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OPEN

Profile of gut microbiota
and serum metabolites associatedwith metabolic syndrome
in a remote island most afflictedby obesity in Japan
Tsugumi Uema1, Jasmine F. Millman 1*, Shiki Okamoto 1*, Takehiro Nakamura1,
Kiyoto Yamashiro1, Moriyuki Uehara1, Ken‑ichiro Honma1, Minoru Miyazato2,
Asuka Ashikari3, Seiichi Saito3, Shiro Maeda4,5, Minako Imamura4,5, Hajime Ishida6,
Masayuki Matsushita7, Koshi Nakamura8 & Hiroaki Masuzaki 1*
Numerous studies have revealed distinct differences in the profiles of gut microbiota between nonobese and obese individuals. To date, however, little is known if any disparities in the communityof gut microbiota exist between metabolically healthy obese (MHO) and metabolically unhealthyobese (MUO) subjects. We therefore aimed to comprehensively characterize the gut microbiota andcirculating metabolites in serum from both MHO and MUO residing in the remote island, Kumejima,
where the prevalence of obesity is one of the highest in Japan, and explored possible correlations
between the gut microbiota profile and markers of metabolic syndrome. Results revealed that MUO
showed significantly higher levels of genera such as g_Succinivibrio, g_Granulicatella, g_Brachyspira,g_Oribacterium and g_Atopobium in comparison to MHO. Moreover, abundance of g_Succinivibrio,
g_Brachyspira and g_Atopobium were positively correlated with value of fasting insulin, HOMA-R,
circulating triglycerides, diastolic blood pressure, BMI, body weight, waist circumference and HbA1c.
In addition, MUO compared to MHO showed an imbalance of serum metabolites, with a significant
elevation in 2-oxoisovaleric acid, pyruvic acid, 2-hydroxybutyric acid, and creatine. Our data highlightunmet needs in precision approaches for the treatment of obesity, targeting the gut microbiota profileand serum metabolites in a distinct population affected by obesity.
Globally, obesity has reached to pandemic levels and represents a major risk for non-communicable diseases
such as cardiovascular diseases and type 2 diabetes mellitus (T2DM); comprising greater than 70% of all deaths
world-wide1. Obesity is generally characterized by a body mass index(BMI) ≥ 30 kg/m2 by the World HealthOrganization (WHO). On the other hand, obesity in Japan is classified as having a BMI ≥ 25 kg/m2, based on
the observation that east and south-east Asians including Japanese represent a considerable risk elevation in
obesity-related comorbidities at a much lower BMI than Caucasians, Hispanics and African ­people2.

1

Division of Endocrinology, Diabetes and Metabolism, Hematology, Rheumatology, (Second Department
of Internal Medicine), Graduate School of Medicine, University of the Ryukyus, 207 Uehara, Nishihara,
Okinawa 903‑0215, Japan. 2Department of Systems Physiology, Graduate School of Medicine, University of the
Ryukyus, Uehara, Nishihara, Okinawa, Japan. 3Department of Urology, Graduate School of Medicine, Universityof the Ryukyus, Uehara, Nishihara, Okinawa, Japan. 4Department of Advanced Genomic and Laboratory
Medicine, Graduate School of Medicine, University of the Ryukyus, Uehara, Nishihara, Okinawa, Japan. 5Divisionof Clinical Laboratory and Blood Transfusion, University of the Ryukyus Hospital, Uehara, Nishihara, Okinawa,
Japan. 6Department of Human Biology and Anatomy, Graduate School of Medicine, University of the Ryukyus,
Uehara, Nishihara, Okinawa, Japan. 7Department of Molecular and Cellular Physiology, Graduate School of
Medicine, University of the Ryukyus, Uehara, Nishihara, Okinawa, Japan. 8Department of Public Health andHygiene, Graduate School of Medicine, University of the Ryukyus, Uehara, Nishihara, Okinawa, Japan. *email:Jasmine.f.millman@gmail.com; shiki@med.u-ryukyu.ac.jp; hiroaki@med.u-ryukyu.ac.jp
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The most southern prefecture, in Japan, Okinawa, comprises of a number of smaller islands, consisting of a
distinctly different lifestyle and culture to that of the mainland of Japan, and was once world-renowned as being
home to a large proportion of especially healthy, long-living individuals. However, Okinawa currently shows oneof the highest prevalence of obesity and T2DM in Japan under the expeditious adoption of western style dietsand sedentary l­ ifestyle3. Among a variety of islands in Okinawa, particularly Kumejima Island has limited foodaccess and availability, in turn creating a highly-obesogenic environment by limiting food choices to unhealthy
and high calorie food items.
Cumulative evidence is emerging regarding the association between obesity and the profile of gut­microbiota4,5, with lean individuals showing distinctly different profile of gut microbiota compared to those whoare ­obese6. Both animal and human studies have demonstrated that a significant decrease in microbial diversity
and also an imbalance in metabolomic and fermentation-related markers in states of obesity and ­T2DM7,8.
Importantly, an elevated Firmicutes to Bacteroidetes ratio appears to be representative in animal models of obesity, however, this finding is not consistent with human ­studies4. In contrast, in humans, certain gut microbiota
such as Oscillospira, Lactobacillus and Alistipes6 as well members of the Clostridia class which are associated with
reduction of systemic inflammation are characteristically depleted in obese people as compared to lean s­ ubjects9.
Animal studies and a number of human studies have also revealed a valuable role of certain microbial
metabolites such as short chain fatty acids (SCFAs), produced by fermentation of fiber, in the prevention andimprovement of obesity and its c­ omorbidities10. Conversely, a series of serum metabolites involved in glucose
and lipid dysmetabolism such as pyruvic acid and hydroxybutyrate are elevated in obese and T2DM humansand ­animals11,12.
Although evidence appears compelling regarding the detrimental effects of obesity on the profile of gut microbiota, there is a controversy regarding the relationship between metabolic risk and obesity per se. Distinctively,
as opposed to metabolically unhealthy obese (MUO), metabolically healthy obese (MHO) individuals show
relatively lower levels of visceral fat a­ ccumulation13, are more sensitive to insulin and exemplify less inflammatory­status14,15. However, a large gap between basic and clinical research still remains regarding the gut microbiota
and circulating metabolites implicated in MUO.
In this context, the present study aimed to characterize the profile of gut microbiota and serum metabolites
in non-obese (NO) and obese (OB) subjects, with a special focus on MHO and MUO inhabitants of the remoteisland, Kumejima, where the prevalence of obesity is one of the highest in Japan (Fig. 1).

