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Original Article
Clinical Study Triglyceride Glucose Index Is Superior to the Homeostasis Model Assessment of Insulin Resistance for Predicting Nonalcoholic Fatty Liver Disease in Korean Adults
Sang Bae Lee1,2*orcid, Min Kyung Kim3*orcid, Shinae Kang1,2, Kahui Park1,2, Jung Hye Kim1,2, Su Jung Baik4, Ji Sun Nam1,2, Chul Woo Ahn1,2, Jong Suk Park1,2orcid
Endocrinology and Metabolism 2019;34(2):179-186.
DOI: https://doi.org/10.3803/EnM.2019.34.2.179
Published online: May 20, 2019

1Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea.

2Severance Institute for Vascular and Metabolic Research, Yonsei University College of Medicine, Seoul, Korea.

3Department of Internal Medicine, Hallym University Kangdong Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Korea.

4Healthcare Research Team of Health Promotion Center, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.

Corresponding author: Jong Suk Park. Department of Internal Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, 211 Eonju-ro, Gangnam-gu, Seoul 06273, Korea. Tel: +82-2-2019-4377, Fax: +82-2-3463-3882, pjs00@yuhs.ac
*These authors contributed equally to this work.
• Received: February 12, 2019   • Revised: March 26, 2019   • Accepted: April 5, 2019

