, H. SB, and R. R, - Waist circumference and abdominal adipose tissue distribution: influence of age. R. TY, and F. JG, - What are the clinical settings and outcomes of lean NAFLD? D - 101500079 - 289-290. Z. P, C. X, Y. X, and G. L, - Markers of insulin resistance associated with non-alcoholic fatty liver disease. - Sci Rep. 2023 Nov 22;13(1):20470. doi: 10.1038/s41598-023-47269-4. - 20470. T. DL, P. LB, F. NA, M. C, W. S, K. F, R. A, T. TJE, H. DS, P. D, and S. P, - Challenges in the diagnosis of insulin resistance: Focusing on the role of. D - 101462250 - 102581. X. Y, X. J, L. M, and G. Y, - Potential screening indicators for early diagnosis of NAFLD/MAFLD and liver. - Front Endocrinol (Lausanne). 2022 Sep 2;13:951689. doi: - 951689. A. M, - Estrogens and the regulation of glucose metabolism. D - 101547524 - 1622-1654. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 04 Dec, 2024 Read the published version in BMC Endocrine Disorders → Version 1 posted Editorial decision: Revision requested 01 Jul, 2024 Reviewers agreed at journal 30 Jun, 2024 Reviews received at journal 28 Jun, 2024 Reviewers agreed at journal 28 Jun, 2024 Reviewers agreed at journal 27 Jun, 2024 Reviewers agreed at journal 26 Jun, 2024 Reviewers agreed at journal 26 Jun, 2024 Reviewers agreed at journal 26 Jun, 2024 Reviewers agreed at journal 26 Jun, 2024 Reviews received at journal 25 Jun, 2024 Reviews received at journal 25 Jun, 2024 Reviewers agreed at journal 25 Jun, 2024 Reviewers agreed at journal 24 Jun, 2024 Reviewers invited by journal 24 Jun, 2024 Editor invited by journal 03 Jun, 2024 Editor assigned by journal 31 May, 2024 Submission checks completed at journal 31 May, 2024 First submitted to journal 27 May, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4482766","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":310825270,"identity":"bc20a804-8d4c-4b31-aa91-f924a16f78a4","order_by":0,"name":"Jie Tian","email":"","orcid":"","institution":"School of Nursing, Nanjing University of Chinese Medicine, Nanjing, China.","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Tian","suffix":""},{"id":310825271,"identity":"1f1a0038-f43d-4f0c-a15c-9aeae6492ab4","order_by":1,"name":"Yutian Cao","email":"","orcid":"","institution":"The First Clinical Medical College of Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yutian","middleName":"","lastName":"Cao","suffix":""},{"id":310825272,"identity":"ea959a54-8c75-4831-ba0d-ecdd84a62141","order_by":2,"name":"Wenhui Zhang","email":"","orcid":"","institution":"The First Clinical Medical College of Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Wenhui","middleName":"","lastName":"Zhang","suffix":""},{"id":310825273,"identity":"ddc43e7c-269b-46cd-9e3b-15151de811bd","order_by":3,"name":"Aiyao Wang","email":"","orcid":"","institution":"School of Nursing, Nanjing University of Chinese Medicine, Nanjing, China.","correspondingAuthor":false,"prefix":"","firstName":"Aiyao","middleName":"","lastName":"Wang","suffix":""},{"id":310825274,"identity":"1c924b61-8c1a-47b5-bc5d-3e6df8ffe3c5","order_by":4,"name":"Xinyi Yang","email":"","orcid":"","institution":"The First Clinical Medical College of Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xinyi","middleName":"","lastName":"Yang","suffix":""},{"id":310825275,"identity":"7e384f10-4d0d-4ec6-9c72-bf5bb2d0cc09","order_by":5,"name":"Yinfeng Dong","email":"","orcid":"","institution":"Department of Pathology and Pathophysiology, School of Medicine, Nanjing University of Chinese Medicine, Nanjing, China","correspondingAuthor":false,"prefix":"","firstName":"Yinfeng","middleName":"","lastName":"Dong","suffix":""},{"id":310825276,"identity":"ee2bb6a7-57f9-46a3-ac4b-25928ba379bc","order_by":6,"name":"Xiqiao Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYDACCRBhwMDDwMB8AMIBcYnUwpZAihYw4IGrxK9FfnbzMWmeAmsZ/vaebw8s2+yiGdibt0kw1NzBqYVxzrE0yRkG6TwSZ85uN5BsS85t4DlWJsFw7BlOLcwSOWYSHwwO8zDcyN0mIdl2ILcBJMLYcBinFjaQggSgFvkbOc8gWuTf4NfCA7PF4EYOG9QWHvxaJCTSki1BfjE8c8xMQuJccm4bT1qxRcIx3FrkZyQfvM3zx9pe7njzM2mJMrvcfvbDG298qMGtBRYK0LAA+Q7ESiCkAaaF8QNhlaNgFIyCUTACAQDaK0rFA77ZcAAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Endocrinology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China","correspondingAuthor":true,"prefix":"","firstName":"Xiqiao","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2024-05-27 06:37:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4482766/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4482766/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12902-024-01794-z","type":"published","date":"2024-12-04T15:57:49+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":58385370,"identity":"959053fd-3945-4c16-a469-883e34b33111","added_by":"auto","created_at":"2024-06-14 18:40:33","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":433576,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReceiver operating characteristic curve of each parameter for predicting Non-alcoholic fatty liver disease.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAUC, area under the receiver operating characteristic curve; WHR,Waist-to-Hip Ratio;TyG, triglyceride glucose; TyG-BMI,Triglyceride glucose-body mass index;TyG-WC, Triglyceride Glucose-Waist Circum\u003cu\u003e \u003c/u\u003eference;HOMA-IR,Homeostasis model assessment of insulin resistance index\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4482766/v1/69f1e18063185a92f9bb0284.jpg"},{"id":58385369,"identity":"5062d485-c681-4383-a7d9-88d18b27c6ba","added_by":"auto","created_at":"2024-06-14 18:40:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":17891,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curve of parameters predicting NAFLD by sex.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA:\u003c/strong\u003e male;\u003cstrong\u003eB:\u003c/strong\u003eFemale.AUC, area under the receiver operating characteristic curve; WHR,Waist-to-Hip Ratio;WC, waist circumference;TyG, triglyceride glucose; TyG-BMI,Triglyceride glucose-body mass index;TyG-WC, Triglyceride Glucose-Waist Circum\u003cu\u003e \u003c/u\u003eference;HOMA-IR,Homeostasis model assessment of insulin resistance index.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4482766/v1/7ca8e2c697830d1c6cac523d.png"},{"id":58385371,"identity":"442b8771-b49c-4605-8e83-e588e9eef12b","added_by":"auto","created_at":"2024-06-14 18:40:34","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1458225,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNAFLD ORs and CIs by quartiles of TyG, TyG-BMI, TyG-WC, TyG-WHR, and HOMA-IR.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBMI, body mass index; WHR,Waist-to-Hip Ratio;WC, waist circumference;TyG, triglyceride glucose; TyG-BMI,Triglyceride glucose-body mass index;TyG-WC, Triglyceride Glucose-Waist Circum\u003cu\u003e \u003c/u\u003eference;HOMA-IR,Homeostasis model assessment of insulin resistance index.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4482766/v1/720c0f1964b9cd3112514d18.jpg"},{"id":70964801,"identity":"845222da-da8e-4fd1-9a54-2793749cd412","added_by":"auto","created_at":"2024-12-09 16:16:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2579043,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4482766/v1/2a66662f-0a19-43fa-9c23-b69628888402.