Nutrition Research 103 (2022) 68–81

Available online at www.sciencedirect.com

journal homepage: www.elsevier.com/locate/NTR

Fermented brown rice beverage distinctively
modulates the gut microbiota in Okinawans withmetabolic syndrome: A randomized controlled trial
Yukari Akamine a,1, Jasmine F. Millman a,1, Tsugumi Uema a,1, Shiki Okamoto a,∗,Masato Yonamine b, Moriyuki Uehara a, Chisayo Kozuka c, Tadashi Kaname d,Michio Shimabukuro e, Kozen Kinjo f, Masayo Mitsuta g, Hirosuke Watanabe h,
Hiroaki Masuzaki a,∗a Division of Endocrinology, Diabetes and Metabolism, Hematology, Rheumatology (Second Department of Medicine), Graduate School of

Medicine, University of the Ryukyus, Okinawa, Japanb Laboratory of Laboratory/Sports Medicine, Division of Clinical Medicine, Faculty of Medicine, University of Tsukuba, Tennodai, Tsukuba,
Ibaraki, Japanc YCI Laboratory for Metabolic Epigenetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japand Department of Genome Medicine, National Center for Child Health and Development, Tokyo, Japane Department of Diabetes, Endocrinology and Metabolism School of Medicine, Fukushima Medical University, Fukushima, Japanf Okinawa Health Promotion Foundation, Okinawa, Japang Aizu Tenpo Co. Ltd, Fukushima, Japanh Industry-Academia Collaboration, Japan Science and Technology Agency (JST), Fukushima, Japana r t i c l e

i n f oa b s t r a c t

Article history:

Accumulating evidence to date suggests that brown rice is superior to white rice in regard to

Received 14 December 2021

its beneficial impact on a number of risk factors of the metabolic syndrome (MetS). However,

Revised 23 March 2022

little is known about the influence of fermented brown rice beverage on the gut microbiota

Accepted 26 March 2022

in humans. We therefore hypothesized that its impact would beneficially alter the gut microbiota composition of patients with MetS. Using a 4-week randomized, single-arm study
design, subjects (n = 40) were advised to consume a daily fermented brown rice beverage

Keywords:(BA) or fermented white rice beverage (WA) as a replacement of their main meal. Clinical and

Brown riceanthropometric measurements as well as fecal samples were collected at baseline and im-

Fermented beverage

mediately after completion of the intervention. Gut microbiota was analyzed using 16S ribo-

Gut microbiota

somal RNA sequencing and capillary electrophoresis-time-of-flight mass spectrometry was

Short chain fatty acids

used to measure plasma short-chain fatty acids. Interestingly, ingestion of BA in contrast to

Metabolic syndrome

WA resulted in a unique elevation in the abundance of number of beneficial species belong-

Obesity syndrome

ing to the Clostridia class, associated with reduced inflammation, and increased short-chain

Abbreviations: BA, fermented brown rice beverage; CE-TOFMS, capillary electrophoresis-time-of-flight mass spectrometry; HbA1c,
hemoglobin A1c; HDL-C, high-density lipoprotein cholesterol; HMT, Human Metabolome Technologies; LDA, linear discriminant analysis;
LDL-C, low-density lipoprotein cholesterol; LEfSe, linear discriminant analysis effect size; MetS, metabolic syndrome; rRNA, ribosomal
RNA; SEM, standard error of the mean; TG, triglycerides; T2DM, type 2 diabetes mellitus; SCFAs, short-chain fatty acids; WA, fermented
white rice beverage.
∗
Corresponding authors at: 207 Uehara, Nishihara, Okinawa, 903-0215, Japan. Tel: +81-98-895-1146, Fax: +81-98-895-1415.
E-mail addresses: shiki@med.u-ryukyu.ac.jp (S. Okamoto), hiroaki@med.u-ryukyu.ac.jp (H. Masuzaki).
1
These authors contributed equally to this work.
https://doi.org/10.1016/j.nutres.2022.03.013
0271-5317/© 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license(http://creativecommons.org/licenses/by/4.0/)

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Nutrition Research 103 (2022) 68–81

fatty acid production: Lactobacillales bacterium DJF B280 (P = .005), Butyrate producing bacterium
A2 207 (P = .012), and Firmicutes bacterium DJF VP44 (P = .038). This study demonstrates that
consumption of BA is effective to beneficially modulate the gut microbiota compared withWA in patients with MetS.
© 2022 The Author(s). Published by Elsevier Inc.
This is an open access article under the CC BY license(http://creativecommons.org/licenses/by/4.0/)

1.

