Abstract
Objectives
Type 2 Diabetes mellitus is a progressive metabolic disease characterized by relative insulin insufficiency and insulin resistance resulting in hyperglycemia. Despite the widespread use of metformin, there is considerable variation in treatment response; with approximately one-third of patients failing to achieve adequate glycemic control. Studies have reported the involvement of single nucleotide polymorphisms and their interactions in genetic pathways i.e., pharmacodynamics and pharmacokinetics. This study aims to investigate the association between 19 pharmacogenetics biomarkers and response to metformin treatment.
Methods
MassARRAY panels were designed and optimized by Inqaba Biotechnical Industries, to genotype 19 biomarkers for 140 type 2 diabetic outpatients.
Results
The CT genotype of the rs12752688 polymorphism was significantly associated with increased response to metformin therapy after correction (OR=0.33, 95% CI [0.16–0.68], p-value=0.006). An association was also found between the GA genotype of SLC47A2 rs12943590 and a decreased response to metformin therapy after correction (OR=2.29, 95% CI [1.01–5.21], p-value=0.01).
Conclusions
This is the first study investigating the association between genetic variants and responsiveness to medication for diabetic patients from the indigenous Nguni population in South Africa. It is suggested that rs12752688 and rs12943590 be included in pharmacogenomics profiling systems to individualize metformin therapy for diabetic patients from African populations.
Acknowledgments
The authors would also like to thank the study participants, Cecilia Makiwane Hospital and the Department of Health Eastern Cape.
Research funding: The work reported herein was made possible through funding by the South African Medical Research Council through its Division of Research Capacity Development under funding received from the South African National Treasury. The content here of is the sole responsibility of the authors and do not necessarily represent the official views of the SAMRC or the funders. In addition, partial funding from the National Research Foundation of South African and the University of the Western Cape was used for this study.
Author contributions: LX conducted investigation, sample collection, data curation, formal data analysis, writing original draft, and editing of subsequent drafts. ZA provided methodology development, formal data analysis, and review and editing of drafts. BP provided methodology development, data curation, formal data analysis, and writing and editing of drafts. CM provided investigation, formal data analysis, and review and writing of drafts. OA provided investigation sites, resources, and data curation. RJ conducted methodology development and provided resources and methodology criteria. JO provided investigation sites, resources and methodology criteria. MB conceived the study, provided resources and data curation. All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
Competing interests: Authors state no conflict of interest. The authors declare: no support from any organization for the submitted work; no financial relationships with any organizations that might have an interest in the submitted work in the previous 3 years; no other relationships or activities that could appear to have influenced the submitted work.
Informed consent: Informed consent was obtained from all individuals included in this study.
Ethical approval: Ethical clearance for this study was obtained from the Senate Research Committee of the University of the Western Cape (Ethics clearance number BM/16/5/19).
References
