Journal of

Research Article
Volume 10:8, 2021

Hypertension: Open Access

ISSN: 2167-1095

Open Access

Relationship between Human Gut Microbiota andNutrition Intake in Hypertensive Discordant Monozygotic
Twins
Rie Tomizawa1,3*, Jonguk Park2, Natsuko Matsumoto3, Koji Hosomi1,4, Hitoshi Kawashima2, Kenji Mizuguchi2,5, Jun Kunisawa1,3
and Chika Honda1,3Center for Twin Research, Osaka University Graduate School of Medicine, Osaka 565-0871, Japan
Laboratory of Bioinformatics, Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health12and Nutrition, Osaka 567-0085, Japan
Laboratory of Vaccine Materials, Center for Vaccine and Adjuvant Research and Laboratory of Gut Environmental System, National Institutes of3

Biomedical Innovation, Health and Nutrition, Osaka 567-0085, Japan
Department of Clinical Laboratory and Biomedical Sciences, Division of Health Sciences, Osaka University Graduate School of Medicine, Osaka

4565-0871, Japan
Institute for Protein Research, Osaka University, Osaka 565-0871, Japan

5

Abstract
Background and Objectives: Intestinal bacteria digest substances, produce metabolites, and influence the host. Blood pressure increases with age, even in healthypeople, and hypertension is said to affect one in four people worldwide. It is a major risk factor for stroke and stroke-related death. Although it is known that blood pressure
is correlated with the ratio of Firmicutes and Bacteroides in gut microbiota and that the intake of peptides in fermented dairy products lowers blood pressure in humans,
the relationship with the gut microbiota at the genus level is still inconsistent. Here, we aimed to examine the association between high blood pressure, gut microbiota, andnutrient intake by removing gender, age, and genetic effects.
Materials and Methods: We selected 25 hypertensive discordant Japanese monozygotic twins and confirmed their zygosity by matching 15 short tandem repeat loci. Their
fecal samples were subjected to 16S rRNA sequencing and bioinformatics analyses to identify and compare fluctuations in intestinal bacteria.
Results: Four genera were extracted by comparing age- and gender-unified hypertensive and non-hypertensive groups, and 15 genera: Actinomyces, Butyricicoccus,
Coprobacter, Coprococcus 1, Eubacterium fissicatena group, Eubacterium rectale group, Eubacterium ruminantium group, Eubacterium eligens group, Lachnospira,Prevotellaceae UCG 001, Ruminiclostridium 9, Ruminococcaceae NK 4A 214 group, Ruminococcaceae UCG 004, Ruminococcaceae UCG 005 and Ruminococcaceae
UCG 014 were extracted by focusing on differences between pairs to account for genetic effects. The correlation between the 15 hypertension-associated bacteria extracted
and nutrient intake showed that most minerals, vitamins, and lipids, including plant fatty acids, had a negative correlation with Actinomyces, while some had a positive
correlation with the Eubacterium rectale group.
Conclusions: When comparing monozygotic twins with hypertension discordance, age, sex, and genetic factors were excluded, and 15 hypertension-related genera wereextracted, including Actinomyces and Eubacterium rectale group, which were associated with several nutrients.

Keywords: Gut microbiome • Hypertension • High blood pressure • Monozygotic twins • Nutrients

Introduction
The human intestinal tract is composed of over 100 trillion microorganisms
with more than 1,000 species [1], which form the gut microbiota. The gut
microbiota has been shown to be involved in the pathogenesis of metabolic
diseases, immune diseases, and malignant tumors. An association withcardiovascular diseases and intestinal bacteria has also been reported. The
Firmicutes: Bacteroidetes (F/B) ratio also increased in a hyper-tensive rat
model [2]. This result suggests that activation of the renin-angiotensin system(RAS), which is a cause of hypertension, may lead to dysbiosis.
In humans, a decrease in intestinal microbiota diversity has been confirmedin hypertensive patients [3]. Additionally, it has been reported that the*Address for Correspondence: Rie Tomizawa, Center for Twin Research, Osaka
University Graduate School of Medicine, Osaka 565-0871, Japan, Tel: +81 6 6871
2556; Fax: +81 6 6879 2557; E-mail: r-tomizawa@sahs.med.osaka-u.ac.jp
Copyright: © 2021 Rie Tomizawa, et al. This is an open-access article distributed
under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided theoriginal author and source are credited.
Received 22 July 2021; Accepted 10 August 2021; Published 25 August 2021

intestinal microflora of rats in which hypertension was induced by a high-fat
diet became Firmicutes-dominant, and short-chain fatty acid (SCFA) butyrate
decreased [4]. Olfr78, an olfactory receptor (OR), is found in the renal arteries
and paraglomerular cells of the kidney, and its binding to SCFAs such as
acetic acid and propionic acid causes an increase in blood pressure [5],whereas Gpr41, which is expressed in blood vessels, has a hypotensive effectwhen bound to SCFAs [6]. Thus, Olfr78 and Gpr41 share a common ligand,propionic acid, and have antagonistic effects on blood pressure regulation.
The prevalence of hypertension (blood pressure of 140/90 mmHg or higher oron antihypertensive medication) accounts for 60% of men and 40% of womenin the range of 40–70 years, and the proportion of hypertensive patients
increases with age. Seventy four percent of men and 70% of women aged
75 years and older fall into this category, with no differences noted between
men and women [7].
Hypertension is a multifactorial genetic disease, and the average systolicblood pressure level of the population has been decreasing owing to theprogress and spread of hypertension treatment, lower salt intake, andchanges in lifestyle and living environment.
However, persistent high blood pressure causes arteriosclerosis, which
is a cause of various complications which include life-threatening stroke,Tomizawa R, et al.

J Hypertens (Los Angel), Volume 10:8, 2021

myocardial infarction, and kidney disease. Given that hypertension is adisease with few subjective symptoms and a large number of patients, it has
a substantial impact on health and welfare and the medical economy.