Results

Subject characteristics. A number of significant differences in anthropometric, clinical and biochemicalparameters were observed between NO (n = 81) and OB (n = 59) (Table 1). Body weight, BMI and waist circumference were significantly higher in OB compared to NO (NO 56.89 ± 1.05, OB 71.66 ± 1.23, NO 22.42 ± 0.26,
OB 27.93 ± 0.3 and NO 83.78 ± 0.81, OB 95.63 ± 0.95, p < 0.0001, respectively). NO and OB subjects also showed
differences in a systolic blood pressure (SBP) (NO 128.89 ± 2.03, OB 138.31 ± 2.38, p = 0.0031) and a diastolicblood pressure (DBP) (NO 75.33 ± 1.25, OB 81.27 ± 1.46, p = 0.0024), with these parameters being elevated in
the OB. Regarding biochemical parameters, compared to NO, the OB showed significantly higher levels of totalcholesterol (TC) (NO 198.33 ± 4.03, OB 215.39 ± 4.72, p = 0.0068), high-density lipoprotein-cholesterol (HDL-C)(NO 63.35 ± 1.78, OB 56.44 ± 2.09, p = 0.013), low-density lipoprotein-cholesterol (LDL-C) (NO 114.4 ± 3.46, OB
131.54 ± 4.05, p = 0.0016) and triglyceride (TG) (NO 81 (59–123), OB 134 (91–165), p < 0.0001), respectively.
OB as opposed to NO subjects also had higher hemoglobin A1c (HbA1c) (NO 5.5 (5.3–5.7), OB 5.6 (5.4–6),
p = 0.015), glucose (NO 81 (75.5–86), OB 83 (79–90), p = 0.047), insulin (NO 3.6 (2.9–5), OB 4.9 (3.5–8.5),
p = 0.001) and Homeostatic Model Assessment of Insulin Resistance (HOMA-R) (NO 0.77 (0.55–1.005), OB
1.09 (0.66–1.8), p = 0.0007), respectively.
When subjects were divided into MHO and MUO, various significant differences remained (Table 2). Specifically, MHO and MUO showed differences in SBP (MHO 133.61 ± 2.31, MUO 142.76 ± 3.71, p = 0.04) and DBP(MHO 78.69 ± 1.34, MUO 85.9 ± 2.14, p = 0.0056), which were higher in the MUO. A number of biochemicalparameters were also elevated in the MUO compared to the MHO, with the MUO revealing significantly higher
levels of TC (MHO 208.06 ± 4.97, MUO 227.14 ± 7.97, p = 0.046), HDL-C (MHO 59.76 ± 1.95, MUO 51.33 ± 3.12,
p = 0.025), TG (MHO 94 (70.75–146), MUO 176 (159–202.5), p < 0.0001), insulin (MHO 4.2 (2.875–6.95), MUO
7.3 (3.9–10), p = 0.0047), glucose (MHO 83 (78–89.25), MUO 88 (80–95), p = 0.074) and HOMA-R (MHO 0.89
(0.59–1.42), MUO 1.66 (0.78–2.25), p = 0.0044), respectively.
Gut microbiota analysis. A number of significant differences were observed between the gut microbiota
communities of NO and OB as well as MHO and MUO. Firstly, beta diversity (dissimilarity index), as demonstrated by Principal Coordinates Analysis (PCoA), with percentage of variation described by Axis 1 (18.22%),
Axis 2 (10.33%) and Axis 3 (4.982%) with the visual observation confirmed by the PERMANOVA test, which
produced significant results for the comparison of NO and OB (p = 0.003) (Fig. 2a). When comparing beta diversity between MHO and MUO, PCoA showed variation described by Axis 1 (16.59%), Axis 2 (7.351%) and Axis
3 (4.477%), and confirmed by the PERMANOVA test, which did not produce significant results for the comparison of MHO and MUO (p = 0.077) (Fig. 2b). Alpha diversity as assessed by the Chao1, Shannon and Observed
features with the Kruskal–Wallis test was significantly higher in NO compared to OB (Chao1 (p = 0.033), Shannon (p = 0.017) and Observed feature (p = 0.024)) (Fig. 2c–e). An elevation in alpha diversity was also observed in
MHO compared to MUO however this did not reach statistical significance (Chao1 (p = 0.6), Shannon (p = 0.18)
and Observed feature (p = 0.85)) (Fig. 2f–h).
Importantly, however, when employing the use of Linear Discriminant Analysis Effect Size (LEfSe) analyses,
a range of marked differences in bacteria were observed at a variety of taxonomic levels between NO and OB as
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Enrollment

Residents of Kumejima aged over 20 years were recruited(n=178)
Excluded (n=38)
Unable to collect fecal sample (n=15)
Unable to measure clinical and biochemical blood samples
due to haemolysis or cloudiness (n=10)
Other reason (n=13)
Declined to participate (n=0)

Total number of participants able to provide required samples (n=140)

Analysis

BMI < 25kg/m2

BMI ≥ 25kg/m2

NO (n=81)

OB (n=59)

Subjects with abdominal obesity defined as a waist circumference≥ 85 cm for men and ≥ 90 cm for women (n=75)

Metabolic syndrome was classified as having
at least two of the following three metabolic abnormalities

AnalysisHigh blood glucose

Dyslipidemia

High blood pressure

Fasting blood glucose≥ 100 mg/dL
or
Use of medication for T2DM

Fasting serum TG ≥ 150 mg/dL
or
HDL-C < 40 mg/dL

SBP ≥ 130 mmHg
DBP ≥ 85 mmHg
or
Use of medication for
hypertension

Participants with none or only one of the threemetabolic abnormalities specified

Participants with at least two of the threemetabolic abnormalities specified

MHO (n=54)

MUO (n=21)

