Prediction of Depression in Women With Metabolic Dysfunction-Associated Fatty Liver Disease Using Routine Blood Tests: A Five-Year Longitudinal Analysis From the UK Biobank.

Abstract

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 (<35 years) as significant risk factors for depression. Among the evaluated models, GANDALF demonstrated superior performance (area under the receiver operating characteristic curve [ROC-AUC] = 0.96 ± 0.03, Matthews' correlation coefficient [MCC] = 0.830 ± 0.068), significantly outperforming conventional machine learning approaches (MCC range: 0.720 to 0.760) and exhibiting better calibration (Brier score: 0.066 vs. 0.093-0.115). SHAP analysis identified red blood cell count, Townsend deprivation index, and neutrophil count as the most influential predictors among the 17-feature panel. RCS analyses revealed nonlinear protective effects of moderate physical activity and higher red blood cell counts, contrasted by adverse effects from elevated white blood cell counts, overweight (body mass index [BMI] >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.