(Illumina, San Diego, CA, USA). The complete flow from fecal sampling to
16SrRNA sequencing was performed according to the protocol described by
Hosomi et al [12].

In addition to susceptibility to diseases other than single genetic disorders,
all human phenotypes are affected by genetic and environmental factors. In
addition to hypertension, the composition of the gut microbiota is known tobe affected by genetic and environ-mental factors [8]. Therefore, the only
human study that can assess the environmental effects on gut microbiota
whilst controlling genetic effects is a twin study. Monozygotic twins share
100% of the intrauterine environment and growth environment in addition totheir genetic background. Thus, differences due to environmental factors canbe evaluated by comparison between twins.

Bioinformatic Analysis

Japan has been one of the longest-lived countries in the world for many years,
and it is known that the composition of the Japanese intestinal flora is unique[9]. As both hypertension and gut microbiota are strongly influenced by diet,
this study is the first to focus on the relationship between hypertension andgut microbiota, as well as nutrient intake in a pair of Japanese identical twins,
one of whom had hypertension and the other did not.

The resulting paired-end FASTQ data were trimmed and merged before
selecting operational taxonomic units (OTUs). OTU classification anddiversity analyses were performed using the QIIME pipeline (v. 1.9.1) [13].
All steps from FASTQ file trimming to gut microbiota diversity analysis were
performed automatically according to a previously described method [14].
OTUs were clustered against the SILVA 128 reference database [15] with
97% similarity using the USEARCH algorithm [16]. Taxonomic classification
was performed using the SILVA 128 reference database down to the genus
level.

Statistical Analyses

The zygosity of the subjects was confirmed by matching 15 short tandem
repeat loci using the PowerPlex®16 system (Promega, Madison, Wisconsin,
USA).

The output of the QIIME pipeline in the Biom table format was imported
and analyzed using R (version 3.5.1). Alpha-diversity indices were calculatedusing the estimate_richness function in the “phyloseq” R-package.
Hierarchical clustering analysis was performed using the R package“vegan”, “stats” based on the Bray-Curtis distance matrix at the genus leveland by using the ward. D 2 method. The beta-diversity index, calculatedby the Bray-Curtis distance matrix using genus-level data, was generatedusing the vegdist function in the “vegan” R-package. Principal coordinates
analysis (PCoA) was performed using the dudi . pco function in the “ade4” R-package. PCoA figures were created using the R package “ggplot 2”.
Covariates of gut microbiome β-diversity were identified by calculating theassociation between continuous or categorical phenotypes and genus-levelcommunity coordinates with the envfit function in the “vegan” R-package.
This function performs the MANOVA and linear correlations for categoricaland continuous variables, respectively. We used the Wilcoxon rank sum
test (wilcox. test function in “stats” R-package) for comparison analysis.
To completely rule out genetic effects, we focused on the differences
between each twin pair. For bacterial relative abundance, the control was
subtracted from Hypertension (HT) to create an integrated table. In addition,
to confirm the relationship between hypertension and gut microbiota morerobustly, the calculated values were converted into increasing, unchanged,
and decreasing categorical variables, and comparative analysis was
performed using Welch's two-sample t-test. Furthermore, for the confirmed
hypertension-related bacteria, the nutrient intake was determined using anutrient intake table that was also integrated by subtracting the control from
HT (Spearman correlation analysis, cor function in “stats” R-package).

This study was approved by the Ethics Committee of Osaka University andthe Ethics Committee of the National Institute of Biomedical Innovation, andwas conducted in accordance with their guidelines.

Heatmaps were created using the “corrplot” and “superheat” R-package, and
PCoA figures and Boxplots were created using the R package “ggplot 2”. All
statistical tests were two-sided, with a significance level of p <0.05.

The aim of this study was to focus on the differences between monozygotic
twin pairs and their gut microbiota by eliminating the influence of genetic
factors, identify bacteria associated with hypertension, and examine which
nutrients these bacteria were associated with.

Materials and Methods
Subjects
Japanese monozygotic twins were recruited from a registry establishedby the Center for Twin Research, Osaka University Graduate School of
Medicine, and written informed consent was obtained from all 52 discordant
individuals (MZ 26 pairs) in whom one of the twins was hypertensive and theother was not, for analysis in this study.
Hypertension was determined based on either confirmation of self-reported
current history by a health care professional or the mean of three bloodpressure readings taken in the sitting position during the face-to-face survey,
which was greater than or equal to degree I hypertension (140 mmHg ≤systolic blood pressure and/or 90 mmHg ≤ diastolic blood pressure) of theHypertension Treatment Guidelines 2019 [10].

Nutrient Intake Data
The brief-type self-administered diet history questionnaire has beendeveloped for nutritional epidemiology studies and has been shown to beaccurate and valid [11]. In this study, the value per 1000 kcal of energy wascalculated and used for analysis, considering the effect of energy intake.
Responses were obtained simultaneously with fecal sample collection.

Fecal Sample Collection
Fecal samples were collected from two twin pairs simultaneously. The
samples were placed in 15 mL vials containing 3 mL of guanidine thiocyanate
solution (TechnoSuruga Laboratory Co., Ltd, Shizuoka, Japan), mixed well,and stored at 4°C until DNA extraction.

DNA Extraction and 16S rRNA Gene Amplicon Sequencing
The fecal sample mixture was mechanically disrupted using the beadbeating method. DNA was extracted using a Gene Prep Star PI-80X device(Kurashiki Boseki Co., Ltd.). Following DNA extraction, the V 3-V 4 region of
the 16S rRNA gene was amplified and sequenced using the Illumina MiSeq
Page 2 of 9

Results
Characteristics of Participants
We analyzed 26 monozygotic twin pairs aged 65.4 ± 13.1 (31–85) years, ofwhich 12 were male and 14 females. The subjects did not heavily consume
alcohol and had not consumed any antibiotic medication for two weeks prior
to the investigation.
There was no significant difference in nutrient intake between hypertensive
and control twins except for iron (Table 1).

