Optimal cutoff for the evaluation of insulin resistance through triglyceride-glucose index: A cross-sectional study in a Venezuelan population [version 1; referees: awaiting peer review]

Background: Insulin resistance (IR) evaluation is a fundamental goal in clinical and epidemiological research. However, the most widely used methods are difficult to apply to populations with low incomes. The triglyceride-glucose index (TGI) emerges as an alternative to use in daily clinical practic...

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Autores:
Salazar, Juan
Bermúdez, Valmore
Calvo, María
Olivar, Luis
Luzardo, Eliana
Navarro, Carla
Mencia, Heysa
Martínez, María
Rivas-Ríos, José
Wilches-Durán, Sandra
Cerda, Marcos
Graterol, Modesto
Graterol, Rosemily
Garicano, Carlos
Hernández, Juan
Rojas, Joselyn
Tipo de recurso:
Fecha de publicación:
2017
Institución:
Universidad Simón Bolívar
Repositorio:
Repositorio Digital USB
Idioma:
eng
OAI Identifier:
oai:bonga.unisimon.edu.co:20.500.12442/1763
Acceso en línea:
http://hdl.handle.net/20.500.12442/1763
Palabra clave:
Blood pressure
Body mass index
Cholesterol
Diabetes mellitus
Glucose metabolism
Insulin resistance
Obesity
Type 2 diabetes
Rights
License
Licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional
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oai_identifier_str oai:bonga.unisimon.edu.co:20.500.12442/1763
network_acronym_str USIMONBOL2
network_name_str Repositorio Digital USB
repository_id_str
dc.title.eng.fl_str_mv Optimal cutoff for the evaluation of insulin resistance through triglyceride-glucose index: A cross-sectional study in a Venezuelan population [version 1; referees: awaiting peer review]
title Optimal cutoff for the evaluation of insulin resistance through triglyceride-glucose index: A cross-sectional study in a Venezuelan population [version 1; referees: awaiting peer review]
spellingShingle Optimal cutoff for the evaluation of insulin resistance through triglyceride-glucose index: A cross-sectional study in a Venezuelan population [version 1; referees: awaiting peer review]
Blood pressure
Body mass index
Cholesterol
Diabetes mellitus
Glucose metabolism
Insulin resistance
Obesity
Type 2 diabetes
title_short Optimal cutoff for the evaluation of insulin resistance through triglyceride-glucose index: A cross-sectional study in a Venezuelan population [version 1; referees: awaiting peer review]
title_full Optimal cutoff for the evaluation of insulin resistance through triglyceride-glucose index: A cross-sectional study in a Venezuelan population [version 1; referees: awaiting peer review]
title_fullStr Optimal cutoff for the evaluation of insulin resistance through triglyceride-glucose index: A cross-sectional study in a Venezuelan population [version 1; referees: awaiting peer review]
title_full_unstemmed Optimal cutoff for the evaluation of insulin resistance through triglyceride-glucose index: A cross-sectional study in a Venezuelan population [version 1; referees: awaiting peer review]
title_sort Optimal cutoff for the evaluation of insulin resistance through triglyceride-glucose index: A cross-sectional study in a Venezuelan population [version 1; referees: awaiting peer review]
dc.creator.fl_str_mv Salazar, Juan
Bermúdez, Valmore
Calvo, María
Olivar, Luis
Luzardo, Eliana
Navarro, Carla
Mencia, Heysa
Martínez, María
Rivas-Ríos, José
Wilches-Durán, Sandra
