請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88964完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 于宏燦 | zh_TW |
| dc.contributor.advisor | Hon-Tsen Yu | en |
| dc.contributor.author | 盧秀真 | zh_TW |
| dc.contributor.author | HSIU-CHEN LU | en |
| dc.date.accessioned | 2023-08-16T16:33:32Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-08-16 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-08-07 | - |
| dc.identifier.citation | 1. Eckburg, P.B., et al., Diversity of the human intestinal microbial flora. Science, 2005. 308(5728): p. 1635-8.
2. Ley, R.E., D.A. Peterson, and J.I. Gordon, Ecological and evolutionary forces shaping microbial diversity in the human intestine. Cell, 2006. 124(4): p. 837-48. 3. Turnbaugh, P.J., et al., The human microbiome project. Nature, 2007. 449(7164): p. 804-10. 4. Ursell, L.K., et al., Defining the human microbiome. Nutr Rev, 2012. 70 Suppl 1(Suppl 1): p. S38-44. 5. Baquero, F. and C. Nombela, The microbiome as a human organ. Clin Microbiol Infect, 2012. 18 Suppl 4: p. 2-4. 6. Lynch, S.V. and O. Pedersen, The Human Intestinal Microbiome in Health and Disease. N Engl J Med, 2016. 375(24): p. 2369-2379. 7. Tasnim, N., et al., Linking the Gut Microbial Ecosystem with the Environment: Does Gut Health Depend on Where We Live? Front Microbiol, 2017. 8: p. 1935. 8. Dominguez-Bello, M.G., et al., Role of the microbiome in human development. Gut, 2019. 68(6): p. 1108-1114. 9. Fan, Y. and O. Pedersen, Gut microbiota in human metabolic health and disease. Nat Rev Microbiol, 2021. 19(1): p. 55-71. 10. Bäckhed, F., et al., The gut microbiota as an environmental factor that regulates fat storage. Proceedings of the National Academy of Sciences, 2004. 101(44): p. 15718-15723. 11. Ley, R.E., et al., Microbial ecology: human gut microbes associated with obesity. Nature, 2006. 444(7122): p. 1022-3. 12. Turnbaugh, P.J., et al., A core gut microbiome in obese and lean twins. Nature, 2009. 457(7228): p. 480-484. 13. Moreno-Indias, I., et al., Impact of the gut microbiota on the development of obesity and type 2 diabetes mellitus. Front Microbiol, 2014. 5: p. 190. 14. Walters, W.A., Z. Xu, and R. Knight, Meta-analyses of human gut microbes associated with obesity and IBD. FEBS Lett, 2014. 588(22): p. 4223-33. 15. Chen, L., et al., Gut microbial co-abundance networks show specificity in inflammatory bowel disease and obesity. Nature communications, 2020. 11(1): p. 4018. 16. Larsen, N., et al., Gut microbiota in human adults with type 2 diabetes differs from non-diabetic adults. PLoS One, 2010. 5(2): p. e9085. 17. Musso, G., R. Gambino, and M. Cassader, Obesity, diabetes, and gut microbiota: the hygiene hypothesis expanded? Diabetes Care, 2010. 33(10): p. 2277-84. 18. Qin, J., et al., A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature, 2012. 490(7418): p. 55-60. 19. Karlsson, F.H., et al., Gut metagenome in European women with normal, impaired and diabetic glucose control. Nature, 2013. 498(7452): p. 99-103. 20. Wang, J., et al., Dysbiosis of maternal and neonatal microbiota associated with gestational diabetes mellitus. Gut, 2018. 67(9): p. 1614-1625. 21. Ahmad, A., et al., Analysis of gut microbiota of obese individuals with type 2 diabetes and healthy individuals. PLoS One, 2019. 14(12): p. e0226372. 22. Scheithauer, T.P.M., et al., Gut Microbiota as a Trigger for Metabolic Inflammation in Obesity and Type 2 Diabetes. Front Immunol, 2020. 11: p. 571731. 