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The potential of Insulin Resistance Indices to predict Non-alcoholic Fatty Liver Disease in Patients with Type 2 Diabetes","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eNon-alcoholic fatty liver disease (NAFLD) is one of the most common liver disorders characterized by abnormal lipid deposition in the liver, it is closely associated with the adverse outcomes of diabetes mellitus[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Research indicates that approximately 55.5% of type 2 diabetes mellitus (T2DM) patients globally have concomitant NAFLD, and more than 90% of T2DM patients eventually progress into NAFLD[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. There is a vicious cycle between T2DM and NAFLD, causing widespread and severe implications. Early diagnosis and treatment is critical for the worse progress. However, liver biopsy remains as the \"gold standard\" for diagnosing fatty liver. Due to the invasive procedure, it is limited in the clinical practice. Therefore, development of non-invasive diagnosis has been bringing into notice. Specially, it is vital for early diagnosis and evaluation of NAFLD.\u003c/p\u003e \u003cp\u003eIncreasing evidence has found that insulin resistance (IR) and obesity are common contributors to the occurrence of T2DM complicated with NAFLD. The triglyceride-glucose (TyG) index has been ranked as a reliable marker for IR[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Mainly, it includes fasting plasma glucose (FPG) and triglycerides (TG). Moreover, studies have demonstrated that TyG combined with obesity indices, such as Triglyceride glucose-body mass index(TyG-BMI) and Triglyceride Glucose-Waist Circumference (TyG-WC), have a potential predictive utility for insulin resistance (IR), and IR is closely correlated to obesity.[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] Subsequently, researchers have evaluated the potential of TyG-related indices in diagnosing NAFLD. However, there are some significant variations in diagnostic performance [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Moreover, the homeostasis model used for the assessment of insulin resistance index (HOMA-IR) was proposed by Matthews et al. in 1985, it serves as an indirect approach for evaluating insulin resistance (IR) in clinical[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Of note, this index utilizes the fasting plasma insulin and glucose concentrations to evaluate IR and β-cell deficiency. However, the value of IR-related indices in diagnosing T2DM complicated with NAFLD needs to be further addressed.\u003c/p\u003e \u003cp\u003eThis study aims to investigate the diagnostic performance of the TyG index and HOMA-IR index in T2DM complicated with NAFLD, and compare their diagnostic values in clinical practice.\u003c/p\u003e"},{"header":"PARTICIPANTS AND METHODS","content":"\u003cp\u003eStudy design and populations\u003c/p\u003e \u003cp\u003eA total of 268 T2DM patients complicated with fatty liver disease, admitted to the Endocrinology Department of Jiangsu Provincial Hospital from January 2021 to October 2023, were collected. Our study obtained approval from the Ethics Committee of Jiangsu Provincial Hospital. Recruitment was conducted by two researchers.\u003c/p\u003e \u003cp\u003eInclusion and exclusion criteria\u003c/p\u003e \u003cp\u003eInclusion criteria:\u003c/p\u003e \u003cp\u003e(1) Age was from 18 to 75 years old;\u003c/p\u003e \u003cp\u003e(2) T2DM patients were selected with the diagnostic criteria outlined in the \"Chinese Guidelines for the Prevention and Treatment of Type 2 Diabetes Mellitus (2017 Edition)\u0026rdquo;[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] ;\u003c/p\u003e \u003cp\u003e(3) The current investigation incorporates a threshold of CAP\u0026thinsp;\u0026ge;\u0026thinsp;258 dB/m to indicate the presence of substantial\u003c/p\u003e \u003cp\u003ehepatic steatosis, which is based on prior research[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eExclusion criteria:\u003c/p\u003e \u003cp\u003e(1) Patients with diabetes complicated with acute infection;\u003c/p\u003e \u003cp\u003e(2) Individuals with a history of long-term alcohol consumption, equivalent to ethanol intake of \u0026gt;\u0026thinsp;140 g/week for males and \u0026gt;\u0026thinsp;70 g/week for females;\u003c/p\u003e \u003cp\u003e(3) Patients with acute or chronic hepatitis, autoimmune hepatitis, hepatic steatosis, drug-induced liver injury, liver malignancies, etc.;\u003c/p\u003e \u003cp\u003eData Collection\u003c/p\u003e \u003cp\u003eGeneral clinical data, including age, gender, hypertension, diabetes, height, and weight, were obtained from medical records. All eligible participants fasted for at least 8 hours overnight, and blood samples were collected on the next morning between 8:00 and 9:00 a.m. Observed index included high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), triglycerides (TG), glycosylated hemoglobin (HbA1c), fasting blood glucose (FBG), serum gamma-glutamyl transferase (GGT), alanine aminotransferase (ALT), and fasting insulin. Two researchers type in and reviewed all these data. The calculating formulas used for analyzing the indices are listed as follows:\u003c/p\u003e \u003cp\u003eBMI\u0026thinsp;=\u0026thinsp;weight (kg)/height2 (m2);\u003c/p\u003e \u003cp\u003eTyG\u0026thinsp;=\u0026thinsp;Ln [TG(mg/dL) \u0026times; FPG (mg/dL) / 2];\u003c/p\u003e \u003cp\u003eTyG-BMI\u0026thinsp;=\u0026thinsp;Ln [TG (mg/dL) \u0026times; FPG (mg/dL) / 2] \u0026times; BMI (kg/m2);\u003c/p\u003e \u003cp\u003eTyG-WC\u0026thinsp;=\u0026thinsp;Ln[TG (mg/dL) \u0026times; FPG (mg/dL) / 2] \u0026times; WC (cm);\u003c/p\u003e \u003cp\u003eTyG-WHR\u0026thinsp;=\u0026thinsp;Ln [TG (mg/dL) \u0026times; FPG (mg/dL) / 2] \u0026times; [WC (cm)/ Height (cm)];\u003c/p\u003e \u003cp\u003eHOMA-IR\u0026thinsp;=\u0026thinsp;FPG(mmol/L)\u0026times;FINS(\u0026micro;U/mL༉/22.5.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was conducted using SPSS V.26.0 (IBM Corp) and MedCalc V.16.2 (MedCalc Software). The categorical data were presented as proportions (%), and the comparisons between two different groups were conducted using the chi-square test. While the continuous data were expressed as the median and interquartile range M (Q1\u0026ndash;Q3), and grouped comparisons were made using independent-sample t-tests. Moreover, Logistic regression analysis was utilized for multivariate analysis. Furthermore, targeted parameters were categorized into quartiles to explore the inner relationships. The diagnostic value of TyG-related indices for NAFLD was evaluated using receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC). Subgroup analyses were conducted based on gender and BMI, and non-parametric DeLong tests were used to reveal the differences in AUC between TyG-related indices and HOMA-IR. A two-\u0026shy;tailed p value\u0026thinsp;\u003cem\u003e\u0026lt;\u0026thinsp;0.05\u003c/em\u003e was considered as a statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eGeneral information of the participants\u003c/p\u003e \u003cp\u003eA total of 268 patients were included in the final analysis. Baseline characteristics of the participants are presented in \u003cb\u003eTable\u0026nbsp;1\u003c/b\u003e. Among the 268 participants, there were 145 T2DM cases complicated with NAFLD, the prevalence of NAFLD was 54.10% among the patients with T2DM. The mean age of the complicated NAFLD group was significantly younger than the T2DM group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with