Association of Triglyceride-Glucose (TyG) Index and Carotid intima-media Thickness in Early Adulthood: Tehran Lipid and Glucose Study

Association of Triglyceride-Glucose (TyG) Index and Carotid intima-media Thickness in Early Adulthood: Tehran Lipid and Glucose Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association of Triglyceride-Glucose (TyG) Index and Carotid intima-media Thickness in Early Adulthood: Tehran Lipid and Glucose Study Amirhosein Seyedhoseinpour, Maryam Barzin, Maryam Mahdavi, Majid Valizadeh, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7106406/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Feb, 2026 Read the published version in Journal of Diabetes & Metabolic Disorders → Version 1 posted You are reading this latest preprint version Abstract Background The association of the Triglyceride-glucose (TyG) index as a novel surrogate marker of IR with atherosclerotic CVD has been suggested previously. However, there is limited data available for young adults. We aimed to define the association of the TyG Index and the carotid intima-media thickness (CIMT) in the Tehran, Lipid and Glucose Study (TLGS) Methods In this cross-sectional study, 1339 participants aged ≥ 18 were categorized into three tertiles based on the TyG Index. The independent contributions of TyG Index tertiles on CIMT and high CIMT were investigated in multiple linear regression and logistic regression models, respectively. Results The mean age of 1339 participants enrolled in the study was 29.77 ± 4.0 years (52% men). The participants were categorized into three tertiles based on the TyG Index. The odds ratio (OR) for the association of TyG Index tertiles with high CIMT was significant in tertile 3 in reference to tertile one after adjustment to age and sex (model 1; OR = 1.73, P = 0.013), which remains significant after further adjustments to smoking, educational status, and physical activity (model 2; OR = 1.70, 95% CI: 1.10–2.64, P = 0.017). Conclusion Our results demonstrated an independent, positive association between the TyG index and higher CIMT levels. These results highlight the potential role of the TyG index as an independent novel variable in the early detection of high-risk individuals for atherosclerotic CVD. Triglyceride-glucose index Cardiovascular disease Insulin resistance Subclinical atherosclerosis carotid intima-media thickness Figures Figure 1 Introduction Despite significant advances in the prevention and treatment of cardiovascular disease (CVD), it remains the leading cause of morbidity and mortality worldwide and a major contributor to the global burden of disease. ( 1 , 2 ) Based on the World Health Organization (WHO) report, CVD accounts for the most mortality caused by non-communicable diseases. ( 3 ) The increasing burden of CVD indicates the importance of early detection of at-risk individuals to improve risk stratification strategies and therapeutic management. Although the role of several risk factors, including age, male sex, positive family history of CVD, diabetes, hypertension, obesity, and hypercholesteremia, on the development of CVD have been established, recent studies indicate that patients without these risk factors may also develop atherosclerotic CVD. ( 4 , 5 ) These findings highlight the importance of identifying novel risk factors in the general population. ( 6 ) Insulin resistance (IR) is a state of impaired tissue sensitivity and responsiveness to circulating insulin. Previous studies have demonstrated the contributing role of IR on the pathogenesis of atherosclerosis and the development of CVD not only in diabetic patients but also amongst the general population and in nondiabetics. ( 7 , 8 ) Furthermore, a causal relationship between IR and CVD has also been suggested in a Mendelian randomized analysis. ( 9 ) Therefore, identifying individuals with IR faces challenges in clinical settings, as hyperinsulinemic-euglycemic, considered a gold standard method, is expensive and invasive, and the homeostasis model assessment- estimated IR (HOMA-IR) index lacks accuracy. ( 10 ) The Triglyceride-glucose (TyG) index is a product of fasting triglyceride and glucose of fasting blood [fasting triglycerides (TG, mg/dL)×fasting blood glucose (FBG, mg/dL)/2], which is considered as a novel and reliable surrogate marker of IR. Along with the simple use of the TyG Index as an alternative marker of IR in clinical settings, its superiority to HOMA-IR has also been demonstrated in previous studies. ( 11 , 12 ) The increasing evidence has shown the association of the TyG Index with atherosclerotic CVD. ( 13 , 14 ) In addition, the predictive role of the TyG Index concerning atherosclerosis and CVD events in the general population has also been established. ( 15 , 16 ) Evaluating carotid intima-media thickness (CIMT) is considered a noninvasive and sensitive contributor to subclinical atherosclerosis ( 17 ) and a suitable marker for early detection of atherosclerosis and evaluation of CVD progression. The study of Li on 59,123 participants demonstrated a positive association between the TyG Index and carotid atherosclerosis. ( 18 ) Furthermore, the findings of another study suggested that a higher TyG Index increases the risk of carotid atherosclerosis incidence in the general population. ( 19 ) Despite promising evidence on the association of the TyG Index and CVD contributors, it is still early to consider it an established independent variable in detecting and evaluating atherosclerosis and CVD. In the presenting study in the Tehran Lipid and Glucose Study (TLGS) with a long-term follow-up of the general population, we aimed to define the association of the TyG Index and the CIMT. The results of this study can help us better understand the role of the TyG Index as a novel marker of atherosclerosis and CVD in the early detection of individuals at risk in clinical settings. Methods and Material Study population : The presenting study is performed in the Tehran Lipid and Glucose Study (TLGS) framework, a large-scale population-based prospective cohort study with more than twenty years of follow-up time. The baseline survey of the TLGS was carried out from 1999 to 2001 on 15005 participants aged ≥ 3 years using a multistage random sampling method in District 13 of Tehran, capital city of Iran, to evaluate risk factors and outcomes of non-communicable diseases. The following surveys were held at three-year intervals in seven phases, with the last follow-up survey (Phase VII) being conducted from 2018 to 2021. In this cross-sectional study in the Tehran Lipid and Glucose Study framework, we included 1450 participants aged more than 18 years with available CIMT measurements in Phase VII. After the exclusion of the participants with a history of cancer, long-term corticosteroid consumption, consumption of antidiabetic medications, missing data of TyG Index, participants with pregnancy and with extreme values of BMI (exceeding ± 3SD), 1339 participants were enrolled. (Fig. 1 ) Informed written consent was obtained from all participants. This study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Shahid Beheshti University of Medical Sciences human research committee. Data collection : The detailed protocol and laboratory procedures of the TLGS were fully described elsewhere. ( 20 ) Demographics and anthropometric data of the participants were obtained utilizing standard questionnaires and established protocols. Weight was measured using a digital electronic scale (Seca 707; range 0.1–150 kg, Hanover, MD, USA) and rounded to 100g. Height assessments were performed with a tape stadiometer when subjects were barefoot, standing against the wall with shoulders in normal alignment; the measurements were rounded to the nearest 0.1cm. BMI was calculated as \(\:Weight\left(kg\right)/〖Height\left(m\right)〗^2\) . Waist circumference (WC) was measured standing at the end of expiration at the narrowest level between the iliac crest and lowest rib, without any pressure on the body's surface. The assessment of fasting plasma glucose (FPG), triglyceride (TG), total cholesterol (TC), and high-density lipoprotein cholesterol (HDL-C) was performed through the obtainment of blood samples after a minimum fasting time of 12 hours at the TLGS Research Laboratory. If TG < 400 mg/dl, the low-density lipoprotein cholesterol (LDL-C) was calculated using the Friedwald formula. ( 21 ) The TyG index was calculated as ln [triglyceride (mg/dL) × fasting glucose (mg/dL)/2]. ( 22 ) Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were obtained utilizing a standard mercury sphygmomanometer, calibrated by the Iranian Institute of Standards and Industrial Researches, from the right brachial artery at the heart level in a sitting position at least twice. Hypertension is defined as systolic blood pressure ≥ 140mmHg and/or diastolic blood pressure ≥ 90mmHg, or the current use of antihypertensive medication is present. Regarding educational status, subjects were categorized into illiteracy, those below diploma, and those in academic education. Physical activity was obtained by a Persian-translated MAQ questionnaire, which measures leisure time, job, and household activities and calculates the metabolic equivalent (MET) based on min/week. ( 23 ) The physical activity levels have been defined as low (MET < 600 min/wk), moderate (MET 600–1499 min/week), and high (MET ≥ 1500 min/week) levels. ( 24 ) The measurements of intima-media thickness of extracranial carotid arteries were obtained utilizing high-resolution B-mode ultrasonography with a linear 7.5– 10 MHz transducer (Samsung Medison SonoAceR3 ultrasound machine) in a supine position with extended and slightly lateral rotated neck. Two expert radiologists obtained all measurements. After performing the transverse plane scan to assess the general anatomy of the artery and atherosclerotic plaques, longitudinal scans were obtained from different angles. After visualization of the far wall arterial interface while luminal content is completely anechoic, the measurements were obtained in a plaque-free arterial segment on the optimal grey scale of the left common carotid artery and the hypoechoic band between the echogenic surfaces of intima and adventitia was considered as CIMT. The measurements were performed in three locations of the left common carotid artery along with sporadic measurement of the distal segment of both carotid arteries, carotid bulb, and internal carotid artery, and the average was considered as the final measurement. The interobserver agreement of the two radiologists was tested in a subsample of 30 participants. The interclass association coefficient (ICC) and 5% confidence interval based on the 2-way mixed-effects model were 0.79 and 0.55–0.90, respectively. ( 25 ) Statistical analysis: Continuous variables with normal and skewed distributions were expressed as mean ± SD and median (IQR, 25th and 75th percentile), respectively. Baseline data regarding the categorical variables were presented as frequency (percentages). Also, baseline data of two groups for continuous and categorical variables were compared using an independent sample t-test, Mann-Whitney U test, and chi-square test. Independent Effects of the TyG Index tertiles on CIMT (in micrometer) were studied using the linear regression model. Test the independent effects of the TyG Index tertiles on CIMT in multiple linear regression adjusting for age, sex, smoking, educational status, and physical activity was tested. We computed the Pearson correlation coefficients to evaluate the correlation between TyG Index with CIMT. The association between the TyG Index tertiles and high c CIMT (> 90 percentile) was also investigated by calculating odd ratios (ORs) using logistic regression models. All analyses were performed using SPSS software version 20 (SPSS, Chicago, IL, USA); the significance level was set at P < 0.05 (two-tailed). Results After applying specific exclusion criteria, 1339 participants were enrolled in our study. The participants were categorized into three tertiles based on the TyG Index. The mean age of the study population was 29.77 ± 4.0 years. It comprises 696 (52%) men and 643 (48%) women. The baseline cardiometabolic characteristics of the subjects are shown in Table 1 . There was a significant difference in all anthropometric indices and cardiometabolic variables, including SBP and DBP, FPG, TC, LDL-C, and TG, with an increasing tendency from tertile 1 to tertile 3 of the TyG Index. However, there was no significant difference in CIMT between tertiles, with a mean value of 0.55 ± 0.09 mm in the total population. Table 1 Baseline characteristics of Triglyceride-Glucose (TyG) Index quartiles T1 (n = 446) T2 (n = 446) T3 (n = 447) Total (n = 1339) P -Value Sex (Men) , n (%) 151 (33.9) 223 (50.0) 322 (72.0) 696 (52.0) < 0.001 Age , year 29.0 ± 4.1 29.7 ± 4.0 30.3 ± 3.9 29.7 ± 4.0 < 0.001 Academic education , n (%) 302 (67.7) 293 (65.8) 283 (63.5) 878 (65.7) 0.406 Weight , kg 66.5 ± 13.6 74.3 ± 14.9 83.9 ± 15.6 74.9 ± 16.33 < 0.001 BMI , kg/m 2 23.9 ± 3.9 26.0 ± 4.3 28.2 ± 4.2 26.0 ± 4.5 < 0.001 WC , cm 81.7 ± 9.7 88.3 ± 10.6 95.3 ± 10.8 88.4 ± 11.8 < 0.001 Hypertension , n (%) 5 (1.1) 15 (3.4) 44 (9.9) 64 (4.8) < 0.001 SBP , mmHg 103.1 ± 11.3 106.5 ± 11.6 112.5 ± 11.9 107.4 ± 12.2 < 0.001 DBP , mmHg 69.5 ± 8.1 72.2 ± 8.3 77.0 ± 9.2 72.9 ± 9.1 < 0.001 FPG , mg⁄dl 85.9 ± 7.2 89.0 ± 6.9 92.1 ± 8.3 89.0 ± 7.9 < 0.001 TC , mg/dl 154.4 ± 26.8 171.4 ± 28.5 195.7 ± 36.7 173.9 ± 35.3 < 0.001 LDL-C ,mg/dl 89.0 ± 23.0 103.8 ± 25.3 115.0 ± 31.6 102.5 ± 28.9 < 0.001 HDL-C , mg/dl 53.1 ± 10.0 47.5 ± 9.3 42.1 ± 9.8 47.6 ± 10.7 < 0.001 TG , mg⁄dl † 63(53–70) 99(89–111) 169(142–222) 99(70–143) < 0.001 Positive family history of CVD , n (%) 12 (2.7) 7 (1.6) 13 (2.9) 32 (2.4) 0.370 Smoking , n (%) 69 (15.7) 90 (20.5) 113 (25.5) 272 (20.6) 0.001 Low physical activity , n (%) 260 (59.5) 238 (54.6) 237 (54.0) 735 (56.0) 0.197 cIMT , mm 0.55 ± 0.08 0.55 ± 0.09 0.55 ± 0.10 0.55 ± 0.09 0.715 cIMT, carotid intima media thickness; DBP, dyastolic blood pressure; FPG, fasting plasma glucose; HDL-C, high density lipoprotein; LDL-C, low density lipoprotein; MET, metabolic equivalent; SBP, systolic blood pressure; TC, total cholesterol; TG, triglycerides; WC, waist circumference. Data are presented mean ± SD or n (%) except TG † is shown median (IQ 25–75) Correlation coefficients between TyG Index with CIMT were − 0.013 ( P = 0.623) in the total population and − 0.012 ( P = 0.792), -0.125 ( P = 0.008), and − 0.012(p = 0.799) in tertiles 1 to 3, respectively. The linear regression analysis for the association of TyG Index tertiles with CIMT is presented in Table 2 . There was a significant negative association in tertile 2 of the TyG Index and CIMT in the unadjusted model (ß=-0.084 P = 0.008) and also after adjustment to age and sex defined as model 1 (ß=-0.068 P = 0.028). The significant association of tertile 2 of the TyG Index and CIMT disappeared after further adjustments to smoking, educational status, and physical activity, which is defined as model 2 (ß=-0.001, p = 0.854). However, the linear regression analysis observed no significant association between tertile 3 of the TyG Index and CIMT. Table 2 Linear regression coefficients for the association of Triglyceride-Glucose (TyG) Index tertiles with cIMT Triglyceride-Glucose Index quartiles ß SE P -Value Unadjusted Model Per 1 unit Increase -0.005 0.018 0.792 T2 -0.084 0.032 0.008 T3 -0.004 0.014 0.798 Model 1 Per 1 unit Increase 0.001 0.017 0.977 T2 -0.068 0.031 0.028 T3 0.000 0.014 0.991 Model 2 Per 1 unit Increase 0.004 0.003 0.137 T2 -0.001 0.006 0.854 T3 0.012 0.006 0.057 Model 1 = age, sex Model 2 = Model 1 + smoking, educational status, physical activity The odds ratio (OR) for the association of TyG Index tertiles with high CIMT is depicted in Table 3 . The odds ratio of the high CIMT in the unadjusted model was not statistically significant in tertiles 2 and 3 in reference to tertile 1. After adjustment to age and sex (model 1), although the association of high CIMT and TyG Index tertile 2 in reference to tertile 1 remains insignificant, tertile 3 shows a significant association with high CIMT in reference to