d i a b e t e s r e s e a r c h a n d c l i n i c a l p r a c t i c e 213 (2024) 111747

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Diabetes Research and Clinical Practicejournal homepage: www.journals.elsevier.com/diabetes-research-and-clinical-practice

Gut microbiota-based prediction for the transition from normal glucose
tolerance (NGT) to impaired glucose tolerance (IGT) in a remote islandcohort study
Tsugumi Uema a, 1 , Mari Tsukita b, 1 , Shiki Okamoto a , Moriyuki Uehara a , Ken-ichiro Honma a ,Yoshiro Nakayama a , Atsuko Tamaki a , Minoru Miyazato c , Asuka Ashikari d , Shiro Maeda e, f ,Minako Imamura e, f , Masayuki Matsushita g , Koshi Nakamura h , Hiroaki Masuzaki a, *aDivision of Endocrinology, Diabetes and Metabolism, Hematology, Rheumatology, (Second Department of Internal Medicine), Graduate School of Medicine, Universityof the Ryukyus, Okinawa, Japanb
Faculty of Medicine, University of the Ryukyus, Okinawa, Japanc
Department of Systems Physiology, Graduate School of Medicine, University of the Ryukyus, Okinawa, Japand
Department of Urology, Graduate School of Medicine, University of the Ryukyus, Okinawa, Japane
Department of Advanced Genomic and Laboratory Medicine, Graduate School of Medicine, University of the Ryukyus, Okinawa, Japanf
Division of Clinical Laboratory and Blood Transfusion, University of the Ryukyus Hospital, Okinawa, Japang
Department of Molecular and Cellular Physiology, Graduate School of Medicine, University of the Ryukyus, Okinawa, Japanh
Department of Public Health and Epidemiology, Graduate School of Medicine, University of the Ryukyus, Okinawa, JapanA R T I C L E I N F O

A B S T R A C T

Keywords:
Impaired glucose tolerance
Type 2 diabetes mellitus (T2DM)
Gut microbiota
Predictive marker
Cohort study

Aim: The present cohort study explored whether specific gut microbiota (GM) profile would predict the devel­
opment of impaired glucose tolerance (IGT) in individuals with normal glucose tolerance (NGT).
Methods: A total of 114 study subjects with NGT in Kumejima island, Japan participated in the present study andunderwent 75 g oral glucose tolerance tests at baseline and one year later. We compared the profile of GM atbaseline between individuals who consistently maintained NGT (NRN, n = 108) and those who transitioned from
NGT to IGT (NTI, n = 6).
Results: Within-individual bacterial richness and evenness as well as inter-individual bacterial composition
showed no significant differences between NRN and NTI. Of note, however, partial least squares discriminant
analyses revealed distinct compositions of GM between groups, with no overlap in their 95 % confidence interval
ellipses. Multi-factor analyses at the genus level demonstrated that the proportions of CF231, Corynebacterium,
Succinivibrio, and Geobacillus were significantly elevated in NTI compared to NRN (p < 0.005, FDR < 0.1,
respectively) after adjusting for age, sex, HbA1c level, and BMI.
Conclusions: Our data suggest that increased proportion of specific GM is linked to the future deterioration of
glucose tolerance, thereby serving as a promising predictive marker for type 2 diabetes mellitus.

1. Introduction
Impaired Glucose Tolerance (IGT) is identified as a prediabetic
condition affecting over 540 million adults worldwide [1]. It has beenreported that IGT is a borderline status with about 25 % of individuals
developing diabetes mellitus (DM) within three to five years [2].
Importantly, even individuals with IGT show a considerably high inci­
dence of cardiovascular events including coronary heart disease andstroke, and all-cause mortality compared to those with normal glucose
tolerance (NGT) [3].
Recently, time course of aggravation in glucose tolerance has beenshown to link with a loss of diversity and/or imbalance of gut micro­
biota, also known as dysbiosis [4], that may negatively affect fuel ho­
meostasis in both humans and rodents [5]. For example, abundance of
Bacteroides is negatively correlated with the host’s insulin resistance [6].
On the other hand, that of Faecalibacterium species are associated with* Corresponding author at: 207 Uehara, Nishihara, Okinawa 903-0215, Japan.
E-mail address: hiroaki@med.u-ryukyu.ac.jp (H. Masuzaki).
1
Tsugumi Uema and Mari Tsukita contributed equally to this work.
https://doi.org/10.1016/j.diabres.2024.111747
Received 4 May 2024; Received in revised form 12 June 2024; Accepted 13 June 2024
Available online 13 June 20240168-8227/© 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).