a higher prevalence in the males than the females (64.1% vs. 57.8%). Moreover, compared to the control group, the NAFLD group exhibited elevated levels of ALT, GGT, TG, and HbA1c (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Notably, participants complicated with NAFLD had significantly higher BMI, WC, WHR, CAP, LSM and TyG-related indices than the T2DM group (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eTable 1. Participant Characteristics\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"104%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.303030303030305%\"\u003e\n \u003cp\u003eNAFLD Group(T2DM with NAFLD) (n=145)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.35353535353536%\"\u003e\n \u003cp\u003eT2DMGroup(T2DM without NAFLD) \u0026nbsp; \u0026nbsp;\u0026nbsp;（n=123）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003eDemographic parameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.303030303030305%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.35353535353536%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003eAge(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.303030303030305%\"\u003e\n \u003cp\u003e49.0(40.0,60.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.35353535353536%\"\u003e\n \u003cp\u003e56.0(47.0,61.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eSex (%)\u003c/p\u003e\n \u003cp\u003eFemale\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMale\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.303030303030305%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e52(35.9%)\u003c/p\u003e\n \u003cp\u003e93(64.1%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.35353535353536%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e52(42.2%)\u003c/p\u003e\n \u003cp\u003e71(57.8%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.315\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003eAnthropometric parameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.303030303030305%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.35353535353536%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eWC(cm)\u003c/p\u003e\n \u003cp\u003eWHR(cm)\u003c/p\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.303030303030305%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e97.40(88.7,106.0)\u003c/p\u003e\n \u003cp\u003e0.96(0.91,1.00)\u003c/p\u003e\n \u003cp\u003e28.50(25.70,31.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.35353535353536%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e89.70(83.0,95.4)\u003c/p\u003e\n \u003cp\u003e0.91(0.87,0.95)\u003c/p\u003e\n \u003cp\u003e26.0(23.60,27.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003eSerum test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.303030303030305%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.35353535353536%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eALT(U/L)\u003c/p\u003e\n \u003cp\u003eGGT(U/L)\u003c/p\u003e\n \u003cp\u003eFBG(umol/L)\u003c/p\u003e\n \u003cp\u003eTC(umol/L)\u003c/p\u003e\n \u003cp\u003eTG(umol/L)\u003c/p\u003e\n \u003cp\u003eHDL-C(umol/L)\u003c/p\u003e\n \u003cp\u003eLDL-C(umol/L)\u003c/p\u003e\n \u003cp\u003eFINS(pmol/L)\u003c/p\u003e\n \u003cp\u003eHbA1c (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.303030303030305%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e28.00(18.5,49.50)\u003c/p\u003e\n \u003cp\u003e45.00(27.25,69.75)\u003c/p\u003e\n \u003cp\u003e7.09(5.47,9.41)\u003c/p\u003e\n \u003cp\u003e4.62(3.93,5.35)\u003c/p\u003e\n \u003cp\u003e2.03(1.52,1.85)\u003c/p\u003e\n \u003cp\u003e1.18(1.00,1.35)\u003c/p\u003e\n \u003cp\u003e2.79(2.31,3.36)\u003c/p\u003e\n \u003cp\u003e8.50(7.40,10.20)\u003c/p\u003e\n \u003cp\u003e12.11(7.3,16.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.35353535353536%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e20.00(14.00,27.00)\u003c/p\u003e\n \u003cp\u003e25.00 (17.75,33.25)\u003c/p\u003e\n \u003cp\u003e6.82(5.80,7.68)\u003c/p\u003e\n \u003cp\u003e4.40(3.63,5.02)\u003c/p\u003e\n \u003cp\u003e0.96(0.96,2.09)\u003c/p\u003e\n \u003cp\u003e1.30(1.08,1.54)\u003c/p\u003e\n \u003cp\u003e2.53(2.04,2.98)\u003c/p\u003e\n \u003cp\u003e9.87(5.55,16.06)\u003c/p\u003e\n \u003cp\u003e7.70(6.70,8.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003cp\u003e0.276\u003c/p\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003eNoninvasive indices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.303030303030305%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.35353535353536%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTyG\u003c/p\u003e\n \u003cp\u003eTyG-BMI\u003c/p\u003e\n \u003cp\u003eTyG-WC\u003c/p\u003e\n \u003cp\u003eTyG-WHR\u003c/p\u003e\n \u003cp\u003eHOMA-IR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.303030303030305%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.05(1.46,2.41)\u003c/p\u003e\n \u003cp\u003e56.98(43.92,74.61)\u003c/p\u003e\n \u003cp\u003e195.67(142.16,249.08)\u003c/p\u003e\n \u003cp\u003e1.89(1.45,2.44)\u003c/p\u003e\n \u003cp\u003e3.86(2.27,5.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.35353535353536%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.56(1.04,2.03)\u003c/p\u003e\n \u003cp\u003e38.41(26.43,56.39)\u003c/p\u003e\n \u003cp\u003e136.35(88.54,186.62)\u003c/p\u003e\n \u003cp\u003e1.43(0.93,1.87)\u003c/p\u003e\n \u003cp\u003e2.71(1.46,5.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003cp\u003e0.006\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eVCTE parameters\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.303030303030305%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.35353535353536%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eCAP (dB/m)\u003c/p\u003e\n \u003cp\u003eLSM (kPa)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.303030303030305%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e313.00(286.0,336.0)\u003c/p\u003e\n \u003cp\u003e7.0(5.3,9.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.35353535353536%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e242(224.0,253.25)\u003c/p\u003e\n \u003cp\u003e4.90(4.0,6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003eMetabolic diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.303030303030305%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.35353535353536%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.252525252525253%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.303030303030305%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e53(36.6%)\u003c/p\u003e\n \u003cp\u003e92(63.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.35353535353536%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e48(39.0%)\u003c/p\u003e\n \u003cp\u003e75(61.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e0.706\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations:WC,waist circumference;WHR,Waist-to-Hip Ratio, BMI, body mass index;ALT,alanine aminotransferase;GGT, \u0026gamma;-glutamyltransferase; FBG, fasting blood glucose; TC, cholesterol; TG, triglycerides; HDL, high-density lipoprotein; LDL, low-density lipoprotein; HbA1c, glycosylated hemoglobin; FINS, fasting insulin; TyG, triglyceride glucose; TyG-BMI,Triglyceride glucose-body mass index;TyG-WC,Triglyceride Glucose-Waist Circumference , HOMA-IR,Homeostasis model assessment of insulin resistance index, CAP Controlled attenuation parameter;LSM,Liver Stiffness Measurement.