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Optimal cutoff for the evaluation of insulin resistance through triglyceride-glucose index: A cross-sectional study in a Venezuelan population.

ABSTRACT: 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 -10). 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.

SUBMITTER: Salazar J

PROVIDER: S-EPMC5760971 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Optimal cutoff for the evaluation of insulin resistance through triglyceride-glucose index: A cross-sectional study in a Venezuelan population.

Salazar Juan J Bermúdez Valmore V Calvo María M Olivar Luis Carlos LC Luzardo Eliana E Navarro Carla C Mencia Heysa H Martínez María M Rivas-Ríos José J Wilches-Durán Sandra S Cerda Marcos M Graterol Modesto M Graterol Rosemily R Garicano Carlos C Hernández Juan J Rojas Joselyn J

F1000Research 20170807

<b>Background:</b> 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. <b>Methods:</b> This is a sub-study of Maracaibo ...[more]

PMID: 29375810

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Project description:BackgroundResearch has shown that insulin resistance (IR) is a known risk factor for diabetic foot (DF), and the triglyceride-glucose (TyG) index is a reliable and simple indicator of IR. However, less is known about the relationship between the TyG and the risk of DF. Here, we investigated the association between the TyG index and the prevalence of DF.MethodsThe eligible records from the Departments of Endocrinology of Shandong Provincial Hospital Affiliated to Shandong First Medical University were screened (from December 1, 2012, to December 31, 2021), and a total of 8866 patients were enrolled. The TyG index was calculated as ln[(fasting triglycerides (mg/dL)×fasting glucose (mg/dL)/2)]. The continuous variables between the DF and the non-DF groups were compared by Student's t test or the Mann-Whitney U test, and categorical variables were compared by the chi-square test. Receiver operating characteristic curve (ROC) analysis was carried out to estimate the predictive value of the TyG index for DF. Logistic regression models were used to evaluate the associations between the quartiles of the TyG index and the risk of DF. Subgroup and sensitivity analyses were conducted.ResultsThe TyG index was significantly lower in the DF group than in the no-DF group. The logistic regression revealed that an increased TyG index was associated with a lower risk of DF after adjusting for potential confounders. In addition, an ROC analysis indicated the discriminatory ability of the TyG index in DF presence with an area under the curve (AUC) of 0.661 (95% CI 0.642-0.680, P < 0.001). Subgroup and sensitivity analysis also supported these robust results.ConclusionsThe TyG index was inversely and dose-dependently associated with the risk of DF in diabetes patients, indicating that elevated TyG index was a protective factor for DF. Future studies are therefore warranted to confirm our finding and to explore the detailed pathological mechanism involved in this process.

Project description:BackgroundTriglyceride glucose (TyG) index is a reliable marker of insulin resistance, which is linked to obstructive sleep apnea (OSA). However, the relationship between TyG index and OSA has not been adequately assessed. This study aimed to evaluate the association between TyG index and OSA.MethodsTyG index was assessed in 180 (mean age: 48.6 ± 13.8 years; 73.9% male) consecutive Korean adults with suspected OSA admitted to the sleep clinic at St. Paul's Hospital between 2010 and 2012. The occurrence of more than 5 apnea-hypopnea index (AHI) events/h was used to define OSA. TyG index was calculated using the following equation: In [fasting triglycerides (mg/dL) × fasting glucose (mg/dL)/2]. All participants were grouped according to TyG index tertiles. Multivariate logistic regression analysis was used to determine factors associated with increased OSA risk.ResultsThe overall prevalence of OSA in study participants was determined to be 83.9%. The prevalence of OSA increased (I [lowest]: 71.6%; II: 88.7%; III [highest]: 91.4%), and lowest peripheral oxygen saturation (SpO2) levels decreased (I: 83.3 ± 8.5%; II: 79.9 ± 8.7%; III: 79.0 ± 8.3%), as TyG index tertile increased (P < 0.05). TyG index was correlated with AHI (r = 0.179) and lowest SpO2 (r = - 0.188) (P < 0.05, respectively). Univariate linear regression analysis revealed an association between TyG and AHI (β = 10.084; P = 0.016). Multivariate logistic regression analysis showed that TyG index (odds ratio [OR]: 3.348; 95% confidence interval [CI]: 1.081-10.372), age ≥ 55 years (OR: 5.426; 95% CI: 1.642-17.935), and obesity (OR: 3.801; 95% CI: 1.468-9.842) were associated with increased OSA risk (all P < 0.05). The optimal TyG index cut-off value for predicting OSA was 8.83 (sensitivity: 61.6%; specificity: 69.0%; area under the curve: 0.688; P = 0.001). The predictive value of the OSA cut-off value improved when age ≥ 55 years and obesity were considered.ConclusionIncreased TyG index was independently associated with increased OSA risk.

