Fen Liu

There is ongoing discussion in the medical community on the effect of metformin in cardiac arrhythmias. Investigating the causal relationship between metformin and arrhythmias was the target of the research. We utilized the Mendelian randomization (MR)-based platform to collect genome-wide association data linking arrhythmias and metformin. We utilized SNPs connected to metformin as instrumental variables (IVs) to evaluate the causative link between metformin and arrhythmia through two-sample MR analysis. Four statistical techniques were employed: MR-Egger regression, weighted median estimator, weighted mode method, and inverse-variance weighted (IVW) method. The impact of individual SNPs on the outcomes of IVW analyses was investigated using the leave-one-out method, and the study’s possible bias was examined using a funnel plot to guarantee the results’ robustness. We identified 40 independent single nucleotide polymorphisms (SNPs) of genome-wide significance from GWAS data for metformin as IVs. The IVW method supported a causal relationship between metformin and reduced incidence of arrhythmia (OR = 0.0093; 95% CI = 0.002–0.055; P = 2.72E−07). MR-Egger regression indicated that there is no need to consider the effect of gene pleiotropy on the results of the study (intercept = 0.0014; P = .874). Although the MR-Egger method didn’t pinpoint a causal link, it did reveal a similar directionality in the β values (OR = 0.0056; 95% CI = 9.384E−06 to 3.369; P = .1207). Fortunately, the weighted median and weighted mode techniques demonstrated a causal link between metformin and a decreased arrhythmia risk (OR = 0.004; 95% CI = 0.0003–0.052, P = .000023; OR = 0.0025; 95% CI = 7.418E−05 to 0.085, P = .001915). Neither Cochran Q test nor the funnel plot showed signs of directional pleiotropy, heterogeneity, or asymmetry. The sensitivity of the method was analyzed by leave-one-out method, and the results suggested that the method was stable. With the MR method, we’ve substantiated a potential cause-and-effect link between metformin usage and cardiac arrhythmia.

Premature coronary artery disease (PCAD) is characterized by early onset, rapid progression, and poor prognosis, which seriously affects patients’ health and quality of life. In this study, we analyzed the proteomic network and biological pathways of PCAD patients by bioinformatics methods, and mined out the key differential proteins, which provided a theoretical basis for clinical intervention. Patients who attended the heart center of the First Affiliated Hospital of Xinjiang Medical University from January 2023 to December 2024 and completed coronary angiography were selected. According to the relevant inclusion and exclusion criteria, a total of 129 patients were included, including 69 in the PCAD group and 60 in the control group. The clinical baseline data of the patients were systematically analyzed. Plasma protein extraction, trypsin digestion and mass spectrometry were completed. The mass spectrometry data were initially separated with the help of proteomics software, and the differential proteins were functionally enriched by RStudio software. Protein interaction networks were constructed by STRING platform and core differential proteins screened were visualized using Cytoscape software (MCODE plug-in). Differences in gender, smoking, alcohol consumption, hypertension, diabetes, HDL-C, Glu, FIB, LPa, NT-pro-BNP, PCT, and IL-6 were statistically significant (P < 0.05). Sex (P = 0.009, OR = 6.782,95% CI: 1.600-28.746), FIB (P = 0.001, OR = 2.662,95% CI: 1.471–4.818), and LPa (P = 0.041, OR = 1.002,95% CI: 1.000-1.004) were independent risk factors for PCAD. A total of 348 up-regulated proteins and 92 down-regulated proteins were screened by bioinformatics analysis. The occurrence of PCAD is associated with protein synthesis, intercellular communication, molecular interactions, ribosomal metabolism, glyoxylate and dicarboxylic acid metabolic pathways. Ribosomal and translational proteins influence the development of PCAD. In this study, we found that gender, FIB, and LPa are risk factors for PCAD. The analysis identified 348 up-regulated and 92 down-regulated proteins. Among them, the differentially expressed proteins DHX9, F7, APCS, and PROC were closely related to the biological process of PCAD. The screened ribosomal and translational proteins showed high-frequency associations in protein-protein interaction networks, providing potential differentially expressed proteins for a deeper understanding of the disease.

