Which surrogate insulin resistance indices best predict coronary artery disease? A machine learning approach.

Details

Serval ID
serval:BIB_804584375B49
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Which surrogate insulin resistance indices best predict coronary artery disease? A machine learning approach.
Journal
Cardiovascular diabetology
Author(s)
Mirjalili S.R., Soltani S., Meybodi Z.H., Marques-Vidal P., Firouzabadi D.D., Eshraghi R., Restrepo D., Ghoshouni H., Sarebanhassanabadi M.
ISSN
1475-2840 (Electronic)
ISSN-L
1475-2840
Publication state
Published
Issued date
21/06/2024
Peer-reviewed
Oui
Volume
23
Number
1
Pages
214
Language
english
Notes
Publication types: Journal Article ; Comparative Study
Publication Status: epublish
Abstract
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.
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.
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.
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.
Keywords
Humans, Insulin Resistance, Coronary Artery Disease/blood, Coronary Artery Disease/diagnosis, Female, Male, Middle Aged, Predictive Value of Tests, Biomarkers/blood, Machine Learning, Aged, Risk Assessment, Adult, Prognosis, Young Adult, Risk Factors, Time Factors, Insulin/blood, Blood Glucose/metabolism, Cardiovascular diseases, Machine learning, Metabolic diseases, Public Health
Pubmed
Open Access
Yes
Create date
28/06/2024 14:14
Last modification date
05/07/2024 7:01
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