1. Pheiffer, C, Pillay-van Wyk, V, Joubert, JD, Levitt, N, Nglazi, MD, Bradshaw, D. The prevalence of type 2 diabetes in South Africa: a systematic review protocol. BMJ Open 2018;8:e021029.10.1136/bmjopen-2017-021029Search in Google Scholar PubMed PubMed Central
2. World Health Organization. World health statistics 2018: monitoring health for the SDGs, sustainable development goals; 2018. Available from: https://www.who.int/gho/publications/world_health_statistics/2018/en/).Search in Google Scholar
3. Ogurtsova, K, da Rocha Fernandes, JD, Huang, Y, Linnenkamp, U, Guariguata, L, Cho, NH, et al. IDF Diabetes Atlas: global estimates for the prevalence of diabetes for 2015 and 2040. Diabetes Res Clin Pract 2017;128:40–50. https://doi.org/10.1016/j.diabres.2018.02.023.Search in Google Scholar PubMed
4. International Diabetes Federation. IDF diabetes Atlas 9th ed. 2019. Available from: https://www.idf.org/our-network/regions-members/africa/welcome.html [Accessed 25 Nov 2019].Search in Google Scholar
5. White, MF. Insulin signaling in health and disease. Science 2003;302:1710–1. https://doi.org/10.1126/science.1092952.Search in Google Scholar PubMed
6. Donath, MY, Ehses, JA, Maedler, K, Schumann, DM, Ellingsgaard, H, Eppler, E, et al. Mechanisms of β-cell death in type 2 diabetes. Diabetes 2005;54:S108–13. https://doi.org/10.2337/diabetes.54.suppl_2.s108.Search in Google Scholar PubMed
7. Nieto-Vazquez, I, Fernández-Veledo, S, Krämer, DK, Vila-Bedmar, R, Garcia-Guerra, L, Lorenzo, M. Insulin resistance associated to obesity: the link TNF-alpha. Arch Physiol Biochem 2008;114:183–94. https://doi.org/10.1080/13813450802181047.Search in Google Scholar PubMed
8. Tara, MD, Pierce, KA, Roix, JJ, Tyler, A, Chen, H, Teixeira, SR. The role of adipocyte insulin resistance in the pathogenesis of obesity-related elevations in endocannabinoids. Diabetes 2008;57:1262–8. https://doi.org/10.2337/db07-1186.Search in Google Scholar PubMed
9. Abdul-Ghani, MA, DeFronzo, RA. Pathogenesis of insulin resistance in skeletal muscle. Bio Med Res Int 2010;2010: 476279. https://doi.org/10.1155/2010/476279.Search in Google Scholar PubMed PubMed Central
10. Babu, PV, Liu, D, Gilbert, ER. Recent advances in understanding the anti-diabetic actions of dietary flavonoids. J Nutr Biochem 2013;24:1777–89. https://doi.org/10.1016/j.jnutbio.2013.06.003.Search in Google Scholar PubMed PubMed Central
11. Inzucchi, SE, Bergenstal, RM, Buse, JB, Diamant, M, Ferrannini, E, Nauck, M, et al. Management of hyperglycaemia in type 2 diabetes: a patient-centered approach. Position statement of the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetologia 2012;55:1577–96. https://doi.org/10.2337/dc12-0413.Search in Google Scholar PubMed PubMed Central
12. Sherifali, D, Sherifali, D, Nerenberg, K, Pullenayegum, E, Cheng, JE, Gerstein, HC. The effect of oral Antidiabetic Agents on HbA1C levels: a systematic review and meta-analysis. Diabetes Care 2010;33:1859–64. https://doi.org/10.2337/dc09-1727.Search in Google Scholar PubMed PubMed Central
13. 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. https://doi.org/10.1038/tpj.2016.54.Search in Google Scholar PubMed
14. Inzucchi, SE, Bergenstal, RM, Buse, JB, Diamant, M, Ferrannini, E, Nauck, M, et al. Management of hyperglycemia in type 2 diabetes, 2015: a patient-centered approach: update to a position statement of the American Diabetes Association and the European Association for the Study of Diabetes. Diabetes Care 2015;38:140–9. https://doi.org/10.2337/dc14-2441.Search in Google Scholar PubMed