T2DM is a major risk factor for CHD. In recent years, machine learning algorithms have demonstrated significant advantages in improving predictive accuracy; however, studies applying these methods for clinical prediction and diagnosis of CHD-DM2 remain limited. This study aims to evaluate the performance of machine learning models and to develop an interpretable model to identify critical risk factors of CHD-DM2, thereby supporting clinical decision-making. Data were collected from cardiovascular inpatients admitted to the First Affiliated Hospital of Xinjiang Medical University between 2001 and 2018. A total of 12,400 patients were included, comprising 10,257 cases of CHD and 2143 cases of CHD-DM2.To address the class imbalance in the dataset, the SMOTENC algorithm was applied in conjunction with the themis package for data preprocessing. Final predictors were identified through a combined approach of univariate analysis and Lasso regression. We then developed and validated seven machine learning models: Logistic, Logistic_Lasso, KNN, SVM, XGBoost, RF, and LightGBM. The predictive performance of the five models was compared using evaluation metrics including accuracy, sensitivity, specificity, AUC, ROC and DCA. Additionally, SHAP values were employed to provide interpretability of the model outputs. The dataset was split into a training set (n = 8460) and a validation set (n = 3680) at a 7:3 ratio. A total of 25 predictive variables were ultimately identified through Lasso regression analysis. Among the seven machine learning models, the RF model demonstrated significantly superior performance and achieved the highest net benefit in the DCA. According to SHAP analysis, Diabetes.History, BG, and HbA1c were identified as the top contributors to CHD-DM2 risk. This study identified Diabetes.History, blood glucose (BG), and HbA1c as the primary risk factors for CHD-DM2. It is recommended that hospitals enhance monitoring of such patients, document the presence of high-risk factors, and implement targeted intervention strategies accordingly.

Objective: The study explores the prevalence of hypertension and evaluates the joint association of sleep quality and physical activity (PA) levels in influencing hypertension among the Kazakh agricultural workers in Xinjiang.Methods: In this chronic disease study conducted in Xinjiang, participants were selected from Habahe County, a predominantly Kazakh region, between October and November 2023. Restricted cubic spline (RCS) analysis and multivariate logistic regression models were used to evaluate the associations between sleep quality, PA levels, and the prevalence of hypertension.Results: In this study of 2,872 participants, the median age was 49 (42–57) years. Among participants, 1,253 (43.63%) were male. The study resulting in a hypertension prevalence of 54.94%. RCS regression showed a linear association between sleep quality and hypertension prevalence in participants (P overall = 0.007, P nonlinear = 0.214), and the association between PA level and hypertension prevalence was also statistically significant (P overall = 0.022). As PA level increased, hypertension prevalence gradually declined but stabilized at higher level. In a multivariate regression analysis adjusting for potential confounders, poorer sleep quality was associated with an increased prevalence of hypertension (OR = 1.33, 95% CI: 1.07–1.65, P = 0.011; OR = 1.38, 95% CI: 1.12–1.69, P = 0.001), while the association between PA level and hypertension prevalence was not statistically significant (P > 0.05). Further analysis showed that in the moderate PA level group, good sleep quality was significantly associated with a lower prevalence of hypertension (OR = 0.71, 95% CI: 0.52–0.97, P = 0.031). However, in individuals with low (12,000 METs × min/week) PA levels, the association between sleep quality and hypertension prevalence was not been observed (P > 0.05).Conclusion: The current study showed that PA and sleep quality are associated with the prevalence of hypertension. Among individuals with moderate PA level, healthy sleep quality may have a protective effect against hypertension.

