Unique Habitual Food Intakes in the Gut Microbiota Cluster Associated with Type 2 Diabetes Mellitus
by
, , , , , and
Yuriko Kondo
1, 
Yoshitaka Hashimoto
1,
Masahide Hamaguchi
1,*,
Shinto Ando
1,
Ayumi Kaji
1,
Ryosuke Sakai
1,
Ryo Inoue
2,
Saori Kashiwagi
3,
Katsura Mizushima
3,
Kazuhiko Uchiyama
3,
Tomohisa Takagi
3,4,
Yuji Naito
5 and
Michiaki Fukui
1
1
Department of Endocrinology and Metabolism, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto 602-8566, Japan
2
Laboratory of Animal Science, Department of Applied Biological Sciences, Faculty of Agriculture, Setsunan University, Hirakata 573-0101, Japan
3
Molecular Gastroenterology and Hepatology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto 602-8566, Japan
4
Department for Medical Innovation and Translational Medical Science, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto 602-8566, Japan
5
Department of Human Immunology and Nutrition Science, Kyoto Prefectural University of Medicine, Kyoto 602-8566, Japan
*
Author to whom correspondence should be addressed.
Nutrients 2021, 13(11), 3816; https://doi.org/10.3390/nu13113816
Submission received: 13 October 2021
/
Revised: 21 October 2021
/
Accepted: 21 October 2021
/
Published: 27 October 2021
(This article belongs to the Special Issue Nutrition and Microbiome)
Abstract
:This cross-sectional study aimed to clarify the characteristic gut microbiota of Japanese patients with type 2 diabetes (T2DM) using t-distributed stochastic neighbor embedding analysis and the k-means method and to clarify the relationship with background data, including dietary habits. The gut microbiota data of 383 patients with T2DM and 114 individuals without T2DM were classified into red, blue, green, and yellow groups. The proportions of patients with T2DM in the red, blue, green, and yellow groups was 86.8% (112/129), 69.8% (81/116), 76.3% (90/118), and 74.6% (100/134), respectively; the red group had the highest prevalence of T2DM. There were no intergroup differences in sex, age, or body mass index. The red group had higher percentages of the Bifidobacterium and Lactobacillus genera and lower percentages of the Blautia and Phascolarctobacterium genera. Higher proportions of patients with T2DM in the red group used α-glucosidase inhibitors and glinide medications and had a low intake of fermented soybean foods, including miso soup, than those in the other groups. The gut microbiota pattern of the red group may indicate characteristic changes in the gut microbiota associated with T2DM in Japan. These results also suggest that certain diabetes drugs and fermented foods may be involved in this change. Further studies are needed to confirm the relationships among traditional dietary habits, the gut microbiota, and T2DM in Japan.
1. Introduction
The number of patients with type 2 diabetes mellitus (T2DM) has been increasing worldwide, including in Japan. The main factors behind the global T2DM epidemic include overweight and obesity, a sedentary lifestyle, and an unhealthy diet [1].
Previous studies suggested that the gut microbiota affects the development of various diseases, such as T2DM, obesity, and inflammatory bowel disease [2,3,4,5]. The human gut microbiota is influenced by many factors, including diet, lifestyle, medications, and genetics [6]. Japanese individuals have a unique gut microbiota compared to other ethnic groups, which is characterized by a high proportion of the Bifidobacterium genera. The high proportion of the Bifidobacterium genera is considered to be the consequence of the intake of various saccharides in traditional and unique Japanese foods [7]. On the other hand, the typical diet in Japan is becoming increasingly Westernized [8,9]. Gut dysbiosis caused by changes in eating habits may be involved in the increased incidence of T2DM [10,11]. However, the association between T2DM and the gut microbiota and the relationship between lifestyle and changes in gut microbiota in Japanese populations have not been fully clarified. One reason for this is that gut microbiota data are vast and difficult to understand.
Recent developments in artificial intelligence technology have enabled the use of various machine-learning methods for analysis. One unsupervised machine learning method, t-distributed stochastic neighbor embedding (t-SNE), visualizes high-dimensional data via a nonlinear reduction to lower dimensions while retaining its original features [12]. Previous studies investigating the relationship between T2DM and the gut microbiota used methods such as principal component analysis, linear discriminant analysis effect size, and hierarchical clustering [2,6,13], but there have been no reports using t-SNE.
This study included a t-SNE analysis of the gut microbiota data of healthy Japanese individuals and patients with T2DM to create a gut microbiota panel. We then identified the groups associated with T2DM and examined their characteristics. We also investigated the relationship between each gut microbiota panel and the lifestyle factors in patients with T2DM.
2. Materials & Methods
2.1. Study Population and Data Collection
The Ethics Committee of Kyoto Prefectural University of Medicine (nos. ERB-C-534, RBMR-E-466-5, and ERB-C-1912) approved this study, which was conducted in accordance with the principles of the Declaration of Helsinki. Written informed consent was obtained from each participant prior to enrollment. None of the participants took antibiotics within 3 months prior to the study. A total of 522 individuals (114 without diabetes, 17 with type 1 diabetes, 383 with T2DM, and 8 with other types of diabetes) were enrolled between November 2016 and December 2017. Thus, the current study included 114 individuals without diabetes and 383 patients with T2DM.
Each participant’s height, body weight, and body mass index (BMI) were recorded. Patients with T2DM were then surveyed with respect to the medications used for diabetes, dyslipidemia, and hypertension, as well as proton pump inhibitor use. The diagnosis of T2DM was based on the Report of the Expert Committee on the Diagnosis and Classification of Diabetes Mellitus [14]. Information on maximum body weight, body weight at 20 years of age, family history of diabetes, and the duration of diabetes were obtained from the patients with T2DM. Based on the questionnaire responses, participants were categorized as non-, past, or current smokers. Regular exercisers were defined as those performing any kind of sport at least once a week [15].
