Metabolites of gut microbiome are associated with glucose metabolism in non-diabetic obese adults: a Chinese monozygotic twin study
Diabetology & Metabolic Syndrome volume 13, Article number: 106 (2021)
Evidence suggests gut microbiome is associated with diabetes. However, it’s unclear whether the association remains in non-diabetic participants. A Chinese monozygotic twin study, in which the participants are without diabetes, and are not taking any medications, was conducted to explore the potential association.
Nine pairs of adult monozygotic twins were enrolled and divided into two twin-pair groups (a and b). Clinical and laboratory measurements were conducted. Visceral adipose tissue (VAT) was assessed. Fecal samples were collected to analyze the microbiome composition by 16S rDNA gene amplicon sequencing. Liquid chromatography mass spectrometry was performed to detect the metabolites.
The participants aged 53 years old averagely, with 8 (88.9%) pairs were women. All the participants were obese with VAT higher than 100 cm2 (152.2 ± 31.6). There was no significant difference of VAT between the twin groups (153.6 ± 30.4 cm2 vs. 150.8 ± 29.5 cm2, p = 0.54). Other clinical measurements, including BMI, lipid profiles, fasting insulin and blood glucose, were also not significantly different between groups (p ≥ 0.056), whereas HbA1c level of group a is significantly higher than group b (5.8 ± 0.3% vs. 5.6 ± 0.2%, p = 0.008). The number and richness of OTUs are relatively higher in group a, and 13 metabolites were significantly different between two groups. Furthermore, several of the 13 metabolites could be significantly linked to special taxons. The potential pathway involved drug metabolism-other enzymes, Tryptophan metabolism and Citrate cycle.
Gut microbiome composition and their metabolites may modulate glucose metabolism in obese adults without diabetes, through Tryptophan metabolism, Citrate cycle and other pathways.
Increasing evidence suggests the gut microbiome is associated with metabolic diseases, especially obesity and type 2 diabetes (T2D) [1,2,3,4]. However, the participants of most previous studies [5,6,7,8,9,10] are patients with type 2 diabetes or pre-diabetes, with different genetic background, and taking different kinds of anti-diabetic drugs, which may have confounding effects on the gut microbiome composition and fecal metabolites [11,12,13]. Thus, it’s unclear whether the association remains in healthy or early subclinical status without obvious confounding factors. Monozygotic twins shared the same genotype and early environmental exposures, and thus, potentially similar gut microbiome composition .
Only few work publications were reported to address the abovementioned issue in PubMed with keywords as “gut microbiome” and “Twin” [14,15,16] (Table 1), one of the which indicated that microbiome changes were associated with sub-clinical state of T2D in participants neither obese nor diabetic . However, none of the three studies included Chinese participants. In this work, a Chinese monozygotic (MZ) twin study was conducted to explore the potential association of gut microbiome with glucose metabolism in healthy obese participants. Twins in the study grew up in the same family, without known diabetes, without taking antibiotics or other medications, to avoid other factors that may influence the gut microbiome.
Microbial metabolites related with host glucose metabolism included Short-Chain Fatty Acids (SCFAs), such as acetate, butyrate, and propionate, which are the major end products of carbohydrates . Besides SCFAs, branched-chain amino acids (BCAAs), bile acids, sulfur-containing amino acids, indole derivatives, trimethylamine N-oxide (TMAO) and vitamins were also involved in the regulation of insulin resistance . In the present study, the above metabolites were tested in the fecal samples to detect the associations with glucose metabolism.
Nine pairs of adult MZ twins who were native residents in Tongzhou District of Beijing were enrolled in our study. The twin pairs grew up in the same family, without diagnosed diabetes, and not taking antibiotics or other medications that may influence the gut microbiome in the last two weeks before coming to hospital. The twin pairs were excluded as long as one of them is pregnant, or with tumor history, or with mental disease, or with recent history of diarrhea or intestinal infection.
The participants were divided into two twin-pair groups (a and b). Clinical and laboratory measurements were conducted. Visceral adipose tissue (VAT) was assessed. Fecal samples were collected to analyze the microbiome composition by 16S rDNA gene amplicon sequencing. Liquid chromatography mass spectrometry was performed to detect the metabolites.
