Mostafa-Hedeab G, Mohamed A. A, Thabet G, Sabry D, Salam R. F, Hassen M. E. Effect of MATE 1, MATE 2 and OCT1 Single Nucleotide Polymorphisms on Metformin Action in Recently Diagnosed Egyptian Type-2 Diabetic Patients. Biomed Pharmacol J 2018;11(1).
Manuscript received on :December 28, 2017
Manuscript accepted on :February 20, 2018
Published online on: --
How to Cite    |   Publication History
Views Views: (Visited 312 times, 1 visits today)   Downloads PDF Downloads: 363
Effect of MATE 1, MATE 2 and OCT1 Single Nucleotide Polymorphisms on Metformin Action in Recently Diagnosed Egyptian Type-2 Diabetic Patients

Gomaa Mostafa-Hedeab1,2, Alaa Abdelhamed Mohamed3,4, Gamal Thabet5, Dina Sabry6, Randa Fayez Salam7 and Manal Ewaiss Hassen3,4

1Department of Pharmacology, Faculty of medicine, Aljouf University, KSA.

2Department of Pharmacology, Faculty of medicine, Beni Suef University, Egypt.

3Department Biochemistry unit- pathology, Faculty of medicine, Aljouf University, KSA.

4Department of Biochemistry, Faculty of medicine, Beni Suef University, Egypt.

5Department of Clinical pathology, National Cancer Institute, Cairo University, Egypt.

6Department of Biochemistry, and Molecular Biology, Faculty of medicine Cairo University, Egypt.

7Department of Internal medicine, Faculty of medicine Cairo University, Egypt.

Corresponding Author E-mail:



Objective: to study the effect of MATE 1, MATE 2 and OCT1 genetic variants on metformin action in recently diagnosed Egyptian Type-2 diabetic patients. Patients & Methods: One hundred type-2 DM patients and forty healthy control were included in the study. All patients were recently diagnosed receiving no treatment before participation in the study. Three single nucleotide polymorphisms (SNPs) were Genotyped using real time PCR, Sequence Detection System: MATE1 (rs2252281), OCT1 coding variants (rs12208357) (SLC22A1) and MATE2 (rs12943590). Results: there is a significant differences between control and patients regarding MATE2 (p<0.05), OCT1 (P<0.005) distribution; in which GG (54%), CC (62%) is the most prevalent among studied patients respectively.  MATE1 SNP; Patients with CC alleles and TT allele had better HBA1C (8.577±.2924), (8.7±.25) compared to CT allele patients (9.584±.3023) (P= .04) (P=.019) respectively. OCT1 SNP; CG allele patients showed better RBS (251±9.565) compared to CC allele (294.42±8.476) (p=0.004). Logistic regression test showed that RBS (p=.00001), ALT (p=.0001) and TLC (p=.025) are independent factors affecting blood glucose. Conclusion: MATE1 and OCT1 SNPs may have a potential role in metformin efficacy.


Diabetes Egyptian;MATE 1; MATE 2 and OCT1; SNPs;

Download this article as: 
Copy the following to cite this article:

Mostafa-Hedeab G, Mohamed A. A, Thabet G, Sabry D, Salam R. F, Hassen M. E. Effect of MATE 1, MATE 2 and OCT1 Single Nucleotide Polymorphisms on Metformin Action in Recently Diagnosed Egyptian Type-2 Diabetic Patients. Biomed Pharmacol J 2018;11(1).

