The pharmacogenetics of metformin and its impact on plasma metformin steady-state levels and...

The pharmacogenetics of metformin and its impacton plasma metformin steady-state levels and glycosylatedhemoglobin A1cMette M.H. Christensena, Charlotte Brasch-Andersenb, Henrik Greene,f,Flemming Nielsena, Per Damkierc, Henning Beck-Nielsend and Kim Brosena

Objective The aim of this study was to evaluate the effect

of genetic variations in OCT1, OCT2, MATE1, MATE 2, and

PMAT on the trough steady-state plasma concentration of

metformin and hemoglobin A1c (Hb1Ac).

Method The South Danish Diabetes Study was a 2�2�2

factorial, prospective, randomized, double-blind, placebo-

controlled, multicentre study. One hundred and fifty-nine

patients received 1 g of metformin, twice daily continuously,

and 415 repeated plasma metformin measurements were

obtained after 3, 6, and 9 months of treatment.

Results The mean trough steady-state metformin plasma

concentration was estimated to be 576 ng/ml (range,

54–4133 ng/ml, q = 0.55) and correlated to the number of

reduced function alleles in OCT1 (none, one or two: 642,

542, 397 ng/ml; P = 0.001). The absolute decrease in Hb1Ac

both initially and long term was also correlated to the

number of reduced function alleles in OCT1 resulting in

diminished pharmacodynamic effect of metformin after

6 and 24 months.

Conclusion In a large cohort of type 2 diabetics, we either

confirm or show for the first time: (a) an enormous

(80-fold) variability in trough steady-state metformin

plasma concentration, (b) OCT1 activity affects metformin

steady-state pharmacokinetics, and (c) OCT1 genotype

has a bearing on HbA1c during metformin

treatment. Pharmacogenetics and Genomics 21:837–850�c 2011 Wolters Kluwer Health | Lippincott Williams &

Wilkins.

Pharmacogenetics and Genomics 2011, 21:837–850

Keywords: MATE1, MATE2, metformin, OCT1, OCT2, personalized medicine,pharmacogenetics, PMAT, steady state, type 2 diabetes

aInstitute of Public Health, Clinical Pharmacology, University of SouthernDenmark, Departments of bClinical Genetics, cBiochemistry and Pharmacology,dEndocrinology, Odense University Hospital, Odense, Denmark, eDivision of DrugResearch, Department of Medicine and Health Sciences, Clinical Pharmacology,Faculty of Health Sciences, Linkoping University, Linkoping and fDivision of GeneTechnology, Science for Life Laboratory, School of Biotechnology, Royal Instituteof Technology, Solna, Sweden

Correspondence to Mette M.H. Christensen, MD, Institute of Public Health,Clinical Pharmacology, University of Southern Denmark, J.B. Winsloews Vej 19, 2,Odense C DK-5000, DenmarkTel: +45 6550 3678; fax: +45 65 91 60 89;e-mail: [email protected]

Received 18 March 2011 Accepted 16 August 2011

IntroductionPandemically speaking, type 2 diabetes is on a worldwide

increase, and there is no indication that this tendency will

change in the decades to come. Metformin has been

known for nearly a century and has experienced a

renaissance in the treatment of type 2 diabetes during

the recent decade because of the documentation that the

drug reduces morbidity and mortality in obese type 2

diabetics [1]. In addition to its blood glucose-lowering

effect, metformin induces a small weight reduction and

has only a minimum risk of inducing hypoglycemia. The

drug frequently induces gastrointestinal side effects but

very rarely causes lactic acidosis [1–3].

The exact molecular mechanism of antidiabetic effects of

metformin has not yet been fully elucidated but it seems

to involve the serine-threonine kinase 11 pathway and

activation of the adenosine monophosphate-activated

protein kinase [4,5]. Metformin increases the peripheral

insulin sensitivity, increases the peripheral uptake of

glucose, and decreases the gluconeogenesis in the

liver [6].

