Assessing the predictive value of insulin resistance indices for metabolic syndrome risk in type 2 diabetes mellitus patients

Limited research has explored the effectiveness of insulin resistance (IR) in forecasting metabolic syndrome (MetS) risk, especially within the Iranian population afflicted with type 2 diabetes mellitus (T2DM). The present investigation aimed to assess the efficacy of IR indices in predicting the risk of MetS among T2DM patients. Convenient sampling was utilized to select four hundred subjects with T2DM. Metabolic factors and IR indices, including the Waist Circumference-Triglyceride Index (WTI), Triglyceride and Glucose Index (TyG index), the product of TyG index and abdominal obesity indices, and the Metabolic Score for Insulin Resistance (METS-IR), were evaluated. Logistic regression, coupled with modeling, was employed to explore the risk of MetS. The predictive performance of the indices for MetS stratified by sex was evaluated via receiver operating characteristic (ROC) curve analysis and estimation of the area under the curve (AUC) values. The TyG-Waist Circumference (TyG-WC) index exhibited the largest AUCs in both males (0.91) and females (0.93), while the TyG-Body Mass Index (TyG-BMI) demonstrated the smallest AUCs (0.77 in males and 0.74 in females). All indices significantly predicted the risk of MetS in all subjects before and after adjustment (p < 0.001 for all). The TyG-WC index demonstrated the highest odds ratios for MetS (8.06, 95% CI 5.41–12.00). In conclusion, all IR indices assessed in this study effectively predicted the risk of MetS among Iranian patients with T2DM, with the TyG-WC index emerging as the most robust predictor across both genders.

Optimal cut-off values for IR indices in predicting MetS risk in patients with T2DM are presented in Table 3.The predictive performance of anthropometric indices (TyG index, TyG-BMI, TyG-WC, TyG-WHR, TyG-WHtR, WTI, and METS-IR) for MetS, stratified by sex, was evaluated using receiver operating characteristic (ROC) curve analysis, and the corresponding area under the curve (AUC) values are depicted in Figs. 1, 2, 3, 4, 5.The TyG-WC index exhibited the largest AUCs in both males and females (0.91 and 0.93, respectively) (Fig. 3, Table 3), while the TyG-BMI demonstrated the smallest AUCs (0.77 in males and 0.74 in females) (Fig. 2, Table 3).
Odds ratios (95% CI) for MetS, with IR indices as independent variables among participants, are presented in Table 4.All indices significantly predicted the risk of MetS in all subjects before and after adjustment (p < 0.001).The TyG-WC index presented the highest odds ratios for MetS (8.06, 95% CI 5.41-12.00).

