Advertisement

The accuracy of triglyceride-glucose (TyG) index for the screening of metabolic syndrome in adults: A systematic review and meta-analysis

Published:September 01, 2022DOI:https://doi.org/10.1016/j.numecd.2022.07.024

      Highlights

      • Relationship between the TyG index and metabolic syndrome: The first meta-analysis.
      • A new index for screening metabolic syndrome.
      • The TyG index is applicable in adults for screening of metabolic syndrome.

      Abstract

      Aims

      To investigate the relationship between the triglyceride-glucose (TyG) index, a novel surrogate index of insulin resistance (IR), and metabolic syndrome (MetS) in a systematic review and meta-analysis.

      Data synthesis

      Studies that report the TyG index in adult subjects with and without MetS were included. Thirteen observational articles were included in this study, with a total of 49,325 participants. Two different categories of meta-analyses were performed. First, the means of the TyG index were compared in participants with and without MetS. The pooled mean difference (MD) of the TyG index between groups was 0.83 units (CI 95: 0.74–0.92, I2 = 98, P-value < 0.001), and the subgroup analyses showed MD significantly differed based on the MetS diagnostic criteria. The pooled MD were 0.80 units (CI 95: 0.70–0.91, I2 = %88, P-value < 0.001) and 0.82 units (CI 95: 0.79–0.86, I2 = %0, P-value > 0.767) for studies reported data for males and females individual, respectively. Second bivariate diagnostic test accuracy (DTA) meta-analysis was performed and determined that the TyG index's pooled sensitivity and specificity for screening of MetS were 80% (CI95: 75%–84%, I2 = 87%, P-value < 0.001) and 81% (CI95: 77%–84%, I2 = 90.45%, P-value < 0.001), respectively. Summary receiver-operating characteristics (sROC) curves were also plotted with the area under the sROC curve of 0.87 (CI 95: 0.84–0.90).

      Conclusions

      The TyG index is a sensitive and specific index for MetS and may be valuable for MetS screening.

      Prospero

      CRD42022316209.

