Multi-Omics and Artificial Intelligence in Advancing Endocrine and Metabolic Disease Research

Endocrine and metabolic diseases represent a critical and evolving area of biomedical research, encompassing conditions such as diabetes, obesity, and metabolic syndrome that are profoundly influenced by genetic, molecular, and environmental factors. The complexity of these disorders arises from interactions across multiple biological layers—genome, transcriptome, proteome, metabolome, and microbiome. In recent years, the emergence of high-throughput multi-omics technologies has significantly improved our understanding of these diseases, allowing for systemic exploration of molecular mechanisms and inter-pathway crosstalk. Simultaneously, artificial intelligence (AI) and machine learning have revolutionized biomedical data analysis, offering tools to extract meaningful patterns from high-dimensional datasets. Despite these advances, substantial challenges remain in translating computational insights into clinical applications. Current studies often lack integration between omics layers or fail to connect molecular data with patient-level outcomes. Therefore, there is an increasing need for collaborative, interdisciplinary research that bridges data-driven discovery and clinical implementation to propel precision endocrinology forward.This Research Topic aims to catalyze progress in the integration of multi-omics and AI methodologies for the study, diagnosis, and management of endocrine and metabolic diseases. It seeks to foster applied and translational research that leverages multi-layered biological and clinical data to enhance understanding of disease pathophysiology and develop predictive, diagnostic, and prognostic models. Key objectives include identifying disease subtypes, improving risk stratification, and discovering actionable biomarkers that can enable tailored therapeutic interventions. The ultimate goal is to strengthen the link between computational innovation and clinical practicability, driving advances in personalized medicine for endocrine and metabolic conditions.To gather further insights into data-driven and clinically relevant approaches in endocrine systems research, we welcome articles addressing, but not limited to, the following themes:• Integration of multi-omics datasets (genomics, transcriptomics, metabolomics, etc.) for mechanistic insights into metabolic diseases• Artificial intelligence and machine learning methods for disease subtyping, risk stratification, and prognosis prediction• Systems biology and network-based modeling for endocrine disease characterization and therapeutic target identification• Applications of digital health technologies and real-world evidence in endocrine and metabolic disease management• Discovery and validation of novel biomarkers with potential clinical utility• Development of computational frameworks enabling precision medicine in diabetes, obesity, and related disorders Topic editors: Tong Yue, Raquel Alarcon Rodriguez, Chaofan Wang Source: https://www.frontiersin.org/research-topics/81207/multi-omics-and-artificial-intelligence-in-advancing-endocrine-and-metabolic-disease-research