Introduction: Alzheimer’s disease (AD) is a severe dementia with clinical and pathological heterogeneity. Our study was aim to explore the roles of endoplasmic reticulum (ER) stress-related genes in AD patients based on interpretable machine learning.Methods: Microarray datasets were obtained from the Gene Expression Omnibus (GEO) database. We performed nine machine learning algorithms including AdaBoost, Logistic Regression, Light Gradient Boosting (LightGBM), Decision Tree (DT), eXtreme Gradient Boosting (XGBoost), Random Forest, K-nearest neighbors (KNN), Naïve Bayes, and support vector machines (SVM) to screen ER stress-related feature genes and estimate their efficiency of these genes for early diagnosis of AD. ROC curves were performed to evaluate model performance. Shapley additive explanation (SHAP) was applied for interpreting the results of these models. AD patients were classified using a consensus clustering algorithm. Immune infiltration and functional enrichment analysis were performed via CIBERSORT and GSVA, respectively. CMap analysis was utilized to identify subtype-specific small-molecule compounds.Results: Higher levels of immune infiltration were found in AD individuals and were markedly linked to deregulated ER stress-related genes. The SVM model exhibited the highest AUC (0.879), accuracy (0.808), recall (0.773), and precision (0.809). Six characteristic genes (RNF5, UBAC2, DNAJC10, RNF103, DDX3X, and NGLY1) were determined, which enable to precisely predict AD progression. The SHAP plots illustrated how a feature gene influence the output of the SVM prediction model. Patients with AD could obtain clinical benefits from the feature gene-based nomogram. Two ER stress-related subtypes were defined in AD, subtype2 exhibited elevated immune infiltration levels and immune score, as well as higher expression of immune checkpoint. We finally identified several subtype-specific small-molecule compounds.Conclusion: Our study provides new insights into the role of ER stress in AD heterogeneity and the development of novel targets for individualized treatment in patients with AD.

Objectives: To explore prevalence of depression and its influencing factors in middle-aged and elderly patients with chronic diseases.Method: Data were extracted from the 2018 China Health and Retirement Tracking Survey (CHARLS) for 6,704 middle-aged and elderly patients ≥45 years with chronic diseases. The influencing variables were selected based on LASSO-logistic regression model, and a nomogram was further drawn to visualize regression results.Results: Comorbidity between chronic diseases and depression symptoms were detected in 3058 individuals (45.6%). Female, rural, lower education, poor, insomnia, multiple chronic disease, and functional impairment were associated with a higher proportion of depression. Meanwhile, family interaction, intergenerational financial support, social activity intensity, and satisfaction with life can protect against depression.Conclusion: Depressive symptoms are common in Chinese older adults with chronic diseases. They need regular assessment and intervention, especially those with multiple diseases, female, rural, alone, impaired, poor sleep, or poor economy. These high-risk elders also need family, medical, and social support and care.

The prevalence of depression among patients with chronic kidney disease (CKD) is high and closely related to poor prognosis. However, the association between sleep onset time, sleep duration, and depression in CKD patients has not been thoroughly studied. This study utilized cross-sectional data from CKD patients who participated in the National Health and Nutrition Examination Survey from 2015 to 2020, analyzing their sleep onset time, sleep duration, and Patient Health Questionnaire-Nine. Logistic regression models and restricted cubic spline models were used to explore the association between sleep onset time, sleep duration, and depression in CKD patients. A total of 2141 CKD patients aged 20 and above were included in this study, among whom 246 (11.5 %) had depression. Compared to those reporting optimal sleep onset (22:00–23:59) and sufficient sleep duration (7–8 h), CKD patients with late sleep onset (≥24:00) and either insufficient (<7 h) or excessive (≥9 h) sleep had a significantly higher risk of depression, with adjusted OR of 2.03 (95 % CI:1.29–3.19) and 2.07 (95 % CI:1.07–4.00), respectively. Additionally, the association between sleep onset time, sleep duration, and depression showed a U-shaped pattern, with the inflection point for sleep onset time at 23:00 and for sleep duration at 7.5 h. Inappropriate sleep onset time and sleep duration are significantly associated with depression in CKD patients. This association may be important to consider in clinical practice for the prevention and management of depressive symptoms in CKD patients.

Objective: Early prediction of long-term outcomes in patients with systemic lupus erythematosus (SLE) remains a great challenge in clinical practice. Our study aims to develop and validate predictive models for the mortality risk.Methods: This observational study identified patients with SLE requiring hospital admission from the Medical Information Mart for Intensive Care (MIMIC-IV) database. We downloaded data from Fujian Provincial Hospital as an external validation set. Variable selection was performed using the Least Absolute Shrinkage and Selection Operator (LASSO) regression. Then, we constructed two predictive models: a traditional nomogram based on logistic regression and a machine learning model employing a stacking ensemble approach. The predictive ability of the models was evaluated by the areas under the receiver operating characteristic curve (AUC) and the calibration curve.Results: A total of 395 patients and 100 patients were enrolled respectively from MIMIC-IV database and the validation cohort. The LASSO regression identified 18 significant variables. Both models demonstrated good discrimination, with AUCs above 0.8. The machine learning model outperformed the nomogram in terms of precision and specificity, highlighting its potential superiority in risk prediction. The SHapley additive explanations analysis further elucidated the contribution of each variable to the model’s predictions, emphasising the importance of factors such as urine output, age, weight and alanine aminotransferase.Conclusions: The machine learning model provides a superior tool for predicting mortality risk in patients with SLE, offering a basis for clinical decision-making and potential improvements in patient outcomes.