Background: Type 2 diabetes (T2DM) significantly elevates coronary heart disease (CHD) risk. This study leverages interpretable machine learning (ML) to identify risk factors for CHD with T2DM, enhancing clinical decision-making. Methods: Clinical data from 5,681 cardiovascular patients (4,396 CHD; 1,285 CHD+T2DM) hospitalized between 2001-2018 were analyzed. The SMOTENC algorithm addressed dataset imbalance. Predictive variables were selected via univariate analysis and Lasso regression. Five ML models (logistic regression, Lasso regression, KNN, SVM, XGBoost) were developed and validated using accuracy, sensitivity, specificity, ROC, and decision curve analysis. SHAP values interpreted model outputs. Results: Data were split into training (n=3,977) and validation (n=1,704) sets. Lasso regression identified 25 predictive variables. XGBoost achieved superior performance (highest accuracy: 0.89; AUC: 0.93) and net benefit in decision curves. SHAP analysis revealed diabetes duration, blood glucose (BG), prothrombin time (PT), and glycated hemoglobin (HbA1c) as primary risk factors. Positive urine glucose and elevated low-density lipoprotein also contributed significantly. Conclusion: Diabetes history, BG, HbA1c, and PT are critical risk factors for CHD-T2DM comorbidity. Prioritizing monitoring of these parameters and implementing targeted interventions may mitigate risk. The XGBoost-SHAP framework provides an interpretable tool for clinical risk stratification.

Introduction: Calcific aortic valve disease (CAVD) is the most common valvular disorder in older adults, characterized by progressive fibrosis, calcification, and impaired blood flow. Despite its growing burden, no effective pharmacological treatments exist. Inflammation and epigenetic dysregulation are increasingly recognized as central to CAVD pathogenesis. This study aimed to identify key inflammatory genes and regulatory mechanisms contributing to disease development. Methods: We performed a meta-analysis of aortic valve transcriptomic datasets, focusing on inflammation-related genes, to identify differentially expressed genes (DEGs) in CAVD. Blood-derived expression quantitative trait loci (eQTL) and DNA methylation QTL (mQTL) data were integrated with CAVD genome-wide association study (GWAS) results (FinnGen database) using a three-step summary data-based Mendelian randomization (SMR) framework. Additionally, we combined aortic eQTL data and inflammatory mediator GWAS to assess tissue-specific regulatory interactions through SMR and co-localization analyses. Results: Fifty-seven inflammation-related DEGs were identified, enriched in immune cells from patients with CAVD. Multi-omics integration prioritized nine causal genes in blood, including PPARG, TLR2, LTA, TNF, C4A, IL6, RELA, C3, and TGFβ1. Among these, TNF and TGFβ1 were strongly associated with increased CAVD risk. In aortic tissue, SMR and co-localization analyses revealed TLR2, C4A, and AGER as key genes linked to inflammatory pathways, highlighting a potential gene-immune axis in disease progression. Conclusions: Our integrative multi-omics analysis reveals that calcific aortic valve disease (CAVD) is driven by genetically and epigenetically regulated inflammatory pathways. We identify TNF and TGFβ1 as epigenetically controlled causal drivers, while AGER, C4A, and TLR2 emerge as tissue-specific mediators of aortic inflammation. These findings establish a causal link between immune dysregulation and CAVD pathogenesis, highlighting promising targets for biomarker development and precision therapies aimed at interrupting disease progression.

