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Prognostic value of triglyceride-glucose index in patients with chronic coronary syndrome undergoing percutaneous coronary intervention
BACKGROUND: The triglyceride-glucose (TyG) index has been proposed as a reliable surrogate marker of insulin resistance and an independent predictor of major adverse cardiovascular events (MACEs). Several recent studies have shown the relationship between the TyG index and cardiovascular outcomes; h...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10685592/ https://www.ncbi.nlm.nih.gov/pubmed/38017540 http://dx.doi.org/10.1186/s12933-023-02060-7 |
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author | Tao, Shiyi Yu, Lintong Li, Jun Xie, Zicong Huang, Li Yang, Deshuang Tan, Yuqing Zhang, Wenjie Huang, Xuanchun Xue, Tiantian |
author_facet | Tao, Shiyi Yu, Lintong Li, Jun Xie, Zicong Huang, Li Yang, Deshuang Tan, Yuqing Zhang, Wenjie Huang, Xuanchun Xue, Tiantian |
author_sort | Tao, Shiyi |
collection | PubMed |
description | BACKGROUND: The triglyceride-glucose (TyG) index has been proposed as a reliable surrogate marker of insulin resistance and an independent predictor of major adverse cardiovascular events (MACEs). Several recent studies have shown the relationship between the TyG index and cardiovascular outcomes; however, the role of the TyG index in chronic coronary syndrome (CCS) progression has not been extensively assessed especially in population after revascularization. This study aimed to investigate the prognostic value of the TyG index in predicting MACEs in CCS patients undergoing percutaneous coronary intervention (PCI). METHODS: The data for the study were taken from the Hospital Information System database in China-Japan Friendship Hospital over the period 2019–2021. Eligible participants were divided into groups according to the TyG index tertiles. The Boruta algorithm was performed for feature selection. Multivariate Cox proportional hazards models and restricted cubic spline (RCS) analysis were applied to examine the dose–response relationship between the TyG index and endpoint, and the results were expressed with hazard ratio (HR) and 95% confidence interval (CI) values. The area under the receiver operating characteristic (ROC) curve (AUC), decision curve analysis (DCA), and clinical impact curve (CIC) were plotted to comprehensively evaluate the predictive accuracy and clinical value of the model. The goodness-of-fit of models was evaluated using the calibration curve and χ(2) likelihood ratio test. RESULTS: After applying inclusion and exclusion criteria, 1353 patients with CCS undergoing PCI were enrolled in the study. After adjusting for all confounders, we found that those with the highest TyG index had a 59.5% increased risk of MACEs over the 1-year follow-up (HR 1.595, 95% CI 1.370 ~ 1.855). Using the lowest TyG index tertile as the reference (T1), the fully adjusted HRs (95% CIs) for endpoints was 1.343 (1.054 ~ 1.711) in the middle (T2) and 2.297 (1.842 ~ 2.864) in highest tertile (T3) (P for trend < 0.001). The TyG index had an excellent predictive performance according to the results of AUC 0.810 (0.786, 0.834) and χ(2) likelihood ratio test (χ(2) = 7.474, P = 0.486). DCA and CIC analysis also suggested a good overall net benefit and clinical impact of the multivariate model. The results in the subgroup analysis were consistent with the main analyses. RCS model demonstrated that the TyG index was nonlinearly associated with the risk of MACEs within one year (P for nonlinear < 0.001). CONCLUSION: The elevated TyG index is associated with an increased risk of cardiovascular events and predicts future MACEs in patients with CCS undergoing PCI independently of known cardiovascular risk factors, indicating that the TyG index may be a potential marker for risk stratification and prognosis in CCS patients undergoing PCI. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12933-023-02060-7. |
format | Online Article Text |