Discussion
The coexistence of Metabolic Syndrome (MetS) in diabetic individuals is associated with the development of both microvascular and macrovascular complications, as evidenced by previous research 24,25 .Within our study sample, MetS was found to be prevalent in 63.3% of participants.Notably, all insulin resistance (IR) indices investigated demonstrated predictive potential for MetS risk.Among these indices, the TyG-WC index exhibited the most www.nature.com/scientificreports/pronounced area under the curve (AUC) values and highest odds ratios for MetS among patients diagnosed with Type 2 Diabetes Mellitus (T2DM).
In line with our findings, prior investigations have also demonstrated the predictive capability of the TyG index for Metabolic Syndrome (MetS).The predictive capacity of the TyG index can be elucidated by mechanisms involving glucotoxicity and lipotoxicity, alongside the intimate associations of its constituent components (triglycerides and fasting plasma glucose) with insulin resistance, a pivotal factor in MetS pathogenesis 18,[26][27][28] .However, combining the TyG index with measures of adiposity such as body mass index (BMI) and waist circumference (WC) may enhance predictive accuracy 29 .Indeed, in our study, the composite of the TyG index with abdominal obesity indices such as WC and waist-to-height ratio (WHtR) demonstrated higher odds ratios for MetS compared to the TyG index alone.Khan et al. revealed that the TyG index, with its robust area under the curve (AUC) of 0.764, outperforms other traditional markers such as fasting blood glucose, triglycerides, small dense LDL-c, non-HDL-c, and HOMA-IR in predicting MetS 26 .Similarly, Gui et al. demonstrated the predictive potential of various obesity-and lipid-related indices for MetS in middle-aged and older adults, with TyG-BMI and the Chinese visceral adiposity index (CVAI) emerging as the most effective markers for predicting MetS in men and women, respectively 30 .In a cross-sectional study, Raimi et al. assessed the utility of the TyG index in identifying MetS among Nigerians, concluding that it was effective in predicting MetS.Furthermore, combining anthropometric and TyG index indicators enhanced predictive accuracy, consistent with our findings 18 .Similarly, in our study, TyG-WC and TyG-WHtR exhibited the largest AUCs in both genders, with overall AUC values higher than those reported by Raimi et al.This suggests that TyG-WC and TyG-WHtR may possess greater predictive utility in our population.Both waist circumference (WC) and waist-to-height ratio (WHtR) serve as markers of visceral adiposity, which correlates more strongly with cardiovascular disease (CVD) risk than BMI, a measure of overall obesity 31 .Notably, WHtR, corrected for height, may offer superior predictive capability compared to WC alone.Indeed, previous studies have demonstrated that WHtR identifies individuals at early health risks more effectively than a composite index combining BMI and WC 18,32,33 .Moreover, in a study by Laurindo et al. conducted among the Brazilian population, the TyG-WC index exhibited the largest AUC (0.849) for detecting MetS using IR indices 34 .Differences in AUC values between studies may be attributed to Table 1.The demographic and anthropometric characteristics of subjects across quartiles of TyG score.Data are means ± SD for quantitative variables and frequency (percent) for qualitative variables.BMI; body mass index, WC; waist circumference, HC; hip circumference, WHR; waist-to-hip ratio, WHtR; waist to height ratio, PA; physical activity.a From ANOVA for quantitative variables, b Chi-square for qualitative variables.www.nature.com/scientificreports/differences in mean fasting plasma glucose and triglyceride levels, variation in study populations (diabetic versus non-diabetic individuals), and ethnic diversity.Mao et al. conducted a study aiming to identify the optimal predictors and cut-off points for Metabolic Syndrome (MetS) among Chinese adults with Type 2 Diabetes Mellitus (T2DM).Their findings indicated that TyG-WC was the most effective predictor of MetS among women, while BMI emerged as the best predictor for both genders combined 35 .In contrast, our study revealed that TyG-WC was the superior predictor of MetS for both women and men.Another study utilizing data from the 2013-2016 US National Health and Nutrition Examination Survey found TyG-WC to be more robust in predicting MetS among the non-Hispanic population, though gender-specific analysis was not conducted 36 .Our findings demonstrated that TyG-WC outperformed TyG-BMI in MetS prediction, with TyG-WC exhibiting the largest area under the curve (AUC) and TyG-BMI the smallest.Body Mass Index (BMI) is commonly regarded as a general indicator of obesity, while waist circumference (WC) is considered a measure of central obesity 37 .However, the distribution of adipose tissue, particularly visceral fat, holds greater significance in metabolic dysfunction and insulin resistance.WC is closely associated with cardiometabolic risks 38 , highlighting its importance in predicting MetS.Moreover, in a study by Song et al., in addition to MetS, the product of the TyG index and anthropometric indices was also employed for predicting non-alcoholic fatty liver disease and Type 2 Diabetes Mellitus.TyG-WC exhibited superiority over TyG-BMI in predicting non-alcoholic fatty liver disease, further emphasizing the utility of WC as a predictor of metabolic disorders 39 .

