Vol 8, No 6 (2019)
Research paper
Published online: 2020-01-08

open access

Page views 1557
Article views/downloads 1026
Get Citation

Connect on Social Media

Connect on Social Media

The effect of linagliptin treatment on gut microbiota in patients with HNF1A-MODY or type 2 diabetes — a preliminary cohort study

Sandra Mrozinska12, Tomasz Gosiewski3, Agnieszka Sroka-Oleksiak3, Magdalena Szopa12, Małgorzata Bulanda3, Maciej T Malecki12, Tomasz Klupa12
Clin Diabetol 2019;8(6):263-270.

Abstract

Introduction. Many studies have evaluated the relationship between diabetes and microbiota. In animal models, the dipeptidyl peptidase-4 inhibitors altered the gut microbiota. We investigated whether linagliptin alters the gastrointestinal flora in humans.

Materials and methods. This prospective cohort study enrolled 24 patients: 5 patients with maturity onset diabetes of the young associated with HNF1A mutation and 19 patients with type 2 diabetes mellitus. Stool samples were collected at baseline and 4 weeks after treatment intensification with either linagliptin or a sulphonylurea alongside current treatment. Faecal 16S rRNA was analysed by next-generation sequencing.

Results. Nine patients initiated linagliptin whereas 15 patients initiated or increased the dose of a sulphonylurea. After linagliptin treatment, we did not observe changes in taxa in L2–L7 based on analysis of composition of microbiomes (ANCOM). The same held true for pairwise alpha diversity (Shannon diversity, p = 0.59; Pielou’s measure of evenness, p = 0.68; and observed operational taxonomic units [OTUs], p = 0.77) and beta diversity distances (unweighted UniFrac, p = 0.99; weighted UniFrac, p = 0.93; Bray-Curtis, p = 0.98; and Jaccard, p = 0.99). Similarly, after sulphonylurea intensification, we did not observe changes in taxa in L2–L7 in ANCOM, nor were there changes in alpha diversity (Shannon diversity, p = 0.19; Pielou’s measure of evenness, p = 0.21; and observed OTUs, p = 0.42) or beta diversity distances (unweighted UniFrac, p = 0.99; weighted UniFrac, p = 0.99; Bray-Curtis, p = 1; and Jaccard, p = 0.99).

Conclusion. We did not observe changes in colonic microbiota 4 weeks after addition of linagliptin to current diabetes treatment. Further studies are required to determine whether linagliptin influences the colonic microbiota in humans.

