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Personalized Early-Warning Signals during Progression of Human Coronary Atherosclerosis by Landscape Dynamic Network
Jing Ge1, Chenxi Song2, Chengming Zhang1,3
1Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China.
Insights
Researchers identified early warning signals for coronary atherosclerosis using a new method. This approach detects the disease
Area of Science:
- Biomedical Science
- Systems Biology
- Metabolomics
Background:
- Coronary atherosclerosis is a primary cause of cardiovascular disease.
- Early detection and identification of disease onset remain challenging.
- Current methods lack individualized predictive capabilities.
Purpose of the Study:
- To identify early-warning signals for coronary atherosclerosis in individual patients.
- To detect the tipping point (predisease state) of the disease.
- To discover key biomarkers driving disease progression.
Main Methods:
- Utilized the landscape dynamic network biomarkers (l-DNB) methodology.
- Applied l-DNB to plasma metabolomics data from patients at various disease stages.
- Employed a single-sample data approach for biomarker identification.
Main Results:
- Accurately detected individualized early-warning signals for coronary atherosclerosis.
- Identified a specific group of dynamic network biomarkers (DNBs) crucial for disease progression.
- Demonstrated the efficacy of the l-DNB methodology in complex disease analysis.
Conclusions:
- The l-DNB methodology enables early, individualized diagnosis of coronary atherosclerosis.
- Discovered novel DNBs that are key drivers of the disease.
- This research offers new insights for personalized medicine in cardiovascular disease management.
Abstract:
Coronary atherosclerosis is one of the major factors causing cardiovascular diseases. However, identifying the tipping point (predisease state of disease) and detecting early-warning signals of human coronary atherosclerosis for individual patients are still great challenges. The landscape dynamic network biomarkers (l-DNB) methodology is based on the theory of dynamic network biomarkers (DNBs), and can use only one-sample omics data to identify the tipping point of complex diseases, such as coronary atherosclerosis. Based on the l-DNB methodology, by using the metabolomics data of plasma of patients with coronary atherosclerosis at different stages, we accurately detected the early-warning signals of each patient. Moreover, we also discovered a group of dynamic network biomarkers (DNBs) which play key roles in driving the progression of the disease. Our study provides a new insight into the individualized early diagnosis of coronary atherosclerosis and may contribute to the development of personalized medicine.
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