23. Tamboli, C.P., et al., Dysbiosis in inflammatory bowel disease. Gut, 2004. 53(1): p. 1-4. 24. Gevers, D., et al., The treatment-naive microbiome in new-onset Crohn's disease. Cell Host Microbe, 2014. 15(3): p. 382-392. 25. Falony, G., et al., Population-level analysis of gut microbiome variation. Science, 2016. 352(6285): p. 560-4. 26. Caruso, R., B.C. Lo, and G. Núñez, Host–microbiota interactions in inflammatory bowel disease. Nature Reviews Immunology, 2020. 20(7): p. 411-426. 27. Sears, C.L. and W.S. Garrett, Microbes, microbiota, and colon cancer. Cell Host Microbe, 2014. 15(3): p. 317-28. 28. Helmink, B.A., et al., The microbiome, cancer, and cancer therapy. Nature Medicine, 2019. 25(3): p. 377-388. 29. Okumura, S., et al., Gut bacteria identified in colorectal cancer patients promote tumourigenesis via butyrate secretion. Nature Communications, 2021. 12(1): p. 5674. 30. Park, E.M., et al., Targeting the gut and tumor microbiota in cancer. Nature Medicine, 2022. 28(4): p. 690-703. 31. Sadrekarimi, H., et al., Emerging role of human microbiome in cancer development and response to therapy: special focus on intestinal microflora. J Transl Med, 2022. 20(1): p. 301. 32. Alexander, J.L., et al., Gut microbiota modulation of chemotherapy efficacy and toxicity. Nature Reviews Gastroenterology & Hepatology, 2017. 14(6): p. 356-365. 33. Roy, S. and G. Trinchieri, Microbiota: a key orchestrator of cancer therapy. Nat Rev Cancer, 2017. 17(5): p. 271-285. 34. Routy, B., et al., Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors. Science, 2018. 359(6371): p. 91-97. 35. Gopalakrishnan, V., et al., Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. Science, 2018. 359(6371): p. 97-103. 36. Matson, V., et al., The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients. Science, 2018. 359(6371): p. 104-108. 37. Chakravorty, S., et al., A detailed analysis of 16S ribosomal RNA gene segments for the diagnosis of pathogenic bacteria. J Microbiol Methods, 2007. 69(2): p. 330-9. 38. Panek, M., et al., Methodology challenges in studying human gut microbiota – effects of collection, storage, DNA extraction and next generation sequencing technologies. Scientific Reports, 2018. 8(1): p. 5143. 39. Johnson, J.S., et al., Evaluation of 16S rRNA gene sequencing for species and strain-level microbiome analysis. Nat Commun, 2019. 10(1): p. 5029. 40. Wensel, C.R., et al., Next-generation sequencing: insights to advance clinical investigations of the microbiome. J Clin Invest, 2022. 132(7). 41. Ahn, J. and R.B. Hayes, Environmental Influences on the Human Microbiome and Implications for Noncommunicable Disease. Annu Rev Public Health, 2021. 42: p. 277-292. 42. Gacesa, R., et al., Environmental factors shaping the gut microbiome in a Dutch population. Nature, 2022. 604(7907): p. 732-739. 43. Nearing, J.T., et al., Microbiome differential abundance methods produce different results across 38 datasets. Nature Communications, 2022. 13(1): p. 342. 44. Sokol, H., et al., Faecalibacterium prausnitzii is an anti-inflammatory commensal bacterium identified by gut microbiota analysis of Crohn disease patients. Proc Natl Acad Sci U S A, 2008. 105(43): p. 16731-6. 45. Sokol, H., et al., Low counts of Faecalibacterium prausnitzii in colitis microbiota. Inflamm Bowel Dis, 2009. 15(8): p. 1183-9. 46. Lopez-Siles, M., et al., Faecalibacterium prausnitzii: from microbiology to diagnostics and prognostics. ISME J, 2017. 