Microbiome and metabolome dysbiosis analysis in impaired glucose tolerance for the prediction of progression to diabetes mellitus

Boxun Zhang; Xuan Zhang; Zhen Luo; Jixiang Ren; Xiaotong Yu; Haiyan Zhao; Yitian Wang; Wenhui Zhang; Weiwei Tian; Xiuxiu Wei; Qiyou Ding; Haoyu Yang; Zishan Jin; Xiaolin Tong; Jun Wang; Linhua Zhao

doi:10.1016/j.jgg.2023.08.005

Volume 51 Issue 1

Jan. 2024

Turn off MathJax

Article Contents

Article Navigation > Journal of Genetics and Genomics > 2024 > 51(1): 75-86

PDF( 2308 KB)

Microbiome and metabolome dysbiosis analysis in impaired glucose tolerance for the prediction of progression to diabetes mellitus

doi: 10.1016/j.jgg.2023.08.005

a. Institute of Metabolic Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China;
b. Faculty of Biological Science and Technology, Baotou Teacher's College, Baotou, Inner Mongolia 014030, China;
c. CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China;
d. Infinitus (China) Company Ltd, Guangzhou, Guangdong 510405, China;
e. Affiliated Hospital of Changchun University of Chinese Medicine, Changchun, Jilin 130021, China;
f. Department of Endocrinology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China;
g. Xinjiekou Community Health Service Center in Xicheng District, Beijing 100035, China;
h. Department of Spleen and Stomach, Shenzhen Traditional Chinese Medicine Hospital, Shenzhen, Guangdong 518033, China;
i. University of Chinese Academy of Sciences, Beijing 100049, China;
j. Beijing University of Chinese Medicine, Beijing 100105, China;
k. Northeast Asia Institute of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, Jilin 130117, China

Funds:

This study was supported by the Innovation Team and Talents Cultivation Program of National Administration of Traditional Chinese Medicine (ZYYCXTD-D-202001) and the National Natural Science Foundation of China (82104835).

Received Date: 2023-04-29
Accepted Date: 2023-08-21
Rev Recd Date: 2023-08-20
Publish Date: 2023-08-29

Abstract

Abstract

Gut microbiota and circulating metabolite dysbiosis predate important pathological changes in glucose metabolic disorders; however, comprehensive studies on impaired glucose tolerance (IGT), a diabetes mellitus (DM) precursor, are lacking. Here, we perform metagenomic sequencing and metabolomics on 47 pairs of individuals with IGT and newly diagnosed DM and 46 controls with normal glucose tolerance (NGT); patients with IGT are followed up after 4 years for progression to DM. Analysis of baseline data reveals significant differences in gut microbiota and serum metabolites among the IGT, DM, and NGT groups. In addition, 13 types of gut microbiota and 17 types of circulating metabolites showed significant differences at baseline before IGT progressed to DM, including higher levels of Eggerthella unclassified, Coprobacillus unclassified, Clostridium ramosum, L-valine, L-norleucine, and L-isoleucine, and lower levels of Eubacterium eligens, Bacteroides faecis, Lachnospiraceae bacterium 3_1_46FAA, Alistipes senegalensis, Megaspaera elsdenii, Clostridium perfringens, α-linolenic acid, 10E,12Z-octadecadienoic acid, and dodecanoic acid. A random forest model based on differential intestinal microbiota and circulating metabolites can predict the progression from IGT to DM (AUC = 0.87). These results suggest that microbiome and metabolome dysbiosis occur in individuals with IGT and have important predictive values and potential for intervention in preventing IGT from progressing to DM.
- Impaired glucose tolerance,
- Diabetes mellitus,
- Gut microbiota,
- Metabolomics

FullText(HTML)

References(56)

References

Al-Madhagy, S., Ashmawy, N.S., Mamdouh, A., Eldahshan, O.A., Farag, M.A., 2023. A comprehensive review of the health benefits of flaxseed oil in relation to its chemical composition and comparison with other omega-3-rich oils. Eur. J. Med. Res. 28, 240.

