Abstract
Background
This work aimed to evaluate the influence of single nucleotide polymorphisms (SNPs) in the SLC47A1 (922-158G>A; rs2289669) and SLC47A2 (−130G>A; rs12943590) genes on the relative change in HbA1c in type 2 diabetes mellitus (T2DM) patients of South India who are taking metformin as monotherapy. It also aims to study the effects of these SNPs on the dose requirement of metformin for glycemic control and the adverse effects of metformin.
Methods
Diabetes patients on metformin monotherapy were recruited based on the eligibility criteria (n=105). DNA was extracted and genotyping was performed with a real-time PCR system using TaqMan® SNP genotyping assay method. The HbA1c levels were measured using Bio-Rad D-10™ Hemoglobin Analyzer.
Results
After adjusting for multiple comparisons (Bonferroni correction) the difference found in the glycemic response between the “GG” genotype and “AG/AA” genotype groups of the SLC47A2 gene was not significant (p=0.027; which was greater than the critical value of 0.025). Patients with “GG” genotype showed a 5.5% decrease in HbA1c from baseline compared to those with the “AG/AA” genotype (0.1% increase). The SNP in the SLC47A1 gene also did not influence the glycemic response to metformin (p=0.079). The median dose requirements based on the genotypes of the rs12943590 variant (p=0.357) or rs2289669 variant (p=0.580) were not significantly different. Similarly, there was no significant difference in the occurrence of adverse effects across the genotypes in both the SLC47A1 (p=0.615) and SLC47A2 (p=0.309) genes.
Conclusions
The clinical response to metformin was not associated with the SNPs in the SLC47A1 and SLC47A2 genes coding for the multidrug and toxin extrusion protein (MATE) transporters. Furthermore, the studied SNPs had no influence on the dose requirement or adverse effects of metformin.
Acknowledgments
We wish to thank Dr. Saranya Vilvanathan, Dr. Neel Shah, Dr. Radhika, Dr. Stanley, Dr. Jose Francis and Dr. Abialbon Paul (Department of Pharmacology, JIPMER, Puducherry, India) for giving us valuable suggestions while designing the study. We would like to express our sincere gratitude to Dr. Srinivas Rao, Dr. Sunitha, Dr. Neel Shah, Dr. C. Indumathi, Mr. Rajan, Ms. Tamijarassy and Ms. Immaculate (Department of Pharmacology, JIPMER, Puducherry, India) for guiding us in the laboratory works. We are also grateful to Ms. Abitha Begam Saleem and Ms. Sujatha Rajish (Department of General Medicine, JIPMER, Puducherry, India) who processed the samples for HbA1c estimation.
Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Research funding: We are greatly indebted to the JIPMER, Puducherry, India for having supported the study through the intramural research grants (JIP/Res/Intra-MD/MS/01/2014; JIP/Res/Intra-MD, MS/sec/05/2014).
Employment or leadership: None declared.
Honorarium: None declared.
Competing interests: The funding organization played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.