Examination of the Effects of Hypertension Medication
We performed a comparative analysis using the hypertension group only
to determine whether hypertension medication affects the gut microbialcommunity. Hy-pertension medication was not significantly correlated with
the PCoA ordination of the microbial community structure (p = 0.608)(Figure 1).

Tomizawa R, et al.

J Hypertens (Los Angel), Volume 10:8, 2021

Table 1.Participant characteristics and mean scores of nutrient intakes.
Variables
male/ female
Age
Body mass index
EN
PRT
APRT
VPRT
FAT
AFAT
VFAT
CHO
NA
K
CA
MG
PP
FE
ZN
CU
MN
RTE
VD
ATC
VK
VB1
VB2
VB6
VB12
VC
SFA
MFA
PFA
CHL
WDF
NDF
TDF
N3
N6

energyproteinanimal protein
vegetable protein
fatty acidanimal fatty acidvegetable fatty acidcarbohydrate
sodiumpotassium
calciummagnesium
phosphorus
iron
zinc
coppermanganese
retinol equivalentvitamin D
alfa tocopherolvitamin K
vitamin B1
vitamin B2
vitamin B6
vitamin B12
vitamin C
saturated fatty acidmonounsaturated fatty acidpolyunsaturated fatty acidcholesterolprovide soluble fiber
insoluble dietary fiber
total dietary fiber
n-3 fatty acidn-6 fatty acidall(n = 52)
pair
kg/m2
kcal
g/1000 kcal
g/1000 kcal
g/1000 kcal
g/1000 kcal
g/1000 kcal
g/1000 kcal
g/1000 kcal
mg/1000 kcal
mg/1000 kcal
mg/1000 kcal
mg/1000 kcal
mg/1000 kcal
mg/1000 kcal
mg/1000 kcal
mg/1000 k cal
mg/1000 kcal
μg /1000 kcal
μg/1000 kcal
μg/1000 kcal
μg/1000 kcal
mg/1000 kcal
mg/1000 kcal
mg/1000 kcal
μg/1000 kcal
mg/1000 kcal
g/1000 kcal
g/1000 kcal
g/1000 kcal
mg/1000 kcal
g/1000 kcal
g/1000 kcal
g/1000 kcal
g/1000 kcal
g/1000 kcal

Mean ± SD
14 / 1265.4 ± 13.1
23.4 ± 3.3
Nutrients
1870.5 ± 610.7
40.8 ± 824.3 ± 8.1
16.5 ± 2.9
33.1 ± 7.6
15.4 ± 5
17.6 ± 5.4
124.9 ± 20.8
2453.4 ± 562.1
1605.2 ± 406.7
363.7 ± 113.9
152.3 ± 32.7
633.5 ± 134.2
4.8 ± 1.2
4.6 ± 0.7
0.6 ± 0.1
1.8 ± 0.5
488.5 ± 241.8
8.7 ± 54.8 ± 1.2
219.9 ± 91.3
0.5 ± 0.1
0.8 ± 0.2
0.7 ± 0.2
5.3 ± 2.4
74.7 ± 28.9
8.9 ± 2.4
11.8 ± 3
7.9 ± 1.8
254.5 ± 92.4
1.9 ± 0.6
5.4 ± 1.4
7.6 ± 2
1.6 ± 0.5
6.2 ± 1.4

Hypertensive (n = 26)

Control (n = 26)(Min - Max)

Mean ± SD(Min - Max)

31 - 8517.4 - 33.2

24 ± 3.2

22.8 ± 3.3

826.8-3256
20.8 - 57.4
7.3 - 41
10.3 - 23.3
12 - 49.3
4.3 - 28.3
6 - 32.5
89 - 187.7
1181.9 - 3749
566.1 - 2497
93.6 - 710.3
71.1 - 211.3
293.9 - 908.7
2 - 103.3 - 6.7
0.3 - 0.9
1 - 3.5
88.3 - 1649
1.1 - 27.4
1.4 - 8.4
41 - 591.6
0.2 - 0.7
0.3 - 1.5
0.3 - 1.2
1 - 12.4
24.5 - 148.5
3 - 14
4.2 - 18.5
3.1 - 12.2
43.3 - 663.6
0.4 - 3.2
2.1 - 9.6
2.6 - 12.8
0.6 - 3.2
2.5 - 10.1

1782.5 ± 531.5
40.2 ± 8.6
24.3 ± 8.5
15.9 ± 2.5
32.5 ± 7.7
15.3 ± 5.1
17.3 ± 5.6
124.7 ± 192500.3 ± 573.2
1541 ± 450.6
358 ± 130147.7 ± 35.9
624.3 ± 150.6
4.7 ± 1.4
4.6 ± 0.8
0.6 ± 0.1
1.7 ± 0.4
472.3 ± 275.8
8.6 ± 5.8
4.6 ± 1.3
207.2 ± 103.5
0.5 ± 0.1
0.8 ± 0.2
0.7 ± 0.2
5.2 ± 2.7
71.4 ± 32.3
8.8 ± 2.4
11.7 ± 2.9
7.7 ± 1.9
266.6 ± 110.2
1.8 ± 0.5
5.2 ± 1.5
7.2 ± 2
1.6 ± 0.5
6.1 ± 1.6

1958.5±669.3
41.5 ± 7.3
24.3 ± 7.8
17.2 ± 3.2
33.6 ± 7.6
15.6 ± 4.8
18 ± 5.1
125.1 ± 22.5
2406.6 ± 546.7
1669.5 ± 345.7
369.3 ± 94.7
156.8 ± 28.5
642.7 ± 114.9
5 ± 1*
4.7 ± 0.6
0.6 ± 0.1
1.8 ± 0.6
504.7 ± 200.7
8.8 ± 4.1
4.9 ± 1.1
232.5 ± 75.2
0.5 ± 0.1
0.8 ± 0.2
0.8 ± 0.2
5.3 ± 2.1
77.9 ± 24.7
9.1 ± 2.4
11.9 ± 3.2
8 ± 1.6
242.5 ± 68.1
2.1 ± 0.6
5.6 ± 1.3
7.9 ± 1.9
1.7 ± 0.5
6.4 ± 1.2

SD: standard deviation. Wilcoxon rank sum test between two groups *p <0.05.
The nutrient intake scores were calculated from the results of brief-type self-administered diet history questionnaire (BDHQ).

