Frontiers in Cardiovascular Medicine最新文献

Coronavirus disease 2019 (COVID-19) is transitioning from a pandemic to an endemic phase through recurring mutations. Initial efforts focused on developing strategies to mitigate infection of lung epithelial cells which are the primary targets of the SARS-CoV-2 virus using the affinity of the spike protein to human ACE2 receptor. SARS-CoV-2, however, infects additional cell types present in the lung such as macrophages through the alternate entry receptor Neuropilin 1 (NRP1). Developing novel therapeutic strategies to prevent SARS-CoV-2 infection of cells crucial for immunosurveillance could thus be integral to treat post-acute sequelae of COVID-19 (PASC). Since traditional drug development process takes a long time, it is imperative to establish new strategies that can be rapidly deployed to combat the dynamic nature of COVID-19 evolution and to contribute to prevention of future pandemics. We obtained the gene expression profiles of THP-1 monocytes from L1000-based Connectivity Map using CLUE, cloud- based software platform for the analysis of perturbational datasets to identify compounds that could reduce the expression level of NRP1. Out of 33,590 compounds, we analyzed the profiles of 45 compounds for their ability to reduce NRP1 expression. We selected the top five small molecule inhibitors predicted to decrease the expression of NRP1 for validation studies. All five selected compounds showed low cytotoxicity at tested doses and their ability to reduce NRP1 surface expression was evaluated in THP-1 monocytes, THP-1-derived macrophage like cells and human peripheral blood mononuclear cell (PBMC)-derived primary macrophages. Five compounds with the largest predicted reduction of NRP1 expression decreased macrophage NRP1 surface expression measured using flow cytometry and fluorescent microscopy assays in both cell line and primary macrophages. Using our computational approach, we identified 45 compounds that could potentially decrease NRP1 surface expression in macrophages based on their effect on THP-1 monocytes. Validation studies showed that such an approach can help to identify compounds for drug repositioning in target cells that are absent in the L1000 database. Our proposed approach can be applicable for the rapid compound exploration to combat novel cell types that SARS-CoV-2 targets for infection and could provide molecular bases for the development of new drugs.

Background: Immune checkpoint inhibitor-associated myocarditis (ICI myocarditis) is an infrequent but potentially fatal immune-related adverse event. This study aimed to identify valuable indicators for risk prediction and evaluation of disease severity and outcomes.

Methods: A total of 79 patients with severe or mild ICI myocarditis and 158 controls without post-ICI immune-related adverse events were enrolled in this retrospective study. The clinical application value of a series of simple biomarkers were tested.

Results: Higher levels of the systemic immune-inflammation index (SII), neutrophil-to-eosinophil ratio (NER), aspartate transferase-to-albumin ratio (AAR), and lactic dehydrogenase-to-albumin ratio (LAR) at myocarditis onset were associated with severe disease conditions. In the receiver operating characteristic analysis, biomarkers areas under the curve (AUC) close to or greater than 0.8 were LAR (AUC: 0.810) and AAR (AUC: 0.806). Patients with higher SII, AAR, and LAR also exhibited poorer overall survival. The SII, NER, AAR, and LAR before the last ICI treatment increased relative to baseline in patients with ICI myocarditis, whereas no significant changes in the tested biomarkers were observed in the control group. For SII, AAR, and LAR, high ratios of the biomarker levels before the last ICI to baseline was associated with the incidence of myocarditis.

Conclusions: Surveillance of these economical biomarkers during ICI therapy might contribute to the risk prediction of ICI myocarditis, as well as the assessment of disease severity and prognosis.

Introduction: Coronary artery disease (CAD) remains the primary cause of death worldwide, and familial hypercholesterolemia (FH) is a common disease that leads to CAD. This study aimed to explore the difference in CAD risk between FH and non-FH patients with high low-density lipoprotein cholesterol (LDL-C) levels.

Methods: Individuals (≥18 years) who underwent coronary angiography (CAG) from June 2016 to September 2020 were consecutively enrolled. Participants with LDL-C levels ≥4.0 mmol/L were ultimately included in this study. For all participants, next-generation sequencing was performed with expanded gene panels including 11 genes (LDLR, APOB, PCSK9, LDLRAP1, ABCG5, ABCG8, LIPA, LPA, APOBR, LRPAP1, and STAP1).