15. Hundal, RS, Krssak, M, Dufour, S, Laurent, D, Lebon, V, Chandramouli, V, et al. Mechanism by which metformin reduces glucose production in type 2 diabetes. Diabetes 2000;49:2063–9. https://doi.org/10.2337/diabetes.49.12.2063.Search in Google Scholar PubMed PubMed Central
16. Johnson, JA, Majumdar, SR, Simpson, SH, Toth, EL. Decreased mortality associated with the use of metformin compared with sulfonylurea monotherapy in type 2 diabetes. Diabetes Care 2002;25:2244–8. https://doi.org/10.2337/diacare.25.12.2244.Search in Google Scholar PubMed
17. Bruijstens, LA, Van Luin, M, Buscher-Jungerhans, PM, Bosch, FH. Reality of severe metformin-induced lactic acidosis in the absence of chronic renal impairment. Neth J Med 2008;66:185–90.Search in Google Scholar
18. Cho, K, Chung, JY, Cho, SK, Shin, HW, Jang, IJ, Park, JW, et al. Antihyperglycemic mechanism of metformin occurs via the AMPK/LXRα/POMC pathway. Sci Rep 2015;5:8145. https://doi.org/10.1038/srep08145.Search in Google Scholar PubMed PubMed Central
19. Roumie, CL, Min, JY, Greevy, RA, Grijalva, CG, Hung, AM, Liu, X, et al. Risk of hypoglycemia following intensification of metformin treatment with insulin versus sulfonylurea. Can Med Assoc J 2016;188:E104–12. https://doi.org/10.1503/cmaj.150904.Search in Google Scholar PubMed PubMed Central
20. Argaud, D, Roth, H, Wiernsperger, N, Leverve, XM. Metformin decreases gluconeogenesis by enhancing the pyruvate kinase flux in isolated rat hepatocytes. Eur J Biochem 1993;213:1341–8. https://doi.org/10.1111/j.1432-1033.1993.tb17886.x.Search in Google Scholar PubMed
21. Alexandre, KB, Smit, AM, Gray, IP, Crowther, NJ. Metformin inhibits intracellular lipid accumulation in the murine pre‐adipocyte cell line, 3T3‐L1. Diabetes Obes Metabol 2008;10:688–90. https://doi.org/10.1111/j.1463-1326.2008.00890.x.Search in Google Scholar PubMed
22. Wang, H, Ni, Y, Yang, S, Li, H, Li, X, Feng, B. The effects of gliclazide, metformin, and acarbose on body composition in patients with newly diagnosed type 2 diabetes mellitus. Curr Ther Res 2013; 75:88–92. https://doi.org/10.1016/j.curtheres.2013.10.002.Search in Google Scholar PubMed PubMed Central
23. Kristensen, JM, Treebak, JT, Schjerling, P, Goodyear, L, Wojtaszewski, JF. Two weeks of metformin treatment induces AMPK-dependent enhancement of insulin-stimulated glucose uptake in mouse soleus muscle. Am J Physiol Endocrinol Metab 2014;306:E1099–109. https://doi.org/10.1152/ajpendo.00417.2013.Search in Google Scholar PubMed PubMed Central
24. Duca, FA, Côté, CD, Rasmussen, BA, Zadeh-Tahmasebi, M, Rutter, GA, Filippi, BM, et al. Metformin activates a duodenal Ampk–dependent pathway to lower hepatic glucose production in rats. Nat Med 2015;21:506–11. https://doi.org/10.1038/nm.3787.Search in Google Scholar PubMed PubMed Central
25. Chen, H, Li, J, Yang, O, Kong, J, Lin, G. Effect of metformin on insulin-resistant endothelial cell function. Oncol Lett 2015;9:1149–53. https://doi.org/10.3892/ol.2015.2883.Search in Google Scholar PubMed PubMed Central
26. Cramer, JA, Pugh, MJ. The influence of insulin use on glycemic control: how well do adults follow prescriptions for insulin? Diabetes Care 2005;28 78–83. https://doi.org/10.2337/diacare.28.1.78.Search in Google Scholar PubMed
27. Fonseca, VA. Defining and characterizing the progression of type 2 diabetes. Diabetes Care 2009;32:S151–6. https://doi.org/10.2337/dc09-S301.Search in Google Scholar PubMed PubMed Central
28. Diabetes Prevention Program Research Group. Long-term safety, tolerability, and weight loss associated with metformin in the diabetes prevention program outcomes study. Diabetes Care 2012;35:731–7. https://doi.org/10.2337/dc11-1299.Search in Google Scholar PubMed PubMed Central
