Low-Density Lipoprotein Cholesterol 4: The Notable Risk Factor of Coronary Artery Disease Development

Wu, Dongmei; Yang, Qiuju; Su, Baohua; Jia, Hao; Ma, Huirong; Yuan, Weilan; Gao, Junhui; Ding, Feifei; Xu, Yue; Wang, Huifeng; Zhao, Jiangman; Li, Bingqiang

doi:10.3389/fcvm.2021.619386

Cited by 10 publications

(16 citation statements)

References 36 publications

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“…In the present study, we use six machine learning tools including KNN, LR, DT, SVM, MLP, and XGBoost-combined clinical features and blood lipid profile including sdLDL-C subfractions to predict CAD risk. We found that all models performed well in the prediction of CAD risk, which is consistent with a previous study that using above-mentioned models combing clinical data and sdLDL-C subfractions showed good performance for predicting CAD risk [ 3 ]. In addition, SVM, KNN, LR, and XGBoost models have also been reported to predict chronic kidney disease [ 67 ] and chronic obstructive pulmonary disease in Chinese population [ 33 ].…”

Section: Discussionsupporting

confidence: 91%

“…The inclusion criteria for participants were as follows: (i) CAD patients were diagnosed by coronary angiography, which is defined as coronary artery stenosis ≥50% in at least one main vessel or its major branches as described [ 29 ]; (ii) the non-CAD controls were diagnosed by coronary angiography without any luminal stenosis or plaque in main vessels and branches; (iii) the age of all participates >18 years. The exclusion criteria were as follows: (i) patients who had prior CAD or revascularization (percutaneous or surgical) [ 30 ]; (ii) participants who do not understand this research study [ 3 ]; (iii) participants who had severe medical disease, such as liver or kidney disease, thyroid disease, and malignant diseases [ 29 ], as well as immune-related sickness, nephropathic diseases, and respiratory diseases and also physiological conditions related to immune responses such as pregnancy [ 31 ]. The exclusion criteria for the non-CAD controls were the same as what mentioned above.…”

Section: Methodsmentioning

confidence: 99%

“…The F 1 score takes the geometric mean of precision and recall [ 35 ]. In addition, the feature score ( F score) rankings were measured by the total_gain metric in XGBoost model [ 3 ].…”

Section: Methodsmentioning

confidence: 99%

“…Coronary artery disease (CAD) is a cardiovascular disease (CVD) which has been found to be the leading cause of mortality worldwide [ 1 ] and caused by atherosclerosis, which can be manifested by typical symptoms such as stable angina, unstable angina, myocardial infarction (MI), or sudden cardiac death without any preceding symptoms [ 2 ]. So far, coronary angiography is the gold method for CAD diagnosis, but it is an invasive and unpractical for universal screening [ 3 ]. Hence, finding cost-effective methods to predict CAD are a major challenge in public health.…”

Section: Introductionmentioning

confidence: 99%

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Development of Machine Learning Tools for Predicting Coronary Artery Disease in the Chinese Population

et al. 2022

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Purpose. Coronary artery disease (CAD) is one of the major cardiovascular diseases and the leading cause of death globally. Blood lipid profile is associated with CAD early risk. Therefore, we aim to establish machine learning models utilizing blood lipid profile to predict CAD risk. Methods. In this study, 193 non-CAD controls and 2001 newly-diagnosed CAD patients (1647 CAD patients who received lipid-lowering therapy and 354 who did not) were recruited. Clinical data and the result of routine blood lipids tests were collected. Moreover, low-density lipoprotein cholesterol (LDL-C) subfractions (LDLC-1 to LDLC-7) were classified and quantified using the Lipoprint system. Six predictive models (k-nearest neighbor classifier (KNN), logistic regression (LR), support vector machine (SVM), decision tree (DT), multilayer perceptron (MLP), and extreme gradient boosting (XGBoost)) were established and evaluated by the confusion matrix, area under the receiver operating characteristic (ROC) curve (AUC), recall (sensitivity), accuracy, precision, and F1 score. The selected features were analyzed and ranked. Results. While predicting the CAD development risk of the CAD patients without lipid-lowering therapy in the test set, all models obtained AUC values above 0.94, and the accuracy, precision, recall, and F1 score were above 0.84, 0.85, 0.92, and 0.88, respectively. While predicting the CAD development risk of all CAD patients in the test set, all models obtained AUC values above 0.91, and the accuracy, precision, recall, and F1 score were above 0.87, 0.94, 0.87, and 0.92, respectively. Importantly, small dense LDL-C (sdLDL-C) and LDLC-4 play pivotal roles in predicting CAD risk. Conclusions. In the present study, machine learning tools combining both clinical data and blood lipid profile showed excellent overall predictive power. It suggests that machine learning tools are suitable for predicting the risk of CAD development in the near future.