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anti-inflammatory status in subjects with obesity or DM, independentlyof calorie intake [7]. Even in subjects with IGT, an apparent decrease ofabundance already occurred in beneficial bacteria such as Alistipes [8]and Akkermansia [9], but conversely, an apparent increase was observedin opportunistic pathogens like Enterobacteriaceae. However, to thebest of our knowledge, a detailed characterization of the gut microbiota
that might predict the transition from NGT to IGT has not yet been fully
conducted.
In this context, we focused our attention on Kumejima island in
Okinawa, the southernmost part of Japan, which provides us with aunique opportunity for epidemiological studies on lifestyle-related dis­eases. Despite historical reputation for healthy longevity, the rapid
adoption of Western diets and sedentary lifestyle has led to a rise in
obesity and T2DM in Okinawa, particularly in remote islands [10].
Taken together, investigating the gut microbiota in Kumejima residents
is of unique value to gain further insight into the prevention of lifestyle-related diseases. In fact, in the precedented study using the same cohort,
we identified specific gut microbiota and serum metabolites capable ofdistinguishing between metabolically healthy obesity (MHO) andmetabolically unhealthy obesity (MUO) [11]. Based on these back­grounds, the present study aimed to explore the potential of specific gut
microbiota as a predictive marker for the risk of transitioning from NGT
to IGT, thereby contributing to the unique proposal of preventive stra­tegies against T2DM.
2. Methods
2.1. Study design and population
As a part of the Kumejima Digital Health Project (KDHP) between
June 2018 and December 2019, this cohort study included residents in
Kumejima island, Okinawa prefecture in Japan. KDHP is a kind of

Fig. 1. Study scheme. bNGT; normal glucose tolerance at baseline (blue) (n = 114), bIGT; impaired glucose tolerance at baseline (red) (n = 18), NRN; consistentnormal glucose tolerance at baseline and one year (green) (n = 108), NTI; normal glucose tolerance at baseline and impaired glucose tolerance at a year later (pink)(n = 6), ITN; impaired glucose tolerance at baseline and normal glucose tolerance at a year later (gray) (n = 6), IRI; consistent impaired glucose tolerance at baselineand one year later (yellow) (n = 12). 75 g OGTT; 75 g oral glucose tolerance test. Graphics were made using Adobe Illustrator 2024 (version: 28.3). (For inter­
pretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)2

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Japanese national project designed to observe health status of residents
in a remote island over time with a particular emphasis on the profile ofgut microbiota. Subjects were residents of Kumejima island aged over
20 years, excluding those with the following conditions: 1) severe renal
dysfunction (estimated glomerular filtration rate < 30 ml/min/1.73 m2)
or hepatic dysfunction (aspartate aminotransferase > 200 IU/L oralanine aminotransferase > 200 IU/L), 2) malignancy, 3) pregnancy, 4)chronic diarrhea, or 5) severe illness requiring hospitalization andtreatment. A total of 178 subjects were recruited, among which 140
individuals were able to provide all the required measurements andsamples including anthropometric measurements, clinical andbiochemical blood samples as well as stool samples. Because we focusedon IGT, 8 of the 140 subjects were excluded due to demonstrating dia­
betic patterns, defined as a fasting plasma glucose level of ≥ 126 mg/dL
or a 120-minute plasma glucose level of ≥ 200 mg/dL during the 75 g
OGTT [12], at baseline and one year later, based on the 75 g oral glucose
tolerance tests (75 g OGTT). Therefore, 132 individuals (65 females and67 males) without diabetic patterns at both time points were finally
included for analyses (Fig. 1, Upper).
All subjects provided written informed consent, and the present
study was approved by the Ethics Committee of the University of the
Ryukyus for Medical and Health Research Involving Human Participants(No. 1194) following the Declaration of Helsinki.