29. Desai, NR, Shrank, WH, Fischer, MA, Avorn, J, Liberman, JN, Schneeweiss, S, et al. Patterns of medication initiation in newly diagnosed diabetes mellitus: quality and cost implications. Am J Med 2012;125:302.e1-7. https://doi.org/10.1016/j.amjmed.2011.07.033.Search in Google Scholar PubMed PubMed Central
30. Evans, WE, Johnson, JA. Pharmacogenomics: the inherited basis for interindividual differences in drug response. Annu Rev Genom Hum Genet 2001;2:9–39. https://doi.org/10.1146/annurev.genom.2.1.9.Search in Google Scholar PubMed
31. 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. https://doi.org/10.2337/db08-1028.Search in Google Scholar PubMed PubMed Central
32. 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. https://doi.org/10.1016/j.diabres.2015.05.003.Search in Google Scholar PubMed
33. Shokri, F, Ghaedi, H, Ghafouri Fard, S, Movafagh, A, Abediankenari, S, Mahrooz, A, et al. Impact of ATM and SLC22A1 polymorphisms on therapeutic response to metformin in Iranian diabetic patients. Int J Mol Cell Medz 2016;5:1–7.Search in Google Scholar
34. 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. https://doi.org/10.1002/cpt.567.Search in Google Scholar PubMed PubMed Central
35. Hardy, BJ, Séguin, B, Ramesar, R, Singer, PA, Daar, AS. South Africa: from species cradle to genomic applications. Nat Rev Genet 2008;9:S19–23. https://doi.org/10.1038/nrg2441.Search in Google Scholar PubMed
36. Tishkoff, SA, Reed, FA, Friedlaender, FR, Ehret, C, Ranciaro, A, Froment, A, et al. The genetic structure and history of Africans and African Americans. Science 2009;324:1035–44. https://doi.org/10.1126/science.1172257.Search in Google Scholar PubMed PubMed Central
37. Kashi, Z, Masoumi, P, Mahrooz, A, Hashemi-Soteh, MB, Bahar, A, Alizadeh, A. The variant organic cation transporter 2 (OCT2)–T201M contribute to changes in insulin resistance in patients with type 2 diabetes treated with metformin. Diabetes Res Clin Pract 2015;108:78–83. https://doi.org/10.1016/j.diabres.2015.01.024.Search in Google Scholar PubMed
38. CDE: Clinical guidelines 2018; 2018. Available from: www.cdediabetes.co.za/ [Accessed 30 Oct 2018].Search in Google Scholar
39. Amod, A The society for endocrinology, metabolism and diabetes of South Africa type 2 diabetes guidelines expert committee. The 2017 SEMDSA guideline for the management of type 2 diabetes guideline committee. JEMDSA 2017;21:S1–196.Search in Google Scholar
40. Lahiri, DK, Nurnberger, JIJr. A rapid non-enzymatic method for the preparation of HMW DNA from blood for RFLP studies. Nucleic Acids Res 1991;19:5444. https://doi.org/10.1093/nar/19.19.5444.Search in Google Scholar PubMed PubMed Central
41. Florez, JC, Jablonski, KA, Taylor, A, Mather, K, Horton, E, White, NH, et al. The C allele of ATM rs11212617 does not associate with metformin response in the diabetes prevention program. Diabetes Care 2012;35:1864–7. https://doi.org/10.2337/dc11-2301.Search in Google Scholar PubMed PubMed Central
42. Xiao, D, Guo, Y, Xi, L, 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;2016:4350712. https://doi.org/10.1155/2016/4350712.Search in Google Scholar PubMed PubMed Central
43. Butler, JM. Forensic DNA typing: biology, technology, and genetics of STR markers. Elsevier Academic Press, London; 2005.Search in Google Scholar
44. Hardy, G.H. Mendelian proportions in a mixed population. Classic papers in genetics. Prentice-Hall, Inc.: Englewood Cliffs, NJ; 1908: pp. 60–2.Search in Google Scholar
45. Lee, S, Kasif, S, Weng, Z, Cantor, CR. Quantitative analysis of single nucleotide polymorphisms within copy number variation. PloS One 2008:3:e3906.10.1371/journal.pone.0003906Search in Google Scholar PubMed PubMed Central