Copyright © 2019 Korean Endocrine Society

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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  • Background
    Recently, the triglyceride glucose (TyG) index has been considered a surrogate marker of insulin resistance which is a well-known pathogenic factor in nonalcoholic fatty liver disease (NAFLD). However, few studies have investigated the relationship between the TyG index and NAFLD. Thus, we investigated the relationship between the TyG index and NAFLD and the effectiveness of the TyG index compared with the homeostasis model assessment of insulin resistance (HOMA-IR) in identifying NAFLD in Korean adults.
  • Methods
    Participants of 4,986 who underwent ultrasonography in a health promotion center were enrolled. The TyG index was calculated as ln [fasting triglycerides (mg/dL)×fasting glucose (mg/dL)/2], and HOMA-IR was estimated. NAFLD was diagnosed by ultrasonography.
  • Results
    Significant differences were observed in metabolic parameters among the quartiles of the TyG index. The prevalence of NAFLD significantly increased with increment in the TyG index. After adjusting for multiple risk factors, a logistic regression analysis was performed. When the highest and lowest quartiles of the TyG index and HOMA-IR were compared, the odds ratios for the prevalence of NAFLD were 2.94 and 1.93 (95% confidence interval, 2.32 to 3.72 and 1.43 to 2.61; both P for trend <0.01), respectively. According to the receiver operating characteristic analysis, the TyG index was superior to HOMA-IR in predicting NAFLD.
  • Conclusion
    The TyG index and prevalence of NAFLD were significantly related and the TyG index was superior to HOMA-IR in predicting NAFLD in Korean adults.
Nonalcoholic fatty liver disease (NAFLD) is one of the most frequently occurring disorders of the liver, with the incidence gradually increasing worldwide [12]. NAFLD encompasses a range of liver diseases, including liver fibrosis, simple steatosis and cirrhosis. Histopathologic changes associated with NAFLD may lead to liver failure, hepatocellular carcinoma and ultimately, hepatic mortality [34]. In recent years, the importance of NAFLD as a metabolic disorder has been realized, not only with regards to hepatic manifestation, but several studies have shown that NAFLD is also related to type 2 diabetes mellitus, abdominal obesity, dyslipidemia, hypertension, and cardiovascular disease [56789]. Although the mechanisms associated with the onset of NAFLD remain poorly understood, insulin resistance (IR) has been found to be associated with the development of NAFLD [101112]. The homeostasis model assessment of insulin resistance (HOMA-IR) is a widely used surrogate marker of IR and is one of several methods used for evaluating IR [13]. Numerous studies have found that an independent relationship exists between NAFLD and HOMA-IR [141516]. In addition, other studies have proposed that HOMA-IR diagnostic criteria can be used for predicting NAFLD [1718].
Recently, the triglyceride glucose (TyG) index, which is calculated on the basis of triglycerides (TGs) and fasting glucose levels, has emerged as a reliable surrogate marker of IR. Moreover, the TyG index correlates with the HOMA-IR and hyperinsulinemic-euglycemic clamp test for recognizing IR [192021]. Despite this, few studies have evaluated the TyG index in the context of NAFLD [222324]. Therefore, in this study, we sought to elucidate the relationship between NAFLD and the TyG index and compare the effectiveness of the TyG index and HOMA-IR in identifying NAFLD in Korean adults.
Study participants
A total of 5,989 Korean subjects (age ≥20 years) who were participants in an in-depth health checkup program at the Gangnam Severance Hospital Health Promotion Center from January 2008 to February 2015, were included in the study. Subjects with elevated levels of TGs (≥400 mg/dL), an acute inflammation, history of malignancy, renal or infectious disease, viral hepatitis (positive for hepatitis B surface antigen or anti-hepatitis C virus antibody), liver cirrhosis or malignancy observed via ultrasound were excluded from this study. Subjects with history of diabetes mellitus or newly diagnosed diabetes in this exam were also excluded. Furthermore, any subjects taking statins, TG-reducing therapies (e.g., fenofibrate or omega-3), thiazolidinediones, or injecting insulin were also excluded, as were males and females with a history of heavy alcohol consumption that exceeded 30 and 20 g/day, respectively. After the exclusion criteria were applied, 4,986 participants were included in the final analysis. All methods were performed in accordance with the ethical standards of the responsible committee on human experimentation and with the World Medical Association Declaration of Helsinki. Written informed consents were provided by all subjects before data collection. The study protocol was approved by the Institutional Review Board of Yonsei University College of Medicine (approval number: 2018-0077).
Clinical characteristics of the study participants
The height and weight of each participant was measured and the body mass index (BMI, kg/m2) was calculated. The social and medical history of each participant was obtained by administering a self-questionnaire, which included questions regarding smoking, alcohol status, medications, and a history of other diseases. Experienced technicians measured the systolic blood pressure (SBP) and diastolic blood pressure (DBP) respectively, after a 5-minute of rest, with the patient's arm placed at the same level as the heart using an automated blood pressure monitor (HEM-7080IC, Omron Healthcare, Lake Forest, IL, USA). A diagnosis of diabetes mellitus was made on the basis of a prior history of diabetes or the American Diabetes Association's diagnostic standards. Subjects with SBP and/or DBP ≥140/90 mm Hg or those presently using antihypertensive medication were defined as having hypertension. Subjects who had regularly smoked cigarettes over the past 6 months were considered to be current smokers.
Biochemical parameters
After an 8-hour fasting period, blood samples were collected from all subjects. The samples were immediately centrifuged and the serum was subsequently stored at −70℃ until further analysis was required. The levels of fasting plasma glucose (FPG), total cholesterol (TC), high density lipoprotein cholesterol (HDL-C), TG, aspartate aminotransferase (AST), and alanine aminotransferase (ALT) were assessed using enzymatic procedures, with an automated chemistry analyzer (Hitachi 7600-120, Hitachi, Tokyo, Japan). The level of low density lipoprotein cholesterol (LDL-C) was calculated using Friedewald formula. The TyG index was computed using the following formula: ln [fasting TGs (mg/dL)×fasting glucose (mg/dL)/2] [19]. The levels of hepatitis B surface antigen and anti-hepatitis C virus antibodies were measured using a Roche E-170 device (Roche Diagnostics, Mannheim, Germany). The fasting serum insulin level was determined using a radioimmunoassay kit (Daiichi Radioisotope Labs, Tokyo, Japan). IR was approximated using the HOMA-IR index which was calculated using following formula: [fasting insulin (µU/mL)×FPG (mg/dL)/405].
Ultrasonographic analyses
A diagnosis of fatty liver disease was made on the basis of the findings of an abdominal ultrasonography scan performed using a 3.5-MHz transducer (HDI 5000, Philips, Bothell, WA, USA). One of three experienced radiologists, who were blinded to the subjects' clinical information, performed the abdominal ultrasonographic examination. Any degree of fat accumulation in the liver was considered to be NAFLD in the present study. The subjects were classified into four groups on the basis of the existence and severity of NAFLD according to the level of hepatic tissue hyperechogenicity, discrepancy between the liver and right kidney, and visibility of the vascular structures [25].
Statistical analysis