Reagent v3 kit in 2 × 250 bp mode.
Raw sequences were filtered using QIIME2 (version: 2021.11) [14]and were demultiplexed by per-sample barcodes and Illuminasequenced amplicon read errors were corrected by DADA2 and clus­
tered into operational taxonomic 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 10,000 sequences in each sample. Alpha diversity met­
rics were calculated for each sample at the sampling depth. To test theassociation for each taxonomy and species, low-abundance species werefirst filtered and then analyses were conducted using 258 OTUs (sharedin more than 50 % of samples).
2.4. Statistical analyses
By 75 g OGTT, we initially compared characteristics between sub­jects with normal glucose tolerance (bNGT) (n = 114) at baseline andthose with impaired glucose tolerance (bIGT) (n = 18) at baseline(Fig. 1, Analysis1). Specifically, according to the WHO criteria [12],NGT was defined as a fasting plasma glucose level < 110 mg/dL and a
120-minute plasma glucose level < 140 mg/dL. In the present study, IGT
encompassed individuals not classified as having neither a diabetic
pattern nor NGT, incorporating the WHO’s definitions of IGT and
Impaired Fasting Glucose (IFG) [12] (Fig. 1, Middle).
Next, we compared the characteristics of subjects who consistently
remained with NGT at both baseline and one year later (NRN, n = 108)with those who consistently remained with IGT at both baseline and one
year later (IRI, n = 12) after excluding subjects transitioning from NGT
at baseline to IGT one year later (NTI, n = 6) and those transitioning
from IGT at baseline to NGT one year later (ITN, n = 6) (Fig. 1, Anal­
ysis1-2).
Finally, after excluding subjects with bIGT (n = 18), we included
only subjects with bNGT in analyses and compared NRN (n = 108) andNTI (n = 6) (Fig. 1, Analysis2).
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 bNGT and bIGT
as well as between NRN and NTI for levels of serum triglycerides,
HbA1c, and plasma glucose were performed using the Mann-Whitney U
test with an unpaired t-test used for the other parameters. Both alphaand beta diversities were calculated using QIIME2 (version: 2021.11)[14]. Unweighted UniFrac distance metrics were obtained to generate
principal coordinate analyses (PCoA). The community structure ofbNGT vs. bIGT, NRN vs. IRI, and NRN vs. NTI were compared bymeasuring Shannon and Observed Features. The mixOmics, plsda.res, andggplot2 from the R-package (version 4.3.2) were used to calculate andplot the Partial Least Squares Discriminant Analyses (PLS-DA). Statisti­
cal differences between bNGT and bIGT as well as between NRN and NTI
at different taxonomic assignments were calculated using Multi-factor
analyses (MFA) with criteria of p < 0.05 and FDR < 0.1. Statisticaldifferences at the genus levels were calculated using MFA betweenbNGT and bIGT as well as between NRN and NTI with criteria of p < 0.05
and FDR < 0.1. MFA was performed on MicrobiomeAnalyst [15].
Spearman’s rank correlation analyses were used to examine the na­
ture of associations between taxa of gut microbiota and clinical indicesin subjects with bNGT using the R-package (version 4.3.2), employing
the cor function, Circlize and ComplexHeatmap from R-package togenerate the heatmap. Other statistical analyses were performed using
JMP version 15.0.0 (SAS Institute Inc., Cary, NC, USA). Statistical sig­
nificance between the two-tailed level set at p < 0.05.

2.2. Sample and basic data collection
Venous blood samples were collected by cubital venipuncture afteran overnight fast. Serum total cholesterol and triglyceride were
measured by Determiner C-TC (Minaris Medical Co., Ltd., Tokyo,
Japan), while serum high-density lipoprotein-cholesterol (HDL-choles­
terol) was measured by Metabolead HDL-C (Minaris Medical Co., Ltd.,Tokyo, Japan). Serum low-density lipoprotein-cholesterol (LDL-choles­
terol) was calculated using the Friedewald equation: total cholesterollevel – HDL-cholesterol level – (triglycerides level/5) [13]. Hemoglobin
A1c (HbA1c) in blood was analyzed with Tosoh Automated Glyco­
hemoglobin Analyzer HLC-723G8 (TOSOH Corporation, Shiga, Japan).
Plasma glucose was measured by l-type Wako Glu2 (FUJIFILM Wako
Pure Chemical Corporation, Osaka, Japan). Plasma immunoreactive
glucagon (IRG) was measured by Glucagon RIA kit (SML) (DIAsource
ImmunoAssays S.A., Brabant Wallon, Belgium). Blood pressure andanthropometric indices were measured by trained nurses in a standard
fashion. Within 0–7 days of blood collection, subjects provided fecal
samples, using a fecal collection container No. 4 with cap, P-sagittate,with round label type 2 affixed (ASIAKIZAI Co., Tokyo, Japan).
2.3. DNA extraction and 16S rRNA sequencing
Fecal samples were stored at -80 ◦ C until processing, and genomic
DNA was extracted using the NucleoSpin Microbial DNA (MachereyNagel, Duren, Germany) according to the manufacturer’s instructions.
All extracted DNA samples were quantified by fluorescence using QuantiT dsDNA Assay Kit (Thermo Fisher Scientific, Massachusetts, USA) andpurified 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′TCGTCGGCAGCGTCAGATGTGTATAAGAGA­
and
806
R
(5′CAGCCTACGGGNGGCWGCAG-3′)
GTCTCGTGGGCTCGGAGATGTGTATAAGAGA­CAGGGACTACHVGGGTWTCTAAT-3′) with Illumina adaptor overhangsequences. The second PCR amplification was performed using the
Nextera XT Index Kit v2 (Illumina, San Diego, USA). Sequencing li­
braries 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 weregenerated and sequenced on a MiSeq system (Illumina) with the MiSeq
3

