Skip to main content
SpringerLink
Log in

The triglycerides and glucose index is more strongly associated with metabolically healthy obesity phenotype than the lipid and obesity indices

  • Original Article
  • Published:
Journal of Endocrinological Investigation Aims and scope Submit manuscript

Abstract

Purpose

The triglycerides and glucose (TyG) index is a reliable biomarker for estimating insulin resistance; however, evidence regarding the use of the TyG index in individuals with metabolically healthy obesity (MHO) is scarce. Thus, we examined the association between the TyG index and the MHO phenotype.

Methods

Apparently healthy men and women aged 18 years or more with obesity (body mass index [BMI] ≥ 30 kg/m2) were allocated into the following groups: MHO and metabolically unhealthy obesity (MUO). The MHO phenotype was defined by obesity and the absence of the following metabolic disorders: elevated triglyceride concentrations, elevated glucose levels, elevated blood pressure, and low HDL-C. The MUO was defined by individuals with obesity and at least one of the aforementioned cardiovascular risk factors.

Results

A total 827 individuals, 605 (73.1%) women and 222 (26.9%) men were enrolled and allocated into the MHO (n = 104) and MUO (n = 723) groups. The adjusted regression analysis by age, sex, BMI, and waist circumference showed that fasting glucose (OR = 0.90; 95% CI: 0.88–0.93), and triglycerides (OR = 0.97; 95% CI: 0.96–0.98), as well as the triglycerides/HDL-C (OR = 0.18; 95% CI: 0.13–0.26), lipid accumulation product (OR = 0.95; 95% CI: 0.93–0.96), visceral adipose index (OR = 0.38; 95% CI: 0.31–0.46), and TyG index (OR = 0.001; 95% CI: 0.000–0.004) are inversely associated with the MHO, while the HDL-C (OR = 1.10; 95% CI: 1.07–1.12) had a direct association.

Conclusions

Our results show that the TyG index is more strongly associated with the MHO phenotype than the lipid and obesity indices.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

Data availability

The data used to support the results of our study are shown within the article.

References

  1. Kivimäki M, Strandberg T, Pentti J et al (2022) Body-mass index and risk of obesity-related complex multimorbidity: an observational multicohort study. lancet Diabetes Endocrinol 10:253–263. https://doi.org/10.1016/S2213-8587(22)00033-X

    Article  PubMed  PubMed Central  Google Scholar 

  2. Neeland IJ, Poirier P, Després JP (2018) Cardiovascular and metabolic heterogeneity of obesity: clinical challenges and implications for management. Circulation 137:1391–1406. https://doi.org/10.1161/CIRCULATIONAHA.117.029617

    Article  PubMed  PubMed Central  Google Scholar 

  3. Bentham J, Di Cesare M, Bilano V et al (2017) Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adults. Lancet (London, England) 390:2627–2642. https://doi.org/10.1016/S0140-6736(17)32129-3

    Article  Google Scholar 

  4. Swinburn BA, Kraak VI, Allender S et al (2019) The Global Syndemic of Obesity, Undernutrition, and Climate Change: The Lancet Commission report. Lancet (London, England) 393:791–846. https://doi.org/10.1016/S0140-6736(18)32822-8

    Article  PubMed  Google Scholar 

  5. Tsatsoulis A, Paschou SA (2020) Metabolically Healthy Obesity: Criteria, Epidemiology, Controversies, and Consequences. Curr Obes Rep 9:109–120. https://doi.org/10.1007/S13679-020-00375-0

    Article  PubMed  Google Scholar 

  6. Liu C, Wang C, Guan S et al (2019) The Prevalence of Metabolically Healthy and Unhealthy Obesity according to Different Criteria. Obes Facts 12:78–90. https://doi.org/10.1159/000495852

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Lavie CJ, Laddu D, Arena R et al (2018) Healthy Weight and Obesity Prevention: JACC Health Promotion Series. J Am Coll Cardiol 72:1506–1531. https://doi.org/10.1016/J.JACC.2018.08.1037

