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Triglyceride Glucose Index: As A Glycemic Control Indicator

Year 2025, Volume: 22 Issue: 2, 357 - 363, 27.06.2025
https://doi.org/10.35440/hutfd.1632098

Abstract

Background: Type-2 Diabetes Mellitus (T2DM) continues to be the most common endocrine disease today. Easily accessible, accurate and reproducible markers are needed in addition to the accepted markers to evaluate insulin resistance (IR) and glycemic control. Therefore, our study aimed to evaluate the use of triglyceride glucose index (TyGI) as an indicator for insulin resistance and glycemic control.
Materials and Methods: Triglyceride(TG), HbA1c, fasting blood glucose (FBG), and total insulin (TI) values of 953 samples, studied simultaneously in our Faculty of Medicine Hospital Laboratory between March 2023 and August 2023, were retrospectively evaluated. The patients were divided into two groups as good and/or poor glycemic control regarding their HbA1c, and the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) values. Receiver Operating Characteristic (ROC) analysis was performed to assess the ability of TyGI to discriminate between good and/or poor glycemic control for each of HOMA-IR and HbA1c. Statistical significance level was accepted as p<0.05. Multivariate logistic regression analysis was performed as well.
Results: A total of 953 patients; with the mean age of 40,83±16,78 participated in the study. According to gender, all parameters except age (p: 0,613) showed significant differences (p<0.001). There were significant differences for FBG, HbA1c, HOMA-IR, TI, TG and TyGI parameters according to cut-off values in all two study groups (p<0.001). TG showed high positive correlation with TyGI (r: 0.796, p<0.001) and moderate positive correlation with FBG (r: 0.616, p<0.001) for both study groups, but low positive correlation with the others. TyGI, had a high selectivity and specificity for HOMA-IR with ≥8,76 cut-off value (AUC:0,72, Se:65%, Sp:70% (p<0.001: 95% CI:0,69-0,75)). In ROC analysis, TyGI had the highest AUC value for HbA1c, and the lowest for HOMA-IR group. The risk of poor glycemic control for HOMA-IR in men is 2.247 times higher than in women. As age increases by one unit, the risk of poor glycemic cont-rol for HOMA-IR increases by 1.045 times.
Conclusions: TyGI was significantly raised in incident T2DM patients with poor glycemic control. TyGI can act as s simple and useful markers that have the strong predictive capability to identify insuline re-sistance and anticipate the development of incident T2DM.

Ethical Statement

We declare that our study was prepared with the approval of the Ethics Committee and in accordance with the Helsinki Declaration, fulfilling all ethical responsibilities.

Supporting Institution

There is no supporting institution

Thanks

We would like to thank our laboratory staff and all other contributors, especially our IT staff who contributed to providing us with the data for the study.