Continuous variables with a normal distribution were presented as the mean±standard deviation. Continuous variables with skewed distributions were presented as the median with the interquartile range and were transformed to a log scale for analysis. Intergroup comparisons were performed using Student's t test or one-way analysis of variance with post hoc analysis. Categorical variables with percentages were compared using chi-square test. After adjusting for any confounding variables, a multivariate logistic regression analysis was used for estimating the odds ratio (OR) and associated 95% confidence interval (CI) for NAFLD on the basis of the TyG index and HOMA-IR. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was calculated for comparing the predictive power of the TyG index and HOMA-IR for the prevalence of NAFLD. Z test was used for comparing the differences between AUCs. Statistical significance was considered for P values less than 0.05. SPSS for Windows version 23.0 (IBM Co., Armonk, NY, USA) and MedCalc (MedCalc software, Olstead, Belgium) were used for performing all statistical analyses in the present study.
A total of 4,986 subjects were included in the present study, 2,069 of whom were diagnosed with NAFLD using ultrasound data (41.5%). The comparison of the baseline characteristics of the subjects enrolled in this study, with and without NAFLD, is presented in Table 1. The subjects with NAFLD were older; more likely to be male; and had a higher BMI, blood pressure, insulin, HOMA-IR, LDL-C, FPG, TC, TG, AST, ALT, and TyG index but lower HDL-C than the subjects without NAFLD. In addition, the proportion of subjects with hypertension was significantly higher among those with NAFLD than those without.
The subjects were divided into four groups on the basis of their TyG indices (Q1: TyG index≤8.04; Q2: 8.05≤TyG index≤8.42; Q3: 8.43≤TyG index≤8.81; Q4: TyG index≥8.82). Table 2 outlines the demographic, clinical, and laboratory information for each group. The metabolic parameters were associated with significant differences between the groups. There was an increase in the number of males, BMI, SBP, DBP, AST, ALT, insulin, HOMA-IR, FPG levels, the prevalence of hypertension, as well as an increase in the levels of TC, TG, and LDL-C. However, the level of HDL-C was found to decrease with increasing TyG index.
The subjects were also classified into four groups according to their HOMA-IR (Q1: HOMA-IR≤0.67; Q2: 0.68≤HOMA-IR≤1.03; Q3: 1.04≤HOMA-IR≤1.56; Q4: TyG index≥1.57) and results demonstrated that both the prevalence and severity of NAFLD increased with increasing TyG index and HOMA-IR quartiles (Fig. 1).
The association between the TyG index and NAFLD was investigated by dividing the TyG index into the following quartiles: the first quartile (Q1) was used as a reference (Table 3). When Q1 was set as a reference, the unadjusted multivariate logistic regression analysis revealed that in all subjects, the TyG indices for Q2, Q3, and Q4 were associated with higher OR for the presence of NAFLD. Moreover, these relationships remained significant (P for trend <0.01) even after adjusting for confounding variables. The association between the HOMA-IR and the presence of NAFLD was further investigated by categorizing the HOMA-IR into quartiles, with the first quartile used as the reference (Table 3). Results showed that there was a significant relationship (P for trend <0.01) between the higher HOMA-IR quartile and the presence of NAFLD, even after adjusting for confounding variables. After adjusting for multiple risk factors, the OR (95% CI) in the highest quartile for NAFLD using HOMA-IR was 1.93 (95% CI, 1.43 to 2.61) compared with 2.94 (95% CI, 2.32 to 3.72) for the TyG index. The AUC (95% CI) of the ROC curve for the TyG index at 0.716 (95% CI, 0.702 to 0.731) was significantly higher than that of the HOMA-IR at 0.672 (95% CI, 0.650 to 0.694) (P<0.01). These results suggest that regarding the prediction of NAFLD, the TyG index is superior to the HOMA-IR.
In the present study, following adjustment for conventional risk factors, an independent relationship between the TyG index and NAFLD was observed. Furthermore, results demonstrated that the TyG index was superior in its ability to identify NAFLD compared with HOMA-IR. To the best of our knowledge, this is the first study wherein the diagnostic effectiveness of the TyG index and HOMA-IR for identifying NAFLD has been compared.
NAFLD is related to IR and metabolic syndromes associated with hyperinsulinemia, hypertriglyceridemia, and hyperglycemia [5262728]. Recently, Zheng et al. [29], even demonstrated that TyG index could predict an incidence of NAFLD in longitudinal, prospective cohort study. In agreement with previous studies, most of the metabolic risk factors evaluated by us increased or decreased according to the TyG index and the presence of NAFLD. In addition, unlike previous studies, which reported an association between the presence of NAFLD and the TyG index [222324], we further examined the relationship between the severity of NAFLD and the TyG index. Results demonstrated that the severity of NAFLD was strongly associated with the TyG index.
IR has been shown to have an important pathological association with NAFLD. Furthermore, both the TyG index and HOMA-IR are well-known representative markers of IR. In the present study, Pearson's correlation analysis was performed and confirmed that there was a significant relationship between the TyG index and HOMA-IR (r=0.466, P<0.001) (data not shown). Interestingly, we observed that the TyG index displayed a higher OR and AUC of the ROC curve than the HOMA-IR for predicting NAFLD. These results suggest that the TyG index is a superior surrogate marker for predicting NAFLD. Recent studies have also reported the superiority of the TyG index for identifying IR [213031]. For example, Vasques et al. [21] reported that the TyG index was superior to HOMA-IR in identifying IR in a Brazilian population. Although the reason for this finding remains unknown, the underlying mechanism for the superiority of the TyG index over HOMA-IR in predicting NAFLD can be explained by the crucial roles of glucotoxicity and lipotoxicity play in the modulation of IR, which shows key pathological association with NAFLD [323334]. Our findings are in line with those of recent studies that demonstrate the superiority of the TyG index to HOMA-IR for evaluating metabolic risk factors associated with IR (e.g., diabetes and subclinical atherosclerosis) [353637]. In contrast, other studies have reported that the HOMA-IR primarily reflects IR in the liver [3839]. The mechanisms associated with this relationship should be clarified in prospective large-scale studies.
The present study had the following limitations. (1) Since this was a cross-sectional observational study, a causality cannot be presumed from the results. (2) The participants comprised Korean adults from a single institution and most of subjects were non-obese, healthy population. For these reasons, levels of fasting insulin and HOMA-IR were relatively low compared with other previous studies. Thus, the generalizability of the results may be limited. (3) The TyG index was compared with the HOMA-IR rather than with the hyperinsulinemic-euglycemic clamp test as the gold standard for assessing IR. However, the IR index derived from a euglycemic clamp has been shown to correlate with the TyG index and HOMA-IR. (4) A liver biopsy with a histological examination was not performed, which is the gold standard technique for identifying steatosis. Moreover, ultrasound was used for assessing the presence of NAFLD in this study; however, ultrasonography, a first-line imaging technique, is a highly useful noninvasive technique that is often used in both clinical practice and epidemiological studies [40].
The results of the present study indicate that there is a significant association between the TyG index and the prevalence of NAFLD in Korean subjects. Moreover, the TyG index was superior to HOMA-IR in predicting NAFLD. The TyG index is a simple and cost-effective marker of IR and appears to be a useful marker for predicting NAFLD.
Acknowledgements
We would like to thank the Gangnam Severance Health Promotion Research team for supporting the construction of the registry of data from the Health Promotion Center of the Gangnam Severance Hospital.