Asnicar, F., Weingart, G., Tickle, T.L., Huttenhower, C., Segata, N., 2015. Compact graphical representation of phylogenetic data and metadata with GraPhlAn. PeerJ 3, e1029.

Bolger, A. M., Lohse, M., & Usadel, B., 2014. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics (Oxford, England), 30(15), 2114-2120.

Brahe, L.K., Le Chatelier, E., Prifti, E., Pons, N., Kennedy, S., Hansen, T., Pedersen, O., Astrup, A., Ehrlich, S.D., Larsen, L.H., 2015. Specific gut microbiota features and metabolic markers in postmenopausal women with obesity. Nutr. Diabetes. 5, e159.

Carter, T.C., Rein, D., Padberg, I., Peter, E., Rennefahrt, U., David, D.E., McManus, V., Stefanski, E., Martin, S., Schatz, P., et al., 2016. Validation of a metabolite panel for early diagnosis of type 2 diabetes. Metabolism 65, 1399-1408.

Chen, L., Zhao, N., Cao, J., Liu, X., Xu, J., Ma, Y., Yu, Y., Zhang, X., Zhang, W., Guan, X., et al., 2022. Short- and long-read metagenomics expand individualized structural variations in gut microbiomes. Nat. Commun. 13, 3175.

Chiasson, J.L., Josse, R.G., Gomis, R., Hanefeld, M., Karasik, A., Laakso, M., STOP-NIDDM Trail Research Group, 2002. Acarbose for prevention of type 2 diabetes mellitus: the STOP-NIDDM randomised trial. Lancet 359, 2072-2077.

Dunn, W.B., Broadhurst, D., Begley, P., Zelena, E., Francis-McIntyre, S., Anderson, N., Brown, M., Knowles, J.D., Halsall, A., Haselden, J.N., et al., 2011. Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat. Protoc. 6, 1060-1083.

Franzosa, E.A., McIver, L.J., Rahnavard, G., Thompson, L.R., Schirmer, M., Weingart, G., Lipson, K.S., Knight, R., Caporaso, J.G., Segata, N., et al., 2018. Species-level functional profiling of metagenomes and metatranscriptomes. Nat. Methods 15, 962-968.

Fujimoto, K., Kawaguchi, Y., Shimohigoshi, M., Gotoh, Y., Nakano, Y., Usui, Y., Hayashi, T., Kimura, Y., Uematsu, M., Yamamoto, T., et al., 2019. Antigen-specific mucosal immunity regulates development of intestinal bacteria-mediated diseases. Gastroenterology 157, 1530-1543.

Gratton, J., Phetcharaburanin, J., Mullish, B.H., Williams, H.R.T., Thursz, M., Nicholson, J.K., Holmes, E., Marchesi, J.R., Li, J.V., 2016. Optimized sample handling strategy for metabolic profiling of human feces. Anal. Chem. 88, 4661-4668.

Guasch-Ferre, M., Hruby, A., Toledo, E., Clish, C.B., Martinez-Gonzalez, M.A., Salas-Salvado, J., Hu, F.B., 2016. Metabolomics in prediabetes and diabetes: a systematic review and meta-analysis. Diabetes Care 39, 833-846.

Hernandez-Perez, S., Oliart-Ros, R.M., Casas-Godoy, L., Sandoval, G., Guarner-Lans, V., Castrejon-Tellez, V., Quevedo-Corona, L., Pena-Montes, C., Ramirez-Higuera, A., 2022. Beneficial effects of fructooligosaccharides esterified with lauric acid in a metabolic syndrome model induced by a high-fat and high-carbohydrate diet in wistar rats. J. Med. Food 25, 828-835.

