Background/Objectives: While some dietary indices have been developed to assess diet quality and chronic disease risk, their comparative effectiveness within the same population remains unclear due to methodological limitations in observational studies. This study employs a causal inference framework to compare nine dietary indices for reducing all-cause and cardiovascular mortality, while investigating inflammatory pathways through multiple mediation analysis. Methods: Using dietary data from 33,881 adults (aged ≥ 20 years, median follow-up 92 months), we applied a causal directed acyclic graph to identify the minimum sufficient adjustment set and implemented generalized propensity score matching to address confounding. Robust Cox proportional hazards regression assessed associations between nine dietary indices—Dietary Inflammatory Index (DII), Composite Dietary Antioxidant Index (CDAI), Healthy Eating Index 2015/2020 (HEI-2015/2020), Alternate Healthy Eating Index (AHEI), Alternate Mediterranean Diet (aMED), Mediterranean Diet Index (MEDI), and Dietary Approaches to Stop Hypertension (DASH/DASHI)—and mortality outcomes. Multiple additive regression trees (MART) algorithm was used for multiple mediation analysis to examine inflammatory markers (PAR, SII, NPR, TyG, LMR, PLR, ELR, CRP) as mechanistic mediators. Results: Among 33,881 participants (mean age 47.07 years, 51.34% women), 4,230 deaths occurred, including 827 cardiovascular deaths. Under the causal inference framework, higher DII scores increased both all-cause (HR: 1.07; 95% CI: 1.02–1.12) and cardiovascular mortality risk (HR: 1.07; 95% CI: 1.04–1.10) by 7%. The aMED demonstrated the strongest protective association, reducing all-cause mortality by 12% (HR: 0.88; 95% CI: 0.80–0.97) and cardiovascular mortality by 11% (HR: 0.89; 95% CI: 0.80–0.98), followed by MEDI with similar magnitude effects. Other healthy dietary indices showed modest 1–3% risk reductions. Multiple mediation analysis revealed that inflammatory markers, particularly neutrophil-to-platelet ratio (NPR) and systemic immune-inflammation index (SII), significantly mediated diet–mortality associations across all indices, with C-reactive protein (CRP) serving as the most frequent mediator. Conclusions: Using causal inference methodology, the Mediterranean dietary pattern (aMED) shows the strongest causal association with reduced mortality risk, with inflammatory pathways serving as key mediating mechanisms. These findings provide robust evidence for prioritizing Mediterranean dietary patterns in public health interventions and clinical practice, while highlighting inflammation as a critical therapeutic target for dietary interventions aimed at reducing mortality risk.

Objective: Meropenem pharmacokinetics vary considerably among critically ill patients, resulting in uncertainty in target concentration attainment. This study aimed to develop a predictive model using real-world therapeutic drug monitoring (TDM) data to assess target attainment and provide a reference for individualized meropenem management. Methods: This single-center retrospective study included critically ill patients administered meropenem treatment and subjected to trough concentration monitoring from August 2023 to January 2026. Meropenem target attainment was defined as a trough concentration (Cmin) exceeding 8.0 mg/L. The analysis included 399 TDM samples from 333 patients, which were partitioned into training and test sets at an 8:2 ratio using a patient-level splitting approach. Following feature selection, eight machine learning algorithms were applied to build prediction models. Predictive performance was assessed primarily by the area under the receiver operating characteristic curve (AUC), with accuracy, sensitivity, specificity, F1 score, and calibration curves used as additional evaluation measures. SHapley Additive exPlanations (SHAP) analysis was then used to interpret the final model. Results: Ten features were ultimately selected, including age, CRRT, ECMO, Scr, ALP, PLT, P-LCR, APACHE II score, CrCl, and dose per body weight. In the independent test set, the random forest showed the best performance, with an AUC of 0.873 (95% CI: 0.779– 0.946), followed by extreme gradient boosting with an AUC of 0.867 (95% CI: 0.763– 0.942) and support vector machine with an AUC of 0.863 (95% CI: 0.771– 0.935). SHAP analysis showed that CrCl, APACHE II score, and Scr were the most important predictors. Conclusion: Machine learning models developed using routine clinical variables showed good performance in predicting meropenem target attainment in critically ill patients and may help identify patients at high risk of target non-attainment, providing a reference for individualized meropenem management.