CONFLICTS OF INTEREST: No potential conflict of interest relevant to this article was reported.

AUTHOR CONTRIBUTIONS:

  • Conception or design: J.S.P., S.B.L., M.K.K.

  • Acquisition, analysis, or interpretation of data: S.K., K.P., J.H.K., S.J.B., J.S.N., C.W.A.

  • Drafting the work or revising: S.B.L., M.K.K., J.S.P.

  • Final approval of the manuscript: J.S.P.

  • 1. Browning JD, Szczepaniak LS, Dobbins R, Nuremberg P, Horton JD, Cohen JC, et al. Prevalence of hepatic steatosis in an urban population in the United States: impact of ethnicity. Hepatology 2004;40:1387–1395. ArticlePubMed
  • 2. Smits MM, Ioannou GN, Boyko EJ, Utzschneider KM. Non-alcoholic fatty liver disease as an independent manifestation of the metabolic syndrome: results of a US national survey in three ethnic groups. J Gastroenterol Hepatol 2013;28:664–670. ArticlePubMed
  • 3. Simeone JC, Bae JP, Hoogwerf BJ, Li Q, Haupt A, Ali AK, et al. Clinical course of nonalcoholic fatty liver disease: an assessment of severity, progression, and outcomes. Clin Epidemiol 2017;9:679–688. ArticlePubMedPMC
  • 4. Ahmed A, Wong RJ, Harrison SA. Nonalcoholic fatty liver disease review: diagnosis, treatment, and outcomes. Clin Gastroenterol Hepatol 2015;13:2062–2070. ArticlePubMed
  • 5. Yki-Jarvinen H. Non-alcoholic fatty liver disease as a cause and a consequence of metabolic syndrome. Lancet Diabetes Endocrinol 2014;2:901–910. ArticlePubMed
  • 6. Anstee QM, Targher G, Day CP. Progression of NAFLD to diabetes mellitus, cardiovascular disease or cirrhosis. Nat Rev Gastroenterol Hepatol 2013;10:330–344. ArticlePubMedPDF
  • 7. Vanni E, Bugianesi E, Kotronen A, De Minicis S, Yki-Jarvinen H, Svegliati-Baroni G. From the metabolic syndrome to NAFLD or vice versa? Dig Liver Dis 2010;42:320–330. ArticlePubMed
  • 8. van den Berg EH, Amini M, Schreuder TC, Dullaart RP, Faber KN, Alizadeh BZ, et al. Prevalence and determinants of non-alcoholic fatty liver disease in lifelines: a large Dutch population cohort. PLoS One 2017;12:e0171502. ArticlePubMedPMC
  • 9. Lee JI, Kim MC, Moon BS, Song YS, Han EN, Lee HS, et al. The relationship between 10-year cardiovascular risk calculated using the pooled cohort equation and the severity of non-alcoholic fatty liver disease. Endocrinol Metab (Seoul) 2016;31:86–92. ArticlePubMedPMC
  • 10. Birkenfeld AL, Shulman GI. Nonalcoholic fatty liver disease, hepatic insulin resistance, and type 2 diabetes. Hepatology 2014;59:713–723. ArticlePubMedPMC
  • 11. Machado M, Cortez-Pinto H. Non-alcoholic fatty liver disease and insulin resistance. Eur J Gastroenterol Hepatol 2005;17:823–826. ArticlePubMed
  • 12. Lee JH, Rhee PL, Lee JK, Lee KT, Kim JJ, Koh KC, et al. Role of hyperinsulinemia and glucose intolerance in the pathogenesis of nonalcoholic fatty liver in patients with normal body weight. Korean J Intern Med 1998;13:12–14. ArticlePubMedPDF
  • 13. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985;28:412–419. ArticlePubMedPDF
  • 14. Brunt EM, Kleiner DE, Wilson LA, Belt P, Neuschwander-Tetri BA. NASH Clinical Research Network (CRN). Nonalcoholic fatty liver disease (NAFLD) activity score and the histopathologic diagnosis in NAFLD: distinct clinicopathologic meanings. Hepatology 2011;53:810–820. ArticlePubMedPMC
  • 15. Pais R, Charlotte F, Fedchuk L, Bedossa P, Lebray P, Poynard T, et al. A systematic review of follow-up biopsies reveals disease progression in patients with non-alcoholic fatty liver. J Hepatol 2013;59:550–556. ArticlePubMed
  • 16. European Association for the Study of the Liver (EASL). European Association for the Study of Diabetes (EASD). European Association for the Study of Obesity (EASO). EASL-EASD-EASO clinical practice guidelines for the management of non-alcoholic fatty liver disease. J Hepatol 2016;64:1388–1402. ArticlePubMed
  • 17. Isokuortti E, Zhou Y, Peltonen M, Bugianesi E, Clement K, Bonnefont-Rousselot D, et al. Use of HOMA-IR to diagnose non-alcoholic fatty liver disease: a population-based and inter-laboratory study. Diabetologia 2017;60:1873–1882. ArticlePubMedPDF
  • 18. Salgado AL, Carvalho Ld, Oliveira AC, Santos VN, Vieira JG, Parise ER. Insulin resistance index (HOMA-IR) in the differentiation of patients with non-alcoholic fatty liver disease and healthy individuals. Arq Gastroenterol 2010;47:165–169. ArticlePubMedPDF
  • 19. Simental-Mendia LE, Rodriguez-Moran M, Guerrero-Romero F. The product of fasting glucose and triglycerides as surrogate for identifying insulin resistance in apparently healthy subjects. Metab Syndr Relat Disord 2008;6:299–304. ArticlePubMed
  • 20. Guerrero-Romero F, Simental-Mendia LE, Gonzalez-Ortiz M, Martinez-Abundis E, Ramos-Zavala MG, Hernandez-Gonzalez SO, et al. The product of triglycerides and glucose, a simple measure of insulin sensitivity. Comparison with the euglycemic-hyperinsulinemic clamp. J Clin Endocrinol Metab 2010;95:3347–3351. ArticlePubMedPDF
  • 21. Vasques AC, Novaes FS, de Oliveira Mda S, Souza JR, Yamanaka A, Pareja JC, et al. TyG index performs better than HOMA in a Brazilian population: a hyperglycemic clamp validated study. Diabetes Res Clin Pract 2011;93:e98–e100. ArticlePubMed
  • 22. Simental-Mendia LE, Simental-Mendia E, Rodriguez-Hernandez H, Rodriguez-Moran M, Guerrero-Romero F. The product of triglycerides and glucose as biomarker for screening simple steatosis and NASH in asymptomatic women. Ann Hepatol 2016;15:715–720.PubMed
  • 23. Zhang S, Du T, Zhang J, Lu H, Lin X, Xie J, et al. The triglyceride and glucose index (TyG) is an effective biomarker to identify nonalcoholic fatty liver disease. Lipids Health Dis 2017;16:15ArticlePubMedPMCPDF
  • 24. Zhang S, Du T, Li M, Jia J, Lu H, Lin X, et al. Triglyceride glucose-body mass index is effective in identifying nonalcoholic fatty liver disease in nonobese subjects. Medicine (Baltimore) 2017;96:e7041. ArticlePubMedPMC
  • 25. Saadeh S, Younossi ZM, Remer EM, Gramlich T, Ong JP, Hurley M, et al. The utility of radiological imaging in nonalcoholic fatty liver disease. Gastroenterology 2002;123:745–750. ArticlePubMed
  • 26. Byrne CD, Targher G. NAFLD: a multisystem disease. J Hepatol 2015;62:S47–S64. ArticlePubMed
  • 27. Abate N, Chandalia M. Risk of obesity-related cardiometabolic complications in special populations: a crisis in Asians. Gastroenterology 2017;152:1647–1655. ArticlePubMed
  • 28. Kim D, Kim WR. Nonobese fatty liver disease. Clin Gastroenterol Hepatol 2017;15:474–485. ArticlePubMed
  • 29. Zheng R, Du Z, Wang M, Mao Y, Mao W. A longitudinal epidemiological study on the triglyceride and glucose index and the incident nonalcoholic fatty liver disease. Lipids Health Dis 2018;17:262ArticlePubMedPMCPDF
  • 30. Guerrero-Romero F, Villalobos-Molina R, Jimenez-Flores JR, Simental-Mendia LE, Mendez-Cruz R, Murguia-Romero M, et al. Fasting triglycerides and glucose index as a diagnostic test for insulin resistance in young adults. Arch Med Res 2016;47:382–387. ArticlePubMed
  • 31. Du T, Yuan G, Zhang M, Zhou X, Sun X, Yu X. Clinical usefulness of lipid ratios, visceral adiposity indicators, and the triglycerides and glucose index as risk markers of insulin resistance. Cardiovasc Diabetol 2014;13:146ArticlePubMedPMCPDF
  • 32. Taniguchi A, Fukushima M, Sakai M, Kataoka K, Nagata I, Doi K, et al. The role of the body mass index and triglyceride levels in identifying insulin-sensitive and insulin-resistant variants in Japanese non-insulin-dependent diabetic patients. Metabolism 2000;49:1001–1005. ArticlePubMed
  • 33. Taniguchi A, Nakai Y, Sakai M, Yoshii S, Hamanaka D, Hatae Y, et al. Relationship of regional adiposity to insulin resistance and serum triglyceride levels in nonobese Japanese type 2 diabetic patients. Metabolism 2002;51:544–548. ArticlePubMed
  • 34. Bergmann K, Sypniewska G. Diabetes as a complication of adipose tissue dysfunction. Is there a role for potential new biomarkers? Clin Chem Lab Med 2013;51:177–185. ArticlePubMedPDF
  • 35. Lee SH, Kwon HS, Park YM, Ha HS, Jeong SH, Yang HK, et al. Predicting the development of diabetes using the product of triglycerides and glucose: the Chungju Metabolic Disease Cohort (CMC) study. PLoS One 2014;9:e90430. ArticlePubMedPMC
  • 36. Irace C, Carallo C, Scavelli FB, De Franceschi MS, Esposito T, Tripolino C, et al. Markers of insulin resistance and carotid atherosclerosis: a comparison of the homeostasis model assessment and triglyceride glucose index. Int J Clin Pract 2013;67:665–672. ArticlePubMed
  • 37. Kim MK, Ahn CW, Kang S, Nam JS, Kim KR, Park JS. Relationship between the triglyceride glucose index and coronary artery calcification in Korean adults. Cardiovasc Diabetol 2017;16:108ArticlePubMedPMCPDF
  • 38. Tripathy D, Almgren P, Tuomi T, Groop L. Contribution of insulin-stimulated glucose uptake and basal hepatic insulin sensitivity to surrogate measures of insulin sensitivity. Diabetes Care 2004;27:2204–2210. ArticlePubMed
  • 39. Bonora E, Targher G, Alberiche M, Bonadonna RC, Saggiani F, Zenere MB, et al. Homeostasis model assessment closely mirrors the glucose clamp technique in the assessment of insulin sensitivity: studies in subjects with various degrees of glucose tolerance and insulin sensitivity. Diabetes Care 2000;23:57–63. ArticlePubMed
  • 40. Loria P, Adinolfi LE, Bellentani S, Bugianesi E, Grieco A, Fargion S, et al. Practice guidelines for the diagnosis and management of nonalcoholic fatty liver disease. A decalogue from the Italian Association for the Study of the Liver (AISF) Expert Committee. Dig Liver Dis 2010;42:272–282. ArticlePubMed
Fig. 1

The prevalence and severity of nonalcoholic fatty liver disease (NAFLD) based on the (A) triglyceride glucose (TyG) index and (B) homeostasis model assessment of insulin resistance (HOMA-IR). Ultrasonography was used for diagnosing the severity of fatty liver disease (all P<0.01).

enm-34-179-g001.jpg
Table 1

Clinical Characteristics of the Study Subjects Based on NAFLD Status

enm-34-179-i001.jpg

Values are expressed as mean±SD, median (interquartile range), or number (%).

NAFLD, nonalcoholic fatty liver disease; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG, fasting plasma glucose; TC, total cholesterol; TG, triglyceride; HDL-C, high density lipoprotein cholesterol; LDL-C, low density lipoprotein cholesterol; AST, aspartate aminotransferase; ALT, alanine aminotransferase; TyG, triglyceride glucose; HOMA-IR, homeostasis model assessment of assessment of insulin resistance.

aIntergroup comparison using Student's t test, all P<0.01.

Table 2

Clinical Characteristics of the Study Participants Based on TyG Index

enm-34-179-i002.jpg

Values are expressed as mean±SD, median (interquartile range), or number (%).

TyG, triglyceride glucose; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG, fasting plasma glucose; TC, total cholesterol; TG, triglyceride; HDL-C, high density lipoprotein cholesterol; LDL-C, low density lipoprotein cholesterol; AST, aspartate aminotransferase; ALT, alanine aminotransferase; HOMA-IR, homeostasis model assessment of insulin resistance.

aIntergroup comparisons using one-way analysis of variance, all P<0.01; bP<0.05 between Q1 and Q2; cP<0.05 between Q1 and Q3; dP<0.05 between Q1 and Q4; eP<0.05 between Q2 and Q4; fP<0.05 between Q3 and Q4; gP<0.05 between Q2 and Q3.