11(4): p. 841-852. 47. Hippe, B., et al., Faecalibacterium prausnitzii phylotypes in type two diabetic, obese, and lean control subjects. Benef Microbes, 2016. 7(4): p. 511-7. 48. Maioli, T.U., et al., Possible Benefits of Faecalibacterium prausnitzii for Obesity-Associated Gut Disorders. Front Pharmacol, 2021. 12: p. 740636. 49. Kootte, R.S., et al., The therapeutic potential of manipulating gut microbiota in obesity and type 2 diabetes mellitus. Diabetes Obes Metab, 2012. 14(2): p. 112-20. 50. Martín, R., et al., Functional Characterization of Novel Faecalibacterium prausnitzii Strains Isolated from Healthy Volunteers: A Step Forward in the Use of F. prausnitzii as a Next-Generation Probiotic. Front Microbiol, 2017. 8: p. 1226. 51. Machado, D., et al., Revealing antimicrobial resistance profile of the novel probiotic candidate Faecalibacterium prausnitzii DSM 17677. Int J Food Microbiol, 2022. 363: p. 109501. 52. Lee, Y., et al., Oral administration of Faecalibacterium prausnitzii and Akkermansia muciniphila strains from humans improves atopic dermatitis symptoms in DNCB induced NC/Nga mice. Sci Rep, 2022. 12(1): p. 7324. 53. Effendi, R., et al., Akkermansia muciniphila and Faecalibacterium prausnitzii in Immune-Related Diseases. Microorganisms, 2022. 10(12). 54. Shin, N.R., et al., An increase in the Akkermansia spp. population induced by metformin treatment improves glucose homeostasis in diet-induced obese mice. Gut, 2014. 63(5): p. 727-35. 55. Gurung, M., et al., Role of gut microbiota in type 2 diabetes pathophysiology. EBioMedicine, 2020. 51: p. 102590. 56. Selber-Hnatiw, S., et al., Human Gut Microbiota: Toward an Ecology of Disease. Front Microbiol, 2017. 8: p. 1265. 57. Baunwall, S.M.D., et al., Faecal microbiota transplantation for first or second Clostridioides difficile infection (EarlyFMT): a randomised, double-blind, placebo-controlled trial. Lancet Gastroenterol Hepatol, 2022. 7(12): p. 1083-1091. 58. Ferring Receives U.S. FDA Approval for REBYOTA™ (fecal microbiota, live-jslm) – A Novel First-in-Class Microbiota-Based Live Biotherapeutic. 2022; Available from: https://ferringusa.com/?press=ferring-receives-u-s-fda-approval-for-rebyota-fecal-microbiota-live-jslm-a-novel-first-in-class-microbiota-based-live-biotherapeutic. 59. Hamady, M. and R. Knight, Microbial community profiling for human microbiome projects: Tools, techniques, and challenges. Genome Res, 2009. 19(7): p. 1141-52. 60. Piquer-Esteban, S., et al., Exploring the universal healthy human gut microbiota around the World. Comput Struct Biotechnol J, 2022. 20: p. 421-433. 61. Shade, A. and J. Handelsman, Beyond the Venn diagram: the hunt for a core microbiome. Environ Microbiol, 2012. 14(1): p. 4-12. 62. Berg, G., et al., Microbiome definition re-visited: old concepts and new challenges. Microbiome, 2020. 8(1): p. 103. 63. Sharon, I., et al., The Core Human Microbiome: Does It Exist and How Can We Find It? A Critical Review of the Concept. Nutrients, 2022. 14(14). 64. Risely, A., Applying the core microbiome to understand host-microbe systems. J Anim Ecol, 2020. 89(7): p. 1549-1558. 65. Neu, A.T., E.E. Allen, and K. Roy, Defining and quantifying the core microbiome: Challenges and prospects. Proc Natl Acad Sci U S A, 2021. 118(51). 66. Maier, L. and A. Typas, Systematically investigating the impact of medication on the gut microbiome. Curr Opin Microbiol, 2017. 39: p. 128-135. 