TY - JOUR
T1 - Classification of the Occurrence of Dyslipidemia Based on Gut Bacteria Related to Barley Intake
AU - Maruyama, Satoko
AU - Matsuoka, Tsubasa
AU - Hosomi, Koji
AU - Park, Jonguk
AU - Nishimura, Mao
AU - Murakami, Haruka
AU - Konishi, Kana
AU - Miyachi, Motohiko
AU - Kawashima, Hitoshi
AU - Mizuguchi, Kenji
AU - Kobayashi, Toshiki
AU - Ooka, Tadao
AU - Yamagata, Zentaro
AU - Kunisawa, Jun
N1 - Funding Information:
The department of ZY has received research grants for other studies from Hakubaku Co., Ltd. SM, TM, MN, and TK are employees of Hakubaku Co., Ltd. The authors declare that this study received funding from Hakubaku Co., Ltd. The funder had the following involvement in the study: conceptualization, project administration, investigation, writing original draft, revising, and editing.
Funding Information:
This study was supported by funding from Hakubaku Co., Ltd., the Japan Agency for Medical Research and Development (AMED), Grant Number JP20gm1010006h004, and the Ministry of Health and Welfare of Japan and Public/Private R&D In-vestment Strategic Expansion PrograM: PRISM, Grant Number 20AC5004.
Publisher Copyright:
Copyright © 2022 Maruyama, Matsuoka, Hosomi, Park, Nishimura, Murakami, Konishi, Miyachi, Kawashima, Mizuguchi, Kobayashi, Ooka, Yamagata and Kunisawa.
PY - 2022/3/24
Y1 - 2022/3/24
N2 - Barley is a grain rich in β-glucan, a soluble dietary fiber, and its consumption can help maintain good health and reduce the risk of metabolic disorders, such as dyslipidemia. However, the effect of barley intake on the risk of dyslipidemia has been found to vary among individuals. Differences in gut bacteria among individuals may be a determining factor since dietary fiber is metabolized by gut bacteria and then converted into short-chain fatty acids with physiological functions that reduce the risk of dyslipidemia. This study examined whether gut bacteria explained individual differences in the effects of barley intake on dyslipidemia using data from a cross-sectional study. In this study, participants with high barley intake and no dyslipidemia were labeled as “responders” to the reduced risk of dyslipidemia based on their barley intake and their gut bacteria. The results of the 16S rRNA gene sequencing showed that the fecal samples of responders (n = 22) were richer in Bifidobacterium, Faecalibacterium, Ruminococcus 1, Subdoligranulum, Ruminococcaceae UCG-013, and Lachnospira than those of non-responders (n = 43), who had high barley intake but symptoms of dyslipidemia. These results indicate the presence of certain gut bacteria that define barley responders. Therefore, we attempted to generate a gut bacteria-based responder classification model through machine learning using random forest. The area under the curve value of the classification model in estimating the effect of barley on the occurrence of dyslipidemia in the host was 0.792 and the Matthews correlation coefficient was 0.56. Our findings connect gut bacteria to individual differences in the effects of barley on lipid metabolism, which could assist in developing personalized dietary strategies.
AB - Barley is a grain rich in β-glucan, a soluble dietary fiber, and its consumption can help maintain good health and reduce the risk of metabolic disorders, such as dyslipidemia. However, the effect of barley intake on the risk of dyslipidemia has been found to vary among individuals. Differences in gut bacteria among individuals may be a determining factor since dietary fiber is metabolized by gut bacteria and then converted into short-chain fatty acids with physiological functions that reduce the risk of dyslipidemia. This study examined whether gut bacteria explained individual differences in the effects of barley intake on dyslipidemia using data from a cross-sectional study. In this study, participants with high barley intake and no dyslipidemia were labeled as “responders” to the reduced risk of dyslipidemia based on their barley intake and their gut bacteria. The results of the 16S rRNA gene sequencing showed that the fecal samples of responders (n = 22) were richer in Bifidobacterium, Faecalibacterium, Ruminococcus 1, Subdoligranulum, Ruminococcaceae UCG-013, and Lachnospira than those of non-responders (n = 43), who had high barley intake but symptoms of dyslipidemia. These results indicate the presence of certain gut bacteria that define barley responders. Therefore, we attempted to generate a gut bacteria-based responder classification model through machine learning using random forest. The area under the curve value of the classification model in estimating the effect of barley on the occurrence of dyslipidemia in the host was 0.792 and the Matthews correlation coefficient was 0.56. Our findings connect gut bacteria to individual differences in the effects of barley on lipid metabolism, which could assist in developing personalized dietary strategies.
KW - barley
KW - dyslipidemia
KW - gut bacteria
KW - machine learning
KW - responder
UR - http://www.scopus.com/inward/record.url?scp=85128305874&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85128305874&partnerID=8YFLogxK
U2 - 10.3389/fnut.2022.812469
DO - 10.3389/fnut.2022.812469
M3 - Article
AN - SCOPUS:85128305874
SN - 2296-861X
VL - 9
JO - Frontiers in Nutrition
JF - Frontiers in Nutrition
M1 - 812469
ER -