46. Dorak, MT. Basic population genetics; 2014. Available from: https://www.dorak.info/genetics/popgen.html.Search in Google Scholar
47. Nei, M, Kumar, S. Molecular evolution and phylogenetics. New York, United States of America: Oxford University Press; 2000.10.1093/oso/9780195135848.001.0001Search in Google Scholar
48. Shikata, E, Yamamoto, R, Takane, H, Shigemasa, C, Ikeda, T, Otsubo, K, et al. Human organic cation transporter (OCT1 and OCT2) gene polymorphisms and therapeutic effects of metformin. J Hum Genet 2007;52:117–122. https://doi.org/10.1007/s10038-006-0087-0.Search in Google Scholar PubMed
49. 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;15:511–17. https://doi.org/10.1007/s10238-014-0322-5.Search in Google Scholar PubMed
50. Van Leeuwen, N, Nijpels, G, Becker, ML, Deshmukh, H, Zhou, K, Stricker, BH, et al. A gene variant near ATM is significantly associated with metformin treatment response in type 2 diabetes: a replication and meta-analysis of five cohorts. Diabetologia 2012;55:1971–7. https://doi.org/10.1007/s00125-012-2537-x.Search in Google Scholar PubMed PubMed Central
51. Sanchez-Rangel, E, Inzucchi, SE Metformin: clinical use in type 2 diabetes. Diabetologia 2017;60:1586–93. https://doi.org/10.1007/s00125-017-4336-x.Search in Google Scholar
52. Maruthur, NM, Tseng, E, Hutfless, S, Wilson, LM, Suarez-Cuervo, C, Berger, Z, 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;164:740–751. https://doi.org/10.7326/M15-2650.Search in Google Scholar
53. Reitman, ML, Schadt, EE. Pharmacogenetics of metformin response: a step in the path toward personalized medicine. J Clin Invest 2007;117:1226–9. https://doi.org/10.1172/JCI32133.Search in Google Scholar
54. Breitenstein, MK, Wang, L, Simon, G, Ryu, E, Armasu, SM, Ray, B, et al. Leveraging an electronic health record-linked biorepository to generate a metformin pharmacogenomics hypothesis. AMIA joint summits on translational science proceedings. AMIA Joint Summits Transl Sci 2015;2015:26–30.Search in Google Scholar
55. Lattard, V, Zhang, J, Cashman, JR. Alternative processing events in human FMO genes. Mol Pharmacol 2004;65:1517–25. https://doi.org/10.1124/mol.65.6.1517.Search in Google Scholar
56. Scott, F, Malagon, SGG, O’Brien, BA, Fennema, D, Veeravalli, S, Coveney, CR, et al. Identification of Flavin-containing monooxygenase 5 (FMO5) as a regulator of glucose homeostasis and a potential sensor of gut bacteria. Drug Metabol Dispos 2017;45:982–9. https://doi.org/10.1124/dmd.117.076612.Search in Google Scholar
57. Hines, RN, Luo, Z, Hopp, KA, Cabacungan, ET, Koukouritaki, SB, McCarver, DG. Genetic variability at the human FMO1 locus: significance of a basal promoter Yin Yang 1 element polymorphism (FMO1*6). J Pharmacol Exp Therapeut 2003;306:1210–8. https://doi.org/10.1124/jpet.103.053686.Search in Google Scholar
58. Yueh, M-F, Krueger, SK, Williams, DE. Pulmonary Flavin-containing monooxygenase (FMO) in rhesus macaque: expression of FMO2 protein, mRNA and analysis of the cDNA. Biochim Biophys Acta Gene Struct Expr 1997;1350:267–71. https://doi.org/10.1016/s0167-4781(97)00004-3.Search in Google Scholar
59. Dolphin, CT, Beckett, DJ, Janmohamed, A, Cullingford, TE, Smith, RL, Shephard, EA, et al. The Flavin-containing monooxygenase 2 gene (FMO2) of humans, but not of other primates, encodes a truncated, nonfunctional protein. J Biol Chem 1998;273:30599–607. https://doi.org/10.1074/jbc.273.46.30599.Search in Google Scholar PubMed
60. Krueger, SK, Yueh, MF, Martin, SR, Pereira, CB, Williams, DE. Characterization of expressed full-length and truncated FMO2 from rhesus monkey. Drug Metab Dispos 2001;29:693–700.Search in Google Scholar