References
1. International Diabetes Federation. IDF Diabetes Atlas, 8th ed. Brussels, Belgium: International Diabetes Federation [Internet]. 2017 [cited 2018 Jul 28]. Available at: http://www.diabetesatlas.org.Search in Google Scholar
2. International Diabetes Federation-India [Internet]. 2017 [cited 2018 Jul 28]. Available at: https://www.idf.org/our-network/regions-members/south-east-asia/members/94-india.Search in Google Scholar
3. American Diabetes Association. 8. Pharmacologic approaches to glycemic treatment. Diabetes Care 2017;40(Suppl. 1):S64–74.10.2337/dc17-S011Search in Google Scholar
4. International Diabetes Federation Guideline Development Group. Global guideline for type 2 diabetes. Diabetes Res Clin Pract 2014;104:1–52.10.1016/j.diabres.2012.10.001Search in Google Scholar
5. Inzucchi SE, Bergenstal RM, Buse JB, Diamant M, Ferrannini E, Nauck M, et al. Management of hyperglycaemia in type 2 diabetes, 2015: a patient-centred approach. Update to a Position Statement of the American Diabetes Association and the European Association for the Study of Diabetes. Diabetologia 2015;58:429–42.10.1007/s00125-014-3460-0Search in Google Scholar
6. Handelsman Y, Bloomgarden ZT, Grunberger G, Umpierrez G, Zimmerman RS, Bailey TS, et al. American Association of Clinical Endocrinologists and American College of Endocrinology–clinical practice guidelines for developing a diabetes mellitus comprehensive care plan–2015. Endocr Pract 2015;21(s1):1–87.10.4158/EP15672.GLSUPPLSearch in Google Scholar
7. National Institute for Health and Care Excellence. NICE guideline [NG28]. Type 2 diabetes in adults: management [Internet]. 2015 [updated 2017 May; cited 2018 Jul 28]. Available at: https://www.nice.org.uk/guidance/ng28.Search in Google Scholar
8. Indian Council of Medical Research. Guidelines for Management of Type 2 Diabetes [Internet]. 2005 [cited 2018 Jul 28]. Available at: http://icmr.nic.in/guidelines_diabetes/guide_diabetes.htm.Search in Google Scholar
9. Research Society for the Study of Diabetes in India. Clinical Practice Recommendations for Management of Type 2 Diabetes Mellitus [Internet]. 2015 [cited 2018 Jul 28]. Available at: http://www.rssdi.in/new/pdf/First_draft_RSSDI_guidelines_2015_Sep_4th_2015.pdf.Search in Google Scholar
10. Graham GG, Punt J, Arora M, Day RO, Doogue MP, Duong J, et al. Clinical pharmacokinetics of metformin. Clin Pharmacokinet 2011; 50: 81–98.10.2165/11534750-000000000-00000Search in Google Scholar
11. Cook MN, Girman CJ, Stein PP, Alexander CM. Initial monotherapy with either metformin or sulphonylureas often fails to achieve or maintain current glycaemic goals in patients with type 2 diabetes in UK primary care. Diabet Med 2007;24:350–8.10.1111/j.1464-5491.2007.02078.xSearch in Google Scholar
12. Kavvoura FK, Pappa M, Evangelou E, Ntzani EE. The genetic architecture of type 2 diabetes pharmacotherapy: the emerging genomic evidence. Curr Pharm Des 2014;20:3610–9.10.2174/13816128113196660675Search in Google Scholar
13. Todd JN, Florez JC. An update on the pharmacogenomics of metformin: progress, problems and potential. Pharmacogenomics 2014;15:529–39.10.2217/pgs.14.21Search in Google Scholar
14. Jablonski KA, McAteer JB, de Bakker PI, Franks PW, Pollin TI, Hanson RL, et al. Common variants in 40 genes assessed for diabetes incidence and response to metformin and lifestyle intervention in the diabetes prevention program. Diabetes 2010;59:2672–81.10.2337/db10-0543Search in Google Scholar
15. Zhou K, Donnelly L, Yang J, Li M, Deshmukh H, Van Zuydam N, et al. Heritability of variation in glycaemic response to metformin: a genome-wide complex trait analysis. Lancet Diabetes Endocrinol 2014;2:481–7.10.1016/S2213-8587(14)70050-6Search in Google Scholar
