A population-based study of precision health assessments using multi-omics network-derived biological functional...

Area of Science:

• Multi-omics data analysis
• Bioinformatics
• Precision health

Background:

• Technological advances enable precision health assessments using big data and bioinformatics.
• Multi-omics data integration is crucial for understanding complex biological systems.

Purpose of the Study:

• To develop biological functional modules (BFMs) from multi-omics data for health assessment.
• To create a model (BFM-ash) for evaluating individual health status and intervention effects.

Main Methods:

• Collected multi-omics data from 4,277 individuals.
• Generated sex-specific BFMs using community detection algorithms.
• Developed and applied the BFM-ash model for health status assessment and supplement evaluation.

Main Results:

• Accurate prediction of carotid plaques using BFMs associated with cardiometabolic health.
• Identification of anomalous BFMs in individuals.
• Demonstrated potential for assessing dietary supplement effects (grape seed extract).

Conclusions:

• BFMs and the BFM-ash model offer a novel approach to precision health assessment.
• The methodology can be applied to chronic disease management and personalized interventions.