Method for estimating disease risk from microbiome data using structural equation modeling

Abstract

The relationship between the human gut microbiota and disease is of increasing scientific interest. Previous investigations have focused on the differences in intestinal bacterial abundance between control and affected groups to identify disease biomarkers. However, different types of intestinal bacteria may have interacting effects and thus be considered biomarker complexes for disease. To investigate this, we aimed to identify a new kind of biomarker for atopic dermatitis using structural equation modeling (SEM). The biomarkers identified were latent variables, which are complex and derived from the abundance data for bacterial marker candidates. Groups of females and males classified as healthy participants [normal control (NC) (female: 321 participants, male: 99 participants)], and patients afflicted with atopic dermatitis only [AS (female: 45 participants, male: 13 participants)], with atopic dermatitis and other diseases [AM (female: 75 participants, male: 34 participants)], and with other diseases but without atopic dermatitis [OD (female: 1,669 participants, male: 866 participants)] were used in this investigation. The candidate bacterial markers were identified by comparing the intestinal microbial community compositions between the NC and AS groups. In females, two latent variables (lv) were identified; for lv1, the associated components (bacterial genera) were Alistipes, Butyricimonas, and Coprobacter, while for lv2, the associated components were Agathobacter, Fusicatenibacter, and Streptococcus. There was a significant difference in the lv2 scores between the groups with atopic dermatitis (AS, AM) and those without (NC, OD), and the genera identified for lv2 are associated with the suppression of inflammatory responses in the body. A logistic regression model to estimate the probability of atopic dermatitis morbidity with lv2 as an explanatory variable had an area under the curve (AUC) score of 0.66 when assessed using receiver operating characteristic (ROC) analysis, and this was higher than that using other logistic regression models. The results indicate that the latent variables, especially lv2, could represent the effects of atopic dermatitis on the intestinal microbiome in females. The latent variables in the SEM could thus be utilized as a new type of biomarker. The advantages identified for the SEM are as follows: (1) it enables the extraction of more sophisticated information when compared with models focused on individual bacteria and (2) it can improve the accuracy of the latent variables used as biomarkers, as the SEM can be expanded.

A risk estimation method for depression based on the dysbiosis of intestinal microbiota in Japanese patients

“…Tokuno et al. reported a method for estimating disease risk from the intestinal microbiota composition using SEM, in which the characteristic intestinal bacteria in the disease group were used as the observational variables ( 13 ). This study attempted to create models to separately estimate depression risk in males and females using this method.…”

“…A depression risk–estimation model using SEM was constructed according to the method described by Tokuno et al. ( 13 ). The details of its construction are described by Hatayama et al.…”

“…Estimating disease risk based on intestinal microbiota composition using structural equation modeling (SEM) has been previously reported. Risk-estimation methods have been developed for atopic dermatitis and mild cognitive impairment based on their association with intestinal microbiota ( 13 , 14 ). Therefore, this method could provide a risk-estimation model for depression which could subsequently be a promising predictive method for depression if indeed the risk of depression is associated with intestinal microbiota.…”

Effects of bathing in different hot spring types on Japanese gut microbiota

“…The fecal samples were sent to Symbiosis Solutions Inc. (Tokyo, Japan) without any temperature control after being suspended in a guanidine thiocyanate solution [100 mM Tris–HCL (pH 8.0), 40 mM Tris–EDTA (pH 9.0), 4 M guanidine thiocyanate, 0.001% bromothymol blue]. Symbiosis Solutions Inc. performed the DNA extraction, sequencing, 16S rRNA data analysis, and occupancy calculations for each bacteria according to previously reported methods 29 . The gut microbiota were identified at the genus level and analyzed to determine the 20 most common genera in all participants.…”

Characteristics of Intestinal Microbiota in Japanese Patients with Mild Cognitive Impairment and a Risk-Estimating Method for the Disorder

“…To estimate the risk of disease from intestinal microbiota using structural equation modeling (SEM), we followed the method reported by Tokuno et al [20] by constructing MCI risk estimation models using R v.4.2.2. The intestinal microbiota data were CLRtransformed using the aldex.clr function in the R v. 4.1.0 package ALDEx2 v. 1.24.0.…”