The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
The TyG index may predict the development of cardiovascular events
Abstract
Background: Cardiovascular disease (CVD) is the worldwide leading cause of morbidity and mortality. An early risk detection of apparently healthy people before CVD onset has clinical relevance in the prevention of cardiovascular events. We evaluated the association between the product of fasting plasma glucose and triglycerides (TyG index) and CVD. Material and methods: A total of 5014 patients of the Vascular Metabolic CUN cohort (VMCUN cohort) were followed up during a median period of 10 years. We used a Cox proportional‐hazard ratio with repeated measures to estimate the risk of incidence of CVD across quintiles of the TyG index, calculated as ln[fasting triglycerides (mg/dL) × fasting plasma glucose (mg(dL)/2], and plotted a receiver‐operating characteristics (ROC) curve to compare a prediction model fitted on the variables used in the Framingham risk score, a new model containing the Framingham variables with the TyG index, and the risk of coronary heart disease. Results: A higher level of TyG index was significantly associated with an increased risk of developing CVD independent of confounding factors with a value of 2·32 (95% CI: 1·65–3·26) for those in the highest quintile and 1·52 (95% CI: 1·07–2·16) for those in the fourth quintile. The areas under the curve (AUC) of the ROC plots were 0·708 (0·68–0·73) for the Framingham model and 0·719 (0·70–0·74) for the Framingham + TyG index model (P = 0·014). Conclusions: The TyG index, a simple measure reflecting insulin resistance, might be useful to early identify individuals at a high risk of developing a cardiovascular event.
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1 retracted
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Explaining the decrease in U.S. deaths from coronary disease, 1980–2000
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Major lipids, apolipoproteins, and risk of vascular disease
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Cited by
Wang, L., Cong, H.-L., Zhang, J.-X., Hu, Y.-C., Wei, A., Zhang, Y.-Y., Yang, H., Ren, L.-B., Qi, W., Li, W.-Y., et al. (2020) · Cardiovascular Diabetology · doi:10.1186/s12933-020-01054-z · Cited by 435
Liu, X., Tan, Z., Huang, Y., Zhao, H., Liu, M., Yu, P., Ma, J., Zhao, Y., Zhu, W., & Wang, J. (2022) · Cardiovascular Diabetology · doi:10.1186/s12933-022-01546-0 · Cited by 409
Che, B., Zhong, C., Zhang, R., Pu, L., Zhao, T., Zhang, Y., & Han, L. (2023) · Cardiovascular Diabetology · doi:10.1186/s12933-023-01762-2 · Cited by 382
da Silva, A., Caldas, A. P. S., Hermsdorff, H. H. M., Bersch-Ferreira, Â. C., Torreglosa, C. R., Weber, B., & Bressan, J. (2019) · Cardiovascular Diabetology · doi:10.1186/s12933-019-0893-2 · Cited by 299
Zhang, S., Du, T., Zhang, J., Lu, H., Lin, X., Xie, J., Yang, Y., & Yu, X. (2017) · Lipids in Health and Disease · doi:10.1186/s12944-017-0409-6 · Cited by 284
Metadata
- Authors
- Sánchez‐Íñigo, L., Navarro‐González, D., Fernández‐Montero, A., Pastrana‐Delgado, J., & Martínez, J. A.
- Venue
- European Journal of Clinical Investigation
- Publisher
- Wiley
- Published
- 2016-01-23
- Volume
- 46
- Issue
- 2
- Pages
- 189-197
- ISSN
- 0014-2972 (ISSN-L)
- 1365-2362
- OpenAlex
- W2217853646
- Cited by
- 663
- References
- 9
- FWCI
- 7.85
- Type
- journal article
- Language
- en
- Open access
- closed
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