References

  • 1. Goyal R, Jialal I. Diabetes Mellitus Type 2.[Updated 2020 Nov 20]. StatPearls[Internet] Treasure Island (FL): StatPearls Pub-lishing. 2021.
  • 2. Hills AP, Arena R, Khunti K, Yajnik CS, Jayawardena R, Henry CJ, et al. Epidemiology and determinants of type 2 diabetes in south Asia. The lancet Diabetes & endocrinology. 2018;6(12):966-78.
  • 3. Zheng Y, Ley SH, Hu FB. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nature re-views endocrinology. 2018;14(2):88-98.
  • 4. Ergör G, Soysal A, Sözmen K, Ünal B, Uçku R, Kılıç B, et al. Balcova heart study: rationale and methodology of the Turkish cohort. International journal of public health. 2012;57:535-42.
  • 5. Süleymanlar G, Utaş C, Arinsoy T, Ateş K, Altun B, Altiparmak MR, et al. A population-based survey of Chronic REnal Disease In Turkey—the CREDIT study. Nephrology Dialysis Transplanta-tion. 2011;26(6):1862-71.
  • 6. Teo K, Chow CK, Vaz M, Rangarajan S, Yusuf S, Group PI-W. The Prospective Urban Rural Epidemiology (PURE) study: exami-ning the impact of societal influences on chronic noncommu-nicable diseases in low-, middle-, and high-income countries. American heart journal. 2009;158(1):1-7. e1.
  • 7. Ünal B, Sözmen K, Uçku R, Ergör G, Soysal A, Baydur H, et al. High prevalence of cardiovascular risk factors in a Western urban Turkish population: a community-based study. Anato-lian Journal of Cardiology/Anadolu Kardiyoloji Dergisi. 2013;13(1).
  • 8. Gillani AH, Aziz MM, Masood I, Saqib A, Yang C, Chang J, et al. Direct and indirect cost of diabetes care among patients with type 2 diabetes in private clinics: a multicenter study in Pun-jab, Pakistan. Expert review of pharmacoeconomics & outco-mes research. 2018;18(6):647-53.
  • 9. Babic N, Valjevac A, Zaciragic A, Avdagic N, Zukic S, Hasic S. The triglyceride/HDL ratio and triglyceride glucose index as predictors of glycemic control in patients with diabetes mel-litus type 2. Medical archives. 2019;73(3):163.
  • 10. Association AD. 6. Glycemic targets: standards of medical care in diabetes—2018. Diabetes care. 2018;41(Supplement_1):S55-S64.
  • 11. Badedi M, Solan Y, Darraj H, Sabai A, Mahfouz M, Alamodi S, et al. Factors associated with long‐term control of type 2 diabe-tes mellitus. Journal of diabetes research. 2016;2016(1):2109542.
  • 12. Patel MB, Sachora WM, Pandya AR, Kothari AD, Patel JK. Can Hba1c Act as A Surrogate Marker for Cardiovascular Risk? Na-tional Journal of Community Medicine. 2014;5(01):29-32.
  • 13. Dobiás̆ová M, Frohlich J. The plasma parameter log (TG/HDL-C) as an atherogenic index: correlation with lipoprotein particle size and esterification rate inapob-lipoprotein-depleted plas-ma (FERHDL). Clinical biochemistry. 2001;34(7):583-8.
  • 14. Buijsse B, Simmons RK, Griffin SJ, Schulze MB. Risk assessment tools for identifying individuals at risk of developing type 2 diabetes. Epidemiologic reviews. 2011;33(1):46-62.
  • 15. Du T, Yuan G, Zhang M, Zhou X, Sun X, Yu X. Clinical usefulness of lipid ratios, visceral adiposity indicators, and the triglyceri-des and glucose index as risk markers of insulin resistance. Cardiovascular diabetology. 2014;13:1-10.
  • 16. Caleyachetty R, Thomas GN, Toulis KA, Mohammed N, Gokhale KM, Balachandran K, et al. Metabolically healthy obese and incident cardiovascular disease events among 3.5 million men and women. Journal of the American College of Cardiology. 2017;70(12):1429-37.
  • 17. Alberti K. International diabetes federation task force on epidemiology and prevention; hational heart, lung, and blood institute; American heart association; world heart federation; international atherosclerosis society; international association for the study of obesity: harmonizing the metabolic syndro-me: a joint interim statement of the international diabetes federation task force on epidemiology and prevention; natio-nal heart, lung, and blood institute; American heart associa-tion; world heart federation; international atherosclerosis so-ciety; and international association for the study of obesity. Circulation. 2009;120:1640-5.
  • 18. Hong S, Han K, Park C-Y. The triglyceride glucose index is a simple and low-cost marker associated with atherosclerotic cardiovascular disease: a population-based study. BMC medi-cine. 2020;18:1-8.
  • 19. Guerrero-Romero F, Simental-Mendía LE, González-Ortiz M, Martínez-Abundis E, Ramos-Zavala MaG, Hernández-González SO, et al. The product of triglycerides and glucose, a simple measure of insulin sensitivity. Comparison with the euglyce-mic-hyperinsulinemic clamp. The Journal of Clinical Endocri-nology & Metabolism. 2010;95(7):3347-51.
  • 20. Vasques ACJ, Novaes FS, de Oliveira MdS, Souza JRM, Yamana-ka A, Pareja JC, et al. TyG index performs better than HOMA in a Brazilian population: a hyperglycemic clamp validated study. Diabetes research and clinical practice. 2011;93(3):e98-e100.