Table 3

Nonalcoholic Fatty Liver Disease OR and 95% CI Based on the TyG Index and HOMA-IR Quartiles

enm-34-179-i003.jpg

OR, odds ratio; CI, confidence interval; TyG, triglyceride glucose; HOMA-IR, homeostasis model assessment of insulin resistance.

aAll P for trends <0.01 in multivariate logistic regression analysis; bAdjusted for age, sex, body mass index, systolic blood pressure, total cholesterol, high density lipoprotein cholesterol, alanine aminotransferase, presence of hypertension.

Figure & Data

References

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      Frontiers in Endocrinology.2024;[Epub]     CrossRef
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      Guoliang Qin, Zhuang Sun, Yuxiang Jin, Xiangguo Ren, Zhaocun Zhang, Shuo Wang, Guanwen Zhou, Kun Huang, Haifeng Zhao, Xianzhou Jiang
      Frontiers in Endocrinology.2024;[Epub]     CrossRef
    • The Triglyceride-Glucose Index is Associated with Vitamin D Status in Metabolic-Associated Fatty Liver Disease
      Zhiping Liu, Wensha Zhang, Zhiwei Zhao, Wenhao Li, Jinhua Zhang
      Diabetes, Metabolic Syndrome and Obesity.2023; Volume 16: 2651.     CrossRef
    • The triglyceride and glucose index and risk of nonalcoholic fatty liver disease: A dose–response meta-analysis
      Qin Ling, Jiawei Chen, Xiao Liu, Yi Xu, Jianyong Ma, Peng Yu, Kai Zheng, Fuwei Liu, Jun Luo
      Frontiers in Endocrinology.2023;[Epub]     CrossRef
    • Triglyceride–Glucose Index as a Potential Indicator of Sarcopenic Obesity in Older People
      Bokun Kim, Gwonmin Kim, Yongkook Lee, Keisuke Taniguchi, Tomonori Isobe, Sechang Oh
      Nutrients.2023; 15(3): 555.     CrossRef
    • Association of insulin resistance with bone mineral density in a nationwide health check-up population in China
      Ming Zhuo, Ze Chen, Mao-Lin Zhong, Fang Lei, Juan-Juan Qin, Shuhua Liu, Ye-Mao Liu, Tao Sun, Xiao-Jing Zhang, Lihua Zhu, Jingjing Cai, Jun-Ming Ye, Erping Yang
      Bone.2023; 170: 116703.     CrossRef
    • Metabolic Dysfunction-Associated Fatty Liver Disease and Mortality: A Population-Based Cohort Study
      Kyung-Soo Kim, Sangmo Hong, Hong-Yup Ahn, Cheol-Young Park
      Diabetes & Metabolism Journal.2023; 47(2): 220.     CrossRef
    • Comparison of the prognostic value of a comprehensive set of predictors in identifying risk of metabolic-associated fatty liver disease among employed adults
      Ze Yang, Bin Yu, Zihang Wang, Zhitao Li, Bo Yang, Honglian Zeng, Shujuan Yang
      BMC Public Health.2023;[Epub]     CrossRef
    • The triglyceride glucose index and CDKAL1 gene rs10946398 SNP are associated with NAFLD in Chinese adults
      Jun ZHU, Dujuan XU, Ruihua YANG, Min LIU, Ying LIU
      Minerva Endocrinology.2023;[Epub]     CrossRef
    • PNPLA3 rs738409 risk genotype decouples TyG index from HOMA2-IR and intrahepatic lipid content
      Ákos Nádasdi, Viktor Gál, Tamás Masszi, Anikó Somogyi, Gábor Firneisz
      Cardiovascular Diabetology.2023;[Epub]     CrossRef
    • Baseline level and change trajectory of the triglyceride-glucose index in relation to the development of NAFLD: a large population-based cohort study
      Yaqin Wang, Jiangang Wang, Lei Liu, Pingting Yang, Shuwen Deng, Xuelian Liu, Linlin Zhao, Changfa Wang, Ying Li
      Frontiers in Endocrinology.2023;[Epub]     CrossRef
    • Assessing temporal differences in the predictive power of baseline TyG-related parameters for future diabetes: an analysis using time-dependent receiver operating characteristics
      Maobin Kuang, Ruijuan Yang, Xin Huang, Chao Wang, Guotai Sheng, Guobo Xie, Yang Zou
      Journal of Translational Medicine.2023;[Epub]     CrossRef
    • Metabolic Score for Insulin Resistance (METS-IR) Predicts Adverse Cardiovascular Events in Patients with Type 2 Diabetes and Ischemic Cardiomyopathy
      Xuehe Zhang, Fen Liu, Wenling Li, Jixin Zhang, Tong Zhang, Xiaolin Yu, Junyi Luo, Qian Zhao, Jinyu Zhang, Binbin Fang, Yining Yang, Xiaomei Li
      Diabetes, Metabolic Syndrome and Obesity.2023; Volume 16: 1283.     CrossRef
    • Prevalence and Risk Factors of Metabolic-Associated Fatty Liver Disease Among Hospital Staff
      Daya Zhang, Lijun Zhang, Shiju Chen, Runxiang Chen, Xiaodong Zhang, Feihu Bai
      Diabetes, Metabolic Syndrome and Obesity.2023; Volume 16: 1221.     CrossRef
    • The metabolic score of insulin resistance is positively correlated with bone mineral density in postmenopausal patients with type 2 diabetes mellitus
      Peng Gu, Bin Pu, Qiao Xin, Dan Yue, LieLiang Luo, JiaSheng Tao, HaiShan Li, Ming Chen, MingHua Hu, XiaoRong Hu, XiaoHui Zheng, ZhanPeng Zeng
      Scientific Reports.2023;[Epub]     CrossRef
    • Prevalence estimates of the insulin resistance and associated prevalence of heart failure among United Status adults
      Xiaozhong Li, Jihong Wang, Liyan Niu, Ziqi Tan, Jianyong Ma, Ling He, Peng Yu, Xiao Liu, Juxiang Li
      BMC Cardiovascular Disorders.2023;[Epub]     CrossRef
    • From NAFLD to HCC: Advances in noninvasive diagnosis
      Qinchen Xu, Maoxiao Feng, Yidan Ren, Xiaoyan Liu, Huiru Gao, Zigan Li, Xin Su, Qin Wang, Yunshan Wang
      Biomedicine & Pharmacotherapy.2023; 165: 115028.     CrossRef
    • Usefulness of two-dimensional shear wave elastography in the assessment of non-alcoholic fatty liver disease in children and adolescents
      Jong Seo Yoon, Kyoung Ja Lim, Il Tae Hwang
      Scientific Reports.2023;[Epub]     CrossRef
    • A non-linear relationship between triglyceride glucose waist circumference and nonalcoholic fatty liver disease in a Japanese population: a secondary analysis
      Xiaojie He, Xinyue Huang, Yafang Qian, Ting Sun
      Frontiers in Endocrinology.2023;[Epub]     CrossRef
    • Neck Circumference as a Predictor of Insulin Resistance in People with Non-alcoholic Fatty Liver Disease
      Da-Hye Son, Jee Hye Han, Jun-Hyuk Lee
      Journal of Obesity & Metabolic Syndrome.2023; 32(3): 214.     CrossRef
