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

Introduction: Early detection of depression is important for preventing depression-related suicides and reducing the risk of recurrence. This study explored the association between depression and intestinal microbiota and developed a depression risk-estimation method based on this. Methods: The intestinal microbiota of Japanese patients with depression (33 males and 35 females) and disease-free controls (246 males and 384 females) in their 20's to 60's were compared by sex using 16S rRNA gene amplicon sequencing. A depression-risk estimation method was developed using structural equation modeling. Results: Intestinal bacteria taxa that differed between depression and control groups were identified based on effect size (absolute value greater than 0.2). Neglecta was more abundant, while Coprobacter, Butyricimonas, Clostridium_XlVb, and Romboutsia were less abundant in the male depression group compared to the male control group. In the female depression group, Massilimicrobiota, Merdimonas, and Sellimonas were more abundant, whereas Dorea and Agathobacter were less abundant compared to the female control group. Several of the intestinal bacterial taxa that were less abundant in depression were associated with butyrate or hydrogen production. Using these depression-associated intestinal bacteria as indicators, risk-estimation models using structural equation modeling for depression were developed. In the risk-estimation models for males and females, the areas under the receiver operating characteristic curve were 0.72 and 0.70, respectively, indicating that these models can distinguish between individuals with and without depression. Conclusions: This study provides insights into depression etiology and aids in its early detection and treatment.

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

Intestinal microbiota may play a significant role in the development and progression of mild cognitive impairment (MCI). In addition, sex differences in the prevalence of MCI and intestinal microbiota are likely to exist. Therefore, this study investigated the association between MCI and intestinal microbiota by comparing Japanese patients in their 70s with MCI (11 males and 18 females) and disease-free controls (17 males and 23 females), taking sex into account. In both sexes, Clostridium_XVIII, Eggerthella, Erysipelatoclostridium, Flavonifractor, and Ruminococcus 2 were the more abundant taxa in the MCI group, whereas Megasphaera, Oscillibacter, Prevotella, Roseburia, and Victivallis were less abundant. Based on these characteristics, it was hypothesized that the composition of the intestinal microbiota in the MCI group leads to dysregulation of the intestinal microbiota, increased intestinal and blood-brain barrier permeability, and increased chronic neuroinflammation, with the long-term persistence of these abnormalities ultimately leading to cognitive decline. Furthermore, risk estimation models for MCI based on intestinal microbiota data were developed using structural equation modeling. These tests discriminated between the MCI and control groups. Incorporating these factors into intestinal microbiota testing using stool samples may be an efficient method to screen individuals with MCI.

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

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.

Characteristics of the Gut Microbiota in Japanese Patients with Premenstrual Syndrome.

Purpose: The present study aimed to characterize the gut microbiota of individuals with premenstrual syndrome. Patients and Methods: The gut microbiota of 24 Japanese women with PMS (PMS group) and 144 healthy Japanese women (control group) were compared. Analysis of the α- and ß-diversities and the gut microbial composition at the genus level were performed using 16S rRNA gene sequence data obtained from stool samples. Results: A significant difference in age was observed between the PMS and control groups; however, no significant difference was observed in BMI. The α-diversity measured using the Simpson index was significantly higher in the PMS group than the control group. Visualization of the ß-diversity using non-metric multidimensional scaling and permutational multivariate analysis of variance (PERMANOVA) showed that the distance of the gut microbiota between the PMS and control groups is significantly different. Furthermore, a significant difference in the composition of the gut microbiota was observed between the PMS and control groups. At the genus level, the abundances of Collinsella, Bifidobacterium, and Blautia were significantly higher in the PMS group than in the control group. In particular, the abundance of Collinsella in the PMS group was approximately 4.5 times higher than that in the control group. To rule out the confounding effect of age in the abundances of Bifidobacterium, Blautia, and Collinsella, the gut microbiota of the PMS and control groups were compared by age group. Results showed that Collinsella had the highest effect size in participants of 30-40 years of age (mean age: 36.39 ± 4.68 years). Conclusion: These results suggest that the PMS group possesses a characteristic gut microbiota. In particular, Collinsella was strongly associated with PMS. Since Collinsella has been reported to be associated with diet, dietary interventions such as prebiotics targeting Collinsella may be effective in preventing, improving, and alleviating PMS.

Fluctuations in Intestinal Microbiota Following Ingestion of Natto Powder Containing Bacillus subtilis var. natto SONOMONO Spores: Considerations Using a Large-Scale Intestinal Microflora Database.

Improving the intestinal microbiota using probiotics, prebiotics, and synbiotics has attracted attention as a method of disease prevention and treatment. This is the first study to discuss the effects of food intake on the intestinal microbiota using a large Japanese intestinal microbiota database. Here, as a case study, we determined changes in the intestinal microbiota caused by ingestion of a processed natto food containing B. subtilisvar. natto SONOMONO spores, SONOMONO NATTO POWDER CAPSULESTM, by analyzing 16S rRNA sequence data generated using next-generation sequencing techniques. The results showed that the relative abundance of Bifidobacterium and Blautia as well as the relative abundance of Bifidobacterium were increased in males and females in the ingesting group, respectively. Additionally, the effects of SONOMONO NATTO POWDER CAPSULESTM intake on Bifidobacterium and Blautia abundance depended on the relative abundance of Bifidobacterium at baseline. Finally, analysis of a large Japanese intestinal microbiota database suggested that the bacterial genera that fluctuated with the ingestion of SONOMONO NATTO POWDER CAPSULESTM may be associated with lifestyle-related diseases such as diabetes.