HFpEF as systemic disease, insight from a diagnostic prediction model reminiscent of systemic inflammation and organ interaction in HFpEF patients.

Systemic inflammation and reciprocal organ interactions are associated with the pathophysiology of heart failure with preserved ejection fraction (HFpEF). However, the clinical value, especially the diagnositc prediction power of inflammation and extra-cardiac organ dysfunction for HfpEF is not explored. In this cross-sectional study, 1808 hospitalized patients from January 2014 to June 2022 in ChiHFpEF cohort were totally enrolled according to inclusion and exclusion criteria. A diagnostic model with markers from routine blood test as well as liver and renal dysfunction for HFpEF was developed using data from ChiHFpEF-cohort by logistic regression and assessed by receiver operating characteristic curve (ROC) and Brier score. Then, the model was validated by the tenfold cross-validation and presented as nomogram and a web-based online risk calculator as well. Multivariate and LASSO regression analysis revealed that age, hemoglobin, neutrophil to lymphocyte ratio, AST/ALT ratio, creatinine, uric acid, atrial fibrillation, and pulmonary hypertension were associated with HFpEF. The predictive model exhibited reasonably accurate discrimination (ROC, 0.753, 95% CI 0.732-0.772) and calibration (Brier score was 0.200). Subsequent internal validation showed good discrimination and calibration (AUC = 0.750, Brier score was 0.202). In additoin to participating in pathophysiology of HFpEF, inflammation and multi-organ interactions have diagnostic prediction value for HFpEF. Screening and optimizing biomarkers of inflammation and multi-organ interactions stand for a new field to improve noninvasive diagnostic tool for HFpEF.

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.

Glucose fluctuations aggravated the late sodium current induced ventricular arrhythmias via the activation of ROS/CaMKII pathway.

BACKGROUND: Recent evidence revealed that glucose fluctuation might be more likely to cause arrhythmia than persistent hyperglycemia, whereas its mechanisms were elusive. We aimed to investigate the effect of glucose fluctuation on the occurrence of ventricular arrhythmia and its mechanism. METHODS: Streptozotocin (STZ) induced diabetic rats were randomized to five groups: the controlled blood glucose (C-STZ) group, uncontrolled blood glucose (U-STZ) group, fluctuated blood glucose (GF-STZ) group, and GF-STZ rats with 100 mg/kg Tempol (GF-STZ + Tempol) group or with 5 mg/kg KN93 (GF-STZ + KN93) group. Six weeks later, the susceptibility of ventricular arrhythmias and the electrophysiological dysfunctions of ventricular myocytes were evaluated using electrocardiogram and patch-clamp technique, respectively. The levels of reactive oxygen species (ROS) and oxidized CaMKII (ox-CaMKII) were determined by fluorescence assay and Western blot, respectively. Neonatal rat cardiomyocytes and H9C2 cells in vitro were used to explore the underlying mechanisms. RESULTS: The induction rate of ventricular arrhythmias was 10%, 55%, and 90% in C-STZ group, U-STZ group, and GF-STZ group, respectively (P < 0.05). The electrophysiological dysfunctions of ventricular myocytes, including action potential duration at repolarization of 90% (APD90), APD90 short-term variability (APD90-STV), late sodium current (INa-L), early after depolarization (EAD) and delayed after depolarizations (DAD), as well as the levels of ROS and ox-CaMKII, were significantly increased in GF-STZ group. In vivo and ex vivo, inhibition of ROS or ox-CaMKII reversed these effects. Inhibition of INa-L also significantly alleviated the electrophysiological dysfunctions. In vitro, inhibition of ROS increase could significantly decrease the ox-CaMKII activation induced by glucose fluctuations. CONCLUSIONS: Glucose fluctuations aggravated the INa-L induced ventricular arrhythmias though the activation of ROS/CaMKII pathway.

Prevalence of neutropenia in US residents: a population based analysis of NHANES 2011-2018.

