The correlation between lipoprotein(a) elevations and the risk of recurrent cardiovascular events in CAD patients with different LDL-C levels

Zhu, Lijun; Zheng, Jiamin; Gao, Beibei; Jin, Xiangbo; Zhou, Lei; Huang, Jinyu

doi:10.1186/s12872-022-02618-5

Cited by 19 publications

(15 citation statements)

References 33 publications

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“…In order to improve risk stratification in patients, efforts have been made to develop prognostic predictive tools or risk scores to identify CTO before CAG [ 4 ]. Although some previous studies have analyzed potential predictors related to the high incidence rate of CAD and established a relevant model for CAD in patients before CAG, no research has attempted to conduct a rigorous analysis of the CTO data contained within clinical features as a means to produce more personalized CTO risk assessments [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Leveraging Machine Learning Techniques to Forecast Chronic Total Occlusion before Coronary Angiography

Shi

Zheng

Liu

et al. 2022

JCM

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Background: Chronic total occlusion (CTO) remains the most challenging procedure in coronary artery disease (CAD) for interventional cardiology. Although some clinical risk factors for CAD have been identified, there is no personalized prognosis test available to confidently identify patients at high or low risk for CTO CAD. This investigation aimed to use a machine learning algorithm for clinical features from clinical routine to develop a precision medicine tool to predict CTO before CAG. Methods: Data from 1473 CAD patients were obtained, including 1105 in the training cohort and 368 in the testing cohort. The baseline clinical characteristics were collected. Univariate and multivariate logistic regression analyses were conducted to identify independent risk factors that impact the diagnosis of CTO. A CTO predicting model was established and validated based on the independent predictors using a machine learning algorithm. The area under the curve (AUC) was used to evaluate the model. Results: The CTO prediction model was developed with the training cohort using the machine learning algorithm. Eight variables were confirmed as ‘important’: gender (male), neutrophil percentage (NE%), hematocrit (HCT), total cholesterol (TC), high-density lipoprotein cholesterol (HDL), ejection fraction (EF), troponin I (TnI), and N-terminal pro-B-type natriuretic peptide (NT-proBNP). The model achieved good concordance indices of 0.724 and 0.719 in the training and testing cohorts, respectively. Conclusions: An easy-to-use tool to predict CTO in patients with CAD was developed and validated. More research with larger cohorts are warranted to improve the prediction model, which can support clinician decisions on the early discerning CTO in CAD patients.

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Section: Introductionmentioning

confidence: 99%

Leveraging Machine Learning Techniques to Forecast Chronic Total Occlusion before Coronary Angiography

Shi

Zheng

Liu

et al. 2022

JCM

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“…Previous studies have demonstrated strong associations of Lp(a) and AIP with CAC. 7 8 9 10 11 12 However, several studies have shown a weakened association between Lp(a) and ASCVD risk in patients without high LDL-C. 15 16 29 30 Therefore, whether a consistent association exists between Lp(a) and CAC is questionable in patients without high LDL-C. In the present study, Lp(a) did not predict CACS ≥400 and CAC ≥90th percentile whereas, AIP independently predicted both CAC >0 and CAC ≥90th percentile.…”

Section: Discussionmentioning

confidence: 99%

“…The biological evidence underlying to this association is not yet fully understood. However, Zhu et al 30 presented the following suggestions. Patients with very low LDL-C levels tend to have high levels of activity of LDL receptors and a strong metabolic capacity for Lp(a).…”

Section: Discussionmentioning

confidence: 99%

The Comparison of the Associations of Lipoprotein(a) and the Atherogenic Index of Plasma With Coronary Artery Calcification in Patients Without High LDL-C: A Comparative Analysis