Metformin is a strong base and at physiological pH, it

exists virtually only (> 99.9%) in its cationic form. Thus,

its passage across cell membranes is heavily dependent on

transporters. The plasma protein binding of metformin is

negligible [7,8]. The intestinal absorption of metformin is

dose dependent and involves an active, saturable uptake

process [9]. The plasma membrane monoamine trans-

porter (PMAT) is a recently discovered proton-activated

transporter belonging to the solute carrier (SLC) family

29 [10–12]. PMAT is located in the apical membrane of

the epithelial lining of the small intestine and renal

tubules, and it probably mediates the intestinal uptake of

metformin [12].

OCT1 (SLC22A1) and OCT2 (SLC22A2) are polyfunc-

tional OCTs located predominantly at the sinusoidal cells

of the liver and basolaterally in the kidney tubular cells,

respectively. OCT1 is also expressed basolaterally in the

enterocytes and in the renal tubular cells [13–15]. They

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Original article 837

1744-6872 �c 2011 Wolters Kluwer Health | Lippincott Williams & Wilkins DOI: 10.1097/FPC.0b013e32834c0010

Copyright © Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.

http://www.pharmacogeneticsandgenomics.com

are both uniporters that mediate facilitated diffusion

of metformin in either direction in the target cells

and hence are the major determinants of the hepatic

uptake of metformin (OCT1) and the renal uptake

(OCT2) from the blood. The multidrug and toxin

extrusion transporters 1 (SLC47A1) and 2 (SLC47A2),

alias MATE1 and MATE2, are located in the apical side of

the renal tubular cells; they are H+/drug antipor-

ters [14,16,17] that facilitate extrusion of metformin

through the urine.

Genetic variation in the genes coding OCT1, OCT2,

MATE1, and MATE2 have been linked to an altered

pharmacodynamic and pharmacokinetic response to

metformin. The genetic component to the variation in

renal metformin clearance has been estimated to be

approximately 90% [18]. In OCT1, minor alleles in the

single nucleotide polymorphisms (SNPs) rs12208357,

rs34104736, rs72552763, rs34130495, and rs34059508

have been associated with a reduced cellular metformin

uptake and adenosine monophosphate-activated protein

kinase activation, and minor effect of metformin in the

oral glucose-tolerance test [5]. Status as heterozygote in

one of the four minor alleles has been linked to a

statistically significant increase in metformin in the area

under the curve, Cmax, oral clearance, and a smaller

volume of distribution (Vd) [19], contrary additive

enhanced renal clearance has also been demonstrated

with an increasing number of minor alleles [20].

For rs622342 in OCT1 and rs2289669 in MATE1, the

number of minor alleles was associated with an additive

pharmacodynamic effect of metformin. Thus, an interac-

tion between the two polymorphisms was seen for

homozygous rs622342 patients who had larger blood

glucose-lowering effects with increasing numbers of

rs2289669 [21–23]. In OCT2, several studies have

analyzed the impact of the minor allele in rs316019 at

the renal metformin clearance but the results have been

contradictory. Some studies found an increased renal

clearance [24], others no effect [20], whereas decreased

renal clearance has been reported as well [25,26]. In the

50UTR region of MATE1, rs2252281 has been found to be

involved in the regulation of transcription and could

affect metformin renal secretion [27]. In MATE2,nonsynonymous SNPs have been linked to reduced

transport of metformin [28]. However, only the non-

synonymous SNP rs34399035 has been found in MATE2in European Caucasians; thus, we believe that this study

is the first to evaluate the pharmacokinetic and dynamic

impacts of this particular SNP.

In this study, we determined the frequency distributions

of the minor alleles in the OCT1, OCT2, PMAT, MATE1,and MATE2 genes including diplotype composition (i.e.

specific combination of two haplotypes) for the reduced

function alleles in OCT1 [rs12208357 (R61C), rs34104736

(S189L), rs34130495 (G401S), rs72552763 (M420del),

and rs34059508(G465R)] in a cohort of Danish type 2

diabetic patients. Furthermore, we examined repeated

trough steady-state metformin plasma concentrations in

159 patients, and to our knowledge, this has not

previously been done in such a large group of individuals.