AIMS: Neutrophils play a pivotal in immunity and inflammation. We aim to investigate the prevalence of neutropenia in the United States. METHODS: In this cross-sectional study, participants from the National Health and Nutrition Examination Survey (NHANES) (2011-2018) were enrolled. Demographic information, hematologic measurements, smoking status of all participants were collected for all participants. All statistical analyses were performed utilizing the NHANES survey weights. Covariate-adjusted linear regression was used to compare hematologic indices in different population grouped by age, sex, ethnicity, and smoking. We also employed multivariate-logistic regression to estimate the weighted odds ratio with a 95% confidence interval and predict the neutropenia risk among. RESULTS: 32,102 participants from NHANES survey were included, represented 286.6 million multiracial population in the United States. Black participants had lower mean leukocyte count (mean difference (MD): 0.71 × 109/L; P < 0.001) and lower neutrophil count (MD: 0.83 × 109/L; P < 0.001) compared with white participants after adjusting for age and sex. Furthermore, t a notable observation was the significant downward shift in the distribution curves of leukocyte count and neutrophil count among black participants. Smokers had a higher mean leukocyte count (MD: 1.10 × 109 cells/L; P < 0.001) and a higher mean neutrophil count (MD: 0.75 × 109 cells/L; P < 0.001) comparing with nonsmokers. The estimated prevalence of neutropenia was 1.24% (95% CI: 1.11 - 1.37%), which corresponds to approximately 35.5 million individuals in the United States. The prevalence of neutropenia in black participants was significantly higher than other races. Results of logistic regression analysis showed that black individuals, male individuals, and children younger than 5 years had a higher risk of neutropenia. CONCLUSIONS: Neutropenia is more common in the general population than we thought, especially in black individuals and children. More attention should be paid to neutropenia.

Association of different obesity patterns with hypertension in US male adults: a cross-sectional study.

Obesity is an important risk factor for hypertension. We aimed to investigate the association between different obesity patterns and hypertension risk in a large male population in the US. Male participants from the National Health and Nutrition Examination Survey (NHANES) (2007-2018) were enrolled in this cross-sectional study. Social demographic information, lifestyle factors, anthropometric measurements and biochemical measurements were collected. Three obesity patterns were classified according to the body mass index (BMI) and waist circumference (WC), including overweight and general obesity, abdominal obesity, and compound obesity. We adopted multivariate logistic regression to investigate the associations between hypertension and different obesity patterns after adjusting for cofounding factors. Subgroup analysis, stratified by age, smoking, drinking and estimated glomerular filtration rate (eGFR), was also conducted to explore the associations between obesity patterns and hypertension risk among different populations. Moreover, the association between WC and hypertension among male individuals was also explored using restricted cubic spline (RCS) analysis. Receiver operating characteristic (ROC) was used to evaluate the discriminatory power of WC for screening hypertension risk. 13,859 male participants from NHANES survey (2007-2018) were enrolled. Comparing with the normal-weight group, the odds ratios (ORs) [95% confidence interval (CI)] for hypertension in individuals with overweight and general obesity, abdominal obesity and compound obesity were 1.41 [1.17-1.70], 1.97 [1.53-2.54] and 3.28 [2.70-3.99], respectively. Subgroup analysis showed that the effect of different obesity patterns on hypertension risk was highly stable among individuals with different clinical conditions. In addition, WC had a positive correlation with the risk of hypertension (OR: 1.43; 95% CI 1.37-1.52; P < 0.001) in fully adjusted multivariate logistic regression model. RCS analysis showed that the association between WC and hypertension risk was in a nonlinear pattern, and WC had a good discriminatory power for hypertension in ROC analysis. Different patterns of obesity have a great impact on the risk of hypertension among male individuals. Increment of WC significantly increased the hypertension risk. More attention should be paid to the prevention of obesity, especially abdominal obesity and compound obesity in male individuals.

Estimated pulse wave velocity is associated with all-cause mortality and cardiovascular mortality among adults with diabetes.