Cerda, Marcos
Graterol, Modesto
Graterol, Rosemily
Garicano, Carlos
Hernández, Juan
Rojas, Joselyn
dc.contributor.author.none.fl_str_mv Salazar, Juan
Bermúdez, Valmore
Calvo, María
Olivar, Luis
Luzardo, Eliana
Navarro, Carla
Mencia, Heysa
Martínez, María
Rivas-Ríos, José
Wilches-Durán, Sandra
Cerda, Marcos
Graterol, Modesto
Graterol, Rosemily
Garicano, Carlos
Hernández, Juan
Rojas, Joselyn
dc.subject.eng.fl_str_mv Blood pressure
Body mass index
Cholesterol
Diabetes mellitus
Glucose metabolism
Insulin resistance
Obesity
Type 2 diabetes
topic Blood pressure
Body mass index
Cholesterol
Diabetes mellitus
Glucose metabolism
Insulin resistance
Obesity
Type 2 diabetes
description Background: Insulin resistance (IR) evaluation is a fundamental goal in clinical and epidemiological research. However, the most widely used methods are difficult to apply to populations with low incomes. The triglyceride-glucose index (TGI) emerges as an alternative to use in daily clinical practice. Therefore the objective of this study was to determine an optimal cutoff point for the TGI in an adult population from Maracaibo, Venezuela. Methods: This is a sub-study of Maracaibo City Metabolic Syndrome Prevalence Study, a descriptive, cross-sectional study with random and multi-stage sampling. For this analysis, 2004 individuals of both genders ≥18 years old with basal insulin determination and triglycerides < 500 mg/dl were evaluated.. A reference population was selected according to clinical and metabolic criteria to plot ROC Curves specific for gender and age groups to determine the optimal cutoff point according to sensitivity and specificity.The TGI was calculated according to the equation: ln [Fasting triglyceride (mg / dl) x Fasting glucose (mg / dl)] / 2. Results: The TGI in the general population was 4.6±0.3 (male: 4.66±0.34 vs. female: 4.56±0.33, p=8.93x10 ). The optimal cutoff point was 4.49, with a sensitivity of 82.6% and specificity of 82.1% (AUC=0.889, 95% CI: 0.854-0.924). There were no significant differences in the predictive capacity of the index when evaluated according to gender and age groups. Those individuals with TGI≥4.5 had higher HOMA2-IR averages than those with TGI <4.5 (2.48 vs 1.74, respectively, p<0.001). Conclusions: The TGI is a measure of interest to identify IR in the general population. We propose a single cutoff point of 4.5 to classify individuals with IR. Future studies should evaluate the predictive capacity of this index to determine atypical metabolic phenotypes, type 2 diabetes mellitus and even cardiovascular risk in our population.
publishDate 2017
dc.date.issued.none.fl_str_mv 2017-08-07
dc.date.accessioned.none.fl_str_mv 2018-03-02T16:22:06Z
dc.date.available.none.fl_str_mv 2018-03-02T16:22:06Z
dc.type.spa.fl_str_mv article
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.identifier.issn.none.fl_str_mv 20461402
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/20.500.12442/1763
identifier_str_mv 20461402
url http://hdl.handle.net/20.500.12442/1763
dc.language.iso.spa.fl_str_mv eng
language eng
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.license.spa.fl_str_mv Licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional
rights_invalid_str_mv Licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional
http://purl.org/coar/access_right/c_abf2
dc.publisher.spa.fl_str_mv F1000 Research Ltd.
