The purpose of this study was to summarize the clinical characteristics and risk factors of major adverse cardiovascular events (MACEs) in patients who had had acute myocardial infarction (AMI) within 1 year of percutaneous coronary intervention (PCI). A total of 421 AMI patients who were treated with PCI and experienced MACEs within 1 year of their admission were included in this retrospective study. In addition, patients were matched for age, sex, and presentation with 561 patients after AMI who had not had MACEs. The clinical characteristics and risk factors for MACEs within 1 year in AMI patients were investigated, to develop a nomogram for MACEs based on univariate and multivariate analyses. The C statistic was used to assess the discriminative performance of the nomogram. In addition, calibration curve and decision curve analyses were conducted to validate the calibration performance and utility, respectively, of the nomogram. After univariate and multivariate analyses, a nomogram was constructed based on age (odds ratio (OR): 1.030; 95% confidence interval (CI): 1.014–1.047), diabetes mellitus (OR: 1.667; 95% CI: 1.151–2.415), low-density lipoprotein cholesterol (OR: 1.332; 95% CI: 1.134–1.565), uric acid (OR: 1.003; 95% CI: 1.001–1.005), lipoprotein (a) (OR: 1.003; 95% CI: 1.002–1.003), left ventricular ejection fraction (OR: 0.929; 95% CI: 0.905–0.954), Syntax score (OR: 1.075; 95% CI: 1.053–1.097), and hypersensitive troponin T (OR: 1.002; 95% CI: 1.002–1.003). The C statistic was 0.814. The calibration curve showed good concordance of the nomogram, while decision curve analysis demonstrated satisfactory positive net benefits. We developed a convenient, practical, and effective prediction model for predicting MACEs in AMI patients within 1 year of PCI. To ensure generalizability, this model requires external validation.
There have been many meta-analyses for statins, ezetimibe and proprotein convertase subtilisin/kexin type 9 inhibitors (PCSK9i) to evaluate clinical outcomes, but the efficacy and safety of different intensity of these three drugs on clinical outcomes was absent. PCSK9i, ezetimibe, and statins were divided into seven interventions as follows: including PCSK9i + high-intensity statins (P9i+HT), PCSK9i + moderate-intensity statins (P9i+MT), ezetimibe + high-intensity statins (Eze+HT), ezetimibe + moderate-intensity statins (Eze+MT), high-intensity statins (HT), moderate-intensity statins (MT), and low-intensity statins (LT). The risk ratios (RR) and 95% confidence intervals (CI) were calculated to evaluate the clinical outcomes in all randomized controlled trials included. In traditional meta-analysis, the more intensive treatment had a lower risk of all-cause mortality (RR 0.91, 95% CI 0.88–0.95), cardiovascular mortality (RR 0.89, 95% CI 0.86–0.92), myocardial infarction (RR 0.79, 95% CI 0.77–0.81), coronary revascularization (RR 0.80, 95% CI 0.76-0.84), and cerebrovascular events (RR 0.84, 95% CI 0.80–0.88) compared with the less intensive treatment. However, the more intensive treatment had a higher risk of new-onset diabetes (RR 1.08, 95% CI 1.04-1.12). The network meta-analysis demonstrated that P9i+HT, P9i+MT, HT, and MT were significantly associated with a risk reduction in coronary revascularization and cerebrovascular events compared with PLBO. LT could effectively reduce the risk of cardiovascular mortality (RR 0.71, 95% CI 0.54–0.92), MI (RR 0.67, 95% CI 0.54-0.82), and coronary revascularization (RR 0.77, 95% CI 0.65–0.91) compared with PLBO. P9i+HT was superior to HT in reducing the risk of MI (RR 0.78, 95% CI 0.68–0.90), coronary revascularization (RR 0.84, 95% CI 0.73–0.96), and cerebrovascular events (RR 0.78, 95% CI 0.64–0.95). However, compared with PLBO, P9i+HT, HT, and MT could increase the risk of new-onset diabetes (RR 1.23, 95% CI 1.11–1.37; RR 1.23, 95% CI 1.14–1.33; RR 1.09, 95% CI 1.02–1.15, respectively). In conclusion, PCSK9i added to background statins may be recommended as preferred lipid-lowering therapy, and did not increase the additional risk of new-onset diabetes. The safety and efficacy of ezetimibe was not superior to that of statins. LT can be recommended as the initial therapy.
