AUTHOR=Vilne Baiba , Ķibilds Juris , Siksna Inese , Lazda Ilva , Valciņa Olga , Krūmiņa Angelika 

TITLE=Could Artificial Intelligence/Machine Learning and Inclusion of Diet-Gut Microbiome Interactions Improve Disease Risk Prediction? Case Study: Coronary Artery Disease

JOURNAL=Frontiers in Microbiology

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2022.627892

DOI=10.3389/fmicb.2022.627892

ISSN=1664-302X

ABSTRACT=Coronary artery disease (CAD) is the leading cause of morbidity and mortality worldwide. Therefore, timely and precise identification of people at high risk of CAD is urgently required. Most current  risk prediction approaches are based on traditional risk factors and are incompletely predictive across all patient groups. CAD is a multi-factorial disease with complex etiology, considered to be driven by both genetic, as well as numerous environmental/lifestyle factors and diet is one of the modifiable factors. However, the "one-size-fits-all" approach may not be efficient, due to significant variation in inter-individual responses. Recently, the gut microbiome has emerged as a potential and previously under-explored contributor to these variations. Efficient integration of dietary and gut microbiome information alongside with genetic variations and clinical data holds a great promise to improve CAD risk prediction. Nevertheless, the highly complex nature of meals combined with the huge inter-individual variability of the gut microbiome poses several Big Data analytics challenges in modeling diet-gut microbiota interactions and integrating these within CAD risk prediction. In this regard, the recent re-emergence of Artificial Intelligence (AI) / Machine Learning (ML) is opening intriguing perspectives. In this Mini-Review, we consider (1) the most used AI/ML approaches and their different use cases for CAD risk prediction (2) modeling of the content, choice and impact of dietary factors on CAD risk; (3) classification of individuals by their gut microbiome composition into CAD cases vs. controls and (4) modeling of the diet-gut microbiome interactions and their impact on CAD risk.