Recent multiomics advancements have improved our understanding of immune dysregulation in dilated cardiomyopathy (DCM). However, specific immune cell subsets and their regulatory genes are still ambiguous. This study aimed to explore immune cell imbalances and regulatory genes in DCM, discover diagnostic biomarkers, and identify potential therapeutic targets. Immune cell infiltration in DCM patients was quantified via deconvolution algorithms and single-cell RNA sequencing. Flow cytometry validation in 40 DCM patients and 40 healthy controls confirmed a notable increase in CD4⁺ effector memory T cells (CD4⁺ TEM cells) in DCM patients. Differential expression analysis of the GSE101585 dataset revealed 1783 genes. Weighted gene coexpression network analysis (WGCNA) identified a core immune-regulatory gene set, and protein–protein interaction (PPI) analysis highlighted 36 hub genes. Machine learning cross-validation identified four diagnostic biomarkers (LRRTM4, PTPN22, FAM175B, and PROM2) whose transcriptional changes had been validated by qPCR. Among these genes, PTPN22 was strongly correlated with CD4⁺ TEM cell abundance. Additionally, DSigDB analysis predicted 87 potential therapeutic drugs, with PTPN22 being the target of the most drugs. This study reveals a CD4⁺ T cell subset-centered immunoregulatory network in DCM, identifying novel diagnostic biomarkers and druggable targets to guide precision immunomodulatory strategies for DCM management.

Background: Various physiological mechanisms are linked to dilated cardiomyopathy (DCM) development, including oxidative stress, immune irregularities, inflammation, fibrosis, and genetic changes. However, precise molecular drivers of DCM, especially regarding abnormal immune responses, remain unclear. This study investigates immune-related long non-coding RNAs (lncRNAs) in DCM’s diagnostic and therapeutic potential. Methods: GSE141910, GSE135055, and GSE165303 datasets were acquired from the GEO database. LASSO, SVM-RFE, and random forest algorithms identified DCM-associated immune-related lncRNAs. Diagnostic capabilities were assessed by Nomogram and receiver operating characteristic (ROC) curves. Multivariate linear regression explored lncRNA correlations with ejection fraction. Single-sample gene set enrichment analysis (ssGSEA) gauged immune cell infiltration/functions. Functional enrichment analyses were performed using Gene set variation analysis (GSVA), gene ontology (GO), and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Consensus clustering categorized DCM cases. Results: Ten immune-related lncRNAs emerged: C10orf71-AS1, FHAD1-AS1, SCIRT, FNDC1-AS1, MELTFAS1, LOC101928834, GDNF-AS1, DCXR-DT, C3orf36, and LOC107985323. These lncRNAs, tied to immunomodulation, showed promising DCM diagnostic accuracy. Adjusted for confounders, they independently correlated with ejection fraction. Using lncRNA expression, DCM patients were grouped into subtypes. Subtype C1 displayed a higher level of immune cell infiltration and immune checkpoint expression compared to subtype C2, emphasizing the variations in the immune microenvironment. Conclusion: This study identifies ten immune-related lncRNAs for further exploration in DCM diagnosis and subtyping. Based on expression patterns, we propose two potential DCM subtypes. Notably, findings are preliminary and hypothesis-generating, demanding validation and further investigation. This research provides insights into DCM diagnosis and classification.