      Keywords

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Nutrition, Metabolism and Cardiovascular Diseases
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Huang P.L.
        A comprehensive definition for metabolic syndrome.
        Dis Model Mech. 2009; 2: 231-237
        • Hirode G.
        • Wong R.J.
        Trends in the prevalence of metabolic syndrome in the United States, 2011-2016.
        JAMA. 2020; 323: 2526-2528
        • Grundy S.M.
        • Cleeman J.I.
        • Daniels S.R.
        • Donato K.A.
        • Eckel R.H.
        • Franklin B.A.
        • et al.
        Diagnosis and management of the metabolic syndrome: an American heart association/national heart, lung, and blood institute scientific statement.
        Circulation. 2005; 112https://doi.org/10.1161/CIRCULATIONAHA.105.169404
        • Alberti K.G.M.M.
        • Eckel R.H.
        • Grundy S.M.
        • Zimmet P.Z.
        • Cleeman J.I.
        • Donato K.A.
        • et al.
        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. 2009; 120https://doi.org/10.1161/CIRCULATIONAHA.109.192644
        • Wang H.H.
        • Lee D.K.
        • Liu M.
        • Portincasa P.
        • Wang D.Q.H.
        Novel insights into the pathogenesis and management of the metabolic syndrome.
        Pediatric Gastroenterology, Hepatology and Nutrition. 2020; 23https://doi.org/10.5223/PGHN.2020.23.3.189
        • Kojadinovic M.
        • Glibetic M.
        • Vucic V.
        • Popovic M.
        • Vidovic N.
        • Debeljak-Martacic J.
        • et al.
        Short-term consumption of pomegranate juice alleviates some metabolic disturbances in overweight patients with dyslipidemia.
        J Med Food. 2021; 24https://doi.org/10.1089/jmf.2020.0122
        • Motamed N.
        • Khonsari M.R.
        • Rabiee B.
        • Ajdarkosh H.
        • Hemasi G.R.
        • Sohrabi M.R.
        • et al.
        Discriminatory ability of visceral adiposity index (VAI) in diagnosis of metabolic syndrome: a population based study.
        Exp Clin Endocrinol Diabetes. 2017; 125https://doi.org/10.1055/s-0042-119032
        • Alberti K.G.M.M.
        • Zimmet P.Z.
        Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus. Provisional report of a WHO consultation.
        Diabet Med. 1998; 15https://doi.org/10.1002/(SICI)1096-9136
        • Gallagher E.J.
        • Leroith D.
        • Karnieli E.
        Insulin resistance in obesity as the underlying cause for the metabolic syndrome.
        MSJM (Mt Sinai J Med). 2010; 77https://doi.org/10.1002/msj.20212
        • Dahan M.H.
        • Abbasi F.
        • Reaven G.
        Relationship between surrogate estimates and direct measurement of insulin resistance in women with polycystic ovary syndrome.
        J Endocrinol Invest. 2019; 42https://doi.org/10.1007/s40618-019-01014-9
        • Raimi T.H.
        • Dele-Ojo B.F.
        • Dada S.A.
        • Fadare J.O.
        • Ajayi D.D.
        • Ajayi E.A.
        • et al.
        Triglyceride-glucose index and related parameters predicted metabolic syndrome in Nigerians.
        Metab Syndr Relat Disord. 2021; 19https://doi.org/10.1089/met.2020.0092
        • Abbasi F.
        • Reaven G.M.
        Comparison of two methods using plasma triglyceride concentration as a surrogate estimate of insulin action in nondiabetic subjects: triglycerides × glucose versus triglyceride/high-density lipoprotein cholesterol.
        Metab Clin Exp. 2011; 60https://doi.org/10.1016/j.metabol.2011.04.006
        • 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; 13https://doi.org/10.1186/s12933-014-0146-3
        • Khan S.H.
        • Sobia F.
        • Niazi N.K.
        • Manzoor S.M.
        • Fazal N.
        • Ahmad F.
        Metabolic clustering of risk factors: evaluation of Triglyceride-glucose index (TyG index) for evaluation of insulin resistance.
        Diabetol Metab Syndrome. 2018; 10https://doi.org/10.1186/s13098-018-0376-8
        • Sánchez-García A.
        • Rodríguez-Gutiérrez R.
        • Mancillas-Adame L.
        • González-Nava V.
        • Díaz González-Colmenero A.
        • Solis R.C.
        • et al.
        Diagnostic accuracy of the triglyceride and glucose index for insulin resistance: a systematic review.
        Internet J Endocrinol. 2020; : 2020
        • Brito ADM de
        • Hermsdorff H.H.M.
        • Filgueiras M.D.S.
        • Suhett L.G.
        • Vieira-Ribeiro S.A.
        • Franceschini S do C.C.
        • et al.
        Predictive capacity of triglyceride-glucose (TyG) index for insulin resistance and cardiometabolic risk in children and adolescents: a systematic review.
        Crit Rev Food Sci Nutr. 2021; : 61https://doi.org/10.1080/10408398.2020.1788501
        • Page M.J.
        • McKenzie J.E.
        • Bossuyt P.M.