Finally, we explored the association between selected

SNPs in the five solute carrier genes listed above and in

the OCT1 diplotype composition and the trough steady-

state plasma concentrations of metformin and on the

decrease in glycosylated hemoglobin A1c (HbA1c) after 6

and 24 months.

MethodsStudy design

The South Danish Diabetes Study (SDDS) has recently

been described in detail in a separate article [29]. In

brief, SDDS was designed as a 2� 2� 2 factorial, pros-

pective, randomized, partly blinded, placebo-controlled,

multicentre study comprising 371 Danish individuals

with type 2 diabetes in eight parallel groups (Fig. 1). The

study enrolled outpatients from eight diabetic clinics in

the Region of Southern Denmark from January 2003 to

July 2007. The patients were treated per protocol for 24

months including 15 visits. For this study, the steady-

state measurements of trough plasma metformin levels

were obtained at visits 8, 9, and 10 (at 3, 6, and 9 months)

and for HbA1c at visits 1, 9, and 15 (at 0, 6, and 24

months).

Study participants

The inclusion criteria were: age of 30–70 years, fasting

C-peptide of more than 300 pmol/l, BMI of more than

25 kg/m2, diabetes for more than 2 years, and 8.0% <

HbA1c < 12.0%, whereas the exclusion criteria included:

intolerance to metformin/glitazones, s-creatinine of less

than 120mmol/l, serum alanine aminotransferase/ serum

aspartate aminotransferase, 2,5� upper normal limit,

total cholesterol of more than 10 mmol/l, total triglyceride

of more than 8 mmol/l, hemoglobin of less than normal

range, treatment with glitazone preceding 30 days, New

York Heart Association III or IV, night work, pregnancy,

poor vision, unawareness of hypoglycemia, mental sick-

ness, alcohol abuse, clinically relevant major organ or

systemic illness, uncontrolled hypertension of more than

180/110 mmHg, systolic or diastolic, steroid treatment,

severe lung disease, and history of malign disease.

Study medication and dosage

The patients were randomized to metformin (Gluco-

phage tablets of 500 mg) or placebo, rosiglitazone

(Avandia tablets of 4 mg) or placebo and insulin neutral

protamine hagedron insulin (NPH; Insulatard FlexPen)

or insulin aspart (NovoRapid FlexPen). Metformin or

placebo was administered at meals initially as one tablet

twice a day (1000 mg), after 4 weeks as two tablets twice

a day (2000 mg). Rosiglitazone or placebo was adminis-

tered as one tablet once a day (4 mg), after 8 weeks as one

838 Pharmacogenetics and Genomics 2011, Vol 21 No 12


Fig. 1

Randomizedn = 371

450 eligiblepatients

Insulin NPH

Placebo

Placebogroup 1 n = 46

Not blinded Blinded Blinded

Included: n = 41

Excluded: n = 4

Excluded: n = 5

Excluded: n = 8

Excluded: n = 8

Included: n = 41

Included: n = 37

Included: n = 40

Patients being insteady state with

metformin∗

Metformingroup 2 n = 45



Metformingroupe 8 n = 48




Rosiglitazone

Rosiglitazone

Placebo

Insulin aspart79 did not fulfilinclusion criteria

The South Danish Diabetes Study enrolment and the outcome number of patients treated with 1 g of metformin, twice daily during visits 8, 9, and 10.Exclusion criteria included: patients with three missing values, three zero values, or no values to evaluate due to a combination of zero values, lackingmeasurements, too low dosage, or not enough plasma for analysis. n: number of patients.