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Section: Discussionsupporting

confidence: 91%

Section: Methodsmentioning

confidence: 99%

“…The F 1 score takes the geometric mean of precision and recall [ 35 ]. In addition, the feature score ( F score) rankings were measured by the total_gain metric in XGBoost model [ 3 ].…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Development of Machine Learning Tools for Predicting Coronary Artery Disease in the Chinese Population

et al. 2022

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“…Another recently performed study based on patients with coronary artery disease (CAD), compared to a control group, showed that triglycerides, LDL-C3, LDL-C4, LDL-C5, LDL-C6, and total LDL-C levels were significantly higher in patients with CAD, while LDL-C1 and HDL-C were significantly lower in the same group. The same review points out that lipid parameters associations with ischemic stroke in patients with non-valvular atrial fibrillation are still contradictory [ 19 ]. Therefore, we agree that sampling LDL-C subfractions on larger populational groups would help create better prediction scores for major CV events in the general population.…”

Section: Discussionmentioning

confidence: 99%

Lipoprofiling Assessed by NMR Spectroscopy in Patients with Acute Coronary Syndromes: Is There a Need for Fasting Prior to Sampling?

et al. 2022

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Most patients presenting in an emergency unit with acute coronary syndromes (ACS) (which include non-ST-elevation myocardial infarction (NSTEMI), ST-elevation MI (STEMI), and unstable angina) usually meet at least two cardiovascular risk factors, such as dyslipidemia, arterial hypertension, diabetes mellitus type 2, obesity, history of or current smoking, etc. Most ACS patients suffer from a type of dyslipidemia, and in addition to this there are ACS patients rushed into the emergency units for which the feeding status is unknown. Thus, we set out to evaluate the effect of fasting on 16 blood metabolite concentrations and 114 lipoprotein parameters on one control group and a group of statin-treated ACS patients hospitalized in a cardiovascular emergency unit, using Nuclear Magnetic Resonance (NMR) spectroscopy. The results indicated trends (in terms of number of cases, but not necessarily in terms of the magnitude of the effect) for as many as four metabolites and 48 lipoproteins. The effect was defined as a trend for results showing over 70% of the cases from either one or both groups that experienced parameter changes in the same direction (i.e., either increased or decreased). In terms of magnitude, the effect is rather low, leading to the overall conclusion that in cardiovascular (CV) emergency units, the blood samples analyzed in any feeding status would provide close results and very valuable information regarding prognosis and for fast decisions on patient’s proper management.

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Atherosclerotic patients with diabetes mellitus may break through the threshold of healthy TMAO levels formed by long-term statins therapy

Liang¹,

Yu²,

Wang³

et al. 2023

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Low-Density Lipoprotein Cholesterol 4: The Notable Risk Factor of Coronary Artery Disease Development

Cited by 10 publications

References 36 publications

Development of Machine Learning Tools for Predicting Coronary Artery Disease in the Chinese Population

Development of Machine Learning Tools for Predicting Coronary Artery Disease in the Chinese Population

Lipoprofiling Assessed by NMR Spectroscopy in Patients with Acute Coronary Syndromes: Is There a Need for Fasting Prior to Sampling?

Atherosclerotic patients with diabetes mellitus may break through the threshold of healthy TMAO levels formed by long-term statins therapy

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