Background: Depression has a significant effect on cardiovascular disease (CVD), but uncertainties persist regarding which modifiable risk factors mediate the causal effects. We aim to determine whether depression is causally linked to CVD and which modifiable risk factors play potential mediating roles. Methods: We used a two-sample Mendelian randomization (MR) approach and NHANES 2007–2018 data to estimate the effects of depression on various CVD cases and investigated 28 potential mediators of the association between depression and CVD. Results: The results of our MR analysis indicated that genetically determined depression was associated with increased risk of several CVD, including coronary heart disease (odds ratio (OR) = 1.14; 95% confidence interval (CI): 1.05,1.22), myocardial infarction (OR = 1.19; 95% CI, 1.09,1.31), atrial fibrillation (OR = 1.14; 95% CI, 1.06,1.22), and stroke (OR = 1.13; 95% CI, 1.05,1.22). However, there was no causal association between depression and heart failure. Four out of 28 cardiometabolic risk factors, including hyperlipidemia, hypertension, diabetes, and prescription opioid use, were identified as mediators of the association between depression and various CVDs. Observational association analyses from NHANES data yielded consistent results. Conclusion: Our findings demonstrated that depression has a causal detrimental effect on various CVDs. Four causal mediators (hyperlipidemia, hypertension, diabetes, and prescription opioid use) were screened to explain the causal effect. Implementing targeted management strategies for these risk factors may be warranted to mitigate the public health burden of CVD among individuals with depression.

The human retina exhibits complex cellular heterogeneity which is critical for visual function, yet comprehensive ethnic-specific references are scarce in ophthalmic transcriptomics. The lack of single-cell RNA sequencing (scRNA-seq) data from Asian populations particularly Chinese donors imposes significant limitations in understanding population-specific retinal biology. We constructed the first comprehensive single-cell transcriptomic atlas of the human retina from Chinese donors, generated through high-throughput scRNA-seq of ∼290,000 viable cells obtained from 18 fresh retinal specimens (living donor and post-mortem specimens). Our multi-level analysis identified 10 distinct retinal cell types. Detailed subcluster analyses further revealed extensive heterogeneity, suggesting distinct subtypes within several cell populations such as 7 amacrine cell subtypes and 14 bipolar cell subtypes. Concurrently, we mapped their associated biological signaling pathways, functions, and mechanistic processes through systematic analysis of delineated subtype-specific molecular mechanisms. This analysis explained the critical involvement of these subpopulations in core biological processes including synaptic organization, neurotransmission, and phototransduction cascades, potentially governing retinal homeostatic regulation and disease mechanisms. Single-cell transcriptomic atlas of the human retina from Chinese donors describes a comprehensive cellular landscape, encompassing major cell types and subtypes including neuronal, glial, and immune populations. This ethnic-specific atlas provided an useful reference for understanding retinal development, cellular interaction and disease pathogenesis in Chinese populations, addressing a longstanding gap in retinal transcriptomic resources.

Background: The association between late-life depressive symptoms and metabolic syndrome (MetS) remains a critical public health concern, yet most existing evidence relies on cross-sectional designs that fail to capture the dynamic nature of depression. This longitudinal study aimed to investigate how depressive symptom trajectories influence MetS risk in middle-aged and older adults, while examining potential effect modification by sociodemographic and lifestyle factors.Methods: Using data from the English Longitudinal Study of Ageing (ELSA), we identified three trajectories of depressive symptoms (persistent low, moderate, and high) through group-based trajectory modeling (GBTM) across four survey waves. Multivariable logistic regression assessed associations between trajectories and incident MetS, adjusted for age, sex, education, marital status, smoking, drinking, and income. Stratified analyses evaluated effect modification by these factors.Results: Participants with persistent moderate (OR=1.08, 95% CI: 1.03–1.15) and high (OR=1.07, 1.01–1.14) trajectories had significantly higher MetS risk versus the low trajectory. Associations were strongest in adults <65 years, married individuals, and those with smoking/drinking habits (p <0.05), but did not vary by sex. Physical activity mediated 18.9% of the total effect (95% CI: 5–37%).Conclusion: Dynamic depressive symptoms independently predict MetS risk, with amplified effects in younger, married, and health-risk subgroups. Targeted interventions addressing both depressive symptoms and modifiable behaviors (e.g., physical activity) may mitigate metabolic risk in aging populations.