Clinical and laboratory measurements
The nurses first administered questionnaires, inquiring into each participant’s medical history, smoking and drinking habits, and intake of medications. Then, venous blood samples were obtained after 8 to 10 h of fasting. Blood samples were analyzed for serum levels of HbA1c, glucose, insulin, triglycerides, total low-density lipoprotein (LDL) and high-density lipoprotein (HDL) cholesterol, glutamic-pyruvic transaminase enzyme (ALT), glutamic-oxaloacetic aminotransferase(AST), gamma-glutamyltransferase (GGT), and serum creatinine. Physical examinations were also performed, including body weight, body height, waist and hip circumference. Visceral adipose fat (VAT) was evaluated for each participant (Inbody 770, Biospace Co. Ltd.).
Blood samples were also obtained and sent to Beijing Genomics Institute to extract DNAs and identify the egg type. Short tandem repeats were applied to identify and confirm the egg type.
Microbiome composition analysis
Total genome DNA from fecal samples was extracted using Soil DNA Kit according to manufacturer’s protocols. DNA concentration was monitored by Qubit® dsDNA HS Assay Kit.
20–30ng DNAs were used to generate amplicons. V3 and V4 hypervariable regions of prokaryotic 16S rDNA were selected for generating amplicons and following taxonomy analysis. The concentration of DNA library was validated by Qubit3.0 Fluorometer. Quantify the library to 10 nM, DNA libraries were multiplexed and loaded on an Illumina MiSeq or NovaSeq instrument according to manufacturer’s instructions (Illumina, San Diego, CA, USA). Sequencing was performed using paired-end. Image analysis and base calling were conducted by the Control Software embedded in the instrument.
The effective sequences were used in final analysis. Sequences were grouped into operational taxonomic units (OTUs) using the clustering program VSEARCH (1.9.6) against the UNITE ITS database (https://unite.ut.ee/) pre-clustered at 97% sequence identity. The Ribosomal Database Program (RDP) classifier was used to assign taxonomic category to all OTUs at confidence threshold of 0.8. The RDP classifier used the UNITE ITS database which has taxonomic categories predicted to the species level.
Fecal metabolites analysis
Various metabolites, including methanol, acetonitrile, 2-chlorophenylalanine, formic acid, ammonium formate and ddH2O, were detected based on liquid chromatography mass spectrometry (LC/MS) in fecal samples.
Fecal samples were thawed at 4 °C. 100 µL of each sample was transferred into 1.5 mL centrifuge tubes, and 400 µL of methanol (pre-cooled at − 20 °C) were added to each tube and vortex for 60 s. Then, the mixtures were centrifuged for 10 min at 12,000 rpm 4 °C and all supernatant in each tube was transferred into another 1.5 mL centrifuge tube, and samples were blow-dried by vacuum concentration. The processed supernatant was dissolved with 150 µL 2-chlorobenzalanine (4 ppm) methanol aqueous solution (4 °C), and filtered through a 0.22 μm membrane to obtain the prepared sample extracts for LC-MS.
Chromatographic separation was accomplished in an Thermo Ultimate 3000 system equipped with an ACQUITY UPLC® HSS T3 (150 × 2.1 mm, 1.8 μm, Waters) column maintained at 40 °C. The temperature of autosampler was 8 °C. Gradient elution of analytes was carried out with 0.1% formic acid in water (C) and 0.1% formic acid in acetonitrile (D) or 5 mM ammonium formate in water (A) and acetonitrile (B) at a flow rate of 0.25 mL/min. Injection of 2µL of each sample was done after equilibration. An increasing linear gradient of solvent B (v/v) was used as follows: 0–1 min, 2% B/D; 1–9 min, 2–50% B/D; 9–12 min, 50–98% B/D; 12–13.5 min, 98% B/D; 13.5–14 min, 98–2% B/D; 14–20 min, 2% D-positive model (14–17 min, 2% B-negative model).
The ESI-MSn experiments were performed on the Thermo Q Exactive Focus mass spectrometer with the spray voltage of 3.8 kV and − 2.5 kV in positive and negative modes, respectively. Sheath gas and auxiliary gas were set at 45 and 15 arbitrary units, respectively. The capillary temperature was 325 °C, respectively. The Orbitrap analyzer scanned over a mass range of m/z 81–1000 for full scan at a mass resolution of 70,000. Data dependent acquisition (DDA) MS/MS experiments were performed with HCD scan. The normalized collision energy was 30 eV. Dynamic exclusion was implemented to remove some unnecessary information in MS/MS spectra.