Purpose To investigate the protective effect and mechanism of enhanced expression of endogenous macrophage migration inhibitory factor (MIF) on cardiac ischemia–reperfusion (I/R) injury. Methods A recombinant double-stranded adeno-associated virus serotype 9 with MIF or green fluorescent protein (GFP) genes (dsAAV9-MIF/GFP) was transduced into mice and neonatal rat ventricular myocytes (NRVMs). The models of cardiac 60 min ischemia and 24 h reperfusion and 12 h hypoxia/12 h reoxygenation (H/R) were established in mice and NRVMs, respectively. Infarct size, cardiac remodeling, and related signaling pathways were assessed. Results The dsAAV9 vector demonstrated strong transduction efficacy and cardiac affinity. Cardiac overexpression of MIF led to a 35.3% reduction in infarct size and improved cardiac function following I/R injury. In the dsAAV9-MIF group, the AMP-activated protein kinase (AMPK) signaling pathway was activated, and autophagy was enhanced during the ischemic period. During reperfusion, the extracellular signal-regulated kinases 1 and 2 (ERK1/2) signaling pathway was upregulated, leading to reduced cardiac apoptosis. In vitro, transfection with MIF in NRVMs also upregulated AMPK and ERK1/2 signaling during hypoxia and reoxygenation, respectively. Furthermore, MIF overexpression significantly improved autophagy and mitochondrial function, evidenced by an increased LC3-II/I ratio and enhanced mitochondrial membrane potential (ΔΨm), with these effects reversed by the AMPK inhibitor compound C. Additionally, MIF overexpression led to a 60% reduction in the apoptosis rate of cardiomyocytes subjected to H/R and decreased the Bax/Bcl-2 ratio, partially through the ERK1/2 signaling pathway. Conclusion Enhanced endogenous MIF expression via the dsAAV9 vector provides significant cardioprotection against I/R injury by activating the AMPK and ERK1/2 signaling pathways. Our findings suggest that targeting MIF may represent a viable therapeutic strategy for severe and prolonged I/R injury.

Metabolic syndrome (MetS) is a collection of metabolic abnormalities including insulin resistance, atherogenic dyslipidemia, central obesity, and hypertension. Recently, long noncoding RNAs (lncRNAs) have emerged as pivotal regulators of metabolic balance, influencing the genes associated with MetS. Although the prevalence of insulin resistance is rising, leading to an increased risk of type 2 diabetes mellitus (T2DM) and its vascular complications, there is still a notable gap in understanding the role of lncRNAs in the context of clinical diabetes. Among lncRNAs, lung adenocarcinoma metastasis‐associated transcript 1 (MALAT1) has been identified as a significant regulator of metabolism‐related disorders, including T2DM and cardiovascular disease (CVD). This review explores the mechanism of lncRNA MALAT1 and suggests that targeting it could offer a promising strategy to combat MetS, thereby enhancing the prognosis of MetS.

Endothelial apoptosis is the core pathological change in atherosclerotic cardiovascular disease, including coronary artery disease (CAD). Determining the molecular mechanisms underlying endothelial apoptosis is important. Nuclear factor kappa B (NF‐κB) is a crucial transcription factor for controlling apoptosis. Our previous study demonstrated that the ‐94 ATTG ins/del mutant in the promoter of NFKB1 gene (rs28362491) is a risk factor for CAD. In the present study, we found that NFKB1 rs28362491 polymorphism was positively associated with increased major adverse cardiac and cerebrovascular events (MACCEs) in CAD patients. After adjusting for confounding factors including age, smoking, hypertension, glucose, and low‐density lipoprotein cholesterol, the mutant DD genotype was an independent predictor of MACCEs (OR = 2.578, 95%CI = 1.64–4.05, P = 0.003). The in vitro study showed that mutant human umbilical vein endothelial cells (DD‐mutant HUVECs) were more susceptible to high‐glucose/palmitate‐induced apoptosis, which was accompanied by decreased p50 expression and increased expression of cleaved caspase‐3, Cytochrome c, and phospho‐p65 (P < 0.05). The mitochondrial membrane potential was significantly lower, while increasing levels of mtROS and more opening of the mPTP were observed in DD‐mutant HUVECs (P < 0.05). Furthermore, the percentage of cells with fragmented or spherical mitochondria was significantly higher in DD‐mutant HUVECs than in wild‐type cells (genotype II HUVECs) (P < 0.05). In addition, after stimulation with high glucose/palmitate, the NFKB1 gene mutant significantly increased the expression of Drp1, which indicated that the NFKB1 gene mutant affected the expression of mitochondrial morphology‐related proteins, leading to excessive mitochondrial fission. In conclusion, the mutant DD genotype of the NFKB1 gene was an independent predictor of worse long‐term prognosis for CAD patients. DD‐mutant HUVECs exhibited abnormal activation of the NF‐κB pathway and increased Drp1 expression, which caused excessive mitochondrial fission and dysfunction, ultimately leading to increased apoptosis.