Figure 1.  Study scheme. NO; non-obese (n = 81), OB; obese, (n = 59), MHO; metabolically healthy obese(n = 54) and MUO; metabolically unhealthy obese (n = 21). Graphics were made using the Adobe Illustrator(version: 5.8.0.592).
well as MHO and MUO. Notably, LEfSe analyses (p < 0.05, LDA score > 5.5) revealed that the ratio of NO versus
OB had significantly higher levels in p_Tenericutes (p = 0.03, LDA score = 6.71) at the phylum level, in c_Molicutes (p = 0.03, LDA score = 6.72) at the class level and in o_SHA_98 (p = 0.049, LDA score = 6.09) and in o_RF39(p = 0.03, LDA score = 6.73) at the order level, respectively. Abundance of microbiota including f_Barnesiellaceae(p = 0.02, LDA score = 6.98), f_Rikenellaceae (p = 0.007, LDA score = 6.86), f_Christensenellaceae (p = 0.034, LDA
score = 6.48) and f_Mogibacteriaceae (p = 0.028, LDA score = 6.07) were elevated in NO compared to OB at
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Anthropometric and clinical parameters

Meann (M:F)81 (37:44)

OB
SEM

Mean

SEM

p value59 (33:26)

Age (years)

56

1.47

56

1.72

0.96

Height (cm)159.02

1.04

159.921.22

0.58< 0.0001

Body weight (kg)

56.89

1.05

71.66

1.23

BMI (kg/m2)

22.42

0.26

27.93

0.3

< 0.0001

Waist circumference (cm)

83.78

0.81

95.63

0.95

< 0.0001

Systolic blood pressure (mmHg)

128.892.03

138.312.38

0.0031

Diastolic blood pressure (mmHg)

75.33

1.25

81.27

1.46

0.0024

Biochemical parameters

Mean

SEM

Mean

SEM

p value

Total-cholesterol (mg/dl)

198.33

4.03

215.394.72

0.0068HDL-cholesterol (mg/dl)

63.35

1.78

56.44

2.09

0.013

LDL-cholesterol (mg/dl)

114.4

3.46

131.544.05

0.0016

Biochemical parameters

Median25th–75th
Percentiles

Median25th–75th
Percentiles

p value

Triglycerides (mg/dl)

81

59–123134

91–165< 0.0001

HbA1c (%)

5.5

5.3–5.7

5.6

5.4–6

0.015

Insulin (uIU/ml)

3.6

2.9–5

4.9

3.5–8.5

0.001

Glucose (mg/dl)

81

75.5–86

83

79–90

0.047

HOMA-R

0.77

0.55–1.005

1.09

0.66–1.8

0.0007

Table 1.  Characteristics of NO and OB. Data were analyzed using unpaired t-test or Mann–Whitney U test tocompare groups. NO non-obese, OB obese.

MHO
Anthropometric and clinical parameters

Meann (M:F)54 (33:21)

MUO
SEM

Mean

SEM

p value21 (12:9)

Age (years)

57

1.62

55

2.61

0.42

Height (cm)161.681.13

161.11

1.81

0.79
0.17

Body weight (kg)

68.45

1.49

72.36

2.4

BMI (kg/m2)

26.16

0.46

27.77

0.73

0.066

Waist circumference (cm)

94.07

1.02

97.1

1.63

0.12

Systolic blood pressure (mmHg)

133.61

2.31

142.763.71

0.04

Diastolic blood pressure (mmHg)

78.69

1.34

85.9

2.14

0.0056

Biochemical parameters

Mean

SEM

Mean

SEM

p value

Total-cholesterol (mg/dl)

208.06

4.97

227.14

7.97

0.046

HDL-cholesterol (mg/dl)

59.76

1.95

51.33

3.12

0.025

LDL-cholesterol (mg/dl)

126.02

4.09

137.136.56

0.15

Biochemical parameters

Median25th–75th
Percentiles

Median25th–75th
Percentiles

p value

Triglycerides (mg/dl)

9470.75–146

176

159–202.5< 0.0001

HbA1c (%)

5.5

5.3–5.7

5.8

5.45–60.056

Insulin (uIU/ml)

4.2

2.875–6.95

7.3

3.9–100.0047

Glucose (mg/dl)

83

78–89.25

88

80–95

0.074

HOMA-R

0.89

0.59–1.42

1.66

0.78–2.25

0.0044

Table 2.  Characteristics of MHO and MUO. Data were analyzed using unpaired t-test or Mann–Whitney U
test to compare groups. MHO metabolic healthy obese, MUO metabolic unhealthy obese.

the family level. The following genera were elevated in NO versus OB; g_Slackia (p = 0.023, LDA score = 6.37),
g_Odoribacter (p = 0.04, LDA score = 6.44), g_Dehalobacterium (p = 0.004, LDA score = 5.9), g_Lachnobacterium(p = 0.03, LDA score = 6.31), g_Anaerotruncus (p = 0.049, LDA score = 5.95) (Fig. 3a, b).
In comparison, several kinds of bacteria at the phylum, class, order, family, and genus level were elevatedin MUO as compared to MHO. At the phylum and class level, these included p_Spirochaetes (p = 0.022, LDA
score = 5.9) and c_Brachyspirae (p = 0.022, LDA score = 6.01), respectively. Microbiota from the order o_Brachyspirales (p = 0.02, LDA score = 5.99) were elevated in MUO in comparison to MHO. At the family level, f_Paraprevotellaceae (p = 0.005, LDA score = 7.01), f_Pasteurellaceae (p = 0.02, LDA score = 5.75), f_Brachyspiraceae(p = 0.02, LDA score = 5.94) and f_Carnobacteriaceae (p = 0.03, LDA score = 6.05), were elevated in MUOScientific Reports |Vol:.(1234567890)(2022) 12:17292 |https://doi.org/10.1038/s41598-022-21708-0