Table 1 Patient characteristics

Characteristics for the randomized patients Mean (95% confidence interval) Range

Number randomized: 371 (male = 229, female = 142)Age at inclusion (years) 57a (52–62)a 30–70BMI (kg/m2)b 34.7 (34.1–35.3) 24.7–68.8Hemoglobin (mmol/l)c 8.7 (8.6–8.7) 6.1–11.1Alanine aminotransferase (U/l)b 24.9 (23.8–26.0) 7–175Creatinine clearance (ml/min)b, d 114.3 (111.0–117.8) 51.5–287.2

Absolute fall in HbA1c from visit 1 to 15 (%)e

Metformin, placebo, insulin NPH – 1.1a ( – 2.1 to – 0.6)a – 4.0 to 0.8Metformin, rosiglitazone, insulin NPH – 1.5a ( – 2.9 to – 0.9)a – 6.3 to 0.1Metformin, placebo, insulin aspart – 1.1a ( – 1.8 to – 0.7)a – 5.4 to 1.0Metformin, rosiglitazone, insulin aspart – 1.4a ( – 2.3 to – 0.6)a – 4.8 to 0.4

Absolute fall in HbA1c from visit 1 to 9(%)f

Metformin, placebo, insulin NPH – 1.5a ( – 2.2 to – 0.7)a – 4.2 to 0.5Metformin, rosiglitazone, insulin NPH – 1.8a ( – 2.9 to –1.0)a – 5.1 to 0.3Metformin, placebo, insulin aspart – 1.6a ( – 2.3 to – 0.9)a – 5.6 to 0.3Metformin, rosiglitazone, insulin aspart – 1.3a ( – 2.2 to – 0.7)a – 4.5 to 0.8

Trough steady-state values for metformin, n = 159 evaluable patientsCss,

b (ng/ml) 576 (520–637) 54–4133Creatinine clearance (ml/min)b 115.5 (110.6–120.7) 51.4–275.4Time since last tablet (h) 13.9 (13.7–14.0) 10–20

HbA1c, hemoglobin A1c; NPH, neutral protamine hagedron.aMedian (25–75th percentile).bGeometric mean values are based on all observations from visits 8, 9, and 10.cMean values are based on all observations from visits 8, 9, and 10.dCrokcroft and Gault [31]: creatinine clerance = (140-age)� weight(kg)� constant (1.23 for men and 1.04 for women)/serum creatinine (mmol/l).eOf the patients enrolled to metformin treatment, 136 patients had HbA1c measured at visits 1 and 15.fOf the patients enrolled to metformin treatment, 151 patients had HbA1c measured at visits 1 and 9.

Metformin pharmacogenetics, new insights Christensen et al. 839


tablet twice a day (8 mg). The group randomized to NPH

insulin took the injection subcutaneously in the thigh at

bedtime. The initial dose of insulin NPH was 12IE. The

insulin doses were adjusted in accordance with a treat to

target algorithm.

Every patient record was looked into to ensure that

patients actually were in steady-state with the correct

dosing (twice daily) when they had their blood samples

taken. More details are provided in the study by Gram

et al. [29].

Study procedures

The study protocol was approved by the Danish

Medicines Agency (J. no. 2612–2056), the Danish Data

Protection Agency (J. no. 2002–41–2176), and the

Scientific Ethical Committee (J. no. M-2417–02). The

study was conducted in accordance with the Helsinki

Declaration and Good Clinical Practice and monitored by

the Good Clinical Practice unit, Odense University

Hospital, Odense, Denmark. The trial was registered in

the US National Institute of Health register (www.clinicaltrials.gov) as trial NCT00121966.

Single nucleotide polymorphism selection

Selection of the relevant genetic variants was based on

the following criteria: (a) the genetic variation had to be

located in a gene coding for a transporter of metformin,

(b) genetic variation influencing the transport of

metformin or the ability to reach a relevant clinical

endpoint ex HbA1c during metformin therapy had to be

reported in the literature, (c) the genetic variation had to

be present in Caucasians, and (d) PMAT was examined as

hypothesis generating including known nonsynonymous

SNPs and tagging SNPs (tagSNPs).

SNPs were selected from OCT1, OCT2, MATE1, MATE2,

and PMATE corresponding to the five known transporters

of metformin. Twenty-seven SNPs and one deletion

fulfilled the selection criteria.