Article available in PDF format

View PDF Download PDF file

References

  1. Kahn SE, Cooper ME, Del Prato S. Pathophysiology and treatment of type 2 diabetes: perspectives on the past, present, and future. Lancet. 2014; 383(9922): 1068–1083.
  2. Fletcher B, Gulanick M, Lamendola C. Risk factors for type 2 diabetes mellitus. J Cardiovasc Nurs. 2002; 16(2): 17–23.
  3. Manolio TA, Collins FS, Cox NJ, et al. Finding the missing heritability of complex diseases. Nature. 2009; 461(7265): 747–753.
  4. McCarthy MI. Genetics of T2DM in 2016: Biological and translational insights from T2DM genetics. Nat Rev Endocrinol. 2017; 13(2): 71–72.
  5. Brunkwall L, Orho-Melander M. The gut microbiome as a target for prevention and treatment of hyperglycaemia in type 2 diabetes: from current human evidence to future possibilities. Diabetologia. 2017; 60(6): 943–951.
  6. Leiva-Gea I, Sánchez-Alcoholado L, Martín-Tejedor B, et al. Gut Microbiota Differs in Composition and Functionality Between Children With Type 1 Diabetes and MODY2 and Healthy Control Subjects: A Case-Control Study. Diabetes Care. 2018; 41(11): 2385–2395.
  7. Mrozinska S, Radkowski P, Gosiewski T, et al. Qualitative Parameters of the Colonic Flora in Patients with HNF1A-MODY Are Different from Those Observed in Type 2 Diabetes Mellitus. J Diabetes Res. 2016; 2016: 3876764.
  8. Vrieze A, Holleman F, Zoetendal EG, et al. The environment within: how gut microbiota may influence metabolism and body composition. Diabetologia. 2010; 53(4): 606–613.
  9. Larsen N, Vogensen FK, van den Berg FWJ, et al. Gut microbiota in human adults with type 2 diabetes differs from non-diabetic adults. PLoS One. 2010; 5(2): e9085.
  10. Forslund K, Hildebrand F, Nielsen T, et al. MetaHIT consortium, MetaHIT consortium. Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota. Nature. 2015; 528(7581): 262–266.
  11. Liu X, Xiao Q, Zhang Li, et al. The long-term efficacy and safety of DPP-IV inhibitors monotherapy and in combination with metformin in 18,980 patients with type-2 diabetes mellitus--a meta-analysis. Pharmacoepidemiol Drug Saf. 2014; 23(7): 687–698.
  12. Montandon SA, Jornayvaz FR. Effects of antidiabetic drugs on gut microbiota composition. Genes (Basel). 2017; 8(10): 250.
  13. Olivares M, Neyrinck AM, Pötgens SA, et al. The DPP-4 inhibitor vildagliptin impacts the gut microbiota and prevents disruption of intestinal homeostasis induced by a Western diet in mice. Diabetologia. 2018; 61(8): 1838–1848.
  14. Zhang Q, Xiao X, Li M, et al. Vildagliptin increases butyrate-producing bacteria in the gut of diabetic rats. PLoS One. 2017; 12(10): e0184735.
  15. Yan X, Feng Bo, Li P, et al. Microflora Disturbance during Progression of Glucose Intolerance and Effect of Sitagliptin: An Animal Study. J Diabetes Res. 2016; 2016: 2093171.
  16. Wang L, Li P, Tang Z, et al. Structural modulation of the gut microbiota and the relationship with body weight: compared evaluation of liraglutide and saxagliptin treatment. Sci Rep. 2016; 6: 33251.
  17. Graefe-Mody U, Retlich S, Friedrich C. Clinical pharmacokinetics and pharmacodynamics of linagliptin. Clin Pharmacokinet. 2012; 51(7): 411–427.
  18. Bolyen E, Rideout JR, Dillon MR, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019; 37(8): 852–857.
  19. Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal. 2011; 17(1): 10.
  20. Callahan BJ, McMurdie PJ, Rosen MJ, et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016; 13(7): 581–583.
  21. Rideout JR, He Y, Navas-Molina JA, et al. Subsampled open-reference clustering creates consistent, comprehensive OTU definitions and scales to billions of sequences. PeerJ. 2014; 2: e545.
  22. Rognes T, Flouri T, Nichols B, et al. VSEARCH: a versatile open source tool for metagenomics. PeerJ. 2016; 4: e2584.
  23. McDonald D, Price MN, Goodrich J, et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J. 2012; 6(3): 610–618.
  24. Bokulich NA, Kaehler BD, Rideout JR, et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2's q2-feature-classifier plugin. Microbiome. 2018; 6(1): 90.
  25. Vázquez-Baeza Y, Pirrung M, Gonzalez A, et al. EMPeror: a tool for visualizing high-throughput microbial community data. Gigascience. 2013; 2(1): 16.
  26. Mandal S, Van Treuren W, White RA, et al. Analysis of composition of microbiomes: a novel method for studying microbial composition. Microb Ecol Health Dis. 2015; 26: 27663.
  27. R Development Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria 2008. URL http://www.R-project.org/.
  28. Claesson MJ, Jeffery IB, Conde S, et al. Gut microbiota composition correlates with diet and health in the elderly. Nature. 2012; 488(7410): 178–184.
  29. Baothman OA, Zamzami MA, Taher I, et al. The role of gut microbiota in the development of obesity and diabetes. Lipids Health Dis. 2016; 15: 108.
  30. Zhang C, Zhang M, Wang S, et al. Interactions between gut microbiota, host genetics and diet relevant to development of metabolic syndromes in mice. ISME J. 2010; 4(2): 232–241.