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a

b
MHO
MUO

NO
OB

c

d

e

*

500

200

Observed features

300

4

2

100

NO

0

OB

f

NO

300

200

100

0

OB

g

h

400

*

400

6
Shannon

Chao1

400

0

*

8

NO

OB

MHO

MUO

300

7
6

200

Observed features

5
Shannon

Chao1

300

4
3
2

100

1
0

MHO

MUO

0

MHO

MUO

200

100

0

Figure 2.  Diversity of gut microbiota in study subjects. Principal component analysis plot of microbiota basedon unweighted UniFrac distances showing beta diversity in (a) NO and OB, and (b) MHO and MUO withpermutational multivariate analysis of variance (PERMANOVA) employed to compare differences between
groups. Alpha diversity was measured using Chao1, Shannon and Observed features indices to compare NO
and OB (c–e), and MHO and MUO (f–h), with the Kruskal–Wallis test used to calculate the level of significancebetween groups. NO; non-obese (blue) (n = 81), OB; obese (red) (n = 59), MHO; metabolically healthy obese(light blue) (n = 54) and MUO; metabolically unhealthy obese (orange) (n = 21). Graphics were made using the
QIIME2 (version: 2019.7). *p < 0.05.

compared to MHO, respectively. At the genus level, g_Atopobium (p = 0.0002, LDA score = 5.73), g_Granulicatella (p = 0.029, LDA score = 6.03), g_Oribacterium (p = 0.022, LDA score = 5.81), g_Succinivibrio (p = 0.037, LDA
score = 6.68), g_Brachyspira (p = 0.022, LDA score = 6.08) were higher in MUO compared to MHO, respectively.
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a

b
NO

OB

NO
OB

f_Barnesiellaceae
f_Rikenellaceaeo_RF39c_Molicutes
p_Tenericutes
f_Christensenellaceae
g_Odoribacter
g_Slackia
g_Lachnobacterium
o_SHA_98
f_Mogibacteriaceae
g_Anaerotruncus
g_Dehalobacterium
0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

LDA SCORE (log 10)

c

d
MHO

MUO

f_Paraprevotellaceae
g_Succinivibrio
g_Brachyspiraf_Carnobacteriaceae
g_Granulicatella
c_Brachyspiraeo_Brachyspirales
f_Brachyspiraceae
p_Spirochaetes
g_Oribacterium
f_Pasteurellaceae
g_Atopobium

-8.0

-6.4

-4.8

-3.2

-1.6

MHO
MUO

c_Fusobacteriia
p_ Fusobacteria

0.0

1.6

3.2

4.8

6.4

8.0

LDA SCORE (log 10)

Figure 3.  Linear discriminant analysis (LDA) score distribution of microbiome between (a) NO; non-obese(blue) and OB; obese (red), and (c) MHO; metabolically healthy obese (light blue) and MUO; metabolicallyunhealthy obese (orange). Alphabetical letters listed before the name of the enterobacteria represent p_; phylum,
c_; class, o_; order, f_; family, and g_; genus levels. Linear discriminant analysis effect size (LEfSe) analyses
of gut microbiota and Cladogram indicating statistical differences of the microbial populations between (b)NO; non-obese (blue) and OB; obese (red), and (d) MHO; metabolically healthy obese (light blue) and MUO;
metabolically unhealthy obese (orange). Cladograms show the phylogenetic distribution of bacterial lineages
associated with groups from kingdom, phylum, class, order, family, and genus levels are listed in kingdom frominside to outside of the cladogram. NO; non-obese (blue) (n = 81), OB; obese (red) (n = 59), MHO; metabolically
healthy obese (light blue) (n = 54) and MUO; metabolically unhealthy obese (orange) (n = 21). Graphics weremade using the Galaxy/Hutlab (https://​hutte​nhower.​sph.​harva​rd.​edu/​galaxy/). p < 0.05, LDA score > 5.5.
Conversely, p_Fusobacteria (p = 0.039, LDA score = 7.17) and c_Fusobacteriia (p = 0.039, LDA score = 7.17)
increased in MHO compared to MUO (Fig. 3c, d).

Correlation of specific genera with components of the metabolic syndrome. Regarding therelationship between various anthropometric, clinical, and biochemical parameters associated with metabolic
syndrome (body weight, BMI, waist circumference, SBP, DBP, TC, HDL-C, LDL-C, TG, glucose, HbA1c, insulinand HOMA-R) and the five genera (g_Atopobium, g_Granulicatella, g_Oribacterium, g_Succinivibrio, g_Brachyspira), significant differences between MHO and MUO were shown in a heat map using Spearman’s rank correlation coefficient (Fig. 4). Significant correlations were observed among three genera and the eight various
anthropometric, clinical, and biochemical parameters associated with metabolic syndrome in the present study.
In particular, g_Atopobium was positively associated with the value of Insulin (ρ = 0.28, p = 0.014), HOMA-R(ρ = 0.3, p = 0.009), TG (ρ = 0.27, p = 0.018), DBP (ρ = 0.3, p = 0.009), BMI (ρ = 0.35, p = 0.002) and body weight(ρ = 0.33, p = 0.004) , respectively, The genus g_Succinivibrio was positively correlated with the value of insu-

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g_Atopobiumg_Succinivibrio

g_Brachyspirag_Granulicatella

HDL-cholesterol

Systolic blood pressure

GlucoseTotal cholesterol

LDL-cholesterol

HbA1c

Waist circumference

Body weight

BMI

Diastolic blood pressure

Triglycerides

HOMA-R

Insuling_Oribacterium

Correlation
1
0.5
0
-0.5
-1

Figure 4.  Spearman’s correlation heat map showing significant correlations of gut microbiota at the genus leveland selected parameters in subjects with abdominal obesity (n = 75). Significant correlations are shown as red(positive) or blue (negative). Graphics were made using the R-package (version 4.0.3). *p < 0.05; **p < 0.01.
lin (ρ = 0.26, p = 0.023), HOMA-R (ρ = 0.25, p = 0.028), TG (ρ = 0.28, p = 0.013), BMI (ρ = 0.33, p = 0.004), bodyweight (ρ = 0.23, p = 0.044), waist circumference (ρ = 0.28, p = 0.016) and HbA1c (ρ = 0.34, p = 0.002), respectively.
Furthermore, g_Brachtspira was positively correlated with the value of insulin (ρ = 0.27, p = 0.02), HOMA-R(ρ = 0.28, p = 0.016), BMI (ρ = 0.28, p = 0.015), body weight (ρ = 0.28, p = 0.013), waist circumference (ρ = 0.27,
p = 0.017) and HbA1c (ρ = 0.27, p = 0.021), respectively.