Blood samples were collected from patients with T2DM for the analysis of hemoglobin A1c, fasting plasma glucose, creatine, and C-peptide levels. The glomerular filtration rate (GFR) was calculated using the following Japanese Society of Nephrology equation: estimated GFR (eGFR) = 194 × creatine−1.094 × age−0.287 (mL/min/1.73 m2) (×0.739 for women) [16]. Insulin resistance was evaluated by 20/(fasting C-peptide [ng/mL] × fasting plasma glucose [mg/dL]) [17]. The insulin secretion capacity was evaluated based on the secretory units of the islet cells in the transplantation index and the C-peptide immunoreactivity index [18]. Early morning spot urine samples were used to measure the urinary creatinine and albumin levels. The mean urinary albumin excretion was determined in three urine samples. Neuropathy was diagnosed according to the criteria of the Diagnostic Neuropathy Study Group [19]. Retinopathy was graded as follows: none, simple diabetic retinopathy, pre-proliferative diabetic retinopathy, or proliferative diabetic retinopathy [20].
2.2. Sampling, DNA Extraction, Sequencing, and Data Analysis
Using previously described methods, the collection of fecal samples and analyses of gut bacterial composition were performed [23,24,25]. Briefly, collected fecal samples were preserved in a guanidine thiocyanate solution (Feces Collection kit; Techno Suruga Lab, Shizuoka, Japan). The isolation of genomic DNA was performed using a NucleoSpin Microbial DNA kit (Macherey-Nagel, Düren, Germany) according to the manufacturer’s instructions. Then, the purification of extracted DNA was performed using Agencourt AMPure XP beads (Beckman Coulter, Brea, CA, USA).
To generate sequencing libraries, a two-step polymerase chain reaction (PCR) of the purified DNA samples was performed. The first PCR was performed for amplification and used a 16S (V3–V4) Metagenomic Library Construction Kit for NGS (Takara Bio Inc., Kusatsu, Japan) with primer pairs 341F (5′-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG-3′) and 806 R (5′-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGGACTACHVGGGTWTCTAAT-3′) corresponding to the V3–V4 region of the 16S rRNA gene. The second PCR was performed to add the index sequences for the Illumina sequencer with a barcode sequence using the Nextera XT Index kit (Illumina, San Diego, CA, USA). The prepared libraries were sequenced for 250 paired-end sequences at Takara Bio’s Biomedical Center using the MiSeq Reagent v3 kit and MiSeq (Illumina) [25].
The generation of a table of the amplicon sequence variants (ASVs), including quality filtering and chimeric variant filtering, was performed using the DADA2 plugin of Quantitative Insights into Microbial Ecology 2 version 2019.4 [26]. Denoising by DADA2 was performed with the trimming length from the left set at 17 and from the right at 19. The truncation length was set to 250 for both reads. The taxonomy of each ASV was assigned using the sklearn classifier algorithm against the Greengenes database version 13_8. The singleton and ASVs assigned to the chloroplasts and mitochondria were removed in this study. The generation of a phylogenetic tree was performed using SATé-enabled phylogenetic placement [27]. Overall, 6,902 ASVs were obtained. The prediction of the functional profiles from the 16S rRNA dataset was performed using Phylogenetic Investigation of Communities by Reconstruction of Unobserved States version 2.1.4 [23] as previously described [25].
2.3. Strategy for Clustering Gut Microbiota
The ASVs were reduced to two dimensions using t-SNE in Python 3.7. Perplexity was determined using the perplexity-decidion.py command. The empirical value of perplexity is 5–50, and a perplexity of 10 was used in this analysis [28]. Two-dimensional ASVs were visualized as scatterplots and clustered using the k-means method. Based on the sum of the squared errors and the number of clusters, the optimal number of clusters was set to K = 4 using the elbow method. Four groups of two-dimensional ASVs were colored and visualized as red, blue, green, and yellow on the scatterplots and defined as the red, blue, green, and yellow groups, respectively.
2.4. Statistical Analysis
After clustering the gut microbiota into four groups, we compared the proportions of phyla and genera among them using the Kruskal–Wallis and Steel–Dwass tests. Furthermore, we compared age and BMI among the four groups using the Kruskal–Wallis test and the proportion of patients with T2DM and men among the four groups using the chi-square test. In addition, logistic regression analysis was performed to calculate the odds ratio for the prevalence of T2DM. Using only the data of patients with T2DM among all the participants, we evaluated the background, examination, and nutritional intake data of the four groups using the chi-square, Kruskal–Wallis, and Steel–Dwass tests. Statistical analyses were performed using JMP version 13.0 (SAS Institute Inc., Cary, NC, USA).
3. Results
This study analyzed the data of 497 individuals (114 without diabetes and 383 with T2DM). According to the t-SNE analysis, we divided the participants into four groups based on the gut microbiota sequencing data. Figure 1 shows a panel of two-dimensional ASVs divided into these four groups and colored red, blue, green, and yellow on the scatterplots. The proportions of patients with T2DM in the red, blue, green, and yellow groups in the t-SNE analysis were 86.8% (112/129), 69.8% (81/116), 76.3% (90/118), and 74.6% (100/134), respectively. Sex, age, and BMI did not differ among the groups (Table 1). A logistic regression analysis showed that the red group was associated with a higher prevalence of T2DM compared to the other groups even after adjusting for covariates (Table 2).
Figure 2 shows the proportions of the phyla among the four groups. The proportion of the Actinobacteria phylum was higher in the red group than in the other groups, while the proportion of the Firmicutes phylum was lower in the red group than in the other groups.
Figure 3 shows the differences in the proportions of genera among the four groups. The proportions of the Bifidobacterium and Lactobacillus genera were significantly higher in the red group than in the other groups, whereas the proportions of the Blautia and Phascolarctobacterium genera were significantly lower in the red group than in the other groups. The proportions of genera of all subjects are listed in Table S1.
