Introduction Sex differences in cardiovascular disease (CVD) risk are examined through biological and clinical factors, with less attention to early-life social exposures. This study examined associations of childhood parental son preference with CVD risk, sex differences, and mediation by modifiable risk factors. Methods This cohort analysis included China Health and Retirement Longitudinal Study participants aged ≥45 years without baseline CVD. Parental son preference was assessed retrospectively in 2014; incident CVD was self-reported physician-diagnosed heart disease or stroke through 2020. Sampling-weighted, community-clustered Cox models estimated adjusted hazard ratios (aHRs) and 95% CIs. Sex was prespecified as an effect modifier; mediation by 13 risk factors used inverse-odds-ratio weighting. Data were collected from 2011 to 2020 and analyzed from 2025 to 2026. Results Among 8,079 participants (mean age, 57.5 years; 4,216 women [52.2%]), 1,820 (22.5%) reported parental son preference. Son preference was associated with higher CVD risk overall (aHR 1.23 [95% CI 1.04, 1.46]) and among women (aHR 1.25 [95% CI 1.03, 1.53]); among men, the estimate was 1.16 (95% CI 0.87, 1.56), with limited heterogeneity by sex (ratio of aHRs 1.06 [95% CI 0.72, 1.58]). Among women, risk was concentrated in the highest paternal (aHR 1.47 [95% CI 1.14, 1.90]) and maternal (aHR 1.50 [95% CI 1.12, 1.99]) preference categories. Modifiable risk factors mediated 5.8% (95% CI 1.9%, 9.7%) of the association among women, mainly through socioeconomic and psychosocial factors. CVD risk was highest with both son preference and high risk-factor burden overall (aHR 1.97 [95% CI 1.43, 2.73]) and among women (aHR 2.23 [95% CI 1.61, 3.10]). Conclusions Parental son preference was associated with higher incident CVD risk, with the largest estimates in the highest paternal or maternal categories among women. Modifiable risk factors explained a modest proportion, supporting life-course cardiovascular prevention that considers sex-differentiated childhood environments alongside risk-factor modification.
Traumatic heterotopic ossification (HO) is a debilitating complication of tendon injury, yet its pathogenic mechanisms remain elusive and effective preventive strategies are lacking. Here, we identify a glycometabolic-epigenetic axis that drives HO progression. Single-cell profiling and functional analyses reveal that hypoxic chondrocytes undergo glycolytic reprogramming, leading to lactate accumulation and transfer into tendon-derived mesenchymal stem cells (TD-MSCs) through MCT1. This metabolic coupling promotes P300-dependent histone H3 lysine 18 lactylation (H3K18la), activating osteogenic programs characterized by Col6a1, Zhx3, Lox and Vdr expression and thereby facilitating pathological endochondral ossification. Guided by this mechanism, we develop a tendon-adhesive hydrogel (GelHA-A485@Exos) that integrates mechanical repair with targeted epigenetic modulation. MSC-derived exosomes enable localized delivery of the P300 inhibitor A485, suppressing H3K18la and aberrant osteogenic activation, while the hydrogel scaffold enhances tendon regeneration. In vivo and in vitro studies demonstrate that GelHA-A485@Exos promotes tendon healing while preventing HO formation. Our findings establish metabolic-epigenetic communication as a central regulator of traumatic HO and provide a translational therapeutic strategy for its clinical prevention.
Introduction Sjögren's syndrome creates a lymphoma-prone inflammatory niche and confers one of the highest autoimmune risks of B-cell non-Hodgkin lymphoma. Yet the genetic and cellular mechanisms that remodel this pre-neoplastic niche into a tumor-supportive immune microenvironment remain unclear. We integrated human genetics, single-cell transcriptomics and functional cell biology to identify an actionable mediator of this transition. Methods We combined bidirectional Mendelian randomization, directionality filtering, MHC-exclusion sensitivity analyses (chromosome 6: 25–35 Mb), colocalization and summary-based HLA fine-mapping. Single-cell RNA sequencing of salivary gland and splenic marginal-zone lymphoma was used to localize genetic risk, reconstruct cross-disease B-cell states and infer intercellular signaling. Mechanistic dependence was tested in IFN-γ-stimulated Raji B-lymphoma cells, and clinical relevance was evaluated in a public chronic lymphocytic leukemia cohort. Results Genetic liability to Sjögren's syndrome increased marginal-zone/MALT, diffuse large B-cell and overall non-Hodgkin lymphoma risk, and effects collapsed after MHC exclusion and resolved to the class II 8.1 ancestral haplotype. Single-cell analysis localized risk to salivary B cells and revealed disease-associated remodeling toward a lymphoid, MIF-rich, CD74-receiving microenvironment, with plasma cells as the largest MIF source. HLA-class-II/CD74 programs increased along the Sjögren-to-lymphoma B-cell trajectory. In Raji cells, MIF activated AKT/NF-κB/BCL-xL signaling and viability. ISO-1 or CD74 knockdown suppressed the pathway and increased cleaved PARP, whereas AKT activation partially rescued signaling and viability. Higher CD74 expression marked adverse outcome in an external B-cell malignancy cohort. Conclusion Convergent genetic, single-cell and perturbation evidence positions CD74 as a candidate organizer of a lymphoma-supportive immune microenvironment linking inherited MHC class II risk to B-cell survival. The MIF–CD74 axis provides a translational tumor-microenvironment target that requires tissue-level and prospective validation.
Objective To explore the feasibility of developing an interpretable machine learning (ML) model that integrates clinical variables and quantitative radiomics features to predict 12-month outcomes of nonsurgical root canal retreatment (NSreRCT). Materials and Methods In this retrospective case-control study, 200 teeth (100 failed and 100 successful NSreRCT) were included, matched by age, tooth type, and periapical status. For each case, 30 preoperative clinical variables and periapical radiographs were collected. Radiomics features were extracted from the core and boundary regions of periapical lesions. Seven ML classifiers were trained and validated using the 5/10 most informative radiomics and/or clinical variables, identified through supervised feature selection nested within stratified 5-fold cross-validation. Within each training partition, 448 candidate pipelines were ranked using an internal hold-out evaluation. Model performance was assessed using accuracy, sensitivity, specificity, Matthew's Correlation Coefficient, Area Under Precision-Recall Curve, mean Area Under Receiver Operating Characteristic Curve (AUC) and stacked AUC. Feature importance was evaluated using SHapley Additive exPlanations analysis. Results The Rad10AndCli10 model incorporating the top 10 radiomics features and top 10 clinical variables achieved the highest performance (stacked AUC: 0.902), outperforming the Rad5AndCli5 model (stacked AUC: 0.896) and Rad10 model (AUC: 0.866) or Cli10 model (AUC: 0.750). The Rad10AndCli10 model demonstrated stable performance across all 5 folds (AUC range: 0.862-0.945). Radiomics features from lesion boundaries contributed most to predictions, and higher lesion heterogeneity was associated with a greater risk of treatment failure. Clinical predictors including deepest periodontal probing depth and size of periapical lesion size, were consistently selected. Conclusion ML models combining radiomics features and clinical variables improved prediction of NSreRCT outcomes compared with models using either data source alone. Radiomics features, particularly those derived from periapical lesion boundaries, contributed substantially to prediction, and greater lesion heterogeneity was associated with a higher risk of NSreRCT failure.
