Vilne Baiba, Ķibilds Juris, Siksna Inese, Lazda Ilva, Valciņa Olga, Krūmiņa Angelika
Bioinformatics Lab, Riga Stradins University, Riga, Latvia.
COST Action CA18131 - Statistical and Machine Learning Techniques in Human Microbiome Studies, Brussels, Belgium.
Front Microbiol. 2022 Apr 11;13:627892. doi: 10.3389/fmicb.2022.627892. eCollection 2022.
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.
冠状动脉疾病（CAD）是最常见的心血管疾病（CVD），也是全球发病和死亡的主要原因，给社会和卫生系统带来了巨大的社会经济负担。因此，迫切需要及时、准确地识别CAD高危人群。目前大多数CAD风险预测方法基于少数传统风险因素（年龄、性别、糖尿病、低密度脂蛋白和高密度脂蛋白胆固醇、吸烟、收缩压），并且在所有患者群体中预测并不完全准确，因为CAD是一种病因复杂的多因素疾病，被认为是由遗传因素以及众多环境/生活方式因素共同驱动的。饮食是改善生活方式和预防疾病的可改变因素之一。然而，目前肥胖、2型糖尿病（T2D）和CVD/CAD的发病率上升表明，由于个体反应存在显著差异，“一刀切”的方法可能并不有效。最近，肠道微生物群已成为这些差异的一个潜在且此前未被充分探索的因素。因此，将饮食和肠道微生物群信息与基因变异和临床数据有效整合，有望改善CAD风险预测。然而，饮食的高度复杂性以及肠道微生物群的巨大个体差异，在建模饮食-肠道微生物群相互作用并将其整合到CAD风险预测方法中以开发个性化决策支持系统（DSS）时，带来了几个大数据分析挑战。在这方面，人工智能（AI）/机器学习（ML）最近的重新兴起开启了有趣的前景，因为这些方法能够捕捉大量复杂的数据矩阵，纳入它们的相互作用并识别线性和非线性关系。在本综述中，我们考虑：（1）最常用的AI/ML方法及其在CAD风险预测中的不同应用案例；（2）饮食因素的内容、选择及其对CAD风险影响的建模；（3）根据肠道微生物群组成将个体分类为CAD病例与对照；（4）饮食-肠道微生物群相互作用及其对CAD风险影响的建模。最后，我们对将所有这些整合起来以改善CAD风险预测给出了展望。