  • 21. Lee DY, Lee ES, Kim JH, Park SE, Park C-Y, Oh K-W, et al. Pre-dictive value of triglyceride glucose index for the risk of inci-dent diabetes: a 4-year retrospective longitudinal study. PloS one. 2016;11(9):e0163465.
  • 22. Zhang M, Wang B, Liu Y, Sun X, Luo X, Wang C, et al. Cumula-tive increased risk of incident type 2 diabetes mellitus with increasing triglyceride glucose index in normal-weight peop-le: The Rural Chinese Cohort Study. Cardiovascular diabeto-logy. 2017;16:1-11.
  • 23. Wang B, Zhang M, Liu Y, Sun X, Zhang L, Wang C, et al. Utility of three novel insulin resistance‐related lipid indices for pre-dicting type 2 diabetes mellitus among people with normal fasting glucose in rural China: 在空腹血糖正常的中国农村人群中使用 3 种新的胰岛素抵抗相关血脂指标来预测 2 型糖尿病. Journal of diabetes. 2018;10(8):641-52.
  • 24. Low S, Khoo KCJ, Irwan B, Sum CF, Subramaniam T, Lim SC, et al. The role of triglyceride glucose index in development of Type 2 diabetes mellitus. Diabetes research and clinical prac-tice. 2018;143:43-9.
  • 25. Navarro-González D, Sánchez-Íñigo L, Pastrana-Delgado J, Fernández-Montero A, Martinez JA. Triglyceride–glucose in-dex (TyG index) in comparison with fasting plasma glucose improved diabetes prediction in patients with normal fasting glucose: the Vascular-Metabolic CUN cohort. Preventive me-dicine. 2016;86:99- 105.
  • 26. Navarro-González D, Sánchez-Íñigo L, Fernández-Montero A, Pastrana-Delgado J, Martinez JA. TyG index change is more determinant for forecasting type 2 diabetes onset than we-ight gain. Medicine. 2016;95(19):e3646.
  • 27. Lee S-H, Kwon H-S, Park Y-M, Ha H-S, Jeong SH, Yang HK, et al. Predicting the development of diabetes using the product of triglycerides and glucose: the Chungju Metabolic Disease Co-hort (CMC) study. PloS one. 2014;9(2):e90430.
  • 28. Chamroonkiadtikun P, Ananchaisarp T, Wanichanon W. The triglyceride-glucose index, a predictor of type 2 diabetes de-velopment: a retrospective cohort study. Primary care diabe-tes. 2020;14(2):161-7.
  • 29. Janghorbani M, Almasi SZ, Amini M. The product of triglyceri-des and glucose in comparison with fasting plasma glucose did not improve diabetes prediction. Acta diabetologica. 2015;52:781-8.
  • 30. Tohidi M, Baghbani-Oskouei A, Ahanchi NS, Azizi F, Hadaegh F. Fasting plasma glucose is a stronger predictor of diabetes than triglyceride–glucose index, triglycerides/high-density lipopro-tein cholesterol, and homeostasis model assessment of insu-lin resistance: Tehran Lipid and Glucose Study. Acta Diabeto-logica. 2018;55:1067-74.
  • 31. Matthews DR, Hosker JP, Rudenski AS, Naylor B, Treacher DF, Turner R. Homeostasis model assessment: insulin resistance and β-cell function from fasting plasma glucose and insulin concentrations in man. diabetologia. 1985;28:412-9.
  • 32. Hajer GR, Van Haeften TW, Visseren FL. Adipose tissue dys-function in obesity, diabetes, and vascular diseases. European heart journal. 2008;29(24):2959-71.
  • 33. Delarue J, Magnan C. Free fatty acids and insulin resistance. Current Opinion in Clinical Nutrition & Metabolic Care. 2007;10(2):142-8.
  • 34. DeFronzo RA, Ferrannini E, Groop L, Henry RR, Herman WH, Holst JJ, et al. Type 2 diabetes mellitus. Nature reviews Dise-ase primers. 2015;1(1):1-22.
  • 35. Man Z-W, Zhu M, Noma Y, Toide K, Sato T, Asahi Y, et al. Impa-ired β-cell function and deposition of fat droplets in the pancreas as a consequence of hypertriglyceridemia in OLETF rat, a model of spontaneous NIDDM. Diabetes. 1997;46(11):1718-24.
  • 36. Robertson RP, Harmon J, Tran POT, Poitout V. β-cell glucose toxicity, lipotoxicity, and chronic oxidative stress in type 2 diabetes. Diabetes. 2004;53(suppl_1):S119-S24.
  • 37. Hameed EK. TyG index a promising biomarker for glycemic control in type 2 Diabetes Mellitus. Diabetes & Metabolic Syndrome: Clinical Research & Reviews. 2019;13(1):560-3.
  • 38. Lee S-H, Yang HK, Ha H-S, Lee J-H, Kwon H-S, Park Y-M, et al. Changes in metabolic health status over time and risk of de-veloping type 2 diabetes: a prospective cohort study. Medici-ne. 2015;94(40). 39. Liu E-q, Weng Y-p, Zhou A-m, Zeng C-l. Association between Triglyceride‐Glucose Index and Type 2 Diabetes Mellitus in the Japanese Population: A Secondary Analysis of a Retrospec-tive Cohort Study. BioMed Research International. 2020;2020(1):2947067.
  • 40. Machann J, Thamer C, Schnoedt B, Stefan N, Stumvoll M, Haring H-U, et al. Age and gender related effects on adipose tissue compartments of subjects with increased risk for type 2 diabetes: a whole body MRI/MRS study. Magnetic Resonance Materials in Physics, Biology and Medicine. 2005;18:128-37.
  • 41. Simental-Mendía LE, Rodríguez-Morán M, Guerrero-Romero F. The product of fasting glucose and triglycerides as surrogate for identifying insulin resistance in apparently healthy sub-jects. Metabolic syndrome and related disorders. 2008;6(4):299-304.
  • 42. Janghorbani M, Amini M. Normal fasting plasma glucose and risk of prediabetes and type 2 diabetes: the Isfahan diabetes prevention study. The review of diabetic studies: RDS. 2011;8(4):490.