Table 3 and Table 4 show the differences in the subjects’ characteristics among the four groups. The proportions of α-glucosidase inhibitor and glinide medication use were significantly higher in the red group than in the other groups.
Table 5 shows the differences in nutritional intake among the four groups. There were no intergroup differences in total energy intake. In contrast, the carbohydrate/energy intake (%) tended to be higher in the red group than in the other groups, although the difference was not statistically significant. In addition, the intake of fermented soybean foods, especially miso soup, was significantly lower, while the intakes of natto and Japanese rice wine, which are also fermented foods, tended to be lower in the red group than in the other groups.
4. Discussion
This study investigated the association between gut microbiota panels and T2DM and the relationship between gut microbiota panels and lifestyle factors. The gut microbiota panels were divided into four groups. Among them, the group with the highest prevalence of T2DM (red group) had a decreased proportion of the Firmicutes phylum and an increased proportion of the Actinobacteria phylum.
Moreover, patients in the gut microbiota group with the highest prevalence of T2DM reported a lower intake of fermented soybean foods, especially miso soup, and tended to have a lower intake of Japanese rice wine, a traditional Japanese fermented beverage. Moreover, a higher proportion of these patients were prescribed α-glucosidase inhibitors compared to those in the other groups.
Evidence collected over the past decade has shown the pivotal role of the gut microbiota in human health and diseases, including T2DM [2,3,4,5]. An association between the presence and/or proportion of bacteria and T2DM has been reported. For example, the abundance of the Firmicutes phylum was increased while that of the Bacteroidetes phylum was decreased in patients with T2DM [13,29]. However, there are vast data on the gut microbiota that are difficult to understand. In this study, we performed dimensionality reduction with t-SNE and divided the gut microbiota into four groups. The proportion of patients with T2DM was higher in the red group than in the other groups. The abundance of the Firmicutes phylum was significantly lower in the red group than in the other groups, which could be related to the increased abundance of the Actinobacteria phylum. The proportions of the Bifidobacterium and Lactobacillus genera were significantly higher in the red group than in the other groups. Previous studies demonstrated an association between these genera and T2DM [5,13,29]. A high proportion of the Bifidobacterium and Lactobacillus genera in Japanese patients with T2DM is associated with the use of α-glucosidase inhibitors [5]. Thus, α-glucosidase inhibitor use may be closely associated with T2DM-related gut microbiota.
A high-fat diet and low dietary fiber intake are associated with dysbiosis. The traditional diet in Japan is low in fat and high in fiber, characterized by the consumption of soybeans, vegetables, seaweed, fish, rice, and fermented foods. These factors may have formed the unique gut microbiota in the Japanese population. The gut microbiota of healthy Japanese individuals is specific, and the functional profiles of carbohydrate and energy metabolism also differ between Japanese individuals and those from other countries [7]. However, in Japan, the Westernization of food continues, and traditional food culture is being lost. Our recent study demonstrated that the gut microbiota and its functional profile differed between patients with T2DM and healthy individuals in Japan and that sucrose intake, which represents diet Westernization, affected gut dysbiosis in Japanese patients with T2DM [26]. In this study, fat and dietary fiber intake did not differ between patients in the gut microbiota group with the highest prevalence of T2DM (red group) and those in the other groups; however, the patients in the red group had a lower intake of fermented foods than those in the other groups. This result suggests that the decreased intake of traditional Japanese fermented foods caused by Westernization of the diet is related to gut dysbiosis in patients with T2DM. At the same time, the proportions of the Blautia genera were lower in the red group than in the other groups. Many Japanese fermented foods, such as fermented soybean paste, are prepared using the nonpathogenic fungus koji. Feeding mice glycosylceramide, which is abundant in koji, reportedly increased the proportion of the Blautia genera [30]. Thus, the lower percentage of the Blautia genera in the red group may be related to the lower intake of fermented foods.
This study has some limitations. A limited number of individuals without diabetes were included in the creation of the gut microbiota panel, and their dietary habits could not be evaluated. In addition, we did not sufficiently examine factors other than dietary content and diabetes medication as factors affecting the gut microbiota. Furthermore, because this was a cross-sectional study, the causal relationship between changes in the gut microbiota shown in this study and T2DM remains unknown. To clarify the relationship between dietary habits and gut dysbiosis in T2DM onset and progression, further studies of patients with T2DM who are not receiving antidiabetic medications and those with prediabetes are needed.
5. Conclusions
In this study, we visualized a huge amount of gut microbiota data by dimensionality reduction using t-SNE and divided them into four groups. We identified characteristic changes in the gut microbiota in patients with T2DM. Our findings suggested that certain diabetes drugs and fermented foods may be involved in these changes in the gut microbiota. To clarify the relationship between dietary habits and the gut microbiota, it will be necessary to reduce the influence of various medications.
Supplementary Materials
The following are available online at https://www.mdpi.com/article/10.3390/nu13113816/s1, Table S1: The proportions of genera of all subjects.
Author Contributions
Conceptualization, M.H.; methodology, S.A.; software, M.F.; validation, Y.K., Y.H. and S.A.; formal analysis, Y.K., Y.H. and S.A.; investigation, Y.H., M.H., S.K., K.M., K.U., T.T. and Y.N.; resources, M.F.; data curation, Y.H., A.K. and R.S.; writing—original draft preparation, Y.K. and Y.H.; writing—review and editing, Y.K., Y.H., M.H., S.A., A.K., R.S., R.I., S.K., K.M., K.U., T.T., Y.N. and M.F.; visualization, Y.K., Y.H. and S.A.; supervision, M.F.; project administration, M.H., T.T., Y.N. and M.F.; funding acquisition, Y.N. and M.F. All authors have read and agreed to the published version of the manuscript.
Funding
This work was partly supported by MAFF Commissioned project study on “Project for the realization of foods and dietary habits to extend healthy life expectancy” Grant Number JPJ009842 to YN. No funding bodies had any role in the study design, data collection and analysis, the decision to publish, or the preparation of the manuscript.