Introduction

Consumption of whole grains, which contain the bran andgerm and are rich in fiber, micronutrients, and phytochemicals, are inversely associated with conditions of the metabolic
syndrome (MetS) [1], a clustering of cardiometabolic risks that
increase the overall likelihood of chronic disease development [2]. Brown rice, a whole grain high in fiber and phytosterols such as γ -oryzanol and once widely consumed by
Asian population groups such as the Japanese, has demonstrated blood glucose and lipid-lowering effects as well as
anti-inflammatory and antiobesity activities in both humans
and animals [3]. Data obtained from Health Professionals
Follow-up Study and the Nurses’ Health Study I and II revealed
that a higher intake of brown rice was associated with a lower
risk of type 2 diabetes mellitus (T2DM), whereas white rice wasassociated with an increased risk [4]. Interestingly, the same
researchers reported that by substituting 50 g/d of white ricefor brown rice, T2DM risk was lowered by 16% [5]. In Japanese
subjects, twice-daily brown rice consumption has been shown
to improve glycemic control as indicated by significant reductions in postprandial blood glucose, hemoglobin A1c (HbA1c),and glycoalbumin levels [6]. Our team also found that daily
ingestion of brown rice for 8 weeks in subjects with MetS in
Okinawa, the Southern tip of Japan, was able to significantly
lower postprandial blood glucose levels and protect endothelial function [7]. Regarding some of the possible mechanisms
at play, research in our laboratory has shown that the bioactive
component, specifically and abundantly contained in brown
rice, γ -oryzanol, can reduce endoplasmic reticulum stress inthe hypothalamus [8], protect pancreatic β cells against apoptosis [9], and act as an inhibitor of DNA methyltransferases inthe brain reward system in mice [10].
Diet and its impact on the gut microbiota have been recognized as significant contributors in the pathogenesis of MetS
and associated chronic diseases [11]. Diets rich in whole grains
have been shown to exert positive effects on the gut microbiota, most notably by increasing microbial diversity and promoting growth of microbial species that can ferment fiber,
producing short-chain fatty acids (SCFAs) [12]; which play diverse roles in appetite and body weight regulation as well as
reducing inflammation and aiding gut barrier maintenance[13, 14]. Even short-term consumption of whole grains as partof a daily diet results in observable positive effects on the gut
microbiota, SCFAs, and a number of markers of immune andgut-mediated inflammation [15, 16]. Notably, habitual brownrice consumption in Japanese adults led an increased abundance of Faecalibacterium prausnitzii, a well-known producer of
the SCFA butyrate and member of the Clostridia class, a groupof bacteria known for their therapeutic and anti-inflammatory
effects [17, 18].
In Japan, much of the good health and longevity experienced is thought to be related to the traditional Japanese diet
(Washoku) [19], which is low in calories and nutrient dense,with meals centered around rice as the staple carbohydrate,
offering a diverse range of fermented foods such as miso,
which are fermented with the fungus Aspergillus oryzae (koji)[20]. Notably, amazake, a traditional Japanese fermented rice
beverage made using rice koji, contains B vitamins and number of bioactive substances, with some research suggesting favorable effects on metabolic parameters [21, 22]. As a result of
rapid Westernization and complex historical factors, Okinawa
currently has one of the highest rates of obesity and T2DM in
Japan, along with a concomitant decrease in consumption ofbrown rice and Amazake [23]. However, little is known about
the impact of traditional foods such as brown rice or amazakeon the gut microbiota in relation to metabolic health, highlighting the need for studies that contribute to a better understanding of the relationship between diet and the gut microbiota, and therapeutic application of food in disease. The aimof this study was therefore to explore the impact of the fermented brown rice beverage (amazake) on the gut microbiota
and major microbial metabolites in Okinawan subjects with
MetS. We hypothesized that acute consumption of brown riceamazake, in contrast to white rice amazake, would result in
an increased abundance of beneficial bacteria and potentially,
plasma SCFA levels, supporting gut health and likely improving aspects of MetS.

2.

Materials and Methods

2.1.

Subjects

This study targeted outpatients with the MetS that visited the
Okinawa Health Promotion Foundation from 1 June 2014 to
31 May 2015. Inclusion criteria were as follows; body mass index  25 and any 2 of the following cardiometabolic risk factors: (1) hypertriglyceridemia (serum triglyceride [TG] concentration  150 mg/dL; (2) low serum high-density lipoproteincholesterol (HDL-C) (HDL-C concentration <40 mg/dL; (3) elevated blood pressure (systolic blood pressure 130 mm Hg
and/or diastolic blood pressure 85 mm Hg); or (4) high fasting glucose (serum glucose concentration 100 mg/dL). Exclusion criteria were as follows: (1) subjects taking medications
that may influence study results (including antidiabetic medications known to impact gut microbiota such as metformin);(2) infectious disease or history of recent operation or injury;

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Nutrition Research 103 (2022) 68–81

Fig. 1 – Flow diagram depicting the study design.

(3) pregnant or nursing women; and (4) severe renal or liver
dysfunction.
The present study was conducted according to the guidelines laid down in the Declaration of Helsinki, and all procedures involving human participants were approved by the
Ethics Committee for the “Okinawa Health Promotion Foundation” (approved at 20140529) and registered at the University Hospital Medical Information Network Clinical Trials Registry (UMIN-CTR registration no. UMIN000017485). Written informed consent was obtained from all participants. All subjects were obliged to report any serious or unexpected adverse
events as well as declare any relations to those included in theintervention immediately to either the principal investigator
and/or research ethics committee.

2.2.

Study design

This study was a single arm, 4-week randomized intervention trial with a total of 40 participants recruited for the study(Fig. 1). Of the 120 outpatients assessed for the study, 4 were
excluded because of moderate or severe liver dysfunction and
76 did not meet the criteria for the MetS. Once selected, subjects were randomly allocated to 1 of 2 groups; receiving a 350g pack of either white rice amazake (WA) (n = 19; 11 males and8 females), or brown rice amazake (BA) (n = 21; 9 males and 12

females), and advised to consume this beverage in replace of
their main meal, once per day for 4 weeks, with the amountof test beverage based on findings from our previous work [7].
Anthropometric and clinical examinations were conducted atbaseline (week 0), as well as blood samples and fecal (stool)
samples collected at baseline (week 0) and at the end of theintervention period (week 4) to assess the impact on gut microbiota composition (primary outcome). A total of 3 plasma
samples (for SCFA analysis) and 2 fecal samples from the WA
group, as well as 2 plasma samples and 4 fecal samples fromthe BA group, were unable to be collected/processed.

2.3.