Aims: We aim to examine the association of estimated pulse wave velocity (ePWV) with all-cause and cardiovascular mortality in patients with diabetes. Methods: All of adult participants with diabetes from the National Health and Nutrition Examination Survey (NHANES) (1999-2018) were enrolled. ePWV was calculated according to the previously published equation based on age and mean blood pressure. The mortality information was obtained from the National Death Index database. Weighted Kaplan-Meier (KM) plot and weighted multivariable Cox regression was used to investigate the association of ePWV with all-cause and cardiovascular mortality risks. Restricted cubic spline was adopted to visualize the relationship between ePWV and mortality risks. Results: 8,916 participants with diabetes were included in this study and the median follow-up duration was ten years. The mean age of study population was 59.0 ± 11.6 years, 51.3% of the participants were male, representing 27.4 million patients with diabetes in weighted analysis. The increment of ePWV was closely associated with increased risks of all-cause mortality (HR: 1.46, 95% CI: 1.42-1.51) and cardiovascular mortality (HR: 1.59, 95% CI: 1.50-1.68). After adjusting for cofounding factors, for every 1 m/s increase in ePWV, there was a 43% increased risk of all-cause mortality (HR: 1.43, 95% CI: 1.38-1.47) and 58% increased of cardiovascular mortality (HR: 1.58, 95% CI: 1.50-1.68). ePWV had positive linear associations with all-cause and cardiovascular mortality. KM plots also showed that the risks of all-cause and cardiovascular mortality were significantly elevated in patients with higher ePWV. Conclusions: ePWV had a close association with all-cause and cardiovascular mortality risks in patients with diabetes.

Identification of key immune-related genes in dilated cardiomyopathy using bioinformatics analysis.

Dilated cardiomyopathy (DCM) is characterized by the left ventricular dilatation and impaired myocardial systolic dysfunction with high mortality and morbidity. However, the underlying mechanisms remain elusive. We first identified the differentially expressed genes (DEGs) between the DCM and control group using two expression profiles from GSE3585 and GSE84796. Enrichment analysis was conducted to explore the potential mechanisms underlying DCM. A total of four algorithms, including key module of MCODE, degree, maximum neighborhood component (MNC), and maximal clique centrality (MCC), were used to identify the hub genes within Cytoscape. The correlation between hub genes and infiltrated immune cells was evaluated to determine potential immune-related genes. The expression analysis and diagnosis value analysis of potential immune-related genes were performed. Finally, the expression analysis with GSE57338 and relationship analysis with the comparative toxicogenomics database (CTD) were performed to identify the key immune-related genes in DCM. A total of 80 DEGs were screened for DCM. Enrichment analysis revealed that DEGs were involved in the immune-related pathological process. Immune infiltration analysis indicated a potentially abnormal immune response in DCM. Four up-regulated genes (COL1A2, COL3A1, CD53, and POSTN) were identified as potential immune-related genes. Finally, three genes (COL1A2, COL3A1, and POSTN) were determined as the key immune-related genes in DCM via expression analysis with a validation set (GSE57338) and relationship analysis with CTD. Our study suggested that the upregulated COL1A2, COL3A1, and POSTN might be the key immune-related genes for DCM. Further studies are needed to validate the underlying mechanisms.

Integrated genomic analysis defines molecular subgroups in dilated cardiomyopathy and identifies novel biomarkers based on machine learning methods.

Aim: As the most common cardiomyopathy, dilated cardiomyopathy (DCM) often leads to progressive heart failure and sudden cardiac death. This study was designed to investigate the molecular subgroups of DCM. Methods: Three datasets of DCM were downloaded from GEO database (GSE17800, GSE79962 and GSE3585). After log2-transformation and background correction with "limma" package in R software, the three datasets were merged into a metadata cohort. The consensus clustering was conducted by the "Consensus Cluster Plus" package to uncover the molecular subgroups of DCM. Moreover, clinical characteristics of different molecular subgroups were compared in detail. We also adopted Weighted gene co-expression network analysis (WGCNA) analysis based on subgroup-specific signatures of gene expression profiles to further explore the specific gene modules of each molecular subgroup and its biological function. Two machine learning methods of LASSO regression algorithm and SVM-RFE algorithm was used to screen out the genetic biomarkers, of which the discriminative ability of molecular subgroups was evaluated by receiver operating characteristic (ROC) curve. Results: Based on the gene expression profiles, heart tissue samples from patients with DCM were clustered into three molecular subgroups. No statistical difference was found in age, body mass index (BMI) and left ventricular internal diameter at end-diastole (LVIDD) among three molecular subgroups. However, the results of left ventricular ejection fraction (LVEF) statistics showed that patients from subgroup 2 had a worse condition than the other group. We found that some of the gene modules (pink, black and grey) in WGCNA analysis were significantly related to cardiac function, and each molecular subgroup had its specific gene modules functions in modulating occurrence and progression of DCM. LASSO regression algorithm and SVM-RFE algorithm was used to further screen out genetic biomarkers of molecular subgroup 2, including TCEAL4, ISG15, RWDD1, ALG5, MRPL20, JTB and LITAF. The results of ROC curves showed that all of the genetic biomarkers had favorable discriminative effectiveness. Conclusion: Patients from different molecular subgroups have their unique gene expression patterns and different clinical characteristics. More personalized treatment under the guidance of gene expression patterns should be realized.

