With the global population aging, the prevention and control of chronic diseases have become a major challenge. Due to the non-invasive nature of ophthalmic examinations, the eye serves as a crucial window for assessing overall health and detecting systemic diseases. Traditional research has established associations between ocular diseases or specific ocular parameters and multi-omics data.
Recent advancements in artificial intelligence have revolutionized the extraction and analysis of large-scale molecular data. High-dimensional data from various omics disciplines are increasingly accessible, and the integration of different omics data is gaining attention. Multi-level and multi-factor analyses based on genomics, transcriptomics, proteomics, and metabolomics provide a comprehensive perspective on the molecular mechanisms driving disease occurrence and progression. This study aims to elucidate the multifactorial nature of common eye diseases and, by integrating multi-omics with ocular health, identify novel ocular biomarkers for early disease diagnosis, pinpoint potential therapeutic targets, and formulate public health strategies for the prevention and management of chronic eye diseases.
This study aims to deepen the understanding of the complex interactions between multi-omics and ocular health, elucidating the mechanisms linking the two. These insights are essential for developing individualized medical treatments and improving health outcomes across diverse populations.