Formulation of WA and BA

White rice and brown rice amazake were developed and commercialized by the Aizu Tempo Co. (Fukushima, Japan) for use
in the present study. Using either white rice (for formulation ofWA) or brown rice (for formulation of BA), rice was first cracked
on its surface physically and then washed with water at 10
to 15◦ C and immersed for 12 to 18 hours. After immersion,
the rice was then steamed at 0.36 to 0.48 MPa (90-110◦ C), for
50 to 90 minutes. Fermentation was then carried out by anautomatic apparatus (stainless steel, made by YAEGAKI Food
& System, Inc., Japan). The apparatus was subsequently filledwith steamed rice and Aspergillus oryzae and kept at 33 to 35◦ CNutrition Research 103 (2022) 68–81

Table 1 – Nutrient composition of experimental beverages
Nutrient

WA

BA

Energy, kcal
Protein, gFat, gCarbohydrate, g
Total fiber, gγ -oryzanol, mg

315
4.9
0.7
72.1
1.1
1.8

340
6
3.2
71.8
3.2
32

Per 350-g pack.
BA, brown rice amazake; WA, white rice amazake.

for 41 to 46 hours. A total of 70 to 160 L of water was then put
into the mixture apparatus (stainless steel, made by YAESU
Co., Ltd., Japan), and heated to 75 to 85◦ C with 30 to 60 kg
steamed rice. At 50 to 70◦ C, 40 to 80 kg of rice koji was thenadded into the mixture apparatus, mixed, and equalized. The
mixture was then maintained at 50 to 70◦ C for 6 to 12 hours
to enable the rice starch granules to be converted to glucose.
Finally, the undiluted, fermented white or brown rice beverages (WA or BA) were prepared and packaged, ready for consumer use. Table 1 shows the nutritional composition of thetest drinks.

2.4.

Measurement of biochemical parameters

Fasting blood samples were collected from participants atbaseline (week 0) and at the end of the intervention (last day
of week 4). Samples were collected in either heparin (for subsequent SCFA analysis) or ethylene-diamine-tetra-acetic acidcontaining tubes and then centrifuged at 3300 rpm for 10 minutes to obtain plasma and serum and sent to SRL Laboratories
for analyses. Using an automatic analyzer JCA-BM8000 series(Japan Electronics Co., Japan), 0.5 mL of serum was analyzed tomeasure total cholesterol, low-density lipoprotein cholesterol(LDL-C), HDL-C, and TG, and using an automatic analyzer JCABM9000 series (Japan Electronics Co., Japan), 0.5 mL of sodium
fluoride plasma was analyzed to measure blood glucose levels. Insulin was measured by analyzing 0.4 mL of serum using
the fully automated chemiluminescent enzyme immunoassay system Lumipulse series (Fujirebio Co., Ltd.) HbA1c wasmeasured by analyzing 2.0 mL of blood (with sodium fluoride)using the HLC-723G8.

2.5.

Analyses of plasma SCFAs

The SCFAs were measured using capillary electrophoresistime-of-flight mass spectrometry (CE-TOFMS). Metabolites
were first extracted by adding 50 μL of heparinized plasma
to 450 μL methanol solution and stirred for about 10 seconds. To this, 500 μL of chloroform and 200 μL of Milli-Q water
were added, stirred, and the centrifuged (2300×g, 4°C, 5 minutes). After centrifugation, the aqueous layer was transferred
to one 400-μL ultrafiltration tube (Ultra-Free MC PLHCC, Human Metabolome Technologies [HMT], Japan) and then centrifuged (9100×g, 4°C, 120 minutes) and subjected to ultrafiltration. The filtrate was dried and solidified using a decompression dryer and then dissolved in 50 μL of Milli-Q water.
SCFA measurements were the carried out through a facility

71

service at HMT. Briefly, CE-TOFMS analysis was carried out using an Agilent CE capillary electrophoresis system equippedwith an Agilent 6210 time-of-flight mass spectrometer (Agilent Technologies, Inc., Santa Clara, CA, USA). The systems
were controlled by Agilent G2201AA ChemStation software
version B.03.01 for CE (Agilent Technologies) and MassHunterfor LC (Agilent Technologies). The spectrometer was scanned
from m/z 50 to 1000 and peaks were extracted using MasterHands, an automatic integration software (Keio University,
Tsuruoka, Yamagata, Japan), to obtain peak information including m/z, peak area, and migration time for CE-TOFMS[24]. Signal peaks corresponding to isotopomers, adduct ions,and other product ions of known metabolites were excluded,
and the remaining peaks were annotated according to HMT’s
metabolite database based on their m/z values and migration
times. Areas of the annotated peaks were then normalized tointernal standards (L-Methionine Sulfone for cationic metabolites and D-Camphor-10-sulfonic for anionic metabolites) andsample amount to obtain relative levels of each of the SCFAs.
Measurement was performed assuming 100% uniformity withrecovery efficiency and reproducibility of this method being
previously reported and confirmed [25].
Plasma levels of acetate were measured using liquid chromatography tandem mass spectrometry as previously described [26], with acetic acid-d4 (FUJIFILM Wako Pure Chemical Corporation, Osaka, Japan) used as an internal standard.
Samples were first deproteinized and acetate was derived using 2-nitrophenylhydrazine, derivatives extracted by methyl
tert-butyl ether and then injected into the liquid chromatography tandem mass spectrometry system using ACQUITY UPLC
system (Waters, Milford, MA, USA), with an analytical column(AQUITY HSS T3 2.1 × 150 mm, 1.8 μm, Waters). Electrosprayionization was carried out with the API4000 (AB Sciex, Foster
City, CA, USA) operating in negative ionization and multiple
reaction-monitoring mode. The multiple reaction-monitoringtransitions labelled for acetic acid and acetic acid-d4 were m/z
194-164 and m/z 197-93, respectively.

2.6.