\u003c/p\u003e \u003cp\u003eOptimal Cut-off Analysis of Various Insulin Resistance Indices for Diagnosing NAFLD\u003c/p\u003e \u003cp\u003eROC curve analysis was performed to clarify the diagnostic capabilities of TyG, TyG-BMI, TyG-WC, TyG-WHR, and HOMA-IR for NAFLD in the patients with T2DM. The results showed that TyG, TyG-BMI, TyG-WC, and TyG-WHR were significantly associated with the occurrence of NAFLD in patients with T2DM (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The cut-off values of predicting NAFLD in patients with T2DM were 2.04 for TyG, 39.58 for TyG-BMI, 211.12 for TyG-WC, 1.52 for TyG-WHR, and 2.12 for HOMA-IR. Among these indices, TyG-BMI demonstrated an optimal effect, with an AUC of 0.738 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The AUC value of HOMA-IR was significantly lower than that of the other parameters (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiagnostic efficacy of different indicators for NAFLD in patients with type 2 diabetes mellitus combined with fatty liver disease\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value for AUROC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCutoff value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.652\u0026ndash;0.764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-BMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.617\u0026ndash;0.778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-WC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.680\u0026ndash;0.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e211.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-WHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.673\u0026ndash;0.782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHOMA-IR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.537\u0026ndash;0.657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations:AUC, area under the receiver operating characteristic curve; BMI, body mass index; WHR,Waist-to-Hip Ratio;WC, waist circumference༛TyG, triglyceride glucose; TyG-BMI,Triglyceride glucose-body mass index;TyG-WC, Triglyceride Glucose-Waist Circum ference;HOMA-IR,Homeostasis model assessment of insulin resistance index.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSubgroup analysis of the Predict values of different indices for NAFLD.\u003c/p\u003e \u003cp\u003eSubgroup analysis based on gender.\u003c/p\u003e \u003cp\u003eAs shown in the \u003cb\u003eFig.\u0026nbsp;2\u003c/b\u003e, the AUC of TyG-BMI was highest for both males and females (0.702 and 0.764). For the males, the optimal predictive performance was observed when the cut-off value of TyG-BMI was 31.54, with a sensitivity of 90.32% and a specificity of 47.89%. However, among TyG and related parameters, the TyG index performed as the poorest for both males and females (AUCs was 0.744 and 0.658, respectively). It is noteworthy that the AUC of HOMA-IR in females (0.659) was significantly higher than the males (0.559), it was shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCut-off values and AUCs (95%CI) of each parameter for predicting Non-alcoholic fatty liver disease according to sex\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCut-off value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSensitivity(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpecificity(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale (n\u0026thinsp;=\u0026thinsp;104)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.658(0.558\u0026ndash;0.748)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e78.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-BMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.702(0.605\u0026ndash;0.788)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e44.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-WC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.670(0.571\u0026ndash;0.759)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e211.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e90.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-WHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.678(0.579\u0026ndash;0.766)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e78.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e48.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHOMA-IR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.659(0.560\u0026ndash;0.749)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e92.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale (n\u0026thinsp;=\u0026thinsp;164)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.744(0.670\u0026ndash;0.809)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e93.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e43.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-BMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.764(0.691\u0026ndash;0.827)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e47.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-WC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.763(0.701\u0026ndash;0.822)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e186.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e78.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-WHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.764(0.691\u0026ndash;0.826)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e93.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHOMA-IR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.559(0.479\u0026ndash;0.636)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e69.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e43.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations:AUC, area under the receiver operating characteristic curve;BMI, body mass index;WHR,Waist-to-Hip Ratio;WC, waist circumference;TyG, triglyceride glucose; TyG-BMI,Triglyceride glucose-body mass index;TyG-WC, Triglyceride Glucose-Waist Circum ference;HOMA-IR,Homeostasis model assessment of insulin resistance index.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSubgroup analysis based on BMI.\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e, In the lean group (BMI\u0026thinsp;\u0026lt;\u0026thinsp;23 kg/m\u0026sup2;), TyG-BMI exhibited particularly strong performance, with an AUC of 0.817, followed closely by TyG-WC and TyG-WHR (AUC of 0.809). When the cut-off value of TyG-BMI was 30.7, the overall performance was optimal, with a sensitivity of 80% and a specificity of 82.61%. HOMA-IR was displayed as the poorest performance among there groups, the AUCs of them were 0.652, 0.533, and 0.552, respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCut-off values and AUCs (95%CI) of each parameter for predicting Non-alcoholic fatty liver disease in different BMI subgroups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCut-off value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSensitivity(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpecificity(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u0026lt;23(n=28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.800(0.607\u0026ndash;0.926)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-BMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.817(0.626\u0026ndash;0.937)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-WC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.809(0.616\u0026ndash;0.932)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e108.