    Article  PubMed  Google Scholar 

  8. Blüher M (2020) Metabolically Healthy Obesity. Endocr Rev 41:405–420. https://doi.org/10.1210/ENDREV/BNAA004

    Article  Google Scholar 

  9. Messier V, Karelis AD, Prud’Homme D et al (2010) Identifying metabolically healthy but obese individuals in sedentary postmenopausal women. Obesity (Silver Spring) 18:911–917. https://doi.org/10.1038/OBY.2009.364

    Article  PubMed  Google Scholar 

  10. Gómez-Zorita S, Queralt M, Vicente MA et al (2021) Metabolically healthy obesity and metabolically obese normal weight: a review. J Physiol Biochem 77:175–189. https://doi.org/10.1007/S13105-020-00781-X

    Article  PubMed  Google Scholar 

  11. Barrea L, Muscogiuri G, Pugliese G et al (2021) Metabolically healthy obesity (MHO) vs. metabolically unhealthy obesity (MUO) phenotypes in PCOS: association with endocrine-metabolic profile, adherence to the mediterranean diet, and body composition. Nutrients. https://doi.org/10.3390/nu13113925

    Article  PubMed  PubMed Central  Google Scholar 

  12. Succurro E, Marini MA, Frontoni S et al (2008) Insulin secretion in metabolically obese, but normal weight, and in metabolically healthy but obese individuals. Obesity (Silver Spring) 16:1881–1886. https://doi.org/10.1038/OBY.2008.308

    Article  CAS  PubMed  Google Scholar 

  13. Simental-Mendía LE, Rodríguez-Morán M, Guerrero-Romero F (2008) The product of fasting glucose and triglycerides as surrogate for identifying insulin resistance in apparently healthy subjects. Metab Syndr Relat Disord 6:299–304. https://doi.org/10.1089/MET.2008.0034

    Article  PubMed  Google Scholar 

  14. Guerrero-Romero F, Simental-Mendía LE, González-Ortiz M et al (2010) The product of triglycerides and glucose, a simple measure of insulin sensitivity. Comparison with the euglycemic-hyperinsulinemic clamp. J Clin Endocrinol Metab 95:3347–3351. https://doi.org/10.1210/JC.2010-0288

    Article  CAS  PubMed  Google Scholar 

  15. Sánchez-Íñigo L, Navarro-González D, Fernández-Montero A et al (2016) The TyG index may predict the development of cardiovascular events. Eur J Clin Invest 46:189–197. https://doi.org/10.1111/ECI.12583

    Article  PubMed  Google Scholar 

  16. Lei L, Liang H, Qu Y et al (2022) Association between triglyceride-glucose index and worsening renal function in the elderly. Front Nutr. https://doi.org/10.3389/FNUT.2022.951564

    Article  PubMed  PubMed Central  Google Scholar 

  17. Beran A, Ayesh H, Mhanna M et al (2022) Triglyceride-glucose index for early prediction of nonalcoholic fatty liver disease: a meta-analysis of 121,975 individuals. J Clin Med. https://doi.org/10.3390/JCM11092666

    Article  PubMed  PubMed Central  Google Scholar 

  18. Zaigham S, Tanash H, Nilsson PM, Muhammad IF (2022) Triglyceride-glucose index is a risk marker of incident copd events in women. Int J Chron Obstruct Pulmon Dis 17:1393–1401. https://doi.org/10.2147/COPD.S360793

    Article  PubMed  PubMed Central  Google Scholar 

  19. Scicali R, Di Pino A, Urbano F et al (2021) Analysis of steatosis biomarkers and inflammatory profile after adding on PCSK9 inhibitor treatment in familial hypercholesterolemia subjects with nonalcoholic fatty liver disease: a single lipid center real-world experience. Nutr Metab Cardiovasc Dis 31:869–879. https://doi.org/10.1016/J.NUMECD.2020.11.009

