Background: Depression constitutes a major public health issue, being one of the leading causes of the burden of disease worldwide. The risk of depression is determined by both genetic and environmental factors. While genetic factors cannot be altered, the identification of potentially reversible environmental factors is crucial in order to try and limit the prevalence of depression. Aim: A cross-sectional, questionnaire-based study on a sample from the multicultural region of Thrace in northeast Greece was designed to assess the potential association of depression with several sociodemographic characteristics, lifestyle, and health status. The study employed four machine learning (ML) methods to assess depression: logistic regression (LR), support vector machine (SVM), XGBoost, and neural networks (NNs). These models were compared to identify the best-performing approach. Additionally, a genetic algorithm (GA) was utilized for feature selection and SHAP (SHapley Additive exPlanations) for interpreting the contributions of each employed feature. Results: The XGBoost classifier demonstrated the highest performance on the test dataset to predict depression with excellent accuracy (97.83%), with NNs a close second (accuracy, 97.02%). The XGBoost classifier utilized the 15 most significant risk factors identified by the GA algorithm. Additionally, the SHAP analysis revealed that anxiety, education level, alcohol consumption, and body mass index were the most influential predictors of depression. Conclusions: These findings provide valuable insights for the development of personalized public health interventions and clinical strategies, ultimately promoting improved mental well-being for individuals. Future research should expand datasets to enhance model accuracy, enabling early detection and personalized mental healthcare systems for better intervention.

Apart from their main function as oxygen carriers in vertebrates, erythrocytes are also involved in immune regulation. By circulating throughout the body, the erythrocytes are exposed and interact with tissues that are damaged as a result of a disease. In this study, we summarize the literature regarding the contribution of erythrocytes to immune regulation and metabolism. Under the circumstances of a disease state, the erythrocytes may lose their antioxidant capacity and release Damage Associated Molecular Patterns, resulting in the regulation of innate and adaptive immunity. In addition, the erythrocytes scavenge and affect the levels of chemokines, circulating cell-free mtDNA, and C3b attached immune complexes. Furthermore, through surface molecules, erythrocytes control the function of T lymphocytes, macrophages, and dendritic cells. Through an array of enzymes, red blood cells contribute to the pool of blood’s bioactive lipids. Finally, the erythrocytes contribute to reverse cholesterol transport through various mechanisms. Our study is highlighting overlooked molecular interactions between erythrocytes and immunity and metabolism, which could lead to the discovery of potent therapeutic targets for immunometabolic diseases.

Background: Lipid accumulation in the liver, skeletal and cardiac muscle, kidneys and pancreas causes cell dysfunction, death and inflammation, a biological phenomenon named lipotoxicity. Erythrocytes participate in the transport of lipids in the circulation, and their lipidome is determined by exchange with blood components. Objective: The objective of this study is to summarize the current knowledge regarding the effect of toxic lipid accumulation in erythrocytes. Results: Erythrocyte lipidome is altered in lipotoxic diseases, such as fatty liver disease, heart failure and diabetes. In addition, ceramide, lysophosphatidylcholine, lysophosphatidic acid, palmitic acid and free cholesterol induce erythrocyte malfunction. Conclusion: Erythrocytes are an additional cell target of lipotoxicity. Further exploration of the implicated molecular mechanisms could lead to novel therapeutic targets for cardiometabolic and hematological diseases.