Institutional Review Board Statement
The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Research Ethics Committee of Kyoto Prefectural University of Medicine (no. ERB-C-534, RBMR-E-466-5, and ERB-C-1912).
Informed Consent Statement
Signed informed consent was obtained from all subjects involved in the study. BioProject accession number DRA009950 in the DDBJ BioProject database (available 1 July 2020).
Data Availability Statement
The sequence data used in this study have been submitted to Sequence Read Archive (SRA) with the accession number PRJNA766337 (Available from 1 November 2021).
Conflicts of Interest
Y.N. received a scholarship from EA Pharma Co., Ltd., a collaboration research fund from Fujifilm Medical Co., Ltd., and has received lecture fees from Janssen Pharma K.K., Mylan EPD Co., Takeda Pharma Co., Ltd., Mochida Pharma Co., Ltd., EA Pharma Co., Ltd., Otsuka Pharma Co., Ltd., Astellas Pharma Co., Ltd., and Miyarisan Pharma Co., Ltd. The research was partly funded by these funds. Neither the funding agency nor any outside organization has participated in the study design or has any competing interest. The other authors declare that they have no competing interests related to the work under consideration.
References
- Zheng, Y.; Ley, S.H.; Hu, F.B. Global Aetiology and Epidemiology of Type 2 Diabetes Mellitus and Its Complications. Nat. Rev. Endocrinol. 2018, 14, 88–98. [Google Scholar] [CrossRef] [PubMed]
- Sikalidis, A.K.; Maykish, A. The Gut Microbiome and Type 2 Diabetes Mellitus: Discussing a Complex Relationship. Biomedicines 2020, 8, 8. [Google Scholar] [CrossRef] [Green Version]
- Saad, M.J.A.; Santos, A.; Prada, P.O. Linking Gut Microbiota and Inflammation to Obesity and Insulin Resistance. Physiology (Bethesda) 2016, 31, 283–293. [Google Scholar] [CrossRef] [PubMed]
- Pedersen, H.K.; Gudmundsdottir, V.; Nielsen, H.B.; Hyotylainen, T.; Nielsen, T.; Jensen, B.A.H.; Forslund, K.; Hildebrand, F.; Prifti, E.; Falony, G.; et al. Human Gut Microbes Impact Host Serum Metabolome and Insulin Sensitivity. Nature 2016, 535, 376–381. [Google Scholar] [CrossRef]
- Gurung, M.; Li, Z.; You, H.; Rodrigues, R.; Jump, D.B.; Morgun, A.; Shulzhenko, N. Role of Gut Microbiota in Type 2 Diabetes Pathophysiology. EBioMedicine 2020, 51, 102590. [Google Scholar] [CrossRef] [Green Version]
- Hashimoto, Y.; Hamaguchi, M.; Fukui, M. Microbe-Associated Metabolites as Targets for Incident Type 2 Diabetes. J. Diabetes Investig. 2021, 12, 476–478. [Google Scholar] [CrossRef]
- Nishijima, S.; Suda, W.; Oshima, K.; Kim, S.-W.; Hirose, Y.; Morita, H.; Hattori, M. The Gut Microbiome of Healthy Japanese and Its Microbial and Functional Uniqueness. DNA Res. 2016, 23, 125–133. [Google Scholar] [CrossRef] [Green Version]
- Gabriel, A.S.; Ninomiya, K.; Uneyama, H. The Role of the Japanese Traditional Diet in Healthy and Sustainable Dietary Patterns around the World. Nutrients 2018, 10, 173. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Murakami, K.; Livingstone, M.B.E.; Sasaki, S. Thirteen-Year Trends in Dietary Patterns among Japanese Adults in the National Health and Nutrition Survey 2003–2015: Continuous Westernization of the Japanese Diet. Nutrients 2018, 10, 994. [Google Scholar] [CrossRef] [Green Version]
- Adachi, K.; Sugiyama, T.; Yamaguchi, Y.; Tamura, Y.; Izawa, S.; Hijikata, Y.; Ebi, M.; Funaki, Y.; Ogasawara, N.; Goto, C.; et al. Gut Microbiota Disorders Cause Type 2 Diabetes Mellitus and Homeostatic Disturbances in Gut-Related Metabolism in Japanese Subjects. J. Clin. Biochem. Nutr. 2019, 64, 231–238. [Google Scholar] [CrossRef] [Green Version]
- Sato, J.; Kanazawa, A.; Ikeda, F.; Yoshihara, T.; Goto, H.; Abe, H.; Komiya, K.; Kawaguchi, M.; Shimizu, T.; Ogihara, T.; et al. Gut Dysbiosis and Detection of “Live Gut Bacteria” in Blood of Japanese Patients with Type 2 Diabetes. Diabetes Care 2014, 37, 2343–2350. [Google Scholar] [CrossRef] [Green Version]
- Jamieson, A.R.; Giger, M.L.; Drukker, K.; Li, H.; Yuan, Y.; Bhooshan, N. Exploring Nonlinear Feature Space Dimension Reduction and Data Representation in Breast Cadx with Laplacian Eigenmaps and T-SNE. Med. Phys. 2010, 37, 339–351. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Qin, J.; Li, Y.; Cai, Z.; Li, S.; Zhu, J.; Zhang, F.; Liang, S.; Zhang, W.; Guan, Y.; Shen, D.; et al. A Metagenome-Wide Association Study of Gut Microbiota in Type 2 Diabetes. Nature 2012, 490, 55–60. [Google Scholar] [CrossRef] [PubMed]
- American Diabetes Association. 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2018. Diabetes Care 2018, 41, S13–S27. [Google Scholar] [CrossRef] [Green Version]
- Okamura, T.; Hashimoto, Y.; Hamaguchi, M.; Obora, A.; Kojima, T.; Fukui, M. Ectopic Fat Obesity Presents the Greatest Risk for Incident Type 2 Diabetes: A Population-Based Longitudinal Study. Int. J. Obes. (Lond) 2019, 43, 139–148. [Google Scholar] [CrossRef]