Copy the following to cite this URL:

Mostafa-Hedeab G, Mohamed A. A, Thabet G, Sabry D, Salam R. F, Hassen M. E. Effect of MATE 1, MATE 2 and OCT1 Single Nucleotide Polymorphisms on Metformin Action in Recently Diagnosed Egyptian Type-2 Diabetic Patients. Biomed Pharmacol J 2018;11(1). Available from:


Metformin (1,1-dimethylbiguanide), a biguanide derivative, is the first choice drug prescribed in type II DM patients (T2D), in conjunction with lifestyle modification.1 It improve insulin sensitivity through up regulation of insulin receptor expression and stimulation of tyrosine kinase activity.2

About fifty percent of  the oral metformin dose is absorbed3,4 passing to blood followed by distribution to different tissues. It is present in unbound form,5 excreted  through renal clearance in unchanged form. The efficacy of  metformin varies substantially, with more than thirty percent of metformin treated patient are classified as non-responders.6 The genetic contribution to this response variability has been studied with a focus has been directed twards metformin pharmacokinetics and pharmacodynamics.

Metformin is transferred into the intestinal cells by plasma monoamine transporters or PMAT (SLC29A4), then into the blood stream and its active hepatic uptake by the organic cation transporter, OCT1 (SLC22A1) 7 Metformin is a good substrate for OCT1, encoded by the solute carrier family 22 member 1 (SLC22A1) gene, which is primarily expressed in the liver.8

Metformin secretion takes place through two consequent step: First from blood to renal tubule cell which is mediated by organic cation transporter, OCT2 (SLC22A2), Second step and final excretion into the urine is mediated by 2 transporters in the multidrug and toxin extrusion family, MATE1 and MATE2-K (SLC47A1 and SLC47A2).7 Biliary excretion of the metformin, although insignificant in human, is mediated through MATE1that is located in the canalicular membrane of hepatocytes.

Based on that, polymorphisms on OCTs and MATEs may change the metformin level and metformin efficacy. The present study was carried out to explore the distribution and the effect of MATE1, MATE2 and OCT1 gene polymorphisms on Metformin efficacy among type 2 diabetic patients

Patients and Method


One hundred subjects clinically diagnosed as type II diabetes mellitus by clinical and laboratory investigations at internal medicine Department, Faculty of Medicine, Cairo University were enrolled present study. Clinical data of all cases was collected including age, sex and family history. Laboratory data were assessed including: glycosylated Hb (HbA1C), blood glucose level, alanine transaminase (ALT), aspartate transaminase (AST), alkaline phosphatase (ALKP), bilirubin, albumin, creatinin, prothrombin concentration (PC), complete blood picture, antinuclear acid (ANA) titer, thyroid stimulating hormone (TSH) and alpha fetoprotein (AFP). None of the patients received treatment before starting this study. In addition, forty age-matched healthy volunteers were recruited after and considered as normal control group. Written informed consent was obtained from each patient and healthy volunteer, this study was approved by the Ethics Committee Board of Faculty of Medicine, Cairo University.

Blood Samples Collection

Five ml blood samples were withdrawn from all study participants .every blood sample was collected into 2 different tubes, the first was collected into vacationer EDTA tubes to measure the biochemical parameters; AST, ALT, total bilirubin (T. Bil), direct bilirubin (D. Bil), albumin, alkaline phosphatise (ALKP), creatinine, and random blood sugar (RBS) measurement were done using Roche Hitachi 911 Chemistry Analyzer (Bunker Lake Blvd. Greater Minneapolis / St. Paul Area. USA). Hemoglobin A1C (HbA1c), PC, international normalized ratio (INR) were detected using (Stanbio Labrotary, Boerne, TX USA) kits. Hemoglobin (Hb), total leucocytes count (TLC), absolute neutrophil counts (ANC), platelets were detected by cell counter (Sysmex XT-4000i Automated Hematology Analyzer Lincolnshire, IL, USA). ELISA kits supplied by (DRG International Inc., Springfield, New Jersey, USA) were used in detection of plasma levels of   Thyroid-stimulating hormone (TSH) and alpha-fetoprotein (AFP).

SNP Genotyping

DNA was extracted from peripheral blood using Zymo research Quick-gDNA™ MiniPrep kit (Catalog No.D3024) according to the instructions guides of the manufacturer’s. All DNA samples extracted were quantitated using the Nano Drop®-1000 spectrophotometer (Nanodrop Technologies, Inc., Wilmington, USA).