    Article  CAS  PubMed  Google Scholar 

  20. Wang A, Tian X, Zuo Y et al (2021) Change in triglyceride-glucose index predicts the risk of cardiovascular disease in the general population: a prospective cohort study. Cardiovasc Diabetol. https://doi.org/10.1186/S12933-021-01305-7

    Article  PubMed  PubMed Central  Google Scholar 

  21. Kouvari M, Boutari C, Chrysohoou C et al (2021) Mediterranean diet is inversely associated with steatosis and fibrosis and decreases ten-year diabetes and cardiovascular risk in NAFLD subjects: Results from the ATTICA prospective cohort study. Clin Nutr 40:3314–3324. https://doi.org/10.1016/J.CLNU.2020.10.058

    Article  CAS  PubMed  Google Scholar 

  22. Lee YC, Park BJ, Lee JH (2021) Sex differences in the relationship between high-risk drinking and the triglyceride-glucose (TyG) index: an analysis using 2013 and 2015 korean national health and nutrition examination survey data. Alcohol Alcohol 56:393–400. https://doi.org/10.1093/ALCALC/AGAA122

    Article  PubMed  Google Scholar 

  23. Moon S, Park JS, Ahn Y (2017) The cut-off values of triglycerides and glucose index for metabolic syndrome in american and korean adolescents. J Korean Med Sci 32:427–433. https://doi.org/10.3346/JKMS.2017.32.3.427

    Article  PubMed  Google Scholar 

  24. Lee SH, Kwon HS, Park YM et al (2014) Predicting the development of diabetes using the product of triglycerides and glucose: the Chungju Metabolic Disease Cohort (CMC) study. PLoS One. https://doi.org/10.1371/JOURNAL.PONE.0090430

    Article  PubMed  PubMed Central  Google Scholar 

  25. Park HM, Lee HS, Lee YJ, Lee JH (2021) The triglyceride-glucose index is a more powerful surrogate marker for predicting the prevalence and incidence of type 2 diabetes mellitus than the homeostatic model assessment of insulin resistance. Diabetes Res Clin Pract. https://doi.org/10.1016/J.DIABRES.2021.109042

    Article  PubMed  PubMed Central  Google Scholar 

  26. Zheng Y, Yin G, Chen F et al (2022) Evaluation of triglyceride glucose index and homeostasis model of insulin resistance in patients with polycystic ovary syndrome. Int J Womens Health 14:1821–1829. https://doi.org/10.2147/IJWH.S387942

    Article  PubMed  PubMed Central  Google Scholar 

  27. Alberti KGMM, Eckel RH, Grundy SM et al (2009) Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation 120:1640–1645. https://doi.org/10.1161/CIRCULATIONAHA.109.192644

    Article  CAS  PubMed  Google Scholar 

  28. Amato MC, Giordano C, Galia M et al (2010) Visceral Adiposity Index: a reliable indicator of visceral fat function associated with cardiometabolic risk. Diabetes Care 33:920–922. https://doi.org/10.2337/DC09-1825

    Article  PubMed  PubMed Central  Google Scholar 

  29. Kahn HS (2005) The “lipid accumulation product” performs better than the body mass index for recognizing cardiovascular risk: a population-based comparison. BMC Cardiovasc Disord. https://doi.org/10.1186/1471-2261-5-26

    Article  PubMed  PubMed Central  Google Scholar 

  30. Gamboa-Gómez CI, Simental-Mendía LE, Rodríguez-Morán M, Guerrero-Romero F (2019) The fat-to-lean mass ratio, a novel anthropometric index, is associated to glucose metabolic disorders. Eur J Intern Med 63:74–78. https://doi.org/10.1016/J.EJIM.2019.03.017

    Article  PubMed  Google Scholar 

  31. Chobanian AV, Bakris GL, Black HR et al (1979) (2003) Seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure Hypertens (Dallas. Tex 42(6):1206–1252. https://doi.org/10.1161/01.HYP.0000107251.49515.C2