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-WHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.809(0.616\u0026ndash;0.932)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHOMA-IR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.652(0.450\u0026ndash;0.821)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e91.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026lt;25(n=47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.607(0.454\u0026ndash;0.746)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-BMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.620(0.466\u0026ndash;0.757)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-WC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.627(0.474\u0026ndash;0.763)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e157.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-WHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.623(0.470\u0026ndash;0.760)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHOMA-IR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.533(0.381\u0026ndash;0.680)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u0026thinsp;\u0026ge;\u0026thinsp;25(n=193)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.671(0.599\u0026ndash;0.736)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e89.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-BMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.701(0.631\u0026ndash;0.764)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e92.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-WC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.698(0.628\u0026ndash;0.762)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e211.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-WHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.690(0.620\u0026ndash;0.755)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e93.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHOMA-IR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.552(0.479\u0026ndash;0.624)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e85.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations:AUC, area under the receiver operating characteristic curve;BMI,body mass index; WHR,Waist-to-Hip Ratio;WC, waist circumference;TyG, triglyceride glucose; TyG-BMI,Triglyceride glucose-body mass index;TyG-WC, Triglyceride Glucose-Waist Circum ference;HOMA-IR,Homeostasis model assessment of insulin resistance index\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRelationship between different indices and NAFLD\u003c/p\u003e \u003cp\u003eThe results showed that even after adjusting for risk factors, elevations in TyG, TyG-BMI, TyG-WC and TyG-WHR remained independent predictors of NAFLD in patients with T2DM (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), as shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Furthermore, after stratifying the parameters into quartiles, we observed a dose-response relationship between TyG-related parameters or HOMA-IR and occurrence of NAFLD (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The odds ratio (OR) of TyG-BMI increased as the highest quartile of the parameters. The ORs and 95% confidence intervals (CIs) of NAFLD for the TyG-BMI were followed as 0.082 (0.036\u0026ndash;0.186), 0.280 (0.129\u0026ndash;0.606), and 0.315 (0.145\u0026ndash;0.685), respectively (Fig.\u0026nbsp;3).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic regression modelling of risk factors for NAFLD in patients with type 2 diabetes mellitus combined with fatty liver disease\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnadjusted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0R(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0R(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.415(2.223\u0026ndash;5.245)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.317(2.138\u0026ndash;5.145)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.875(1.521\u0026ndash;5.434)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-BMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.047(1.032\u0026ndash;1.063)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.047(1.031\u0026ndash;1.062)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.038(1.014\u0026ndash;1.062)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-WC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.014(1.010\u0026ndash;1.018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.014(1.009\u0026ndash;1.018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.011(1.004\u0026ndash;1.017)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-WHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.512(2.631\u0026ndash;6.553)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.041(2.532,6.447)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.478(1.764,6.861)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHOMA-IR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.086(1.010\u0026ndash;1.168)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.080(1.003\u0026ndash;1.163)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.985(0.899,1.080)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eAbbreviations: Model 1: adjusted for age and sex; TyG, triglyceride glucose; TyG-BMI,Triglyceride glucose-body mass index;TyG-WC, Triglyceride Glucose-Waist Circum ference;HOMA-IR,Homeostasis model assessment of insulin resistance index.Model 2:adjusted for age, sex, blood pressure,BMI,fasting glucose, blood lipids and liver and kidney function.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe relationship between TyG, TyG-BMI, TyG-WC, TyG-WHR and CAP, LSM\u003c/p\u003e \u003cp\u003eGeneralized linear regression models were performed to evaluate the relationship between CAP, LSM and the above indexes. The results showed that TyG and its combination indexes TyG-BMI,TyG-WC,TyG-WHR were positively correlated with the βs(95%CI) of 21.30(95%CI14.292,28.323), 0.745(95%CI0.623,1.047), 0.247(95%CI 0.184,0.310)and24.40(95%CI 17.305\u0026ndash;31.505),(P\u0026lt;0.001) respectively. HOMA-IR also had a positive relationship with CAP [β\u0026thinsp;=\u0026thinsp;2.549(95%CI 1.081,4.017), P\u0026thinsp;=\u0026thinsp;0.001]. Additionally, we observed a positive association between TyG and its combination indexes with LSM. See details in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe relationship between TyG, TyG-BMI, TyG-WC, TyG-WHR and CAP, LSM\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCAP (dB/m) [β(95%CI)]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLSM (kpa) [β(95%CI)]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.30(14.292\u0026ndash;28.323)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.872(0.237,1.507)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-BMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.745(0.623,1.047)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.042(0.022,0.061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-WC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.247(0.184,0.310)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.012(0.006,0.018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG-WHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.40(17.305\u0026ndash;31.505)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.122(0.474,1.769)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHOMA-IR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.549(1.081,4.017)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.330(0.208,0.452)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations:BMI,body mass index; WHR,Waist-to-Hip Ratio;WC, waist circumference;TyG, triglyceride glucose; TyG-BMI,Triglyceride glucose-body mass index;TyG-WC, Triglyceride Glucose-Waist Circum ference;HOMA-IR,Homeostasis model assessment of insulin resistance index;CAP,Controlled attenuation parameter;LSM,Liver Stiffness Measurement.