Glisemik Kontrol Belirteci Olarak Trigliserid Glukoz İndeksi

Year 2025, Volume: 22 Issue: 2, 357 - 363, 27.06.2025
https://doi.org/10.35440/hutfd.1632098

Abstract

Amaç: Tip-2 Diyabetes Mellitus (T2DM) günümüzde en yaygın endokrin hastalık olmaya devam etmekte-dir. İnsülin direncini (İD) ve glisemik kontrolü değerlendirmek için kabul görmüş belirteçlere ek olarak kolay erişilebilir, doğru ve tekrarlanabilir belirteçlere ihtiyaç duyulmaktadır. Bu nedenle çalışmamızda insülin direnci ve glisemik kontrol için bir gösterge olarak trigliserid glikoz indeksinin (TyGI) kullanımını değerlendirmeyi amaçladık.
Materyal ve Metod: Mart 2023 ile Ağustos 2023 tarihleri arasında Tıp Fakültesi Hastanesi Laboratuvarı-mızda eş zamanlı olarak incelenen 953 örneğin trigliserid (TG), HbA1c, açlık kan şekeri (AKŞ) ve toplam insülin (Tİ) değerleri geriye dönük olarak incelendi. Hastalar; HbA1c ve homeostasis model değerlendir-mesiyle tahmin edilen insülin direnci (HOMA-İD) değerleri açısından iyi glisemik kontrol ve/veya kötü glisemik kontrol olmak üzere iki gruba ayrıldı. TyGI'nin, HOMA-İD ve HbA1c'nin her biri için iyi ve/veya kötü glisemik kontrol arasında ayrım yapabilme yeteneğini değerlendirmek amacıyla Duyarlılık ve Özgül-lük Analizi (DÖA) yapıldı. İstatistiksel anlamlılık düzeyi p<0,05 olarak kabul edildi. Ayrıca çok değişkenli lojistik regresyon analizi yapıldı.
Bulgular: Çalışmaya yaş ortalaması 40,83±16,78 olan toplam 953 hasta katıldı. Cinsiyete göre yaş (p: 0,613) hariç tüm parametreler anlamlı farklılıklar gösterdi (p<0,005). Her iki çalışma grubunda da kesme değerlerine göre AKŞ, HbA1c, HOMA-İD, Tİ, TG ve TyGI parametreleri için anlamlı farklılıklar vardı (p<0,001). TG, her iki çalışma grubu için de TyGI ile yüksek pozitif korelasyon (r: 0,796, p<0,001) ve AKŞ ile orta derecede pozitif korelasyon (r: 0,616, p<0,001) gösterdi ancak diğerleri ile düşük pozitif korelas-yon gösterdi. TyGI, ≥8,76 kesim değeri (Eğri Altı Alan (EAA):0,72, Duyarlılık:%65, Seçicilik:%70 (p<0.001: %95 GA:0,69-0,75)) ile HOMA-İD için yüksek seçiciliğe ve duyarlılığa sahipti. TyGI; HbA1c için en yüksek EAA’ya ve HOMA-İD grubu için ise en düşük EAA’ya sahipti. Erkeklerde HOMA-İD için kötü glisemik kontrol riski kadınlara göre 2.247 kat daha fazladır. Yaş bir birim arttıkça, HOMA-İD için kötü glisemik kontrol riski 1.045 kat artmaktadır.
Sonuç: TyGI, kötü glisemik kontrole sahip yeni T2DM hastalarında önemli ölçüde yükselmiştir. TyGI, insülin direncini belirlemek ve yeni T2DM gelişimini önceden tespit etmek için güçlü öngörücü yetene-ğe sahip basit ve kullanışlı bir belirteç olarak işlev görebilir.