- Matsuo, S.; Imai, E.; Horio, M.; Yasuda, Y.; Tomita, K.; Nitta, K.; Yamagata, K.; Tomino, Y.; Yokoyama, H.; Hishida, A.; et al. Revised Equations for Estimated GFR from Serum Creatinine in Japan. Am. J. Kidney Dis. 2009, 53, 982–992. [Google Scholar] [CrossRef]
- Ohkura, T.; Shiochi, H.; Fujioka, Y.; Sumi, K.; Yamamoto, N.; Matsuzawa, K.; Izawa, S.; Kinoshita, H.; Ohkura, H.; Kato, M.; et al. 20/(Fasting C-Peptide × Fasting Plasma Glucose) is a Simple and Effective Index of Insulin Resistance in Patients with Type 2 Diabetes Mellitus: A Preliminary Report. Cardiovasc. Diabetol. 2013, 12, 21. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Iwata, M.; Matsushita, Y.; Fukuda, K.; Wakura, T.; Okabe, K.; Koshimizu, Y.; Fukushima, Y.; Kobashi, C.; Yamazaki, Y.; Honoki, H.; et al. Secretory Units of Islets in Transplantation Index Is a Useful Predictor of Insulin Requirement in Japanese Type 2 Diabetic Patients. J. Diabetes Investig. 2014, 5, 570–580. [Google Scholar] [CrossRef] [Green Version]
- Yasuda, H.; Sanada, M.; Kitada, K.; Terashima, T.; Kim, H.; Sakaue, Y.; Fujitani, M.; Kawai, H.; Maeda, K.; Kashiwagi, A. Rationale and Usefulness of Newly Devised Abbreviated Diagnostic Criteria and Staging for Diabetic Polyneuropathy. Diabetes Res. Clin. Pract. 2007, 77 (Suppl. 1), S178–S183. [Google Scholar] [CrossRef]
- Mineoka, Y.; Ishii, M.; Hashimoto, Y.; Tanaka, M.; Nakamura, N.; Katsumi, Y.; Isono, M.; Fukui, M. Relationship between Limited Joint Mobility of Hand and Carotid Atherosclerosis in Patients with Type 2 Diabetes. Diabetes Res. Clin. Pract. 2017, 132, 79–84. [Google Scholar] [CrossRef]
- Hashimoto, Y.; Kaji, A.; Sakai, R.; Osaka, T.; Ushigome, E.; Hamaguchi, M.; Yamazaki, M.; Fukui, M. Skipping Breakfast Is Associated with Glycemic Variability in Patients with Type 2 Diabetes. Nutrition 2020, 71, 110639. [Google Scholar] [CrossRef] [PubMed]
- Kobayashi, S.; Murakami, K.; Sasaki, S.; Okubo, H.; Hirota, N.; Notsu, A.; Fukui, M.; Date, C. Comparison of Relative Validity of Food Group Intakes Estimated by Comprehensive and Brief-Type Self-Administered Diet History Questionnaires against 16 d Dietary Records in Japanese Adults. Public Health Nutr. 2011, 14, 1200–1211. [Google Scholar] [CrossRef]
- Inoue, R.; Ohue-Kitano, R.; Tsukahara, T.; Tanaka, M.; Masuda, S.; Inoue, T.; Yamakage, H.; Kusakabe, T.; Hasegawa, K.; Shimatsu, A.; et al. Prediction of Functional Profiles of Gut Microbiota from 16S RRNA Metagenomic Data Provides a More Robust Evaluation of Gut Dysbiosis Occurring in Japanese Type 2 Diabetic Patients. J. Clin. Biochem. Nutr. 2017, 61, 217–221. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Nishino, K.; Nishida, A.; Inoue, R.; Kawada, Y.; Ohno, M.; Sakai, S.; Inatomi, O.; Bamba, S.; Sugimoto, M.; Kawahara, M.; et al. Analysis of Endoscopic Brush Samples Identified Mucosa-Associated Dysbiosis in Inflammatory Bowel Disease. J. Gastroenterol. 2018, 53, 95–106. [Google Scholar] [CrossRef] [Green Version]
- Takagi, T.; Naito, Y.; Kashiwagi, S.; Uchiyama, K.; Mizushima, K.; Kamada, K.; Ishikawa, T.; Inoue, R.; Okuda, K.; Tsujimoto, Y.; et al. Changes in the Gut Microbiota Are Associated with Hypertension, Hyperlipidemia, and Type 2 Diabetes Mellitus in Japanese Subjects. Nutrients 2020, 12, 2996. [Google Scholar] [CrossRef]
- Hashimoto, Y.; Hamaguchi, M.; Kaji, A.; Sakai, R.; Osaka, T.; Inoue, R.; Kashiwagi, S.; Mizushima, K.; Uchiyama, K.; Takagi, T.; et al. Intake of Sucrose Affects Gut Dysbiosis in Patients with Type 2 Diabetes. J. Diabetes Investig. 2020, 11, 1623–1634. [Google Scholar] [CrossRef]
- Janssen, S.; McDonald, D.; Gonzalez, A.; Navas-Molina, J.A.; Jiang, L.; Xu, Z.Z.; Winker, K.; Kado, D.M.; Orwoll, E.; Manary, M.; et al. Phylogenetic Placement of Exact Amplicon Sequences Improves Associations with Clinical Information. Msystems 2018, 3, e00021-18. [Google Scholar] [CrossRef] [Green Version]
- van der Maaten, L.; Hinton, G. Visualizing Data Using T-SNE. J. Mach. Learn. Res. 2008, 9, 2431–2456. [Google Scholar]
- Karlsson, F.H.; Tremaroli, V.; Nookaew, I.; Bergström, G.; Behre, C.J.; Fagerberg, B.; Nielsen, J.; Bäckhed, F. Gut Metagenome in European Women with Normal, Impaired and Diabetic Glucose Control. Nature 2013, 498, 99–103. [Google Scholar] [CrossRef]
- Hamajima, H.; Matsunaga, H.; Fujikawa, A.; Sato, T.; Mitsutake, S.; Yanagita, T.; Nagao, K.; Nakayama, J.; Kitagaki, H. Japanese Traditional Dietary Fungus Koji Aspergillus Oryzae Functions as a Prebiotic for Blautia Coccoides through Glycosylceramide: Japanese Dietary Fungus Koji Is a New Prebiotic. SpringerPlus 2016, 5, 1321. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Figure 1.