id | pubmed-10685592 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-106855922023-11-30 Prognostic value of triglyceride-glucose index in patients with chronic coronary syndrome undergoing percutaneous coronary intervention Tao, Shiyi Yu, Lintong Li, Jun Xie, Zicong Huang, Li Yang, Deshuang Tan, Yuqing Zhang, Wenjie Huang, Xuanchun Xue, Tiantian Cardiovasc Diabetol Research BACKGROUND: The triglyceride-glucose (TyG) index has been proposed as a reliable surrogate marker of insulin resistance and an independent predictor of major adverse cardiovascular events (MACEs). Several recent studies have shown the relationship between the TyG index and cardiovascular outcomes; however, the role of the TyG index in chronic coronary syndrome (CCS) progression has not been extensively assessed especially in population after revascularization. This study aimed to investigate the prognostic value of the TyG index in predicting MACEs in CCS patients undergoing percutaneous coronary intervention (PCI). METHODS: The data for the study were taken from the Hospital Information System database in China-Japan Friendship Hospital over the period 2019–2021. Eligible participants were divided into groups according to the TyG index tertiles. The Boruta algorithm was performed for feature selection. Multivariate Cox proportional hazards models and restricted cubic spline (RCS) analysis were applied to examine the dose–response relationship between the TyG index and endpoint, and the results were expressed with hazard ratio (HR) and 95% confidence interval (CI) values. The area under the receiver operating characteristic (ROC) curve (AUC), decision curve analysis (DCA), and clinical impact curve (CIC) were plotted to comprehensively evaluate the predictive accuracy and clinical value of the model. The goodness-of-fit of models was evaluated using the calibration curve and χ(2) likelihood ratio test. RESULTS: After applying inclusion and exclusion criteria, 1353 patients with CCS undergoing PCI were enrolled in the study. After adjusting for all confounders, we found that those with the highest TyG index had a 59.5% increased risk of MACEs over the 1-year follow-up (HR 1.595, 95% CI 1.370 ~ 1.855). Using the lowest TyG index tertile as the reference (T1), the fully adjusted HRs (95% CIs) for endpoints was 1.343 (1.054 ~ 1.711) in the middle (T2) and 2.297 (1.842 ~ 2.864) in highest tertile (T3) (P for trend < 0.001). The TyG index had an excellent predictive performance according to the results of AUC 0.810 (0.786, 0.834) and χ(2) likelihood ratio test (χ(2) = 7.474, P = 0.486). DCA and CIC analysis also suggested a good overall net benefit and clinical impact of the multivariate model. The results in the subgroup analysis were consistent with the main analyses. RCS model demonstrated that the TyG index was nonlinearly associated with the risk of MACEs within one year (P for nonlinear < 0.001). CONCLUSION: The elevated TyG index is associated with an increased risk of cardiovascular events and predicts future MACEs in patients with CCS undergoing PCI independently of known cardiovascular risk factors, indicating that the TyG index may be a potential marker for risk stratification and prognosis in CCS patients undergoing PCI. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12933-023-02060-7. BioMed Central 2023-11-28 /pmc/articles/PMC10685592/ /pubmed/38017540 http://dx.doi.org/10.1186/s12933-023-02060-7 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Tao, Shiyi Yu, Lintong Li, Jun Xie, Zicong Huang, Li Yang, Deshuang Tan, Yuqing Zhang, Wenjie Huang, Xuanchun Xue, Tiantian Prognostic value of triglyceride-glucose index in patients with chronic coronary syndrome undergoing percutaneous coronary intervention |
title | Prognostic value of triglyceride-glucose index in patients with chronic coronary syndrome undergoing percutaneous coronary intervention |
title_full | Prognostic value of triglyceride-glucose index in patients with chronic coronary syndrome undergoing percutaneous coronary intervention |
title_fullStr | Prognostic value of triglyceride-glucose index in patients with chronic coronary syndrome undergoing percutaneous coronary intervention |
title_full_unstemmed | Prognostic value of triglyceride-glucose index in patients with chronic coronary syndrome undergoing percutaneous coronary intervention |
title_short | Prognostic value of triglyceride-glucose index in patients with chronic coronary syndrome undergoing percutaneous coronary intervention |
title_sort | prognostic value of triglyceride-glucose index in patients with chronic coronary syndrome undergoing percutaneous coronary intervention |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10685592/ https://www.ncbi.nlm.nih.gov/pubmed/38017540 http://dx.doi.org/10.1186/s12933-023-02060-7 |
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