Estimated Pulse Wave Velocity Predicts All-Cause and Cardiovascular-Cause Mortality in Individuals With Hypertension　- Findings From a National Health and Nutrition Examination Study 1999-2018.

BACKGROUND: This study aimed to investigate the association between estimated pulse wave velocity (ePWV) and mortality outcomes among individuals with hypertension.Methods and Results: Based on the National Health and Nutrition Examination Survey (NHANES) 1999-2018, a total of 14,396 eligible participants with hypertension were enrolled. The ePWV was calculated using the equation based on blood pressure and age. The mortality outcomes of included participants were directly acquired from the National Death Index database. The multivariable Cox regression analysis was used to examine the relationship between ePWV and mortality outcomes. Moreover, the restricted cubic spline (RCS) was also used to explore this relationship. Receiver operating characteristics curves (ROC) were adopted to evaluate the prognostic ability of ePWV for predicting mortality outcomes of patients with hypertension. The median follow-up duration was 10.8 years; individuals with higher an ePWV had higher risks of mortality from both all causes (HR: 2.79, 95% CI: 2.43-3.20) and cardiovascular diseases (HR: 3.41, 95% CI: 2.50-4.64). After adjusting for confounding factors, each 1 m/s increase in ePWV was associated with a 43% increase in all-cause mortality risk (HR: 1.43, 95% CI: 1.37-1.48) and a 54% increase in cardiovascular mortality risk (HR: 1.54, 95% CI: 1.43-1.66). CONCLUSIONS: This study indicates that ePWV is a novel prognostic indicator for predicting the risks of mortality among patients with hypertension.

Predictive Biomarkers for Postmyocardial Infarction Heart Failure Using Machine Learning: A Secondary Analysis of a Cohort Study.

BACKGROUND: There are few biomarkers with an excellent predictive value for postacute myocardial infarction (MI) patients who developed heart failure (HF). This study aimed to screen candidate biomarkers to predict post-MI HF. METHODS: This is a secondary analysis of a single-center cohort study including nine post-MI HF patients and eight post-MI patients who remained HF-free over a 6-month follow-up. Transcriptional profiling was analyzed using the whole blood samples collected at admission, discharge, and 1-month follow-up. We screened differentially expressed genes and identified key modules using weighted gene coexpression network analysis. We confirmed the candidate biomarkers using the developed external datasets on post-MI HF. The receiver operating characteristic curves were created to evaluate the predictive value of these candidate biomarkers. RESULTS: A total of 6,778, 1,136, and 1,974 genes (dataset 1) were differently expressed at admission, discharge, and 1-month follow-up, respectively. The white and royal blue modules were most significantly correlated with post-MI HF (dataset 2). After overlapping dataset 1, dataset 2, and external datasets (dataset 3), we identified five candidate biomarkers, including FCGR2A, GSDMB, MIR330, MED1, and SQSTM1. When GSDMB and SQSTM1 were combined, the area under the curve achieved 1.00, 0.85, and 0.89 in admission, discharge, and 1-month follow-up, respectively. CONCLUSIONS: This study demonstrates that FCGR2A, GSDMB, MIR330, MED1, and SQSTM1 are the candidate predictive biomarker genes for post-MI HF, and the combination of GSDMB and SQSTM1 has a high predictive value.

His-Purkinje conduction system pacing: A systematic review and network meta-analysis in bradycardia and conduction disorders.

BACKGROUND: His-Purkinje conduction system pacing (HPCSP) has emerged as an effective alternative to overcome the limitations of right ventricular pacing (RVP) via physiological left ventricular activation, but there remains a paucity of comparative information for His bundle pacing (HBP) and left bundle branch pacing (LBBP). METHODS: A Bayesian random-effects network analysis was conducted to compare the relative effects of HBP, LBBP, and RVP in patients with bradycardia and conduction disorders. PubMed, Embase, Cochrane Library, and Web of Science were systematically searched from database inception until September 21, 2021. RESULTS: Twenty-eight studies involving 4160 patients were included in this meta-analysis. LBBP significantly improved success rate, pacing threshold, pacing impedance, and R-wave amplitude compared with HBP. LBBP also demonstrated a nonsignificant trend towards superior outcomes of lead complications, heart failure hospitalization, atrial fibrillation, and all-cause death. However, HBP was associated with significantly shorter paced QRS duration relative to LBBP. Despite higher success rates, shorter procedure/fluoroscopy duration, and fewer lead complications, patients receiving RVP were more likely to experience reduced left ventricular ejection fraction, longer paced QRS duration, and higher rates of heart failure hospitalization than those receiving HPCSP. No statistical differences were observed in the remaining outcome measures. CONCLUSIONS: This network meta-analysis demonstrates the efficacy and safety of HPCSP for the treatment of bradycardia and conduction disorders, with differences in pacing parameters, electrophysiology characteristics, and clinical outcomes between HBP and LBBP. Larger-scale, long-term comparative studies are warranted for further verification.

Identification of key genes in calcific aortic valve disease via weighted gene co-expression network analysis.

BACKGROUND: Calcific aortic valve disease (CAVD) is the most common subclass of valve heart disease in the elderly population and a primary cause of aortic valve stenosis. However, the underlying mechanisms remain unclear. METHODS: The gene expression profiles of GSE83453, GSE51472, and GSE12644 were analyzed by 'limma' and 'weighted gene co-expression network analysis (WGCNA)' package in R to identify differentially expressed genes (DEGs) and key modules associated with CAVD, respectively. Then, enrichment analysis was performed based on Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, DisGeNET, and TRRUST database. Protein-protein interaction network was constructed using the overlapped genes of DEGs and key modules, and we identified the top 5 hub genes by mixed character calculation. RESULTS: We identified the blue and yellow modules as the key modules. Enrichment analysis showed that leukocyte migration, extracellular matrix, and extracellular matrix structural constituent were significantly enriched. SPP1, TNC, SCG2, FAM20A, and CD52 were identified as hub genes, and their expression levels in calcified or normal aortic valve samples were illustrated, respectively. CONCLUSIONS: This study suggested that SPP1, TNC, SCG2, FAM20A, and CD52 might be hub genes associated with CAVD. Further studies are required to elucidate the underlying mechanisms and provide potential therapeutic targets.