Three single nucleotide polymorphisms (SNPs) were screened. Genotyping was identified using real time PCR (StepOne, Applied biosystem) Sequence Detection System (ABI Inc. CA, USA) according to the Applied Biosystem protocol. MATE1 (g.−66T>C, rs2252281), OCT1 coding variants (R61C, rs12208357) and MATE2 (g.−130G>A, rs12943590) SNPs (Cat No. 4351379) were subjected to analysis in the extracted DNA using specific primers , FAM and VIC probes (Taqman SNP genotyping assays, Applied Biosystems, Foster City, CA). PCR mixture incorporated 20 ng of whole blood genomic extracted DNA and the following reagents: 1.25 µl FAM and VIC probes and primers (Taqman SNP genotyping assays), 12.5 µl Taqman universal master mix II No UNG (Cat No. 4440040) and complete volume to 25 µl using DNase free water. Negative control was included in each reaction to exclude DNA contamination. The thermal cycling profile was 10 minutes at 95 ºC for enzyme activation then by 40 cycles of 15 seconds DNA denaturation at 95ºC, 20 seconds primers and probes annealing at 55 ºC and 30 second at 72 ºC for the amplification step. The genotyping data were analyzed using SDS 2.1 software (ABI Inc. CA, USA).


Distribution of gene alleles showed significant differences between control and patients as regard MATE2 (rs12943590) (p<0.05) in which GG is the most prevalent allele among patients (54%) while the homozygous allele (AA) is the least prevalent one (12%) and OCT1 (rs12208357) (P<0.005) in which CC allele is the most prevalent among studied patients (62%), whereas the GG allele is the least prevalent one (12%) Table (1)

Table 1: Subject genetic distribution of the studied population

Group   MATE1rs2252281 MATE2rs12943590 ** OCT1rs12208357**
Control Count (40) 6 16 18 20 12 8 6 10 24
% 15% 40% 45% 50% 30% 20% 15% 25% 60%
Patients Count (100) 26 38 36 12 34 54 62 26 12
% 26% 38% 36% 12% 34% 54.0% 62.0% 26% 12%


** Significant differences between control and patients

Studied patients group showed high significant differences compared to control group as regard AST (44.6±13.6, 32.5±5.2), ALT (52.6±10.1, 30.9±5.4), HBA1C (8.988±1.6907, 5±.6381), RBS (279.66±62.528, 100.7±16.302), TLC (6.634±1.9917, 5.685±.9694), Platelet (272.98±94.1970, 239.050±67.6942), AFP (2.9776±2.04391, 1.7365±.92572) (P< 0.0001) Table (2).

Table 2: comparison between Patients and control group

  Group Mean
age Control 43.25±9.71
Patients 40.12± 11.459
AST ** Control 32.45±5.208
Patients 44.58±13.559
ALT ** Control 30.85±5.447
Patients 52.58±10.071
Bilirubin Total * Control .849±.22942
Patients .742±.16834
Direct Bilirubin Control .226±.11082
Patients .2208±.10017
Albumin * Control 4.295±.4082
Patients 4.058±.4795
Alk Phosphatase Control 71.4±22.74
Patients 67.18±26.44
Creatinine Control .949±.1838
Patients .912±.291
HBA1C *** Control 5±.6381
Patients 8.988±1.6907
RBS *** Control 100.7±16.302
Patients 279.66±62.528
PC Control 89.85±7.646
Patients 88.48±7.432
INR Control 1.121±.1373
Patients 1.328±1.8218
Hb Control 13.845±1.6568
Patients 13.444±1.9758
TLC * Control 5.685±.9694
Patients 6.634±1.9917
Platelet * Control 239.05±67.6942
Patients 272.98±94.197
TSH Control 1.66±.75671
Patients 1.7234±99360
AFP *** Control 1.7365±.92572
Patients 2.9776±2.04391


*significant difference (p<0.05)

** Highly significant difference (p<0.005)

Among MATE1 SNP, patients with CC allele showed better HBA1C (8.577±.2924) compared to patients with CT allele  (9.584±.3023) (P= .04), also patients with TT allele showed better HBA1C (8.7±.25) compared to CT (9.6±.3) (P=.019).