Involvement of pyroptosis pathway in epicardial adipose tissue - myocardium axis in experimental heart failure with preserved ejection fraction.

Epicardial adipose tissue (EAT) is a metabolically active organ which generates inflammatory cytokines. Thickness of EAT is associated with onset and development of heart failure with preserved ejection fraction (HFpEF). However, it is still unclear the specific mechanisms and pharmacological targets on EAT induced inflammation in HFpEF. A two-hit protocol with western diet and Nω-nitrol-arginine methyl ester (L-NAME) was used to establish HFpEF mouse model. In HFpEF mice, inflammatory biomarkers, such as tumor necrosis factor (TNF)-α, interleukin (IL)-1ß and von willebrand factor (vWF) elevated in myocardium compared to control. Inflammatory cell infiltration in myocardium was increased. In HFpEF mice, inflammasome-mediated pyroptosis pathway was activated in the EAT. Suppression of pyroptosis-related protein gasdermin D (GSDMD) in cultured EAT could lower cardiomyocyte inflammation and autophagy. Furthermore, spironolactone and rosuvastatin, the two-hit anti-inflammatory agents, reduced NLR family pyrin domain containing 3 (NLRP3)/GSDMD pyroptosis in EAT and autophagy in myocardium of HFpEF mouse. The combination treatment also enhanced exercise tolerance and appeased inflammatory injuries in HFpEF mice. CONCLUSION: Pyroptosis signaling is involved in EAT-myocardium axis in mouse model of HFpEF. Targeting adipocyte-derived inflammation in EAT bears potential to treatment HFpEF.

Associations between novel anthropometric measures and the prevalence of hypertension among 45,853 adults: A cross-sectional study.

Aims: Traditional anthropometric measures, including body mass index (BMI), are insufficient for evaluating the risk of hypertension. We aimed to investigate the association between novel anthropometric indices and hypertension risk in a large population in the United States. Methods: Forty-five thousand eight hundred fifty-three participants from the National Health and Nutrition Examination Survey (NHANES) (1999-2018) were enrolled. Social demographic information, lifestyle factors, blood biochemical measurements and anthropometric indices, including body weight, body mass index (BMI), waist circumference, waist-to-height ratio (WtHR), conicity index (CI), a body shape index (ABSI), body roundness index (BRI) and lipid accumulation product (LAP) were collected. Multivariable logistic regression and restricted cubic spline were adopted to investigate the associations between hypertension risk and anthropometric indices. We also performed receiver operating characteristic (ROC) curve analyses to further evaluate the discriminatory powers of anthropometric measurements for screening hypertension risk. Moreover, participants were randomly assigned to the training group and the validation group in a ratio of 3 to 1. A nomogram model based on anthropometric measures was established and validated in the training group and validation group, respectively. Results: All of the anthropometric measurements investigated were positively and independently associated with the hypertension risk. Among all anthropometric indices, per-SD increment in ABSI had the highest OR (OR: 3.4; 95% CI: 2.73-4.24) after adjusting for age, sex, race/ethnicity, education, smoking, drinking, diabetes, and eGFR. Moreover, results from restricted cubic splines revealed the non-linear association between anthropometric measurements and hypertension risk. In ROC analyses, CI had superior discriminatory power for hypertension (area under the curve: 0.71; 95% CI: 0.706-0.715; optimal cutoff value: 1.3) compared with other indices. Nomogram model based on age, sex, diabetes, CI and LAP showed favorable predicting ability of hypertension risk with an AUC (95% CI) in training group of 80.2% (79.7-80.6%), and the AUC (95% CI) in validation group was 79.5% (78.3-80.1%). Meanwhile, calibration plot showed good consistency. Conclusions: Anthropometric measurements including BMI, WtHR, CI, ABSI, BRI and LAP are closely associated with hypertension risk in the present study. For better prevention and treatment of hypertension, more attention should be paid to anthropometric indices, especially novel anthropometric indices.