dc.source.eng.fl_str_mv F1000 Research
Vol. 6, No.1337 (2017)
institution Universidad Simón Bolívar
dc.source.uri.none.fl_str_mv https://f1000research.com/articles/6-1337/v1
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spelling Licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2Salazar, Juanfbd053e7-5aea-424c-812f-92153ecb9181-1Bermúdez, Valmore29f9aa18-16a4-4fd3-8ce5-ed94a0b8663a-1Calvo, María3038ee2e-e515-49a5-ac75-96a0d81ff3c8-1Olivar, Luisf2fa2efd-f10f-4f8f-a731-c59a2c25964f-1Luzardo, Eliana1b6358ac-0e35-4d5a-817b-45cdaeea6c0b-1Navarro, Carla9892bf34-10a9-479a-8b3a-6769d4804207-1Mencia, Heysa3839fb1c-8414-476c-b555-018d40b17560-1Martínez, María9aa9b2df-abae-4b77-9dc6-375422bdff38-1Rivas-Ríos, José150fd0a6-0769-46ee-8541-c44afb598193-1Wilches-Durán, Sandra57727544-0054-45e6-997c-6d75c266cea0-1Cerda, Marcosf544118b-7bb4-4bf7-b8e8-fb4d2017eaf5-1Graterol, Modesto59713475-4607-4bff-90c1-a66ad9f0a173-1Graterol, Rosemily9e55a756-71ff-4945-99cf-ab0ecd38182f-1Garicano, Carlos96ac943f-0dd6-4a77-a76d-9c0024646fe5-1Hernández, Juan1e343f6e-5122-40d2-b89a-7287a485337c-1Rojas, Joselyn2aa91570-0516-424d-8f76-25cd7b39be6e-12018-03-02T16:22:06Z2018-03-02T16:22:06Z2017-08-0720461402http://hdl.handle.net/20.500.12442/1763Background: Insulin resistance (IR) evaluation is a fundamental goal in clinical and epidemiological research. However, the most widely used methods are difficult to apply to populations with low incomes. The triglyceride-glucose index (TGI) emerges as an alternative to use in daily clinical practice. Therefore the objective of this study was to determine an optimal cutoff point for the TGI in an adult population from Maracaibo, Venezuela. Methods: This is a sub-study of Maracaibo City Metabolic Syndrome Prevalence Study, a descriptive, cross-sectional study with random and multi-stage sampling. For this analysis, 2004 individuals of both genders ≥18 years old with basal insulin determination and triglycerides < 500 mg/dl were evaluated.. A reference population was selected according to clinical and metabolic criteria to plot ROC Curves specific for gender and age groups to determine the optimal cutoff point according to sensitivity and specificity.The TGI was calculated according to the equation: ln [Fasting triglyceride (mg / dl) x Fasting glucose (mg / dl)] / 2. Results: The TGI in the general population was 4.6±0.3 (male: 4.66±0.34 vs. female: 4.56±0.33, p=8.93x10 ). The optimal cutoff point was 4.49, with a sensitivity of 82.6% and specificity of 82.1% (AUC=0.889, 95% CI: 0.854-0.924). There were no significant differences in the predictive capacity of the index when evaluated according to gender and age groups. Those individuals with TGI≥4.5 had higher HOMA2-IR averages than those with TGI <4.5 (2.48 vs 1.74, respectively, p<0.001). Conclusions: The TGI is a measure of interest to identify IR in the general population. We propose a single cutoff point of 4.5 to classify individuals with IR. Future studies should evaluate the predictive capacity of this index to determine atypical metabolic phenotypes, type 2 diabetes mellitus and even cardiovascular risk in our population.engF1000 Research Ltd.F1000 ResearchVol. 6, No.1337 (2017)https://f1000research.com/articles/6-1337/v1Blood pressureBody mass indexCholesterolDiabetes mellitusGlucose metabolismInsulin resistanceObesityType 2 diabetesOptimal cutoff for the evaluation of insulin resistance through triglyceride-glucose index: A cross-sectional study in a Venezuelan population [version 1; referees: awaiting peer review]articlehttp://purl.org/coar/resource_type/c_6501Wilcox G: Insulin and insulin resistance. Clin Biochem Rev. 2005; 26(2): 19–39.Morigny P, Houssier M, Mouisel E, et al.: Adipocyte lipolysis and insulin resistance. Biochimie. 