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eTo our knowledge, it is the first time to explore the promising of insulin-based insulin resistance markers (HOMA-IR) and non-insulin-based insulin resistance markers (TyG, TyG-BMI, TyG-WC, TyG-WHR) to identify NAFLD in patients with type 2 diabetes. In this study, we ovserved the performance of TyG-related indices and HOMA-IR for diagnosing NAFLD in patients with type 2 diabetes. We found that TyG-WC, TyG-BMI, and TyG-WHR had better diagnostic value for NAFLD than TyG index and HOMA-IR, especially the TyG-BMI, these results were consistent in subgroup analysis. Although the results showed that TyG-WC and TyG-WHR indices had some predictive potential, they were not as stable as TyG-BMI. To investigate the risk factors for NAFLD, we conducted binary logistic regression analysis. Findings showed that TyG, TyG-BMI, TyG-WC, TyG-WHR and HOMA-IR were independent risk factors for NAFLD in T2DM patients. Additionally, we found that TyG and its related indices, as well as HOMA-IR, were positively correlated with CAP values and LSM. For every unit increase in TyG, TyG-BMI, TyG-WC, TyG-WHR, and HOMA-IR, CAP increased by 21.30, 0.745, 0.747, 24.40, and 2.549 dB/m, respectively.\u003c/p\u003e \u003cp\u003eNAFLD is presently among the most widespread chronic liver diseases worldwide, with rising burden of disease. Nonetheless, the pathogenesis and early diagnosis of NAFLD remains incompletely understood. Insulin resistance (IR) plays a critical role in the development of NAFLD. The hyperinsulinemia-euglycemic clamp (HEC) method is widely accepted as the gold standard for measuring human insulin resistance/sensitivity[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, it is time-consuming, labor-intensive, and expensive. All these characteristics limit its widespread application. HOMA-IR, a non-invasive measure based on fasting insulin (FINS), is extensively employed in clinical assessment of IR. Some scholars have considered other fasting insulin-independent indices, such as TyG-related indices. The TyG index was initially proposed in 2008 as a substitute for HOMA-IR, perhaps due to the close association between the primary indices (triglycerides, fasting blood glucose) and \"glucotoxicity\" or \"lipotoxicity\". Furthermore, the TyG index is derived from fasting measurements, cost-effective, and can be measured in all clinical laboratories without the need for quantifying serum insulin levels. Therefore, the TyG index can serve as a convenient and reliable method for detecting insulin resistance. The parameters related to the TyG index, combining TyG index with WC, BMI, and WHR, were first reported by Leay-Kiaw Er et al[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Since these parameters are essential components of glucose and fat metabolism, they are closely associated with the occurrence of NAFLD and can serve as reliable predictive indicators. Sheng et al. conducted an epidemiological survey in the general population to assess the optimal obesity and lipid-related indicators for predicting NAFLD[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. They found that compared to visceral obesity indicators, lipid parameters, lipid ratios, and fat factors, TyG index-related parameters were good predictors of NAFLD. Subsequently, Li et al. used them to measure the diagnostic rate of NAFLD in non-obese populations in the United States, findings demonstrated that TyG-WC had better discriminatory power for NAFLD than other indicators, with an AUC of 0.806[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn contrast to previous studies focused on either obese populations or the general populace, our research concentrated on the ability of TyG index, TyG-BMI, TyG-WC, TyG-WHR, and HOMA-IR to diagnose NAFLD in the individuals with type 2 diabetes. ROC curve analysis demonstrated that the AUC of TyG and related parameters were higher than that of HOMA-IR, with TyG-BMI exhibiting the largest AUC (0.738), which indicated the ability of TyG index in identifying NAFLD were better than the others. This finding was consistent with Chang et al.'s study[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. It was worth noting that TyG-BMI performs well in both males and females. But our data showed that it was much better for predicting NAFLD in male T2DM patients. Furthermore, Wang et al. found that the TyG index performed better in females than in males[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Research indicates that sex hormones play a protective role in body composition and metabolic risk. The predictive ability of the TyG index may decrease in postmenopausal women[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In our study, the average age of the women was 53.6 years, compared to 38.3 years in Wang et al.'s study. This age difference may explain the variation in results. Moreover, BMI is easily measurable and widely regarded as a key indicator for evaluating overall obesity and various metabolic irregularities. In this study, TyG-BMI displayed as more efficient in identifying NAFLD than other TyG-related indices. This might be attributed to the comprehensive assessment of individuals' metabolic status and insulin resistance risk, and BMI was combined with the TyG index in our study. This is particularly crucial for predicting NAFLD, given its close association with systemic metabolic abnormalities[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. WC and WHR primarily assess the abdominal fat-to-lower body fat ratio, offering unique insights into specific obesity types and cardiovascular risk assessment.[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]Nevertheless, BMI offers more comprehensive insights into assessing the risk of NAFLD, especially for type 2 diabetes mellitus. Additionally, due to differences in the location, the data of WC measurements may vary.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]Therefore, precise measurement of body fat and its distribution is critical in epidemiological investigations.