16. Maruthur NM, Gribble MO, Bennett WL, Bolen S, Wilson LM, Balakrishnan P, et al. The pharmacogenetics of type 2 diabetes: a systematic review. Diabetes Care 2014;37:876–86.10.2337/dc13-1276Search in Google Scholar PubMed PubMed Central
17. Daniels MA, Kan C, Willmes DM, Ismail K, Pistrosch F, Hopkins D, et al. Pharmacogenomics in type 2 diabetes: oral antidiabetic drugs. Pharmacogenomics J 2016;16:399–410.10.1038/tpj.2016.54Search in Google Scholar PubMed
18. Gong L, Goswami S, Giacomini KM, Altman RB, Klein TE. Metformin pathways: pharmacokinetics and pharmacodynamics. Pharmacogenet Genomics 2012;22:820–7.10.1097/FPC.0b013e3283559b22Search in Google Scholar PubMed PubMed Central
19. Motohashi H, Inui K. Organic cation transporter OCTs (SLC22) and MATEs (SLC47) in the human kidney. Aaps J 2013;15:581–8.10.1208/s12248-013-9465-7Search in Google Scholar PubMed PubMed Central
20. Staud F, Cerveny L, Ahmadimoghaddam D, Ceckova M. Multidrug and toxin extrusion proteins (MATE/SLC47); role in pharmacokinetics. Int J Biochem Cell Biol 2013;45:2007–11.10.1016/j.biocel.2013.06.022Search in Google Scholar PubMed
21. Motohashi H, Inui K. Multidrug and toxin extrusion family SLC47: physiological, pharmacokinetic and toxicokinetic importance of MATE1 and MATE2-K. Mol Aspects Med 2013;34:661–8.10.1016/j.mam.2012.11.004Search in Google Scholar PubMed
22. Yonezawa A, Inui K. Importance of the multidrug and toxin extrusion MATE/SLC47A family to pharmacokinetics, pharmacodynamics/toxicodynamics and pharmacogenomics. Br J Pharmacol 2011;164:1817–25.10.1111/j.1476-5381.2011.01394.xSearch in Google Scholar PubMed PubMed Central
23. Christensen MM, Brasch-Andersen C, Green H, Nielsen F, Damkier P, Beck-Nielsen H, et al. The pharmacogenetics of metformin and its impact on plasma metformin steady-state levels and glycosylated hemoglobin A1c. Pharmacogenet Genomics 2011;21:837–50.10.1097/FPC.0b013e32834c0010Search in Google Scholar PubMed
24. Nies AT, Damme K, Kruck S, Schaeffeler E, Schwab M. Structure and function of multidrug and toxin extrusion proteins (MATEs) and their relevance to drug therapy and personalized medicine. Arch Toxicol 2016;90:1555–84.10.1007/s00204-016-1728-5Search in Google Scholar PubMed
25. Umamaheswaran G, Praveen RG, Damodaran SE, Das AK. Adithan C. Influence of SLC22A1 rs622342 genetic polymorphism on metformin response in South Indian type 2 diabetes mellitus patients. Clin Exp Med 2015;15:511–7.10.1007/s10238-014-0322-5Search in Google Scholar PubMed
26. Raj GM, Mathaiyan J, Wyawahare M, Rao KS, Priyadarshini R. Genetic polymorphisms of multidrug and toxin extrusion proteins (MATE1 and MATE2) in South Indian population. BioImpacts 2017;7:25–30.10.15171/bi.2017.04Search in Google Scholar PubMed PubMed Central
27. Ndebele P. The declaration of Helsinki, 50 years later. JAMA 2013;310:2145–6.10.1001/jama.2013.281316Search in Google Scholar PubMed
28. Dujic T, Zhou K, Yee SW, van Leeuwen N, de Keyser CE, Javorský M, et al. Variants in pharmacokinetic transporters and glycemic response to metformin: a metgen meta-analysis. Clin Pharmacol Ther 2017;101:763–72.10.1002/cpt.567Search in Google Scholar PubMed PubMed Central
29. Choi JH, Yee SW, Ramirez AH, Morrissey KM, Jang GH, Joski PJ, et al. A Common 5′-UTR Variant in MATE2-K Is Associated With Poor Response to Metformin. Clin Pharmacol Ther 2011;90: 674–84.10.1038/clpt.2011.165Search in Google Scholar PubMed PubMed Central