The relationship between major depressive disorder (MDD) and liver diseases is complex, with causal links poorly understood. This study aimed to disentangle causal, mediated, and confounded links between MDD and liver diseases. We integrated three approaches: a trend analysis of 2004 to 2024 publications; a two-sample Mendelian randomization, or MR, analysis assessing genetic causal effects of MDD on liver diseases; and an observational analysis of the NHANES 2007 to 2020 cohort using survey-weighted regression and formal causal mediation analysis to evaluate clinical pathways. MR analysis provided genetic evidence for a causal effect of MDD on NAFLD (OR = 1.233, 95% CI: 1.015-1.496), chronic HBV (OR = 1.607, 95% CI: 1.155-2.235), and viral hepatitis (OR = 1.515, 95% CI: 1.053-2.180), but not chronic HCV. In NHANES, the clinical MDD-NAFLD association (AOR = 1.35) became non-significant after adjusting for BMI (AOR = 1.07, P = 0.500), with formal mediation analysis revealing that BMI significantly mediates 80.70% of this risk. Furthermore, MDD showed no independent association with advanced liver fibrosis (P = 0.500). Conversely, a strong observational association was found for chronic HCV (AOR = 2.52) despite the null MR finding; however, this was entirely attenuated after adjusting for illicit drug use (AOR = 1.59, P = 0.072). No significant association was found for chronic HBV. MDD increases liver disease risk via distinct pathways. The genetic MDD-NAFLD link appears largely mediated by obesity clinically, manifesting primarily as steatosis rather than direct fibrogenesis. The strong MDD-HCV association reflects socio-behavioral confounding, while the causal MDD-HBV link remains an under-recognized risk. These findings underscore the importance of managing obesity in psychiatric care and integrating mental health screening in hepatology.

Background: Metabolic dysfunction-associated fatty liver disease (MAFLD) affects 38.9% of the global adult population and is associated with increased mortality when it co-occurs with depression. Women exhibit a 1.5– to 3–fold higher prevalence of depression, with postmenopausal hormonal imbalances amplifying susceptibility. This underscores the urgent need for sex-specific predictive models. The aim of this study was to develop a lightweight, high-accuracy model to identify key predictors of depression risk in female MAFLD patients using a five-year longitudinal UK Biobank cohort. Methods: We analyzed routine blood biomarkers (hematology and metabolites), lifestyle factors, and reproductive history from female participants with MAFLD identified within the UK Biobank cohort. Logistic regression was adjusted for socioeconomic, lifestyle, multimorbidity, and medication use, and was employed to evaluate sex-specific factors. Feature selection employed a two-stage approach to ensure balanced covariate distributions between cases and controls: Random Forest-based stratified bootstrap resampling (1000 iterations) instead of traditional random sampling, followed by recursive feature elimination. Five models—Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), Random Forest, Feature Tokenizer Transformer (FT-Transformer), and Gated Adaptive Network for Deep Automated Learning of Features (GANDALF)—were assessed via 10–fold cross-validation. SHapley Additive exPlanations (SHAP) and restricted cubic splines (RCS) elucidated feature importance and nonlinear effects. Results: A total of 39,430 female MAFLD patients were included in the final analysis, among whom 611 (1.55%) developed incident depression over the five-year follow-up. Adjusted logistic regression identified younger age at first live birth (<20 years) and early menopause (25 kg/m2) and obesity (body mass index [BMI] >30 kg/m2), and early reproductive milestones. Additionally, our feature set showed enhanced predictive validity compared to established biomarker panels from prior studies (ROC-AUC 0.96 vs. 0.886–0.940). Conclusions: This study introduces a highly accurate, lightweight predictive model tailored for female MAFLD patients, leveraging 17 key features to improve the prediction of depression risk. By enabling personalized risk assessment and targeted interventions, our model offers a transformative approach to improve mental health outcomes and care quality in this vulnerable population.