    Article  Google Scholar 

  32. Friedewald WT, Levy RI, Fredrickson DS (1972) Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem 18:499–502

    Article  CAS  PubMed  Google Scholar 

  33. Lopes WA, de Oliveira GH, Locateli JC, Simões CF (2020) TyG in insulin resistance prediction. J Pediatr (Rio J) 96:132–133. https://doi.org/10.1016/J.JPED.2019.09.002

    Article  PubMed  Google Scholar 

  34. Huang R, Cheng Z, Jin X et al (2022) Usefulness of four surrogate indexes of insulin resistance in middle-aged population in Hefei, China. Ann Med 54:622–632. https://doi.org/10.1080/07853890.2022.2039956

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Liu X, Tan Z, Huang Y et al (2022) Relationship between the triglyceride-glucose index and risk of cardiovascular diseases and mortality in the general population: a systematic review and meta-analysis. Cardiovasc Diabetol. https://doi.org/10.1186/S12933-022-01546-0

    Article  PubMed  PubMed Central  Google Scholar 

  36. Akbar MR, Pranata R, Wibowo A et al (2021) The association between triglyceride-glucose index and major adverse cardiovascular events in patients with acute coronary syndrome - dose-response meta-analysis. Nutr Metab Cardiovasc Dis 31:3024–3030. https://doi.org/10.1016/J.NUMECD.2021.08.026

    Article  CAS  PubMed  Google Scholar 

  37. Wang Y, Yang W, Jiang X (2021) Association between triglyceride-glucose index and hypertension: a meta-analysis. Front Cardiovasc Med. https://doi.org/10.3389/FCVM.2021.644035

    Article  PubMed  PubMed Central  Google Scholar 

  38. Van MH, Tien HA, Sinh CT et al (2021) Assessment of preferred methods to measure insulin resistance in Asian patients with hypertension. J Clin Hypertens (Greenwich) 23:529–537. https://doi.org/10.1111/JCH.14155

    Article  Google Scholar 

  39. Wei A, Liu J, Wang L et al (2022) Correlation of triglyceride-glucose index and dyslipidaemia with premature coronary heart diseases and multivessel disease: a cross-sectional study in Tianjin. BMJ Open, China. https://doi.org/10.1136/BMJOPEN-2022-065780

    Book  Google Scholar 

  40. Che B, Zhong C, Zhang R et al (2023) Triglyceride-glucose index and triglyceride to high-density lipoprotein cholesterol ratio as potential cardiovascular disease risk factors: an analysis of UK biobank data. Cardiovasc Diabetol. https://doi.org/10.1186/S12933-023-01762-2

    Article  PubMed  PubMed Central  Google Scholar 

  41. Pranata R, Huang I, Irvan et al (2021) The association between triglyceride-glucose index and the incidence of type 2 diabetes mellitus-a systematic review and dose-response meta-analysis of cohort studies. Endocrine 74:254–262. https://doi.org/10.1007/S12020-021-02780-4

    Article  CAS  PubMed  Google Scholar 

  42. da Silva A, Caldas APS, Rocha DMUP, Bressan J (2020) Triglyceride-glucose index predicts independently type 2 diabetes mellitus risk: a systematic review and meta-analysis of cohort studies. Prim Care Diabetes 14:584–593. https://doi.org/10.1016/J.PCD.2020.09.001

    Article  PubMed  Google Scholar 

  43. Wang Z, Zhao L, He S (2021) Triglyceride-glucose index as predictor for future type 2 diabetes mellitus in a Chinese population in southwest China: a 15-year prospective study. Endocrine 72:124–131. https://doi.org/10.1007/S12020-020-02589-7

    Article  CAS  PubMed  Google Scholar 

  44. Nabipoorashrafi SA, Seyedi SA, Rabizadeh S et al (2022) The accuracy of triglyceride-glucose (TyG) index for the screening of metabolic syndrome in adults: a systematic review and meta-analysis. Nutr Metab Cardiovasc Dis 32:2677–2688. https://doi.org/10.1016/J.NUMECD.2022.07.024

