Which surrogate insulin resistance indices best predict coronary artery disease? A machine learning approach
Name
12933_2024_Article_2306.pdf
Size
2.07 MB
Format
Adobe PDF
Checksum (MD5)
ef9eff546ed3f68cca761d56060e607c
Author(s)
Mirjalili, Seyed Reza
Soltani, Sepideh
Meybodi, Zahra Heidari
Marques-Vidal, Pedro
Firouzabadi, Danial Dehghani
Eshraghi, Reza
Restrepo, David
Ghoshouni, Hamed
Sarebanhassanabadi, Mohammadtaghi
Date Issued
June 21, 2024
Journal
Cardiovascular Diabetology
Publisher
Springer Science and Business Media LLC
Citation
Mirjalili, S.R., Soltani, S., Meybodi, Z.H. et al. Which surrogate insulin resistance indices best predict coronary artery disease? A machine learning approach. Cardiovasc Diabetol 23, 214 (2024).
Version
Final published version
Abstract
Background
Various surrogate markers of insulin resistance have been developed, capable of predicting coronary artery disease (CAD) without the need to detect serum insulin. For accurate prediction, they depend only on glucose and lipid profiles, as well as anthropometric features. However, there is still no agreement on the most suitable one for predicting CAD.
Methods
We followed a cohort of 2,000 individuals, ranging in age from 20 to 74, for a duration of 9.9 years. We utilized multivariate Cox proportional hazard models to investigate the association between TyG-index, TyG-BMI, TyG-WC, TG/HDL, plus METS-IR and the occurrence of CAD. The receiver operating curve (ROC) was employed to compare the predictive efficacy of these indices and their corresponding cutoff values for predicting CAD. We also used three distinct embedded feature selection methods: LASSO, Random Forest feature selection, and the Boruta algorithm, to evaluate and compare surrogate markers of insulin resistance in predicting CAD. In addition, we utilized the ceteris paribus profile on the Random Forest model to illustrate how the model’s predictive performance is affected by variations in individual surrogate markers, while keeping all other factors consistent in a diagram.
Results
The TyG-index was the only surrogate marker of insulin resistance that demonstrated an association with CAD in fully adjusted model (HR: 2.54, CI: 1.34–4.81). The association was more prominent in females. Moreover, it demonstrated the highest area under the ROC curve (0.67 [0.63–0.7]) in comparison to other surrogate indices for insulin resistance. All feature selection approaches concur that the TyG-index is the most reliable surrogate insulin resistance marker for predicting CAD. Based on the Ceteris paribus profile of Random Forest the predictive ability of the TyG-index increased steadily after 9 with a positive slope, without any decline or leveling off.
Conclusion
Due to the simplicity of assessing the TyG-index with routine biochemical assays and given that the TyG-index was the most effective surrogate insulin resistance index for predicting CAD based on our results, it seems suitable for inclusion in future CAD prevention strategies.
MIT Department
Harvard--MIT Program in Health Sciences and Technology. Laboratory for Computational Physiology
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1186/s12933-024-02306-y