Metabolite and SCFAs.

The concentration of SCFAs; acetate, propionate, butyrate and valerate, showedno significant differences between NO and OB as well as MHO and MUO (Supplementary Figs. S1 and S2).
However, a number of significant differences were observed for other serum metabolites between NO and OB as
well as MHO and MUO (Fig. 5). These included, 2-oxoisovaleric acid and pyruvic acid which were significantly
elevated in OB versus NO (NO 15.96 ± 0.37, OB 18.01 ± 0.43, p = 0.0005 and NO 18.0 ± 5.07, OB 44.61 ± 5.94,
p = 0.0009, respectively) (Fig. 5a, b). Whilst Asparagine was lower in OB compared to NO (NO 52.4 ± 0.82,
OB 48.44 ± 0.97, p = 0.0022) (Fig. 5c). Between MHO and MUO, significant differences were observed for;2-oxoisovaleric acid (MHO 16.55 ± 0.45, MUO 20.26 ± 0.73, p < 0.0001), pyruvic acid (MHO 29.39 ± 7.0, MUO
64.89 ± 11.22, p = 0.009), 2-hydroxybutyric acid (MHO 51.11 ± 2.85, MUO 62.35 ± 4.57, p = 0.04), and creatine(MHO 46.62 ± 2.76, MUO 57.21 ± 4.43, p = 0.046) (Fig. 5d–g) which were higher in MUO. In comparison, MHO
compared to MUO showed higher levels of citrulline (MHO 32.97 ± 0.85, MUO 28.36 ± 1.36, p = 0.0051) andbetaine (MHO 60.96 ± 1.86, MUO 53.88 ± 2.98, p = 0.047) (Fig. 5h, i).

Discussion

The present study demonstrates that NO versus OB residents of Kumejima island, Okinawa, Japan showeddistinctly different composition and diversity in gut microbiota. When subjects with abdominal obesity weredivided into MHO and MUO, unique differences in the community of gut microbiota between the two groups
were identified that correlated with aspects of the metabolic syndrome. Furthermore, MHO and MUO showed

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b
***

15
10
5
0

30
20

0

OB

d

NO

0

MHO

60
40
20
0

MUO

MHO

MHO

20

60

20

0

40

20

10

MUO

MUO

*

Betaine (μM)

Citrulline (μM)

Creatine (μM)

20

***

40

MHO

40

0

30

0

60

i

*

60

OB
*

MUO

h

g

NO

***
2-Hydroxybutyric acid (μM)

Pyruvic acid (μM)

5

20

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OB

20

10

30

f
80

15

40

10

e
****

2-Oxoisovaleric acid (μM)

40

10
NO

**
50

Asparagine (μM)

20

c

***

50
Pyruvic acid (μM)

2-Oxoisovaleric acid (μM)