Results: A total of 223 individuals were included in this study. According to the CAG findings, 199 CAD patients and 24 non-CAD patients were included. The proportions of FH genes, regardless of whether 3 major genes or all 11 genes were sequenced, were not significantly different between the CAD and non-CAD groups (P > 0.05). In addition, all CAD patients were divided into a triple vessel disease (TVD) group and a non-TVD group. The TVD group had a greater proportion of patients with mutations in 3 FH major genes (P < 0.05). In addition, TC, LDL-C and modified LDL-C (MLDL-C) levels were higher and the estimated glomerular filtration rate (eGFR) was lower in the TVD group than in the non-TVD group (all P < 0.05). However, multivariate logistic regression analyses revealed that only the eGFR was an independent risk factor for TVD (OR 0.99; 95% CI: 0.98-1.00, P < 0.05). To eliminate the impact of the eGFR, subgroup analysis was conducted, and the results indicated that among CAD patients in the high-eGFR group, having FH mutations in 3 major genes was an independent risk factor for TVD (OR 3.00; 95% CI: 1.16-7.79, P < 0.05). In total, 104 FH-related mutations were detected in this study.

Conclusions: FH mutation did not increase the rate of CAD in individuals with an MLDL-C level ≥4.0 mmol/L. However, among CAD patients (MLDL-C level ≥4.0 mmol/L) with almost normal renal function (≥87.4 ml/min/1.73 m²), the probability of enduring TVD in those with FH mutations in 3 major genes was 3.00 times greater than that in those without FH mutations.

Objective: To investigate whether postoperative systemic immune-inflammation index (SII) is associated with acute kidney injury (AKI) after cardiac surgery.

Methods: We included patients undergoing cardiac surgery from the Medical Information Mart for Intensive Care-Ⅳ database to conduct a retrospective cohort study. The outcomes are AKI, severe AKI, and 30-day mortality after cardiac surgery. Analytical techniques including receiver operating characteristic (ROC) analysis, restricted cubic splines (RCS), and multivariable logistic regression were used to assess the association between SII and outcomes. Sensitivity analyses using inverse probability of treatment weighting (IPTW) and the E-value were conducted to validate the stability of the results.

Results: 3,799 subjects were included in this study. We used ROC to calculate an optimal cutoff value for predicting AKI after cardiac surgery, and subsequently patients were divided into two groups based on the cutoff value (Low SII: ≤ 949 × 10⁹/L; High SII: > 949 × 10⁹/L). ROC showed moderately good performance of SII for predicting AKI, while RCS also indicated a positive association between SII and AKI. The multivariate logistic analysis further affirmed the heightened risk of AKI in patients in the high SII group (OR, 5.33; 95%CI, 4.34-6.53; P < 0.001). Similar associations were observed between SII and severe AKI. Sensitivity and subgroup analyses indicated the robustness of the findings.

Conclusion: Elevated SII was independently associated with a higher risk of AKI in adults undergoing cardiac surgery. The potential causal relationship between postoperative SII and cardiac surgery associated AKI warrants prospective research.

Introduction: A rapid, accurate, and specific ultrafiltration with ultra-performance liquid chromatographic-tandem mass spectrometry method was validated for the simultaneous determination of the protein binding rate of atorvastatin in uremic patients. Methods: The plasma samples were centrifuged at 6,000 r/min for 15 min at 37°C and the ultrafiltrate was collected. An ACQUITY UPLC® BEH C18 Column with gradient elution of water (0.1% formic acid) and acetonitrile was used for separation at a flow rate of 0.4 ml/min.

Results: The calibration curves of two analytes in the serum showed excellent linearity over the concentration ranges of 0.05-20.00 ng/ml for atorvastatin, and 0.05-20.00 ng/ml for orthohydroxy atorvastatin, respectively. This method was validated according to standard US food and drug administration and European medicines agency guidelines in terms of selectivity, linearity, detection limits, matrix effects, accuracy, precision, recovery, and stability. This assay can be easily implemented in clinical practice to determine the free and combined concentrations of atorvastatin in the plasma of uremic patients. The final result showed that the average plasma protein binding rate in uremic patients was 86.58 ± 2.04%, relative standard deviation (RSD) (%) = 1.98, while the plasma protein binding rate in patients with normal renal function was 97.62 ± 1.96%, RSD (%) = 2.04. There was a significant difference in the protein binding rate in different types of plasma (P < 0.05), and the protein binding rate decreased with increasing creatinine until it stabilized at nearly 80%. The mean metabolite/prototype ratio of atorvastatin in patients with normal renal function and in patients with uremia was 1.085 and 0.974, respectively.