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Diabetes Research and Clinical Practice 213 (2024) 111747

3. Results

63.2 ± 3.0, p = 0.02), while no significant differences were observed inheight (bNGT 159.8 ± 0.90, bIGT 157.7 ± 2.26, p = 0.40), body weight(bNGT 62.9 ± 1.15, bIGT 64.0 ± 2.89, p = 0.73), BMI (bNGT 24.5 ±
0.33, bIGT 25.0 ± 0.84, p = 0.64), or waist circumference (bNGT 88.2
± 0.88, bIGT 90.9 ± 2.21, p = 0.25), respectively. Systolic blood pres­sure (bNGT 131.7 ± 1.75, bIGT 132.4 ± 4.40, p = 0.88) and diastolicblood pressure (bNGT 77.2 ± 1.06, bIGT 76.5 ± 2.66, p = 0.80) also did
not significantly differ between two groups. Biochemical parameters,
the levels of total cholesterol (bNGT 204.4 ± 3.5, bIGT 209.0 ± 8.8, p =0.63), HDL-cholesterol (bNGT 61.0 ± 1.5, bIGT 56.8 ± 3.9, p = 0.31),

3.1. Analyses 1: bNGT vs. bIGT
3.1.1. Characteristics of subjects
Using a 75 g OGTT, we initially divided the characteristics between
subjects with bNGT (n = 114) and those with bIGT (n = 18). Significant
differences in anthropometric, clinical, and biochemical parameters
were observed between bNGT and bIGT at baseline (Supplementary
Table S1). Age in bIGT was higher than bNGT (bNGT 55.3 ± 1.2, bIGT

Fig. 2. Diversity and composition of gut microbiota in subjects studied of bNGT and bIGT at baseline. (A) Alpha diversity assessed by (a) Shannon and (b) Observed
Features indices were compared between bNGT and bIGT. The Kruskal-Wallis test assessed the statistical significance between two groups. Graphics were made using
QIIME2 (version 2021.11) 28. *p < 0.05. (B) Principal component analyses plot of microbiota based on unweighted UniFrac distances shows beta diversity betweenbNGT and bIGT. Permutational multivariate analyses of variance (PERMANOVA) were employed to compare differences between two groups. (C) Partial LeastSquares Discriminant Analyses (PLS-DA) of the comparison of gut microbiota. The PLS-DA score plot illustrates the differences in gut microbiota profiles be­
tween bNGT and bIGT. Each point represents the composition of gut microbiota in each subject, and the 95 % confidence ellipses demonstrate the group differ­entiation based on the PLS-DA model. The explained variance is based on X-variate (normalized bacterial abundances). bNGT; normal glucose tolerance at baseline(blue) (n = 114); bIGT; impaired glucose tolerance at baseline (red) (n = 18). Graphics were made using the R-package (version 4.3.2). (For interpretation of thereferences to colour in this figure legend, the reader is referred to the web version of this article.)4

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and LDL-cholesterol (bNGT 121.1 ± 3.1, bIGT 122.5 ± 7.7, p = 0.87)
also did not differ significantly.
However, serum triglycerides level was notably different, with thebIGT group showing an increased median level compared to bNGT
(bNGT 93.5 (67.0–143.0), bIGT 144.5 (93.0–193.5), p = 0.03). Serum
HbA1c level was also significantly different, with higher levels observedin the bIGT group (bNGT 5.5 (5.3–5.7), bIGT 5.8 (5.6–6.0), p = 0.0003).
In the 75 g OGTT, bIGT showed significantly higher levels of fasting
plasma glucose as well as postprandial glucose exclusion at all time
points (fasting: bNGT 81.0 (76.0–85.3), bIGT 90.0 (82.0–105.0), p <0.0001, after 30 min: bNGT 136.0 (121.0–150.3), bIGT 166.5(148.0–191.3), p < 0.0001, after 60 min: bNGT 126.5 (97.8–150.3),
bIGT 198.5 (154.8–217.0), p < 0.0001, after 90 min: bNGT 109.0(92.8–130.0), bIGT 193.5 (150.8–212.5), p < 0.0001, after 120 min:bNGT 104.5 (85.0–120.0), bIGT 152.0 (146.0–171.5), p < 0.0001,
respectively).

