Prognostic implication of lipidomics in patients with coronary total occlusion undergoing PCI

Effectiveness of acupuncture on glycolipid metabolism in patients with coronary heart disease: A systematic review and meta-analysis

OBJECTIVE

Disorders of glycolipid metabolism are important pathogenic factors leading to coronary artery disease, and there is no safe and effective comprehensive treatment, while acupuncture has a certain efficacy in heart disease and disorders of glycolipid metabolism. To assess the effects of acupuncture on glycolipid metabolism in patients with coronary heart disease, we conducted a systematic review and meta-analysis.

METHODS

From the time of library construction to August 18, 2023, Searches were conducted in eight databases, with no language restriction. Only RCTs that included acupuncture as a sole or secondary effect on glucose-lipid metabolism in coronary heart disease were included.Our primary outcome indicators were Low-density lipoprotein cholesterol(LDL-C), Hemoglobin A1c(HbA1c). Our analyses were conducted in strict accordance with the PRISMA statement, and the researchers used the Cochrane Handbook for literature screening and data extraction, the "Risk of Bias" tool (ROB.2) for assessing risk of bias, and RevMan (version 5.3) for meta-analysis of outcome metrics, and the GRADE criteria for assessing quality of evidence. Assessing the quality of acupuncture literature using the Standards for reporting interventions in clinical trials of acupuncture.

RESULTS

Our analysis included 18 eligible RCTs (N = 1346 participants). For the primary outcome metrics, acupuncture combined with standard treatment was effective in reducing LDL-C (SMD =-0.56; 95 % CI, -0.75 to -0.38; P < 0.00001), and HbA1c (MD = -1.15; 95 % CI, -1.73 to -0.58; P < 0.0001). For secondary outcome measures, combination therapy improved TC (SMD = -0.97; 95 % CI, -1.44 to -0.51; P < 0.0001), TG (MD = -0.39; 95 % CI, -0.58 to -0.20; P < 0.0001), hs-CRP (MD = -0.98; 95 % CI, -1.43 to -0.52; P <0.0001), 2hPG (MD = -1.45; 95 % CI, -1.74 to -1.16; P < 0.00001), and ORR (RR, 1.27; 95 % CI, 1.19-1.36; P < 0.00001) levels more than standard therapy alone.However, the combination therapy did not prevail in lowering HDL-C (MD = 0.11; 95 % CI, 0.07-0.14; P < 0.00001) compared with standard therapy alone. Meanwhile heterogeneity analysis showed that After coronary heart disease intervention, acupuncture was able to reduce TC (SMD = -0.85; 95 % CI, -1.37 to -0.33; P = 0.001), TG (MD = -0.14; 95 % CI, -0.24 to -0.04; P = 0.004) levels, but did not dominate in lowering LDL-C.

CONCLUSIONS

Acupuncture effectively regulates glycolipid metabolism in coronary artery disease, serving as an adjuvant treatment. It may aid post-PCI healing via lipid metabolism regulation, but rigorous, large-scale, long-term RCTs are needed for validation.

Coronary Artery Disease with Elevated Levels of HDL Cholesterol Is Associated with Distinct Lipid Signatures

Levels of high-density lipoprotein cholesterol (HDL-C) are inversely associated with the incidence of coronary artery disease (CAD). However, the underlying mechanism of CAD in the context of elevated HDL-C levels is unclear. Our study aimed to explore the lipid signatures in patients with CAD and elevated HDL-C levels and to identify potential diagnostic biomarkers for these conditions. We measured the plasma lipidomes of forty participants with elevated HDL-C levels (men with >50 mg/dL and women with >60 mg/dL), with or without CAD, using liquid chromatography-tandem mass spectrometry. We analyzed four hundred fifty-eight lipid species and identified an altered lipidomic profile in subjects with CAD and high HDL-C levels. In addition, we identified eighteen distinct lipid species, including eight sphingolipids and ten glycerophospholipids; all of these, except sphingosine-1-phosphate (d20:1), were higher in the CAD group. Pathways for sphingolipid and glycerophospholipid metabolism were the most significantly altered. Moreover, our data led to a diagnostic model with an area under the curve of 0.935, in which monosialo-dihexosyl ganglioside (GM3) (d18:1/22:0), GM3 (d18:0/22:0), and phosphatidylserine (38:4) were combined. We found that a characteristic lipidome signature is associated with CAD in individuals with elevated HDL-C levels. Additionally, the disorders of sphingolipid as well as glycerophospholipid metabolism may underlie CAD.

Chewing the fat: How lipidomics is changing our understanding of human health and disease in 2022

Lipids are biological molecules that play vital roles in all living organisms. They perform many cellular functions, such as 1) forming cellular and subcellular membranes, 2) storing and using energy, and 3) serving as chemical messengers during intra- and inter-cellular signal transduction. The large-scale study of the pathways and networks of cellular lipids in biological systems is called "lipidomics" and is one of the fastest-growing omics technologies of the last two decades. With state-of-the-art mass spectrometry instrumentation and sophisticated data handling, clinical studies show how human lipid composition changes in health and disease, thereby making it a valuable medium to collect for clinical applications, such as disease diagnostics, therapeutic decision-making, and drug development. This review gives a comprehensive overview of current workflows used in clinical research, from sample collection and preparation to data and clinical interpretations. This is followed by an appraisal of applications in 2022 and a perspective on the exciting future of clinical lipidomics.

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

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.