DNA extraction and microbiota analysis

After collection from subjects at baseline and at theend of the intervention, stool samples were stored at 80°C until DNA extraction was performed. Briefly, microbial DNA was then isolated from fecal contents using QIAamp Fast DNA Stool Mini Kit (QIAGEN, Tokyo, Japan) as
per the manufacturer’s instructions. Polymerase chain reaction amplification was then performed targeting the V1V2 region; forward primer (CCATCTCATCCCTGCGTGTCTCCGACTCAGNNNNNNNNNNagrgtttgatymtggctcag) and reverse
primer (CCTATCCCCTGTGTGCCTTGGCAGTCTCAGtgctgcctcccgtaggagt) containing the GS Junior adapter sequences A andB, respectively, and a unique 10-bp barcode sequence for forward primer for each sample. Polymerase chain reaction amplification for 20 ng of extracted DNA was performed using
Tks Gflex DNA Polymerase (Takara Bio, Inc., Shiga, Japan) under conditions of 1 minutes at 94°C, 10 cycles of 96°C for 10
seconds, 55°C for 15 seconds, and 68°C for 60 seconds, plus 25
cycles of 96°C for 10 seconds, 60°C for 15 seconds, and 68°C
for 60 seconds. The amplification gave approximately 370-bp
products, confirmed by agarose gel electrophoresis. Then, the72

Nutrition Research 103 (2022) 68–81

products were purified by AMPure XP magnetic beads (Beckman Coulter, Inc., CA, USA) and quantified using the PicoGreen
dsDNA Assay kit (Thermo Fisher Scientific). Equal amounts ofproducts were mixed, and sequencing was performed according to the manufacturer’s instructions (GS Junior: Roche, Basel,
Switzerland) as previously reported [27] with some modifications. An original pipeline was used for 16S ribosomal RNA(rRNA) analysis, which was constructed and provided by Kim
et al. [27] and was built into the GS Junior PC (CentOS 6). The
reference sequences for 16S rRNA analysis were created from
2 databases, the bacterial 16S rRNA sequences from the Ribosomal Database Project and the bacterial genome sequences
obtained from the National Center for Biotechnology Information FTP site. The 16SrRNA sequences pyrosequenced by GS
Junior, were filtered by QV (>25), amplicon length of V1-V2 region (>250 bp), and removed homopolymers (>7 bp). Read sequences with a BLAST match length of less than 90% to thereference sequences were also removed to eliminate the possibility of chimeric sequences. Reads that passed the above filters were clustered using the UCLUST program with a cutoff of
96% pairwise identity to produce operational taxonomic units.
Representative sequences of each operational taxonomic unit
were mapped by BLAST search to the reference sequenceswith a 96 % pairwise-identity cutoff and assigned to bacterial
species.

2.7.

Statistical analysis

Because no previous studies had assessed the effect of brownor white rice amazake on gut microbiota, GPower [28] wasused to confirm a sample size of 40, P < .05 (2-sided), at
the recommended power of 80%, as suitable for this study.
Subject characteristics and plasma SCFA data are shown asmeans ± standard error of the mean (SEM), with nonparametric Mann-Whitney test used to analyze differences between
WA and BA groups. MicrobiomeAnalyst [29] was used analyze
differences in community composition and to calculate alphaand beta diversity within and between WA and BA groups.
Briefly, data were filtered by removing very small counts using a 20% prevalence filter (for a given feature to be retained,
at least 20% of its values contain at least 4 counts) and thennormalized to address sparsity and ensure more meaningful comparisons. Nonparametric Mann-Whitney tests werethen used to compare alpha diversity (Chao1), and beta diversity (Bray-Curtis differences) using permutational multivariate analysis of variance (PERMANOVA). Nonparametric MannWhitney tests were also used to assess differences in gut microbiota at major taxonomic levels and depicted using heattree analysis. Significant differences in gut microbiota wereat the species level were identified using linear discriminant
analysis (LDA) effect size (LEfSe) analysis with features classified as significant using the adjusted P value of .05 and LDA
score of 2.0. Differential abundance analysis of microbiota at
the species level was performed using nonparametric MannWhitney tests. Spearman rank correlation method was used
to analyze the relationship between the microbiome and clinical indices using the R-package, version 4.0.3, using the corfunction, and Circlize and ComplexHeatmap from R-package
to generate heat maps. MicrobiomeAnalyst was also used forother relevant statistical analyses [29]. Levels of statistical sig-

nificance were set at ∗ P < .05, ∗∗ P < .01, and ∗∗∗ P < .001. Statistical analyses were performed in Graph Pad Prism, version 9.2(Graph Pad Software, San Diego, CA, USA).

3.