Objective. This study is aimed at exploring the underlying molecular mechanisms of ST-segment elevation myocardial infarction (STEMI) and provides potential clinical prognostic biomarkers for STEMI. Methods. The GSE60993 dataset was downloaded from the GEO database, and the differentially expressed genes (DEGs) between STEMI and control groups were screened. Enrichment analysis of the DEGs was subsequently performed using the DAVID database. A protein–protein interaction network was constructed, and hub genes were identified. The hub genes in patients were then validated by quantitative reverse transcription-PCR. Furthermore, hub gene-miRNA interactions were evaluated using the miRTarBase database. Finally, patient data on classical cardiovascular risk factors were collected, and plasma microRNA-146a (miR-146a) levels were detected. An individualized nomogram was constructed based on multivariate Cox regression analysis. Results. A total of 239 DEGs were identified between the STEMI and control groups. Expression of S100A12 and miR-146a was significantly upregulated in STEMI samples compared with controls. STEMI patients with high levels of miR-146a had a higher risk of major adverse cardiovascular events (MACEs) than those with low levels of miR-146a (log-rank P = 0.034 ). Multivariate Cox regression analysis identified five statistically significant variables, including age, hypertension, diabetes mellitus, white blood cells, and miR-146a. A nomogram was constructed to estimate the likelihood of a MACE at one, two, and three years after STEMI. Conclusion. The incidence of MACEs in STEMI patients expressing high levels of miR-146a was significantly greater than in those expressing low levels. MicroRNA-146a can serve as a biomarker for adverse prognosis of STEMI and might function in its pathogenesis by targeting S100A12, which may exert its role via an inflammatory response. In addition, our study presents a valid and practical model to assess the probability of MACEs within three years of STEMI.
Objective Percutaneous coronary intervention (PCI) is one of the most effective treatments for acute coronary syndrome (ACS). However, the need for postoperative revascularization remains a major problem in PCI. This study was to develop and validate a nomogram for prediction of revascularization after PCI in patients with ACS. Methods A retrospective observational study was conducted using data from 1083 patients who underwent PCI (≥6 months) at a single center from June 2013 to December 2019. They were divided into training (70%; n = 758) and validation (30%; n = 325) sets. Multivariate logistic regression analysis was used to establish a predictive model represented by a nomogram. The nomogram was developed and evaluated based on discrimination, calibration, and clinical efficacy using the concordance statistic (C-statistic), calibration plot and decision curve analysis (DCA), respectively. Results The nomogram was comprised of ten variables: follow-up time (odds ratio (OR): 1.01; 95% confidence interval (CI): 1.00–1.03), history of diabetes mellitus (OR: 1.83; 95% CI: 1.25–2.69), serum creatinine level on admission (OR: 0.99; 95% CI: 0.98–1.00), serum uric acid level on admission (OR: 1.005; 95% CI: 1.002–1.007), lipoprotein-a level on admission (OR: 1.0021; 95% CI: 1.0013–1.0029), low density lipoprotein cholesterol level on re-admission (OR: 1.33; 95% CI: 0.10–0.47), the presence of chronic total occlusion (OR: 3.30; 95% CI: 1.93–5.80), the presence of multivessel disease (OR: 4.48; 95% CI: 2.85–7.28), the presence of calcified lesions (OR: 1.63; 95% CI: 1.11–2.39), and the presence of bifurcation lesions (OR: 1.82; 95% CI: 1.20–2.77). The area under the receiver operating characteristic curve values for the training and validation sets were 0.765 (95% CI: 0.732–0.799) and 0.791 (95% CI: 0.742–0.830), respectively. The calibration plots showed good agreement between prediction and observation in both the training and validation sets. DCA also demonstrated that the nomogram was clinically useful. Conclusion We developed an easy-to-use nomogram model to predict the risk of revascularization after PCI in patients with ACS. The nomogram may provide useful assessment of risk for subsequent treatment of ACS patients undergoing PCI.