While in OCT1 SNP, patients with CG allele showed better RBS (251±9.565) compared to patients with CC allele (294.42±8.476) (p= 0.004), Table (3).

Table 3: Effect of MATE 1, AMTE 2 and OCT-1 gene polymorphisms on metformin actions

26 38 36 12 34 54 62 26 12
RBS 281.8±10.3 281.68±11.594 276±10.094 264.17±18.360 268.12±8.696 290.37±9.2 294.42±8.476£ 251±9.565 265.5±11.89
HBA1C 8.577±.2924 9.58±0.3  * ¥ 8.656±.2473 9.033±.5322 8.741±.22 9.133±.26 9.235±.8 8.438±.2469 8.9±.4777


* Significant compared to CC (P= .040)

¥ Significant compared to TT (P=.019)

£ Significant compared to CT (P=0.004).

Regarding to MATE1 SNP, patients with CC alleles had better HBA1C (8.577±.2924) compared to patients with CT alleles (9.584±.3023) (P= .04), also patients with TT allele showed better HBA1C (8.7±.25) compared to CT (9.6±.3) (P=.019).

While in OCT-1 SNP, patients with CT allele showed better RBS (251±9.565) compared to patients with CC allele (294.42±8.476) (p= 0.004), Table (3).

Logistic regression test was done showed the independent factors that may affect blood glucose level as indicated by the HBA1C are RBS (.00001), ALT (.0001) and TLC (.025) Table (4).

Table 4: Logistic Stepwise Regression Test

  R R2 Adjusted R2 Coefficient SE Standardized coefficient p
RBS .734a .539 .536 11.142581 1.5858 0.734 .00001
ALT .763b .583 .577 9.096 1.5146 0.248 .0001
TLC .773c .598 .589 -2.259263 1.4924 -0.1307 .025


Dependent variable: HBA1C, R: correlation coefficient; SE: standard error; P: significance probability. RBS (random blood sugar)

Comparison between MATE1 variance and references who are MATE2 references showed insignificant differences except TSH (p<0.0002). Comparison between MATE2 variance and references who are MATE1 references showed significant differences regarding total bilirubin (p<0.033), Direct Bilirubin (p<0.029) and Albumin (p<0.011), Table (5).

Table 5: Comparison between MATE 1, MATE 2 references and variants effect on metformin efficacy

Variant (TC & CC) (n=64) Reference (TT) (n=36) Variant  (AA) (n=12) Reference (GG&GA) (n=87)
HBA1C 9.175 ±.2225 8.656±.2473 9.03±.5322 8.979±.1811
RBS 281.72±7.998 276±10.094 264.17±18.36 280.92±6.679



Individualized glycemic control is essential in patients’ with DM2 to make an equilibrium between age, comorbidities, and the risk of development of hypoglycemia. Proper control of blood glucose by keeping the  HbA1c below 6.5 % significantly decrease nephropathy and cardiovascular complications development.9

Although the metformin neither bound to plasma protein nor metabolized,7 about sixty third percent of metformin treated patients experience gastrointestinal symptoms leading to discontinuation of the metformin by about from 5-10 % of these patients.10 However, it is actively transported and distributed,11 with respected  inter-individual variability in metformin’s action.12

These change in metformin efficacy is primarily due to the variation in the activity of the transporters either at the action site (hepatocyte) or at the excretory site (renal tubules).

In the present study; the distribution of gene alleles showed significant differences between control and patients as regard MATE2 (rs12943590) (p<0.05) and OCT1 (rs12208357) (P<0.005).

