Oral microbiome-systemic link studies: perspectives on current limitations and future artificial intelligence-based approaches. (3rd May 2020)

• Record Type: Journal Article
• Authors: Seneviratne, Chaminda Jayampath; Balan, Preethi; Suriyanarayanan, Tanujaa; Lakshmanan, Meiyappan; Lee, Dong-Yup; Rho, Mina; Jakubovics, Nicholas; Brandt, Bernd; Crielaard, Wim; Zaura, Egija
• Is Part Of: Critical reviews in microbiology. Volume 46:Number 3(2020)
• Page Start: 288
• Page End: 299
• Publication Date: 2020-05-03
• Subjects: High-throughput DNA sequencing -- oral health -- saliva -- metagenomics -- deep learning -- machine learning; Microbiology -- Periodicals; Microbiology -- Periodicals; Review Literature -- Periodicals; 579
• DOI: 10.1080/1040841X.2020.1766414
• Languages: English
• ISSNs: 1040-841X

Abstract

Abstract: 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.