Titre
Which surrogate insulin resistance indices best predict coronary artery disease? A machine learning approach.
Type
article
Institution
UNIL/CHUV/Unisanté + institutions partenaires
Périodique
Auteur(s)
Mirjalili, S.R.
Auteure/Auteur
Soltani, S.
Auteure/Auteur
Meybodi, Z.H.
Auteure/Auteur
Marques-Vidal, P.
Auteure/Auteur
Firouzabadi, D.D.
Auteure/Auteur
Eshraghi, R.
Auteure/Auteur
Restrepo, D.
Auteure/Auteur
Ghoshouni, H.
Auteure/Auteur
Sarebanhassanabadi, M.
Auteure/Auteur
Liens vers les personnes
Liens vers les unités
ISSN
1475-2840
Statut éditorial
Publié
Date de publication
2024-06-21
Volume
23
Numéro
1
Première page
214
Peer-reviewed
Oui
Langue
anglais
Notes
Publication types: Journal Article ; Comparative Study
Publication Status: epublish
Publication Status: epublish
Résumé
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.
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.
Sujets
PID Serval
serval:BIB_804584375B49
PMID
Open Access
Oui
Date de création
2024-06-28T12:14:53.011Z
Date de création dans IRIS
2025-05-21T01:35:24Z
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Nom
38907271_BIB_804584375B49.pdf
Version du manuscrit
published
Licence
https://creativecommons.org/licenses/by/4.0
Taille
2.07 MB
Format
Adobe PDF
PID Serval
serval:BIB_804584375B49.P001
URN
urn:nbn:ch:serval-BIB_804584375B499
Somme de contrôle
(MD5):ef9eff546ed3f68cca761d56060e607c