Characteristics (mean (SD) or N (%)
In the present study, both the Waist-Triglyceride Index (WTI) and Metabolic Syndrome-Insulin Resistance (METS-IR) significantly predicted the risk of Metabolic Syndrome (MetS) in all participants, both before and after adjusting for relevant factors.Yang et al. highlighted the Waist-Triglyceride (WT) index, calculated as the product of waist circumference (WC) and triglyceride levels, as strongly associated with coronary heart disease risk 40 .Additionally, the WT index demonstrated effectiveness in screening for MetS in individuals with Type 2 Diabetes Mellitus (T2DM) 41 .Recently, Liu et al. introduced a modified form of the WT index, termed WTI, which exhibited a robust ability to identify MetS 42 .Similarly, Endukuru et al. demonstrated that WTI had the highest predictive ability for detecting low high-density lipoprotein cholesterol (HDL-C), elevated blood pressure, and high triglyceride levels in women compared to other indices 43 .Several studies have also demonstrated the high predictive capacity of WTI for discriminating MetS 44,45 .The METS-IR was developed by Chavolla et al. to evaluate insulin sensitivity, validated against the euglycemic-hyperinsulinemic clamp.Moreover, they found Table 2.The biochemical characteristics and insulin resistance indices of subjects across quartiles of TyG index score.Data are means ± SD for quantitative variables and frequency (percent) for qualitative variables.FBG; fasting blood glucose, HbA1c; hemoglobin A1C, TG; triglyceride, TC; total cholesterol, HDL-C; highdensity lipoprotein cholesterol, LDL-C; low-density lipoprotein cholesterol, AIP; atherogenic index of plasma, SBP; systolic blood pressure, DBP; diastolic blood pressure, MAP; mean arterial pressure, PP; pulse pressure, MetS; metabolic syndrome, TyG index; triglyceride and glucose index, WTI; waist circumference-triglyceride index, METS-IR; metabolic score for insulin resistance.a From ANOVA for quantitative variables, b Chi-square for qualitative variables.www.nature.com/scientificreports/ that METS-IR was associated with ectopic fat accumulation and could better predict incident T2DM than the triglyceride to high-density lipoprotein cholesterol ratio (TG/HDL-C) and TyG index in the Mexican population 46 .Han et al. investigated the association of various insulin resistance indicators, including METS-IR, TG/HDL-C, TyG-BMI, and TyG index, with serum uric acid levels in patients with T2DM, revealing significant associations between all indices and serum uric acid levels 47 .Furthermore, Zhang et al. demonstrated that METS-IR could predict the incidence of major adverse cardiovascular events in individuals with ischemic cardiomyopathy and T2DM 48 .It has been reported that METS-IR is strongly associated with hypertension even in individuals with normal weight 49 .Pathophysiological studies have elucidated that insulin resistance can perturb the lipid metabolism of the entire body, increase cardiac lipotoxicity, and induce oxidative stress and endothelial dysfunction, ultimately culminating in dyslipidemia, hypertension, and cardiovascular disease 50 .

Characteristics (mean (SD) or N (%)
Variations in the literature may stem from differences in chosen anthropometric indices, gender, ethnicity, underlying conditions, participant age, confounder variables, and criteria used to define Metabolic Syndrome (MetS), such as those by WHO, IDF, ATP III, and AHA/NHLBI.A limitation of our study is its cross-sectional design, which doesn't establish causality.Additionally, our focus on the Iranian population may limit generalizability.However, our study is the first to explore IR indices in predicting MetS risk among Iranian T2DM patients, and it includes both genders and employs multivariable logistic regression across three models.
All IR indices examined predicted MetS risk in our study, with the TyG-WC index emerging as the most effective predictor for both genders among Iranian T2DM patients.