Integrated identification of key immune related genes and patterns of immune infiltration in calcified aortic valvular disease: A network based meta-analysis.

Background: As the most prevalent valvular heart disease, calcific aortic valve disease (CAVD) has become a primary cause of aortic valve stenosis and insufficiency. We aim to illustrate the roles of immune related genes (IRGs) and immune cells infiltration in the occurrence of CAVD. Methods: Integrative meta-analysis of expression data (INMEX) was adopted to incorporate multiple gene expression datasets of CAVD from Gene Expression Omnibus (GEO) database. By matching the differentially expressed genes (DEGs) to IRGs from "ImmPort" database, differentially expressed immune related genes (DEIRGs) were screened out. We performed enrichment analysis and found that DEIRGs in CAVD were closely related to inflammatory response and immune cells infiltration. We also constructed protein-protein interaction (PPI) network of DEIRGs and identified 5 key DEIRGs in CAVD according to the mixed character calculation results. Moreover, CIBERSORT algorithm was used to explore the profile of infiltrating immune cells in CAVD. Based on Spearman's rank correlation method, correlation analysis between key DEIRGs and infiltrating immune cells was performed. Results: A total of 220 DEIRGs were identified and the enrichment analysis of DEIRGs showed that they were significantly enriched in inflammatory responses. PPI network was constructed and PTPN11, GRB2, SYK, PTPN6 and SHC1 were identified as key DEIRGs. Compared with normal aortic valve tissue samples, the proportion of neutrophils, T cells CD4 memory activated and macrophages M0 was elevated in calcified aortic valves tissue samples, as well as reduced infiltration of macrophages M2 and NK cells activated. Furthermore, key DEIRGs identified in the present study, including PTPN11, GRB2, PTPN6, SYK, and SHC1, were all significantly correlated with infiltration of various immune cells. Conclusion: This meta-analysis suggested that PTPN11, GRB2, PTPN6, SYK, and SHC1 might be key DEIRGs associated with immune cells infiltration, which play a pivotal role in pathogenesis of CAVD.

Do Elderly Patients with Atrial Fibrillation Have Comparable Ablation Outcomes Compared to Younger Ones? Evidence from Pooled Clinical Studies.

Background: Age is an independent risk factor of the progress and prognosis of atrial fibrillation (AF). However, ablation outcomes between elderly and younger patients with AF remain elusive. Methods: Cochrane Library, Embase, PubMed, and Web of Science were systematically searched up to 1 April 2022. Studies comparing AF ablation outcomes between elderly and younger patients and comprising outcomes of AF ablation for elderly patients were included. Trial sequential analysis (TSA) was performed to adjust for random error and lower statistical power in our meta-analysis. Subgroup analysis identified possible determinants of outcome impact for elderly patients after ablation. Moreover, linear and quadratic prediction fit plots with confidence intervals were performed, as appropriate. Results: A total of 27 studies with 113,106 AF patients were eligible. Compared with the younger group, the elderly group was significantly associated with a lower rate of freedom from AF (risk ratio [RR], 0.95; p = 0.008), as well as a higher incidence of safety outcomes (cerebrovascular events: RR, 1.64; p = 0.000; serious hemorrhage complications: RR, 1.50; p = 0.035; all-cause death: RR, 2.61; p = 0.003). Subgroup analysis and quadratic prediction fit analysis revealed the follow-up time was the potential determinant of freedom from AF for elderly patients after AF ablation. Conclusions: Our meta-analysis suggests that elderly patients may have inferior efficacy and safety outcomes to younger patients with AF ablation. Moreover, the follow-up time may be a potential determinant of outcome impact on freedom from AF for elderly patients after AF ablation.

Glucose fluctuation promotes cardiomyocyte apoptosis by triggering endoplasmic reticulum (ER) stress signaling pathway in vivo and in vitro.