Results

At baseline, no significant differences in anthropometric or
clinical parameters were observed between WA and BA groups(Table 2). Regarding biochemical parameters, level of blood
glucose was significantly higher in the WA group with a meanof 112.7 ± 3.8 mg/dL compared with the BA group with a meanof 100.4 ± 2.7 mg/dL (P = .018). The number of subjects with
T2DM was significantly higher in WA group (n = 10) versus theBA group (n = 3) (P = .017).
Regarding alpha and beta diversity as well as the abundance of bacteria, at the phylum, class, order, and family levels, no significant differences were observed between those
allocated to the WA or BA group. However, a number of significant differences between groups at the genus and species
level were observed (Figure 2). Specifically, heat tree analyses
showed that the WA group exhibited a lower abundance of
Bacteroides intestinalis (P = .042) and Faecalibacterium sp DJF VR20
(P = .036) in comparison to the BA group (Figure 2A). In addition, an altered abundance was observed for genus Sutterella(P = .011), and the comprising species, Sutterella wadsworthensis (P = .011) as well as Parabacteroides distasonis (P = .003) andAlistipes onderdonkii (P = .048), which were elevated in the BA
group in comparison to the WA group at baseline (Figure 2A).
Moreover, LEfSe analysis, showing significant taxa ranked indecreasing order based on their LDA scores, further confirmedthat these 5 species—Bacteroides intestinalis, Faecalibacterium sp
DJF VR20, Sutterella wadsworthensis, Parabacteroides distasonis,and Alistipes onderdonkii—were significantly lower and higher
in WA and BA groups, respectively (Figure 2B).
Following consumption of the test beverages after 4 weeks,
no significant differences in anthropometric, clinical, or biochemical parameters were detected between the WA and BA
groups (Table 3). Also, no significant differences were observedin the concentration of plasma levels of the SCFAs acetate, butyrate, propionate, or valerate between the WA and BA groups
after 4 weeks or within the WA or BA groups (baseline vs 4
weeks) (Fig. 3A-D). However, within-group comparisons did reveal an increasing trend in the BA group for butyrate (baseline:
0.431± 0.670 vs 4 weeks: 0.613 ± 0.129, P = .491) compared with
the WA group (baseline: 0.506 ± 0.076 vs 4 weeks: 0.475 ± 0.081,
P = .996) (Fig. 3C). Similarly, within-group comparisons showedthat valerate had increasing tendency in the BA group (baseline: 0.217 ± 0.032 vs 4 weeks: 0.2981 ± 0.037, P = .298) in comparison to the WA group (baseline: 0.243 ± 0.036 vs 4 weeks:
0.240 ± 0.030, P =1.00) (Fig. 3D).
Moreover, no significant differences in alpha diversity (diversity within samples) using Chao1 (Fig. 4A) were detected
between WA and BA groups after 4 weeks. Principle coordinates analysis using Bray-Curtis distances, with the percentage of variation explained by axis 1 (22.3%) and axis 2 (10.8%),
also showed no significant differences in beta diversity (dissimilarity in community composition) between WA or BA subjects (Fig. 4B). Taxonomy-based abundance analyses of gut
microbiota at phylum level revealed no statistically signifi-

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Table 2 – – Baseline characteristics of subjects
Anthropometric andclinical parameters
Age (y)Height (cm)Body weight (kg)BMI (kg/m2 )Waist circumference (cm)
Systolic blood pressure(mmHg)
Diastolic blood pressure(mmHg)
Biochemical parameters
Total cholesterol (mg/d)
HDL-cholesterol (mg/dL)
LDL-cholesterol (mg/dL)
Triglycerides (mg/dL)
Glucose (mg/dL)
HbA1c (%)
Insulin (μIU/mL)
HOMA-IR
Comorbidities (N, %)High blood pressure
Dyslipidemia
Type 2 diabetes mellitus

WA

BA

Mean

Mean

P value

56.7 ± 2.2
158.4 ± 1.7
72.7 ± 3.2
28.8 ± 0.8
95.9 ± 1.7
138.2 ± 2.9

58.5 ± 2.2
159.8 ± 1.8
73.4 ± 2.0
28.8 ± 0.7
95.6 ± 1.9
136.4 ± 2.8

.722
.578
.569
.569
.846
.673

81.5 ± 1.8

81.4 ± 1.8.595205.3 ± 6.3
53.0 ± 2.9
120.5 ± 6.1
180.4 ± 33.1
112.7 ± 3.8
6.3 ± 0.1
10.5 ± 11.8
2.9 ± 0.5

209.6 ± 6.6
53.0 ± 2.8
132.1 ± 6.3
134.5 ± 10.0
100.4 ± 2.7
5.9 ± 0.1
10.0 ± 1.0
2.6 ± 0.3

.379
.995
.344
.984
.018∗
.059
.615
.836

17 (89%)16 (84%)10 (53%)20 (95%)18 (86%)
3 (14%)

0.596>0.9990.017∗

Data indicate mean values ± standard errors of the mean. All data were analyzed using nonparametric Mann-Whitney test to compare WA and
BA groups. WA, n = 19; BA, n = 21; ∗ P < .05.
Abbreviations: BA, brown rice amazake; BMI, body mass index; HbA1c, hemoglobin A1c; HDL, high-density lipoprotein; HOMA-IR, Homeostatic Model Assessment for Insulin Resistance; LDL, low-density lipoprotein; WA; white rice amazake.

Table 3 – – Characteristics of subjects at 4 weeks
Anthropometric and clinical parameters
Body weight (kg)BMI (kg/m2 )Waist circumference (cm)
Systolic blood pressure (mmHg)
Diastolic blood pressure (mmHg)
Biochemical parameters
Total cholesterol (mg/d)
HDL-cholesterol (mg/dL)
LDL-cholesterol (mg/dL)
Triglycerides (mg/dL)
Glucose (mg/dL)
HbA1c (%)
Insulin (μIU/mL)
HOMA-IR

WA
Mean

BA
Mean

P value72.8±3.2
28.8±0.8
95.8±1.5
137.6±3.2
81.5±1.873.6±2.1
28.8±0.795.8±1.9
133.9±2.6
80.9±1.8

.507
.606
.857
.386
.614

200.6±4.5
52.7±3.2
118.7±4.5161.1±21.2
111.6±4.8
6.3±0.1
9.3±1.1
2.5±0.3

205.2±4.9
51.0±2.5
128.4±4.3154.9±20.1
102.2±3.0
5.9±0.1
10.1±1.0
2.6±0.3

.386
.663
.100
.984
.092
.063
.634
.888

Data indicate mean values ± standard error of the mean. All data were analyzed using nonparametric Mann-Whitney test to compare WA and
BA groups. WA, n = 19; BA, n = 21; ∗ P < .05.
Abbreviations: BA, brown rice amazake; BMI, body mass index; HbA1c, hemoglobin A1c; HDL, high-density lipoprotein; HOMA-IR, Homeostatic Model Assessment for Insulin Resistance; LDL, low-density lipoprotein; WA; white rice amazake.