tertile 1 with an odds ratio of 1.73 ( P = 0.013). This association remained significant when further adjustments to smoking, educational status, and physical activity (model 2) were applied (OR = 1.70, P = 0.017). Table 3 Odds ratios for the association of Triglyceride-Glucose Index tertiles with high cIMT Triglyceride-Glucose Index quartiles OR (95% CI) P -Value Unadjusted Model T1 ref T2 1.02 (0.67, 1.57) 0.913 T3 1.48 (0.99, 2.21) 0.057 Model 1 T1 ref T2 1.09 (0.71, 1.69) 0.692 T3 1.73 (1.12, 2.66) 0.013 Model 2 T1 ref T2 1.00 (0.64, 1.56) 0.999 T3 1.70 (1.10, 2.64) 0.017 Model 1 = age, sex Model 2 = Model 1 + smoking, educational status, physical activity Discussion In the presenting cross-sectional study, in a framework of a large-scale population-based cohort study of the Iranian population in the TLGS, we evaluated the TyG Index's and CIMT's association in early adulthood. Our results demonstrated that compared to subjects with the lowest TyG Index category, those with the highest category significantly have higher risks of developing high CIMT levels as a contributor to subclinical atherosclerosis. The significance of the results was independent of age, sex, smoking, physical activity, and educational status. Insulin resistance has been shown to play a role in the pathogenesis of atherosclerotic cardiovascular diseases (ASCVD). Several pathophysiological pathways have been suggested to define the role of insulin resistance in the initiation and progression of ASCVD. ( 8 , 9 ) These pathways include causing oxidative stress and persistent low-grade inflammation, ( 26 ) directly affecting endothelial dysfunction, ( 27 ) and increasing sympathetic nervous system activity, ( 28 ) which play key roles in the pathogenesis. The TyG index is a highly sensitive and specific marker of insulin resistance, demonstrating better performance compared to the homeostasis model assessment (HOMA) for measuring insulin resistance. Furthermore, it can be easily calculated using routine blood biochemical tests, highlighting its potential role as a contributing or independent risk factor for ASCVD. ( 29 ) Several previous studies evaluated the association of the TyG index with cardiovascular disease. In the meta-analysis of cohort studies comprising 5,731,294 participants without ASCVD at baseline, patients in the highest TyG index category had an increased incidence of ASCVDs, coronary artery disease (CAD), and stroke compared with patients in the lowest TyG index category, independently. ( 14 ) In compliance with the previous study, another meta-analysis of twelve cohort studies demonstrated a higher risk of CAD, myocardial infarction, and composite CVD in participants in the higher TyG index category. ( 30 ) Another study of Tehran lipid and glucose study (TLGS) with 16 years of follow-up showed 61% and 84% increased risk of CVD and CA in participants with elevated TyG index at baseline. ( 31 ) A study conducted in 2017 showed that the prevalence of subclinical atherosclerosis in the middle-aged population without cardiovascular risk factors is as high as 50% ( 32 ), indicating the critical importance of early detection of at-risk patients. The results of our study demonstrated an association between higher levels of TyG index and high CIMT levels as a contributor to subclinical atherosclerosis. These findings are consistent with previous studies. In Li W. et al. ( 18 ) study on 59,123 participants aged > 40, the TyG-index was significantly associated with the prevalence of carotid atherosclerosis, CIMT, carotid plaques, and carotid stenosis severity. In the subgroup analysis, the significant association between the TyG index and carotid atherosclerosis was only observed in participants with age > 60 years old. Another study on 1,523 patients with ischemic stroke showed an odds ratio of 1.56 for abnormal CIMT and 1.46 for abnormal maximum CIMT in quartile four versus quartile 1 of the TyG index. ( 33 ) Also, in the study of Li Z. et al. ( 34 ) in patients with established coronary heart disease (CHD), the odds ratio of carotid artery plaque was 1.37 in quartile 4 of the TyG index compared with quartile 1. In our study, the odds ratio for high CIMT was 1.73 in tertile three compared to tertile 1 of the TyG index, which was consistent with previous studies. Despite the study of Li W. et al. ( 18 ), which was performed on middle-aged and elderly participants, and the study of Miao M. et al. ( 33 ) and Li Z. et al. ( 34 ), which included subjects with established ischemic stroke and CHD, respectively, our study was performed on the general population with a mean age of 29.77 ± 4.0 years which appears to be distinct in this regard. However, several other studies ( 15 , 19 , 35 ) found a significant positive association between the TyG index and carotid atherosclerosis. In the study of Zhao et al. ( 36 ) on the Chinese elderly population, no significant association between the TyG index and carotid plaque was observed. Several strengths and limitations of the study must be noted. Regarding limitations, first, the study was conducted on a general population of a metropolitan city in Iran and may not be national or representative of another population. Second, only participants with available CIMT data were eligible for the study. Third, CIMT has been regarded as a surrogate marker of subclinical atherosclerosis; however, it has been reported that the incidence of high CIMT might be driven by the adaptive remodeling of the media (not intima). Fourth, some possible confounders were not considered, such as dietary habits and socioeconomic status. Last but not least, regarding the study's cross-sectional nature, we can not provide conclusive evidence in favor of causality. Regarding the study's strengths, this study was performed in a well-designed, large-scale, population-based study. In conclusion, the results of our study demonstrated an independent, positive association between the TyG index and higher CIMT levels as a contributor to atherosclerosis. These results highlight the potential role of the TyG index as an independent novel variable in the early detection of high-risk individuals for atherosclerotic cardiovascular disease, which can be further evaluated in future studies. Declarations Acknowledgments : The authors express their appreciation to participants of District 13, Tehran, for their enthusiastic support in this study Human Ethics: This study has been approved by the National Research Council of the Islamic Republic of Iran (No. 181) and has been performed with the approval of the Human Research Review Committee of the Endocrine Research Center, Shahid Beheshti University, Tehran, Iran. The Endocrine Science ethics committee was conducted in accordance with the principles of the Declaration of Helsinki. Consent to Participate declarations: At the beginning of this study, all parents or legal guardians provided written informed consent for participants under 18 years of age. Written informed consent was acquired from all participants ≥ 18 years before taking part in the investigations. Clinical trial number: not applicable Conflict of interest : The authors declare no conflicts of interest. Funding Sources : The authors disclose no financial relationships relevant to this article. Author Contributions : AS : Conceptualization, Data curation, Writing - Original draft preparation. MB : Data curation, Writing - Original draft preparation. MM : Software, Formal analysis. MV : Validation, Writing - Review & Editing. SG: Validation, Writing - Review & Editing. FA : Resources, Supervision. FH : Conceptualization, Methodology, Supervision. Data Availability Statement : The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. Consent for publication : Not applicable. References Sacco RL, Roth GA, Reddy KS, Arnett DK, Bonita R, Gaziano TA et al. The Heart of 25 by 25: Achieving the Goal of Reducing Global and Regional Premature Deaths From Cardiovascular Diseases and Stroke: A Modeling Study From the American Heart Association and World Heart Federation. Circulation. 2016;133(23). Kivimäki M, Steptoe A. 