Could Artificial Intelligence/Machine Learning and Inclusion of Diet-Gut Microbiome Interactions Improve Disease Risk...

Area of Science:

• Cardiovascular Disease Research
• Nutritional Science
• Microbiome Research

Background:

• Coronary artery disease (CAD) is a leading cause of global mortality, necessitating improved risk prediction.
• Current models using traditional risk factors are insufficient due to CAD's complex, multifactorial nature.
• Individual responses to diet vary, highlighting the need for personalized approaches beyond "one-size-fits-all".

Purpose of the Study:

• To review Artificial Intelligence (AI) and Machine Learning (ML) applications in CAD risk prediction.
• To explore the role of diet and gut microbiome in CAD.
• To discuss the integration of diet-gut microbiome interactions for personalized CAD risk assessment.

Main Methods:

• Review of AI/ML approaches for CAD risk prediction.
• Analysis of dietary factors' impact on CAD.
• Classification of individuals based on gut microbiome composition.
• Modeling of diet-gut microbiome interactions.

Main Results:

• AI/ML can capture complex data and interactions for improved prediction.
• Diet and gut microbiome are significant, yet complex, contributors to CAD risk.
• Integrating these factors presents Big Data challenges but holds promise.

Conclusions:

• Personalized CAD risk prediction requires integrating diet, gut microbiome, and clinical data.
• AI/ML offers powerful tools to model these complex interactions.
• Future research should focus on developing integrated, personalized decision support systems for CAD prevention.