cant differences between the WA and BA groups (Fig. 4C). Importantly, significant differences were observed at the family,
genus, and species levels between the WA and BA groups, withheat tree analysis graphically highlighting these differences(Fig. 4D). Specifically, an altered abundance of bacteria fromthe family Porphyromonadaceae was observed in BA subjects
versus WA subjects (P = .013) and similarly for the comprising
genera Parabacteriodes (P = .011). Compared with the BA group,

the WA group exhibited a significantly lower abundance of Butyricicoccus (P = .012) genus and comprising species Butyrate
producing bacterium A2 207 (P = .012). A significantly different abundance of bacteria in the WA and BA groups from theSutterella genus (P = .001) and Sutterella wadsworthensis at the
species level (P = .001) were also observed. Moreover, the WA
and BA groups differed in the abundance of Lactobacillales bacterium DJF B280 (P = .005), and Firmicutes bacterium DJF VP44

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Nutrition Research 103 (2022) 68–81

Fig. 2 – Composition of gut microbiota in WA and BA subjects at baseline. (A) Heat tree analysis showing taxonomic
differences between WA and BA microbial communities with significant alterations in taxa shown by name of the bacteria
at the corresponding node. Position and size of nodes indicate hierarchical structure of taxa and colors of nodes andbranches indicate either an increase (red) or decrease (blue) in abundance level based on the log2 ratio of medianabundance. (B) Linear discriminant analysis effect size (LEfSe) between WA and BA groups with significant taxa graded indecreasing order by LDA score and heatmap showing corresponding taxa that are either higher (red) or lower (blue). Data
were analyzed using nonparametric Mann-Whitney tests to compare WA and BA groups at baseline. WA, n = 17; BA, n = 17.
∗ P < .05. BA, fermented brown rice beverage; WA, fermented white rice beverage.

(P = .038), both from the Firmicutes phylum. LEfSe analysis
further highlighted significant differences in Butyrate producing bacterium A2 207, Sutterella wadsworthensis, and Firmicutes
bacterium DJF VP44, which were all increased and decreasedin the BA and WA groups, respectively (Fig. 4E). Differential
abundance analyses further showed that these species clearly
demonstrated a significant elevation in the BA group in comparison to WA group (Fig. 5A-D).
When comparing baseline and 4-week microbiota data
within groups (baseline vs 4 weeks), no significant differencesin alpha diversity (diversity within samples) using Chao1(Supplementary Figure S1a and b) were observed within either WA or BA groups. Principle coordinates analysis using
Bray-Curtis distances, with the percentage of variation explained by axis 1 (23.7%) and axis 2 (13.3%), in the WA group(Supplementary Figure S1c) and (18.3%) and axis 2 (12.3%) in
the BA group (Supplementary Figure S1d), also showed no significant differences at baseline and 4 weeks. Taxonomy-basedabundance analyses of gut microbiota at phylum level revealed no statistically significant differences within WA (Sup-plementary Figure S2a) and BA (Supplementary Figure S2b)groups at baseline and 4 weeks. In contrast, heat map analysis showed a significant alteration of the species Flavonifractor plautii (P = .026) in the WA group after 4 weeks in comparison to baseline (Supplementary Figure S2c), with differential abundance analyses visually showing an increase of thisspecies within the WA group at 4 weeks (Supplementary Figure S2d).
To explore the relationship between the gut microbiota andcomponents of the metabolic syndrome, Spearman rank correlation coefficient method was used to assess the correlation
between each subject’s gut microbiota at the species level andanthropometric, clinical, and biochemical indices related tothe metabolic syndrome (body weight, body mass index, waist
circumference, systolic blood pressure, diastolic blood pressure, total cholesterol, HDL-C, LDL-cholesterol, triglycerides,
glucose, HbA1c, insulin, and Homeostatic Model Assessment
for Insulin Resistance). Significant correlations (correction r >0.4 or r < − 0.4, P< .05) were observed among 41 taxa and the
13 clinical indices (Fig. 6), with the abundance level of only 3

Nutrition Research 103 (2022) 68–81

75

Fig. 3 – Plasma SCFA profile of subjects comparing WA or BA groups at 4 weeks and within WA and BA groups (baseline and
4 -weeks). (A-D) Individual plasma SCFA concentrations (acetate, propionate, butyrate, and valerate, respectively). Data
indicate means and standard errors of the mean (SEM). All data were analyzed using 2-way ANOVA followed by Tukey post
hoc test. WA, n = 16; BA, n = 19. ∗ P < .05. BA, fermented brown rice beverage; SCFA, short-chain fatty acid; WA, fermented
white rice beverage.

species differing significantly between the WA and BA groups.
Interestingly, a significantly negative correlation observed between Sutterella wadsworthensis and blood glucose (P = .032)
and significantly negative correlations also observed between
Lactobacillales bacterium DJF B280 and triglyceride level (P =.006) as well as Butyrate producing bacterium A2 207 and triglyceride level (P = .001). A strong positive correlation was foundbetween Lactobacillales bacterium DJF B280 and HDL-C (P = .037)
and a more moderate positive correlation for Butyrate producing bacterium A2 207 and LDL-C (P = .046).

4.