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Association between macro- and microvascular damage and the triglyceride glucose index in community-dwelling elderly individuals: The Northern Shanghai Study. Cardiovasc Diabetol. 2019;18(1). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 21 Feb, 2026 Read the published version in Journal of Diabetes & Metabolic Disorders → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-7106406","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":491704114,"identity":"a4f76167-0844-4dcd-a107-f2bedf014a3c","order_by":0,"name":"Amirhosein Seyedhoseinpour","email":"","orcid":"","institution":"Shahid Beheshti University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Amirhosein","middleName":"","lastName":"Seyedhoseinpour","suffix":""},{"id":491704115,"identity":"caed0273-f24b-41a8-9939-584dd3e8f4fe","order_by":1,"name":"Maryam Barzin","email":"","orcid":"","institution":"Shahid Beheshti University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Maryam","middleName":"","lastName":"Barzin","suffix":""},{"id":491704116,"identity":"86d7b393-e540-4a17-a1df-1514444393be","order_by":2,"name":"Maryam Mahdavi","email":"","orcid":"","institution":"Shahid Beheshti University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Maryam","middleName":"","lastName":"Mahdavi","suffix":""},{"id":491704117,"identity":"c3caebef-860c-41c2-bf6e-7c598ecd423e","order_by":3,"name":"Majid Valizadeh","email":"","orcid":"","institution":"Shahid Beheshti University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Majid","middleName":"","lastName":"Valizadeh","suffix":""},{"id":491704118,"identity":"f6c48fb8-4914-4466-8a72-fbe8fda3a02c","order_by":4,"name":"Fereidoun Azizi","email":"","orcid":"","institution":"Shahid Beheshti University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Fereidoun","middleName":"","lastName":"Azizi","suffix":""},{"id":491704119,"identity":"5f6e9ed5-3696-4df1-8d65-a66371719af3","order_by":5,"name":"Sahar Ghareh","email":"","orcid":"","institution":"Islamic Azad University","correspondingAuthor":false,"prefix":"","firstName":"Sahar","middleName":"","lastName":"Ghareh","suffix":""},{"id":491704120,"identity":"54e7b3ce-5da7-45a6-8cd5-f5f9bed6602a","order_by":6,"name":"Farhad Hosseinpanah","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYBAC9hlQhgF7AwMDD5DBBhXgwaWF5wZMC88BkrVIJOBRhqJFuvnw54KaO/Lmko+PPXhTYZfPJ918gOFHDYOMeQMOLTLH0qRnHHtmuHN2WrrhnDPJlm0yxxIYe44BZQ5g12IvkWPGzMN2mHHD7Rwzad62AwZsEjkGDLwNDDwSuBwmkWP8meffYfsNN89/g2rJ/8D4F78WA6DKw4kbbvCwwWxhYMZvS1qa9My+w8kbzqSZSQL9AtSSZnBY5pgEHi3JwBD7dth2w/HDzySAIWYgPyP54cM3NTb2uLSAADOGyAEGBnwasGkZBaNgFIyCUYAMAB24UYTgEGx1AAAAAElFTkSuQmCC","orcid":"","institution":"Shahid Beheshti University of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Farhad","middleName":"","lastName":"Hosseinpanah","suffix":""}],"badges":[],"createdAt":"2025-07-12 07:23:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7106406/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7106406/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s40200-026-01867-x","type":"published","date":"2026-02-21T15:59:39+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87885458,"identity":"031a43e2-afea-45ef-8c5e-3bdaac638dda","added_by":"auto","created_at":"2025-07-30 05:08:01","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":215789,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of study participants.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7106406/v1/18d94801584805c6ca35cc40.jpg"},{"id":103251522,"identity":"787cd974-d948-4a7b-8dff-630135e10ca2","added_by":"auto","created_at":"2026-02-23 16:10:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1027588,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7106406/v1/0dc3e3eb-c876-4d79-8c91-d869bccec0c3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of Triglyceride-Glucose (TyG) Index and Carotid intima-media Thickness in Early Adulthood: Tehran Lipid and Glucose Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDespite significant advances in the prevention and treatment of cardiovascular disease (CVD), it remains the leading cause of morbidity and mortality worldwide and a major contributor to the global burden of disease. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Based on the World Health Organization (WHO) report, CVD accounts for the most mortality caused by non-communicable diseases. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) The increasing burden of CVD indicates the importance of early detection of at-risk individuals to improve risk stratification strategies and therapeutic management. Although the role of several risk factors, including age, male sex, positive family history of CVD, diabetes, hypertension, obesity, and hypercholesteremia, on the development of CVD have been established, recent studies indicate that patients without these risk factors may also develop atherosclerotic CVD. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) These findings highlight the importance of identifying novel risk factors in the general population. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eInsulin resistance (IR) is a state of impaired tissue sensitivity and responsiveness to circulating insulin. Previous studies have demonstrated the contributing role of IR on the pathogenesis of atherosclerosis and the development of CVD not only in diabetic patients but also amongst the general population and in nondiabetics. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) Furthermore, a causal relationship between IR and CVD has also been suggested in a Mendelian randomized analysis. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) Therefore, identifying individuals with IR faces challenges in clinical settings, as hyperinsulinemic-euglycemic, considered a gold standard method, is expensive and invasive, and the homeostasis model assessment- estimated IR (HOMA-IR) index lacks accuracy. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) The Triglyceride-glucose (TyG) index is a product of fasting triglyceride and glucose of fasting blood [fasting triglycerides (TG, mg/dL)\u0026times;fasting blood glucose (FBG, mg/dL)/2], which is considered as a novel and reliable surrogate marker of IR. Along with the simple use of the TyG Index as an alternative marker of IR in clinical settings, its superiority to HOMA-IR has also been demonstrated in previous studies. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eThe increasing evidence has shown the association of the TyG Index with atherosclerotic CVD. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) In addition, the predictive role of the TyG Index concerning atherosclerosis and CVD events in the general population has also been established. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) Evaluating carotid intima-media thickness (CIMT) is considered a noninvasive and sensitive contributor to subclinical atherosclerosis (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) and a suitable marker for early detection of atherosclerosis and evaluation of CVD progression. The study of Li on 59,123 participants demonstrated a positive association between the TyG Index and carotid atherosclerosis. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) Furthermore, the findings of another study suggested that a higher TyG Index increases the risk of carotid atherosclerosis incidence in the general population. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eDespite promising evidence on the association of the TyG Index and CVD contributors, it is still early to consider it an established independent variable in detecting and evaluating atherosclerosis and CVD. In the presenting study in the Tehran Lipid and Glucose Study (TLGS) with a long-term follow-up of the general population, we aimed to define the association of the TyG Index and the CIMT. The results of this study can help us better understand the role of the TyG Index as a novel marker of atherosclerosis and CVD in the early detection of individuals at risk in clinical settings.