Singapore Oral Microbiomics Initiative (SOMI), National Dental Research Institute Singapore, National Dental Centre Singapore, Duke NUS Medical School, Singapore, Singapore.
Bioprocessing Technology Institute (BTI), ASTAR - Agency for Science, Technology and Research, Singapore, Singapore.
Crit Rev Microbiol. 2020 May;46(3):288-299. doi: 10.1080/1040841X.2020.1766414. Epub 2020 May 21.
In the past decade, there has been a tremendous increase in studies on the link between oral microbiome and systemic diseases. However, variations in study design and confounding variables across studies often lead to inconsistent observations. In this narrative review, we have discussed the potential influence of study design and confounding variables on the current sequencing-based oral microbiome-systemic disease link studies. The current limitations of oral microbiome-systemic link studies on type 2 diabetes mellitus, rheumatoid arthritis, pregnancy, atherosclerosis, and pancreatic cancer are discussed in this review, followed by our perspective on how artificial intelligence (AI), particularly machine learning and deep learning approaches, can be employed for predicting systemic disease and host metadata from the oral microbiome. The application of AI for predicting systemic disease as well as host metadata requires the establishment of a global database repository with microbiome sequences and annotated host metadata. However, this task requires collective efforts from researchers working in the field of oral microbiome to establish more comprehensive datasets with appropriate host metadata. Development of AI-based models by incorporating consistent host metadata will allow prediction of systemic diseases with higher accuracies, bringing considerable clinical benefits.
在过去的十年中，关于口腔微生物组与全身疾病之间联系的研究有了巨大的增长。然而，研究设计和混杂变量的变化往往导致研究结果不一致。在这篇叙述性综述中，我们讨论了研究设计和混杂变量对当前基于测序的口腔微生物组-全身疾病关联研究的潜在影响。本文还讨论了当前关于 2 型糖尿病、类风湿关节炎、妊娠、动脉粥样硬化和胰腺癌的口腔微生物组与全身疾病关联研究的局限性，并就人工智能（AI），特别是机器学习和深度学习方法，如何用于从口腔微生物组预测全身疾病和宿主元数据提出了看法。为了从口腔微生物组预测全身疾病和宿主元数据，需要建立一个具有微生物组序列和注释宿主元数据的全球数据库存储库。然而，这一任务需要从事口腔微生物组领域研究的研究人员共同努力，建立具有适当宿主元数据的更全面的数据集。通过纳入一致的宿主元数据，开发基于 AI 的模型将允许更准确地预测全身疾病，带来可观的临床效益。