Gut microbiota panel according to the t-distributed stochastic neighbor embedding method.
Figure 2.
Phylum proportions by group. The differences among groups were evaluated using the Kruskal–Wallis and Steel–Dwass tests. Actinobacteria: red vs. the others, all p < 0.0001; blue vs. yellow, p = 0.0369; green vs. yellow, p < 0.0001. Bacteroidetes: red vs. blue, p = 0.0008; red vs. yellow, p < 0.0001; blue vs. green, p < 0.0001; blue vs. yellow, p < 0.0001; green vs. yellow, p < 0.0001. Firmicutes: red vs. blue and yellow, p < 0.0001; red vs. green, p = 0.0067; blue vs. yellow, p < 0.000; green vs. yellow, p < 0.0001.
Figure 2.
Phylum proportions by group. The differences among groups were evaluated using the Kruskal–Wallis and Steel–Dwass tests. Actinobacteria: red vs. the others, all p < 0.0001; blue vs. yellow, p = 0.0369; green vs. yellow, p < 0.0001. Bacteroidetes: red vs. blue, p = 0.0008; red vs. yellow, p < 0.0001; blue vs. green, p < 0.0001; blue vs. yellow, p < 0.0001; green vs. yellow, p < 0.0001. Firmicutes: red vs. blue and yellow, p < 0.0001; red vs. green, p = 0.0067; blue vs. yellow, p < 0.000; green vs. yellow, p < 0.0001.
Figure 3.
Gut microbiota at the genus level with significant differences seen in the red group. The proportions of genera among the four groups were evaluated using the Kruskal–Wallis and Steel–Dwass tests. * p < 0.05.
Figure 3.
Gut microbiota at the genus level with significant differences seen in the red group. The proportions of genera among the four groups were evaluated using the Kruskal–Wallis and Steel–Dwass tests. * p < 0.05.
Table 1.
Basic characteristics of all subjects.
| Group | Red | Blue | Green | Yellow | p Value * |
|---|---|---|---|---|---|
| Number | 129 | 116 | 118 | 134 | - |
| Type 2 diabetes, % (n) | 86.8 (112) | 69.8 (81) | 76.3 (90) | 74.6 (100) | 0.012 |
| Male sex, % (n) | 48.1 (62) | 57.8 (67) | 57.6 (68) | 48.5 (65) | 0.237 |
| Age, years, mean (SD) | 67.8 (10.9) | 64.5 (12.5) | 67.5 (10.6) | 65.3 (11.2) | 0.057 |
| BMI, kg/m2, mean (SD) | 23.8 (4.0) | 24.2 (4.8) | 23.3 (3.6) | 23.6 (3.9) | 0.424 |
BMI, body mass index; SD, standard deviation. * Kruskal–Wallis test was applied.
Table 2.
Odds ratio for the prevalence of type 2 diabetes.
| Odds Ratio (95% CI) | p Value | |
|---|---|---|
| Red group | Ref. | - |
| Blue group | 0.34 (0.18–0.66) | 0.001 |
| Green group | 0.46 (0.24–0.90) | 0.024 |
| Yellow group | 0.47 (0.24–0.89) | 0.021 |
| Men | 1.66 (1.08–2.55) | 0.022 |
| Age, years | 1.01 (0.99–1.03) | 0.257 |
Table 3.
Characteristics of subjects with type 2 diabetes mellitus.