Project description:BackgroundThe triglyceride-glucose index (TyG) has been proposed as a surrogate marker of insulin resistance, which is a typical trait of pregnancy. However, very few studies analyzed TyG performance as marker of insulin resistance in pregnancy, and they were limited to insulin resistance assessment at fasting rather than in dynamic conditions, i.e., during an oral glucose tolerance test (OGTT), which allows more reliable assessment of the actual insulin sensitivity impairment. Thus, first aim of the study was exploring in pregnancy the relationships between TyG and OGTT-derived insulin sensitivity. In addition, we developed a new version of TyG, for improved performance as marker of insulin resistance in pregnancy.MethodsAt early pregnancy, a cohort of 109 women underwent assessment of maternal biometry and blood tests at fasting, for measurements of several variables (visit 1). Subsequently (26 weeks of gestation) all visit 1 analyses were repeated (visit 2), and a subgroup of women (84 selected) received a 2 h-75 g OGTT (30, 60, 90, and 120 min sampling) with measurement of blood glucose, insulin and C-peptide for reliable assessment of insulin sensitivity (PREDIM index) and insulin secretion/beta-cell function. The dataset was randomly split into 70% training set and 30% test set, and by machine learning approach we identified the optimal model, with TyG included, showing the best relationship with PREDIM. For inclusion in the model, we considered only fasting variables, in agreement with TyG definition.ResultsThe relationship of TyG with PREDIM was weak. Conversely, the improved TyG, called TyGIS, (linear function of TyG, body weight, lean body mass percentage and fasting insulin) resulted much strongly related to PREDIM, in both training and test sets (R2 > 0.64, p < 0.0001). Bland-Altman analysis and equivalence test confirmed the good performance of TyGIS in terms of association with PREDIM. Different further analyses confirmed TyGIS superiority over TyG.ConclusionsWe developed an improved version of TyG, as new surrogate marker of insulin sensitivity in pregnancy (TyGIS). Similarly to TyG, TyGIS relies only on fasting variables, but its performances are remarkably improved than those of TyG.

Project description:Aims/introductionThe triglyceride-glucose (TyG) index has been proposed as a reliable and simple marker of insulin resistance. We investigated the association between TyG index and diabetic nephropathy (DN) in patients with type 2 diabetes.Materials and methodsA consecutive case series of 682 adult patients with type 2 diabetes hospitalized in the Department of Endocrinology at the Tongji Hospital (Wuhan, Hubei, China) from January 2007 to December 2009 was included in this cross-sectional analysis. Receiver operating characteristics curve analysis, correlation analysis and multiple logistic regression analysis were carried out.ResultsA total of 232 (34.0%) participants were identified with DN. Compared with the non-DN group, the DN group had longer disease duration, and higher bodyweight, systolic blood pressure, diastolic blood pressure, glycated hemoglobin, triglycerides, total cholesterol, serum uric acid, 24 h-urinary albumin, TyG index and homeostasis model assessment 2 estimates for insulin resistance (HOMA2-IR; P < 0.05 for each). The TyG index with an optimal cut-off point >9.66 showed a higher area under the receiver operating characteristic curve of 0.67 (P = 0.002) than HOMA2-IR (area under the curve 0.61, P = 0.029) on receiver operating characteristic curve analysis for DN identification. Additionally, the TyG index positively correlated with the levels of metabolic indicators (bodyweight, glycated hemoglobin, triglycerides, total cholesterol, serum uric acid, fasting glucose and HOMA2-IR) and natural logarithmic 24 h-urinary albumin (P < 0.05 for each), but not natural logarithm of estimated glomerular filtration rate. On multiple regression analysis, an increased TyG index was shown to be an independent risk factor (odds ratio 1.91, P = 0.001) for DN.ConclusionsThe TyG index was independently associated with DN in patients with type 2 diabetes, and was a better marker than HOMA2-IR for identification of DN in type 2 diabetes patients.

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Optimal cutoff for the evaluation of insulin resistance through triglyceride-glucose index: A cross-sectional study in a Venezuelan population.

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Optimal cutoff for the evaluation of insulin resistance through triglyceride-glucose index: A cross-sectional study in a Venezuelan population.

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