In the evaluation procedure of PMAT, tagSNPs in

SLC29A4 ± 1000 base pairs from the coding sequence

were obtained from phase I and II Hapmap SNPs in the

HapMap CEU database, and tagSNPs with the threshold

of r2 > 0.8 and minor allele frequency of more than 10%

were selected using Haploview version 4.2. The selected

SNPs are shown in Table 2.

Analytical method for determination of metformin in

plasma

The plasma concentrations of metformin were determined

using solid-phase extraction (SPE) and a high-performance

liquid chromatography system with UV-detection (La-

Chrome 7000 serie, Merck-Hitachi, Darmstadt, Germany).

C4 (100 mg, 3 ml) cartridges (International Sorbent

Technology, Mid Glamorgan, UK) were used for sample

preparation. In brief, the SPE cartridge was precondi-

tioned with 1 ml of methanol and 1 ml of Milli-Q-treated

water. The sample consisting of 1 ml of plasma sample

vortex mixed with 75 ml of a 50 mg/ml of buformin was

applied to the SPE cartridge and washed with 3� 1 ml of

Milli-Q-treated water. The cartridges were briefly dried

by use of full vacuum for 30 s. The samples were eluted

with 1 ml of 2% formic acid in methanol and evaporated

to dryness at 401C under a gentle stream of nitrogen. The

samples were reconstituted in 150 ml of eluent. The

samples were transferred to 300 ml of conical high-

performance liquid chromatography sample vials and

centrifuged for 2 min at 6.800 g to precipitate minor

protein residues from the liquid phase. An aliquot of

100 ml of the sample was then transferred to new sample

vials, and a volume of 50 ml was injected into the column.

The separation was performed on a Synergi Polar RP

column (250� 4.6 mm, 3.5 mm particles; Phenomenex,

Torrance, California, USA) using a mobile phase consist-

ing of 5 : 95 v/v % methanol : 0.02 mol/l NaH2PO4 buffer

(pH 3.4) at a flow rate of 1 ml/min. The retention time

was 3.87 min for metformin and 6.96 min for buformin

(internal standard).

The validation of the method was performed over 5

consecutive days. The intraday precision was investigated

at the concentrations 50, 100, 500, 1000, and 4000 ng/ml

(n = 10), and the coefficient of variation ranged from 1.2

to 7.0%. The standard curve showed an excellent linearity

with a correlation coefficient R2 = 0.992. The mean

recovery of the extraction procedure was 77.9% for

metformin and 87.3% for buformin. The interday

variability was investigated at three concentration

levels (300, 900, and 1800 ng/ml): the interday variation

(CV %) did not exceed 2%; the accuracy of the method

ranged from 101 to 108%. The lower limit of detection

was 20 ng/ml and the lower limit of quantification was

30 ng/ml.

Genotyping methods

The genomic DNA was extracted by The Maxwell 16

Blood DNA Purification Kit (Promega Corporation,

Woods Hollow, Madison, Wisconsin, USA) from an aliquot

of venous blood drawn at the initiation of the study and

stored at – 201C until analysis.

Most SNPs were genotyped using TaqMan realtime PCR

predesigned assays or File-builder primers and probes and

conducted on a StepOne Plus (Applied Biosystems,

Foster City, California, USA) in accordance with the

manufacturer’s protocol. The SNP rs11760365 was

genotyped using pyrosequencing technique [30];

rs72552763, rs34130495, and rs4299914 were genotyped

by sequencing. All assay numbers, sequences of the

primers and probes used for genotyping are summarized

in Supplementary Table 1, Supplemental digital content

1, http://links.lww.com/FPC/A324. All call rates were above

98%.



http://www.clinicaltrials.gov

http://www.clinicaltrials.gov

http://links.lww.com/FPC/A324

Statistical analysis and considerations

The repeated measurements collected during the study

were estimated using mixed-effect modeling. Data are

presented as median, 25–75th percentiles, and range;

however, longitudinal data are presented as geometric

means with 95% confidence intervals (CIs) and range.