61. Phani, NM, Vohra, M, Kakar, A, Adhikari, P, Nagri, SK, D’Souza, SC, et al. Implication of critical pharmacokinetic gene variants on therapeutic response to metformin in type 2 diabetes. Pharmacogenomics 2018;19:905–11. https://doi.org/10.2217/pgs-2018-0041.Search in Google Scholar PubMed
62. 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. https://doi.org/10.1038/clpt.2011.165.Search in Google Scholar PubMed PubMed Central
63. 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. https://doi.org/10.1038/clpt.2012.210.Search in Google Scholar PubMed PubMed Central
64. Sakata, T, Anzai, N, Kimura, T, Miura, D, Fukutomi, T, Takeda, M, et al. Functional analysis of human organic cation transporter OCT3 (SLC22A3) polymorphisms. J Pharmacol Sci 2010;113:263–6. https://doi.org/10.1254/jphs.09331sc.Search in Google Scholar PubMed
65. Chen, L, Pawlikowski, B, Schlessinger, A, More, SS, Stryke, D, Johns, SJ, et al. Role of organic cation transporter 3 (SLC22A3) and its missense variants in the pharmacologic action of metformin. Pharmacogenet Genom 2010;20:687–99. https://doi.org/10.1097/FPC.0b013e32833fe789.Search in Google Scholar PubMed PubMed Central
66. Jacobs, C, Pearce, B, Du Plessis, M, Hoosain, N, Benjeddou, M. Genetic polymorphisms and haplotypes of the organic cation transporter 1 gene (SLC22A1) in the Xhosa population of South Africa. Genet Mol Biol 2014;37:350–9. https://doi.org/10.1590/s1415-47572014005000002.Search in Google Scholar PubMed PubMed Central
67. Tzvetkov, MV, Vormfelde, SV, Balen, D, Meineke, I, Schmidt, T, Sehrt, D, et al. The effects of genetic polymorphisms in the organic cation transporters OCT1, OCT2, and OCT3 on the renal clearance of metformin. Clin Pharmacol Ther 2009;86:299–306. https://doi.org/10.1038/clpt.2009.92.Search in Google Scholar PubMed
68. Chen, L, Takizawa, M, Chen, E, Schlessinger, A, Segenthelar, J, Choi, JH, et al. Genetic polymorphisms in organic cation transporter 1 (OCT1) in Chinese and Japanese populations exhibit altered function. J Pharmacol Exp Therapeut 2010;335:42–50. https://doi.org/10.1124/jpet.110.170159.Search in Google Scholar PubMed PubMed Central
69. Bonferroni, CE Teoria statistica delle classi e calcolo delle probabilità.’ Pubblicazioni del R Istituto Superiore di Scienze Economiche e Commerciali di Firenze 1936;8:3–62.Search in Google Scholar
70. Ellis, JA, Ong, B. The MassARRAY® system for targeted SNP genotyping. In: White, S, Cantsilieris, S, editors. Genotyping. Methods in molecular biology, vol 1492. Humana Press, New York, NY; 2017.10.1007/978-1-4939-6442-0_5Search in Google Scholar PubMed
71. Chen, S, Zhou, J, Xi, M, Jia, Y, Wong, Y, Zhao, J, et al. Pharmcogenomic variation and metformin response. Curr Drug Metab 2013;14:1070–82. https://doi.org/10.2174/1389200214666131211153933.Search in Google Scholar PubMed
72. Zolk, O. Disposition of metformin: variability due to polymorphisms of organic cation transporters. Ann Med 2012;44:119–29. https://doi.org/10.3109/07853890.2010.549144.Search in Google Scholar PubMed
73. Lucassen, A, Ehlers, K, Grobler, PJ, Shezi, AL. Allele frequency data of 15 autosomal STR loci in four major population groups of South Africa. Int J Legal Med 2014;128:275–6. https://doi.org/10.1007/s00414-013-0898-4.Search in Google Scholar PubMed
74. Lane, AB, Soodyall, H, Arndt, S, Ratshikhopha, ME, Jonker, E, Freeman, C, et al. Genetic substructure in South African Bantu-speakers: evidence from autosomal DNA and Y-chromosome studies. Am J Phys Anthropol 2002;119:175–85. https://doi.org/10.1002/ajpa.10097.Search in Google Scholar PubMed
© 2020 Walter de Gruyter GmbH, Berlin/Boston