Objectives: To explore whether the triglyceride–glucose (TyG) index and the triglyceride to high-density lipoprotein cholesterol (TG/HDL-C) ratio are associated with the trajectories of depressive symptoms. Methods: In this longitudinal study, 4215 participants aged 45 years and older were recruited from the China Health and Retirement Longitudinal Study from 2011 to 2018. The trajectories of depressive symptoms, measured by the 10-item Center for Epidemiologic Studies Depression Scale (CESD-10), were identified using group-based trajectory modeling. Multinomial logistic models and restricted cubic spline analysis were used to investigate the relationships between the TyG index and the TG/HDL-C ratio and the trajectories of depressive symptoms. Stratified analyses were conducted based on sex, age, place of residence, and body mass index (BMI). Results: Five distinct trajectories of depressive symptoms characterized by stable low, stable moderate, decreasing, increasing, and stable high were identified during a follow-up of 7 years. The associations of the TyG index and the TG/HDL-C ratio with trajectories of depressive symptoms are not entirely consistent. After adjusting for covariates, a higher TyG index at baseline was associated with lower odds of being on the decreasing trajectory of depressive symptoms (OR_ad = 0.61, 95% CI: 0.40–0.92) compared to the stable low trajectory, and restricted cubic spline analysis revealed a negative linear relationship between the TyG index and the likelihood of a decreasing trajectory of depressive symptoms. However, the relationship between the TG/HDL-C ratio and the decreasing trajectory of depressive symptoms was no longer statistically significant when all confounders were controlled (OR_ad = 0.72, 95% CI: 0.50−1.04). Additionally, this negative association between the TyG index and decreasing trajectory of depressive symptoms was observed among 45–64-year-old individuals, female participants, those living in rural areas, and those with a normal BMI. Limitations: This study was conducted in a middle-aged and elderly population in China, and extrapolation to other regions and populations requires further confirmation. Conclusions: Compared to the TG/HDL-C ratio, the TyG index may be a better predictor for trajectories of depressive symptoms in middle-aged and older adults. Considering that the pathology of depression progresses long term, our findings may have utility for identifying available and reliable markers for the development of depression.

The global prevalence and burden of anxiety disorders (ADs) are increasing. However, findings on the acute effects of air pollution on ADs remain inconclusive. We evaluated the effects of short-term exposure to ambient air pollutants, including fine particulate matter (PM_2.5), inhalable particulate matter (PM₁₀), nitrogen dioxide (NO₂), carbon monoxide (CO), sulfur dioxide (SO₂), and ozone (O₃), on daily hospital visits for ADs. A generalized additive model was used to perform a time-series analysis on data from a Southern China city’s medical insurance system between 1 March 2021, and 31 July 2023. Although the daily levels of most pollutants (PM₁₀, SO₂, CO, NO₂ and O₃) were consistently below China and WHO’s Ambient Air-Quality Standards, significant associations were observed between daily hospital visits for ADs and all six air pollutants. Each interquartile range increase in concentrations resulted in the largest odds ratios of 1.08 (95% CI: 1.01, 1.16) at lag1 for PM_2.5, 1.19 (95% CI: 1.05, 1.34) at lag07 for NO₂, 1.14 (95% CI: 1.05, 1.23) at lag02 for CO, 1.12 (95% CI: 1.01, 1.25) at lag07 for PM₁₀, 1.06 (95% CI: 1.01, 1.12) at lag7 for SO₂ and 1.08 (95% CI: 1.01, 1.15) at lag7 for O₃, respectively. The effects of NO₂ and CO remained robust across subgroup analyses and sensitivity analyses. Females and middle-aged individuals showed stronger associations than other subgroups. The findings underscore the necessity for public health efforts to alleviate the impact of air pollution on mental health, even in low-concentration settings.

Multi-omics molecular subtyping is complicated by small cohorts, heterogeneous data layers, and sensitivity to data splitting and feature selection. We developed multi-channel attention-based omics fusion (MCOF), an end-to-end framework that combines omics-specific projection, one-dimensional convolution, and integrated gradients for candidate biomarker prioritization. MCOF was evaluated against six baselines using five repeated stratified holdouts across four multi-omics datasets. In the restricted four-class breast invasive carcinoma task, MCOF achieved the highest observed mean for all five metrics, including an accuracy of 0.900 ± 0.022 and a macro-averaged area under the receiver operating characteristic curve (macro-AUC) of 0.982 ± 0.003. In the restricted three-class stomach adenocarcinoma task, it achieved the highest observed macro-F1 (0.786 ± 0.035), macro-AUC (0.934 ± 0.020), and weighted-AUC (0.930 ± 0.016), whereas support vector machines had a slightly higher accuracy and weighted F1. It also achieved the highest or near-highest AUC on the two non-cancer datasets. Compared with MOGONET, MCOF used 30.6% fewer trainable parameters on both cancer tasks. Sensitivity analyses indicated that the contribution of multi-omics fusion varied by dataset and metric. Excluding the 28 prediction analysis of microarray 50 (PAM50) genes present in the retained BRCA RNA panel produced only a modest reduction in the macro-AUC, from 0.982 ± 0.003 to 0.978 ± 0.004. Implementation tests across 20 trained checkpoints detected no batch-order effect or cross-sample gradient, while the IG rankings were moderately stable across repeated holdouts and numerically consistent across class-weighting schemes and 25–100 integration steps. MCOF provides a practical framework for multi-omics classification and candidate prioritization.