Discussion: The metabolic process of atorvastatin may be inhibited in uremic hemodialysis patients, but the total concentration of atorvastatin did not change significantly; due to the decrease of protein binding rate increase the drug distribution of atorvastatin in the liver or muscle tissue, which may increase the risk of certain adverse reactions. We recommend that clinicians use free drug concentration monitoring to adjust the dose of atorvastatin to ensure patient safety for uremic hemodialysis patients.

Background: Right ventricular outflow tract (RVOT) stenting as an alternative palliation for patients with stenotic RVOTs is increasingly recognized. However, a notable gap remains in the literature regarding the efficacy and the comparative outcomes between RVOT stenting and the modified Blalock-Taussig shunt (mBTS) in children older than one year.

Methods: We conducted a retrospective review of clinical data from patients aged one year to 18 years with stenotic RVOTs who underwent RVOT stenting or mBTS procedures at our institution between December 2019 and October 2022. We compared major adverse cardiovascular events (MACE) including re-hospitalization, re-intervention, and mortality, hospital and ICU length of stay, and discharge oxygen saturation between the groups.

Results: We identified 58 patients (51.7% male) with a median age of 2.6 years (IQR: 2-8.1) and a median weight of 9.7 kg (IQR: 7.5-17.5). Among them, 18 (31%) patients received RVOT stenting, and 40 (68.9%) patients had mBTS. The median age for the RVOT stenting group was 92.5 months (IQR: 31.2-152) compared to 26.5 months (IQR: 23.0-54.0) for the mBTS group (p = 0.218). MACEs occurred in 4 (22.2%) patients with RVOT stents and 8 (20%) patients with mBTS (p = 0.624). Patients with mBTS had a longer ICU stay (median 3.5 days, IQR, 2-5) compared to those with RVOT stents (median 2 days, IQR: 1-2) (p = 0.295). Conversely, the hospital stay for patients with mBTS was shorter (median 10 days, IQR, 7.5-13.7) than for those with RVOT stents (median 11.5 days, IQR, 7-19) (p = 0.045). The median discharge oxygen saturation was 87% (IQR: 83-88) in the mBTS group and 80% (IQR: 75-87) in the RVOT stenting group (p = 0.212).

Conclusions: RVOT stenting as palliation to stenotic RVOTs in children older than one year demonstrated outcomes comparable to mBTS in terms of MACE and achieving oxygen saturation targets.

Introduction: Pericoronary adipose tissue (PCAT) plays a significant role in the occurrence and progression of coronary artery disease (CAD). This study investigates the relationship between PCAT and CAD, focusing on the occurrence of the disease, the severity of vascular narrowing, and the characteristics of arterial plaques.

Methods: We analyzed a cohort of 152 individuals with CAD and 55 individuals with non-coronary artery disease (N-CAD). Participants underwent both coronary computed tomography angiography (CCTA) and digital subtraction angiography (DSA). Utilizing United Imaging software for artificial intelligence delineation, we measured the fat attenuation index (FAI) and volume of PCAT in the left anterior descending (LAD), left circumflex (LCX), and right coronary arteries (RCA).

Results: Our findings demonstrate that while CCTA is effective in diagnosing CAD compared to DSA, its diagnostic power for individual coronary arteries remains limited. Further analysis revealed that the FAI of the RCA and the overall PCAT volume independently influenced CAD (OR: 1.057, 95% CI: 1.002 to 1.116; OR: 0.967, 95% CI: 0.936 to 0.999). FAI showed a significant independent effect on RCA stenosis (OR: 1.041, 95% CI: 1.003 to 1.081), while the fat volume of the LAD had a significant independent effect on LAD stenosis (OR: 0.884, 95% CI: 0.809 to 0.965). A higher FAI and a lower fat volume were significantly correlated with more severe vascular stenosis percentages in all three arteries (p < 0.05), except for the fat volume and stenosis of the LCX. Moreover, we found the significant differences in the fat volume of the LCX between different plaque types (H = 8.869, p = 0.012), with calcified plaques consistently exhibiting the lowest fat volume across all three arteries. Finally, the likelihood ratio test confirmed that incorporating the PCAT fat volume parameter of LAD significantly improved the diagnostic ability of CCTA for both CAD (p = 0.01543) and LAD stenosis (p = 0.001585).

Conclusion: The quantification of PCAT has potential application value in the comprehensive assessment of CAD. It is recommended that cardiology and radiology departments consider incorporating PCAT into the assessment criteria for patients suspected of having CAD.

