Causal Association Between Anemia and Cardiovascular Disease: A 2-Sample Bidirectional Mendelian Randomization Study.

Background Although previous observational studies have shown an association between anemia and cardiovascular disease (CVD), the underlying causal relationship between anemia and CVD remains uncertain. Methods and Results We conducted a 2-sample bidirectional Mendelian randomization (MR) study to assess the causal association between anemia and CVD. We extracted summary statistics data for anemia, heart failure (HF), coronary artery disease (CAD), atrial fibrillation, any stroke, and any ischemic stroke (AIS) from relevant published genome-wide association studies. After rigorous quality control steps, independent single-nucleotide polymorphisms for each disease were selected as instrumental variables. Inverse-variance weighting was used as the primary method to estimate the causal association between anemia and CVD in the 2-sample MR analysis. Simultaneously, we performed a series of multiple methods analyses (median weighting, maximum likelihood [MR robust adjusted profile score]), sensitivity analyses (Cochran's Q test and MR-Egger intercept, leave-one-out test [MR pleiotropy residual sum and outlier]), instrumental variable strength evaluations (F statistic), and statistic power estimates to verify the robustness and reliability of our results. Furthermore, the associations between anemia and CVD from different studies, including the UK Biobank and FinnGen studies, were combined by meta-analysis. The MR analysis showed that genetically predicted anemia was significantly associated with HF risk at the Bonferroni-corrected significance level (odds ratio [OR], 1.11 [95% CI, 1.04-1.18]; P=0.002) and was suggestively associated with CAD risk (OR, 1.11 [95% CI, 1.02-1.22]; P=0.020). However, the associations between anemia and atrial fibrillation, any stroke, or AIS were not statistically significant. In the reverse MR analysis, we found that genetic susceptibility to HF, CAD, and AIS was significantly associated with anemia risk. The ORs of HF, CAD, and AIS were 1.64 (95% CI, 1.39-1.94; P=7.60E-09), 1.16 (95% CI, 1.08-1.24; P=2.32E-05), and 1.30 (95% CI, 1.11-1.52; P=0.001), respectively. Genetically predicted atrial fibrillation was suggestively associated with anemia (OR, 1.06 [95% CI, 1.01-1.12]; P=0.015). Sensitivity analyses found weak evidence of horizontal pleiotropy and heterogeneity, which ensured the robustness and reliability of the results. Meta-analysis also showed the statistically significant association between anemia and HF risk. Conclusions Our study supports bidirectional causality between anemia and HF and significant associations between genetic predisposition to CAD and AIS with anemia, which contributes to the clinical management of both diseases.

Exploring the pathogenesis and immune infiltration in dilated cardiomyopathy complicated with atrial fibrillation by bioinformatics analysis.

Background: Atrial fibrillation (AF) is a serious complication of dilated cardiomyopathy (DCM), which increases the risk of thromboembolic events and sudden death in DCM patients. However, the common mechanism of DCM combined with AF remains unclear. This study aims to explore the molecular mechanism and analyze immune infiltration in DCM complicated with AF through comprehensive bioinformatics analysis. Methods: The gene expression datasets of DCM (GSE141910) and AF (GSE41177 and GSE79768) were obtained from the Gene Expression Omnibus database. Gene enrichment analyses were performed after screening the common differentially expressed genes (DEGs) of DCM and AF. Protein-protein interaction network was constructed in the STRING database and visualized in Cytoscape software, which helped to further screen the central functional modules of DEGs and hub genes. In addition, ImmuCellAI algorithm was performed to estimate immune infiltration patterns, and Spearman correlation was conducted to investigate the correlation between the abundance of multiple immune cells and the expression levels of hub immune-related genes after obtaining hub immune-related genes from the ImmPort database. The hub immune-related genes expression and immune infiltration patterns were additionally verified in the validation datasets (GSE57338, GSE115574, and GSE31821). The diagnostic effectiveness of hub immune-related genes was evaluated through Receiver Operator Characteristic Curve analysis. Results: A total of 184 common DEGs in DCM and AF were identified for subsequent analyses. The functions of hub genes were significantly associated with immune responses. We identified 7 hub immune-related genes (HLA-DRA, LCK, ITK, CD48, CD247, CD3D, and IL2RG) and a spectrum of immune cell subsets including Monocyte, Neutrophil, and follicular helper T (Tfh) cells were found to be concurrently dysregulated in both DCM and AF. 7 hub immune-related genes were predominantly favorably correlated with Tfh cells and were primarily negatively correlated with Neutrophil infiltrations in DCM and AF. CD48+CD3D were verified to diagnose DCM and AF with excellent sensitivity and specificity, showing favorable diagnostic value. Conclusions: Our study reveals that immune cells (Tfh cells) disorders caused by hub immune-related genes (CD48 and CD3D) may be the common pathogenesis of DCM combined with AF, which lays a foundation for further immune mechanism research.