Please use this identifier to cite or link to this item:
10.3389/fmicb.2022.627892
Title: | Could Artificial Intelligence/Machine Learning and Inclusion of Diet-Gut Microbiome Interactions Improve Disease Risk Prediction? Case Study : Coronary Artery Disease |
Authors: | Vilne, Baiba Ķibilds, Juris Siksna, Inese Lazda, Ilva Valciņa, Olga Krūmiņa, Angelika Bioinformatics Group Department of Infectology |
Keywords: | artificial intelligence;coronary artery disease;diet;gut microbiome;machine learning;personalized nutrition;risk prediction;3.1 Basic medicine;1.1. Scientific article indexed in Web of Science and/or Scopus database;Microbiology;Microbiology (medical);SDG 3 - Good Health and Well-being |
Issue Date: | 11-Apr-2022 |
Citation: | Vilne , B , Ķibilds , J , Siksna , I , Lazda , I , Valciņa , O & Krūmiņa , A 2022 , ' Could Artificial Intelligence/Machine Learning and Inclusion of Diet-Gut Microbiome Interactions Improve Disease Risk Prediction? Case Study : Coronary Artery Disease ' , Frontiers in Microbiology , vol. 13 , 627892 , pp. 627892 . https://doi.org/10.3389/fmicb.2022.627892 |
Abstract: | Coronary artery disease (CAD) is the most common cardiovascular disease (CVD) and the main leading cause of morbidity and mortality worldwide, posing a huge socio-economic burden to the society and health systems. Therefore, timely and precise identification of people at high risk of CAD is urgently required. Most current CAD risk prediction approaches are based on a small number of traditional risk factors (age, sex, diabetes, LDL and HDL cholesterol, smoking, systolic blood pressure) and are incompletely predictive across all patient groups, as CAD is a multi-factorial disease with complex etiology, considered to be driven by both genetic, as well as numerous environmental/lifestyle factors. Diet is one of the modifiable factors for improving lifestyle and disease prevention. However, the current rise in obesity, type 2 diabetes (T2D) and CVD/CAD indicates that 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. Hence, 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 approaches for the development of personalized decision support systems (DSS). In this regard, the recent re-emergence of Artificial Intelligence (AI) / Machine Learning (ML) is opening intriguing perspectives, as these approaches are able to capture large and complex matrices of data, incorporating their interactions and identifying both linear and non-linear relationships. 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. Finally, we provide an outlook for putting it all together for improved CAD risk predictions. |
Description: | Funding Information: This research was funded by the Latvian Council of Science within the project Gut microbiome composition and diversity among health and lifestyle induced dietary regimen, project No. lzp-2018/2-0266. Publisher Copyright: Copyright © 2022 Vilne, Ķibilds, Siksna, Lazda, Valciņa and Krūmiņa. |
DOI: | 10.3389/fmicb.2022.627892 |
ISSN: | 1664-302X |
Appears in Collections: | Research outputs from Pure / Zinātniskās darbības rezultāti no ZDIS Pure |
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