± 14.9, p = 0.97), HDL-cholesterol (NRN 61.0 ± 1.6, NTI 60.7 ± 6.7, p= 0.96), LDL-cholesterol (NRN 120.9 ± 3.1, NTI 125.3 ± 13.2, p =0.74), or triglycerides (NRN 94.0 (67.0–145.0), NTI 85.5 (66.0–137.5),
p = 0.68), respectively.
However, NTI was significantly higher in the value HbA1c (NRN 5.5(5.3–5.6), NTI 5.9 (5.6–6.2), p = 0.008) compared to NRN. In the 75 g
OGTT, significant differences were observed at the point after 30 min(NRN 134.5 (120.3–150.0), NTI 151.5 (137.5–195.8), p < 0.05). How­ever, no significant differences were noted at other time points (fasting:
NRN 80.5 (75.3–85.0), NTI: 86.0 (79.8–93.5), p = 0.08, after 60 min:
NRN 124.0 (97.0–145.8), NTI 145.0 (122.8–219.5), p = 0.09, after 90
min: NRN 108.0 (92.0–128.0), NTI 138.5 (105.3–162.8), p = 0.06, 120
min: NRN 104.5 (85.0–120.0), NTI 111.0 (93.0–123.8), p = 0.47,
respectively).
3.3.2. Analyses of gut microbiota
Comparative analyses of gut microbiota between NRN and NTI
showed no significant differences in both alpha and beta diversity, but
detailed multivariate analyses revealed evident differences in bacterial
composition. Alpha diversity showed no difference between NRN and
NTI as assessed by the Shannon (p = 0.18) (Fig. 3A-a) and Observed
Features (p = 0.57) (Fig. 3A-b) with the Kruskal–Wallis test. Beta di­versity showed no difference (p = 0.77) in the comparison between NRN
and NTI as demonstrated by PCoA. The percentage of variation
explained by Axis 1 was 17.7 %, by Axis 2 was 6.3 %, and by Axis 3 was
4.6 %, with the visual observation being confirmed by the PERMANOVA
test (Fig. 3B).
Notably, however, PLS-DA performed to visually compare gut
microbiota profiles revealed distinct clustering patterns between NRN
and NTI with the 95 % confidence ellipse regions, showing no overlap(Fig. 3C).
Furthermore, after adjusting for age, sex, the value of HbA1c, and
BMI, MFA at the genus level identified four gut microbiota genera as
significantly prevalent in NTI compared to NRN: CF231 (Log2FC =-0.003, p = 0.000006, FDR = 0.004), Corynebacterium (log2FC =-0.0002, p = 0.00001, FDR = 0.004), Succinivibrio (Log2FC = -0.02, p =0.0007, FDR = 0.07), and Geobacillus (Log2FC = -0.00006, p = 0.001,
FDR = 0.08) (Table 1). Additionally, analyses conducted without
covariates (crude) and with age and sex revealed that all these genera
were significantly abundant in NTI compared to NRN (Supplementary
Table S3).
In subjects with bNGT (n = 114), the correlations between param­
eters associated with impaired glucose tolerance (circulating IRG, fast­ing blood glucose levels, and the value of HbA1c) and four gut
microbiota genera (CF231, Corynebacterium, Succinivibrio, and Geo­bacillus) were analyzed using a heatmap based on Spearman’s rankcorrelation coefficients (Fig. 3D). The results showed a positive corre­
lation between Succinivibrio and the value of HbA1c (p < 0.01).
Results of IRI (n = 12) and ITN (n = 6) subjects were also examined.
However, there were no significant differences in both alpha and beta
diversity between two groups in the present study.

