Journal of clinical lipidology · 2025
Noninvasive indices of hepatic steatosis and fibrosis for the prediction of obstructive coronary artery disease.
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Trygoniaris Dimitrios, Mittas Nikolaos, Papazoglou Andreas S, Karagiannidis Efstratios, Chatzopoulou Fani, Polyzos Stergios A, Chatzidimitriou Dimitrios, Angelis Lefteris, Vizirianakis Ioannis S
Аннотация
BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD) emerges as a significant risk enhancer for cardiovascular disease, yet the predictive role of specific noninvasive tests (NITs) of MASLD for obstructive coronary artery disease (CAD) remains unclear. This study aimed to evaluate the prognostic value of various NITs in predicting obstructive CAD among individuals with symptoms suspected of CAD.
METHODS AND RESULTS: This study analyzed data from the GEnetic Syntax Score (GESS) dataset, including 754 patients undergoing invasive coronary angiography (ICA) for suspected CAD. Six NITs, including the triglyceride-glucose (TyG) index, the Framingham steatosis index (FSI), the acNASH index, the fibrosis-4 (FIB-4) index, the aspartate aminotransferase to platelet ratio index (APRI), and the hepatic steatosis index (HSI) were assessed for their association with obstructive CAD using logistic regression models, unadjusted and adjusted for potential covariates. Model performance was evaluated using receiver operating characteristic (ROC) and precision-recall (PR) curves by calculating the area under the curve (AUC) and performing formal hypothesis testing procedures through a stratified 10-fold cross-validation approach. TyG (odds ratio [OR]: 3.009, 95% CI: 2.223-4.126), FSI (OR: 1.185, 95% CI: 1.050-1.342), and APRI (OR: 2.350, 95% CI: 1.085-5.367) were found to be significantly associated with obstructive CAD. The adjusted TyG model exhibited superior predictive performance (AUC-ROC: 0.701, 95% CI: 0.673-0.724; AUC-PR: 0.825, 95% CI: 0.797-0.858), outperforming all the other models.
CONCLUSION: TyG demonstrated the highest predictive value for identifying individuals at increased risk of obstructive CAD. These findings highlight a potential role of NITs in cardiovascular risk stratification, though further external validation is needed.
METHODS AND RESULTS: This study analyzed data from the GEnetic Syntax Score (GESS) dataset, including 754 patients undergoing invasive coronary angiography (ICA) for suspected CAD. Six NITs, including the triglyceride-glucose (TyG) index, the Framingham steatosis index (FSI), the acNASH index, the fibrosis-4 (FIB-4) index, the aspartate aminotransferase to platelet ratio index (APRI), and the hepatic steatosis index (HSI) were assessed for their association with obstructive CAD using logistic regression models, unadjusted and adjusted for potential covariates. Model performance was evaluated using receiver operating characteristic (ROC) and precision-recall (PR) curves by calculating the area under the curve (AUC) and performing formal hypothesis testing procedures through a stratified 10-fold cross-validation approach. TyG (odds ratio [OR]: 3.009, 95% CI: 2.223-4.126), FSI (OR: 1.185, 95% CI: 1.050-1.342), and APRI (OR: 2.350, 95% CI: 1.085-5.367) were found to be significantly associated with obstructive CAD. The adjusted TyG model exhibited superior predictive performance (AUC-ROC: 0.701, 95% CI: 0.673-0.724; AUC-PR: 0.825, 95% CI: 0.797-0.858), outperforming all the other models.
CONCLUSION: TyG demonstrated the highest predictive value for identifying individuals at increased risk of obstructive CAD. These findings highlight a potential role of NITs in cardiovascular risk stratification, though further external validation is needed.