Background: Varicose veins are a common issue for employees in jobs that require prolonged standing compared with all other employees. However, its relationship with presentations of traditional Chinese medicine constitution is unknown. This study aimed to investigate their association.

Material and methods: Data in the study were obtained from questionnaires of patients in Taiwan Biobank, enrolled from 2008 to 2020. The responses to the statement "I can see distorted blood vessels on my four limbs (varicose veins)." were categorized into none, mild, moderate, severe. and more severe, and the same scale was also used to classify breathing difficulties and hypotension.

Results: A total of 11,293 participants were enrolled in the study. The prevalence of women was higher in the studied group compared with the control. Patients complained of breathing difficulties with moderate (30.49%) and severe discomfort (12.44%) in the diseased group. Regarding hypotension, 28.81% and 9.82% of the patients presented with moderate and severe hypotension, respectively. The cofactor odds ratio was 1.775 for severe breathing difficulty/moderate hypotension and 2.235 for severe breathing difficulty/severe hypotension, with statistical significance. The combined impact of breathing difficulties and hypotension increased with severity.

Conclusions: Varicose veins had a higher association with breathing difficulties and hypotension as the severity of the condition worsened. The combined impact of breathing difficulties and hypotension increased as the disease progressed. Therefore, self-reported assessments can be a useful tool for evaluating patients with asymptomatic varicose veins before the development of "heart-failure-like symptoms" to reduce the risk of underdiagnosis.

Objective: In this study, we aimed to compare the levels of inflammatory markers (C-reactive protein, CRP; procalcitonin, PCT) and blood lipids (total cholesterol, TC; triglyceride, TG; high-density lipoprotein cholesterol, HDL-C; low-density lipoprotein cholesterol, LDL-C) between patients with stable angina and control group, and to explore the correlation between these parameters and the severity and prognosis of stable angina.

Methods: We retrospectively selected 113 patients with stable angina and 128 control group from the medical record system, and compared their inflammatory factors and blood lipids. We also assessed the severity of angina using the Canadian Cardiovascular Society (CCS) classification and followed up the patients for 1 year to record any cardiovascular events.

Results: We found that patients with stable angina had significantly higher levels of CRP, TC, TG, and LDL-C, and lower levels of HDL-C than control group. Moreover, CRP, TC, TG, and LDL-C were positively correlated with the severity of angina, while HDL-C was negatively correlated. During the follow-up period, 37 patients with stable angina experienced cardiovascular events, and they had higher levels of CRP, TC, TG, and LDL-C, and lower levels of HDL-C than those who did not.

Conclusion: Our study suggests that inflammation and dyslipidemia are closely related to stable angina, and that inflammatory factors and blood lipids can be used as indicators of the severity and prognosis of stable angina.

Background: Computer-interpreted electrocardiogram (CIE) data is provided by almost all commercial software used to capture and store digital electrocardiograms. CIE is widely available, inexpensive, and accurate. We tested the potential of CIE in long-term sudden cardiac death (SCD) risk prediction.

Methods: This is a retrospective of 8,568 consecutive patients treated for acute coronary syndrome. The primary endpoint was five-year occurrence of SCDs or equivalent events (SCDs aborted by successful resuscitation or adequate ICD therapy). CIE statements were extracted from summary statements and measurements made by the GE Muse 12SL algorithm from ECGs taken during admission. Three supervised machine learning algorithms (logistic regression, extreme gradient boosting, and random forest) were then used for analysis to find risk features using a random 70/30% split for discovery and validation cohorts.

Results: Five-year SCD occurrence rate was 3.3% (n = 287). Regardless of the used ML algorithm, the most significant risk ECG risk features detected by the CIE included known risk features such as QRS duration and factors associated with QRS duration, heart rate-corrected QT time (QTc), and the presence of premature ventricular contractions (PVCs). Risk score formed by using most significant CIE features associated with the risk of SCD despite adjusting for any clinical risk factor (including left ventricular ejection fraction). Sensitivity of CIE data to correctly identify patients with high risk of SCD (over 10% 5-year risk of SCD) was usually low, but specificity and negative prediction value reached up to 96.9% and 97.3% when selecting only the most significant features identified by logistic regression modeling (p-value threshold <0.01 for accepting features in the model). Overall, CIE data showed a modest overall performance for identifying high risk individuals with area under the receiver operating characteristic curve values ranging between 0.652 and 0.693 (highest for extreme gradient boosting and lowest for logistic regression).

Conclusion: This proof-of-concept study shows that automatic interpretation of ECG identifies previously validated risk features for SCD.