30. Stocker SL, Morrissey KM, Yee SW, Castro RA, Xu L, Dahlin A, et al. The effect of novel promoter variants in MATE1 and MATE2 on the pharmacokinetics and pharmacodynamics of metformin. Clin Pharmacol Ther 2013;93:186–94.10.1038/clpt.2012.210Search in Google Scholar PubMed PubMed Central
31. Klen J, Goričar K, Janež A, Dolžan V. The role of genetic factors and kidney and liver function in glycemic control in type 2 diabetes patients on long-term metformin and sulphonylurea cotreatment. Biomed Res Int 2014;2014:1–7.10.1155/2014/934729Search in Google Scholar PubMed PubMed Central
32. Tarasova L, Kalnina I, Geldnere K, Bumbure A, Ritenberga R, Nikitina-Zake L, et al. Association of genetic variation in the organic cation transporters OCT1, OCT2 and multidrug and toxin extrusion 1 transporter protein genes with the gastrointestinal side effects and lower BMI in metformin-treated type 2 diabetes patients. Pharmacogenet Genomics 2012;22:659–66.10.1097/FPC.0b013e3283561666Search in Google Scholar PubMed
33. Christensen MM, Brasch-Andersen C, Green H, Nielsen F, Damkier P, Beck-Nielsen H, et al. The pharmacogenetics of metformin and its impact on plasma metformin steady-state levels and glycosylated hemoglobin A1c: corrigendum. Pharmacogenet Genomics 2015;25:48–50.10.1097/FPC.0b013e32834c0010Search in Google Scholar PubMed
34. Xiao D, Guo Y, Li X, Yin J-Y, Zheng W, Qiu X-W, et al. The impacts of SLC22A1 rs594709 and SLC47A1 rs2289669 polymorphisms on metformin therapeutic efficacy in chinese type 2 diabetes patients. Int J Endocrinol 2016;2016:1–7.Search in Google Scholar
35. Becker ML, Visser LE, van Schaik RH, Hofman A, Uitterlinden AG, Stricker BH. Genetic variation in the multidrug and toxin extrusion 1 transporter protein influences the glucose-lowering effect of metformin in patients with diabetes: a preliminary study. Diabetes 2009;58:745–9.10.2337/db08-1028Search in Google Scholar PubMed PubMed Central
36. Tkáč I, Klimčáková L, Javorský M, Fabianová M, Schroner Z, Hermanová H, et al. Pharmacogenomic association between a variant in SLC47A1 gene and therapeutic response to metformin in type 2 diabetes. Diabetes Obes Metab 2013;15:189–91.10.1111/j.1463-1326.2012.01691.xSearch in Google Scholar PubMed
37. He R, Zhang D, Lu W, Zheng T, Wan L, Liu F, et al. SLC47A1 gene rs2289669 G>A variants enhance the glucose-lowering effect of metformin via delaying its excretion in Chinese type 2 diabetes patients. Diabetes Res Clin Pract 2015;109:57–63.10.1016/j.diabres.2015.05.003Search in Google Scholar PubMed
38. Liang H, Xu W, Zhou L, Yang W, Weng J. Differential increments of basal glucagon-like-1 peptide concentration among SLC47A1 rs2289669 genotypes were associated with inter-individual variability in glycaemic response to metformin in Chinese people with newly diagnosed Type 2 diabetes. Diabet Med 2017;34:987–92.10.1111/dme.13351Search in Google Scholar PubMed
39. Mousavi S, Kohan L, Yavarian M, Habib A. Pharmacogenetic variation of SLC47A1 gene and metformin response in type 2 diabetes patients. Mol Biol Res Commun 2017;6:91–4.Search in Google Scholar
40. Ji L, Li H, Guo X, Li Y, Hu R, Zhu Z. Impact of baseline BMI on glycemic control and weight change with metformin monotherapy in Chinese type 2 diabetes patients: phase IV open-label trial. PLoS One 2013;8:e57222.10.1371/journal.pone.0057222Search in Google Scholar PubMed PubMed Central
41. United States Food and Drug Administration. Glucophage Label [Internet]. 1995 [updated 2017 Apr; cited 2018 Jul 28]. Available at: https://www.accessdata.fda.gov/drugsatfda_docs/label/2017/020357s037s039,021202s021s023lbl.pdf.Search in Google Scholar