    Article  CAS  PubMed  Google Scholar 

  45. Hoddy KK, Axelrod CL, Mey JT et al (2022) Insulin resistance persists despite a metabolically healthy obesity phenotype. Obesity (Silver Spring) 30:39–44. https://doi.org/10.1002/OBY.23312

    Article  CAS  PubMed  Google Scholar 

  46. Manu P, Ionescu-Tirgoviste C, Tsang J et al (2012) Dysmetabolic signals in “metabolically healthy” obesity. Obes Res Clin Pract 6:e1–e90. https://doi.org/10.1016/j.orcp.2011.04.003

    Article  PubMed  Google Scholar 

  47. Sejooti SS, Naher S, Hoque MM et al (2019) Frequency of insulin resistance in nondiabetic adult Bangladeshi individuals of different obesity phenotypes. Diabetes Metab Syndr 13:62–67. https://doi.org/10.1016/J.DSX.2018.08.022

    Article  PubMed  Google Scholar 

  48. Gonçalves CG, Glade MJ, Meguid MM (2016) Metabolically healthy obese individuals: key protective factors. Nutrition 32:14–20. https://doi.org/10.1016/J.NUT.2015.07.010

    Article  PubMed  Google Scholar 

  49. Liao C, Gao W, Cao W et al (2021) Associations of metabolic/obesity phenotypes with insulin resistance and c-reactive protein: results from the CNTR study. Diabetes Metab Syndr Obes 14:1141–1151. https://doi.org/10.2147/DMSO.S298499

    Article  PubMed  PubMed Central  Google Scholar 

  50. Marini MA, Frontoni S, Succurro E et al (2014) Differences in insulin clearance between metabolically healthy and unhealthy obese subjects. Acta Diabetol 51:257–261. https://doi.org/10.1007/S00592-013-0511-9

    Article  CAS  PubMed  Google Scholar 

  51. Bell JA, Hamer M, Batty GD et al (2015) Incidence of metabolic risk factors among healthy obese adults: 20-Year follow-up. J Am Coll Cardiol 66:871–873. https://doi.org/10.1016/J.JACC.2015.06.014

    Article  PubMed  PubMed Central  Google Scholar 

  52. Wildman RP, Muntner P, Reynolds K et al (2008) The obese without cardiometabolic risk factor clustering and the normal weight with cardiometabolic risk factor clustering: prevalence and correlates of 2 phenotypes among the US population (NHANES 1999–2004). Arch Intern Med 168:1617–1624. https://doi.org/10.1001/ARCHINTE.168.15.1617

    Article  PubMed  Google Scholar 

  53. van Vliet-Ostaptchouk JV, Nuotio ML, Slagter SN et al (2014) The prevalence of metabolic syndrome and metabolically healthy obesity in Europe: a collaborative analysis of ten large cohort studies. BMC Endocr Disord. https://doi.org/10.1186/1472-6823-14-9

    Article  PubMed  PubMed Central  Google Scholar 

  54. Ortega FB, Lavie CJ, Blair SN (2016) Obesity and cardiovascular disease. Circ Res 118:1752–1770. https://doi.org/10.1161/CIRCRESAHA.115.306883

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Unidad de Investigación Biomédica, Instituto Mexicano del Seguro Social, Delegación Durango, Durango, México

    Y. Weyman-Vela, F. Guerrero-Romero & L. E. Simental-Mendía

Authors
  1. Y. Weyman-Vela
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. F. Guerrero-Romero
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. L. E. Simental-Mendía
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to L. E. Simental-Mendía.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Weyman-Vela, Y., Guerrero-Romero, F. & Simental-Mendía, L.E. The triglycerides and glucose index is more strongly associated with metabolically healthy obesity phenotype than the lipid and obesity indices. J Endocrinol Invest 47, 865–871 (2024). https://doi.org/10.1007/s40618-023-02201-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40618-023-02201-5

Keywords

Search

Navigation