The studied patients group in the present work showed the MATE1 allele distribution heterozygous CT allele is the most prevalent one (38%), whereas homozygous variant CC is the least prevalent one (26%), while MATE2 alleles distribution showed that GG is the most prevalent alleles among patients (54%) while the homozygous variant (AA) is the least prevalent variant (12%); whereas OCT1 allele distribution showed that CC allele is the most prevalent among studied patients (62%), while the GG is the least prevalent one (12%).

OCT1, located on the enterocytes basolateral membrane, may be responsible for transport of metformin into the intersitial fluid 13 and on the hepatocytes sinusoidal membrane, mediates the first step of metformin entry into hepatocytes.14

The action of metformin may depend on the expression of OCTs which act as influx transporters to transport metformin intracellularly.15 T allele of SLC22A1 rs12208357 (Arg61Cys) polymorphism is strongly correlated with decreased OCT1 protein expression in Caucasian liver tissue samples.16

OCT transporters are Na + -independent carriers that mediate the facilitated uniport transport of different organic cations through the plasma membrane.17

OCT1 showed high expressions variability among different individuals’ human liver samples.16

Wang  et al., approve the role of  OCT1 in active uptake of the metformin into hepatocyte as the lactate production is completely abolished in a transgenic mouse model, knockout of liver SLC22A1.18 The polymorphisms of SLC22A1 gene that results in change in the OCT1 function are associated with changes in metformin pharmacokinetics and dynamic.19

A non-synonymous coding variants of OCT1 (rs12208357) is associated with reduction in OCT1 expression.16

In the present study, OCT-1 SNP, patients with CT allele showed better RBS compared to patients with CC allele (p= 0.004), where there is insignificant differences regarding the effect of different alleles on HbA1c, RBS couldn’t be considered as a respected parameter for determine the effect, hence OCT1 SNPs in our study didn’t result in any differences in metformin action.

In agreement with our results was  the GoDARTS study which  didn’t showed  an associations between SLC22A1 rs12208357 variants and glycemic response.14

On the other hand, our result is against shu et al.,  who showed the total  abolishment  of  metformin action in the knockout mice,15 or those which showed association with rs12208357 variants and impaired or reduced  glucose response14 20 21-23 in healthy volunteer?? 15,24  and diabetic patients.25,21

On the same way, Nies et al., showed decreasing OCT1 protein expression in Caucasian liver sample in T allele of SLC22A1 subject.16

Many factor can affect OCT1 action; it showed  substrate specificity, where 41C > T variant was associated with increased uptake of MPP + 26 but diminished  metformin uptake.15 Races also affect OCT1 genetic variants distribution and function; where the genetic variants R61C, G401S, 420del, and G465R, which are related to decreased metformin uptake, have not been identified in Korean, Asian American or Japanese populations and four variants of SLC22A1 (480C > G, 848C > T 1022C > T and 1222A > G) which are found in the Korean population were not associated with functional changes of metformin uptake, suggesting that OCT1 polymorphisms may not be the main contributors to the inter-individual pharmacokinetic variation of metformin in Korean and other East Asian populations,27 although this can’t be considered as a rule all over the world.

The SLC47 family has two members named and toxin extrusion 1 (MATE1) and 2 (MATE2).28 MATE2 has two variants, MATE+2-K and MATE2-B, which have a different splicing pattern between exons 6 and 7. This codes for a 566 amino acid protein (MATE2-K) and a truncated protein with 220 amino acids (MATE2-B).