67. Jackson, M.A., et al., Gut microbiota associations with common diseases and prescription medications in a population-based cohort. Nat Commun, 2018. 9(1): p. 2655. 68. Maier, L., et al., Extensive impact of non-antibiotic drugs on human gut bacteria. Nature, 2018. 555(7698): p. 623-628. 69. Whang, A., R. Nagpal, and H. Yadav, Bi-directional drug-microbiome interactions of anti-diabetics. EBioMedicine, 2019. 39: p. 591-602. 70. Weersma, R.K., A. Zhernakova, and J. Fu, Interaction between drugs and the gut microbiome. Gut, 2020. 69(8): p. 1510-1519. 71. Klunemann, M., et al., Bioaccumulation of therapeutic drugs by human gut bacteria. Nature, 2021. 597(7877): p. 533-538. 72. Sevcikova, A., et al., The Impact of the Microbiome on Resistance to Cancer Treatment with Chemotherapeutic Agents and Immunotherapy. Int J Mol Sci, 2022. 23(1). 73. Merkevicius, K., et al., Microbiome Changes after Type 2 Diabetes Treatment: A Systematic Review. Medicina (Kaunas, Lithuania), 2021. 57(10). 74. Rawshani, A., et al., Risk Factors, Mortality, and Cardiovascular Outcomes in Patients with Type 2 Diabetes. N Engl J Med, 2018. 379(7): p. 633-644. 75. Association, A.D., 4. Comprehensive Medical Evaluation and Assessment of Comorbidities: Standards of Medical Care in Diabetes—2020. Diabetes Care, 2019. 43(Supplement_1): p. S37-S47. 76. 臺灣糖尿病年鑑2019第2型糖尿病. 2019: 社團法人中華民國糖尿病衛教學會. 77. WHO. Fact sheets of Diabetes. Available from: https://www.who.int/news-room/fact-sheets/detail/diabetes. 78. Association, A.D., 5. Facilitating Behavior Change and Well-being to Improve Health Outcomes: Standards of Medical Care in Diabetes—2020. Diabetes Care, 2019. 43(Supplement_1): p. S48-S65. 79. Khan, M.A.B., et al., Epidemiology of Type 2 Diabetes - Global Burden of Disease and Forecasted Trends. J Epidemiol Glob Health, 2020. 10(1): p. 107-111. 80. Collaborators, G.B.D.D.i.t.A., Burden of diabetes and hyperglycaemia in adults in the Americas, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Diabetes Endocrinol, 2022. 10(9): p. 655-667. 81. Williams, D.M., H. Jones, and J.W. Stephens, Personalized Type 2 Diabetes Management: An Update on Recent Advances and Recommendations. Diabetes Metab Syndr Obes, 2022. 15: p. 281-295. 82. Association, A.D., 3. Prevention or Delay of Type 2 Diabetes: Standards of Medical Care in Diabetes—2020. Diabetes Care, 2019. 43(Supplement_1): p. S32-S36. 83. Petroni, M.L., et al., Nutrition in Patients with Type 2 Diabetes: Present Knowledge and Remaining Challenges. Nutrients, 2021. 13(8). 84. Borse, S.P., et al., Management of Type 2 Diabetes: Current Strategies, Unfocussed Aspects, Challenges, and Alternatives. Medical Principles and Practice, 2021. 30(2): p. 109-121. 85. Davies, M.J., et al., Management of Hyperglycemia in Type 2 Diabetes, 2022. A Consensus Report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetes Care, 2022. 45(11): p. 2753-2786. 86. Chaudhury, A., et al., Clinical Review of Antidiabetic Drugs: Implications for Type 2 Diabetes Mellitus Management. Front Endocrinol (Lausanne), 2017. 8: p. 6. 87. Samson, S.L. and A.J. Garber, Type 2 Diabetes☆, in Encyclopedia of Endocrine Diseases (Second Edition), I. Huhtaniemi and L. Martini, Editors. 2018, Academic Press: Oxford. p. 116-121. 88. Association, A.D., 9. Pharmacologic Approaches to Glycemic Treatment: Standards of Medical Care in Diabetes—2020. Diabetes Care, 2019. 43(Supplement_1): p. S98-S110. 