3.1.2. Analyses of gut microbiota
Comparative analyses of gut microbiota between bNGT and bIGT
showed no significant differences in both alpha and beta diversity
measurements, but detailed multivariate analyses revealed some dif­ferences in bacterial composition. Alpha diversity showed no significant
differences between bNGT and bIGT as assessed by the Shannon (p =0.20) (Fig. 2A-a) and Observed Features (p = 0.12) (Fig. 2A-b) with the
Kruskal–Wallis test. Beta diversity showed no difference (p = 0.06) inthe comparison between bNGT and bIGT as demonstrated by Principal
Coordinates Analyses (PCoA). The percentage of variation explained by
Axis 1 was 18.2 %, by Axis 2 was 6.2 %, and by Axis 3 was 4.6 %, withthe visual observation being confirmed by the PERMANOVA test
(Fig. 2B).
Importantly, the PLS-DA performed to visually compare gut micro­
biota profiles revealed distinct clustering patterns between bNGT andbIGT, while there was a bit of overlap in the 95 % confidence ellipse
regions (Fig. 2C). Furthermore, detailed analyses using MFA at the genus
level revealed that Clostridium was significantly abundant in bIGT
compared to bNGT. (Log2FC = − 0.02, p = 0.0007, FDR = 0.067).
3.2. Analyses 1–2: Consistent NGT (NRN) vs. Consistent IGT (IRI) at
baseline and after one year
3.2.1. Analyses of gut microbiota
Alpha diversity showed a significant difference between NRN (n =108) and IRI (n = 12) as assessed by the Observed Features with the
Kruskal-Wallis test (Shannon: p = 0.05, Observed Features: p < 0.05)(Supplementary Fig. S1A-a, b). Beta diversity showed a significant
difference (p = 0.04) in the comparison between NRN and IRI as
demonstrated by PCoA. The percentage of variation explained by Axis 1
was 18.5 %, by Axis 2 was 6.2 %, and by Axis 3 was 4.6 % with the visualobservation being confirmed by the PERMANOVA test (Supplementary
Fig. S1B).
3.3. Analyses 2: NRN vs. NTI

4. Discussion

3.3.1. Subject characteristics
Comparing anthropometric, clinical, and biochemical parameters
between NRN (n = 108) and NTI (n = 6) revealed no significant dif­ferences in most of the clinical parameters (Supplementary Table S2).
No significant differences were found in age (NRN 54.9 ± 1.3, NTI 62.3
± 5.4, p = 0.18), height (NRN 159.7 ± 0.95, NTI 162.6 ± 4.04, p =0.49), body weight (NRN 62.7 ± 1.16, NTI 67.0 ± 4.94, p = 0.40), BMI(NRN 24.4 ± 0.35, NTI 26.4 ± 1.49, p = 0.20), or waist circumference(NRN 87.9 ± 0.91, NTI 93.6 ± 3.85, p = 0.15). Systolic blood pressure(NRN 131.5 ± 1.80, NTI 135.2 ± 7.83, p = 0.65) and diastolic bloodpressure (NRN 77.2 ± 1.12, NTI 77.2 ± 4.77, p = 0.99) were also
comparable between two groups. Biochemical parameters revealed nosignificant differences in total cholesterol (NRN 204.4 ± 3.5, NTI 205.0

The major findings in the present study include distinct differences in
the composition of baseline gut microbiota between NRN and NTI.
Detailed analyses of gut microbiota showed no significant increase in
specific gut microbiota in NRN. However, CF231, Corynebacterium,
Succinivibrio, and Geobacillus were substantially abundant in NTI
compared to NRN even after adjusting for age, sex, HbA1c level, and
BMI.

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Fig. 3. Diversity and composition of gut microbiota in subjects studied of NRN and NTI at baseline and heatmap of subjects with normal glucose tolerance atbaseline. (A) Alpha diversity assessed by (a) Shannon and (b) Observed Features indices were compared between NRN and NTI. The Kruskal-Wallis test assessed thestatistical significance between two groups. Graphics were made using QIIME2 (version 2021.11) 28. *p < 0.05. (B) Principal component analyses plot of microbiota
based on unweighted UniFrac distances shows beta diversity between NRN and NTI. Permutational multivariate analyses of variance (PERMANOVA) were employed
to compare differences between two groups. (C) Partial Least-Squares Discriminant Analyses (PLS-DA) of the composition of gut microbiota. The PLS-DA score plot
illustrates the differences in gut microbiota profiles between NRN and NTI. Each point represents the composition of gut microbiota in each subject, and the 95 %confidence ellipses demonstrate the group differentiation based on the PLS-DA model. The explained variance is based on X-variate (normalized bacterial abun­dances). NRN; consistent normal glucose tolerance at baseline and one year later (green) (n = 108); NTI; normal glucose tolerance at baseline and impaired glucose
tolerance one year later (pink) (n = 6). Graphics were made using the R-package (version 4.3.2). (D) Spearman’s correlation heatmap showing significant corre­
lations of gut microbiota at the genus level and selected parameters in subjects with normal glucose tolerance at baseline (n = 114). Significant correlations areshown as red (positive) or blue (negative). Graphics were made using the R-package (version 4.3.2). **p < 0.01. (For interpretation of the references to colour in thisfigure legend, the reader is referred to the web version of this article.)