\u003c/p\u003e \u003cp\u003eInterestingly, this study revealed that the predictive potential of TyG and its related indicators varies across different BMI subgroups. Stratification by BMI showed that TyG and its related parameters have better predictive value for the lean patients, especially for TyG-BMI. Lean NAFLD was first reported in Asian populations, with a prevalence rate of 25.2%[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Unlike previous research, an increasing number of studies suggest that compared to obese NAFLD patients, lean NAFLD patients have a higher risk of developing diabetes, cardiovascular diseases, and all-cause mortality, and lean NAFLD is challenged to indentify and therapy[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Due to lifestyle and genetic factors, unlike Western populations, East Asians are more prone to lean NAFLD, they are also more susceptible to insulin resistance (IR). In our study, the positive correlation between TyG-BMI and NAFLD in the leans suggests that the increase in TyG may outweigh the effect of BMI reduction in individuals with NAFLD. This is consistent with previous findings[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. This implies that insulin resistance caused by excessive visceral fat accumulation may play a more significant role in the development of NAFLD for the lean patients. Therefore, relying on reduced BMI or increased TyG may not be sufficient to accurately predict lean NAFLD. Combinding TyG with BMI is crucial for better diagnozing lean NAFLD.\u003c/p\u003e \u003cp\u003eIn addition, the diagnostic capability of HOMA-IR for NAFLD was limited in this study. However, Zeng et al[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].demonstrated an AUC of 0.724 for HOMA-IR, which was much higher than other insulin markers. We speculate that this discrepancy may be related to variations in the diagnostic cut-off points, study populations, dietary habits, ultrasound diagnostic expertise, and regional differences. Furthermore, in the female subgroup, HOMA-IR outperformed the males, possibly due to differences in body fat, muscle mass, hormone levels, and fat distribution[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. It was consistent with the findings of Xue's study[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Research indicates that estrogen reduces the risk of fatty liver and diabetes by promoting insulin secretion and regulating energy allocation. The decline in estrogen levels in late menopause is associated with various metabolic disorders, including lipid abnormalities and insulin resistance (IR), making it a significant risk factor for female NAFLD. In this study, the average age of females was over 53 years, emphasizing the role of IR in the progression of NAFLD. However, the underlying mechanisms require to be further elucidated[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study has several limitations. Firstly, the cross-sectional design of the study precludes establishing causal relationships, only the correlations were observed. Secondly, the data was based on the participants from one institution. Considering variations in TG levels among different ethnic groups, further research is need to evaluate the applicability of TyG-BMI across different populations. In the future, we shall enlarge the sample size and aquire more comprehensive and reliable findings.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study demonstrates that TyG-BMI is promising to predict NAFLD, especially for male and lean T2D patients. Furthermore, individuals with the normal BMI should undergo a more comprehensive assessment for NAFLD. These parameters also have a potential to evaluate the risk of NAFLD in the general population.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003ethe study was performed in accordance with the Declaration of Helsinki. All experimental protocols were approved and carried out following the guidelines of the Ethics Committee of Jiangsu Provincial Hospital (Approval Number: 2022NL-071-02). All participants in this research were completely voluntary.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Disclosures:\u0026nbsp;\u003c/strong\u003eNo potential conflict of interest was reported by the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThe study was funded by Administration of Traditional Chinese Medicine of Jiangsu National Science Foundation of China (No.82004286) and Jiangsu Province Postgraduate Innovation Project (No. SJCX23-0730).\u003c/p\u003e\n\u003cp\u003eThe funding source was only used for article processing charges.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability statement:\u003c/strong\u003e The data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publication:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment:\u0026nbsp;\u003c/strong\u003eWe would like to thank the participants in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eL.J. H.KT, W.ZS,S.QY, andG.J, Correlation between degree of steatosis and insulin resistance in type 2 diabetes mellitus （T2DM） with nonalcoholic fatty liver disease. . Chinese Imaging Journal of Integrated Traditional and Western Medicine 21 (2023) 319-323.\u003c/li\u003e\n\u003cli\u003eY. ZM, G. P, d.A. L, P. JM, S. M, F. N, Q. Y, B. L, A. A, and N. F, - The global epidemiology of NAFLD and NASH in patients with type 2 diabetes: A. D - 8503886 - 793-801.\u003c/li\u003e\n\u003cli\u003eH. Zou, X. Ma, F. Zhang, and Y. Xie, Comparison of the diagnostic performance of twelve noninvasive scores of metabolic dysfunction-associated fatty liver disease. Lipids in Health and Disease 22 (2023).\u003c/li\u003e\n\u003cli\u003eL. J, O. Id, K. J, K. SH, K. GC, and O. Id, - Comparison of triglyceride glucose index, and related parameters to predict. D - 101285081 (2019) - e0212963.\u003c/li\u003e\n\u003cli\u003eL. J, O. Id, K. J, K. SH, K. GC, and O. Id, - Comparison of triglyceride glucose index, and related parameters to predict. D - 101285081 (2019) - e0212963.\u003c/li\u003e\n\u003cli\u003eM. DR, H. JP, R. AS, N. BA, T. DF, and T. RC, - Homeostasis model assessment: insulin resistance and beta-cell function from.\u003c/li\u003e\n\u003cli\u003eC.D. Society, Chinese guideline for the prevention and treatment of type 2 diabetes mellitus(2017 edition). Chinese Journal of Diabetes Mellitus (2018) 4-67.\u003c/li\u003e\n\u003cli\u003eK. T, P. D, S. M, F. JG, M. YQ, d.L. V, K. M, L.-P. M, H. KH, C. AC, F. G, C. WK, W. VW, M. RP, C. K, F.-R. M, B. M, S. F, H. JB, S. SK, B. R, J. KS, M. P, F. C, M. S, W. GL, C. P, M. K, B. J, B. P, K. V, and W. J, - Individual patient data meta-analysis of controlled attenuation parameter (CAP). - J Hepatol. 2017 May;66(5):1022-1030. doi: 10.1016/j.jhep.2016.12.022. Epub 2016 - 1022-1030.\u003c/li\u003e\n\u003cli\u003eP. SY, G. JF, and C. S, - Assessment of Insulin Secretion and Insulin Resistance in Human. D - 101556588 - 641-654.\u003c/li\u003e\n\u003cli\u003eE. LK, W. S, C. HH, H. LA, T. MS, S. YC, and K. YL, - Triglyceride Glucose-Body Mass Index Is a Simple and Clinically Useful Surrogate. - PLoS One. 2016 Mar 1;11(3):e0149731. doi: 10.1371/journal.pone.0149731. - e0149731.\u003c/li\u003e\n\u003cli\u003eS. G, L. S, X. Q, P. N, K. M, Z. Y, and X. Id- Orcid, - The usefulness of obesity and lipid-related indices to predict the presence of. D - 101147696 - 134.\u003c/li\u003e\n\u003cli\u003eL. S, F. L, D. J, Z. W, Y. T, and M. J, - Triglyceride glucose-waist circumference: the optimum index to screen. D - 100968547 - 376.\u003c/li\u003e\n\u003cli\u003eC. M, O. Id, S. Z, and S. G, - Association between triglyceride glucose-related markers and the risk of. D - 101552874 - e070189.\u003c/li\u003e\n\u003cli\u003eW. Y, W. J, L. L, Y. P, D. S, L. X, Z. L, W. C, and L. Y, - Baseline level and change trajectory of the triglyceride-glucose index in. - Front Endocrinol (Lausanne). 2023 May 8;14:1137098. doi: - 1137098.\u003c/li\u003e\n\u003cli\u003eG. EB, and S. W, - Gender differences in insulin resistance, body composition, and energy balance. D - 101225178 - 60-75.