89. Maruthur, N.M., et al., Diabetes Medications as Monotherapy or Metformin-Based Combination Therapy for Type 2 Diabetes: A Systematic Review and Meta-analysis. Ann Intern Med, 2016. 164(11): p. 740-51. 90. Singh, A.K., R. Singh, and P.P. Chakraborty, Diabetes Monotherapies versus Metformin-Based Combination Therapy for the Treatment of Type 2 Diabetes. Int J Gen Med, 2021. 14: p. 3833-3848. 91. Baynes, K.C., The evolving world of GLP-1 agonist therapies for type 2 diabetes. Ther Adv Endocrinol Metab, 2010. 1(2): p. 61-7. 92. Mata-Cases, M., et al., Glucagon-like peptide-1 receptor agonists in patients with type 2 diabetes:real-world evidence from a Mediterranean area. Curr Med Res Opin, 2019. 35(10): p. 1735-1744. 93. Drucker, D.J., Mechanisms of Action and Therapeutic Application of Glucagon-like Peptide-1. Cell Metab, 2018. 27(4): p. 740-756. 94. Sharma, D., et al., Recent updates on GLP-1 agonists: Current advancements & challenges. Biomedicine & Pharmacotherapy, 2018. 108: p. 952-962. 95. Nauck, M.A., et al., GLP-1 receptor agonists in the treatment of type 2 diabetes - state-of-the-art. Mol Metab, 2021. 46: p. 101102. 96. Simioni, N., et al., Predictors of treatment response to liraglutide in type 2 diabetes in a real-world setting. Acta Diabetol, 2018. 55(6): p. 557-568. 97. Grasset, E., et al., A Specific Gut Microbiota Dysbiosis of Type 2 Diabetic Mice Induces GLP-1 Resistance through an Enteric NO-Dependent and Gut-Brain Axis Mechanism. Cell Metab, 2017. 25(5): p. 1075-1090.e5. 98. Forslund, K., et al., Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota. Nature, 2015. 528(7581): p. 262-266. 99. Montandon, S.A. and F.R. Jornayvaz, Effects of Antidiabetic Drugs on Gut Microbiota Composition. Genes (Basel), 2017. 8(10). 100. Wu, H., 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): p. 850-858. 101. Kyriachenko, Y., et al., Crosstalk between gut microbiota and antidiabetic drug action. World J Diabetes, 2019. 10(3): p. 154-168. 102. Adeshirlarijaney, A. and A.T. Gewirtz, Considering gut microbiota in treatment of type 2 diabetes mellitus. Gut Microbes, 2020. 11(3): p. 253-264. 103. Nakajima, H., et al., The Effects of Metformin on the Gut Microbiota of Patients with Type 2 Diabetes: A Two-Center, Quasi-Experimental Study. Life (Basel), 2020. 10(9). 104. Pascale, A., et al., The role of gut microbiota in obesity, diabetes mellitus, and effect of metformin: new insights into old diseases. Curr Opin Pharmacol, 2019. 49: p. 1-5. 105. Pedersen, H.K., et al., Human gut microbes impact host serum metabolome and insulin sensitivity. Nature, 2016. 535(7612): p. 376-81. 106. Smits, M.M., et al., Liraglutide and sitagliptin have no effect on intestinal microbiota composition: A 12-week randomized placebo-controlled trial in adults with type 2 diabetes. Diabetes & Metabolism, 2021. 47(5): p. 101223. 107. Wang, Z., et al., Gut microbiome differences between metformin- and liraglutide-treated T2DM subjects. Endocrinol Diabetes Metab, 2018. 1(1): p. e00009. 108. Shang, J., et al., Liraglutide-induced structural modulation of the gut microbiota in patients with type 2 diabetes mellitus. PeerJ, 2021. 9: p. e11128. 109. Hisada, T., K. Endoh, and K. Kuriki, Inter- and intra-individual variations in seasonal and daily stabilities of the human gut microbiota in Japanese. Arch Microbiol, 2015. 197(7): p. 919-34. 110. Huang, M.C., et al., Validity of a Short Food Frequency Questionnaire Assessing Macronutrient and Fiber Intakes in Patients of Han Chinese Descent with Type 2 Diabetes. Int J Environ Res Public Health, 2018. 