Objective: The present study aims to elucidate the significance of immune cell infiltration in Coronavirus disease 2019 (COVID‐19) myocarditis and identify potential diagnostic markers for this condition. Myocarditis, an inflammatory cardiac disease, primarily results from viral infections. Although the association between COVID‐19 and myocarditis is well‐established, the specific mechanism(s) underlying this relationship remain incompletely understood. Methods: The GSE53607 and GSE35182 datasets were obtained from the GEO database, which contains samples from a mouse model for viral myocarditis. Differentially expressed genes (DEGs) and candidate biomarkers were selected using the LASSO regression model and support vector machine recursive feature elimination (SVM‐RFE) analysis. Subsequently, the diagnostic potential of these biomarkers was evaluated by calculating the area under the receiver operating characteristic curve (AUC). Further validation of the biomarkers was conducted using the GSE183850 dataset, which consists of samples from patients with COVID‐19 myocarditis. In addition, CIBERSORT analysis was employed to estimate the compositional patterns of 22 types of immune cell fractions in merged cohorts. Results: Thirty genes were identified, with a significant proportion of the DEGs being associated with carbohydrate binding, endopeptidase activity, and pathogenic organisms such as Staphylococcus aureus and coronavirus disease. Importantly, gene sets related to the IL6‐JAK‐STAT3 signaling pathways, inflammatory response, and interferon response exhibited differential activation in viral myocarditis compared to the control group. In addition, in the context of COVID‐19 myocarditis patients from the GSE183850 dataset, B2M and C3 were established as diagnostic markers that were subsequently validated (AUC = 0.978 and AUC = 0.956, respectively). Furthermore, analysis of immune cell infiltration revealed correlations between B2M and C3 expression levels and the activation of NK cells, dendritic cells, T cells CD4 memory resting, as well as eosinophils. Conclusion: B2M and C3 have been identified as potential biomarkers for viral myocarditis, providing valuable insights for future investigations into the pathogenesis of COVID‐19‐associated myocarditis.

Current human retina studies predominantly utilize post-mortem tissue, and the sample accessibility constraints make the characterization of the living human retina at single-cell resolution a challenge. Although single-nucleus RNA-seq expands the utility of frozen samples, it provides a nuclear-centric view, potentially missing key cytoplasmic information and transient biological processes. Thus, it is important to generate resources directly from living human retinal tissue to complement existing datasets. We profiled 106,829 single cells from nine unfrozen human retina samples. Living samples were collected within 10 min of therapeutic enucleation and four postmortem samples were collected within 6 h. After standardized dissociation, single-cell transcriptomes were generated using 10x Genomics 3’ RNA-seq and applied scVI to generate batch-corrected integrated atlas. Major cell types and subtypes were annotated through iterative Leiden clustering, canonical markers. Subsequent analyses included differential expression comparisons between cell states and regulon activity profiling to further characterize cellular identities and regulatory networks. Transcriptional dynamics were assessed using RNA velocity, and cell-cell signaling pathways were inferred with CellChat. Key findings were validated in independent samples from two additional donors (four samples) using the identical workflow. We contribute to establishing a reference for retinal cell type proportions and cellular states. Our analysis revealed ELF1-mlCone, a distinct cluster of mlCone photoreceptors identified by distinct transcriptional features. The presence and transcriptional features of this cluster were validated in independent samples. Additionally, by comparing living and post-mortem samples, our study highlights differences in transcriptional dynamics: living tissue preserved coherent RNA velocity streams, enabling clear dynamic state transitions, while post-mortem tissue exhibited disorganized patterns. These findings suggest that using living tissue can improve the capture of active cellular states and transitions. Our atlas provides a single-cell reference contrasting living versus early postmortem human retina, integrating cell type composition, transcriptional diversity, and functional insights. It may serve as a useful resource for retinal research and for understanding aspects of human retinal biology, particularly given its inclusion of living tissue and diverse pathological states.