Database management and statistical analysis were carried out using SAS 9.4 software (Cary, NC). The central tendency (spread) was represented by the arithmetic mean (SD). To compare means and proportions, paired t-test and the χ2-statistic were applied, respectively. Significance was a 2-tailed α-level of 0.05 or less.
Characteristics and glucose levels of participants
Nine obese twin pairs without diabetes were enrolled in this study, aged 53 years old averagely, with 8 (88.9%) pairs were women. The twins were divided to two groups (a vs. b) for further analysis. All of the participants were obese with VAT higher than 100 cm2 (152.2 ± 31.6), and there was no difference between the twin groups (153.6 ± 30.4 cm2 vs. 150.8 ± 29.5 cm2, P = 0.54). However, HbA1c levels were significantly different, averaged 5.8 ± 0.3% vs. 5.6 ± 0.2% (P = 0.008). Fasting blood glucose and body mass index averaged 5.82 ± 0.71 vs. 5.58 ± 0.34 mmol/L (P = 0.29), 29.7 ± 3.4 vs. 28.8 ± 3.2 (P = 0.056), respectively. The other characteristics of participants were described in Table 2.
Differences of gut microbiome composition
Two hundred and seventy-nine OTUs were detected in all samples. The twin groups shared 249 same OTUs, with 18 unique OTUs for group a and 12 for group b (Fig. 1A). Thirty OTUs with the highest richness in each participant were displayed in the clustered heatmap, with the depths of colors representing the richness (Fig. 1B). The corresponding top five taxons were k__Bacteria, p__Firmicutes, c__Clostridia, o__Clostridiales and f__Lachnospiraceae.
Differences of fecal metabolite profiles
Thirteen metabolites were significantly different between two groups (Fig. 2), which includes anabasine, DDAO, lumazine, S-Allyl-l-cysteine, citric acid, alloxan, indoleacetic acid, mercaptopurine, Methyl Jasmonate, N-methyl-l-glutamic acid, N-Methyldioctylamine, n-Pentadecylamine, and Salicylic Acid. The former four metabolites were significantly lower in group a, while the others were significantly higher.
Correlation analysis were performed between the above 13 metabolites with Pearson Correlation Coefficient. Nineteen significant correlations were detected between each two metabolites. Four significant correlations were detected for n-Pentadecylamine, three for alloxan, anabasine and DDAO and two for mercaptopurine (Fig. 2).
Pathway impacts were also performed. Seven potential pathways were found related with gut microbiome and the metabolites. The three pathways with greatest impacts were Drug metabolism, Tryptophan metabolism and Citrate cycle, with impacts from 0.048 to 0.11 (Fig. 3).
Correlation analysis of gut microbiome and metabolites
Correlation analysis were performed to explore the potential associations of significantly different metabolites with special taxons. Twenty significant associations were found at family levels, with five metabolites (N-methyl-l-glutamic acid, Alloxan, Mercaptopurine, Citric acid and Anabasine) significant for o__Coriobacteriales_Unclassified family, and Mercaptopurine was the only metabolite that was significantly different in four families (Fig. 4).
This study aimed to explore whether the gut microbiome composition and the metabolites were associated with the glucose metabolism in nine obese MZ twin pairs without history of diabetes. The twins were divided into group a and group b. There were no significant differences for VAT or BMI or other characteristic between two groups, whereas the level of HbA1c is significantly higher in group a. 16S rDNA-based high-throughput sequencing and LC/MS were performed in 18 fecal samples from these nine MZ twin pairs. Analysis showed that the number and richness of OTUs are relatively higher in group a, and 13 metabolites were significantly different between two groups. Furthermore, several of the 13 metabolites were significantly associated with special taxons, which indicated that the gut microbiome may modulate the glucose metabolism through the gut microbiome composition and their metabolites. The potential pathways involved Drug metabolism, Tryptophan metabolism and Citrate cycle.