        • Boutron I.
        • Hoffmann T.C.
        • Mulrow C.D.
        • et al.
        The PRISMA 2020 statement: an updated guideline for reporting systematic reviews.
        Syst Rev. 2021; 10: 1-11
        • Whiting P.F.
        • Rutjes A.W.S.
        • Westwood M.E.
        • Mallett S.
        • Deeks J.J.
        • Reitsma J.B.
        • et al.
        QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies.
        Ann Intern Med. 2011; 155: 529-536
        • Higgins J.P.T.
        • Thomas J.
        • Chandler J.
        • Cumpston M.
        • Li T.
        • Page M.J.
        • et al.
        Cochrane handbook for systematic reviews of interventions.
        John Wiley & Sons, 2019
        • Schroll J.B.
        • Moustgaard R.
        • Gøtzsche P.C.
        Dealing with substantial heterogeneity in Cochrane reviews. Cross-sectional study.
        BMC Med Res Methodol. 2011; 11: 1-8
        • Endukuru C.K.
        • Gaur G.S.
        • Yerrabelli D.
        • Sahoo J.
        • Vairappan B.
        Cut-off values and clinical utility of surrogate markers for insulin resistance and beta-cell function to identify metabolic syndrome and its components among southern indian adults.
        Journal of Obesity and Metabolic Syndrome. 2021; 29https://doi.org/10.7570/JOMES20071
        • Haijing C.
        • Zhong X.I.N.
        • Xuhong W.
        • Caiguo Y.U.
        Comparison of the value of anthropometric indicators and triglycerides glucose index in the diagnosis of metabolic syndrome.
        Chinese General Practice. 2020; 23: 813
        • Liu P.J.
        • Lou H.P.
        • Zhu Y.N.
        Screening for metabolic syndrome using an integrated continuous index consisting of waist circumference and triglyceride: a preliminary cross-sectional study.
        Diabetes, Metab Syndrome Obes Targets Ther. 2020; 13https://doi.org/10.2147/DMSO.S259770
        • Li R.
        • Li Q.
        • Cui M.
        • Yin Z.
        • Li L.
        • Zhong T.
        • et al.
        Clinical surrogate markers for predicting metabolic syndrome in middle-aged and elderly Chinese.
        Journal of Diabetes Investigation. 2018; 9https://doi.org/10.1111/jdi.12708
        • Unger G.
        • Benozzi S.F.
        • Perruzza F.
        • Pennacchiotti G.L.
        Índice triglicéridos y glucosa: un indicador útil de insulinorresistencia.
        Endocrinol Nutr. 2014; 61https://doi.org/10.1016/j.endonu.2014.06.009
        • Ferreira J.R.S.
        • Zandonade E.
        • de Paula Alves Bezerra O.M.
        • Salaroli L.B.
        Cutoff point of TyG index for metabolic syndrome in Brazilian farmers.
        Arch Endocrinol Metab. 2021; 65https://doi.org/10.20945/2359-3997000000401
        • Chiu T.H.
        • Huang Y.C.
        • Chiu H.
        • Wu P.Y.
        • Chiou H.Y.C.
        • Huang J.C.
        • et al.
        Comparison of various obesity-related indices for identification of metabolic syndrome: a population-based study from taiwan biobank.
        Diagnostics. 2020; 10https://doi.org/10.3390/diagnostics10121081
        • Son D.-H.
        • Lee H.S.
        • Lee Y.-J.
        • Lee J.-H.
        • Han J.-H.
        Comparison of triglyceride-glucose index and HOMA-IR for predicting prevalence and incidence of metabolic syndrome.
        Nutr Metabol Cardiovasc Dis. 2022; 32: 596-604
        • Shin K.A.
        • Kim Y.J.
        Usefulness of surrogate markers of body fat distribution for predicting metabolic syndrome in middle-aged and older Korean populations.
        Diabetes, Metab Syndrome Obes Targets Ther. 2019; 12https://doi.org/10.2147/DMSO.S217628
        • Pandit K.
        • Mukhopadhyay P.
        • Chatterjee P.
        • Majhi B.
        • Chowdhury S.
        • Ghosh S.
        Assessment of insulin resistance indices in individuals with lean and obese metabolic syndrome compared to normal individuals: a population based study.
        J Assoc Phys India. 2020; : 68
        • Xu H.
        • Han G.
        • Wang L.
        • Ding H.
        • Wang C.
        • Ping X.
        • et al.
        25-hydroxyvitamin D levels are inversely related to metabolic syndrome risk profile in northern Chinese subjects without vitamin D supplementation.
        Diabetol Metab Syndrome. 2022; 14: 1-11
        • Hui Z.
        • Yang C.
        • Donghua Y.I.N.
        • Yin Hongli
        • Gu Liubao
        • Zhixiang S.
        Predictive value of non-high-density lipoprotein cholesterol, triglyceride-glucose index, and ratio of triglyceride to high-density lipoprotein cholesterol in metabolic syndrome: a comparative study.
        Chinese General Practice. 2021; 24: 322
      1. Macaskill P, Gatsonis C, Deeks J, Harbord R, Takwoingi Y. Cochrane handbook for systematic reviews of diagnostic test accuracy 2010.