\u003c/p\u003e"},{"header":"Methods and Material","content":"\u003cp\u003e\u003cb\u003eStudy population\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eThe presenting study is performed in the Tehran Lipid and Glucose Study (TLGS) framework, a large-scale population-based prospective cohort study with more than twenty years of follow-up time. The baseline survey of the TLGS was carried out from 1999 to 2001 on 15005 participants aged\u0026thinsp;\u0026ge;\u0026thinsp;3 years using a multistage random sampling method in District 13 of Tehran, capital city of Iran, to evaluate risk factors and outcomes of non-communicable diseases. The following surveys were held at three-year intervals in seven phases, with the last follow-up survey (Phase VII) being conducted from 2018 to 2021.\u003c/p\u003e\u003cp\u003eIn this cross-sectional study in the Tehran Lipid and Glucose Study framework, we included 1450 participants aged more than 18 years with available CIMT measurements in Phase VII. After the exclusion of the participants with a history of cancer, long-term corticosteroid consumption, consumption of antidiabetic medications, missing data of TyG Index, participants with pregnancy and with extreme values of BMI (exceeding\u0026thinsp;\u0026plusmn;\u0026thinsp;3SD), 1339 participants were enrolled. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e Informed written consent was obtained from all participants. This study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Shahid Beheshti University of Medical Sciences human research committee.\u003c/p\u003e\u003cp\u003e\u003cb\u003eData collection\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eThe detailed protocol and laboratory procedures of the TLGS were fully described elsewhere. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) Demographics and anthropometric data of the participants were obtained utilizing standard questionnaires and established protocols. Weight was measured using a digital electronic scale (Seca 707; range 0.1\u0026ndash;150 kg, Hanover, MD, USA) and rounded to 100g. Height assessments were performed with a tape stadiometer when subjects were barefoot, standing against the wall with shoulders in normal alignment; the measurements were rounded to the nearest 0.1cm. BMI was calculated as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Weight\\left(kg\\right)/〖Height\\left(m\\right)〗^2\\)\u003c/span\u003e\u003c/span\u003e. Waist circumference (WC) was measured standing at the end of expiration at the narrowest level between the iliac crest and lowest rib, without any pressure on the body's surface. The assessment of fasting plasma glucose (FPG), triglyceride (TG), total cholesterol (TC), and high-density lipoprotein cholesterol (HDL-C) was performed through the obtainment of blood samples after a minimum fasting time of 12 hours at the TLGS Research Laboratory. If TG\u0026thinsp;\u0026lt;\u0026thinsp;400 mg/dl, the low-density lipoprotein cholesterol (LDL-C) was calculated using the Friedwald formula. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) The TyG index was calculated as ln [triglyceride (mg/dL) \u0026times; fasting glucose (mg/dL)/2]. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were obtained utilizing a standard mercury sphygmomanometer, calibrated by the Iranian Institute of Standards and Industrial Researches, from the right brachial artery at the heart level in a sitting position at least twice. Hypertension is defined as systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140mmHg and/or diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;90mmHg, or the current use of antihypertensive medication is present.\u003c/p\u003e\u003cp\u003eRegarding educational status, subjects were categorized into illiteracy, those below diploma, and those in academic education. Physical activity was obtained by a Persian-translated MAQ questionnaire, which measures leisure time, job, and household activities and calculates the metabolic equivalent (MET) based on min/week. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) The physical activity levels have been defined as low (MET\u0026thinsp;\u0026lt;\u0026thinsp;600 min/wk), moderate (MET 600\u0026ndash;1499 min/week), and high (MET\u0026thinsp;\u0026ge;\u0026thinsp;1500 min/week) levels. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eThe measurements of intima-media thickness of extracranial carotid arteries were obtained utilizing high-resolution B-mode ultrasonography with a linear 7.5\u0026ndash; 10 MHz transducer (Samsung Medison SonoAceR3 ultrasound machine) in a supine position with extended and slightly lateral rotated neck. Two expert radiologists obtained all measurements. After performing the transverse plane scan to assess the general anatomy of the artery and atherosclerotic plaques, longitudinal scans were obtained from different angles. After visualization of the far wall arterial interface while luminal content is completely anechoic, the measurements were obtained in a plaque-free arterial segment on the optimal grey scale of the left common carotid artery and the hypoechoic band between the echogenic surfaces of intima and adventitia was considered as CIMT. The measurements were performed in three locations of the left common carotid artery along with sporadic measurement of the distal segment of both carotid arteries, carotid bulb, and internal carotid artery, and the average was considered as the final measurement. The interobserver agreement of the two radiologists was tested in a subsample of 30 participants. The interclass association coefficient (ICC) and 5% confidence interval based on the 2-way mixed-effects model were 0.79 and 0.55\u0026ndash;0.90, respectively. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e)\u003c/p\u003e\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis:\u003c/h2\u003e\u003cp\u003eContinuous variables with normal and skewed distributions were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD and median (IQR, 25th and 75th percentile), respectively. Baseline data regarding the categorical variables were presented as frequency (percentages). Also, baseline data of two groups for continuous and categorical variables were compared using an independent sample t-test, Mann-Whitney U test, and chi-square test. Independent Effects of the TyG Index tertiles on CIMT (in micrometer) were studied using the linear regression model. Test the independent effects of the TyG Index tertiles on CIMT in multiple linear regression adjusting for age, sex, smoking, educational status, and physical activity was tested. We computed the Pearson correlation coefficients to evaluate the correlation between TyG Index with CIMT. The association between the TyG Index tertiles and high c CIMT (\u0026gt;\u0026thinsp;90 percentile) was also investigated by calculating odd ratios (ORs) using logistic regression models. All analyses were performed using SPSS software version 20 (SPSS, Chicago, IL, USA); the significance level was set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (two-tailed).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eAfter applying specific exclusion criteria, 1339 participants were enrolled in our study. The participants were categorized into three tertiles based on the TyG Index. The mean age of the study population was 29.77\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0 years. It comprises 696 (52%) men and 643 (48%) women. The baseline cardiometabolic characteristics of the subjects are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. There was a significant difference in all anthropometric indices and cardiometabolic variables, including SBP and DBP, FPG, TC, LDL-C, and TG, with an increasing tendency from tertile 1 to tertile 3 of the TyG Index. However, there was no significant difference in CIMT between tertiles, with a mean value of 0.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09 mm in the total population.