4.1. Differences in the gut microbiota between individuals with baseline
NGT (bNGT) and baseline IGT (bIGT)

consistent with a previous report [16].
PLS-DA visualization of differences in gut microbiota composition
between bNGT and bIGT revealed distinct clustering patterns between
two groups, while there was some overlap in the 95 % confidence ellipse
regions. Additionally, the MFA at the genus level identified the presenceof Clostridium in bIGT. Given that some of the Clostridium species
decrease in patients with T2DM [14] while others conversely proliferatein IGT or T2DM as opportunistic pathogens [15], observed increase of

A previous study showed an apparent trend of reduced diversity andimbalance of gut microbiota in IGT compared to NGT [16]. Regardingalpha and beta diversity in the present study, no significant differenceswere observed between bNGT and bIGT. On the other hand, both alphaand beta diversities were significantly lower in IRI than in NRN,
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deterioration, this finding may suggest a potential role for a line of in­testinal bacteria in the risk of glucose intolerance.
We do acknowledge several critical limitations in the present study.
First, due to its cross-sectional nature for some analyses, it is not possible
to infer causality between specific profiles of gut microbiota and ab­
normalities in glucose homeostasis. Second, the relatively small sample
size limits the power to detect changes in the gut microbiota, necessi­
tating further validation in larger sample populations. Considering theimpact of the relatively small sample size, we also evaluated statisticalsignificance using an FDR < 0.1 in addition to a p < 0.05 to reduce thelikelihood of incidental findings. This approach is beneficial to consid­
erably enhance the reliability of our results. Furthermore, it should be
noted that Kumejima residents, a remote island in the southernmost partof Japan, exhibit similar trends among residents in overall lifestyle
habits, including daily food consumption and physical activity. Thismight be advantageous for obtaining reliable data on the profile of gut
microbiota.

Table 1
Multi-factor analyses of gut microbiota composition (NRN vs. NTI).
Microbiota (genus)

Log2FC

St.Errorp-value

FDR

CF231
Corynebacterium
Succinivibrio
Geobacillus− 0.003− 0.0002− 0.02− 0.00006

0.00060.00003
0.004
0.000020.000006
0.00001
0.0007
0.001

0.004
0.004
0.07
0.08

This table displays differences in the abundance of gut microbiota identified
between NRN and NTI when age, sex, the value of HbA1c, and BMI were all
included as covariates in a linear model. p < 0.05. FDR < 0.1.
Log2FC; Log2 Fold Change represents the logarithmic fold change in the abun­dance of gut microbiota between two groups. A positive value indicates a higher
abundance in NRN, while a negative value indicates a higher abundance in NTI.
St. Error; The standard error of the Log2FC. The p-value is obtained from t-tests.
FDR; False Discovery Rate.
NRN; consistent normal glucose tolerance at baseline and one year later (n =108), NTI (NGT to IGT); normal glucose tolerance at baseline and impaired
glucose tolerance one year later (n = 6). Data were analyzed using Multi-factor
analyses of MicrobiomeAnalyst 2.0 [15].

5. Conclusion

Clostridium in bIGT could potentially be explained by the activation ofopportunistic pathogens within this genus, which may subsequently
contribute to glucose intolerance.
Conversely, no genera were found to be significantly prevalent inbNGT compared to bIGT, although it has been well documented thatgenera such as Alistipes[7], Akkermansia[8], Bacteroides[5] and Faecali­
bacterium[6] are significantly prevalent in subjects with NGT comparedto IGT. Such a discrepancy may be attributable to geographical differ­ences. In this sense, further studies are warranted to test the reproduc­
ibility in different geographic areas.

This study identified specific gut microbiota that may predict thetransition from NGT to IGT in residents of a remote island with similar
lifestyle trends. The findings suggest that differences in gut microbiota
profiles could be used to predict the transition from NGT to IGT.
Although the applicability of our findings needs to be validated byextensive studies, results may provide unique insight into early
screening and intervention in subjects with a higher risk of T2DM.
6. Authors’ relationships and activities
The authors declare no competing interests.

4.2. Profile of gut microbiota as a predictive marker for transitioning from
NGT to IGT

7. Funding.
The present study was supported by Regional Development Projects
in the Okinawan Remote Islands, as part of the 2017–2020 KDHP.