Gut Microbiota Diversity in Hypertension group and Control
Group
We calculated Chao 1, Shannon diversity index, and Simpson diversity
index and found no significant difference between the two groups (p = 0.405,
0.146, 0.151) (Table 2).
As a result of comparative analysis between the HT group and the control
group using PCoA ordination of the microbial community structure, nodifference was observed. After controlling for genetic changes, it was
confirmed that hypertension had no effect on the microbial community of thegut (p = 0.608) (Figure 2).

Characteristics of Bacteria in Hypertension group and Control Group
Page 3 of 9

The characteristic bacteria found in the case and control groups were theEubacterium ruminantium group, Lachnospira, Ruminococcaceae UCG 004
and four other genera, all of which had higher occupancy rates in the control
group (Table 3).

Characteristics of Bacteria in Co-Twins
We focused on the differences between each twin pair to completely rule out
genetic effects, and 15 genera were extracted: Actinomyces, Butyricicoccus,
Coprobacter, Coprococcus 1, Eubacterium fissicatena group, Eubacterium
rectale group, Eubacterium ruminantium group, Eubacterium eligens
group, Lachnospira, Prevotellaceae UCG 001, Ruminiclostridium 9,
Ruminococcaceae NK 4A 214 group, Ruminococcaceae UCG 004,
Ruminococcaceae UCG 005, and Ruminococcaceae UCG 014. All four
genera mentioned above were also included (Table 4).

Tomizawa R, et al.

J Hypertens (Los Angel), Volume 10:8, 2021

Figure 1. Principal coordinates analysis (PCoA) ordination of microbial community structure in the presence and absence of therapeutic drugs for hypertension.
Table 2. Alpha diversity at genera level between Hypertension group and Control group.
Estimators
Chao1
Shannon
SimpsonHT_mean
1447.583
3.949
0.932

Control_mean
1550.957
4.115
0.941

FDR
0.405
0.377
0.377

p-value
0.405012
0.145685
0.150819

Figure 2. Principal coordinates analysis (PCoA) ordination of microbial community structure between hypertension and control groups.

Nutrient Meta-Analysis with the 15 extracted hypertensionassociated bacteria

In addition, minerals, vitamins, and fats are inversely correlated tocarbohydrates (Figure 3).

The correlation between the 15 extracted hypertension-associated bacteria
and nutritional intake was calculated (Figure 3). As indicated by thedendrogram, nutrients were divided into three major groups. Most minerals,
vitamins, and some lipids and proteins were largely classified. Dietary fiber
was grouped with vegetable protein, vegetable fat, manganese, and vitamin
C. Energy and carbohydrates were classified in the last group.

Discussion

Several minerals, vitamins, and fats, including vegetable fatty acids, werenega-tively correlated with Actinomyces and some positively correlated with
the Eubacterium rectale group.

Additionally, in the Healthy Life in an Urban Setting study, gut microbiota
ac-counted for 4.4% of the total unadjusted systolic blood pressure variance;
however, the proportion of variance varied widely by ethnicity (4.8% in the

Page 4 of 9

In the present study, we investigated the human gut microbiota and its
nutrient intake in relation to hypertension by removing genetic effects using
identical Japanese twins discordant for hypertension.

Tomizawa R, et al.

J Hypertens (Los Angel), Volume 10:8, 2021

Table 3. Four genera extracted from two group comparisons of high blood pressure group and healthy group.
PhylumFirmicutes
Firmicutes
Firmicutes
Firmicutes

Genus
Eubacterium ruminantium group
Lachnospira
Ruminococcaceae.UCG.004
Ruminococcaceae.UCG.014

HT_mean
0.769
71.0383.462
0.000

Control_mean
55.769128.808
7.500
10.346

fdr
0.886
0.886
0.886
0.719

p-value
0.037329
0.038243
0.031139
0.005245
Wilcoxon rank sum test

Table 4. Fifteen genera extracted by twin-pair difference.
PhylumActinobacteria
Firmicutes
Bacteroidetes
Firmicutes
Firmicutes
Firmicutes
Firmicutes
Firmicutes
Firmicutes
Bacteroidetes
Firmicutes
Firmicutes
Firmicutes
Firmicutes
Firmicutes

Genus
Actinomyces
Butyricicoccus
Coprobacter
Coprococcus 1
Eubacterium fissicatena group
Eubacterium rectale group
Eubacterium ruminantium group
Eubacterium eligens group
Lachnospira
Prevotellaceae UCG 001
Ruminiclostridium 9
Ruminococcaceae NK4A214 group
Ruminococcaceae UCG 004
Ruminococcaceae UCG 005
Ruminococcaceae UCG 014

HT_plus (%)
38.46269.23134.61523.07730.76957.6920.000
34.61526.9233.846
19.23115.38519.231
19.231
0.000

Control_plus (%)
15.38530.76911.53857.692
11.53830.76923.077
11.53869.2310.000
65.38550.00053.84657.69226.923p-val
0.031881
0.012420.022199
0.048661
0.031326
0.043127
0.026433
0.019722
0.025172
0.025191
0.009561
0.026421
0.033175
0.018991
0.006366
Welch’s t-test

Figure 3. Association between hypertension-related bacteria and nutrient intake.
Dutch and less than 0.8% in others) [17], suggesting that a study of Japanesemonozygotic twins would have important consequences.
Page 5 of 9