a

MHO

MUO

0

MHO

MUO

Figure 5.  (a–c) Composition of selected metabolites in serum of NO and OB. (d–i) Composition of metabolites
in serum of MHO and MUO. NO; non-obese (blue) (n = 81), OB; obese (red) (n = 59), MHO; metabolically
healthy obese (light blue) (n = 54) and MUO; metabolically unhealthy obese (orange) (n = 21). Data are shownas mean ± SEM. Data were analyzed using unpaired t-test to compare groups. *p < 0.05; **p < 0.01; ***p < 0.001;****p < 0.0001.
considerable differences in a series of serum metabolites that closely correlated with gut microbiota genera
implicated in the pathophysiology of obesity.
Regarding the gut microbiota analyses, beta diversity was significantly different in NO compared to OB andalpha diversity, as measured by Chao1, Shannon and Observed features were significantly lower in OB as compared to NO. This finding is in line with majority of the literature, reproducing a consistent relationship between alower alpha diversity and obesity ­phenotype7,16 whilst higher diversity is associated with stability and health of gut
microbiota. It should be noted that measures of alpha and beta diversity were not significantly different between
MHO and MUO in the present study, suggesting that such a relationship between microbial diversity and bodyweight would be stronger than other aspects of metabolic health including value of circulating lipids or HbA1c.
Furthermore, composition of gut microbiota showed a number of disparities between NO and OB as well
as MHO and MUO. In particular, bacteria from the order SHA_98, which have been negatively correlated with
Scientific Reports |Vol:.(1234567890)(2022) 12:17292 |https://doi.org/10.1038/s41598-022-21708-08www.nature.com/scientificreports/­BMI17, as well as RF39, which are correlated with reduced visceral fat and intestinal glucose i­ ntake18, were predominantly increased in NO compared to OB. Moreover, a series of bacteria which are characteristically reducedin obesity and obesity-related metabolic diseases including those from the Barnesiellaceae, Rikenellaceae andChristensenellaceae ­families9,19,20, were significantly elevated in NO compared to OB. Furthermore, bacteria
from the Mogibacteriaceae family which are reported to be abundant in the intestinal bacteria of lean ­Japanese21,
were significantly increased in NO as compared to OB. At the genus level, a number of genera were elevatedin NO compared to OB including Dehalobacterium and Odoribacter, which have been shown to be reduced in
the obese ­phenotype9, as well as Slackia, which have been implicated in host lipid and xenobiotic ­metabolism22,
and Anaeroturuncus, which has been found to negatively correlate with ­BMI23. Also, bacteria from the genus
Lachnobacterium were increased in NO compared to OB, which has been shown to have a negative correlation
with body fat, LDL-C and ­TC24.
Moreover, when subjects were divided into MHO and MUO, a number of significant differences in the gut
microbiota were identified. Notably, bacteria from the family Paraprevotellaceae, which were elevated in MUO
compared to MHO, have been shown in previous studies to be positively correlated with circulating blood TG,
TC, and glucose ­tolerance25. Regarding differences between groups at the genus level, Atopobium has been known
to increase under conditions of hormonal and metabolic dysfunction including polycystic ovary syndrome(PCOS). To extend on this, the Coriobacteriaceae family, to which Atopobium belong, have been associatedwith increased risk of the metabolic ­syndrome26. Following on from this, Spearman’s correlation analyses in thepresent study support a line of previous findings, revealing that Atopobium showed a significant positive correlation with plasma levels of fasting insulin, HOMA-R, TG and Diastolic blood pressure, in addition to BMI andbody weight. These results suggest that increased Atopobium would play a role in the pathophysiology of MUO.
Among a line of other genera significantly increased in MUO in the present study, Granulicatella is known todecrease in the oral cavity of obese patients after the metabolic ­surgery27, and Oribacterium has been reported
as a potential obesogenic ­bacteria28. On the other hand, Succinivibrio, a bacterium known to correlate with riskof type 2 diabetes and obesity d­ isease29 was significantly correlated with plasma fasting insulin, HOMA-R, TG,
BMI, weight, waist circumference, and HbA1c in present study. Furthermore, Brachyspira is known to colonizein the intestinal mucosa, implicating in the pathogenesis of irritable bowel s­ yndrome30. Conversely, the only
gut microbiota that increased in MHO compared MUO were bacteria from the class Fusobacteriia, phylum
Fusobacteria. Fusobacteriia are butyrate-producing b­ acteria31, suggesting that elevated levels of this bacteria
may result in preserved metabolic health among obese individuals.
Pertaining to analyses of gut microbiota associated serum metabolites, we did not observe any significant
differences in the concentration of SCFAs among the groups. However, we did observe a number of notable differences in the levels of specific serum metabolites implicated in energy homeostasis and glucose metabolism
between NO and OB as well as MUO and MHO. Of note, we found that 2-oxoisovaleric acid and pyruvic acid
were significantly elevated in OB and MUO in comparison to NO and MHO. In line with our data, 2-oxoisovaleric acid is implicated in mitochondrial dysfunction and elevated in subjects with obesity and ­T2DM32,33.
Furthermore, higher levels of pyruvic acid are known to promote gluconeogenesis and also commonly observedin patients with T­ 2DM34. On the other hand, we found that level of serum asparagine was significantly reducedin OB in comparison to NO. In fact, serum level of asparagine has been shown to increase after weight loss in­humans35 as well as to associate with adaptive thermogenesis via mTORC1 signaling in m
­ ice36. Therefore, elevation in serum level of asparagine would facilitate heat production, thereby contributing to maintenance of body
shape. In addition, level of creatine in serum, which was significantly elevated in MUO compared to MHO in thepresent study, has been shown to positively associate with certain pathogenic bacteria and dysbiosis in a mousemodel of acute kidney injury, which often ensues with metabolic ­syndrome37. Furthermore, 2-hydroxybutyricacid, which was significantly elevated in serum from MUO versus MHO in the present study, has been shown to
be implicated in oxidative stress, thereby highlighting its usefulness as an early biomarker for insulin ­resistance38.
In contrast, citrulline, which was significantly reduced in serum of MUO compared to MHO in the present study,
appears to play a role in arterial dilation and glucose ­homeostasis39. A randomized double-blind-placebo controlled study revealed that citrulline supplementation was able to markedly reduce value of fasting blood glucose
and HbA1c in h
­ umans40. Lastly, concentration of betaine in serum, a naturally occurring methyl donor, which
was significantly decreased in the MUO compared to MHO, is known to associate with metabolically-beneficial
profile in Asian men with mild fatty liver ­disease41. The possible relationships between four kinds of serum
metabolites (2-oxoisovaleric acid, pyruvic acid, 2-hydroxybutyric acid, and creatine) and the five kinds of genera(g_Atopobium, g_Granulicatella, g_Oribacterium, g_Succinivibrio, g_Brachyspira) showing significant differences
between MHO and MUO were explored. However, overall, the strength of the correlation (ρ) among respective
parameters was extremely subtle, yielding no definite links worth consideration (Supplementary Fig. S3).
We do recognize that there are a couple of limitations in the present study. Firstly, the cross-sectional nature
limits the ability to make causal inferences regarding the relationship between specific microbiota and serum
metabolites with obesity and markers of metabolic health. Second, abdominal obesity in the present study wasclassified according to the Japanese s­ tandard42, with waist circumference measured at the umbilical level andtherefore generalizability to other population groups needs to be cautioned. However, results obtained are valuable when taking other Asian populations into consideration who commonly show signs of metabolic dysfunction
at a relatively low BMI. Although the size of the sample may present another limitation, it should be noted that
overall lifestyle including daily food consumption and physical activities in the remote island show a similar
trend among inhabitants, which may be advantageous to obtain solid data on the profile of gut microbiota andmicrobial metabolites given the relatively small population ­size43.
In conclusion, our data demonstrate that stark differences in the gut microbiota and metabolite profile exist
between not only obese and healthy weight individuals but also between MHO and MUO inhabitants of theremote island, where the prevalence of obesity is one of the highest in Japan. Overall, our data may help to pave
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a new avenue in approaching the multidimensional nature of obesity and the need for a tailored approach for thetreatment of MHO and MUO that better focuses on the unique profile of gut microbiota and serum metabolites.

