Data-driven microbiota biomarker discovery for personalized drug therapy of cardiovascular disease

Area of Science:

• Microbiome research
• Computational biology
• Precision medicine

Background:

• Cardiovascular disease (CVD) is a leading global health issue.
• Personalized diagnosis, treatment, and prevention of CVD remain significant challenges.
• Advancements in metagenome sequencing and data-driven discovery enable new approaches.

Purpose of the Study:

• To summarize available data resources, knowledgebases, and computational models for CVD microbiota biomarker discovery.
• To review current findings on microbiota patterns linked to CVD therapeutic effects.
• To discuss future opportunities in translational informatics for personalized CVD management.

Main Methods:

• Review of existing literature and databases.
• Analysis of computational models for biomarker discovery.
• Synthesis of findings on CVD-associated microbiota patterns.

Main Results:

• Emerging computational tools facilitate microbiota biomarker discovery for CVD.
• Specific microbiota patterns are associated with therapeutic responses in CVD.
• Translational informatics holds promise for personalized CVD treatments.

Conclusions:

• Computer-aided microbiota biomarker discovery is crucial for advancing precision cardiovascular medicine.
• Understanding host-microbiota interactions is key for effective CVD management.
• Future research should focus on integrating multi-omics data for personalized therapeutic strategies.