Metabolomics data, featuring high-throughput and highly accurate quantitative characteristics, can comprehensively mirror the alterations in the metabolic state. When combined with multi-modal data such as genomics, imaging and clinical phenotypes, it can reveal the occurrence, development process of diseases, and the health status of the human body from multiple perspectives. Among them, metabolomics can accurately reflect the metabolic changes in disease states, genomics can identify disease!associated genes, and imaging can vividly display phenotypic characteristics. By integrating these multimodal data and combining big data and artificial intelligence technologies, it becomes possible to delve deeply into the potential correlations and patterns within the data, and investigate the mechanisms underlying disease occurrence and development.
This study leverages the UK Biobank to conduct in-depth integration and analysis of multi!omics data, aiming to uncover the biological mechanisms and key biomarkers of osteoporosis and psychiatric disorders (such as depression, bipolar disorder, and schizophrenia), thereby offering support for quantitative assessment and precise diagnosis.
Specifically, this research is centered on predicting the risk of osteoporotic fractures among individuals in a depressive state. Given that individuals with depression often undergo substantial lifestyle alterations, including decreased physical activity, mood swings, alterations in dietary patterns, and disruption of the circadian rhythm, it is hypothesized that their risk of osteoporotic fractures may be elevated compared to that of healthy populations. Consequently, this study intends to leverage the UK Biobank to establish a metabolic regulatory network for individuals with mental disorders and those suffering from osteoporotic fractures. The objectives are to identify potential biomarkers, explore the mechanisms underlying the development of these diseases, and deduce the interactive