| Group | Red | Blue | Green | Yellow | p Value * |
|---|---|---|---|---|---|
| Number | 112 | 81 | 90 | 100 | - |
| Male sex, % (n) | 47.3 (53) | 60.5 (49) | 61.1 (55) | 55.0 (55) | 0.172 |
| Age, years, mean (SD) | 67.5 (10.9) | 66.2 (10.9) | 68.2 (9.5) | 64.8 (10.9) | 0.122 |
| BMI, kg/m2, mean (SD) | 24.1 (4.0) | 25.0 (4.9) | 23.8 (3.6) | 24.2 (3.8) | 0.295 |
| Duration of diabetes, years, mean (SD) | 14.8 (10.1) | 13.6 (9.3) | 15.1 (11.7) | 12.2 (8.8) | 0.164 |
| Family history of diabetes, % (n) | 46.4 (52) | 40.7 (33) | 37.8 (34) | 53 (53) | 0.16 |
| Habitual alcohol intake, % (n) | 5.4 (6) | 13.6 (11) | 5.6 (5) | 18 (18) | 0.006 |
| Smoking status | 0.403 | ||||
| Nonsmoker, % (n) | 66.1 (74) | 51.9 (42) | 60 (54) | 57 (57) | |
| Past smoker, % (n) | 25 (28) | 28.4 (23) | 25.6 (23) | 29 (29) | |
| Current smoker, % (n) | 8.9 (10) | 19.8 (16) | 14.4 (13) | 14 (14) | |
| Exercise, % (n) | 42 (47) | 50.6 (41) | 44.4 (40) | 54 (54) | 0.293 |
| Neuropathy, % (n) | 24.1 (27) | 28.4 (23) | 24.4 (22) | 21 (21) | 0.721 |
| Retinopathy, % (n) | 23.2 (26) | 22.2 (18) | 27.8 (25) | 12 (12) | 0.052 |
| Nephropathy, % (n) | 43.8 (49) | 49.4 (40) | 44.4 (40) | 35 (35) | 0.252 |
| History of cardiovascular disease, % (n) | 18.8 (21) | 12.4 (10) | 18.9 (17) | 6 (6) | 0.025 |
| Medication use | |||||
| Sulfonylurea, % (n) | 25 (28) | 16.1 (13) | 24.4 (22) | 24 (24) | 0.452 |
| Glinide, % (n) | 16.1 (18) | 7.4 (86) | 3.3 (3) | 4 (4) | 0.002 |
| Dipeptidyl peptidase-4 inhibitor, % (n) | 59.8 (67) | 43.2 (35) | 54.4 (49) | 42 (42) | 0.029 |
| Biguanide, % (n) | 40.2 (45) | 45.7 (37) | 27.8 (25) | 49 (49) | 0.019 |
| Thiazolidinedione, % (n) | 6.3 (7) | 4.9 (4) | 3.3 (3) | 0 (0) | 0.096 |
| α-Glucosidase inhibitor, % (n) | 30.4 (34) | 6.2 (5) | 4.4 (4) | 4 (4) | <0.001 |
| Sodium glucose co-transporter 2 inhibitor, % (n) | 14.3 (16) | 23.5 (19) | 15.6 (14) | 13 (13) | 0.238 |
| Glucagon-like peptide-1 analog, % (n) | 12.5 (14) | 14.8 (12) | 16.7 (15) | 21 (21) | 0.398 |
| Insulin, % (n) | 26.8 (30) | 23.5 (19) | 25.6 (23) | 17 (17) | 0.355 |
| Renin angiotensin system inhibitor, % (n) | 48.2 (54) | 48.2 (39) | 45.6 (41) | 36 (36) | 0.258 |
| Calcium channel blocker, % (n) | 32.1 (36) | 25.9 (21) | 31.1 (28) | 23 (23) | 0.427 |
| Diuretic, % (n) | 13.4 (15) | 11.1 (9) | 7.8 (7) | 8 (8) | 0.488 |
| α blocker, % (n) | 1.8 (2) | 7.4 (6) | 6.7 (6) | 1 (1) | 0.045 |
| β blocker, % (n) | 9.8 (11) | 6.2 (5) | 5.6 (5) | 2 (2) | 0.124 |
| Statin, % (n) | 42.0 (47) | 40.7 (3) | 36.7 (33) | 35 (35) | 0.71 |
| Fibrate, % (n) | 2.7 (3) | 9.9 (8) | 2.2 (2) | 3 (3) | 0.038 |
| Eicosapentaenoic acid, % (n) | 7.1 (8) | 2.5 (2) | 1.1 (1) | 6 (6) | 0.133 |
| Ezetimibe, % (n) | 2.7 (3) | 7.4 (6) | 2.2 (2) | 0 (0) | 0.282 |
BMI, body mass index; SD, standard deviation. * Kruskal–Wallis test was applied.
Table 4.
Examination results of subjects with type 2 diabetes mellitus.
| Group | Red | Blue | Green | Yellow | p Value * |
|---|---|---|---|---|---|
| Number | 112 | 81 | 90 | 100 | - |
| Systolic blood pressure, mmHg, mean (SD) | 134.0 (17.7) | 134.7 (19.3) | 134.8 (20.0) | 133.5 (18.3) | 0.963 |
| Diastolic blood pressure, mmHg, mean (SD) | 77.9 (11.6) | 80.3 (11.6) | 78.9 (11.5) | 78.7 (10.3) | 0.524 |
| Glucose, mg/dL (SD) | 147.5 (50.9) | 149.7 (46.5) | 150.5 (50.0) | 148.9 (49.0) | 0.977 |
| Hemoglobin A1c, % (SD) | 7.30 (1.41) | 7.54 (1.38) | 7.38 (1.16) | 7.27 (1.14) | 0.508 |
| C-peptide index, mean (SD) | 1.23 (0.74) | 1.33 (0.72) | 1.18 (0.69) | 1.26 (0.70) | 0.628 |
| Aspartate aminotransferase, IU/L, mean (SD) | 23.8 (12.1) | 26.1 (11.5) | 21.2 (8.8) | 23.7 (8.8) | 0.026 |
| Alanine aminotransferase, IU/L, mean (SD) | 24.8 (19.6) | 26.0 (16.4) | 21.5 (15.1) | 23.7 (14.1) | 0.303 |
| Gamma-glutamyltransferase, IU/L, mean (SD) | 31.9 (24.3) | 42.3 (48.4) | 29.4 (20.8) | 38.4 (29.0) | 0.025 |
| Creatinine, μmol/L (SD) | 69.9 (30.8) | 74.9 (25.7) | 80.0 (44.1) | 71.8 (23.2) | 0.134 |
| eGFR, mL/min/1.73 m2, mean (SD) | 72.0 (21.4) | 67.6 (16.4) | 66.7 (20.8) | 69.9 (17.7) | 0.208 |
| Uric acid, μmol/L, mean (SD) | 299.7 (69.7) | 306.1 (75.0) | 306.1 (78.2) | 317.7 (76.5) | 0.373 |
| Triglycerides, mmol/L, mean (SD) | 1.44 (1.06) | 1.63 (0.91) | 1.37 (0.71) | 1.60 (0.93) | 0.16 |
| HDL cholesterol, mmol/L, mean (SD) | 1.62 (0.47) | 1.53 (0.46) | 1.50 (0.39) | 1.56 (0.42) | 0.278 |
eGFR, estimated glomerular filtration rate; HDL, high-density lipoprotein; SD, standard deviation. * Kruskal–Wallis test was applied.
Table 5.