Glucose fluctuation is more harmful than sustained hyperglycemia, but the effect on cardiomyocyte apoptosis have not yet been clarified. In this study, we aim to identify the effect of glucose fluctuation on cardiomyocyte apoptosis and explore the underlying mechanism. Sprague-Dawley rats were intraperitoneally injected with streptozotocin (STZ) and divided into three groups: controlled diabetic group (C-STZ); uncontrolled diabetic group (U-STZ) and glucose fluctuated diabetic group (GF-STZ). After twelve weeks, echocardiography, Hematoxylin-eosin (HE) staining, and Masson staining were adopted to assess the cardiac function and pathological changes. TUNEL staining was used to detect apoptotic cells. Expressions of apoptosis-related proteins and key molecules in the endoplasmic reticulum (ER) stress pathway were determined via western blots. Further, primary cardiomyocytes incubated in different glucose conditions were treated with the inhibitor of ER stress to explore the causative role of ER stress in glucose fluctuation-induced cardiomyocyte apoptosis. In vivo, we demonstrated that glucose fluctuation promoted cardiomyocyte apoptosis, and were more harmful to cardiomyocytes than sustained hyperglycemia. Moreover, glucose fluctuation significantly triggered ER stress signaling pathway. In vitro, primary cardiomyocyte apoptosis induced by glucose fluctuation and the activation of ER stress were significantly attenuated by 4-PBA, which is an ER stress inhibitor. Above all, glucose fluctuation can promote cardiomyocyte apoptosis through triggering the ER stress signaling pathway in diabetic rats and in primary cardiomyocytes.

The Long-Term Outcomes of Ablation With Vein of Marshall Ethanol Infusion vs. Ablation Alone in Patients With Atrial Fibrillation: A Meta-Analysis.

Background: The long-term outcomes of ablation with vein of Marshall ethanol infusion (VOM-ABL) compared with ablation alone in patients with atrial fibrillation (AF) remains elusive. We aimed to explore whether VOM-ABL showed better long-term benefits and screen the potential determinants of outcome impact of VOM-ABL procedure. Methods: PubMed, Cochrane Library, Web of Science, and Embase were searched up to 1st September 2021. Studies comparing the long-term (one-year or longer) outcomes between VOM-ABL and ablation alone were included. Subgroup analysis identified potential determinants for VOM-ABL procedure. Results: Compared with ablation alone, VOM-ABL was associated with a significantly higher rate of long-term freedom from AF/AT (risk ratio [RR], 1.28; 95% confidence interval [CI], 1.12-1.47; p = 0.00) and successful mitral isthmus (MI) block (RR, 1.52; 95% CI, 1.16-1.99; p = 0.00), whereas, there was no significant difference in pericardial effusion, stroke/transient ischemic attack (TIA), and all-cause death. Subgroup analysis identified two significant treatment-covariate interactions: one was ablation strategy subgroup (pulmonary vein isolation plus linear and/or substrate ablation [PVI+]; RR, 1.41; 95% CI, 1.27-1.56 vs. PVI; RR, 1.05; 95% CI, 0.92-1.19, p = 0.00 for interaction) for freedom from AF/AT, while the other was VOM-ABL group sample size subgroup (≥ 100; RR, 1.98; 95% CI, 1.24-3.17 vs. <100; RR, 1.20; 95% CI, 1.10-1.30, p = 0.04 for interaction) for MI block. Conclusions: This meta-analysis demonstrates that VOM-ABL has superior efficacy and comparable safety over ablation alone in AF patients with long-term follow-up. Moreover, PVI+ and VOM-ABL group sample size ≥ 100 may be associated with a great impact on freedom from AF/AT and MI block, respectively.

Effects of sitagliptin on serum lipid levels in patients with type 2 diabetes: a systematic review and meta-analysis.

AIM: Lipid abnormalities often occur in patients with diabetes mellitus and the coexistence of diabetes mellitus and dyslipidaemia will increase the risk of cardiovascular diseases. However, the specific effects of sitagliptin on lipid control remain elusive in diabetic patients. The aim of this meta-analysis is to investigate the effects of sitagliptin alone or with other antidiabetic agents on serum lipid control. METHODS: PubMed, Cochrane Library, Embase and the ClinicalTrials.gov website were systematically searched from 2006 (the first year that sitagliptin entered market) to 16 January 2021. Eligible studies were randomized clinical trials (RCTs) of sitagliptin including outcomes of serum total cholesterol (TC), triglycerides, high-density lipoprotein cholesterol (HDL-C) or low-density lipoprotein cholesterol (LDL-C). RESULTS: A total of 14 RCTs with 2654 patients were identified. Treatment with sitagliptin alone or in combination with other antidiabetic agents significantly reduced serum TC [mean difference (MD) = -5.52 95% confidence interval (95% CI), -7.88 to -3.15; P < 0.00001] and LDL-C (MD = -0.07; 95% CI, -0.14 to 0.00; P < 0.00001) in patients with type 2 diabetes. No statistical significances were found in serum triglycerides (MD = 1.53; 95% CI, -8.22 to 11.28; P = 0.76) or HDL-C (MD = 0.65; 95% CI, -1.59 to 0.29; P = 0.18). Subgroup analyses suggest that sitagliptin can significantly decrease serum LDL-C, TC and triglyceride levels compared with placebo alone, and no statistical significance was found in comparison with the serum HDLC levels. CONCLUSION: Sitagliptin alone or in combination with other antidiabetic agents significantly reduces serum TC and LDL-C in patients with type 2 diabetes mellitus, while no significant difference was observed in serum triglycerides or HDL-C.

