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. 2023 May 9;11(5):1246.
doi: 10.3390/microorganisms11051246.

Characteristic Gut Bacteria in High Barley Consuming Japanese Individuals without Hypertension

Satoko Maruyama  1   2 , Tsubasa Matsuoka  1   2   3 , Koji Hosomi  2 , Jonguk Park  4 , Mao Nishimura  1   2 , Haruka Murakami  5 , Kana Konishi  5 , Motohiko Miyachi  5 , Hitoshi Kawashima  4 , Kenji Mizuguchi  4   6 , Toshiki Kobayashi  1 , Tadao Ooka  3 , Zentaro Yamagata  3 , Jun Kunisawa  2   7   8   9   10   11   12   13
Affiliations

Characteristic Gut Bacteria in High Barley Consuming Japanese Individuals without Hypertension

Satoko Maruyama et al. Microorganisms. .

Abstract

Background: Barley, a grain rich in soluble dietary fiber β-glucan, is expected to lower blood pressure. Conversely, individual differences in its effects on the host might be an issue, and gut bacterial composition may be a determinant.

Methods: Using data from a cross-sectional study, we examined whether the gut bacterial composition could explain the classification of a population with hypertension risks despite their high barley consumption. Participants with high barley intake and no occurrence of hypertension were defined as "responders" (n = 26), whereas participants with high barley intake and hypertension risks were defined as "non-responders" (n = 39).

Results: 16S rRNA gene sequencing revealed that feces from the responders presented higher levels of Faecalibacterium, Ruminococcaceae UCG-013, Lachnospira, and Subdoligranulum and lower levels of Lachnoclostridium and Prevotella 9 than that from non-responders. We further created a machine-learning responder classification model using random forest based on gut bacteria with an area under the curve value of 0.75 for estimating the effect of barley on the development of hypertension.

Conclusions: Our findings establish a link between the gut bacteria characteristics and the predicted control of blood pressure provided by barley intake, thereby providing a framework for the future development of personalized dietary strategies.

Keywords: barley; gut bacteria; hypertension; machine learning; stratification.

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Conflict of interest statement

The department of Z.Y. has received research grants for other studies from Hakubaku Co., Ltd. S.M., T.M., M.N. and T.K. are employees of Hakubaku Co., Ltd.

Figures

Figure 1
Figure 1
Flowchart of the recruitment and selection of participants for this study.
Figure 2
Figure 2
Comparison of the gut microbiome composition in the responders (n = 39), non-responders (n = 26), and low barley group (n = 65). (a) Scatter plots of PCo1 and PCo2. (b) Comparison of PCo1. (c) Comparison of PCo2. PCoA of the gut microbiome was based on an abundance of 266 genera. PCo1 and PCo2 between groups were compared using the Mann–Whitney U-test.
Figure 3
Figure 3
Random forest classification model generated based on 64 genera included in the training data set. (a) ROC curve and AUC of the microbiome-based model for the discrimination between the responders and the non-responders. (b) Top 20 genera of intestinal bacteria (explanatory variables), sorted by importance. Dots show the importance of each intestinal bacterium.
See this image and copyright information in PMC

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