Statistical inferences of the trough steady-state concen-

trations (Css) and genotypes were analyzed by the mixed-

effect model with restricted maximum likelihood. Post

estimations were performed using the Wald test for

repeated measurements. The null hypothesis in the Wald

test indicates that there is no difference between mean

trough metformin steady-state concentrations for the

different genotypes. The post estimation takes into

account the covariables time of blood sampling and

creatinine clearance. Statistical inferences of the decrease

in HbA1c from visit 1 to 9 and from 1 to 15 and genotypes

were evaluated using parametric multiple regression and

Wald tests adjusted for randomization. For OCT1, both

genotype and diplotype were tested. For the loss-of-

function alleles in OCT1, the Cuzick nonparametric trend

test was used in addition to the Wald test. The

significance level was set to 0.05 for the well-established

genes in OCT1, OCT2, MATE1, and MATE; albeit 0.0045

(= 0.05/11, number of SNPs11) for the explorative

analysis of PMAT. Before statistical analysis, visually

guided by qq-plots, metformin concentration was loga-

rithm transformed to create a Gaussian distribution. All

statistical analyses were conducted using the STATA 11.0

(StataCorp, Texas, USA).

For all metformin plasma samples in the analysis applied,

the patient had to be in complete steady state with 1 g of

metformin, twice daily and the blood sample collected

within the time interval (t) 10–20 h after the last tablet

of metformin. The crude Css was adjusted for creatinine

clearance (Clcr) and time of sampling. The Clcr is a

compound variable consisting of weight, sex, age, and

serum creatinine, and it estimates the glomerular

filtration of metformin. The tubular clearance of metfor-

min is expected to be affected by the genotypes [31].

Only patients with normal liver function were enroled in

the study and the amount of OCT1 in the liver was not

affected by cholestasis [32].

The multiple linear regression analysis of the decrease in

HbA1c was adjusted for the randomization group. The

Table 2 Genotype characteristics

Genotyped (n)

Gene Database SNP ID Transcript position Amino acid changeb MAF CEU (%) MAF} Total wt/wt wt/v v/v

Primary analysisOCT1 rs12208357 C286T R61C 7.2d–9.1e 8.2 364 304 60 —

rs34104736 C671T S189L 0.5d 0 365 365 — —rs34130495 G1306A G401S 1.1d–3.2e 4.4 362 330 32 —rs72552763 GAT1365Del M420del 16e–18.5d 17.5 361 244 108 9rs34059508 G1498A G465R 1.5e–4.0d 2.1 364 350 13 1rs461473 Intron (G > A) — 12.7c 11.4 365 290 67 8rs622342 Intron (A > C) — 37f–41.0c 38 364 137 177 50

OCT2 rs316019 G978T A270S 7.5c–15.7g 9.7 365 295 69 1MATE1 rs2289669 Intron (G > A) — 43f–43.3c 42.0 364 120 182 62

rs2252281 50UTR (T > C) — 32.1h–36.8c 42.0 362 122 176 64MATE2 rs34399035 C1352T G393R 1.7c 1.0 365 358 7 —

Explorative analysisPMAT rs73332823 A234C M24L NDc — 365 365 — —

rs17854505 T400A V79E NDc — 361 361 — —rs17855675 C536A N124K NDc — 365 365 — —rs17857336 C1449A P429T NDc — 361 361 — —rs11760365 Intron(A > T)a — 45.8c 42.3 362 128 162 72rs4724512 Intron(A > G)a — 21.7c 24.4 362 213 121 28rs6959643 Intron(T > A)a — 42.5c 46.2 365 108 177 80rs6958502 Intron(G > A)a — 21.2c 15.3 365 259 100 6rs6963810 Intron(G > A)a — 35.0c 33.2 364 161 164 39rs6965716 Intron(G > A)a — 49.1c 48.3 362 102 170 90rs2685753 Intron(A > G)a — 26.7c 23.5 364 224 109 31rs3889348 Intron(C > T)a — 37.5c 31.4 365 180 141 44rs4720572 Intron(T > C)a — 27.5c 31.9 362 175 143 44rs4299914 Intron(G > A)a — 45.8c 49.7 363 93 179 91rs6971788 30URT(T > A)a — 22.8c 20.8 364 239 101 24