        • Grundy S.M.
        • Brewer H.B.
        • Cleeman J.I.
        • Smith S.C.
        • Lenfant C.
        Definition of metabolic syndrome: report of the national heart, lung, and blood institute/American heart association conference on scientific issues related to definition.
        Circulation. 2004; 109https://doi.org/10.1161/01.CIR.0000111245.75752.C6
        • Hardy O.T.
        • Czech M.P.
        • Corvera S.
        What causes the insulin resistance underlying obesity?.
        Curr Opin Endocrinol Diabetes Obes. 2012; 19: 81
        • Tchernof A.
        • Després J.P.
        Pathophysiology of human visceral obesity: an update.
        Physiol Rev. 2013; 93https://doi.org/10.1152/physrev.00033.2011
        • Coker R.H.
        • Williams R.H.
        • Yeo S.E.
        • Kortebein P.M.
        • Bodenner D.L.
        • Kern P.A.
        • et al.
        Visceral fat and adiponectin: associations with insulin resistance are tissue-specific in women.
        Metab Syndr Relat Disord. 2009; 7: 61-67
        • Amato M.C.
        • Pizzolanti G.
        • Torregrossa V.
        • Misiano G.
        • Milano S.
        • Giordano C.
        Visceral adiposity index (VAI) is predictive of an altered adipokine profile in patients with type 2 diabetes.
        PLoS One. 2014; 9e91969
        • Sung H.H.
        • Park C.E.
        • Gi M.Y.
        • Cha J.A.
        • Moon A.E.
        • Kang J.K.
        • et al.
        The association of the visceral adiposity index with insulin resistance and beta-cell function in Korean adults with and without type 2 diabetes mellitus.
        Endocr J. 2020; 67: 613-621
        • Mazidi M.
        • Kengne A.-P.
        • Katsiki N.
        • Mikhailidis D.P.
        • Banach M.
        Lipid accumulation product and triglycerides/glucose index are useful predictors of insulin resistance.
        J Diabetes Complicat. 2018; 32: 266-270
        • Motamed N.
        • Razmjou S.
        • Hemmasi G.
        • Maadi M.
        • Zamani F.
        Lipid accumulation product and metabolic syndrome: a population-based study in northern Iran, Amol.
        J Endocrinol Invest. 2016; 39: 375-382
        • Nascimento-Ferreira M.V.
        • Rendo-Urteaga T.
        • Vilanova-Campelo R.C.
        • Carvalho H.B.
        • da Paz Oliveira G.
        • Landim M.B.P.
        • et al.
        The lipid accumulation product is a powerful tool to predict metabolic syndrome in undiagnosed Brazilian adults.
        Clin Nutr. 2017; 36: 1693-1700
        • Goldani H.
        • Adami F.S.
        • Antunes M.T.
        • Rosa L.H.
        • Fassina P.
        • Grave M.T.Q.
        • et al.
        Applicatility of the visceral adiposity index (VAI) in the prediction of the components of the metabolic syndrome in elderly.
        Nutr Hosp. 2015; 32: 1609-1615
        • Vizzuso S.
        • del Torto A.
        • Dilillo D.
        • Calcaterra V.
        • di Profio E.
        • Leone A.
        • et al.
        Visceral adiposity index (VAI) in children and adolescents with obesity: no association with daily energy intake but promising tool to identify metabolic syndrome (MetS).
        Nutrients. 2021; 13: 413
        • Wang Y.
        • Yang W.
        • Jiang X.
        Association between triglyceride-glucose index and hypertension: a meta-analysis.
        Frontiers in Cardiovascular Medicine. 2021; 8644035
        • Wu S.
        • Xu L.
        • Wu M.
        • Chen S.
        • Wang Y.
        • Tian Y.
        Association between triglyceride-glucose index and risk of arterial stiffness: a cohort study.
        Cardiovasc Diabetol. 2021; 20: 1-8
        • Barzegar N.
        • Tohidi M.
        • Hasheminia M.
        • Azizi F.
        • Hadaegh F.
        The impact of triglyceride-glucose index on incident cardiovascular events during 16 years of follow-up: tehran Lipid and Glucose Study.
        Cardiovasc Diabetol. 2020; 19: 1-12
        • Simental-Mendía L.E.
        • Hernández-Ronquillo G.
        • Gómez-Díaz R.
        • Rodríguez-Morán M.
        • Guerrero-Romero F.
        The triglycerides and glucose index is associated with cardiovascular risk factors in normal-weight children and adolescents.
        Pediatr Res. 2017; 82: 920-925
        • Xuan X.
        • Hamaguchi M.
        • Cao Q.
        • Okamura T.
        • Hashimoto Y.
        • Obora A.
        • et al.
        U-shaped association between the triglyceride-glucose index and the risk of incident diabetes in people with normal glycemic level: a population-base longitudinal cohort study.
        Clin Nutr. 2021; 40: 1555-1561
        • Wen J.
        • Wang A.
        • Liu G.
        • Wang M.
        • Zuo Y.
        • Li W.
        • et al.
        Elevated triglyceride-glucose (TyG) index predicts incidence of Prediabetes: a prospective cohort study in China.
        Lipids Health Dis. 2020; 19: 1-10
        • Bijari M.
        • Jangjoo S.
        • Emami N.
        • Raji S.
        • Mottaghi M.
        • Moallem R.
        • et al.
        The accuracy of visceral adiposity index for the screening of metabolic syndrome: a systematic review and meta-analysis.
        International Journal of Endocrinology. 2021; : 2021