15(6). 111. Gallwitz, B., et al., Effect of once-weekly dulaglutide on glycated haemoglobin (HbA1c) and fasting blood glucose in patient subpopulations by gender, duration of diabetes and baseline HbA1c. Diabetes Obes Metab, 2018. 20(2): p. 409-418. 112. Zinman, B., et al., Liraglutide and Glycaemic Outcomes in the LEADER Trial. Diabetes Ther, 2018. 9(6): p. 2383-2392. 113. Chang, K.C., et al., Comparative effectiveness of dulaglutide versus liraglutide in Asian type 2 diabetes patients: a multi-institutional cohort study and meta-analysis. Cardiovasc Diabetol, 2020. 19(1): p. 172. 114. Illumina. 16S Metagenomic Sequencing Library Preparation. 2013 [cited 2021 March 9]; Available from: https://support.illumina.com/downloads/16s_metagenomic_sequencing_library_preparation.html. 115. Comeau, A.M., G.M. Douglas, and M.G. Langille, Microbiome Helper: a Custom and Streamlined Workflow for Microbiome Research. mSystems, 2017. 2(1). 116. Bolyen, E., et al., Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol, 2019. 37(8): p. 852-857. 117. Martin, M., Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal, 2011. 17(1): p. 3. 118. Callahan, B.J., et al., DADA2: High-resolution sample inference from Illumina amplicon data. Nature Methods, 2016. 13(7): p. 581-583. 119. Bokulich, N.A., et al., Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2's q2-feature-classifier plugin. Microbiome, 2018. 6(1): p. 90. 120. Yarza, P., et al., Uniting the classification of cultured and uncultured bacteria and archaea using 16S rRNA gene sequences. Nat Rev Microbiol, 2014. 12(9): p. 635-45. 121. R_Core_Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2018; Available from: https://www.R-project.org/. 122. Oksanen, F.J., et al. vegan: Community Ecology Package. R package version 2.6-2. 2017; Available from: https://CRAN.R-project.org/package=vegan. 123. Anderson, M.J., A new method for non-parametric multivariate analysis of variance. Austral Ecology, 2001. 26(1): p. 32-46. 124. Buttigieg, P.L. and A. Ramette, A guide to statistical analysis in microbial ecology: a community-focused, living review of multivariate data analyses. FEMS Microbiology Ecology, 2014. 90(3): p. 543-550. 125. Warton, D.I., S.T. Wright, and Y. Wang, Distance-based multivariate analyses confound location and dispersion effects. Methods in Ecology and Evolution, 2012. 3(1): p. 89-101. 126. Khomich, M., et al., Analysing microbiome intervention design studies: Comparison of alternative multivariate statistical methods. PLOS ONE, 2021. 16(11): p. e0259973. 127. Friedman, J. and E.J. Alm, Inferring correlation networks from genomic survey data. PLoS Comput Biol, 2012. 8(9): p. e1002687. 128. Kurtz, Z.D., et al., Sparse and compositionally robust inference of microbial ecological networks. PLoS Comput Biol, 2015. 11(5): p. e1004226. 129. Nepusz, G.C.a.T., The igraph software package for complex network research. InterJournal, 2006. Complex Systems: p. 1695. 130. Wibowo, M.C., et al., Reconstruction of ancient microbial genomes from the human gut. Nature, 2021. 594(7862): p. 234-239. 131. iquer-Esteban, S., et al., Exploring the universal healthy human gut microbiota around the World. Comput Struct Biotechnol J, 2022. 20: p. 421-433. 132. Ochman, H., et al., Evolutionary relationships of wild hominids recapitulated by gut microbial communities. PLoS Biol, 2010. 8(11): p. e1000546. 