| Title: | Unraveling the Multi-Omic and Causal Risk Architecture of Myasthenia Gravis |
| Lead Institution: | Sun Yat-Sen University |
| Principal investigator: |
Application 1196491
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About
Myasthenia gravis (MG) is an autoimmune disease characterized by the attack of antibodies on the neuromuscular junction, leading to muscle weakness. The etiology of MG is multifaceted, resulting from genetic and several environmental risk factors. However, the exact factors that result in an individual's susceptibility to MG are unknown. To address this gap, the present undertaking aims to employ an integrated approach, leveraging the wealth of genetics, metabolomics, proteomics, and comprehensive epidemiological data. Through multidimensional analyses, we aim to discover new risk factors, identify potential biomarkers, and understand causal relationships genetic risk factors and Myasthenia gravis. The systematic profiling of circulating proteins offers unique opportunities to uncover novel biomarkers and enhance our understanding of disease etiology. Similarly, the blood metabolome is regarded as an important readout that integrates information from genetic factors, gene expression, and protein abundance, as well as external environmental factors (evidence from existing studies on the associations between exposure factors and disease has been summarized). To this end, we will integrate multi-omics (i.e., proteomics and metabolomics) and epidemiological data to identify novel risk factors and biomarkers, and to provide definitive evidence for known disease risk associations reported by traditional observational studies. Our research will focus on neuroimmune disorders, particularly MG. By analyzing multi-omics and epidemiological information, we aim to uncover new risk factors, identify potential biomarkers, and elucidate the complex causal network underlying various neuroimmune diseases. This comprehensive exploration will contribute to clarifying the immune-mediated mechanisms and pathophysiology of these disorders, providing new scientific insights for disease prediction, early diagnosis, and treatment strategies.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 21574 | Dual-Axis Pattern of Peripheral Immunity in Myasthenia Gravis Susceptibility: A Prospective Cohort Study and Mendelian Randomization Analysis | Jiang Lei (+4) | 2026 | Biomedicines |
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