The study was approved by the Yamanashi University Ethics Committee (approval No. 1824), the National Institutes of Biomedical Innovation, Health, and Nutrition Ethics Committee (approval No. 169–04), and the Chiyoda Paramedical Care Clinic Ethics Committee (approval No. 15000088). This study was conducted in accordance with the Declaration of Helsinki (2013) and was based on a registered study (UMIN000033479). Cross-sectional data were evaluated from the first year of the study to obtain an exploratory overview of the gut microbiome of a population that consumes barley. Sampling was conducted from August 2018 to March 2019.
We enrolled 272 participants, which were employees of the barley processing company Hakubaku Co., Ltd. Our target sample included at least 100 participants. We excluded those with disorders (Risk 2) and pre-disorders (Risk 1) of diabetes, hypertension, and dyslipidemia from the main analysis. Although gastrointestinal disorders were not included in the exclusion criteria, none of the participants had a history of ulcerative colitis or Crohn’s disease. There were also four participants with a history of irritable bowel syndrome, but none were undergoing treatment. Details of the exclusion criteria for disorders are shown in Table S1 (see Additional file 1). We classified the participants into two groups based on their median barley consumption rate (high, 3.5–28 and low, 0–3.5 g/1000 kcal).
The primary outcome was the association between barley consumption and the alpha-diversity of the microbiome, and the secondary outcome was the abundance of the 50 dominant genera sorted by mean relative abundance. We collected a copy of the participants’ medical check-up results. The participants’ medical check-ups were conducted at different hospitals, and measurements of body measurements, blood pressure, and biochemical markers were obtained. Blood pressure was measured at rest in a sitting position. Blood samples were taken after fasting for more than 7 h and measured with a calibrated measuring device. Body mass index (BMI) was calculated by dividing the weight by the square of the height. Dietary habits other than barley consumption were assessed using a brief self-administered diet history questionnaire (BDHQ; Gender Medical Research, Inc., Tokyo, Japan). Barley consumption (g/1000 kcal) was calculated using a questionnaire and the daily energy value from the BDHQ. Rice bowl size (200, 160, 140, and 100 g), proportion of barley mixed with white rice (0, 5, 10, 15, 30, and 50%), barley-mixed rice consumed per month (0, 0.5, 1, 4, 8, and 16 days/month), and barley consumption rate (g/d) were determined. Medical history, including medication (especially during the month of sampling), and consumption of fermented foods and supplements were determined using questionnaires.
DNA extraction and 16S rRNA gene amplicon sequencing
Fecal samples were collected at home with guanidine thiocyanate (GuSCN) solution, and DNA was extracted and stored at 10–30 °C for up to 30 d . Briefly, 0.2 mL of fecal samples, 0.3 mL of No. 10 lysis buffer (Kurabo Industries Ltd., Osaka, Japan), and 0.5 g of 0.1 mm glass beads (WakenBtech Co., Ltd., Tokyo, Japan) were homogenized using a PS1000 Cell Destroyer (Bio Medical Science, Tokyo, Japan) at 4260 rpm for 50 s at 25 °C. The homogenate was centrifuged at 13000×g for 5 min at 25 °C, and the DNA was extracted from the supernatant using a Gene Prep Star PI-80X automated DNA isolation system (Kurabo Industries Ltd). DNA concentration was determined with the ND-1000 NanoDrop Spectrophotometer (Thermo Fisher Scientific Inc., Waltham, MA, USA). The samples were stored at − 30 °C. The 16S rRNA gene was amplified from fecal DNA and sequenced . The V3–V4 region of the 16S rRNA gene was amplified using the following primers (5′→3′): TCGTCGGCAGCGTCAGATGTGTATAAGCGACAGCCTACGGGNGGCWGCAG and GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC. The DNA library for Illumina MiSeq was prepared using Nextera XT Index Kit v2 Set A (Illumina Inc., San Diego, CA, USA), and its concentration was determined with the QuantiFluor dsDNA System (Promega Corp., Madison, WI, USA). The 16S rRNA gene was sequenced using Illumina MiSeq (Illumina) as described by the manufacturer.
The sequence reads from Illumina MiSeq were analyzed using the Quantitative Insights Into Microbial Ecology (QIIME) software package (version 1.9.1) . We used QIIME Analysis Automating Script (Auto-q)  to proceed from trimming paired-end reads to operational taxonomic unit (OTU) selection. We used open-reference OTUs picked with the UCLUST software against the SILVA (version 128) reference sequence to select OTUs based on sequence similarity (> 97%). The taxonomy (phylum, class, order, family, and genus) and relative abundance were calculated using the SILVA database (version 128) [13, 14]. The intestinal bacteria was compared based on 10,000 randomly selected reads per sample.
Calculation of alpha-diversity
Data were exported as BIOM files and imported into R (version 3.6.0). Diversity was analyzed using the phyloseq R-package. Alpha-diversity indices of observed OTUs, Chao1, Shannon, and Simpson indices were calculated using the estimate_richness function.
Comparison of barley groups
To compare the results of the medical check-ups and dietary habits between the high and low barley groups, we used Student’s t-test. The alpha-diversity and relative abundance of each genus were analyzed using Mann-Whitney U-test. P values were adjusted using false discovery rate (FDR) methods. To confirm the reliability of the analyses, we explored the relationship between barley consumption groups (0 = low and 1 = high) and each bacteria using multiple regression analyses with all participants. We expressly set the amounts of Bifidobacterium, Butyricicoccus, Collinsella, Ruminococcus 2, and Dialister as outcomes. We adjusted the model for age, sex, risk of diabetes, dyslipidemia, and hypertension for model 1. In addition to model 1, we adjusted the model for consumption rate (g/1000 kcal) of cereals, sugar and sweetener, legumes, and beverages for model 2 and for cereals, sugar and sweetener, legumes, beverages, green vegetables, other vegetables, fish, and confectionery for model 3. We used the vif function of the car R-package to evaluate variance inflation factors (VIFs). All VIFs were < 5 and considered acceptable for these analyses .
Principal coordinate analysis of gut bacteria
We classified the participants into enterotypes A, B, and C using the pam function of the cluster R-package. We then summarized the composition of the intestinal bacteria by principal coordinate analysis (PCoA) using the vegdist function of the vegan R-package and the quasieuclid and dudi.pco functions of the ade4 R-package. Data were calculated using the Bray–Curtis method. Subsequently, the environmental factor arrows were fit to the PCoA figure using the envfit function of the vegan R-package. Significant genera were assessed using permutations of environmental variables.
Network analysis of significant bacteria and barley
To visualize the associations between barley and 20 genera selected based on a P < 0.1, we implemented a network analysis. The network is shown with lines of correlation with |r| > 0.15 (Kendall rank correlation tests). Different colors on the plots indicate different community groups. We fit the correlation data frame to the cc.df function of the igraph R-package using the reshape2 R-package and calculated bacteria community groups using the leading.eigenvector.community function. All analyses were carried out in R (version 3.6.0), and tests were two-sided; P < 0.05 was considered significant. All graphs except for those from the network analysis were created with the ggplot2 R-package .