133. Iatcu, C.O., A. Steen, and M. Covasa, Gut Microbiota and Complications of Type-2 Diabetes. Nutrients, 2021. 14(1). 134. Tong, X., et al., Structural Alteration of Gut Microbiota during the Amelioration of Human Type 2 Diabetes with Hyperlipidemia by Metformin and a Traditional Chinese Herbal Formula: a Multicenter, Randomized, Open Label Clinical Trial. mBio, 2018. 9(3). 135. Yamashita, M., et al., Alteration of gut microbiota by a Westernized lifestyle and its correlation with insulin resistance in non-diabetic Japanese men. Journal of Diabetes Investigation, 2019. 10(6): p. 1463-1470. 136. Huber-Ruano, I., et al., Orally administered Odoribacter laneus improves glucose control and inflammatory profile in obese mice by depleting circulating succinate. Microbiome, 2022. 10(1): p. 135. 137. Zhang, X., et al., Effects of Acarbose on the Gut Microbiota of Prediabetic Patients: A Randomized, Double-blind, Controlled Crossover Trial. Diabetes Ther, 2017. 8(2): p. 293-307. 138. Van Hul, M., et al., From correlation to causality: the case of Subdoligranulum. Gut Microbes, 2020. 12(1): p. 1-13. 139. Arumugam, M., et al., Enterotypes of the human gut microbiome. Nature, 2011. 473(7346): p. 174-80. 140. Le, T.K., et al., Bifidobacterium species lower serum glucose, increase expressions of insulin signaling proteins, and improve adipokine profile in diabetic mice. Biomed Res, 2015. 36(1): p. 63-70. 141. Adachi, K., 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(3): p. 231-238. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88964 | - |
| dc.description.abstract | 人類腸道微生物與宿主長期共演化在人類健康扮演重要的角色，一些疾病的發展證實和腸道微生物相不平衡有關，如近年來盛行率快速竄起的第2型糖尿病。過去橫斷性研究比較第2型糖尿病人、與健康人、及使用傳統藥物病人的腸道微生物相的差異，找出一些特定微生物與疾病發展有關，但部分研究觀察到的結果相反，因此不僅找出特定微生物與疾病狀態的關係，也需從生態的觀點探討微生態系裡核心腸道微生物群與宿主的交互影響，此外，只有部分研究有納入亞洲族群。為了處理這些問題，我們的研究在台灣林口長庚醫院取樣第2型糖尿病人。首先，比較使用新興糖尿病藥物第1型類升糖素胜肽受體促效劑(Glucagon-like peptide-1 receptor agonist，簡稱 GLP-1 RA)後，有療效與無療效反應兩群病人其腸道微生物相的特徵及差異，其次是探討病人用藥前後的腸道微生物相的變化，找出宿主-腸道微生物相-藥物在第2型糖尿病人的可能因果關係。本研究結果顯示有療效和無療效反應的腸道微生物相有顯著差異，但病人於GLP-1 RA用藥前後的腸道微生物相並無變化，推論第2型糖尿病人的腸道微生物組成與GLP-1 RA療效反應有關。無療效反應的病人可能有微生態失調(dysbiosis)，包括具有高豐度的微生物群，如，戴阿利斯特桿菌屬(Dialister)、戴阿里斯氏屬(Alistipes)、病毒丁酸單胞菌(Butyricimonas virosa)、光岡菌屬(Mitsuokella)及莫亞拉屬(Moryella)，以及失去或減少核心微生物群，糞桿菌屬(Faecalibacterium)、厭氧棒狀菌屬(Anaerostipes)、糞球菌屬(Coprococcus)及布勞特氏菌屬(Blautia)。我們的發現將有助於預測糖尿病人對於GLP-1 RA的療效反應，使健康照護者提供病人個人化治療選項。 | zh_TW |
| dc.description.abstract | The Human gut microbiota which co-evolved with the host plays an important role in human health. Evidence has showed that the imbalance in the microbial community is associated with the development of some diseases such as type 2 diabetes (T2D), of which prevalence has been rising rapidly in recent years. Previous cross-sectional and comparison studies had characterized the difference in gut microbiota between T2D patients, and healthy people, as well as T2D patients who received traditional treatment. Some specific microbes which may be linked to disease development were identified, however several studies observed opposite results for these specific microbes. Therefore, there is a need to take an ecological view of gut microbiota in this micro-ecosystem, to investigate relationships not only between several microbes and a diseased state but also a cluster of core gut microbiota and their interactions with the host. Furthermore, limited work was done in Asian populations. Thus, our studies to address these questions and to