Atherogenic index of plasma combined with carotid crouse score predicts coronary artery disease in young and middle-aged adults

Abstract This retrospective study included 642 patients aged 18–59 years who underwent carotid ultrasound and coronary angiography. Patients were grouped by coronary artery disease (CAD) status and median Gensini score. Clinical characteristics, the atherogenic index of plasma (AIP), and carotid Cro...

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Main Authors: Xi Wu, Mingxing Wu, Haobo Huang, Zhe Liu, He Huang, Lei Wang
Format: Article
Language:English
Published: Nature Portfolio 2025-08-01
Series:Scientific Reports
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Online Access:https://doi.org/10.1038/s41598-025-15944-3
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author Xi Wu
Mingxing Wu
Haobo Huang
Zhe Liu
He Huang
Lei Wang
author_facet Xi Wu
Mingxing Wu
Haobo Huang
Zhe Liu
He Huang
Lei Wang
author_sort Xi Wu
collection DOAJ
description Abstract This retrospective study included 642 patients aged 18–59 years who underwent carotid ultrasound and coronary angiography. Patients were grouped by coronary artery disease (CAD) status and median Gensini score. Clinical characteristics, the atherogenic index of plasma (AIP), and carotid Crouse scores were compared. Multivariate logistic regression analysis was performed to identify independent predictors of CAD and severe coronary stenosis. Receiver operating characteristic (ROC) curve analyses assessed the diagnostic performance of AIP, Crouse score, and their combination. Both AIP and Crouse score were significantly higher in CAD patients compared to non-CAD controls (P < 0.001) and in high versus low Gensini score groups (P < 0.001). Multivariate analyses identified AIP (odds ratio [OR] = 1.219 for CAD; OR = 1.575 for severe stenosis) and Crouse score (OR = 1.543 for CAD; OR = 1.253 for severe stenosis) as independent predictors. ROC curve analysis revealed that the combined model had higher area under the curve (AUC) values for predicting CAD (AUC = 0.700) and severe coronary stenosis (AUC = 0.754) compared to either AIP or Crouse score alone. Both the AIP and carotid Crouse score are independent predictors of CAD presence and severity. Their combined use significantly improves diagnostic performance, offering a practical, non-invasive strategy for early CAD screening and risk stratification in this population.
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spelling doaj-art-7e72d2ef4ef045ec93c9cf0b3c666d012025-08-24T11:29:08ZengNature PortfolioScientific Reports2045-23222025-08-0115111210.1038/s41598-025-15944-3Atherogenic index of plasma combined with carotid crouse score predicts coronary artery disease in young and middle-aged adultsXi Wu0Mingxing Wu1Haobo Huang2Zhe Liu3He Huang4Lei Wang5Department of Cardiology, Xiangtan Central Hospital (The Affiliated Hospital of Hunan University)Department of Cardiology, Xiangtan Central Hospital (The Affiliated Hospital of Hunan University)Department of Cardiology, Xiangtan Central Hospital (The Affiliated Hospital of Hunan University)Department of Cardiology, Xiangtan Central Hospital (The Affiliated Hospital of Hunan University)Department of Cardiology, Xiangtan Central Hospital (The Affiliated Hospital of Hunan University)Department of Cardiology, Xiangtan Central Hospital (The Affiliated Hospital of Hunan University)Abstract This retrospective study included 642 patients aged 18–59 years who underwent carotid ultrasound and coronary angiography. Patients were grouped by coronary artery disease (CAD) status and median Gensini score. Clinical characteristics, the atherogenic index of plasma (AIP), and carotid Crouse scores were compared. Multivariate logistic regression analysis was performed to identify independent predictors of CAD and severe coronary stenosis. Receiver operating characteristic (ROC) curve analyses assessed the diagnostic performance of AIP, Crouse score, and their combination. Both AIP and Crouse score were significantly higher in CAD patients compared to non-CAD controls (P < 0.001) and in high versus low Gensini score groups (P < 0.001). Multivariate analyses identified AIP (odds ratio [OR] = 1.219 for CAD; OR = 1.575 for severe stenosis) and Crouse score (OR = 1.543 for CAD; OR = 1.253 for severe stenosis) as independent predictors. ROC curve analysis revealed that the combined model had higher area under the curve (AUC) values for predicting CAD (AUC = 0.700) and severe coronary stenosis (AUC = 0.754) compared to either AIP or Crouse score alone. Both the AIP and carotid Crouse score are independent predictors of CAD presence and severity. Their combined use significantly improves diagnostic performance, offering a practical, non-invasive strategy for early CAD screening and risk stratification in this population.https://doi.org/10.1038/s41598-025-15944-3Coronary artery diseaseAtherogenic index of plasmaCrouse scoreCarotid ultrasoundYoung and middle-aged populationsRisk prediction
spellingShingle Xi Wu
Mingxing Wu
Haobo Huang
Zhe Liu
He Huang
Lei Wang
Atherogenic index of plasma combined with carotid crouse score predicts coronary artery disease in young and middle-aged adults
Scientific Reports
Coronary artery disease
Atherogenic index of plasma
Crouse score
Carotid ultrasound
Young and middle-aged populations
Risk prediction
title Atherogenic index of plasma combined with carotid crouse score predicts coronary artery disease in young and middle-aged adults
title_full Atherogenic index of plasma combined with carotid crouse score predicts coronary artery disease in young and middle-aged adults
title_fullStr Atherogenic index of plasma combined with carotid crouse score predicts coronary artery disease in young and middle-aged adults
title_full_unstemmed Atherogenic index of plasma combined with carotid crouse score predicts coronary artery disease in young and middle-aged adults
title_short Atherogenic index of plasma combined with carotid crouse score predicts coronary artery disease in young and middle-aged adults
title_sort atherogenic index of plasma combined with carotid crouse score predicts coronary artery disease in young and middle aged adults
topic Coronary artery disease
Atherogenic index of plasma
Crouse score
Carotid ultrasound
Young and middle-aged populations
Risk prediction
url https://doi.org/10.1038/s41598-025-15944-3
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