Hu, L., Xing, Y., Jiang, P., Gan, L., Zhao, F., Peng, W., Li, W., Tong, Y., Deng, S., 2021. Predicting the postmortem interval using human intestinal microbiome data and random forest algorithm. Sci. Justice 61, 516-527.

Huang, Y., Cai, X., Mai, W., Li, M., Hu, Y., 2016. Association between prediabetes and risk of cardiovascular disease and all cause mortality: systematic review and meta-analysis. BMJ. 355, i5953.

International Diabetes Federation. IDF Diabetes Atlas, 10th edn. Brussels, Belgium: 2021. Available at: https://www.diabetesatlas.org.

Karlsson, F.H., Tremaroli, V., Nookaew, I., Bergstrom, G., Behre, C.J., Fagerberg, B., Nielsen, J., Backhed, F., 2013. Gut metagenome in European women with normal, impaired and diabetic glucose control. Nature 498, 99-103.

Knowler, W.C., Barrett-Connor, E., Fowler, S.E., Hamman, R.F., Lachin, J.M., Walker, E.A., Nathan, D.M., Diabetes Prevention Program Research Group, 2002. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N. Engl. J. Med. 346, 393-403.

Letchumanan, G., Abdullah, N., Marlini, M., Baharom, N., Lawley, B., Omar, MR., Mohideen, FBS., Addnan, FH., Nur-Fariha, MM., Ismail, Z., et al., 2022. Gut microbiota composition in prediabetes and newly diagnosed type 2 diabetes: a systematic review of observational studies. Front. Cell Infect. Mi. 12, 943427.

Li, G., Zhang, P., Wang, J., Gregg, E.W., Yang, W., Gong, Q., Li, H., Li, H., Jiang, Y., An, Y., et al., 2008. The long-term effect of lifestyle interventions to prevent diabetes in the China Da Qing Diabetes Prevention Study: a 20-year follow-up study. Lancet 371, 1783-1789.

Li, S., Xu, L., Qing, J., Wu, X., Li, H., Chen, H., Liu, X., 2023. Multiple biological activities and biosynthesis mechanisms of specific conjugated linoleic acid isomers and analytical methods for prospective application. Food Chem. 409, 135257.

Liu, X., Dai, M., Ma,Y., Zhao, N., Wang, Z., Yu, Y., Xu, Y., Zhang, H., Xiang, L., Tian, H., et al., 2022. Reconstruction and dynamics of the human intestinal microbiome observed. Engineering 15, 89-101.

Luan, Z., Sun, G., Huang, Y., Yang, Y., Yang, R., Li, C., Wang, T., Tan, D., Qi, S., et al., 2020. Metagenomics study reveals changes in gut microbiota in centenarians: a cohort study of hainan centenarians. Front. Microbiol. 11, 1474.

Luo, M., Sun, M., Wang, T., Zhang, S., Song, X., Liu, X., Wei, J., Chen, Q., Zhong, T., Qin, J., 2023. Gut microbiota and type 1 diabetes: a two-sample bidirectional Mendelian randomization study. Front. Cell. Infect. Microbiol. 13, 1163898.

Lynch, C.J., Adams, S.H., 2014. Branched-chain amino acids in metabolic signalling and insulin resistance. Nat. Rev. Endocrinol. 10, 723-736.

Mohebali, N., Ekat, K., Kreikemeyer, B., Breitruck, A., 2020. Barrier protection and recovery effects of gut commensal bacteria on differentiated intestinal epithelial cells in vitro. Nutrients 12, 2251.

Nie, X., Chen, J., Ma, X., Ni, Y., Shen, Y., Yu, H., Panagiotou, G., Bao, Y., 2020. A metagenome-wide association study of gut microbiome and visceral fat accumulation. Comput. Struct. Biotechnol. J. 18, 2596-2609.

Nuli, R., Cai, J., Kadeer, A., Zhang, Y., Mohemaiti, P., 2019. Integrative analysis toward different glucose tolerance-related gut microbiota and diet. Front. Endocrinol. (Lausanne) 10, 295.