Methods

Study design and population. This cross-sectional study targeted residents in Kumejima Island, Okinawa
prefecture, in Japan aged over 20 years as a part of the KDHP (Kumejima Digital Health Project) between June2018 and December 2019. KDHP is a Japanese national project designed to observe the health status of residents in Kumejima Island over time and with a particular emphasis on the profile of gut microbiota and serum
metabolites, to characterize lifestyle-related diseases, including obesity and T2DM which this population groupis vulnerable to. Participants were residents in Kumejima island over 20 years old, except for those met thefollowing exclusion criteria; (1) severe renal dysfunction (eGFR < 30 ml/min/1.73 ­m2) or hepatic dysfunction(AST > 200 IU/L or ALT > 200 IU/L), (2) malignancy, (3) pregnancy, (4) chronic diarrhea, or (5) severe illness
requiring hospitalization and treatment. A total of 178 subjects were recruited with 140 individuals (70 females
and 70 males) able to provide all the required measurements and samples including anthropometric measurements, clinical and biochemical blood samples as well as stool samples.
Initially, all subjects were classified into NO and OB defined as a BMI < 25 and ≥ 25 kg/m22, respectively, tocompare the profiles of interest. Next, with reference to the Japanese definition of the metabolic ­syndrome42,
subjects with abdominal obesity defined as a waist circumference ≥ 85 cm for men and ≥ 90 cm for women
were further divided into MHO and MUO. Specifically, subjects were classed as MHO if they had no apparent
metabolic disorders, whilst MUO subjects were classified as having at least two of the following three metabolic
abnormalities; high blood glucose, dyslipidemia, and high blood pressure. High blood glucose was defined asa fasting blood glucose ≥ 100 mg/dL or use of medication for T2DM, dyslipidemia defined as a fasting serumTG ≥ 150 mg/dL or HDL-C < 40 mg/dL, high blood pressure defined as SBP ≥ 130 mmHg, DBP ≥ 85 mmHg, or
use of medication for h­ ypertension42 (Fig. 1). All participants provided written informed consent and the study
was approved by the Ethics Committee of University of the Ryukyus for Medical and Health Research Involving
Human Subjects (No.1194) in accordance with the Declaration of Helsinki.
Sample and basic data collection. Participant’s fasting blood samples were drawn from an ulnar mediancutaneous vein using a 20 ml disposable syringe (Nipro Medical, Osaka, Japan) with a 23 G butterfly needle(Nipro Medical, Osaka, Japan) and transferred to a blood-collection tube (NP-FN0205, Nipro Neo-tube; Nipro
Medical, Osaka, Japan). Circulating TC was measured by Determiner C-TC (Minaris Medical Co., Ltd., Tokyo,
Japan), circulating HDL-C was measured by Metabolead HDL-C (Minaris Medical Co., Ltd., Tokyo, Japan)
and circulating LDL-C calculated by F
­ riedewald44. Value of circulating TG was measured by Determiner C-TG
(Minaris Medical Co., Ltd., Tokyo, Japan). Value of HbA1c in blood was analyzed with Tosoh Automated Glycohemoglobin Analyzer HLC-723G8 (TOSOH Corporation, Shiga, Japan). Plasma insulin concentrations were
measured by ARCHITECT Insulin (Abbott Japan LLC, Tokyo, Japan). Plasma glucose was measured by l-type
Wako Glu2 (FUJIFILM Wako Pure Chemical Corporation, Osaka, Japan). HOMA-R calculations were performed according to the following formula: (concentration of fasting blood glucose (mmol/l)) × (concentrationof fasting blood insulin (mU/l))/22.545. Blood pressure and anthropometric indices were measured by trained
nurses using standard manners. Within 0–7 days of blood collection, participants provided fecal samples, usinga fecal collection container No. 4 with cap, P-sagittate, with round label type 2 affixed (ASIAKIZAI Co., Tokyo,
Japan).
Metabolite analysis. Fasting serum samples were collected in 1.5 ml micro centrifuge tubes (CF-0150,

BIO-BIK; Ina Optika, Osaka, Japan) and stored at − 80 °C until processing. Human serum was analyzed using
capillary electrophoresis time-of-flight mass spectrometry (CE-TOFMS); (Agilent Technologies, CA, USA) at
Human Metabolome Technology Inc. (HMT) (Human Metabolome Technologies, Yamagata, Japan). For measurements using CE-TOFMS, 200 µl of methanol solution adjusted to concentration of 20 µl of internal standards(Human Metabolome Technologies, Yamagata, Japan), 50 µl of human serum was added and stirred. To this wasadded 150 µl of Milli-Q water, stirred, and 300 µl transferred to an ultrafiltration tube (Centrifugal Filter Unit
5 kDa; Ultra-free MC PLHCC, Human Metabolome Technologies, Yamagata, Japan), and centrifuged (9,100 × g,
4 °C, 120 min) and subjected to ultrafiltration. The filtrate was allowed to dry, dissolved again in 50 µl of Milli-Q
water for Capillary electrophoresis is coupled to mass spectrometry (CE-MS) analysis. CE-TOFMS was used tomeasure cation and anion mode under the conditions shown in Supplementary Table S146–48. Judging from thepeak intensities and shapes obtained, the dilution factors were standardized to cation:1, anion:5, for all samples.
The detected peaks were automatically extracted using the automatic integration software MasterHands (ver.
2.17.1.11; Keio University, Tokyo, Japan) for peaks with a signal-to-noise (S/N) ratio of 3 or higher, and themass-to-charge ratio (m/z), peak area value, and Migration time (MT) in CE-TOFMS. The obtained peak areavalues were converted to relative area values using the following equation (Relative area value = Area ratio of target peak/Area value of internal RMs*Sample volume). Since these data contain adduct ions such as ­Na+ and ­K+
and fragment ions such as dehydration and diammonium, these molecular weight-related ions were removed.
However, since there were also substance-specific adducts and fragments, it was not possible to scrutinize all ofthem. Peaks that were scrutinized were matched and aligned between each sample based on m/z and MT values.
The detected peaks were matched and searched against all substances registered in the HMT Metabolite Libraryand the Known-Unknown Library based on m/z and MT values. Tolerances for searching candidate metabolites
in serum were ± 0.5 min for MT and ± 10 ppm (CE-TOFMS) for m/z.

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SCFAs analysis.

Concentration of SCFAs (acetic acid, propionic acid, butyric acid and valeric acid) infrom subjects’ serum were measured by Liquid chromatography-tandem mass spectrometry (LC–MS/MS). The
serum samples and the internal standard solution (40 µmol/L; Acetic acid-d4) were deproteinized with methanol. For the calibration curves, standard solutions of SCFAs (Merck, Darmstadt, Germany) were spiked into thesurrogate matrix for the ultrapure water instead of serum to prepared calibration curves. These samples were
derivatization with 2-nitrophenyl hydrazine and each of samples were extracted by using t-butyl methyl ether.
After the solvent was evaporated to dryness under a stream of nitrogen gas, the residue was dissolved in final
solvent. The processed sample was injected into the LC–MS/MS system. Regarding the LC–MS/MS system, liquid
chromatography was performed using an ACQUITY UPLC system (Waters Corporation, Massachusetts, USA),separated using an analytical column (AQUITY BEH C18, 2.1 × 100 mm, 1.7 μm; Waters Corporation, Massachusetts, US). Electrospray ionization (ESI) was carried out with the API4000, Tripre Quad 500 and QTRAP
5500 (Sciex, Massachusetts, USA) operating in the negative ionization and SRM mode. The SRM transitions
for acetic acid, propionic acid, butyric acid and valeric acid were m/z 194–164, m/z 208–178, m/z 222–192 and
236–206 respectively.