sample a set of T2D patients were conducted at Chang Gung Memorial Hospital, Taiwan. Firstly, we characterized and compared gut microbiota of T2D patients who had a response or non-response to an emerging anti-diabetic drug, Glucagon-like peptide-1 receptor agonist (GLP-1 RA) after treatment. Secondly, we explored the gut microbiota changes between before and after patients taking GLP-1 RA, and to identify potential casual relationships of host-microbiota-drug interactions in T2D patients. This study results showed that gut microbiota significantly differed between responders and non-responders but no change before and after taking GLP-1 RA, which suggested that the microbial composition in T2D patients were associated with treatment response to GLP-1 RA. Non-responders may have gut microbiota dysbiosis, including harbored high abundance of identified microbial features, e.g., Dialister, Alistipes, Butyricimonas virosa, Mitsuokella and Moryella, and lost or decreased abundance of core genera Faecalibacterium, Anaerostipes, Coprococcus and Blautia. Our findings may be helpful for predicting T2D patients’ responses to GLP-1 RA, enabling healthcare providers to offer personalized treatment options to patients. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-08-16T16:33:32Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-08-16T16:33:32Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 誌謝 i
中文摘要 ii 英文摘要 iii Chapter 1 Introduction 1 1.1 Human gut microbiota in health and disease 1 1.2 Host -microbiota- drug interactions in T2D patients 3 1.3 Research objectives and hypotheses 5 Chapter 2 Materials and Methods 7 2.1 Study population and sample collection 7 2.2 Dataset A and B and Justification of Sample Size 8 2.3 DNA extraction, PCR and sequencing 8 Analyses of sequences 9 2.4 Statistical and microbiota analyses 10 2.5 Correlation between Hb1Ac reduction ratio and ASVs in Study A 11 2.6 Core microbiota and network analysis in Study B 12 Chapter 3 Results 13 3.1 Subject population and their clinical characters 13 3.2 Alpha and beta analysis among groups 14 3.3 Associations between gut microbiota and treatment responses in Dataset A 16 3.4 Core microbiome and network analyses in Dataset B 16 3.5 Tables and Figures 18 Chapter 4 Discussion 33 Chapter 5 Conclusion 39 參考文獻 41 附錄-已發表的SCI期刊一篇 53 | - |
| dc.language.iso | en | - |
| dc.title | 第2 型糖尿病人腸道微生物相及與GLP-1 RA 交互作用研究 | zh_TW |
| dc.title | Characterization of gut microbiota and its interactions with GLP-1 RA in type 2 diabetic patients | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 博士 | - |
| dc.contributor.oralexamcommittee | 陳示國;江皓森;廖本揚;黃孟娟 | zh_TW |
| dc.contributor.oralexamcommittee | Shih-Kuo Chen;Hao-Sen Chiang;Ben-Yang Liao ;Meng-Chuan Huang | en |
| dc.subject.keyword | 腸道微生物相,第2型糖尿病,第1型類升糖素胜肽受體促效劑,核心微生物相,共演化, | zh_TW |
| dc.subject.keyword | gut microbiota,Type 2 diabetes,Glucagon-like peptide-1 receptor agonist,GLP-1 RA,core microbiota,coevolution, | en |
| dc.relation.page | 64 | - |
| dc.identifier.doi | 10.6342/NTU202303307 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2023-08-09 | - |
| dc.contributor.author-college | 生命科學院 | - |
| dc.contributor.author-dept | 生命科學系 | - |
| dc.date.embargo-lift | 2024-01-07 | - |
| 顯示於系所單位： | 生命科學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-111-2.pdf 授權僅限NTU校內IP使用（校園外請利用VPN校外連線服務） | 4.83 MB | Adobe PDF | 檢視/開啟 |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。