Jianhui Liu

Background: Cardiometabolic multimorbidity (CMM) has emerged as a primary public health challenge, significantly increasing mortality risk and reducing quality of life. Although the metabolic score for visceral fat (METS‐VF) and functional limitation are recognized individually as risk factors for cardiometabolic health, the bidirectional relationship between functional limitation and CMM suggests complex underlying mechanisms. However, research investigating the independent and joint effects of METS‐VF (representing visceral obesity) and functional limitation on the risk of CMM onset is lacking. This study is aimed at systematically elucidating these associations to provide evidence for targeted interventions. Methods: This prospective cohort study utilized data from the China Health and Retirement Longitudinal Study (CHARLS). A total of 8140 participants were included in the cross‐sectional analysis, and a sub‐cohort of 5356 individuals free of CMM at baseline was followed longitudinally. CMM was defined as the coexistence of at least two cardiometabolic diseases: heart disease, diabetes, stroke, or hypertension. Functional limitation was assessed using activities of daily living (ADL) and instrumental ADL (IADL) scales. Multivariable Cox proportional hazards regression and restricted cubic spline (RCS) analysis were employed to evaluate the association between METS‐VF, functional limitations, and CMM incidence. Results: During the follow‐up period, METS‐VF demonstrated a nonlinear positive dose‐response relationship with CMM risk. In the longitudinal analysis, participants in the high METS‐VF group (defined by the RCS inflection point) exhibited a significantly elevated risk of developing CMM compared to the low‐risk group (HR = 1.77, 95% CI: 1.39–2.26) after adjusting for confounders. Continuous functional limitations were also independently associated with incident CMM (HR = 1.12, 95% CI: 1.08–1.16). Notably, a joint effect was observed; participants with both high METS‐VF and functional limitations faced the highest risk (HR = 2.43, 95% CI: 1.78–3.31). Receiver operating characteristic (ROC) analysis indicated that the combined model incorporating both factors achieved superior predictive accuracy (AUC = 0.680) compared to single‐factor models. Conclusion: METS‐VF and functional limitations are strong independent predictors of CMM, and their simultaneous presence exerts a significant joint effect on disease risk. The integration of METS‐VF and functional assessment into clinical practice can enhance CMM risk stratification and facilitate the early identification of vulnerable high‐risk subgroups.

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.

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.

Aims: Numerous observational studies have reported an association between calcific aortic valve stenosis (CAVS) and an increased risk of other cardiovascular diseases (CVDs). However, the potential causal association is uncertain. Methods and results: A bidirectional Mendelian randomization (MR) analysis was performed to confirm the causal association between CAVS and the risk of seven common CVDs. The meta-analysis was used to estimate the combined causal effect with multiple MR results. The MR analysis indicated that genetically predicted CAVS is associated with the increased risk of heart failure (odds ratio [OR] = 1.064; 95% confidence interval [CI] [1.008, 1.123]; P = 0.025), large-artery stroke (OR = 1.256; 95% CI [1.112, 1.418]; P < 0.001), cardioembolic stroke (OR = 1.228; 95% CI [1.117, 1.349]; P < 0.001) and atrial fibrillation (OR = 1.100; 95% CI [1.061, 1.140]; P < 0.001) without heterogeneity and pleiotropy. Genetically predicted hypertension (FinnGen: OR = 1.170; 95% CI [1.055, 1.297]; P = 0.003 and UK Biobank: OR = 2.456; 95% CI: 1.571, 3.840; P = 8.16 × 10−5) increased the risk of CAVS. Conclusions: This comprehensive MR study revealed the causal relationship between genetically predicted CAVS and the increased risk of various CVDs and the causal relationship between hypertension and CAVS risk. Given the harmful causal impact of CAVS on adverse cardiovascular events, it is crucial to prioritize the prevention and management of CVD in individuals with CAVS. Blood pressure management is an effective preventive intervention.

Diabetic retinopathy (DR) remains a principal cause of vision impairment worldwide, involved complex retinal cellular pathophysiology that remains incompletely understood. To elucidate cell-type-specific molecular signatures underlying DR, we generated a high-resolution single-cell transcriptomic atlas of 297,121 retinal cells from 20 Chinese donors, including non-diabetic controls (26.4%), diabetic without retinopathy (23.4%) and DR (50.2%). Following rigorous quality control, batch-effect correction, and clustering and annotation, 10 major retinal cell populations were delineated. Differential expression analyses across disease states within each cell type yielded candidate gene sets, which were further refined via a multi-stage machine-learning pipeline combining L1-regularized logistic regression and recursive feature elimination with cross-validation, alongside bootstrap stability selection. Resulting cell-type-specific classifiers achieved high accuracy (79–95%) and AUCs (0.85–0.99) in distinguishing DR disease states. Enrichment analyses implicated immune activation, oxidative stress, neurodegeneration and synaptic dysfunction pathways across multiple cell types in retina. Integrating 567 unique marker genes from all cell types, a general multilayer perceptron classifier achieved 95.31% overall accuracy on held-out test data, demonstrating the translational potential of these signatures for non-diabetic controls, diabetic without retinopathy and DR classification. This high-resolution atlas and the accompanying analytic framework provide a robust computational framework for biomarker discovery, mechanistic insight and targeted intervention strategies in diabetic retinal diseases.

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, encompassing all major neuronal lineages, Müller glia, astrocytes, microglia. Detailed subcluster analyses further revealed extensive heterogeneity, identifying distinct subtypes within several cell populations such as 7 amacrine cell subtypes and 14 bipolar cell subtypes. Concurrently, through systematic analysis of delineated subtype-specific molecular programs, we mapped their associated biological signaling pathways, functions, and mechanistic processes. 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 important reference for understanding retinal development, cellular interaction and disease pathogenesis in Chinese populations, addressing a longstanding gap in ophthalmic transcriptomic resources.

Diabetic retinopathy (DR) is a major cause of vision loss worldwide. Here, we conduct single-cell RNA sequencing of twenty human retina samples (from living and post-mortem donors) across non-diabetic, diabetic, and DR states to create a comprehensive transcriptomic atlas. We identify two stable microglial populations—homeostatic and inflammatory—that exist along a functional continuum, plus a neutrophil cluster within C1QA+ myeloid cells with dynamic transitions occurring throughout disease progression. Module-level analysis reveals divergent transcriptional trajectories: homeostatic microglia maintain energetic programs while selectively upregulating stress elements, whereas inflammatory microglia layer additional pro-inflammatory programs onto preserved biosynthetic foundations. Eleven co-expression modules organize into two major axes: an inflammatory-stress axis, and a regulatory/metabolic-motility axis, with a stable translation module persisting across disease stages. Cell communication analysis further highlights sophisticated neural-immune interactions, particularly between photoreceptors and microglia. Our findings provide insights into the complex cellular dynamics of DR progression and suggest potential therapeutic targets for early intervention.

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.