Parker, B.J., Wearsch, P.A., Veloo, A.C.M., Rodriguez-Palacios, A., 2020. The Genus Alistipes: Gut bacteria with emerging implications to inflammation, cancer, and mental health. front. immunol. 11, 906.

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al., 2011. Scikit-learn: machine learning in python. J. Mach. Learn. Res., 12: 2825-2830.

Pietrucci, D., Teofani, A., Unida, V., Cerroni, R., Biocca, S., Stefani, A., Desideri, A., 2020. Can gut microbiota be a good predictor for Parkinson’s disease? A machine learning approach. Brain Sci. 10, 242.

Qin, J., Li, Y., Cai, Z., Li, S., Zhu, J., Zhang, F., Liang, S., Zhang, W., Guan, Y., Shen, D., et al., 2012. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 490, 55-60.

Ren, Y., Hao, L., Liu, J., Wang, P., Ding, Q., Chen, C., Song, Y., 2023. Alterations in the gut microbiota in pregnant women with pregestational type 2 diabetes mellitus. mSystems 8, e0114622.

RISE Consortium, 2019. Lack of Durable Improvements in β-Cell Function Following Withdrawal of pharmacological interventions in adults with impaired glucose tolerance or recently diagnosed type 2 diabetes. Diabetes Care 42, 1742-1751.

Schlesinger, S., Neuenschwander, M., Barbaresko, J., Lang, A., Maalmi, H., Rathmann, W., Roden, M., Herder, C., 2022. Prediabetes and risk of mortality, diabetes-related complications and comorbidities: umbrella review of meta-analyses of prospective studies. Diabetologia 65, 275-285.

Semnani-Azad, Z., Connelly, P.W., Bazinet, R.P., Retnakaran, R., Jenkins, D.J.A., Harris, S.B., Zinman, B., Hanley, A.J, 2021. Adipose tissue insulin resistance is longitudinally associated with adipose tissue dysfunction, circulating lipids, and dysglycemia: The PROMISE Cohort. Diabetes Care 44, 1682-1691.

Siddiqui, M.T., Cresci, G.A.M., 2021. the immunomodulatory functions of butyrate. J Inflamm. Res. 14, 6025-6041.

The Chinese Diabetes Society of the Chinese Medical Association, 2021. Chinese Guidelines forthe Prevention and Treatment of Type 2 Diabetes (2020 Edition). Chin. J. Diabetes Mellit. 13, 315-409.

Truong, D.T., Franzosa, E.A., Tickle, T.L., Matthias, S., George, W., Edoardo, P., Adrian. T., Curtis, H., Nicola, S., 2015. MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nat. methods 12, 902-903.

Tuomilehto, J., Lindstrom, J., Eriksson, J.G., Valle, T.T., Hamalainen, H., Ilanne-Parikka, P., Keinanen-Kiukaanniemi, S., Laakso, M., Louheranta, A., Rastas, M., et al., 2001. Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. N. Engl. J. Med. 344, 1343-1350.

Turner, J.W., Cheng, X., Saferin, N., Yeo, J.-Y., Yang, T., Joe, B., 2022. Gut microbiota of wild fish as reporters of compromised aquatic environments sleuthed through machine learning. Physiol. Genomics 54, 177-185.

Vangipurapu, J., Fernandes Silva, L., Kuulasmaa, T., Smith, U., Laakso, M., 2020. microbiota-related metabolites and the risk of type 2 diabetes. Diabetes Care 43, 1319-1325.

Vernekar, M., Singhal, R., Joshi, K., Amarapurkar, D., 2018. Variation in the plasma levels of polyunsaturated fatty acids in control vis-a-vis nonalcoholic fatty liver disease subjects and its possible association with gut microbiome. Metab. Syndr. Relat. Disord. 16, 329-335.