Nutritional intakes of subjects with type 2 diabetes mellitus.
| Group | Red | Blue | Green | Yellow | p Value * |
|---|---|---|---|---|---|
| Number | 112 | 81 | 90 | 100 | - |
| Total energy intake, kcal/day, mean (SD) | 1687 (621) | 1804 (534) | 1769 (733) | 1714 (626) | 0.294 |
| Total energy intake, kcal/day/IBW, mean (SD) | 30.5 (11.4) | 31.6 (8.6) | 29.6 (9.9) | 29.4 (9.8) | 0.29 |
| Protein intake, g/day/IBW, mean (SD) | 1.27 (0.58) | 1.28 (0.41) | 1.25 (0.47) | 1.26 (0.52) | 0.78 |
| Protein intake/energy intake, % (SD) | 16.6 (3.6) | 16.3 (3.3) | 17.1 (3.4) | 17.1 (3.5) | 0.341 |
| Animal protein intake, g/day/IBW, mean (SD) | 0.78 (0.47) | 0.78 (0.32) | 0.76 (0.36) | 0.79 (0.41) | 0.794 |
| Vegetable protein intake, g/day/IBW, mean (SD) | 0.50 (0.17) | 0.50 (0.15) | 0.49 (0.18) | 0.47 (0.15) | 0.402 |
| Fat intake, g/day/IBW, mean (SD) | 0.97 (0.49) | 0.98 (0.32) | 0.94 (0.37) | 0.95 (0.36) | 0.696 |
| Fat intake/energy intake, % (SD) | 28.6 (6.3) | 28.0 (5.6) | 28.8 (7.0) | 29.0 (6.3) | 0.811 |
| Animal fat intake, g/day/IBW, mean (SD) | 0.48 (0.31) | 0.48 (0.19) | 0.46 (0.21) | 0.46 (0.23) | 0.623 |
| Vegetable fat intake, g/day/IBW, mean (SD) | 0.49 (0.22) | 0.50 (0.18) | 0.48 (0.20) | 0.49 (0.18) | 0.925 |
| Carbohydrate intake, g/day/IBW, mean (SD) | 3.94 (1.41) | 4.00 (1.27) | 3.80 (1.49) | 3.58 (1.37) | 0.071 |
| Carbohydrate intake/energy intake, % (SD) | 52.2 (8.2) | 50.7 (8.8) | 51.2 (9.5) | 48.8 (8.6) | 0.06 |
| Fiber intake, g/day, mean (SD) | 11.8 (5.0) | 11.9 (4.9) | 13.0 (5.4) | 12.1 (5.0) | 0.337 |
| Sucrose intake, g/day, mean (SD) | 12.8 (8.6) | 13.5 (9.7) | 10.3 (8.6) | 10.5 (6.7) | 0.032 |
| Salt intake, g/day, mean (SD) | 10.5 (3.7) | 10.9 (3.3) | 10.9 (4.8) | 10.7 (3.8) | 0.778 |
| Seaweed intake, g/day, mean (SD) | 14.5 (17.0) | 12.8 (12.4) | 12.8 (13.0) | 13.6 (13.7) | 0.990 |
| Soybean food intake, g/day, mean (SD) | 160.5 (110.1) | 174.0 (115.5) | 202.7 (126.7) | 186.5 (132.4) | 0.048 |
| Tofu and fried tofu intake, g/day, mean (SD) | 51.2 (36.2) | 48.4 (40.3) | 52.1 (41.4) | 47.7 (35.3) | 0.689 |
| Fermented soybean food intake, g/day, mean (SD) | 109.3 (96.1) | 125.6 (103.0) | 150.7 (113.2) | 138.9 (118.7) | 0.011 |
| Natto intake, g/day, mean (SD) | 11.1 (15.7) | 11.0 (14.3) | 14.0 (16.7) | 15.8 (20.3) | 0.062 |
| Miso soup intake, g/day, mean (SD) | 98.2 (10.3) | 114.6 (12.0) | 136.7 (11.4) | 123.0 (10.7) | 0.03 |
| Alcohol consumption, g/day, mean (SD) | 4.75 (14.9) | 11.1 (25.6) | 5.23 (15.2) | 10.71 (21.1) | 0.011 |
| Japanese sake intake, g/day, mean (SD) | 4.08 (2.52) | 9.38 (2.93) | 4.79 (2.79) | 3.21 (2.62) | 0.06 |
IBW, ideal body weight; SD, standard deviation. * Kruskal–Wallis test was applied.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
MDPI and ACS Style
Kondo, Y.; Hashimoto, Y.; Hamaguchi, M.; Ando, S.; Kaji, A.; Sakai, R.; Inoue, R.; Kashiwagi, S.; Mizushima, K.; Uchiyama, K.; et al. Unique Habitual Food Intakes in the Gut Microbiota Cluster Associated with Type 2 Diabetes Mellitus. Nutrients 2021, 13, 3816. https://doi.org/10.3390/nu13113816
AMA Style
Kondo Y, Hashimoto Y, Hamaguchi M, Ando S, Kaji A, Sakai R, Inoue R, Kashiwagi S, Mizushima K, Uchiyama K, et al. Unique Habitual Food Intakes in the Gut Microbiota Cluster Associated with Type 2 Diabetes Mellitus. Nutrients. 2021; 13(11):3816. https://doi.org/10.3390/nu13113816
Chicago/Turabian StyleKondo, Yuriko, Yoshitaka Hashimoto, Masahide Hamaguchi, Shinto Ando, Ayumi Kaji, Ryosuke Sakai, Ryo Inoue, Saori Kashiwagi, Katsura Mizushima, Kazuhiko Uchiyama, and et al. 2021. "Unique Habitual Food Intakes in the Gut Microbiota Cluster Associated with Type 2 Diabetes Mellitus" Nutrients 13, no. 11: 3816. https://doi.org/10.3390/nu13113816
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.