References

  • 1. Goyal R, Jialal I. Diabetes Mellitus Type 2.[Updated 2020 Nov 20]. StatPearls[Internet] Treasure Island (FL): StatPearls Pub-lishing. 2021.
  • 2. Hills AP, Arena R, Khunti K, Yajnik CS, Jayawardena R, Henry CJ, et al. Epidemiology and determinants of type 2 diabetes in south Asia. The lancet Diabetes & endocrinology. 2018;6(12):966-78.
  • 3. Zheng Y, Ley SH, Hu FB. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nature re-views endocrinology. 2018;14(2):88-98.
  • 4. Ergör G, Soysal A, Sözmen K, Ünal B, Uçku R, Kılıç B, et al. Balcova heart study: rationale and methodology of the Turkish cohort. International journal of public health. 2012;57:535-42.
  • 5. Süleymanlar G, Utaş C, Arinsoy T, Ateş K, Altun B, Altiparmak MR, et al. A population-based survey of Chronic REnal Disease In Turkey—the CREDIT study. Nephrology Dialysis Transplanta-tion. 2011;26(6):1862-71.
  • 6. Teo K, Chow CK, Vaz M, Rangarajan S, Yusuf S, Group PI-W. The Prospective Urban Rural Epidemiology (PURE) study: exami-ning the impact of societal influences on chronic noncommu-nicable diseases in low-, middle-, and high-income countries. American heart journal. 2009;158(1):1-7. e1.
  • 7. Ünal B, Sözmen K, Uçku R, Ergör G, Soysal A, Baydur H, et al. High prevalence of cardiovascular risk factors in a Western urban Turkish population: a community-based study. Anato-lian Journal of Cardiology/Anadolu Kardiyoloji Dergisi. 2013;13(1).
  • 8. Gillani AH, Aziz MM, Masood I, Saqib A, Yang C, Chang J, et al. Direct and indirect cost of diabetes care among patients with type 2 diabetes in private clinics: a multicenter study in Pun-jab, Pakistan. Expert review of pharmacoeconomics & outco-mes research. 2018;18(6):647-53.
  • 9. Babic N, Valjevac A, Zaciragic A, Avdagic N, Zukic S, Hasic S. The triglyceride/HDL ratio and triglyceride glucose index as predictors of glycemic control in patients with diabetes mel-litus type 2. Medical archives. 2019;73(3):163.
  • 10. Association AD. 6. Glycemic targets: standards of medical care in diabetes—2018. Diabetes care. 2018;41(Supplement_1):S55-S64.
  • 11. Badedi M, Solan Y, Darraj H, Sabai A, Mahfouz M, Alamodi S, et al. Factors associated with long‐term control of type 2 diabe-tes mellitus. Journal of diabetes research. 2016;2016(1):2109542.
  • 12. Patel MB, Sachora WM, Pandya AR, Kothari AD, Patel JK. Can Hba1c Act as A Surrogate Marker for Cardiovascular Risk? Na-tional Journal of Community Medicine. 2014;5(01):29-32.
  • 13. Dobiás̆ová M, Frohlich J. The plasma parameter log (TG/HDL-C) as an atherogenic index: correlation with lipoprotein particle size and esterification rate inapob-lipoprotein-depleted plas-ma (FERHDL). Clinical biochemistry. 2001;34(7):583-8.
  • 14. Buijsse B, Simmons RK, Griffin SJ, Schulze MB. Risk assessment tools for identifying individuals at risk of developing type 2 diabetes. Epidemiologic reviews. 2011;33(1):46-62.
  • 15. Du T, Yuan G, Zhang M, Zhou X, Sun X, Yu X. Clinical usefulness of lipid ratios, visceral adiposity indicators, and the triglyceri-des and glucose index as risk markers of insulin resistance. Cardiovascular diabetology. 2014;13:1-10.
  • 16. Caleyachetty R, Thomas GN, Toulis KA, Mohammed N, Gokhale KM, Balachandran K, et al. Metabolically healthy obese and incident cardiovascular disease events among 3.5 million men and women. Journal of the American College of Cardiology. 2017;70(12):1429-37.