Introduction: Atrial fibrillation (AF) is the most prevalent sustained cardiac arrhythmia, but the molecular mechanisms underlying AF are not known. We aimed to identify the pivotal genes and pathways involved in AF pathogenesis because they could become potential biomarkers and therapeutic targets of AF. Methods: The microarray datasets of GSE31821 and GSE41177 were downloaded from the Gene Expression Omnibus database. After combining the two datasets, differentially expressed genes (DEGs) were screened by the Limma package. MicroRNAs (miRNAs) confirmed experimentally to have an interaction with AF were screened through the miRTarBase database. Target genes of miRNAs were predicted using the miRNet database, and the intersection between DEGs and target genes of miRNAs, which were defined as common genes (CGs), were analyzed. Functional and pathway-enrichment analyses of DEGs and CGs were performed using the databases DAVID and KOBAS. Protein-protein interaction (PPI) network, miRNA-messenger(m) RNA network, and drug-gene network was visualized. Finally, reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) was used to validate the expression of hub genes in the miRNA-mRNA network. Results: Thirty-three CGs were acquired from the intersection of 65 DEGs from the integrated dataset and 9777 target genes of miRNAs. Fifteen "hub" genes were selected from the PPI network, and the miRNA-mRNA network, including 82 miRNAs and 9 target mRNAs, was constructed. Furthermore, with the validation by RT-qPCR, macrophage migration inhibitory factor (MIF), MYC proto-oncogene, bHLH transcription factor (MYC), inhibitor of differentiation 1 (ID1), and C-X-C Motif Chemokine Receptor 4 (CXCR4) were upregulated and superoxide Dismutase 2 (SOD2) was downregulated in patients with AF compared with healthy controls. We also found MIF, MYC, and ID1 were enriched in the transforming growth factor (TGF)-β and Hippo signaling pathway. Conclusion:We identified several pivotal genes and pathways involved in AF pathogenesis. MIF, MYC, and ID1 might participate in AF progression through the TGF-β and Hippo signaling pathways. Our study provided new insights into the mechanisms of action of AF.
BackgroundAt present, effective clinical therapies for myocardial ischemia-reperfusion injury (MIRI) are lacking. We investigated if luteolin conferred cardioprotective effects against MIRI and elucidated the potential underlying mechanisms.MethodFour databases were searched for preclinical studies of luteolin for the treatment of MIRI. The primary outcomes were myocardial infarct size (IS) and intracardiac hemodynamics. The second outcomes were representative indicators of apoptosis, oxidative stress, and inflammatory. The Stata and RevMan software packages were utilized for data analysis.ResultsLuteolin administration was confirmed to reduce IS and ameliorate hemodynamics as compared to the control groups (p < 0.01). IS had decreased by 2.50%, 2.14%, 2.54% in three subgroups. Amelioration of hemodynamics was apparent in two different myocardial infarct models (model of left anterior descending branch ligation and model of global heart ischemia), as left ventricular systolic pressure improved by 21.62 and 35.40 mmHg respectively, left ventricular end-diastolic pressure decreased by 7.79 and 4.73 mmHg respectively, maximum rate of left ventricular pressure rise increased by 737.48 and 750.47 mmHg/s respectively, and maximum rate of left ventricular pressure decrease increased by 605.66 and 790.64 mmHg/s respectively. Apoptosis of cardiomyocytes also significantly decreased, as indicated by thelevels of MDA, an oxidative stress product, and expression of the inflammatory factor TNF-α (p < 0.001).ConclusionPooling of the data demonstrated that luteolin exerts cardioprotective effects against MIRI through different signaling pathways. As possible mechanisms, luteolin exerts anti-apoptosis, anti-oxidation, and anti-inflammation effects against MIRI.
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