Previous studies performed in diabetic patients with different ethnicities mostly suggested the gut microbiome composition changes are associated with the onset or the development of the disease. However, the differential communities or taxons are inconsistent. Three Chinese studies were conducted in participants at different stages of glucose intolerance status: normal glucose tolerance (NGT), prediabetes (Pre-DM), and T2DM patients. Zhang et al.  analyzed 121 participants: 44 NGTs, 64 Pre-DMs and 13 newly diagnosed T2DM. Results showed that Verrucomicrobiae had a significantly lower abundance in both the pre-DM and T2DM groups. Zhao et al.  performed analysis in T2DM patients with or without complications, and the healthy controls. Results suggested higher abundance of Proteobacteria and higher ratio of Firmicutes/Bacteroidetes in T2DM patients. Zhong et al.  explored the gut metagenomics and metaproteomics signatures in Pre-DMs, T2DMs without treatment and NGTs. They found a significantly higher abundance of Megasphaera elsdenii (MLG-1568) in both T2DMs and Pre-DMs than in NGTs. A recent African study  also revealed that gut microbiota composition is associated with T2DM, with identification of higher richness of Desulfovibrio piger, Prevotella, Peptostreptococcus, and Eubacterium in T2DM group. Bhute et al.  assessed gut microbial diversity in 49 Indian participants who were also divided into three groups: New-DMs, Known-DMs and healthy participants. Results indicated that microbial dysbiosis may not be just limited to eubacteria in diabetes, which may also extend into other two domains leading to trans-domain dysbiosis in microbiota. Another recent study conducted by Gaike et al.  suggested that gut microbial diversity of newly diagnosed T2DMs is significantly different from that of NGTs, whereas this difference was not observed between the Pre-DM group and NGT group. The inconsistent microbiota composition results from these above studies indicate the complexity of gut microbiome changes in human participants. On the other hand, it may also indicate the potential interference of various confounding factors, such as the genotype, the different environment exposures and the ethnicities.
A Korean MZ twin study  conducted gut microbiome analysis in 20 MZ twins neither obese nor diabetic, with 36 fecal samples collected. They analyzed the association of changes in microbiome composition with different factors, such as BMI and glucose levels. Results suggested the decrease in Akkermansia muciniphila may occur prior to the onset of diabetes, and strain-level differences in composition were observed despite of species-level similarities in the twin pairs.
The potential mechanisms of the gut microbiota and glucose metabolism mainly involve several metabolites , including beneficial metabolites, such as short-chain fatty acids (SCFAs), sulfur-containing amino acids, bile acids, and indole derivatives, and also potentially harmful metabolites, such as branched-chain amino acids(BCAAs) and lipopolysaccharide (LPS).
In the current study, anabasine, DDAO, lumazine, S-Allyl-l-cysteine were identified to be lower, while citric acid, alloxan, indoleacetic acid, mercaptopurine, methyl jasmonate, N-methyl-l-glutamic acid, N-Methyldioctylamine, n-Pentadecylamine, and Salicylic acid were higher in group a with higher HbA1c levels. And some of the above metabolites could be linked to special taxons, which indicated that the impaired glucose metabolism may be associated with gut micorbiome composition and their metabolites.
In comparison with previous studies, the present study was performed in MZ twins. The participants shared same genetic background and growth environment, and none of them was taking any medications when enrolled in this study, which can eliminate the effect of medications on gut microbiome. Thus, the design of our study is more favorable to identify the unique microbial changes and to explore the potential taxons and their metabolites associated with the development or prevention of impaired glucose metabolism. Nevertheless, reported findings must be interpreted within the context of their limitations. First, the sample size is relatively small. Second, most of the participants are female, the gender bias cannot be excluded. Third, the study design is cross-sectional, which cannot make causal explanations.
Results of the study indicate that higher levels of HbA1c in the twin pairs may be associated with some metabolites of the gut microbiome, including anabasine, DDAO, lumazine, S-Allyl-l-cysteine, citric acid, alloxan, indoleacetic acid, mercaptopurine, methyl jasmonate, N-methyl-l-glutamic acid, N-Methyldioctylamine, n-Pentadecylamine, and Salicylic acid. Some of the above metabolites could be significantly linked to special taxons, which indicated that impaired glucose metabolism may be associated with gut micorbiome composition and their metabolites. The potential pathway involved Drug metabolism, Tryptophan metabolism and Citrate cycle.
Availability of data and materials
The data of this study is available on request.
Clemente JC, Ursell LK, Parfrey LW, Knight R. The impact of the gut microbiota on human health: an integrative view. Cell. 2012;148(6):1258–70.
Jackson MA, Verdi S, Maxan ME, Shin CM, Zierer J, Bowyer RCE, et al. Gut microbiota associations with common diseases and prescription medications in a population-based cohort. Nat Commun. 2018;9(1):2655.
