Dimension reduction of microbiome data linkedBifidobacteriumto allergic rhinitis

Abstract

AbstractDimension reduction has been used to visualise the distribution of multidimensional microbiome data, but the composite variables calculated by the dimension reduction methods have not been widely used to investigate the relationship of the human gut microbiome with lifestyle and disease. In the present study, we applied several dimension reduction methods, including principal component analysis (PCA), principal coordinate analysis (PCoA), non-metric multidimensional scaling (NMDS), and non-negative matrix factorization (NMF), to a microbiome dataset from 186 subjects with symptoms of AR and 106 controls. All the dimension reduction methods supported that the enterotype clusters were overlapped in the dimension reduction plots, and that the distribution of microbial data points appeared to be continuous rather than discrete. Comparison of the composite variables calculated from the different dimension reduction methods showed that the characteristics of the composite variables differed between the dimension reduction methods. The second composite variable calculated from PCoA was significantly associated with the intake of several nutrients, including omega-3 polyunsaturated fatty acids, and the risk of AR. The composite variable was also correlated with the relative abundance ofBifidobacterium, and thus,Bifidobacteriumwas related to the risk of AR and intake of several nutrients through dimension reduction. Our results highlight the usefulness of the dimension reduction methods for investigating the association of microbial composition with lifestyle and disease in clinical research.