Study design and participants
In this cross-sectional investigation, 400 Iranian patients diagnosed with Type 2 Diabetes Mellitus (T2DM) were prospectively enrolled from the Endocrine and Metabolism Clinic of Golestan Hospital, located in Ahvaz City, during the period spanning from March to May 2023.Patients were selected utilizing a convenient consecutive sampling method.Inclusion criteria comprised willingness to participate, age between 18 and 60 years, and a minimum of 2 years since the diagnosis of T2DM.Exclusion criteria consisted of insulin usage, pregnancy or lactation, smoking, alcohol consumption, incomplete demographic or anthropometric data, adherence to specialized diets, recent intake of antioxidant supplements within the last 3 months, and presence of comorbidities such as renal, hepatic, thyroidal, neoplastic, HIV, or infectious diseases.
A structured questionnaire was employed to collect demographic and baseline characteristics, encompassing sociodemographic factors such as gender, age, educational level, occupation, ethnicity, duration of diabetes, physical activity, and medication history.The study protocol adhered to the principles outlined in the Declaration of Helsinki and was approved by the Ethics Committee in Research of Sirjan University of Medical Sciences (Ethical code: IR.SIRUMS.REC.1401.017,Approval date: 18-03-2023).Written informed consent was obtained from all participants before their involvement.The sample size was determined based on the study conducted by Zhang et al. 51 and the utilization of the TyG-WHtR index, employing the formula (n = (z1 − a/2) 2 .SD 2 /d 2 ) with a precision (d) of 0.05, a standard deviation (SD) of 0.45, and a confidence level of 95%, resulting in a final sample size of 400 subjects.

Definition of MetS
Metabolic Syndrome (MetS) was defined according to the criteria established by the International Diabetes Federation (IDF), which includes the presence of central obesity, defined as a waist circumference (WC) equal to or greater than 95 cm for both genders based on guidelines provided by the Iranian National Obesity Committee 52 , in addition to meeting two or more of the following criteria: fasting blood glucose (FBG) levels equal to or greater than 100 mg/dL, or receiving medications for hyperglycemia; triglyceride (TG) levels equal to or greater than 150 mg/dL, or receiving medications for hypertriglyceridemia; low levels of high-density lipoprotein cholesterol (HDL-C), defined as less than 40 mg/dL in men and less than 50 mg/dL in women, or receiving drug treatment for low HDL-C; and elevated blood pressure, indicated by systolic blood pressure (SBP) equal to or greater than

Blood pressure (BP) measurement
Blood pressure (BP) measurements were taken by a trained professional following a 20-min rest period for the patients, between 8:00 and 9:00 AM.This procedure was iterated thrice consecutively, and the average of the three successive readings was utilized for analysis.The mean arterial pressure (MAP) and pulse pressure (PP) were calculated employing the following formulas 54 : where SBP represents systolic blood pressure and DBP represents diastolic blood pressure, both measured in millimeters of mercury (mmHg).

Biochemical assessment
Serum levels of fasting blood glucose (FBG) with a coefficient of variation (CV) interassay of 1.2% and lipid profile parameters, including triglycerides (TG) with a CV interassay of 1.6%, total cholesterol (TC) with a CV interassay of 2%, high-density lipoprotein cholesterol (HDL-C) with a CV interassay of 1.8%, low-density lipoprotein cholesterol (LDL-C) with a CV interassay of 1.29%, and very low-density lipoprotein (VLDL), were measured following a 12-h fasting period.Blood samples of 5 cc were drawn from each participant.FBG and the lipid profile were determined utilizing the enzymatic method with Pars Azmoon kits (Tehran, Iran) and analyzed on an auto analyzer (Hitachi 902, Japan).The Atherogenic Index of Plasma (AIP) was calculated using the logarithm of the TG to HDL-C ratio 55 .Hemoglobin A1c (HbA1c) levels in whole blood were quantified via www.nature.com/scientificreports/Physical activity levels assessed using the International Physical Activity Questionnaire (IPAQ), and results were reported as metabolic equivalent hours per week (METs hr/wk) 60 .

Statistical analysis
Data analysis was conducted using SPSS version 23 software.The normal distribution of the data was assessed using the Kolmogorov-Smirnov statistical test.Quantitative variables were compared between two groups using the independent t-test, while qualitative variables were compared using the chi-square test.Differences in variables across quartiles of the Triglyceride and glucose index (TyG index) were examined using One-way ANOVA with Post hoc (Least Significant Difference, LSD) analysis.To investigate the risk of Metabolic Syndrome

Figure 1 .
Figure 1.Roc Curve for TyG index (a male, b female, c total).

Figure 4 .
Figure 4. Roc Curve for total TyG-WHR index (a male, b female, c total).

Table 3 .
The optimal cut-off value for indices of insulin resistance in predicting the risk of MetS in patients with T2DM.