| Title: | Exploring the patterns associated with the co-morbidity in metabolic diseases |
| Lead Institution: | Shanghai Sixth People's Hospital |
| Principal investigator: |
Application 171860
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About
Co-morbidity, is a clinical state in which a patient has two or more chronic diseases at the same time, which is increasingly occurring in general medical practice and has become a major challenge to the global health care delivery system. Multidimensional phenomics has a role in predicting and signaling disease onset and is significantly correlated with the development of multiple chronic diseases. Mining and analyzing metabolic markers specific to the occurrence of co-morbidities plays an important role in predicting chronic disease co-morbidities. However, other important associations remain to be investigated through large sample cohort studies. There are currently a lack of valuable predictive models developed for complications of metabolic disease. In this 3-year project, we aim to identify potential metabolic biomarkers associated with the progression of metabolic disease co-morbidities (diabetes mellitus, chronic liver disease, cardiovascular disease, psychiatric disorders, etc.) using data from the UK Biobank cohort. The application of big data analytics to the medical community will allow for more accurate morbidity and mortality risk assessment of metabolic-related diseases from cohort data with large sample sizes and variable sizes, further contributing to the accuracy and precision of metabolic disease co-morbidity prevention. The results of our research related to the progression of co-morbidities will be freely available to the research community.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 17721 | Metabolomic signatures connect and mediate sedentary time-driven mortality risk in patients with cardiovascular disease | Min Zhu (+6) | 2026 | Frontiers in Sports and Active Living |
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