    • Prediction and Validation of Metabolic Dysfunction-Associated Fatty Liver Disease Using Insulin Resistance-Related Indices in the Japanese Population
      Kengo Moriyama, Nagamu Inoue, Jin Imai, Yumi Masuda, Chizumi Yamada, Noriaki Kishimoto, Shinji Takashimizu, Akira Kubo, Yasuhiro Nishizaki
      Metabolic Syndrome and Related Disorders.2023; 21(9): 489.     CrossRef
    • Application value of triglyceride-glucose index and triglyceride-glucose body mass index in evaluating the degree of hepatic steatosis in non-alcoholic fatty liver disease
      Mengyuan Wang, Mingxing Chang, Peipu Shen, Wei Wei, Huayao Li, Guifang Shen
      Lipids in Health and Disease.2023;[Epub]     CrossRef
    • Homeostatic Model Assessment of Insulin Resistance as a Potential Screening Test for Non-Alcoholic Fatty Liver Disease in Korean Adults without Diabetes
      Hyejung Lee, Jae-Ho Lee
      Korean Journal of Family Practice.2023; 13(4): 233.     CrossRef
    • Comparison of the Triglyceride Glucose Index and Modified Triglyceride Glucose Indices to Predict Nonalcoholic Fatty Liver Disease in Youths
      Kyungchul Song, Goeun Park, Hye Sun Lee, Myeongseob Lee, Hae In Lee, Han Saem Choi, Junghwan Suh, Ahreum Kwon, Ho-Seong Kim, Hyun Wook Chae
      The Journal of Pediatrics.2022; 242: 79.     CrossRef
    • The triglyceride-glucose index as a clinical useful marker for metabolic associated fatty liver disease (MAFLD): a population-based study among Iranian adults
      Ehsaneh Taheri, Mohammad Amin Pourhoseingholi, Alireza Moslem, Amir Hossein Hassani, Alireza Mousavi Jarrahi, Hamid Asadzadeh Aghdaei, Mohammad Reza Zali, Behzad Hatami
      Journal of Diabetes & Metabolic Disorders.2022; 21(1): 97.     CrossRef
    • Comparison of triglyceride-glucose index and HOMA-IR for predicting prevalence and incidence of metabolic syndrome
      Da-Hye Son, Hye Sun Lee, Yong-Jae Lee, Jun-Hyuk Lee, Jee-Hye Han
      Nutrition, Metabolism and Cardiovascular Diseases.2022; 32(3): 596.     CrossRef
    • Triglycerides/Glucose Index Is Associated with Sperm Parameters and Sperm DNA Fragmentation in Primary Infertile Men: A Cross-Sectional Study
      Federico Belladelli, Luca Boeri, Edoardo Pozzi, Giuseppe Fallara, Christian Corsini, Luigi Candela, Walter Cazzaniga, Daniele Cignoli, Luca Pagliardini, Alessia D’Arma, Paolo Capogrosso, Eugenio Ventimiglia, Francesco Montorsi, Andrea Salonia
      Metabolites.2022; 12(2): 143.     CrossRef
    • Association of triglyceride-glucose index and stroke recurrence among nondiabetic patients with acute ischemic stroke
      Xiaomeng Yang, Guangyao Wang, Jing Jing, Anxin Wang, Xiaoli Zhang, Qian Jia, Xia Meng, Xingquan Zhao, Liping Liu, Hao Li, Yongjun Wang, Yilong Wang
      BMC Neurology.2022;[Epub]     CrossRef
    • The triglycerides and glucose (TyG) index: A new marker associated with nonalcoholic steatohepatitis (NASH) in obese patients
      Benjamin Rivière, Audrey Jaussent, Valérie Macioce, Stéphanie Faure, Nicolas Builles, Patrick Lefebvre, Philippe Géraud, Marie-Christine Picot, Sandra Rebuffat, Eric Renard, Valérie Paradis, Marie-Dominique Servais, Nathalie de Preville, David Nocca, Anne
      Diabetes & Metabolism.2022; 48(4): 101345.     CrossRef
    • Association of triglyceride-glucose with cardiac hemodynamics in type 2 diabetes
      Chenxi Wang, Zhicong Zhao, Xia Deng, Zhensheng Cai, Tian Gu, Lian Li, Chang Guo, Dong Wang, Ling Yang, Li Zhao, Guoyue Yuan
      Diabetes and Vascular Disease Research.2022; 19(2): 147916412210833.     CrossRef
    • Comparison of the Modified TyG Indices and Other Parameters to Predict Non-Alcoholic Fatty Liver Disease in Youth
      Kyungchul Song, Hae Won Lee, Han Saem Choi, Goeun Park, Hye Sun Lee, Su Jin Kim, Myeongseob Lee, Junghwan Suh, Ahreum Kwon, Ho-Seong Kim, Hyun Wook Chae
      Biology.2022; 11(5): 685.     CrossRef
    • Triglyceride-Glucose Index for Early Prediction of Nonalcoholic Fatty Liver Disease: A Meta-Analysis of 121,975 Individuals
      Azizullah Beran, Hazem Ayesh, Mohammed Mhanna, Waseem Wahood, Sami Ghazaleh, Ziad Abuhelwa, Wasef Sayeh, Nameer Aladamat, Rami Musallam, Reem Matar, Saif-Eddin Malhas, Ragheb Assaly
      Journal of Clinical Medicine.2022; 11(9): 2666.     CrossRef
    • Triglyceride and Glucose Index as a Screening Tool for Nonalcoholic Liver Disease in Patients with Metabolic Syndrome
      Anca Maria Amzolini, Mircea-Cătălin Forțofoiu, Anca Barău Alhija, Ionela Mihaela Vladu, Diana Clenciu, Adina Mitrea, Maria Forțofoiu, Daniela Matei, Magdalena Diaconu, Marinela Sinziana Tudor, Elena Simona Micu
      Journal of Clinical Medicine.2022; 11(11): 3043.     CrossRef
    • Triglyceride glucose index is superior biomarker for predicting type 2 diabetes mellitus in children and adolescents
      Jong Seo Yoon, Hye Jin Lee, Hwal Rim Jeong, Young Suk Shim, Min Jae Kang, Il Tae Hwang
      Endocrine Journal.2022; 69(5): 559.     CrossRef
    • Triglyceride and glucose index is a simple and easy‐to‐calculate marker associated with nonalcoholic fatty liver disease
      Kyung‐Soo Kim, Sangmo Hong, Hong‐Yup Ahn, Cheol‐Young Park
      Obesity.2022; 30(6): 1279.     CrossRef
    • Evaluating Triglyceride and Glucose Index as a Simple and Easy-to-Calculate Marker for All-Cause and Cardiovascular Mortality
      Kyung-Soo Kim, Sangmo Hong, You-Cheol Hwang, Hong-Yup Ahn, Cheol-Young Park
      Journal of General Internal Medicine.2022; 37(16): 4153.     CrossRef
    • The Role of Insulin Resistance in Fueling NAFLD Pathogenesis: From Molecular Mechanisms to Clinical Implications
      Rossella Palma, Annamaria Pronio, Mario Romeo, Flavia Scognamiglio, Lorenzo Ventriglia, Vittorio Maria Ormando, Antonietta Lamazza, Stefano Pontone, Alessandro Federico, Marcello Dallio
      Journal of Clinical Medicine.2022; 11(13): 3649.     CrossRef