2016; 125: 259–66.Patel TP, Rawal K, Bagchi AK, et al.: Insulin resistance: an additional risk factor in the pathogenesis of cardiovascular disease in type 2 diabetes. Heart Fail Rev. 2016; 21(1): 11–23.Luchsinger J: Insulin resistance, type 2 diabetes, and AD: cerebrovascular disease or neurodegeneration? Neurology. 2010; 75(9): 758–759.Rojas J, Bermúdez V, Leal E, et al.: Insulinorresistencia E Hiperinsulinemia Como Factores De Riesgo Para Enfermedad Cardiovascular. AVTF. 2008; 27(1): 30–40.Gastaldelli A: Role of beta-cell dysfunction, ectopic fat accumulation and insulin resistance in the pathogenesis of type 2 diabetes mellitus. Diabetes Res Clin Pract. 2011; 93(suppl 1): S60–S65.Randle PJ, Garland PB, Hales CN, et al.: The glucose fatty-acid cycle. Its role in insulin sensitivity and the metabolic disturbances of diabetes mellitus. Lancet. 1963; 1(7285): 785–789.Shulman GI: Cellular mechanisms of insulin resistance. J Clin Invest. 2000; 106(2): 171–6.Borai A, Livingstone C, Kaddam I, et al.: Selection of the appropriate method for the assessment of insulin resistance. BMC Med Res Methodol. 2011; 11(1): 158.DeFronzo RA, Tobin JD, Andres R: Glucose clamp technique: a method for quantifying insulin secretion and resistance. Am J Physiol. 1979; 237(3): E214–223.Singh B, Saxena A: Surrogate markers of insulin resistance: A review. World J Diabetes. 2010; 1(2): 36–47.Matthews DR, Hosker JP, Rudenski AS, et al.: Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985; 28(7): 412–9.Bermudez V, Salazar J, Martínez MS, et al.: Prevalence and Associated Factors of Insulin Resistance in Adults from Maracaibo City, Venezuela. Adv Prev Med. 2016; 2016: 9405105.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 subjects. Metab Syndr Relat Disord. 2008; 6(4): 299–304.Guerrero-Romero F, Simental-Mendía LE, González-Ortiz M, 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(7): 3347–51.Bermúdez V, Marcano RP, Cano C, et al.: The Maracaibo city metabolic syndrome prevalence study: design and scope. Am J Ther. 2010; 17(3): 288–294.World Health Organization: Obesity: preventing and managing the global epidemic. Report of a WHO consultation. World Health Organ Tech Rep Ser. Geneva: The Organization; 2000; 894: i–xii, 1–253.Health Statistics: NHANES III reference manuals and reports (CDROM). Hyattsville, MD: Centers for Disease Control and Prevention, 1996.Bermúdez V, Rojas J, Salazar J, et al.: Optimal Waist Circumference Cut-Off Point for Multiple Risk Factor Aggregation: Results from the Maracaibo City Metabolic Syndrome Prevalence Study. Epidemiol Res Int. 2014; 2014: 718571.Bermúdez V, Rojas J, Martínez MS, et al.: Epidemiologic Behavior and Estimation of an Optimal Cut-Off Point for Homeostasis Model Assessment- 2 Insulin Resistance: A Report from a Venezuelan Population. Int Sch Res Notices. 2014; 2014: 616271.Unger G, Benozzi SF, Perruzza F, et al.: Triglycerides and glucose index: a useful indicator of insulin resistance. Endocrinol Nutr. 2014; 61(10): 533–40.Akobeng AK: Understanding diagnostic tests 3: Receiver operating characteristic curves. Acta Paediatr. 2007; 66(5): 644–7.Demler OV, Pencina MJ, D'Agostino RB Sr: Misuse of DeLong test to compare AUCs for nested models. Stat Med. 2012; 31(23): 2577–87.Böhning D, Böhning W, Holling H: Revisiting Youden’s index as a useful measure of the misclassification error in meta-analysis of diagnostic studies. Stat Methods Med Res. 2008; 17(6): 543–54.Perkins NJ, Schisterman EF: The inconsistency of “optimal” cutpoints obtained using two criteria based on the receiver operating characteristic curve. Am J Epidemiol. 