  • 17. Alberti K. International diabetes federation task force on epidemiology and prevention; hational heart, lung, and blood institute; American heart association; world heart federation; international atherosclerosis society; international association for the study of obesity: harmonizing the metabolic syndro-me: a joint interim statement of the international diabetes federation task force on epidemiology and prevention; natio-nal heart, lung, and blood institute; American heart associa-tion; world heart federation; international atherosclerosis so-ciety; and international association for the study of obesity. Circulation. 2009;120:1640-5.
  • 18. Hong S, Han K, Park C-Y. The triglyceride glucose index is a simple and low-cost marker associated with atherosclerotic cardiovascular disease: a population-based study. BMC medi-cine. 2020;18:1-8.
  • 19. Guerrero-Romero F, Simental-Mendía LE, González-Ortiz M, Martínez-Abundis E, Ramos-Zavala MaG, Hernández-González SO, et al. The product of triglycerides and glucose, a simple measure of insulin sensitivity. Comparison with the euglyce-mic-hyperinsulinemic clamp. The Journal of Clinical Endocri-nology & Metabolism. 2010;95(7):3347-51.
  • 20. Vasques ACJ, Novaes FS, de Oliveira MdS, Souza JRM, Yamana-ka A, Pareja JC, et al. TyG index performs better than HOMA in a Brazilian population: a hyperglycemic clamp validated study. Diabetes research and clinical practice. 2011;93(3):e98-e100.
  • 21. Lee DY, Lee ES, Kim JH, Park SE, Park C-Y, Oh K-W, et al. Pre-dictive value of triglyceride glucose index for the risk of inci-dent diabetes: a 4-year retrospective longitudinal study. PloS one. 2016;11(9):e0163465.
  • 22. Zhang M, Wang B, Liu Y, Sun X, Luo X, Wang C, et al. Cumula-tive increased risk of incident type 2 diabetes mellitus with increasing triglyceride glucose index in normal-weight peop-le: The Rural Chinese Cohort Study. Cardiovascular diabeto-logy. 2017;16:1-11.
  • 23. Wang B, Zhang M, Liu Y, Sun X, Zhang L, Wang C, et al. Utility of three novel insulin resistance‐related lipid indices for pre-dicting type 2 diabetes mellitus among people with normal fasting glucose in rural China: 在空腹血糖正常的中国农村人群中使用 3 种新的胰岛素抵抗相关血脂指标来预测 2 型糖尿病. Journal of diabetes. 2018;10(8):641-52.
  • 24. Low S, Khoo KCJ, Irwan B, Sum CF, Subramaniam T, Lim SC, et al. The role of triglyceride glucose index in development of Type 2 diabetes mellitus. Diabetes research and clinical prac-tice. 2018;143:43-9.
  • 25. Navarro-González D, Sánchez-Íñigo L, Pastrana-Delgado J, Fernández-Montero A, Martinez JA. Triglyceride–glucose in-dex (TyG index) in comparison with fasting plasma glucose improved diabetes prediction in patients with normal fasting glucose: the Vascular-Metabolic CUN cohort. Preventive me-dicine. 2016;86:99- 105.
  • 26. Navarro-González D, Sánchez-Íñigo L, Fernández-Montero A, Pastrana-Delgado J, Martinez JA. TyG index change is more determinant for forecasting type 2 diabetes onset than we-ight gain. Medicine. 2016;95(19):e3646.
  • 27. Lee S-H, Kwon H-S, Park Y-M, Ha H-S, Jeong SH, Yang HK, et al. Predicting the development of diabetes using the product of triglycerides and glucose: the Chungju Metabolic Disease Co-hort (CMC) study. PloS one. 2014;9(2):e90430.
  • 28. Chamroonkiadtikun P, Ananchaisarp T, Wanichanon W. The triglyceride-glucose index, a predictor of type 2 diabetes de-velopment: a retrospective cohort study. Primary care diabe-tes. 2020;14(2):161-7.
  • 29. Janghorbani M, Almasi SZ, Amini M. The product of triglyceri-des and glucose in comparison with fasting plasma glucose did not improve diabetes prediction. Acta diabetologica. 2015;52:781-8.