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline characteristics of Triglyceride-Glucose (TyG) Index quartiles\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=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT1\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;446)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eT2\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;446)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eT3\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;447)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1339)\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\u003e\u003cb\u003eSex (Men)\u003c/b\u003e, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e151 (33.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e223 (50.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e322 (72.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e696 (52.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e, year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e29.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAcademic education\u003c/b\u003e, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e302 (67.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e293 (65.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e283 (63.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e878 (65.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.406\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWeight\u003c/b\u003e, kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e66.5\u0026thinsp;\u0026plusmn;\u0026thinsp;13.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74.3\u0026thinsp;\u0026plusmn;\u0026thinsp;14.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e83.9\u0026thinsp;\u0026plusmn;\u0026thinsp;15.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e74.9\u0026thinsp;\u0026plusmn;\u0026thinsp;16.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWC\u003c/b\u003e, cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e81.7\u0026thinsp;\u0026plusmn;\u0026thinsp;9.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e88.3\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95.3\u0026thinsp;\u0026plusmn;\u0026thinsp;10.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e88.4\u0026thinsp;\u0026plusmn;\u0026thinsp;11.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (3.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44 (9.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e64 (4.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSBP\u003c/b\u003e, mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e103.1\u0026thinsp;\u0026plusmn;\u0026thinsp;11.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e106.5\u0026thinsp;\u0026plusmn;\u0026thinsp;11.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e112.5\u0026thinsp;\u0026plusmn;\u0026thinsp;11.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e107.4\u0026thinsp;\u0026plusmn;\u0026thinsp;12.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDBP\u003c/b\u003e, mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e69.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e72.2\u0026thinsp;\u0026plusmn;\u0026thinsp;8.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e77.0\u0026thinsp;\u0026plusmn;\u0026thinsp;9.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e72.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFPG\u003c/b\u003e, mg\u0026frasl;dl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e85.9\u0026thinsp;\u0026plusmn;\u0026thinsp;7.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e89.0\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e92.1\u0026thinsp;\u0026plusmn;\u0026thinsp;8.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e89.0\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTC\u003c/b\u003e, mg/dl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e154.4\u0026thinsp;\u0026plusmn;\u0026thinsp;26.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e171.4\u0026thinsp;\u0026plusmn;\u0026thinsp;28.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e195.7\u0026thinsp;\u0026plusmn;\u0026thinsp;36.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e173.9\u0026thinsp;\u0026plusmn;\u0026thinsp;35.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLDL-C\u003c/b\u003e ,mg/dl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e89.0\u0026thinsp;\u0026plusmn;\u0026thinsp;23.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e103.8\u0026thinsp;\u0026plusmn;\u0026thinsp;25.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e115.0\u0026thinsp;\u0026plusmn;\u0026thinsp;31.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e102.5\u0026thinsp;\u0026plusmn;\u0026thinsp;28.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHDL-C\u003c/b\u003e, mg/dl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47.5\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42.1\u0026thinsp;\u0026plusmn;\u0026thinsp;9.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e47.6\u0026thinsp;\u0026plusmn;\u0026thinsp;10.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTG\u003c/b\u003e, mg\u0026frasl;dl \u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63(53\u0026ndash;70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e99(89\u0026ndash;111)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e169(142\u0026ndash;222)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e99(70\u0026ndash;143)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePositive family history of CVD\u003c/b\u003e, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12 (2.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (1.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13 (2.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e32 (2.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.370\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSmoking\u003c/b\u003e, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e69 (15.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e90 (20.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e113 (25.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e272 (20.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLow physical activity\u003c/b\u003e, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e260 (59.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e238 (54.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e237 (54.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e735 (56.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.197\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ecIMT\u003c/b\u003e, mm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.715\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003ecIMT, carotid intima media thickness; DBP, dyastolic blood pressure; FPG, fasting plasma glucose; HDL-C, high density lipoprotein; LDL-C, low density lipoprotein; MET, metabolic equivalent; SBP, systolic blood pressure; TC, total cholesterol; TG, triglycerides; WC, waist circumference.\u003c/p\u003e\u003cp\u003eData are presented mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or n (%) except TG \u003csup\u003e\u0026dagger;\u003c/sup\u003e is shown median (IQ 25\u0026ndash;75)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eCorrelation coefficients between TyG Index with CIMT were \u0026minus;\u0026thinsp;0.013 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.623) in the total population and \u0026minus;\u0026thinsp;0.012 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.792), -0.125 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008), and \u0026minus;\u0026thinsp;0.012(p\u0026thinsp;=\u0026thinsp;0.799) in tertiles 1 to 3, respectively.\u003c/p\u003e\u003cp\u003eThe linear regression analysis for the association of TyG Index tertiles with CIMT is presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. There was a significant negative association in tertile 2 of the TyG Index and CIMT in the unadjusted model (\u0026szlig;=-0.084 \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008) and also after adjustment to age and sex defined as model 1 (\u0026szlig;=-0.068 \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028). The significant association of tertile 2 of the TyG Index and CIMT disappeared after further adjustments to smoking, educational status, and physical activity, which is defined as model 2 (\u0026szlig;=-0.001, p\u0026thinsp;=\u0026thinsp;0.854). However, the linear regression analysis observed no significant association between tertile 3 of the TyG Index and CIMT.