42. Masharani U. Diabetes mellitus & hypoglycemia. In: Papadakis MA, Mcphee SJ, editors. Current medical diagnosis & treatment, 56th ed. New York: McGraw-Hill Education, 2017:1210–58.Search in Google Scholar
43. Powers AC, D’ALessio D. Endocrine pancreas and pharmacotherapy of diabetes mellitus and hypoglycemia. In: Brunton LL, Hilal-Dandan R, Knollmann BC, editors. Goodman & Gilman’s the pharmacological basis of therapeutics, 13th ed. New York: McGraw-Hill Education, 2018:863–86.Search in Google Scholar
44. Becker ML, Visser LE, van Schaik RH, Hofman A, Uitterlinden AG, Stricker BH. Interaction between polymorphisms in the OCT1 and MATE1 transporter and metformin response. Pharmacogenet Genomics 2010;20:38–44.10.1097/FPC.0b013e328333bb11Search in Google Scholar PubMed
45. Christensen MM, Pedersen RS, Stage TB, Brasch-Andersen C, Nielsen F, Damkier P, et al. A gene–gene interaction between polymorphisms in the OCT2 and MATE1 genes influences the renal clearance of metformin. Pharmacogenet Genomics 2013;23:526–34.10.1097/FPC.0b013e328364a57dSearch in Google Scholar PubMed
46. Kajiwara M, Terada T, Ogasawara K, Iwano J, Katsura T, Fukatsu A, et al. Identification of multidrug and toxin extrusion (MATE1 and MATE2-K) variants with complete loss of transport activity. J Hum Genet 2009;54:40–6.10.1038/jhg.2008.1Search in Google Scholar PubMed
47. Chen Y, Teranishi K, Li S, Yee SW, Hesselson S, Stryke D, et al. Genetic variants in multidrug and toxic compound extrusion-1, hMATE1, alter transport function. Pharmacogenomics J 2009;9:127–36.10.1038/tpj.2008.19Search in Google Scholar PubMed PubMed Central
48. Nishimura K, Ide R, Hirota T, Kawazu K, Kodama S, Takesue H, et al. Identification and functional characterization of novel nonsynonymous variants in the human multidrug and toxin extrusion 2-K. Drug Metab Dispos 2014;42:1432–7.10.1124/dmd.114.056887Search in Google Scholar PubMed
49. Cho SK, Chung J-Y. The MATE1 rs2289669 polymorphism affects the renal clearance of metformin following ranitidine treatment. Int J Clin Pharmacol Ther 2016;54:253–62.10.5414/CP202473Search in Google Scholar PubMed
50. Ivanyuk A, Livio F, Biollaz J, Buclin T. Renal Drug Transporters and Drug Interactions. Clin Pharmacokinet 2017;56:825–92.10.1007/s40262-017-0506-8Search in Google Scholar PubMed
Supplementary Material
The online version of this article offers supplementary material (https://doi.org/10.1515/dmpt-2018-0030).
©2018 Walter de Gruyter GmbH, Berlin/Boston
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Articles in the same Issue
- Frontmatter
- Editorial
- Gérard Siest Prize awarded to Alžběta Hlaváčková at the 9th Santorini Conference
- Opinion Paper
- Personalized medicine into health national services: barriers and potentialities
- Case Report
- Early disease relapse in a patient with colorectal cancer who harbors genetic variants of DPYD, TYMS, MTHFR and DHFR after treatment with 5-fluorouracil-based chemotherapy
- Original Articles
- Effect of baseline renal and hepatic function on the incidence of adverse drug events: the Japan Adverse Drug Events study
- Lack of effect of the SLC47A1 and SLC47A2 gene polymorphisms on the glycemic response to metformin in type 2 diabetes mellitus patients
- Effects of CYP2C19*17 polymorphisms on the efficacy and safety of bromodigyrochlorophenylbenzodiazepine in patients with anxiety disorder and comorbid alcohol use disorder
- Pharmacogenetic testing by polymorphic markers G1846A (CYP2D6*4) and C100T (CYP2D6*10) of the CYP2D6 gene in coronary heart disease patients taking ββ-blockers in the Republic of Sakha (YAKUTIA)
- Acknowledgment