  • 30. Tohidi M, Baghbani-Oskouei A, Ahanchi NS, Azizi F, Hadaegh F. Fasting plasma glucose is a stronger predictor of diabetes than triglyceride–glucose index, triglycerides/high-density lipopro-tein cholesterol, and homeostasis model assessment of insu-lin resistance: Tehran Lipid and Glucose Study. Acta Diabeto-logica. 2018;55:1067-74.
  • 31. Matthews DR, Hosker JP, Rudenski AS, Naylor B, Treacher DF, Turner R. Homeostasis model assessment: insulin resistance and β-cell function from fasting plasma glucose and insulin concentrations in man. diabetologia. 1985;28:412-9.
  • 32. Hajer GR, Van Haeften TW, Visseren FL. Adipose tissue dys-function in obesity, diabetes, and vascular diseases. European heart journal. 2008;29(24):2959-71.
  • 33. Delarue J, Magnan C. Free fatty acids and insulin resistance. Current Opinion in Clinical Nutrition & Metabolic Care. 2007;10(2):142-8.
  • 34. DeFronzo RA, Ferrannini E, Groop L, Henry RR, Herman WH, Holst JJ, et al. Type 2 diabetes mellitus. Nature reviews Dise-ase primers. 2015;1(1):1-22.
  • 35. Man Z-W, Zhu M, Noma Y, Toide K, Sato T, Asahi Y, et al. Impa-ired β-cell function and deposition of fat droplets in the pancreas as a consequence of hypertriglyceridemia in OLETF rat, a model of spontaneous NIDDM. Diabetes. 1997;46(11):1718-24.
  • 36. Robertson RP, Harmon J, Tran POT, Poitout V. β-cell glucose toxicity, lipotoxicity, and chronic oxidative stress in type 2 diabetes. Diabetes. 2004;53(suppl_1):S119-S24.
  • 37. Hameed EK. TyG index a promising biomarker for glycemic control in type 2 Diabetes Mellitus. Diabetes & Metabolic Syndrome: Clinical Research & Reviews. 2019;13(1):560-3.
  • 38. Lee S-H, Yang HK, Ha H-S, Lee J-H, Kwon H-S, Park Y-M, et al. Changes in metabolic health status over time and risk of de-veloping type 2 diabetes: a prospective cohort study. Medici-ne. 2015;94(40). 39. Liu E-q, Weng Y-p, Zhou A-m, Zeng C-l. Association between Triglyceride‐Glucose Index and Type 2 Diabetes Mellitus in the Japanese Population: A Secondary Analysis of a Retrospec-tive Cohort Study. BioMed Research International. 2020;2020(1):2947067.
  • 40. Machann J, Thamer C, Schnoedt B, Stefan N, Stumvoll M, Haring H-U, et al. Age and gender related effects on adipose tissue compartments of subjects with increased risk for type 2 diabetes: a whole body MRI/MRS study. Magnetic Resonance Materials in Physics, Biology and Medicine. 2005;18:128-37.
  • 41. Simental-Mendía LE, Rodríguez-Morán M, Guerrero-Romero F. The product of fasting glucose and triglycerides as surrogate for identifying insulin resistance in apparently healthy sub-jects. Metabolic syndrome and related disorders. 2008;6(4):299-304.
  • 42. Janghorbani M, Amini M. Normal fasting plasma glucose and risk of prediabetes and type 2 diabetes: the Isfahan diabetes prevention study. The review of diabetic studies: RDS. 2011;8(4):490.
There are 41 citations in total.

Details

Primary Language English
Subjects Clinical Chemistry
Journal Section Research Article
Authors

Muzaffer Katar 0000-0002-6296-2390

Osman Demir 0000-0002-1322-2716

Early Pub Date June 24, 2025
Publication Date June 27, 2025
Submission Date February 3, 2025
Acceptance Date June 16, 2025
Published in Issue Year 2025 Volume: 22 Issue: 2

Cite

Vancouver Katar M, Demir O. Triglyceride Glucose Index: As A Glycemic Control Indicator. Harran Üniversitesi Tıp Fakültesi Dergisi. 2025;22(2):357-63.