\u003c/li\u003e\n\u003cli\u003eF. DH, F. JM, A. PW, and H. CB, - Development and Progression of Non-Alcoholic Fatty Liver Disease: The Role of. D - 101092791 T - epublish.\u003c/li\u003e\n\u003cli\u003eK. JL, L. S, H. SB, and R. R, - Waist circumference and abdominal adipose tissue distribution: influence of age.\u003c/li\u003e\n\u003cli\u003eG. EB, and S. W, - Gender differences in insulin resistance, body composition, and energy balance. D - 101225178 - 60-75.\u003c/li\u003e\n\u003cli\u003eX. R, P. J, Z. W, J. G, and D. Y, - Recent advances in lean NAFLD. D - 8213295 - 113331.\u003c/li\u003e\n\u003cli\u003eK. JL, L. S, H. SB, and R. R, - Waist circumference and abdominal adipose tissue distribution: influence of age.\u003c/li\u003e\n\u003cli\u003eR. TY, and F. JG, - What are the clinical settings and outcomes of lean NAFLD? D - 101500079 - 289-290.\u003c/li\u003e\n\u003cli\u003eZ. P, C. X, Y. X, and G. L, - Markers of insulin resistance associated with non-alcoholic fatty liver disease. - Sci Rep. 2023 Nov 22;13(1):20470. doi: 10.1038/s41598-023-47269-4. - 20470.\u003c/li\u003e\n\u003cli\u003eT. DL, P. LB, F. NA, M. C, W. S, K. F, R. A, T. TJE, H. DS, P. D, and S. P, - Challenges in the diagnosis of insulin resistance: Focusing on the role of. D - 101462250 - 102581.\u003c/li\u003e\n\u003cli\u003eX. Y, X. J, L. M, and G. Y, - Potential screening indicators for early diagnosis of NAFLD/MAFLD and liver. - Front Endocrinol (Lausanne). 2022 Sep 2;13:951689. doi: - 951689.\u003c/li\u003e\n\u003cli\u003eA. M, - Estrogens and the regulation of glucose metabolism. D - 101547524 - 1622-1654.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-endocrine-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bend","sideBox":"Learn more about [BMC Endocrine Disorders](http://bmcendocrdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bend/default.aspx","title":"BMC Endocrine Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"type 2 diabetes mellitus, non-alcoholic fatty liver disease, TyG index-related parameters, BMI, ROC curves","lastPublishedDoi":"10.21203/rs.3.rs-4482766/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4482766/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: The triglyceride-glucose (TyG) index and related parameters, and as well as Homeostatic Model Assessment for Insulin Resistance(HOMA-IR), are recently developed as insulin resistance markers, which can identify the individuals with a risk of non-alcoholic fatty liver disease (NAFLD). However, whether it can be used to predict NAFLD among patients with type 2 diabetes mellitus (T2DM) remains unclear. This study aims to observe the performance of insulin resistance indices in diagnosing NAFLD combined with T2DM, and compare the diagnostic values in clinical practice.\u003c/p\u003e\n\u003cp\u003ePatients and Methods: 268 patients with T2DM from the Endocrinology Department of Jiangsu Provincial Hospital of Traditional Chinese Medicine were enrolled in this study, they were divided into two groups: the NAFLD group (T2DM with NAFLD) and the T2DM group (T2DM without NAFLD). General information and blood indicators of the pariticipants were collected, and insulin resistance indices were calculated based on the data. Furthermore, receiver operating characteristic (ROC) analysis was conducted to calculate the area under the curve (AUC) of the insulin resistance-related indices.\u003c/p\u003e\n\u003cp\u003eResults：ROC analysis revealed that among the five insulin resistance-related indices, four parameters (TyG、TyG-BMI、TyG-WC and TyG-WHR) exhibit high predictive performance for identifying NAFLD except for HOMA-IR. Of particular, TyG-BMI demonstrated the superior predictive value, especially in the males and individuals with a BMI less than 23 kg/m². For the male and the lean patients, AUC for TyG-BMI was 0.764 (95% CI 0.691 - 0.827) and 0.817 (95% CI 0.626 - 0.937), respectively. The sensitivity and specificity for the male NAFLD were 90.32% and 47.89%. While for the lean patients, the sensitivity and specificity were 80% and 82.6%, respectively. Moreover, In the fully adjusted models, there were positive associations of TyG, TyG-BMI, TyG-WC, TyG-WHR and HOMA-IR to CAP, with the βs of 21.30, 0.745, 0.247 and 2.549 (all p\u0026lt;0.001), respectively.\u003c/p\u003e\n\u003cp\u003eConclusion: TyG-BMI is promising to predict NAFLD combined with T2DM, especially for the lean and male T2DM patients.\u003c/p\u003e","manuscriptTitle":"The potential of Insulin Resistance Indices to predict Non-alcoholic Fatty Liver Disease in Patients with Type 2 Diabetes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-14 18:40:29","doi":"10.21203/rs.3.rs-4482766/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-01T04:12:38+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"317823172110093748290594591534753171449","date":"2024-06-30T11:37:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-28T06:46:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"41643586000252977741517569356003260148","date":"2024-06-28T05:14:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"271565468188730450577064393976784637378","date":"2024-06-27T06:44:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"135054508505448015385272863747777195467","date":"2024-06-27T00:32:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"337222589216652000650380703900601090972","date":"2024-06-26T14:47:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"96875896028451973733232165324612218231","date":"2024-06-26T12:13:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"238832596253893499021656986753878663790","date":"2024-06-26T10:42:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-25T22:52:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-25T05:50:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"38556066022861837985768247404801143641","date":"2024-06-25T05:07:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"232255968679583948897775866779831360380","date":"2024-06-24T15:50:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-24T15:03:15+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-06-03T06:14:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-31T09:01:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-31T09:01:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Endocrine Disorders","date":"2024-05-27T06:35:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-endocrine-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bend","sideBox":"Learn more about [BMC Endocrine Disorders](http://bmcendocrdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bend/default.aspx","title":"BMC Endocrine Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8c7cd736-a0d6-410d-9a80-22633f1b39a5","owner":[],"postedDate":"June 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-09T16:05:54+00:00","versionOfRecord":{"articleIdentity":"rs-4482766","link":"https://doi.org/10.1186/s12902-024-01794-z","journal":{"identity":"bmc-endocrine-disorders","isVorOnly":false,"title":"BMC Endocrine Disorders"},"publishedOn":"2024-12-04 15:57:49","publishedOnDateReadable":"December 4th, 2024"},"versionCreatedAt":"2024-06-14 18:40:29","video":"","vorDoi":"10.1186/s12902-024-01794-z","vorDoiUrl":"https://doi.org/10.1186/s12902-024-01794-z","workflowStages":[]},"version":"v1","identity":"rs-4482766","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4482766","identity":"rs-4482766","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}