Analysis of potential genetic biomarkers using machine learning methods and immune infiltration regulatory mechanisms underlying atrial fibrillation.

OBJECTIVE: We aimed to screen out biomarkers for atrial fibrillation (AF) based on machine learning methods and evaluate the degree of immune infiltration in AF patients in detail. METHODS: Two datasets (GSE41177 and GSE79768) related to AF were downloaded from Gene expression omnibus (GEO) database and merged for further analysis. Differentially expressed genes (DEGs) were screened out using "limma" package in R software. Candidate biomarkers for AF were identified using machine learning methods of the LASSO regression algorithm and SVM-RFE algorithm. Receiver operating characteristic (ROC) curve was employed to assess the diagnostic effectiveness of biomarkers, which was further validated in another independent validation dataset of GSE14975. Moreover, we used CIBERSORT to study the proportion of infiltrating immune cells in each sample, and the Spearman method was used to explore the correlation between biomarkers and immune cells. RESULTS: 129 DEGs were identified, and CYBB, CXCR2, and S100A4 were identified as key biomarkers of AF using LASSO regression and SVM-RFE algorithm. Both in the training dataset and the validation dataset, CYBB, CXCR2, and S100A4 showed favorable diagnostic effectiveness. Immune infiltration analysis indicated that, compared with sinus rhythm (SR), the atrial samples of patients with AF contained a higher T cells gamma delta, neutrophils and mast cells resting, whereas T cells follicular helper were relatively lower. Correlation analysis demonstrated that CYBB, CXCR2, and S100A4 were significantly correlated with the infiltrating immune cells. CONCLUSIONS: In conclusion, this study suggested that CYBB, CXCR2, and S100A4 are key biomarkers of AF correlated with infiltrating immune cells, and infiltrating immune cells play pivotal roles in AF.

The dietary inflammatory index and its association with the prevalence of hypertension: A cross-sectional study.

Aims: We aim to investigate the association of the Dietary Inflammatory Index (DII) with the prevalence of hypertension in a large multiracial population in the United States. Methods: Participants from the National Health and Nutrition Examination Survey (NHANES) (1999-2018) were included in this cross-sectional study. Dietary information was obtained and used to calculate DII. Blood pressures of participants were measured by experienced examiners. The NHANES used the method of "stratified multistage probability sampling," and this study is a weight analysis following the NHANES analytic guidance. Weight logistic regression analysis was adopted to investigate the association of hypertension with DII. Least Absolute Shrinkage and Selection Operator (LASSO) regression was carried out to screen the most important dietary factors associated with the risk of hypertension. Moreover, a nomogram model based on key dietary factors was established; the receiver operating characteristic (ROC) curve was used to evaluate the diagnostic power of the nomogram model for screening hypertension risk. Results: A total of 45,023 participants were included in this study, representing 191 million residents in the United States. Participants with hypertension had an elevated DII compared with those without hypertension. Weight logistic regression showed that an increment of DII was strongly associated with hypertension after adjusting for confounding factors. The nomogram model, based on key dietary factors screened by LASSO regression, showed a favorable discriminatory power with an area under the curve (AUC) of 78.5% (95% CI: 78.5%-79.3%). Results of the sensitivity analysis excluding participants who received any drug treatment were consistent with those in the main analysis. Conclusion: An increment of DII is associated with the risk of hypertension. For better prevention and treatment of hypertension, more attention should be paid to controlling dietary inflammation.

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.