Discussion

In line with our hypothesis, consumption of BA, as opposed toWA, was able to significantly modulate the gut microbiota andcontributed to an increasing trend in the level of plasma butyrate and valerate. Importantly, ingestion of BA as opposed to
WA most notably impacted gut microbiota composition at the
species level. Interestingly, an increase in Butyrate producing
bacterium A2 207 from the Butyricicoccus genus was observedin BA subjects versus WA subjects. As the name suggests, thisspecies of bacteria is capable of producing butyrate, with reduced levels of butyrate-producing bacteria observed in individuals with a variety of metabolic and chronic diseases [30,31]. Although we did not observe a significant difference inplasma butyrate levels between groups, we did observe an increasing tendency in the BA group. In addition, a greater abundance of Lactobacillales bacterium DJF B280, often found in fermented foods, as well as Firmicutes bacterium DJF VP44, weresignificantly elevated in the BA group in comparison to the
WA group. These species, belonging to Clostridium cluster IV,
have an indispensable role in creating immune tolerance andlowering levels of inflammatory cytokines and increasing regulatory T-cell induction via both SCFA-dependent and independent mechanisms [32, 33]. Interestingly, Lactobacillales bacterium DJF B280 and Firmicutes bacterium DJF VP44 as well as
Butyrate producing bacterium A2 207 are capable of producing
butyrate, lactate, and ethanol [18]. Lactate can also act as a
substrate for other bacteria that produce butyrate and propionate [34], and production of valerate is also possible via thecoupling of propionate with ethanol [18]. In this context, an increased abundance of these bacterial species in the BA group
may have contributed to the tendency of plasma butyrate andvalerate levels to increase observed in the BA group after 4
weeks.
The potential mechanisms as to how to BA may characteristically modulate the gut microbiota may be due to a number of beneficial components. First, fiber, which is higher in BA
compared with WA, can lead to an increase in abundance offiber-degrading bacteria, capable of producing SCFAs such as
those species seen elevated in the BA group [35]. In addition,

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Fig. 4 – Diversity and composition of gut microbiota in WA and BA subjects at 4 weeks. Box plot showing alpha diversity as
measured by (A) chao1 index, estimating taxa richness by accounting for low abundance; (B) principal coordinate analysis ofgut microbiota composition depicting beta diversity and using Bray-Curtis distance matrix highlighting differences between
groups; (C) actual abundance of major phyla composition; and (D) heat tree analysis showing taxonomic differences
between WA and BA microbial communities with significant alterations in taxa shown by name of the bacteria at thecorresponding node. Position and size of nodes indicate hierarchical structure of taxa and colors of nodes and branches
indicate either an increase (red) or decrease (blue) in abundance level based on the log2 ratio of median abundance. (E)Linear discriminant analysis effect size (LEfSe) between WA and BA groups with significant taxa graded in decreasing orderby LDA score and heatmap showing corresponding taxa that are either higher (red) or lower (blue). Data were analyzed
using nonparametric Mann-Whitney tests to compare differences in alpha diversity and microbiota composition (heat tree,
LEfSe). Beta diversity was analyzed using permutational multivariate analysis of variance (PERMANOVA). WA, n = 17, BA,n = 17. ∗ P < .05. BA, fermented brown rice beverage; WA, fermented white rice beverage.

Nutrition Research 103 (2022) 68–81

77

Fig. 5 – (A-D) Differential abundance analysis (log-transformed count) of selected gut microbiota species showing significant
differences between WA and BA groups. Data indicate means and their standard errors of the means (SEM). Data wereanalyzed using nonparametric Mann-Whitney tests. WA, n = 17; BA, n = 17. ∗ P < .05. BA, fermented brown rice beverage;
WA, fermented white rice beverage.

BA abundantly contains γ -oryzanol, whereas WA does not somuch. Intriguingly, the main metabolite of γ -oryzanol, ferulic
acid, a phenolic compound that possesses potent antioxidant
and anti-inflammatory activity [36], relies on microbial transformation to be converted to its bioactive form by species suchas those from the Clostridium cluster IV, which were seen elevated in the BA group [32].
When examining differences within groups at baseline andafter 4 weeks, no apparent differences were observed in the
BA group. However, ingestion of WA resulted in somewhat
negative alterations to gut microbiota. Specifically, Flavonifractor plautii, a flavonoid-degrading bacteria [37], was reduced at
4 weeks in comparison to baseline levels in the WA group.
Although data on the nutrient intake of subjects were notobtained, there may be a possibility that a reduction in thisspecies could occur as a result of the fiber and flavonoidcontaining foods eaten in the subject’s usual diet at base-line, being replaced with WA, which instead contains minimal
amounts of phytochemicals and fiber. The lack of differences
observed in the BA group at 4 weeks versus baseline may havebeen partially because of the acute nature of the intervention
period, with more beneficial changes in gut microbiota likely
observed after a longer duration of daily BA ingestion [38].
Importantly, at baseline, although differences were not observed between groups for most subject characteristics, a significantly greater number of subjects with T2DM, and thus
correspondingly higher levels of blood glucose, were observedin the WA group. In addition to this, a significantly greater
abundance of bacterial species—Alistipes onderdonkii, Parabacteroides distasonis, Bacteroides intestinalis, and Faecalibacterium sp
DJF VR20–were observed in the BA group at baseline and abundance of Sutterella wadsworthensis was found elevated in the
BA group both at baseline and after the 4-week intervention
in comparison to WA group. Although these findings represent

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Fig. 6 – Heat map of Spearman correlation analysis highlighting significant correlations of gut microbial species andselected anthropometric, clinical, and biochemical indices of study subjects at 4 weeks. Significant correlations (correction r> 0.4 or r < −0.4) are shown as blue (negative) or red (positive). n = 34 (WA and BA). ∗ P < .05, ∗∗ P < .01 and ∗∗∗ P < .001. BA,
fermented brown rice beverage; WA, fermented white rice beverage.