In previous studies, hypertension decreased richness and diversity [2,3,18];
however, no difference in diversity was observed in this study. Although

Tomizawa R, et al.

diversity was observed in previous studies of patients with mild hypertension[3], the present study did not focus on the severity of hypertension because
the hypertension group was identified using self-reported questionnaires, thenumber of years of medical history was not considered, and the criteria for
judging the hypertension group from the measured values was the grade
I hypertension group. Including and comparing more severe hypertensive
cases should be considered in the future study.
By equally grouping one of the pairs into the hypertensive group and theother into the control group, four genera were extracted for comparison after
adjusting for age and sex between the two groups. Age [19] and sex [20]contribute to microbiota variation; however, there was no need to adjust for age
or gender because each pair of monozygotic twins was divided into two groups.
In addition, when the effects of age, sex, and genetic background were
removed by focusing on the differences between pairs, 15 genera wereextracted, including the four genera mentioned above, all of which showed
higher occupancy in the control group. Twelve genera belonged to thephylum Firmicutes, two to the phylum Bacteroidetes, and the remaining
one to the phylum Actinobacteria. At the phylum level, Firmicutes has beenreported to be associated with coronary artery disease and Actinobacteriawith cerebral infarction, suggesting that Bacteroidetes may be involved in theprevention of coronary artery disease and cerebral infarction. In addition, theF/B ratio has been found to be higher in studies of hypertension [2,21], andseveral genera extracted in this study were also found in the Firmicutes andBacteroidetes phyla, supporting the previous findings.
Of the 15 genera selected for this study, six had already been reported to be
associated with blood pressure.
Four genera showed the same trend as in a previous study [5,22-24].
Actinomyces was observed in a significant amount in the hypertensive
group, showing the same trend as in a previous study [22], having a positive
correlation with blood pressure indices.
The Eubacterium rectale group was observed in a significant (0.043) amountin the hypertensive group in this study, showing the same trend as in aprevious study [23].
The Eubacterium eligens group had high occupancy in the hypertension
group, similar to a study examining the differences in gut bacteria by dietwhen saturated fatty acid was replaced with walnuts and vegetable oil in
people that were high risk for cardiovascular disease. An increase in the
Eubacterium eligens group showed the same trend as reported [24], whichcorrelated with a decrease in brachial mean arterial pressure, central meanarterial pressure, and central diastolic blood pressure (BP).
The Ruminococcaceae NK 4A 214 group was observed in a significant(0.026) amount in the control group, with the same trend in a previous study[5].
Two genera showed a different trend from the previous study.
Butyricoccus was observed in a significant (0.012) amount in the hypertension
group, and showed an opposite trend to previous studies, which reported an
association with enrichment in normotension or negatively associated withBP indices [3].
Coprobacter was found in a significant (0.022) amount in the hypertensive
group, whereas it was high in the control group in a previous study [25], thus
showing the opposite trend. The difference was that the previous study did
not exclude patients who had received antihypertensive treatment.
Given that present study included hypertensive discordant monozygotic
twins, indicates the possibility of age, sex, and genetic effects in these twogenera.
Persistent high blood pressure causes arteriosclerosis and lead to
cardiovascular diseases including cardiac disease. Ruminococcaceae
UCG 014, which had a higher occupancy in the control group in this study,
was not reported to be related to hypertension in previous studies, but it
has been reported to be cardiac disease [26]. An increased presence ofPage 6 of 9

J Hypertens (Los Angel), Volume 10:8, 2021

Ruminococcaceae UCG 005, Ruminococcaceae UCG 014 and Eubacterium
fissicatena group was noted in the acute coronary syndrome (ACS)
group compared to the healthy control group, as well as increased serum
trimethylamine N-oxide (TMAO) concentration [26]. Cardiovascular events,
such ACS, are caused by the disruption of atherosclerotic plaques andthrombus occlusion. Atherosclerosis is a chronic inflammatory disease of theblood vessels that occurs under the influence of lifestyle-related diseases,
including hypertension and smoking. Its mechanism has been clarified
since the report that TMAO, a metabolite derived from intestinal bacteria, isassociated with the onset of neovascular events [27]. Choline and L-carnitine
contained in eggs, shrimp, and meat are metabolized to TMA by intestinalbacteria, absorbed from the intestinal tract, and converted to TMAO via
enzymes in the liver [28]. TMAO increases cholesterol accumulation in
macrophages and decreases cholesterol withdrawal from plaques. TMAO
also increases platelet aggregation capacity, promotes thrombus formation,and increases cardiovascular events [27]. A previous study confirmed that
serum TMAO correlated with ACS, suggesting that these bacteria may be
predictive biomarkers for ACS development [26]; however, the results of thisstudy showed that Ruminococcaceae UCG 005, Ruminococcaceae UCG
014 and Eubacterium eligens group may also be affected by age, gender,and genetics, and therefore further research concerning these factors needs
to be considered.
In a previous study, Ruminococcaceae UCG 014 showed that the intestinal
flora may mediate the effect of periodontitis on diabetes [26]. In the gut
microbiota, some butyric acid-producing bacteria, such as Eubacterium_
fissicatena group and Ruminococcaceae_UCG-014 were reduced, anda negative correlation with serum HbA 1c was confirmed. These results
suggest that Ruminococcaceae UCG 014 and Eubacterium fissicatena group
may have similar functions; however the Eubacterium fissicatena group wasobserved in a significant amount in the HT group. And Ruminococcaceae
UCG 005 was observed in a significant (0.033) amount in the control group.
This was also observed in the results for intestinal diseases in the nondisease group [33].
Further studies, including those on its mechanism, are required.
For the other five bacteria extracted in this study, there were no reports
showing an association with cardiovascular disease.
Lachnospira was observed in a significant (0.025) amount in the control
group. Previous studies in humans also reported a decrease in patientswith gallstones compared to the normal group [29] and significantly morein the normal group than in patients with idiopathic nephrotic syndrome withmembranous nephropathy [30]. This was consistent with the present study inthat it was more common in the normal group, despite differences in disease.
A study focusing on differences in gut microbiota between normal-weight
and overweight children living in urban areas in the Philippines reported that
Lachnospira was more common in the overweight group [31]; however, it isdifficult to compare the two groups because the median body mass index inthis study was 22.8 and there were differences in age.
In addition, Lachnospira is associated with SNP rs59846192 (DMRTB1), andit has been reported that populations with the major allele (GG) are rich in
Lachnospira [32]. However, this study classified monozygotic twin pairs withidentical genetic backgrounds into two groups; therefore, this effect was notconsidered.
Prevotellaceae UCG 001 was observed in a significant (0.025) amount in
the hypertensive group, whereas a decrease in this amount was observedin chronic kidney disease patients [34]. A significant decrease was also
observed in the high fat diet group in a mouse experiment [35]; however,it increased when combined with alcoholic beverages. This also caused anincrease in the value and activity of antioxidant enzymes and a decreasein the content of lipid peroxidation products [36], suggesting that it may be
influenced by diet.
Coprococcus 1 was observed in a significant (0.049) amount in the control
group. There are a few human studies on Coprococcus 1, and it has been