While MATE1 is expressed in many tissues specially  the liver and kidney,29  MATE2 and MATE2-K are predominantly expressed in the human kidney.29  MATE2-B has been detected in many organs but not in the kidney 29; however, the physiological roles of MATE2 and MATE2-B remain unclear.28

Metformin is excreted by kidney through action of  OCT2,30 and MATE1 and MATE2 K.31 32 29  MATE1 acting as an efflux pump in many tissue tissues such as the liver which play an important role in metformin pharmacodynamics.31,32

The reduction of promoter expression of MATE1 result in reduction of transport level leading to decrease metformin efflux and increase tissue level which are predicted to result in greater metformin pharmacological action. The MATE1 variant appear to affect mainly metformin pharmacodynamics rather than kinetic as it may be the single metformin transporter on the bile.33 

In the present work, Among MATE1 SNP, patients with CC or TT allele responds better to metformin compared to those with CT allele patients as evident by better HBA1C level  (P= .04) and (P=.019) respectively.

Our result is against Stocker  et al., who showed the T/C variant in the promoter of SLC47A1 gene is associated with a greater metformin action in T2DM  patients using  HbA1c as an indicator of efficacy.33

Comparison between MATE1 variance and references who are MATE2 references nullify such effect.

Our present results are against Stocker et al., 2013 which showed that both MATE genotypes were associated with altered post-metformin glucose tolerance, with variant carriers of MATE2 reduced response in healthy volunteer.33

MATEs  are inhibited by many drugs e.g., pyrimethamine, baclofen, ketoconazole, propranolol, naloxone34  and cimetidine35 that may result in significant drug-drug interaction.

All patients’ included in the current study didn’t receive the above mentioned drugs prior or during this study.

In the current study, MATE2 SNPs or reference to variant alleles has insignificant differences in the effects on metformin action. Comparison between MATE2 variance and references who are MATE1 references showed insignificant effect differences.

Our results are against results of Stocker et al., 2013 which showed alteration of glucose level in both MATE genotypes of metformin treated healthy volunteer.33

The expression of MATE in the apical part and the expression of OCT in the basolateral may result in the transcellular movement of cations substrates that play an important role in transportation of these cations from the blood to the urine or bile.36

In the present study, comparing MATE1 and MATE2 reference to variant alleles showed insignificant differences in their effects on metformin action

Our result is against Stocker  et al., who showed that GG and GA carriers experience  more improvement in HbA1c level  following  metformin administration  compared to  AA carriers,33 and Choi  et al., in metformin treated patients, demonstrated that GG carriers better HbA1c level compared AA carriers after metformin treatment.37

The beneficial effect of MATE2 could be explained by increase in renal gluconeogenesis and glucose uptake increases in type II diabetes.38 Thus, enhanced-expression of MATE2 associated with homozygous variant would have lower renal metformin level resulting in reduction of its pharmacologic effect.

The conflicting results between the present study and other studies may be related to small number included in the present study, different parameters used to evaluate the metformin effect, and racial factor; however multicenter study is needed to clarify such conflict.

In the present study, some limitation should be acknowledged such evaluation of metformin level which didn’t assessed and highly encouraged to be explored in further studies.

From the current work we can conclude that MATE1 and OCT1 SNPs may have a potential role in metformin efficacy; however multi-centre study is needed for more clarification.


We acknowledge all patients and healthy volunteer for their agreement to participate in this study.

Conflict of interest

There is no conflict of interest.