Qin J, Li Y, Cai Z, Li S, Zhu J, Zhang F, et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature. 2012;490(7418):55–60.
Karlsson FH, Tremaroli V, Nookaew I, Bergstrom G, Behre CJ, Fagerberg B, et al. Gut metagenome in European women with normal, impaired and diabetic glucose control. Nature. 2013;498(7452):99–103.
Zhang X, Shen D, Fang Z, Jie Z, Qiu X, Zhang C, et al. Human gut microbiota changes reveal the progression of glucose intolerance. PLoS ONE. 2013;8(8):e71108.
Bhute SS, Suryavanshi MV, Joshi SM, Yajnik CS, Shouche YS, Ghaskadbi SS. Gut microbial diversity assessment of indian type-2-diabetics reveals alterations in Eubacteria, Archaea, and Eukaryotes. Front Microbiol. 2017;8:214.
Zhong H, Ren H, Lu Y, Fang C, Hou G, Yang Z, et al. Distinct gut metagenomics and metaproteomics signatures in prediabetics and treatment-naïve type 2 diabetics. EBioMedicine. 2019;47:373–83.
Zhao L, Lou H, Peng Y, Chen S, Zhang Y, Li X. Comprehensive relationships between gut microbiome and faecal metabolome in individuals with type 2 diabetes and its complications. Endocrine. 2019;66(3):526–37.
Gaike AH, Paul D, Bhute S, Dhotre DP, Pande P, Upadhyaya S, et al. The gut microbial diversity of newly diagnosed diabetics but not of prediabetics is significantly different from that of healthy nondiabetics. mSystems. 2020;5(2):e00578-19.
Doumatey AP, Adeyemo A, Zhou J, Lei L, Adebamowo SN, Adebamowo C, et al. Gut microbiome profiles are associated with type 2 diabetes in urban Africans. Front Cell Infect Microbiol. 2020;10:63.
Wu H, Esteve E, Tremaroli V, Khan MT, Caesar R, Mannerås-Holm L, et al. Metformin alters the gut microbiome of individuals with treatment-naive type 2 diabetes, contributing to the therapeutic effects of the drug. Nat Med. 2017;23(7):850–8.
Moreira GV, Azevedo FF, Ribeiro LM, Santos A, Guadagnini D, Gama P, et al. Liraglutide modulates gut microbiota and reduces NAFLD in obese mice. J Nutr Biochem. 2018;62:143–54.
Liao X, Song L, Zeng B, Liu B, Qiu Y, Qu H, et al. Alteration of gut microbiota induced by DPP-4i treatment improves glucose homeostasis. EBioMedicine. 2019;44:665–74.
Turnbaugh PJ, Hamady M, Yatsunenko T, Cantarel BL, Duncan A, Ley RE, et al. A core gut microbiome in obese and lean twins. Nature. 2009;457(7228):480–4.
Yassour M, Lim MY, Yun HS, Tickle TL, Sung J, Song YM, et al. Sub-clinical detection of gut microbial biomarkers of obesity and type 2 diabetes. Genome Med. 2016;8(1):17.
Bowyer RCE, Jackson MA, Le Roy CI, Lochlainn MN, Spector TD, Dowd JB, et al. Socioeconomic status and the gut microbiome: a twins UK cohort study. Microorganisms. 2019;7(1):17.
Canfora EE, Jocken JW, Blaak EE. Short-chain fatty acids in control of body weight and insulin sensitivity. Nat Rev Endocrinol. 2015;11(10):577–91.
Khan MT, Nieuwdorp M, Backhed F. Microbial modulation of insulin sensitivity. Cell Metab. 2014;20(5):753–60.
The authors gratefully acknowledge the study participants and nursing staff involved in this study.
No funding was received for this work.
Ethics approval and consent to participate
The study complied with the Helsinki Declaration for investigation of humans. The Ethical Committee of Beijing Luhe Hospital, Capital Medical University granted ethical approval to of this study. Written informed consent was obtained from all patients included in the study.
Consent for publication
The authors have no conflicts of interest.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Yu, K., Yu, CG., Yin, XQ. et al. Metabolites of gut microbiome are associated with glucose metabolism in non-diabetic obese adults: a Chinese monozygotic twin study. Diabetol Metab Syndr 13, 106 (2021). https://doi.org/10.1186/s13098-021-00724-6