Wang-Sattler, R., Yu, Z., Herder, C., Messias, A.C., Floegel, A., He, Y., Heim, K., Campillos, M., Holzapfel, C., Thorand, B., et al., 2012. Novel biomarkers for pre-diabetes identified by metabolomics. Mol. Syst. Biol. 8, 615.

White, P.J., McGarrah, R.W., Herman, M.A., Bain, J.R., Shah, S.H., Newgard, C.B., 2021. Insulin action, type 2 diabetes, and branched-chain amino acids: A two-way street. Mol. Metab. 52, 101261.

Worley, B., Halouska, S., Powers, R., 2013. Utilities for quantifying separation in PCA/PLS-DA scores plots. Anal. Biochem. 433, 102-104.

Woting, A., Pfeiffer, N., Loh, G., Klaus, S., Blaut, M., 2014. Clostridium ramosum promotes high-fat diet-induced obesity in gnotobiotic mouse models. mBio 5, e01530-01514.

Wu, H., Tremaroli, V., Schmidt, C., Lundqvist, A., Olsson, L.M., Kramer, M., Gummesson, A., Perkins, R., Bergstrom, G., Backhed, F., 2020. The Gut Microbiota in Prediabetes and Diabetes: A Population-Based Cross-Sectional Study. Cell Metab. 32, 379-390.

Wurtz, P., Tiainen, M., Makinen, V.-P., Kangas, A.J., Soininen, P., Saltevo, J., Keinanen-Kiukaanniemi, S., Mantyselka, P., Lehtimaki, T., Laakso, M., et al., 2012. Circulating metabolite predictors of glycemia in middle-aged men and women. Diabetes Care 35, 1749-1756.

Xu, S., Coleman, R.L., Wan, Q., Gu, Y., Meng, G., Song, K., Shi, Z., Xie, Q., Tuomilehto, J., Holman, R.R., et al., 2022. Risk prediction models for incident type 2 diabetes in Chinese people with intermediate hyperglycemia: a systematic literature review and external validation study. Cardiovasc. Diabetol. 21, 182.

Xu, Z., Jiang, W., Huang, W., Lin, Y., Chan, F.K.L., Ng, S.C., 2022. Gut microbiota in patients with obesity and metabolic disorders - a systematic review. Genes Nutr. 17, 2.

Zeng, M.Y., Inohara, N., Nunez, G., 2017. Mechanisms of inflammation-driven bacterial dysbiosis in the gut. Mucosal. Immunol. 10, 18-26.

Zhang, X., Osaka, T., Tsuneda, S., 2015. Bacterial metabolites directly modulate farnesoid X receptor activity. Nutr. Metab. (Lond) 12, 48. https://doi.org/10.1186/s12986-015-0045-y.

Zhao, D., Liu, H., Zheng, Y., He, Y., Lu, D., Lyu, C., 2019. A reliable method for colorectal cancer prediction based on feature selection and support vector machine. Med. Biol. Eng. Comput. 57, 901-912.

Zhong, H., Ren, H., Lu, Y., Fang, C., Hou, G., Yang, Z., Chen, B., Yang, F., Zhao, Y., Shi, Z., et al., 2019. Distinct gut metagenomics and metaproteomics signatures in prediabetics and treatment-naive type 2 diabetics. EBioMedicine 47, 373-383.

DREAM (Diabetes REduction Assessment with ramipril and rosiglitazone Medication) Trial Investigators, Gerstein, H.C., Yusuf, S., Bosch, J., Pogue, J., Sheridan, P., Dinccag, N., Hanefeld, M., Hoogwerf, B., Laakso, M., et al., 2006. Effect of rosiglitazone on the frequency of diabetes in patients with impaired glucose tolerance or impaired fasting glucose: a randomised controlled trial. Lancet 368, 1096-1105.

Relative Articles

Supplements(0)

Cited By

Proportional views