Functional Nutrition as Integrated Intervention for In- and Outpatient with Schizophrenia

Navigating Nutritional Inequality in Schizophrenia: A Comprehensive Exploration of Diet, Genetics, and Holistic Management Across the Life Cycle

This review explores the understudied topic of nutritional inequality among individuals with schizophrenia, highlighting the complex interplay between diet, genetics, and mental health. Unhealthy dietary patterns, socioeconomic factors, and disordered eating behaviors contribute to malnutrition, increasing the risk of physical health issues and premature mortality. Socioeconomic factors exacerbate nutritional disparities, necessitating targeted interventions. Genetic influences on nutrient metabolism remain under-researched, although nutritional genomics shows potential for personalized interventions. Current research reveals methodological gaps, urging larger sample sizes and standardized approaches. The integration of nutrigenomics, encompassing various omics disciplines, emerges as a transformative tool. The holistic life-cycle approach to schizophrenia management underscores the vital role of nutrition, calling for personalized interventions to enhance mental health outcomes.

Employing graph attention networks to decode psycho-metabolic interactions in Schizophrenia

Schizophrenia is a severe mental disorder characterized by intricate and underexplored interactions between psychological symptoms and metabolic health, presenting challenges in understanding the disease mechanisms and designing effective treatment strategies. To delve deeply into the complex interactions between mental and metabolic health in patients with schizophrenia, this study constructed a psycho-metabolic interaction network and optimized the Graph Attention Network (GAT). This approach reveals complex data patterns that traditional statistical analyses fail to capture. The results show that weight management and medication management play a central role in the interplay between psychiatric disorders and metabolic health. Furthermore, additional analysis revealed significant correlations between the history of psychiatric symptoms and physical health indicators, as well as the key roles of biochemical markers(e.g., triglycerides and low-density lipoprotein cholesterol), which have not been sufficiently emphasized in previous studies. This highlights the importance of medication management approaches, weight management, psychological treatment, and biomarker monitoring in comprehensive treatment and underscores the significance of the biopsychosocial model. This study is the first to utilize a GNN to explore the interactions between schizophrenia symptoms and metabolic features, providing new insights into understanding psychiatric disorders and guiding the development of more comprehensive treatment strategies for schizophrenia.