    • Comparison of the diagnostic value between triglyceride-glucose index and triglyceride to high-density lipoprotein cholesterol ratio in metabolic-associated fatty liver disease patients: a retrospective cross-sectional study
      Zhi Liu, He He, Yuzhao Dai, Lidan Yang, Shenling Liao, Zhenmei An, Shuangqing Li
      Lipids in Health and Disease.2022;[Epub]     CrossRef
    • Insulin Resistance Markers to Detect Nonalcoholic Fatty Liver Disease in a Male Hispanic Population
      Maritza Pérez-Mayorga, Jose P. Lopez-Lopez, Maria A. Chacon-Manosalva, Maria G Castillo, Johanna Otero, Daniel Martinez-Bello, Diego Gomez-Arbelaez, Daniel D. Cohen, Patricio Lopez-Jaramillo, Federico Ravaioli
      Canadian Journal of Gastroenterology and Hepatology.2022; 2022: 1.     CrossRef
    • Risk factors and prediction model for nonalcoholic fatty liver disease in northwest China
      Danting Li, Meiyu Zhang, Shengli Wu, Huiwen Tan, Nong Li
      Scientific Reports.2022;[Epub]     CrossRef
    • The triglyceride glucose index is associated with the cerebral small vessel disease in a memory clinic population
      Jiayu Zhang, Ming Hu, Yanqiu Jia, Shicong Zhao, Peiyuan Lv, Mingyue Fan, Yuanyuan Shi, Wei Jin
      Journal of Clinical Neuroscience.2022; 104: 126.     CrossRef
    • Integrating experimental model, LC-MS/MS chemical analysis, and systems biology approach to investigate the possible antidiabetic effect and mechanisms of Matricaria aurea (Golden Chamomile) in type 2 diabetes mellitus
      Yassin Ismail, Dina M. Fahmy, Maivel H. Ghattas, Mai M. Ahmed, Walaa Zehry, Samy M. Saleh, Dina M. Abo-elmatty
      Frontiers in Pharmacology.2022;[Epub]     CrossRef
    • Laboratory data clustering in defining population cohorts: Case study on metabolic indicators
      Ivan Pavicevic, Goran Miljus, Olgica Nedic
      Journal of the Serbian Chemical Society.2022; 87(9): 1025.     CrossRef
    • Association of triglyceride glucose-body mass index with non-small cell lung cancer risk: A case-control study on Chinese adults
      Feifei Wang, Ting He, Guoliang Wang, Tuo Han, Zhongqiang Yao
      Frontiers in Nutrition.2022;[Epub]     CrossRef
    • Association Between Triglyceride-Glucose Index and Risk of Metabolic Dysfunction-Associated Fatty Liver Disease: A Cohort Study
      Ru Zhang, Qing Guan, Mengting Zhang, Yajie Ding, Zongzhe Tang, Hongliang Wang, Wei Zhang, Yue Chen, Rong Jiang, Yan Cui, Jie Wang
      Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy.2022; Volume 15: 3167.     CrossRef
    • Association between the triglyceride-glucose (TyG) index and increased blood pressure in normotensive subjects: a population-based study
      Dong-Hwa Lee, Jong Eun Park, So Young Kim, Hyun Jeong Jeon, Jong-Hyock Park
      Diabetology & Metabolic Syndrome.2022;[Epub]     CrossRef
    • Fructose consumption correlates with triglyceride-glucose index and glycemic status in healthy adults
      Eda Keskin, Havvanur Yoldas Ilktac
      Clinical Nutrition ESPEN.2022; 52: 184.     CrossRef
    • Associations Between Type 2 Diabetes Subtypes and Complications: Analysis of the Malaysia National Diabetes Registry
      Rasa Kazlauskaite, Nathan Ellermeier, Carrie Ngongo, Arunah Chandran, Pankaja Desai, Ethan Ritz, Rachel Nugent, Feisul Idzwan Mustapha
      SSRN Electronic Journal .2022;[Epub]     CrossRef
    • Independent and combined effects of triglyceride-glucose index on prehypertension risk: a cross-sectional survey in China
      Hong Xie, Jian Song, Liangliang Sun, Xinxin Xie, Yehuan Sun
      Journal of Human Hypertension.2021; 35(3): 207.     CrossRef
    • Association of Triglyceride-Glucose Index with Bone Mineral Density in Non-diabetic Koreans: KNHANES 2008–2011
      Jee Hee Yoon, A Ram Hong, Wonsuk Choi, Ji Yong Park, Hee Kyung Kim, Ho-Cheol Kang
      Calcified Tissue International.2021; 108(2): 176.     CrossRef
    • Triglyceride‐Glucose Index (TyG) is associated with erectile dysfunction: A cross‐sectional study
      Mehmet Yilmaz, Mustafa Karaaslan, Senol Tonyali, Mecit Celik, Tuncay Toprak, Oner Odabas
      Andrology.2021; 9(1): 238.     CrossRef
    • Triglyceride Glucose Index and Related Parameters (Triglyceride Glucose-Body Mass Index and Triglyceride Glucose-Waist Circumference) Identify Nonalcoholic Fatty Liver and Liver Fibrosis in Individuals with Overweight/Obesity
      Mohammad E. Khamseh, Mojtaba Malek, Rowshanak Abbasi, Hoda Taheri, Maryam Lahouti, Fariba Alaei-Shahmiri
      Metabolic Syndrome and Related Disorders.2021; 19(3): 167.     CrossRef
    • Triglyceride and glucose index and the risk of gestational diabetes mellitus: A nationwide population-based cohort study
      Jung A Kim, Jinsil Kim, Eun Roh, So-hyeon Hong, You-Bin Lee, Sei Hyun Baik, Kyung Mook Choi, Eunjin Noh, Soon Young Hwang, Geum Joon Cho, Hye Jin Yoo
      Diabetes Research and Clinical Practice.2021; 171: 108533.     CrossRef
    • Association of the triglyceride and glucose index with low muscle mass: KNHANES 2008–2011
      Jung A. Kim, Soon Young Hwang, Ji Hee Yu, Eun Roh, So-hyeon Hong, You-Bin Lee, Nam Hoon Kim, Hye Jin Yoo, Ji A. Seo, Nan Hee Kim, Sin Gon Kim, Sei Hyun Baik, Kyung Mook Choi
      Scientific Reports.2021;[Epub]     CrossRef
    • The triglycerides and glucose index is strongly associated with hepatic steatosis in children with overweight or obesity
      Luis E. Simental-Mendía, César Javier Ortega-Pacheco, Elvira García-Guerrero, María Alejandra Sicsik-Aragón, Fernando Guerrero-Romero, Gerardo Martínez-Aguilar
      European Journal of Pediatrics.2021; 180(6): 1755.     CrossRef
    • Triglyceride-glucose index and the risk of stroke and its subtypes in the general population: an 11-year follow-up
      Anxin Wang, Guangyao Wang, Qian Liu, Yingting Zuo, Shuohua Chen, Boni Tao, Xue Tian, Penglian Wang, Xia Meng, Shouling Wu, Yongjun Wang, Yilong Wang
      Cardiovascular Diabetology.2021;[Epub]     CrossRef