Funding Source

There is no Funding source


  1. Adler A.I, Shaw E.J, Stokes T, Ruiz F. Newer agents for blood glucose control in type 2 diabetes: summary of NICE guidance. BMJ: British Medical Journal (Online). 2009.
  2. Gunton J.E, Delhanty P.J, Takahashi S.I, Baxter R.C.  Metformin rapidly increases insulin receptor activation in human liver and signals preferentially through insulin-receptor substrate-2. The Journal of Clinical Endocrinology & Metabolism. 2003;3:1323-32.
  3. Pentikäinen P, Neuvonen P, Penttilä A. Pharmacokinetics of metformin after intravenous and oral administration to man. European journal of clinical pharmacology. 1979;3:195-202.
  4. Tucker G, Casey C, Phillips P, Connor H, Ward J, Woods H. Metformin kinetics in healthy subjects and in patients with diabetes mellitus. British journal of clinical pharmacology. 1981;2:235-46.
  5. Sirtori C.R, Franceschini G, Galli-Kienle M, et al. Disposition of metformin (N, N‐dimethylbiguanide) in man. Clinical Pharmacology & Therapeutics. 1978;6:683-93.
  6. Cook M, Girman C, Stein P, Alexander C. 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. Diabetic medicine 2007;4:350-58.
  7. Gong L, Goswami S, Giacomini K.M, Altman R.B, Klein T.E.  Metformin pathways: pharmacokinetics and pharmacodynamics. Pharmacogenetics and genomics. 2012;11:820.
  8. Kimura N, Masuda S, Tanihara Y, et al. Metformin is a superior substrate for renal organic cation transporter OCT2 rather than hepatic OCT1. Drug metabolism and pharmacokinetics. 2005;5:379-86.
  9. Garber A.J, Abrahamson M.J, Barzilay J.I, et al. Consensus statement by the American Association of Clinical Endocrinologists and American College of Endocrinology on the comprehensive type 2 diabetes management algorithm–2016 executive summary. Endocrine Practice 2016;1:84-113.
  10. DeFronzo R.A. Pharmacologic therapy for type 2 diabetes mellitus. Annals of internal medicine. 1999;4:281-303.
  11. Wang L, Weinshilboum R. Metformin pharmacogenomics. biomarkers to mechanisms. Diabetes. 2014;8:2609-10.
  12. Goswami S, Yee S, Stocker S, et al. Genetic variants in transcription factors are associated with the pharmacokinetics and pharmacodynamics of metformin. Clinical Pharmacology & Therapeutics. 2014;3:370-79.
  13. Müller J, Lips K.S, Metzner L, Neubert R.H, Koepsell H, Brandsch M. Drug specificity and intestinal membrane localization of human organic cation transporters (OCT). Biochemical pharmacology. 2005;12:1851-60.
  14. Zhou K, Donnelly L.A, Kimber C.H, et al. Reduced-function SLC22A1 polymorphisms encoding organic cation transporter 1 and glycemic response to metformin: a GoDARTS study. Diabetes. 2009;6:1434-39.
  15. Shu Y, Sheardown SA, Brown C, et al. Effect of genetic variation in the organic cation transporter 1 (OCT1) on metformin action. Journal of Clinical Investigation 2007;5:1422.
  16. Nies AT, Koepsell H, Winter S, et al. Expression of organic cation transporters OCT1 (SLC22A1) and OCT3 (SLC22A3) is affected by genetic factors and cholestasis in human liver. Hepatology 2009;4:1227-40.
  17. Koepsell H, Schmitt B, Gorboulev V. Organic cation transporters. In: Reviews of physiology. biochemistry and pharmacology. 2003:36-90. Springer,
  18. Wang D.S, Jonker J.W, Kato Y, Kusuhara H, Schinkel A.H, Sugiyama Y. Involvement of organic cation transporter 1 in hepatic and intestinal distribution of metformin. Journal of Pharmacology and Experimental Therapeutics. 2002;2:510-15.
  19. Chen L, Takizawa M, Chen E, et al. Genetic polymorphisms in organic cation transporter 1 (OCT1) in Chinese and Japanese populations exhibit altered function. Journal of Pharmacology and Experimental Therapeutics. 2010;1:42-50.
  20. Jablonski K.A, McAteer J.B, de Bakker P.I, 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;10:2672-81.
