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BackgroundAlzheimer’s disease (AD) represents a progressive neurodegenerative disorder characterized by the accumulation of misfolded amyloid beta protein, leading to the formation of amyloid plaques and the aggregation of tau protein into neurofibrillary tangles within the cerebral cortex. The role of carbohydrates, particularly apolipoprotein E (ApoE), is pivotal in AD pathogenesis due to its involvement in lipid and cholesterol metabolism, and its status as a genetic predisposition factor for the disease. Despite its significance, the mechanistic contributions of Lipid Metabolism-related Genes (LMGs) to AD remain inadequately elucidated. This research endeavor seeks to bridge this gap by pinpointing biomarkers indicative of early-stage AD, with an emphasis on those linked to immune cell infiltration. To this end, advanced machine-learning algorithms and data derived from the Gene Expression Omnibus (GEO) database have been employed to facilitate the identification of these biomarkers.MethodsDifferentially expressed genes (DEGs) were identified by comparing gene expression profiles between healthy individuals and Alzheimer’s disease (AD) patients, using data from two Gene Expression Omnibus (GEO) datasets: GSE5281 and GSE138260. Functional enrichment analysis was conducted to elucidate the biological relevance of the DEGs. To ensure the reliability of the results, samples were randomly divided into training and validation sets. The analysis focused on lipid metabolism-related DEGs (LMDEGs) to explore potential biomarkers for AD. Machine learning algorithms, including Support Vector Machine-Recursive Feature Elimination (SVM-RFE) and the Least Absolute Shrinkage and Selection Operator (LASSO) regression model, were applied to identify a key gene biomarker. Additionally, immune cell infiltration and its relationship with the gene biomarker were assessed using the CIBERSORT algorithm. The Integrated Traditional Chinese Medicine (ITCM) database was also referenced to identify Chinese medicines related to lipid metabolism and their possible connection to AD. This comprehensive strategy aims to integrate modern computational methods with traditional medicine to deepen our understanding of AD and its underlying mechanisms.ResultsThe study identified 137 genes from a pool of 751 lipid metabolism-related genes (LMGs) significantly associated with autophagy and immune response mechanisms. Through the application of LASSO and SVM-RFE machine-learning techniques, four genes—choline acetyl transferase (CHAT), member RAS oncogene family (RAB4A), acyl-CoA binding domain-containing protein 6 (ACBD6), and alpha-galactosidase A (GLA)—emerged as potential biomarkers for Alzheimer’s disease (AD). These genes demonstrated strong therapeutic potential due to their involvement in critical biological pathways. Notably, nine Chinese medicine compounds were identified to target these marker genes, offering a novel treatment approach for AD. Further, ceRNA network analysis revealed complex regulatory interactions involving these genes, underscoring their importance in AD pathology. CIBERSORT analysis highlighted a potential link between changes in the immune microenvironment and CHAT expression levels in AD patients, providing new insights into the immunological dimensions of the disease.ConclusionThe discovery of these gene markers offers substantial promise for the diagnosis and understanding of Alzheimer’s disease (AD). However, further investigation is necessary to validate their clinical utility. This study illuminates the role of Lipid Metabolism-related Genes (LMGs) in AD pathogenesis, offering potential targets for therapeutic intervention. It enhances our grasp of AD’s complex mechanisms and paves the way for future research aimed at refining diagnostic and treatment strategies.

Objectives The purpose of this study was to explore whether diabetes distress mediated the relationship between loneliness and health promotion in older adults with diabetes.Design A cross-sectional study.Setting The study was conducted at three tertiary hospitals in Changsha, Hunan Province, China.Participants The sample included 140 patients with diabetes (65 years and older, mean age 72.6 years, SD=4.6).Methods We employed path models to analyse data on diabetes distress, loneliness and health promotion behaviours. We collected diabetes distress, loneliness and health promotion behaviour with self-reported questionnaires including the Diabetes Distress Scale, the University of California at Los Angeles (UCLA) Loneliness Scale and the Elderly Health Promotion Scale from January 2022 to October 2022. Mediation analysis was performed by SPSS V.26.0’s PROCESS macro.Result The findings of this study indicated diabetes distress acted as a mediator between loneliness and health promotion behaviour. According to bootstrapping results, the total effect of loneliness on health promotion behaviour was significantly negative (β=−0.312, p=0.006). Loneliness significantly and negatively correlated with diabetes distress (β=−0.043, p

ObjectivesTo investigate the status of nurses’ work engagement and the relationship among resilience, organizational support, and innovative behaviors.MethodsIn this cross-sectional study, we investigated 496 nurses in Hunan, China, from July 2022 to December 2022. A descriptive statistical approach, Pearson’s correlation analysis and Hayes’ PROCESS Macro Models 4 and 14 were used to analyze the available data.ResultsThe level of work engagement among nurses was found to be moderate. Resilience positively predicted work engagement among nurses. Organizational support played a partially mediating role in the association between resilience and work engagement. Furthermore, innovative behavior played a moderating role in the association between adaptive resilience and work engagement.ConclusionBased on the results, greater attention needs to be paid to nurses’ work engagement. A high level of resilience, organizational support, and innovative behavior may increase work engagement among nurses. Nursing leaders can take measures to increase work engagement among nurses by improving nurses’ resilience and organizational support, and cultivating innovative behavior.