Increased triglyceride-glucose index predicts contrast-induced nephropathy in non-diabetic NSTEMI patients: A prospective study

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Abstract

The triglyceride-glucose (TyG) index is a new reliable marker of insulin resistance (IR) and has recently been reported to be associated with renal dysfunction and contrast-induced nephropathy (CIN). Our aim in this study is to investigate the relationship between the TyG index and CIN in non-diabetic non-ST elevation acute myocardial infarction (NSTEMI) patients. The study included 272 non-diabetic patients who applied with NSTEMI and underwent coronary angiography (CAG). Patient data were divided into quartiles according to the TyG index: Q1: TyG < 8.55; Q2: 8.55 ≤ TyG ≤ 8.87; Q3: 8.88 ≤ TyG ≤ 9.29; and Q4: TyG > 9.29. Baseline characteristics, laboratory measurements, angiography data, and the incidence of CIN were compared between the groups. CIN was observed in 18 (6.6%) patients in the study. The incidence of CIN was lowest in the Q1 group and highest in the Q4 group (1 (1.5%) in Q1; 3 (4.4%) in Q2; 5 (7.4%) in Q3; 9 (13.2%) in Q4; p = 0.040). TyG index was found to be an independent risk factor for the development of CIN in multivariate logistic regression analysis (odds ratio = 6.58; confidence interval (CI) = 2.12–20.40; p = 0.001). TyG index value of 9.17 was identified as an effective cut-off point for the prediction of CIN (Area under the curve: 0.712, CI: 0.590–0.834, p = 0.003), and it had a sensitivity of 61% and a specificity of 72%. The results of this study showed that a high TyG index increases the incidence of CIN after CAG in non-diabetic NSTEMI patients and is an independent risk factor for the development of CIN.

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Use and abuse of HOMA modeling.

T Wallace, J Levy, D. Matthews (2004)

Homeostatic model assessment (HOMA) is a method for assessing beta-cell function and insulin resistance (IR) from basal (fasting) glucose and insulin or C-peptide concentrations. It has been reported in >500 publications, 20 times more frequently for the estimation of IR than beta-cell function. This article summarizes the physiological basis of HOMA, a structural model of steady-state insulin and glucose domains, constructed from physiological dose responses of glucose uptake and insulin production. Hepatic and peripheral glucose efflux and uptake were modeled to be dependent on plasma glucose and insulin concentrations. Decreases in beta-cell function were modeled by changing the beta-cell response to plasma glucose concentrations. The original HOMA model was described in 1985 with a formula for approximate estimation. The computer model is available but has not been as widely used as the approximation formulae. HOMA has been validated against a variety of physiological methods. We review the use and reporting of HOMA in the literature and give guidance on its appropriate use (e.g., cohort and epidemiological studies) and inappropriate use (e.g., measuring beta-cell function in isolation). The HOMA model compares favorably with other models and has the advantage of requiring only a single plasma sample assayed for insulin and glucose. In conclusion, the HOMA model has become a widely used clinical and epidemiological tool and, when used appropriately, it can yield valuable data. However, as with all models, the primary input data need to be robust, and the data need to be interpreted carefully.

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The product of fasting glucose and triglycerides as surrogate for identifying insulin resistance in apparently healthy subjects.

Luis E Simental-Mendía, Martha Rodríguez-Morán, Fernando Guerrero-Romero (2008)

Because the insulin test is expensive and is not available in most laboratories in the cities of undeveloped countries, we tested whether the product of fasting triglycerides and glucose levels (TyG) is a surrogate for estimating insulin resistance compared with the homeostasis model assessment of insulin resistance (HOMA-IR) index. We performed a population-based cross-sectional study. Sampling strategy was based on a randomized two-stage cluster sampling procedure. Only apparently healthy subjects, men and nonpregnant women aged 18-65 years, with newly diagnosed impaired fasting glucose (IFG), impaired glucose tolerance (IGT), or IFG + IGT were enrolled. Renal disease, malignancy, and diabetes were exclusion criteria. Sensitivity, specificity, predictive values, and the probability of disease given a positive test were calculated. The optimal TyG index for estimating insulin resistance was established using a receiver operating characteristic scatter plot analysis. A total of 748 apparently healthy subjects aged 41.4 +/- 11.2 years were enrolled. Insulin resistance was identified in 241 (32.2%) subjects (HOMA-IR index 4.4 +/- 1.6). New diagnoses of IFG, IGT, and IFG + IGT were established in 145 (19.4%), 54 (7.2%), and 75 (10.0%) individuals. respectively. The best TyG index for diagnosis of insulin resistance was Ln 4.65, which showed the highest sensitivity (84.0%) and specificity (45.0%) values. The positive and negative predictive values were 81.1% and 84.8%, and the probability of disease, given a positive test, was 60.5%. The TyG index could be useful as surrogate to identify insulin resistance in apparently healthy subjects.

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A More Accurate Method To Estimate Glomerular Filtration Rate from Serum Creatinine: A New Prediction Equation

Andrew Levey (1999)

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Author and article information

Journal

Title: Journal of Investigative Medicine

Abbreviated Title: Journal of Investigative Medicine

Publisher: SAGE Publications

ISSN (Print): 1081-5589

ISSN (Electronic): 1708-8267

Publication date Created: December 2023

Publication date (Electronic): June 28 2023

Publication date (Print): December 2023

Volume: 71

Issue: 8

Pages: 838-844

Affiliations

[1 ]Department of Cardiology, Faculty of Medicine, Aksaray University, Aksaray, Turkey

[2 ]Department of Internal Medicine, Faculty of Medicine, Aksaray University, Aksaray, Turkey

[3 ]Department of Cardiology, Aksaray Education and Research Hospital, Aksaray, Turkey

Article

DOI: 10.1177/10815589231182317

PubMed ID: 37377036

SO-VID: b8ca0bae-42b7-401c-9275-630618beed7d

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http://journals.sagepub.com/page/policies/text-and-data-mining-license

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