  21. Becker M.L, Visser L.E, Van Schaik R.H, Hofman A, Uitterlinden A.G, Stricker B.H.C.  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;3:745-49.
  22. Todd J.N, Florez J.C.  An update on the pharmacogenomics of metformin: progress. problems and potential. Pharmacogenomics 2014;4:529-39.
  23. Dujic T, Zhou K, Donnelly L.A, Tavendale R, Palmer C.N, Pearson E.R. Association of organic cation transporter 1 with intolerance to metformin in type 2 diabetes: a GoDARTS study. Diabetes. 2015;5:1786-93.
  24. Shu Y, Brown C, Castro R, et al. Effect of genetic variation in the organic cation transporter 1, OCT1, on metformin pharmacokinetics. Clinical Pharmacology & Therapeutics. 2008;2:273-80.
  25. Christensen M.M, Brasch-Andersen C, Green H, et al.  The pharmacogenetics of metformin and its impact on plasma metformin steady-state levels and glycosylated hemoglobin A1c. Pharmacogenetics and genomics. 2011;12:837-50.
  26. Shu Y, Leabman M.K, Feng B, et al. Evolutionary conservation predicts function of variants of the human organic cation transporter, OCT1. Proceedings of the National Academy of Sciences. 2003;10:5902-07.
  27. Song I, Shin H, Shin J. Genetic variants of organic cation transporter 2 (OCT2) significantly reduce metformin uptake in oocytes. Xenobiotica the fate of foreign compounds in biological systems. 2008;9:1252-62.
  28. Yonezawa A, Inui K.i.  Importance of the multidrug and toxin extrusion MATE/SLC47A family to pharmacokinetics, pharmacodynamics/toxicodynamics and pharmacogenomics. British journal of pharmacology. 2011;7:1817-25.
  29. Masuda S, Terada T, Yonezawa A, et al. Identification and functional characterization of a new human kidney–specific H+/organic cation antiporter, kidney-specific multidrug and toxin extrusion 2. Journal of the American Society of Nephrology. 2006;8:2127-35.
  30. Takane H, Shikata E, Otsubo K, Higuchi S, Ieiri I. Polymorphism in human organic cation transporters and metformin action. 2008.
  31. Otsuka M, Matsumoto T, Morimoto R, Arioka S, Omote H, Moriyama Y. A human transporter protein that mediates the final excretion step for toxic organic cations. Proceedings of the National Academy of Sciences of the United States of America. 2005;50:17923-28.
  32. Tanihara Y, Masuda S, Sato T, Katsura T, Ogawa O, Inui K.i. Substrate specificity of MATE1 and MATE2-K, human multidrug and toxin extrusions/H+-organic cation antiporters. Biochemical pharmacology 2007;2:359-71.
  33. Stocker S.L, Morrissey K.M, Yee S.W, et al. The effect of novel promoter variants in MATE1 and MATE2 on the pharmacokinetics and pharmacodynamics of metformin. Clinical Pharmacology & Therapeutics. 2013;2:186-94.
  34. Astorga B, Ekins S, Morales M, Wright S.H. Molecular determinants of ligand selectivity for the human multidrug and toxin extruder proteins MATE1 and MATE2-K. Journal of Pharmacology and Experimental Therapeutics. 2012;3:743-55.
  35. Klatt S, Fromm M.F, König J. Transporter-mediated drug-drug interactions with oral antidiabetic drugs. Pharmaceutics. 2011;4:680-705.
  36. Sato T, Masuda S, Yonezawa A, Tanihara Y, Katsura T, Inui K.i. Transcellular transport of organic cations in double-transfected MDCK cells expressing human organic cation transporters hOCT1/hMATE1 and hOCT2/hMATE1. Biochemical pharmacology. 2008;7:894-903.
  37. Choi J, Yee S, Ramirez A, et al. A Common 5′‐UTR Variant in MATE2‐K Is Associated With Poor Response to Metformin. Clinical Pharmacology & Therapeutics. 2011;5:674-84.
  38. Meyer C, Woerle H.J, Dostou J.M, Welle S.L, Gerich J.E.  Abnormal renal, hepatic, and muscle glucose metabolism following glucose ingestion in type 2 diabetes. American Journal of Physiology-Endocrinology and Metabolism. 2004;6:E1049-E56.
Share Button
(Visited 312 times, 1 visits today)

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.