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLinear regression coefficients for the association of Triglyceride-Glucose (TyG) Index tertiles with cIMT\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eTriglyceride-Glucose Index quartiles\u003c/p\u003e\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\u003e\u003cb\u003e\u0026szlig;\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eSE\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003cb\u003e-Value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eUnadjusted Model\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePer 1 unit Increase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.792\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.798\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eModel 1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePer 1 unit Increase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.977\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.028\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.991\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eModel 2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePer 1 unit Increase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.137\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.854\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.057\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eModel 1\u0026thinsp;=\u0026thinsp;age, sex\u003c/p\u003e\u003cp\u003eModel 2\u0026thinsp;=\u0026thinsp;Model 1\u0026thinsp;+\u0026thinsp;smoking, educational status, physical activity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe odds ratio (OR) for the association of TyG Index tertiles with high CIMT is depicted in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The odds ratio of the high CIMT in the unadjusted model was not statistically significant in tertiles 2 and 3 in reference to tertile 1. After adjustment to age and sex (model 1), although the association of high CIMT and TyG Index tertile 2 in reference to tertile 1 remains insignificant, tertile 3 shows a significant association with high CIMT in reference to tertile 1 with an odds ratio of 1.73 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013). This association remained significant when further adjustments to smoking, educational status, and physical activity (model 2) were applied (OR\u0026thinsp;=\u0026thinsp;1.70, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOdds ratios for the association of Triglyceride-Glucose Index tertiles with high cIMT\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eTriglyceride-Glucose Index quartiles\u003c/p\u003e\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\u003eOR (95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e -Value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eUnadjusted Model\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.02 (0.67, 1.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.913\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.48 (0.99, 2.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.057\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eModel 1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.09 (0.71, 1.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.692\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.73 (1.12, 2.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.013\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eModel 2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (0.64, 1.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.999\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.70 (1.10, 2.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.017\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eModel 1\u0026thinsp;=\u0026thinsp;age, sex\u003c/p\u003e\u003cp\u003eModel 2\u0026thinsp;=\u0026thinsp;Model 1\u0026thinsp;+\u0026thinsp;smoking, educational status, physical activity\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the presenting cross-sectional study, in a framework of a large-scale population-based cohort study of the Iranian population in the TLGS, we evaluated the TyG Index's and CIMT's association in early adulthood. Our results demonstrated that compared to subjects with the lowest TyG Index category, those with the highest category significantly have higher risks of developing high CIMT levels as a contributor to subclinical atherosclerosis. The significance of the results was independent of age, sex, smoking, physical activity, and educational status.\u003c/p\u003e\u003cp\u003eInsulin resistance has been shown to play a role in the pathogenesis of atherosclerotic cardiovascular diseases (ASCVD). Several pathophysiological pathways have been suggested to define the role of insulin resistance in the initiation and progression of ASCVD. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) These pathways include causing oxidative stress and persistent low-grade inflammation, (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) directly affecting endothelial dysfunction, (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) and increasing sympathetic nervous system activity, (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) which play key roles in the pathogenesis. The TyG index is a highly sensitive and specific marker of insulin resistance, demonstrating better performance compared to the homeostasis model assessment (HOMA) for measuring insulin resistance. Furthermore, it can be easily calculated using routine blood biochemical tests, highlighting its potential role as a contributing or independent risk factor for ASCVD. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eSeveral previous studies evaluated the association of the TyG index with cardiovascular disease. In the meta-analysis of cohort studies comprising 5,731,294 participants without ASCVD at baseline, patients in the highest TyG index category had an increased incidence of ASCVDs, coronary artery disease (CAD), and stroke compared with patients in the lowest TyG index category, independently. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) In compliance with the previous study, another meta-analysis of twelve cohort studies demonstrated a higher risk of CAD, myocardial infarction, and composite CVD in participants in the higher TyG index category. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) Another study of Tehran lipid and glucose study (TLGS) with 16 years of follow-up showed 61% and 84% increased risk of CVD and CA in participants with elevated TyG index at baseline. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eA study conducted in 2017 showed that the prevalence of subclinical atherosclerosis in the middle-aged population without cardiovascular risk factors is as high as 50% (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), indicating the critical importance of early detection of at-risk patients. The results of our study demonstrated an association between higher levels of TyG index and high CIMT levels as a contributor to subclinical atherosclerosis. These findings are consistent with previous studies. In Li W. et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) study on 59,123 participants aged\u0026thinsp;\u0026gt;\u0026thinsp;40, the TyG-index was significantly associated with the prevalence of carotid atherosclerosis, CIMT, carotid plaques, and carotid stenosis severity. In the subgroup analysis, the significant association between the TyG index and carotid atherosclerosis was only observed in participants with age\u0026thinsp;\u0026gt;\u0026thinsp;60 years old. Another study on 1,523 patients with ischemic stroke showed an odds ratio of 1.56 for abnormal CIMT and 1.46 for abnormal maximum CIMT in quartile four versus quartile 1 of the TyG index. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) Also, in the study of Li Z. et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) in patients with established coronary heart disease (CHD), the odds ratio of carotid artery plaque was 1.37 in quartile 4 of the TyG index compared with quartile 1. In our study, the odds ratio for high CIMT was 1.73 in tertile three compared to tertile 1 of the TyG index, which was consistent with previous studies. Despite the study of Li W. et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), which was performed on middle-aged and elderly participants, and the study of Miao M. et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) and Li Z. et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e), which included subjects with established ischemic stroke and CHD, respectively, our study was performed on the general population with a mean age of 29.77\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0 years which appears to be distinct in this regard. However, several other studies (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e) found a significant positive association between the TyG index and carotid atherosclerosis. In the study of Zhao et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e) on the Chinese elderly population, no significant association between the TyG index and carotid plaque was observed.\u003c/p\u003e\u003cp\u003eSeveral strengths and limitations of the study must be noted. Regarding limitations, first, the study was conducted on a general population of a metropolitan city in Iran and may not be national or representative of another population. Second, only participants with available CIMT data were eligible for the study. Third, CIMT has been regarded as a surrogate marker of subclinical atherosclerosis; however, it has been reported that the incidence of high CIMT might be driven by the adaptive remodeling of the media (not intima). Fourth, some possible confounders were not considered, such as dietary habits and socioeconomic status. Last but not least, regarding the study's cross-sectional nature, we can not provide conclusive evidence in favor of causality. Regarding the study's strengths, this study was performed in a well-designed, large-scale, population-based study.\u003c/p\u003e\u003cp\u003eIn conclusion, the results of our study demonstrated an independent, positive association between the TyG index and higher CIMT levels as a contributor to atherosclerosis. These results highlight the potential role of the TyG index as an independent novel variable in the early detection of high-risk individuals for atherosclerotic cardiovascular disease, which can be further evaluated in future studies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e: The authors express their appreciation to participants of District 13, Tehran, for their enthusiastic support in this study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics:\u0026nbsp;\u003c/strong\u003eThis study has been approved by the National Research Council of the Islamic Republic of Iran (No. 181) and has been performed with the approval of the Human Research Review Committee of the Endocrine Research Center, Shahid Beheshti University, Tehran, Iran. The Endocrine Science ethics committee was conducted in accordance with the principles of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate declarations:\u003c/strong\u003eAt the beginning of this study, all parents or legal guardians provided written informed consent for participants under 18 years of age. Written informed consent was acquired from all participants ≥ 18 years before taking part in the investigations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e: The authors declare no conflicts of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Sources\u003c/strong\u003e: The authors disclose no financial relationships relevant to this article.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAS\u003c/strong\u003e: Conceptualization, Data curation, Writing - Original draft preparation. \u003cstrong\u003eMB\u003c/strong\u003e: Data curation, Writing - Original draft preparation. \u003cstrong\u003eMM\u003c/strong\u003e: Software, Formal analysis. \u003cstrong\u003eMV\u003c/strong\u003e: Validation, Writing - Review \u0026amp; Editing. \u003cstrong\u003eSG:\u003c/strong\u003e Validation, Writing - Review \u0026amp; Editing. \u003cstrong\u003eFA\u003c/strong\u003e: Resources, Supervision. \u003cstrong\u003eFH\u003c/strong\u003e: Conceptualization, Methodology, Supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e: The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e: Not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSacco RL, Roth GA, Reddy KS, Arnett DK, Bonita R, Gaziano TA et al. 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Cardiovasc Diabetol. 2019;18(1).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Triglyceride-glucose index, Cardiovascular disease, Insulin resistance, Subclinical atherosclerosis, carotid intima-media thickness","lastPublishedDoi":"10.21203/rs.3.rs-7106406/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7106406/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThe association of the Triglyceride-glucose (TyG) index as a novel surrogate marker of IR with atherosclerotic CVD has been suggested previously. However, there is limited data available for young adults. We aimed to define the association of the TyG Index and the carotid intima-media thickness (CIMT) in the Tehran, Lipid and Glucose Study (TLGS)\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eIn this cross-sectional study, 1339 participants aged\u0026thinsp;\u0026ge;\u0026thinsp;18 were categorized into three tertiles based on the TyG Index. The independent contributions of TyG Index tertiles on CIMT and high CIMT were investigated in multiple linear regression and logistic regression models, respectively.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe mean age of 1339 participants enrolled in the study was 29.77\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0 years (52% men). The participants were categorized into three tertiles based on the TyG Index. The odds ratio (OR) for the association of TyG Index tertiles with high CIMT was significant in tertile 3 in reference to tertile one after adjustment to age and sex (model 1; OR\u0026thinsp;=\u0026thinsp;1.73, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013), which remains significant after further adjustments to smoking, educational status, and physical activity (model 2; OR\u0026thinsp;=\u0026thinsp;1.70, 95% CI: 1.10\u0026ndash;2.64, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eOur results demonstrated an independent, positive association between the TyG index and higher CIMT levels. These results highlight the potential role of the TyG index as an independent novel variable in the early detection of high-risk individuals for atherosclerotic CVD.\u003c/p\u003e","manuscriptTitle":"Association of Triglyceride-Glucose (TyG) Index and Carotid intima-media Thickness in Early Adulthood: Tehran Lipid and Glucose Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-30 05:07:56","doi":"10.21203/rs.3.rs-7106406/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4ae85a82-2ea8-4ca6-96a6-892c187ba759","owner":[],"postedDate":"July 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-23T16:06:34+00:00","versionOfRecord":{"articleIdentity":"rs-7106406","link":"https://doi.org/10.1007/s40200-026-01867-x","journal":{"identity":"journal-of-diabetes-and-metabolic-disorders","isVorOnly":false,"title":"Journal of Diabetes \u0026 Metabolic Disorders"},"publishedOn":"2026-02-21 15:59:39","publishedOnDateReadable":"February 21st, 2026"},"versionCreatedAt":"2025-07-30 05:07:56","video":"","vorDoi":"10.1007/s40200-026-01867-x","vorDoiUrl":"https://doi.org/10.1007/s40200-026-01867-x","workflowStages":[]},"version":"v1","identity":"rs-7106406","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7106406","identity":"rs-7106406","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}