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...
- 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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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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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. 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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. 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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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