2006; 163(7): 670–5.Samson SL, Garber AJ: Metabolic syndrome. Endocrinol Metab Clin North Am. 2014; 43(1): 1–23.Lyssenko V, Jonsson A, Almgren P, et al.: Clinical risk factors, DNA variants, and the development of type 2 diabetes. N Engl J Med. 2008; 359(21): 2220–2232.Miller M, Stone NJ, Ballantyne C, et al.: Triglycerides and cardiovascular disease: a scientific statement from the American Heart Association. Circulation. 2011; 123(20): 2292–2333.Er LK, Wu S, Chou HH, et al.: Triglyceride Glucose-Body Mass Index Is a Simple and Clinically Useful Surrogate Marker for Insulin Resistance in Nondiabetic Individuals. PLoS One. 2016; 11(3): e0149731.Du T, Yuan G, Zhang M, et al.: Clinical usefulness of lipid ratios, visceral adiposity indicators, and the triglycerides and glucose index as risk markers of insulin resistance. Cardiovasc Diabetol. 2014; 13(1): 146.Guerrero-Romero F, Villalobos-Molina R, Jiménez-Flores JR, et al.: Fasting triglycerides and glucose index as a diagnostic test for insulin resistance in Young adults. Arch Med Rev. 2016; 47(5): 382–387.Irace C, Carallo C, Scavelli FB, 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(7): 665–672.Vasques AC, Novaes FS, de Oliveira Mda S, et al.: TyG index performs better than HOMA in a Brazilian population: a hyperglycemic clamp validated study. Diabetes Res Clin Pract. 2011; 93(3): e98–e100.Lee SH, Han K, Yang HK, et al.: A novel criterion for identifying metabolically obese but normal weight individuals using the product of triglycerides and glucose. Nutr Diabetes. 2015; 5(4): e149.Lee SH, Yang HK, Ha HS, et al.: Changes in Metabolic Health Status Over Time and Risk of Developing Type 2 Diabetes: A Prospective Cohort Study. Medicine (Baltimore). 2015; 94(40): e1705.Abbasi F, Reaven GM: 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. Metabolism. 2011; 60(12): 1673–1676.Hosseini SM: Triglyceride-Glucose (TyG) Index Computation and Cut-Off. Acta Endo (Buc). 2015; 11(1): 130–131.Navarro-González D, Sánchez-Íñigo L, Pastrana-Delgado J, et al.: Triglyceride-glucose index (TyG index) in comparison with fasting plasma glucose improved diabetes prediction in patients with normal fasting glucose: The Vascular-Metabolic CUN cohort. Prev Med. 2016; 86: 99–105.Cuda G, Lentini M, Gallo L, et al.: Fasting triglycerides and glucose index in an unselected consecutive Italian population of outpatients. Riv Ital Med Lab. 2011; 7(4): 226–227.Monickaraj F, Aravind S, Nandhini P, et al.: Accelerated fat cell aging links oxidative stress and insulin resistance in adipocytes. J Biosci. 2013; 38(1): 113–122.Lee SH, Kwon HS, Park YM, 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(2): e90430.Lee SH, Han K, Yang HK, et al.: Identifying subgroups of obesity using the product of triglycerides and glucose: the Korea National Health and Nutrition Examination Survey, 2008–2010. Clin Endocrinol (Oxf). 2014; 82(2): 213–220.Navarro-González D, Sánchez-Íñigo L, Fernández-Montero A, et al.: TyG Index Change Is More Determinant for Forecasting Type 2 Diabetes Onset Than Weight Gain. Medicine (Baltimore). 2016; 95(19): e3646.Lee DY, Lee ES, Kim JH, et al.: Predictive value of triglyceride glucose index for the risk of incident diabetes: A 4-year retrospective longitudinal study. PLoS One. 2016; 11(9): e0163465.Salazar J, Bermúdez V, Calvo M, et al.: Dataset 1 in: Optimal cutoff for the evaluation of insulin resistance through triglyceride-glucose index: A crosssectional study in a Venezuelan population. 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