Acute Myocardial Infarction in Chinese Medicine Hospitals in China from 2006 to 2013: An Analysis of 2311 Patients from Hospital Data

Identification of potential crucial cuproptosis-related genes in myocardial ischemia-reperfusion injury through the bioinformatic analysis

BACKGROUND

Cuproptosis is known to regulate diverse physiological functions in many diseases, but its role in regulating Myocardial Ischemia-Reperfusion Injury (MI/RI) remains unclear.

METHODS

For this purpose, the MI/RI microarray datasets GSE61592 were downloaded from the Gene Expression Omnibus (GEO) database, and the Differently Expressed Genes (DEGs) in MI/RI were identified using R software. Moreover, the MI/RI mice model was established to confirm further the diagnostic value of Pyruvate Dehydrogenase B (Pdhb), Dihydrolipoamide S-acetyltransferase (Dlat), and Pyruvate dehydrogenase E1 subunit alpha 1 (Pdhα1).

RESULTS

The analysis of microarray datasets GSE61592 revealed that 798 genes were upregulated and 768 were downregulated in the myocardial tissue of the ischemia-reperfusion injury mice. Furthermore, Dlat, Pdhb, Pdhα1, and cuproptosis-related genes belonged to the downregulated genes. The receiver operating characteristics curve analysis results indicated that the Dlat, Pdhb, and Pdhα1 levels were downregulated in MI/RI and were found to be potential biomarkers for MI/RI diagnosis and prognosis. Similarly, analysis of Dlat, Pdhb, and Pdhα1 levels in the MI/RI mice revealed Pdhb being the key diagnostic marker.

CONCLUSIONS

This study demonstrated the prognostic value of cuproptosis-related genes (Dlat, Pdhb, and Pdhα1), especially Pdhb, MI/RI, providing new insight into the MI/RI treatment.

Short-term effects of nitrogen dioxide on inpatient acute myocardial infarction in Lanzhou, China

Nitrogen dioxide (NO2) represents a deleterious effect on acute myocardial infarction (AMI), but few relevant studies have been conducted in China. We aim to evaluate the acute effects of NO2 exposure on hospitalization for AMI in Lanzhou, China. In this study, we applied a distributional lag nonlinear model (DLNM) to assess the association between NO2 exposure and AMI hospitalization. We explored the sensitivity of various groups through stratified analysis by gender, age, and season. The daily average concentration of NO2 is 47.50 ± 17.38 µg/m3. We observed a significant exposure-response relationship between NO2 concentration and AMI hospitalization. The single pollutant model analysis shows that NO2 is positively correlated with AMI hospitalization at lag1, lag01, lag02, and lag03. The greatest lag effect estimate occurs at lag01, where a 10 µg/m3 increase in NO2 concentrations is significantly associated with a relative risk (RR) of hospitalization due to AMI of 1.027 [95% confidence interval (CI): 1.013, 1.042]. The results of the stratified analysis by gender, age, and season indicate that males, those aged ≥65 years, and the cold season are more sensitive to the deleterious effects caused by NO2 exposure. Short-term exposure to NO2 can enhance the risk of AMI hospitalization in urban Lanzhou.Implications: Exposure to particulate matter can lead to an increased incidence of AMI. Our study once again shows that NO2 exposure increases the risk of AMI hospital admission. AMI is a common and expensive fatal condition. Reducing NO2 exposure will benefit cardiovascular health and save on healthcare costs.

Plasma Quantitative Lipid Profiles: Identification of CarnitineC18:1-OH, CarnitineC18:2-OH and FFA (20:1) as Novel Biomarkers for Pre-warning and Prognosis in Acute Myocardial Infarction

This study was aimed to determine the association between potential plasma lipid biomarkers and early screening and prognosis of Acute myocardial infarction (AMI). In the present study, a total of 795 differentially expressed lipid metabolites were detected based on ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS). Out of these metabolites, 25 lipid metabolites were identified which showed specifical expression in the AMI group compared with the healthy control (HC) group and unstable angina (UA) group. Then, we applied the least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) methods to obtain three lipid molecules, including CarnitineC18:1-OH, CarnitineC18:2-OH and FFA (20:1). The three lipid metabolites and the diagnostic model exhibited well predictive ability in discriminating between AMI patients and UA patients in both the discovery and validation sets with an area under the curve (AUC) of 0.9. Univariate and multivariate logistic regression analyses indicated that the three lipid metabolites may serve as potential biomarkers for diagnosing AMI. A subsequent 1-year follow-up analysis indicated that the three lipid biomarkers also had prominent performance in predicting re-admission of patients with AMI due to cardiovascular events. In summary, we used quantitative lipid technology to delineate the characteristics of lipid metabolism in patients with AMI, and identified potential early diagnosis biomarkers of AMI via machine learning approach.