DNA extraction and 16S rRNA sequencing. Fecal samples were stored at -80 °C until processing andthen genomic DNA was extracted using the NucleoSpin Microbial DNA (Macherey–Nagel, Duren, Germany)
according to the manufacturer’s instructions. All extracted DNA samples were quantified by fluorescence using
Quant-iT dsDNA Assay Kit (Thermo Fisher Scientific, Massachusetts, USA) and purified using the Agencourt
AMPure XP (Beckman Coulter, CA, USA). Sequencing libraries were prepared using the 16S (V3-V4) Metagenomic Library Construction Kit for NGS (Takara Bio, Kusatsu, Japan). The first PCR amplification was performed
using the primer pair 341 F (5’-TCG​TCG​GCA​GCG​TCA​GAT​GTG​TAT​AAG​AGA​CAG​CCT​ACGGGNGGC​
WGC​AG-3’) and 806 R (5’-GTC​TCG​TGG​GCT​CGG​AGA​TGT​GTA​TAA​GAG​ACA​GGG​ACTACHVGGG​TWT​
CTAAT-3’) with Illumina adaptor overhang sequences. The second PCR amplification was performed using the
Nextera XT Index Kit v2 (Illumina, San Diego, USA). Sequencing libraries were purified using the Agencourt
AMPure XP (Beckman Coulter) and quantified by fluorescence using the Quant-iT dsDNA Assay Kit (Thermo
Fisher Scientific). Clonal clusters of the libraries were generated and sequenced on a MiSeq system (Illumina)
with the MiSeq Reagent v3 kit in 2 × 250 bp mode.
Raw sequences were filtered using QIIME2 (version:2019.7)49 and were demultiplexed by per-sample barcodes and Illumina-sequenced amplicon read errors were corrected by DADA2 and clustered into operationaltaxonomic units (OTU) at 99% identity using the VSEARCH. Taxonomy was classified using the GreenGenes
99% OTUs database (version 13_8). A rarefaction curve was generated after random sampling up to 50,000
sequences in each sample. Alpha diversity metrics were calculated for each sample at the sampling depth. To
test the association for each taxonomy and species, low abundance species were first filtered and then analyses
were conducted using 369 OTUs (shared in more than 50% of samples).

Statistical analysis. Data were shown as mean ± standard error of the mean (SEM) for normal distribution
variables, and as median and 25th–75th percentiles for skewed distribution variables. Comparisons between
NO and OB, as well as between MHO and MUO, for serum TG, HbA1c, insulin, glucose and HOMA-R were
performed using Mann–Whitney U test with unpaired t-test used for the other parameters. Both alpha andbeta diversities were calculated using QIIME2 (version: 2019.7)49. Unweighted UniFrac distance metrics wereobtained to generate principal coordinate analysis (PCoA). The community structure between NO and OB, andMHO and MUO, was compared by measuring Chao1, Shannon and Observed features. Statistical differences
between NO and OB, as well as MHO and MUO at different taxonomic assignments were calculated using
Linear Discriminant Analysis Effect Size (LEfSe) using criteria, p < 0.05, LDA score > 5.5. LEfSe were performed
Galaxy Huttenhower Lab ­platform50. Comparisons of metabolites and SCFAs between NO and OB, and MHO
and MUO groups were analyzed using unpaired t-test, and correlation analysis of metabolites and gut microbiota was analyzed using Spearman’s rank correlation analysis with JMP version 15.0.0. Spearman’s rank correlation analysis was used to examine the nature of associations between gut microbiota taxa and clinical indices aswell as selected metabolites in the serum of subjects with abdominal obesity using the R-package (version 4.0.3),
employing the cor function, Circlize and ComplexHeatmap from R-package to generate heat maps. Other statistical analyses were performed using JMP version 15.0.0 (SAS Institute Inc., Cary, NC, USA). Probability values
were two-tailed, with levels of statistical significance set at p < 0.05.

Data availability

The datasets generated and/or analysed during the current study are available in Mendeley Data repository,
https://​data.​mende​ley.​com/​datas​ets/​z483h​65xrp/2
Received: 24 March 2022; Accepted: 30 September 2022

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Acknowledgements

The authors thank Maya Kodama, Miwa Taira and Shigeko Iizuka for their research assistance, as well as Haruo
Ota, Junko Hirata, Ayumi Uehara and staff of Kumejima Town Hall for their assistance with recruitment ofparticipants. We are also grateful to Chikako Noguchi, Mamiko Hirata, Yukari Inamine and Tomoko Ikematsu
for their excellent secretarial assistance.

Author contributions

All authors contributed to the study conception and design. Biological sample and analysis as well as subject
data collection were undertaken by T.U., T.N., K.Y., M.U., and K.H. Statistical analyses were performed by T.U.
and K.Y. under the supervision of K.N. The first draft of the manuscript was written by T.U. and J.F.M. T.U.,
J.F.M., M.M., A.A., S.S., S.M., M.I., H.I., M.M., K.N. and H.M. reviewed and edited the final manuscript. Project
administration was undertaken by T.U. J.F.M., S.O. and H.M. supervised the study. All authors read and approved
the final manuscript.

Funding

The present study was supported by Regional Development Projects in the Okinawan Remote Islands, as part ofthe 2017–2020 Kumejima Digital Health Project (KDHP).

Competing interests

The authors declare no competing interests.

Additional information

Supplementary Information The online version contains supplementary material available at https://​doi.​org/​
10.​1038/​s41598-​022-​21708-0.
Correspondence and requests for materials should be addressed to J.F.M., S.O. or H.M.
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