    • Association Between Triglyceride Glucose Index and Non-Small Cell Lung Cancer Risk in Chinese Population
      Xin Yan, Yujuan Gao, Jingzhi Tong, Mi Tian, Jinghong Dai, Yi Zhuang
      Frontiers in Oncology.2021;[Epub]     CrossRef
    • Triglyceride Glucose Index Is Associated With Arterial Stiffness and 10-Year Cardiovascular Disease Risk in a Chinese Population
      Wen Guo, Wenfang Zhu, Juan Wu, Xiaona Li, Jing Lu, Pei Qin, Cheng Zhu, Nianzhen Xu, Qun Zhang
      Frontiers in Cardiovascular Medicine.2021;[Epub]     CrossRef
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      Zongyi Hou, Yuesong Pan, Yindong Yang, Xiaofan Yang, Xianglong Xiang, Yilong Wang, Zixiao Li, Xingquan Zhao, Hao Li, Xia Meng, Yongjun Wang
      Frontiers in Neurology.2021;[Epub]     CrossRef
    • Role of Endocrine-Disrupting Chemicals in the Pathogenesis of Non-Alcoholic Fatty Liver Disease: A Comprehensive Review
      Raquel Cano, José Pérez, Lissé Dávila, Ángel Ortega, Yosselin Gómez, Nereida Valero-Cedeño, Heliana Parra, Alexander Manzano, Teresa Véliz Castro, María Albornoz, Gabriel Cano, Joselyn Rojas-Quintero, Maricarmen Chacín, Valmore Bermúdez
      International Journal of Molecular Sciences.2021; 22(9): 4807.     CrossRef
    • Triglyceride glucose (TyG) index and the progression of liver fibrosis: A cross-sectional study
      Helda Tutunchi, Fatemeh Naeini, Majid Mobasseri, Alireza Ostadrahimi
      Clinical Nutrition ESPEN.2021; 44: 483.     CrossRef
    • Newly proposed insulin resistance indexes called TyG-NC and TyG-NHtR show efficacy in diagnosing the metabolic syndrome
      M. Mirr, D. Skrypnik, P. Bogdański, M. Owecki
      Journal of Endocrinological Investigation.2021; 44(12): 2831.     CrossRef
    • Association Between the Triglyceride–Glucose Index and Outcomes of Nonalcoholic Fatty Liver Disease: A Large-Scale Health Management Cohort Study
      Jing Liu, Liying Guan, Meng Zhao, Qihang Li, An Song, Ling Gao, Haiyan Lin, Jiajun Zhao
      Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy.2021; Volume 14: 2829.     CrossRef
    • Association between triglyceride-glucose index and thyroid function in euthyroid adults: The Korea National Health and Nutritional Examination Survey 2015
      Wonsuk Choi, Ji Yong Park, A. Ram Hong, Jee Hee Yoon, Hee Kyung Kim, Ho-Cheol Kang, Sun Young Lee
      PLOS ONE.2021; 16(7): e0254630.     CrossRef
    • Triglyceride glucose-waist to height ratio: a novel and effective marker for identifying hepatic steatosis in individuals with type 2 diabetes mellitus
      Mojtaba Malek, Mohammad E. Khamseh, Haleh Chehrehgosha, Sohrab Nobarani, Fariba Alaei-Shahmiri
      Endocrine.2021; 74(3): 538.     CrossRef
    • The association between triglyceride-glucose index, cardio-cerebrovascular diseases, and death in Korean adults: A retrospective study based on the NHIS-HEALS cohort
      Joungyoun Kim, Sang-Jun Shin, Hee-Taik Kang, Claudio Passino
      PLOS ONE.2021; 16(11): e0259212.     CrossRef
    • A population-based study of TyG index distribution and its relationship to cardiometabolic risk factors in children and adolescents
      Jong Seo Yoon, Young Suk Shim, Hae Sang Lee, Il Tae Hwang, Jin Soon Hwang
      Scientific Reports.2021;[Epub]     CrossRef
    • Comparison of several blood lipid-related indexes in the screening of non-alcoholic fatty liver disease in women: a cross-sectional study in the Pearl River Delta region of southern China
      Jingrui Wang, Zhenzhen Su, Yijin Feng, Ruihan Xi, Jiamin Liu, Peixi Wang
      BMC Gastroenterology.2021;[Epub]     CrossRef
    • The value of the triglyceride-glucose index in the diagnosis of insulin resistance in early forms of non-alcoholic fatty liver disease
      A. A. Shipovskaya, N. A. Larina, I. V. Kurbatova, O. P. Dudanova
      Experimental and Clinical Gastroenterology.2021; (10): 43.     CrossRef
    • Triglyceride Glucose-Waist Circumference Is Superior to the Homeostasis Model Assessment of Insulin Resistance in Identifying Nonalcoholic Fatty Liver Disease in Healthy Subjects
      Hwi Seung Kim, Yun Kyung Cho, Eun Hee Kim, Min Jung Lee, Chang Hee Jung, Joong-Yeol Park, Hong-Kyu Kim, Woo Je Lee
      Journal of Clinical Medicine.2021; 11(1): 41.     CrossRef
    • Homeostasis model assessment of insulin resistance and lobular inflammation in nondiabetic patients with nonalcoholic fatty liver disease: methodological considerations
      Denis Monneret, Dominique Bonnefont-Rousselot
      European Journal of Gastroenterology & Hepatology.2020; 32(4): 542.     CrossRef
    • Helicobacter pylori infection may increase the severity of nonalcoholic fatty liver disease via promoting liver function damage, glycometabolism, lipid metabolism, inflammatory reaction and metabolic syndrome
      Chen Chen, Caiyun Zhang, Xuelin Wang, Feijuan Zhang, Ze Zhang, Pengchai Ma, Shuzhi Feng
      European Journal of Gastroenterology & Hepatology.2020; 32(7): 857.     CrossRef
    • Beneficial effect of anti-diabetic drugs for nonalcoholic fatty liver disease
      Kyung-Soo Kim, Byung-Wan Lee
      Clinical and Molecular Hepatology.2020; 26(4): 430.     CrossRef
    • The triglyceride-glucose index is associated with the severity of hepatic steatosis and the presence of liver fibrosis in non-alcoholic fatty liver disease: a cross-sectional study in Chinese adults
      Wen Guo, Jing Lu, Pei Qin, Xiaona Li, Wenfang Zhu, Juan Wu, Nianzhen Xu, Qun Zhang
      Lipids in Health and Disease.2020;[Epub]     CrossRef
    • Association between triglyceride glucose-body mass index and non-alcoholic fatty liver disease in the non-obese Chinese population with normal blood lipid levels: a secondary analysis based on a prospective cohort study
      Yaling Li, Rui Zheng, Jie Li, Shuyi Feng, Li Wang, Zhiming Huang
      Lipids in Health and Disease.2020;[Epub]     CrossRef

    Figure

    Endocrinol Metab : Endocrinology and Metabolism