Objective: This study investigates the hemoglobin-to-red blood cell distribution width ratio (HRR) and its association with risk of elevated depressive symptoms in Chinese adults, addressing a gap in evidence for non-older populations and exploring potential effect modifiers.Methods: In this cross-sectional analysis of 30,427 adults from routine health screenings (July 2020–June 2021), depression was assessed using the Self-Rating Depression Scale. HRR was calculated as hemoglobin (g/dL) divided by red cell distribution width (%). Multivariable logistic regression, restricted cubic splines (RCS), sensitivity analyses with alternative depression definitions, E-value analysis, and ROC curve comparisons with the RDW-to-albumin ratio (RAR) were conducted, adjusted for sociodemographic, lifestyle, and clinical confounders.Results: In a population of 30,427 adults (46.3% female; mean age 44.9 ± 10.7 years; 56.7% with college education or above) undergoing routine health screenings, a strong inverse graded statistical association was observed between hemoglobin-to-red cell distribution width ratio (HRR) and elevated depressive symptoms (SDS ≥ 53). Each unit increase in HRR reduced elevated depressive symptoms (SDS ≥ 53) by 39% in fully adjusted models (OR = 0.61, 95% CI: 0.51–0.72, p < 0.0001). After adjusting covariates, compared to the lowest quartile (Q1: HRR ≤ 1.031), participants in Q2 (1.031–1.142), Q3 (1.142–1.25), and Q4 (≥1.25) exhibited 10% (OR = 0.90, 95% CI: 0.84–0.98), 13% (OR = 0.87, 95% CI: 0.80–0.94), and 24% (OR = 0.76, 95% CI: 0.70–0.83,) reductions in elevated depressive symptoms (SDS ≥ 53), respectively (p-trend<0.0001). RCS confirmed a linear association without threshold effects. Age modified the relationship (p-interaction = 0.004), with stronger protection in adults ≥45 years (OR = 0.40, 95% CI: 0.32–0.51) than younger individuals (OR = 0.64, 95% CI: 0.50–0.82).Conclusion: HRR may indicate an independent, linear inverse association with elevated depressive symptoms (SDS ≥ 53) in Chinese adults, with strong effects in older populations. As a cost-effective hematological biomarker, HRR could support scalable elevated depressive symptoms (SDS ≥ 53) stratification and prevention strategies to complement other risk factors for elevated depressive symptoms, particularly in aging groups, and these findings warrant validation in prospective studies.

Objectives: This study investigates the nonlinear association between myeloperoxidase (MPO) levels and Helicobacter pylori (H. pylori) infection risk in Chinese adults, evaluating potential modifiers and clinical implications for infection prevention. Methods: An analysis was conducted on cross-sectional data from 15,180 adults who underwent routine health examinations between January and December 2021. H. pylori infection was diagnosed using the 14C-urea breath test with a threshold of disintegrations per minute (DPM) ≥ 100. ELISA was used to measure plasma MPO levels. Nonlinear associations were assessed through logistic regression, restricted cubic splines, threshold effect analysis, and subgroup interactions. Results: The study identified a U-shaped correlation between MPO levels and the risk of H. pylori infection. Compared to the middle tertile (T2: 20.6–31 ng/mL), participants in the lowest (T1: ≤20.6 ng/mL; OR = 1.36, 95% CI: 1.24–1.49) and highest tertiles (T3: ≥31 ng/mL; OR = 1.12, 1.02–1.22) exhibited elevated infection risk after full adjustment (p < 0.001). DPM levels were notably elevated in T1 (β = 37.1, 26.66–47.57) and T3 (β = 19.27, 8.81–29.72) relative to T2 (p < 0.0001). RCS-based threshold analysis identified a nonlinear inflection at 24.0 ng/mL of MPO, where each additional 1 ng/mL of MPO below this threshold was associated with a reduced infection risk (OR = 0.959, 95% CI: 0.947–0.971), whereas levels above increased the risk (OR = 1.004, 95% CI: 1.002–1.007). This pattern aligned with H. pylori breath test values, which mirrored the U-shaped trend across MPO tertiles. Subgroup analyses revealed uniform associations between MPO and H. pylori infection risk/DPM across various factors such as age, sex, BMI, and metabolic comorbidities, with all interaction p-values exceeding 0.05. Conclusions: MPO levels exhibit a robust U-shaped association with H. pylori infection risk, independent of anthropometric and metabolic confounders. Monitoring MPO may aid in identifying individuals at bidirectional infection risk, suggesting novel insights into the inflammation–infection interplay. The study’s cross-sectional design limits the ability to establish causal relationships, necessitating further longitudinal research to validate these findings and elucidate their clinical implications.