No significant differences in alpha and beta diversity were observed
between NRN and NTI. As both groups were normoglycemic at baseline,
this result was within our expectation. Surprisingly, however, PLS-DA
visualization of the composition of gut microbiota formed distinct
clusters without overlapping in the 95 % confidence ellipsoidal regions
between NRN and NTI, indicating clear compositional differences,which were never identified from diversity analyses.
After adjusting for age, sex, the value of HbA1c, and BMI, MFA at thegenus level identified a higher prevalence of CF231, Corynebacterium,
Succinivibrio, and Geobacillus in NTI compared to NRN. These genera
may serve as predictive markers for transitioning from NGT to IGT.
CF231, related to the progression from cirrhosis to hepatocellular
carcinoma [17], belongs to the family Paraprevotellaceae, which is
associated with BMI [18], dyslipidemia, and glucose intolerance [19],and also shows an increase in subjects with MUO in our previous study[11].
While some species of Corynebacterium exert anti-inflammatory ef­
fects in humans [20], an increase in Corynebacterium was conversely
shown to associate with T2DM [21].
Succinivibrio, associated with the risk of T2DM and obesity disease in
humans [22], was found to increase in MUO correlating positively withthe value of HOMA-R, levels of serum triglycerides, BMI, body weight,
waist circumference, and HbA1c in our previous study [11].
Geobacillus is known to increase in patients with cholangiocarcinoma[23] and bladder cancer [24], but its clinical implication in glucose
homeostasis still remains unclear.
Despite no differences except for the value of serum HbA1c and 75 g
OGTT plasma glucose after 30 min in the baseline profiles between NRN
and NTI, and even after adjusting for covariates to align backgroundfactors, abundance of CF231, Corynebacterium, Succinivibrio, and Geo­bacillus were significantly higher in NTI compared to NRN. Coupled withaforementioned previous studies that link these genera to metabolic

8. Author contributions.
All authors contributed to the study’s conception and design. Bio­logical sample and analyses as well as subject data collection were un­dertaken by T.U. and M.U., and K.H. Statistical analyses were performed
by T.U. under the supervision of K.N. The first draft of the manuscript
was written by T.U. and M.T. T.U., M.T., K.Y., S.O., M.U., K. H., Y.N., A.
T., MI.M., A.A., S.M., M.I., MA.M., K.N., and H.M reviewed and edited
the final manuscript. Project administration was undertaken by T.U., K.
Y., M.U., K.H., MI.M., A.A., MA.M., and H.M. supervised the study. All
authors read and approved the final manuscript.
9. Informed Consent Statement
Written informed consent was obtained from all of the subjects
involved in the present study.
CRediT authorship contribution statement
Tsugumi Uema: Writing – review & editing, Writing – original draft,
Visualization, Resources, Methodology, Investigation, Formal analysis,
Data curation, Conceptualization. Mari Tsukita: Writing – review &editing, Writing – original draft, Visualization. Shiki Okamoto: Writing– review & editing, Methodology. Moriyuki Uehara: Writing – review &editing, Investigation. Ken-ichiro Honma: Writing – review & editing,
Investigation. Yoshiro Nakayama: Writing – review & editing. Atsuko
Tamaki: Writing – review & editing. Minoru Miyazato: Writing – re­view & editing, Project administration, Investigation. Asuka Ashikari:
Writing – review & editing, Investigation. Shiro Maeda: Writing –7

T. Uema et al.

Diabetes Research and Clinical Practice 213 (2024) 111747

review & editing, Methodology, Investigation. Minako Imamura:Writing – review & editing, Investigation. Masayuki Matsushita:
Writing – review & editing, Supervision, Project administration, Fund­ing acquisition. Koshi Nakamura: Writing – review & editing, Super­
vision, Methodology, Formal analysis. Hiroaki Masuzaki: Writing –review & editing, Supervision, Project administration, Methodology,
Funding acquisition, Conceptualization.

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Declaration of competing interest
The authors declare the following financial interests/personal re­lationships which may be considered as potential competing interests.
The sponsors were not involved in the study design; collection, analyses,
or interpretation of the data; the writing of this manuscript; or the de­cision to submit the manuscript for publication. The authors, their im­
mediate families, and any research foundations with which they areaffiliated have not received any financial payment or other benefits fromany commercial entity related to the subject of this article.
Acknowledgments
Authors express cordial thanks to Kiyoto Yamashiro, Takehiro
Nakamura, Junko Hirata, Maya Kodama, Miwa Taira and Shigeko Iizuka
for dedicated contribution as well as to the staff of Kumejima Town Hallfor excellent help. We are also grateful to Chikako Noguchi and Mamiko
Hirata for secretarial assistance.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.diabres.2024.111747.
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