Hong

Kim

Jung

2023

J Lipid Atheroscler

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Objective Lipoprotein(a) (Lp[a]) and the atherogenic index of plasma (AIP) have been reported as predictive markers of coronary artery calcium (CAC). However, previous studies demonstrated that the cardiovascular risk associations with Lp(a) are attenuated in patients with low-density lipoprotein cholesterol (LDL-C) levels ≤135 mg/dL. However, few articles have identified the risk factors of CAC in patients without high LDL-C. Therefore, we performed this study to investigate the association of Lp(a) and AIP with CAC in patients with LDL-C levels ≤135 mg/dL. Methods This study included 625 lipid-lowering agent naive patients with LDL-C levels ≤135 mg/dL who underwent coronary computed tomographic angiography. We performed multivariate logistic regression analysis to evaluate the risk factors for a coronary artery calcium score (CACS) >0, CACS ≥400, and CAC ≥90th percentile. Results The mean age of the patients was 55.0±7.9 years and their mean LDL-C level was 94.7 ±23.3 mg/dL. Multivariate regression analysis showed that age, male sex, diabetes, hypertension, Lp(a), and AIP were independent predictors of CAS>0. Age, male sex, and diabetes were independent predictors of CACS≥400. Diabetes, hypertension, and AIP were independent predictors of CAC ≥90th percentile (all p <0.05). Unlike Lp(a), higher AIP tertiles were associated with significantly higher CAC percentiles and greater proportions of patients with CACS ≥400 and CAC ≥90th percentile. Conclusion In patients without high LDL-C, AIP could be a more reliable predictor of CAC than Lp(a).

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“…25 , 26 Recent studies showed that vascular disease recurrence in patients with high levels of Lp(a) (≥50 mg/dL) is attenuated only at very low LDL-C (<55 mg/dL) but not when LDL levels ≥70 mg/dL. 27 , 28 Kinetic studies suggest a preferential accumulation of Lp(a) particles in vessel walls which may increase the artherogenic potential of Lp(a) cholesterol over LDL-C. High levels of Lp(a) are highly correlated with rapid progression of coronary atherosclerosis, a strong predictor of adverse outcome. Patients with high Lp(a) levels develop high risk unstable plaques with complex morphology which are prone to rupture.…”

Section: Discussionmentioning

confidence: 99%

Elevated Lp(a) Levels Correlate with Severe and Multiple Coronary Artery Stenotic Lesions

Hageh

Chacar

Ghassibe-Sabbagh

et al. 2023

VHRM

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Backgrounds and Aims The role of Lipoprotein(a) (Lp(a)) in increasing the risk of cardiovascular diseases is reported in several populations. The aim of this study is to investigate the correlation of high Lp(a) levels with the degree of coronary artery stenosis. Methods Two hundred and sixty-eight patients were enrolled for this study. Patients who underwent coronary artery angiography and who had Lp(a) measurements available were included in this study. Binomial logistic regressions were applied to investigate the association between Lp(a) and stenosis in the four major coronary arteries. The effect of LDL and HDL Cholesterol on modulating the association of Lp(a) with coronary artery disease (CAD) was also evaluated. Multinomial regression analysis was applied to assess the association of Lp(a) with the different degrees of stenosis in the four major coronary arteries. Results Our analyses showed that Lp(a) is a risk factor for CAD and this risk is significantly apparent in patients with HDL-cholesterol ≥35 mg/dL and in non-obese patients. A large proportion of the study patients with elevated Lp(a) levels had CAD even when exhibiting high HDL serum levels. Increased HDL with low Lp(a) serum levels were the least correlated with stenosis. A significantly higher levels of Lp(a) were found in patients with >50% stenosis in at least two major coronary vessels arguing for pronounced and multiple stenotic lesions. Finally, the derived variant (rs1084651) of the LPA gene was significantly associated with CAD. Conclusion Our study highlights the importance of Lp(a) levels as an independent biological marker of severe and multiple coronary artery stenosis.

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The correlation between lipoprotein(a) elevations and the risk of recurrent cardiovascular events in CAD patients with different LDL-C levels

Cited by 19 publications

References 33 publications

Leveraging Machine Learning Techniques to Forecast Chronic Total Occlusion before Coronary Angiography

Leveraging Machine Learning Techniques to Forecast Chronic Total Occlusion before Coronary Angiography

The Comparison of the Associations of Lipoprotein(a) and the Atherogenic Index of Plasma With Coronary Artery Calcification in Patients Without High LDL-C: A Comparative Analysis

Elevated Lp(a) Levels Correlate with Severe and Multiple Coronary Artery Stenotic Lesions

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