Tomizawa R, et al.

J Hypertens (Los Angel), Volume 10:8, 2021

reported to be associated with weight loss [37]; however, there is still muchto be learned.

studies with a larger number of subjects are needed to confirm these
relationships in the future.

Eubacterium ruminantium group, an opportunistic bacterium, was not found in
the hypertensive group. Few studies have been conducted on humans, and it
was reported that it was more common in patients that had miscarriages [38].

Our results extracted 15 genera of gut microbiota associated withhypertension, taking into account age, gender, and genetic factors that affectgut microbiota, by targeting hypertensive discordant Japanese monozygotic
twins. Furthermore, by investigating the relationship between the extracted
hypertension-related bacteria and nutrients that strongly affect the intestinalbacteria, Actinomyces and Eubacterium rectale groups were associated withsome nutrients, but other bacteria were not associated with nutrients. The
cross-sectional of this study inherently limits our ability to delineate fully theobserved associations among hypertension and gut bacteria and nutrition.
Further exploration of specific associations presented here will requirecomparison with cohort studies or increasing the number of subjects.

Ruminiclostridium 9 was observed in a significant (0.010) amount in thecontrol group. To date, no study has reported its relationship with the disease.
Ruminococcaceae UCG 004 was observed in a significant amount in thecontrol group. There was a report associated with inattentive symptoms in astudy of people with attention deficit/hyperactivity disorder [39].
In a cohort study, no association was found between 68 different microbiota
markers and self-reported hypertension after correction for multiple testing[40]; however, we suggest that 15 bacteria are hypertension-associatedbacteria by studying hypertensive discordant monozygotic twins. Even
though subtypes of hypertension, isolated systolic hypertension, anddiastolic hypertension share different gut microbiota profiles [25], for rigor,
actual measurements and severity of hypertension should be considered. In
a previous study, the association between gut microbiota and hypertension
was not significant in the British, Africans, Ghanaians, South Asians, andTurks, and significant associations were observed in Americans, Finns,
Moroccans, and the Dutch. Only the gut microbiota of the Dutch cohort
showed a high level of explained variance [40], suggesting that the Japanese
may be the ethnic group that shows an association between gut bacteria andhypertension.
The gut microbiome is affected by diet, and the relationship between diet andnutrients has already been reported in the 15 genera identified in this study.
For example, Actinomyces and Lachnospira have been found to be affectedby dietary fiber; for Actinomyces, it has been reported that the higher thefiber intake, the lower the percentage of Actinomyces [23], and the present
results are not statistically significant [24]. However, as in previous studies,
the higher the fiber intake, the lower the percentage of Actinomyces andActinomyces was lower in the control group. Alternatively, for Lachnospira,l-Theanine increased the percentage of Lachnospira and an increase intotal SCFA (acetate, propionate, butyrate) was reported [41]. The present
results also showed a positive correlation between dietary fiber intake andLachnospira, although this was not statistically significant (r = -0.1).
A diet high in dietary fiber has an antihypertensive effect in hypertensive
patients [42], and the results are consistent with the association between
these two bacteria and dietary fiber. One of the mechanisms is the responseof the intestinal bacterial tumor metabolite SCFA to its receptors Olfr78 [5]and Gpr41 [6]. In addition, dietary fiber is effective in lowering blood pressure
by adsorbing sodium and removing it from the body; however, when we
focused on sodium, which has a negative impact on BP and cardio-vascular
health, we found a significant positive correlation with Eubacterium rectale
group (r = 0.48), and Eubacterium rectale group was more common in thehypertensive group. Therefore, Eubacterium rectale group may play somerole in this study. In addition, there was a negative correlation between
Ruminococcaceae NK 4A 214 group and Ruminococcaceae UCG 005.
In addition, fruits that increase SCFA are rich in potassium. Potassium
intake was negatively correlated with Actinomyces (r = -0.4) and positively
correlated with the Eubacterium rectale group (r = 0.4). In a previous study,
Lachnospira was also positively correlated with fruit [43]; however, this result
was not statistically significant (r = 0.18). Here, a positive correlation withpotassium was observed, although this was not statistically significant.

Conclusions
This study had several limitations. The target sample size was insufficientfor strong statistical power (26 pairs), which may have led to bias in thedata. Owing to these limitations, we were unable to detect several genera
associated with Blood Pressure, as identified in previous studies. Further
Page 7 of 9

Author Contribution
Rie Tomizawa and Jonguk Park contributed equally to this work.