nonnegligible limitations to the present study, with the difference in gut microbiota possibly from the differences in number of subjects with T2DM between groups, this likely does not
represent the only factor involved in causing these disparities.
First, only differences in the bacteria Sutterella wadsworthensis was observed at both baseline and remained at the endof the 4-week intervention between the WA and BA groups.
This raises the question that the lower abundance of Sutterellawadsworthensis in the WA group in comparison to BA group
could be impacted by the presence of T2DM. Intriguingly, a
reduced abundance of Sutterella genus (containing Sutterellawadsworthensis) has been observed in subjects with prediabetes and newly diagnosed diabetes and T2DM in comparison

to healthy controls [39]. Also, a number of other variables thatwere not measured in the present study, such as habitual diet,exercise, and sleep, may have also contributed to the differences in gut microbiota observed between groups [38, 40, 41].
At the end of the 4-week intervention, no significant differences in anthropometric, clinical, or biochemical parameters were observed between WA and BA groups, which again is
likely a result of the relatively brief intervention period. However, given that a number of significant changes in the gut microbiota were observed between WA and BA groups, this raises
the question as to whether changes in gut microbiota may
precede changes in, or be correlated with, various metabolic
markers. To explore this, we undertook correlation analyses

Nutrition Research 103 (2022) 68–81

to identify if there were any significant relationships between
the gut microbiota and anthropometric, clinical, and biochemical parameters of interest in the context of the metabolic syndrome.
We found that Sutterella wadsworthensis, which was elevated in the BA group at baseline and 4 weeks, was negatively
correlated with blood glucose. These findings are in line withprevious studies reporting a reduced abundance of Sutterella insubjects with T2DM [39, 42]. Interestingly, an increase in Sutterella and improvement in glucose metabolism has been reported after Roux-en-Y gastric bypass surgery in diabetic rats[42]. Significantly negative correlations were also observed between Lactobacillales bacterium DJF B280 and Butyrate producing
bacterium A2 207 and triglyceride level. These findings are inline with a large population-based cohort study of 893 subjects that found that gut microbiota from the Clostridiaceae
family, containing both Lactobacillales bacterium DJF B280 and
Butyrate producing bacterium A2 207, which were elevated in
the BA group, were negatively correlated with triglyceride levels [43]. Our study also found that Lactobacillales bacterium DJF
B280 was significantly correlated with HDL-C level. In particular, SCFA-producing bacteria from Clostridiaceae, containing Lactobacillales bacterium DJF B280, have been shown to bepositively correlated with HDL-C [43] and demonstrate overall favorable effects on cholesterol metabolism in both animals and humans [44]. Although a positive correlation was observed between Butyrate producing bacterium A2 207 and LDL-C
in our study, the majority of published literature points towarda beneficial effect of this species and other butyrate-producing
bacteria in lipid metabolism [43–45].
In conclusion, consumption of BA in comparison to WA,
resulted in beneficial changes to the gut microbiota in subjects with MetS. These results support recent research regarding the beneficial effects of whole grains and fermented foods
such as BA on the gut microbiota. Longer term interventional
studies are required to further strengthen the impact of BA on
the gut microbiome and the potential impact on markers of
metabolic health.

79

tion, and Resources. Hirosuke Watanabe: Formal Analysis, Investigation, and Resources. Hiroaki Masuzaki: Conceptualization, Methodology, Writing – Review & Editing, Supervision,
Project Administration, and Funding Acquisition. All authors
read and approved the final manuscript.

Acknowledgments
The authors thank Ikumi Nomura, Chie Horiguchi, andTomoko Ikematsu for technical assistance, as well as Chikako
Noguchi, Mamiko Hirata, and Yukari Inamine for secretarial
assistance.

Sources of support
The present study was supported in part by Grants-in-Aid
from the Japan Society for the Promotion of Science (JSPS),
KAKENHI (grant numbers: 20K08912, 18K11079); Japan Agency
for Medical Research and Development (AMED) (grant number: JP19ek0410049); Construction of Okinawa Science andTechnology Innovation System; Setsuro Fuji Memorial the Osaka Foundation for Promotion of Fundamental Medical Research; Medical Rice Association and Council for Science,
Technology and Innovation, Cross-ministerial Strategic Innovation Promotion Program, “Technologies for Creating Nextgeneration Agriculture, Forestry and Fisheries.” JSPS, AMED,
Construction of Okinawa Science and Technology Innovation
System, Setsuro Fuji Memorial the Osaka Foundation for Promotion of Fundamental Medical Research, Medical Rice Association and Council for Science, Technology and Innovation, Cross-ministerial Strategic Innovation Promotion Program, “Technologies for Creating Next-generation Agriculture,
Forestry and Fisheries,” had no role in the study design, collection, analysis, interpretation, or writing of this article.

Supplementary materials
Declaration of Competing Interest
There are no conflicts of interest to declare.

Author contributions
Yukari Akamine: Formal Analysis, Writing Original Draft,
Writing – Review & Editing, and Visualization. Jasmine F. Millman: Formal Analysis, Writing Original Draft, Writing – Review & Editing, and Visualization. Tsugumi Uema: Formal
Analysis, Writing Original Draft, Writing – Review & Editing,and Visualization. Shiki Okamoto: Writing – Review & Editing
and Supervision. Masato Yonamine: Investigation and Project
Administration. Moriyuki Uehara: Investigation and Project
Administration. Chisayo Kozuka: Methodology and Investigation. Tadashi Kaname: Software, Resources, and Data Curation. Michio Shimabukuro: Conceptualization and Project
Administration. Kozen Kinjo: Formal Analysis, Investigation,
and Resources. Masayo Mitsuta: Formal Analysis, Investiga-

Supplementary material associated with this article can befound, in the online version, at doi:10.1016/j.nutres.2022.03.
013.

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