Acknowledgments
We would like to thank the twin subjects for their participation, the researcher
Kanako Akada, and all technical and secretary staff at the Center for Twin
Research at Osaka University Graduate School of Medicine. The authors
would like to thank all consultants from the Osaka Twin Research Group(Norio Sakai, Masanori Takahashi, Hisashi Tanaka, Kei Kamide, Shinji
Kihara, Hiroko Watanabe, Mikio Watanabe, and Hiroto Takahashi, Center
for Twin Research, Osaka University Graduate School of Medicine). This
work was supported by JSPS KAKENHI, Grant Numbers JP16K15385,
JP17H04134 and JP 21K15267, University Grants from the Japanese
Ministry of Education, Culture, Sports, Science and Tech-nology, and theMinistry of Health and Welfare of Japan and Public/Private R&D Investment
Strategic Expansion PrograM (PRISM) and AMED-CREST (JP 20 gm
1010006 h 004).

Conflicts of Interest
The authors declare no conflict of interest.

References
1. Kamada Nobuhiko, Sang-Uk Seo, Grace Y Chen and Gabriel Núñez, etal. “Role of the gut microbiota in immunity and inflammatory disease.”
Nat Rev Immunol 13(2013): 321-335.
2. Yang Tao, Santisteban Monica M, Rodriguez Vermali and Li Eric et al.
“Gut dysbiosis is linked to hypertension.” Hypertension 65(2015): 13311340.
3. Li Jing, Fangqing, Yidan Wang and Junru Chen et al. “Gut microbiota
dysbiosis contributes to the development of hypertension.” Micro-biome5(2017): 14.
4. Durgan David J, Bhanu P Ganesh, Julia L Cope and Nadim J Ajami
et al. “Role of the gut microbiome in obstructive sleep apnea-induced
hypertension.” Hypertension 67(2016): 469-474.
5. Pluznick Jennifer L, Ryan J Protzko, Haykanush Gevorgyan and Zita
Peterlin et al. “Olfactory receptor responding to gut microbiota-derived
signals plays a role in renin secretion and blood pressure regulation.”
Proc Natl Acad Sci U S A 110(2013): 4410-4415.
6. Pluznick Jennifer L. “Renal and cardiovascular sensory receptors andblood pressure regulation.” Am J Physiol Ren Physiol 305(2013): 439-44.
7. Portal site of Official Statistics of Japan(e-Stat). National Health andnutrition Survey, Japan.

Tomizawa R, et al.

J Hypertens (Los Angel), Volume 10:8, 2021

8. Awany Denis, Imane Allali, Shareefa Dalvie and Sian Hemmings et al.
“Host and Microbiome Genome-Wide Association Studies: Current State
and Challenges.” Front Genet 22(2019): 637.

25. Xie Dan, Zhang Mushi, Wang Baili and Lin Han et al. “Differential
analysis of hypertension-associated intestinal microbiota.” Int J Med Sci
16(2019): 872-881.

9. Nishijima Suguru, Wataru Suda, Kenshiro Oshima and Seok-Won Kim
et al. “The gut microbiome of healthy Japanese and its microbial andfunctional uniqueness.” DNA Res 23(2016): 125-133.

26. JingGao, Kun-TaoYan, Ji-XiangWang and Jing Dou, et al. “Gut microbial
taxa as potential predictive biomarkers for acute coronary syndrome andpost-STEMI cardiovascular events.” Sci Rep 1(2020): 2639.

10. Guidelines for the management of hypertension. Japanese Societyof Hypertension. Committee for the Development of Hypertension
Treatment Guidelines, Japan (2019).

27. Weifei Zhu, Jill C Gregory, Elin Org and Jennifer A Buffa, et al. “Gutmicrobial metabolite TMAO enhances platelet hyperreactivity andthrombosis risk.” Cell 165(2016): 111-124.

11. Kobayashi Satomi, Kentaro Murakami, Satoshi Sasaki and Hitomi
Okubo et al. “Comparison of relative validity of food group intakes
estimated by comprehensive and brief-type self-administered diethistory questionnaires against 16 d dietary records in Japanese adults.”
Public Health Nutr 14(2011): 1200-1211.

28. Simó C and García-Cañas V. “Dietary bioactive ingredients to modulate
the gut microbiota-derived metabolite TMAO. New opportunities forfunctional food development.” Food Funct 11(2020): 6745-6776.

12. Hosomi Koji, Harumi Ohno, Haruka Murakami and Yayoi NatsumeKitatani et al. “Method for preparing DNA from feces in guanidine
thiocyanate solution affects 16S rRNA-based pro-filing of humanmicrobiota diversity.” Sci Rep 7(2017): 4339.

29. Tao Wu, Zhigang Zhang, Bin Liu and Dezhi Hou, et al. “Gut microbiota
dysbiosis and bacterial community assembly associated with cholesterol
gallstones in large-scale study.” BMC Genomics 14(2013): 669.
30. JunZhanga, DanLuob, ZhimingLinc and Wenying Zhou, et al. “Dysbiosis
of gut microbiota in adult idiopathic membranous nephropathy withnephrotic syndrome.” Microb Pathog 147(2020): 104359.

13. Caporaso J Gregory, Justin Kuczynski, Jesse Stombaugh and Kyle
Bittinger et al. “QIIME Allows Analysis of High-Throughput Community
Sequencing data.” Nat Methods 7(2010): 335-336.

31. Maria Julia Golloso-Gubat, Quinten R Ducarmon, Robby Carlo A Tan
and Romy D Zwittink, et al. “Gut microbiota and dietary intake of normalweight and overweight Filipino children.” Microorganisms 8(2020): 1015.

14. Mohsen Attayeb, Jonguk Park, Yi-An Chen and Hitoshi Kawashima,
et al. “Impact of quality trimming on the efficiency of reads joining anddiversity analysis of Illumina Paired-end reads in the context of QIIME
1 and QIIME 2 microbiome analysis frameworks.” BMC Bioinformatics
20(2019): 581.

32. Williams Turpin, Osvaldo Espin-Garcia, Wei Xu and Mark S Silverberg.
“Association of host genome with intestinal microbial composition in alarge healthy cohort.” Nat Genet 48(2016): 1413-1417.

15. Christian Quast, Elmar Pruesse, Pelin Yilmaz and Jan Gerken, et al.
“The SILVA ribosomal RNA gene database project: Improved dataprocessing and web-based tools.” Nucleic Acids Res 41(2013): 590-596.
16. Edgar Robert C. “Search and clustering orders of magnitude faster than
BLAST.” Bioinformatics 26(2010): 2460-2461.
17. Verhaar Barbara J H, Didier Collard, Andrei Prodan and Johannes H M
Levels JHM, et al. “Associations between gut microbiota, faecal shortchain fatty acids, and blood pressure across ethnic groups: The HELIUS
study.” Eur Heart J 41(2020): 4259-4267.
18. Yan Qiulong, Yifang Gu, Xiangchun Li and Wei Yang, et al. “Alterationsof the gut microbiome in hypertension.” Front Cell Infect Microbiol
7(2017): 381.
19. Paul W O’Toole, Ian B Jeffery. “Gut microbiota and aging.” Science350(2015): 1214–1215.
20. Cuesta-Zuluaga Jacobo de la, Scott T. Kelley, Yingfeng Chen and JuanS. Escobar, et al. “Age- and Sex-Dependent Patterns of Gut Microbial
Diversity in Human Adults.” M Systems 4(2019): 00261-00219.
21. Jose Pedro A, Dominic Raj. “Gut microbiota in hypertension.” Curr Opin
Nephrol Hypertens 24(2015): 403-409.
22. Palmu Joonatan, Aaro Salosensaari, Aki S. Havulinna and Susan Cheng,
et al. “Association between the gut microbiota and blood pressure ina population cohort of 6953 individuals.” J Am Heart Assoc 9(2020):
016641.
23. Liu Jilun, Ning An, Cong Ma and Xiaofeng Li, et al. “Correlation analysis
of intestinal flora with hypertension.” Exp Ther Med 16(2018): 23252330.
24. Tindall Alyssa M, Christopher J McLimans, Kristina S Petersen andPenny M Kris-Etherton, et al. “Walnuts and vegetable oils containing
oleic acid differentially affect the gut microbiota and associations withcardiovascular risk factors: Follow-up of a randomized, controlled,
feeding trial in adults at risk for cardiovascular disease.” J Nutr
150(2020): 806-817.
Page 8 of 9

33. Leonardo Mancabelli, Christian Milani, Gabriele Andrea Lugli andFrancesca Turroni, et al. “Identification of universal gut microbial
biomarkers of common human intestinal diseases by meta-analysis.”
FEMS Microbiol Ecol 93(2017).
34. Jin Zhao, Xiaoxuan Ning, Baojian Liu and Ruijuan Dong, et al. “Specificalterations in gut microbiota in patients with chronic kidney disease: An
updated systematic review.” Ren Fail 43(2021): 102-112.
35. Zibin Zheng, Wentao Lyu, Ying Ren, Xiaoqiong Li et al. “Allobaculum
involves in the modulation of intestinal ANGPTLT 4 expression in micetreated by high-fat diet.” Front Nutr 8(2021): 690138.
36. Liang Zhao, Hanying Ouyang, Nanhai Zhang and Chengtao Wanget al. “Effects of Huangjiu, Baijiu and red wine combined with high-fat
diet on glucose and lipid metabolism: Aggravate or alleviate?” Alcohol56(2021): 334-347.
37. Junsheng Liu, Jun Liu, Liu Liu and Guangwen Zhang, et al. “The gut
microbiota alteration and the key bacteria in Astragalus polysac-charides(APS)-improved osteoporosis.” Food Res Int 138(2020): 109811.
38. Yongjie Liu, Hainan Chen, Liping Feng and Jun Zhang. “Interactions
between gut microbiota and metabolites modulate cytokine network
im-balances in women with unexplained miscarriage.” NPJ Biofilms
Microbiomes 7(2021): 24.
39. Joanna Szopinska-Tokov, Sarita Dam, Jilly Naaijen and Prokopis
Konstanti, et al. “Investigating the gut microbiota composition ofindividuals with attention-deficit/hyperactivity disorder and association
with symptoms.” Microorganisms 8(2020): 406.
40. Matthew A. Jackson, Serena Verdi, Maria-Emanuela Maxan and CheolMin Shin, et al. “Gut microbiota associations with common diseases andprescription medications in a population-based cohort.” Nat Commun
9(2018): 2655.
41. Wei Xu, Ling Lin, An Liu and Tuo Zhang, et al. “L-theanine affects
intestinal mucosal immunity by regulating short-chain fatty acidmetabolism under dietary fiber feeding.” Food Funct 11(2020): 83698379.
42. Whelton SP, Hyre AD, Pedersen B and Yi Y, et al. “Effect of dietary fiber

Tomizawa R, et al.

intake on blood pressure: A me-ta-analysis of randomized, controlled
clinical trials.” J Hypertens 23(2005):475-481.
43. Dena R Herman, Nicholas Rhoades, Jasmine Mercado and Pedro
Argueta, et al., “Dietary habits of 2- to 9-year-old American children
are associated with gut microbiome composition.” J Acad Nutr Diet
120(2020): 517-534.

Page 9 of 9

J Hypertens (Los Angel), Volume 10:8, 2021

How to cite this article: Rie Tomizawa, Jonguk Park, Koji Hosomi, Natsuko
Matsumoto, Hitoshi Kawashima, Kenji Mizuguchi, Jun Kunisawa and Chika
Honda. "Relationship between Human Gut Microbiota and Nutrition Intake
in Hypertensive Discordant Monozygotic Twins." J Hypertens (Los Angel) 10(2021): 297.