dbSNP ID, single nucleotide polymorphism database identification; MAF, minor allele frequency = (numbers of SNP alleles/total number of alleles) = [(heterozygote�1+homozygote�2)/no. individuals�2]; ND, not determined; PMAT, plasma membrane monoamine transporter; v, the genetic variant; wt, wild-type.aSNPs used to tag PMAT.bSingle letter nomenclature amino acid substitution.cHapMap-CEU or PMT-272 Caucasian at http://www.ncbi.nlm.nih.gov/projects/SNP, January 2011.dShu et al. [38].eKerb et al. [39].fBecker et al. [22,23].gLeabman et al. [40]hChoi et al. [27].



http://www.ncbi.nlm.nih.gov/projects/SNP

randomization group takes into account systematic

differences including medication, age, weight, organ

functions, and so on. Patients treated with interacting

drugs, that is prednisolon or rifampicin [33] for more than

1 month were excluded; similarly were patients who

stopped the per protocol treatment for more than 1

month. In-vitro models have suggested that patients with

M420del have more pronounced inhibition of OCT1

metformin transport with verapamil and amitriptyline

than the wild-type patient [34]. In this study, one patient

in the metformin group was treated with verapamil and

one with amitriptyline. However, the clinical relevance of

the interactions needs to be confirmed.

Linkage disequilibrium, haplotype and diplotype

inference

The software Haploview (version 4.2, Broad Institute of

MIT and Harvard, Cambridge, Massachusetts, USA) was

used to estimate the Hardy–Weinberg equilibrium (HWE),

SNP, and haplotype frequencies, as well as visualize the

structure of pairwise linkage disequilibrium (LD) between

the SNPs in OCT1, OCT2, MATE1, MATE2, and

PMATE [35]. The diplotypes of the four reduced function

alleles in OCT1 were inferred using the software package

PHASE (Stephens, University of Washington, Seattle,

Washington, USA), version 2.1.1 by Stephens et al. [36,37].

ResultsThe study cohort

Three hundred and seventy-one type 2 diabetic

patients were randomized for the study (men = 229,

women = 142). Patient characteristics are tabulated

in Table 1 and elaborated in the recent article on the

SDDS [29].

One hundred and eighty-four patients were randomized

to metformin; repeated plasma metformin measurements

(415 samples) were obtained from 159 patients, all being

in steady state receiving 1 g of metformin, twice daily.

Twenty-five patients were excluded due to missing values

(n = 3), zero values (n = 3), combination of zero values,

lacking measurements, too low dosage or not enough

plasma for analysis (n = 19), blood sampling outside the

interval of 10–20 h (no patients excluded, number of

samples excluded = 46; Fig. 1). The mean trough steady-

state metformin concentration was determined to be

576 ng/l (95% CI: 520–637), however, with a huge range

from 54 to 4133 ng/l (Table 1). The intraclass correlation

r for the steady-state metformin concentrations was

estimated to be 0.55. Hence, 55% of the total variation

observed was due to interpatient variation. [r= standard

deviation (SD, between patients)2/(SD (within patients)2

+SD(between patients)2]. The variation for the individ-

ual patient was smaller and thus accounted for 45% of the

Fig. 2

8

6

4Per

cent

age

2

00 1000

Mean trough steady-state metformin concentration (ng/ml)

2000 3000 4000

Mean trough steady-state metformin plasma concentration in 159 type 2 diabetic patients treated with 1 g of metformin, twice daily in three repeatedvisits.



total variation. However, this does not undermine the fact

that the patients with very high metformin trough steady-

state concentration values (> 2000 ng/ml) will experience

this repeatedly (Fig. 2 shows the mean trough steady-

state metformin concentration measurements for the

individual patients). This was illustrated by the patient

with the highest metformin trough steady-state concen-

tration measurements: at visit 8, 3958 ng/ml 12 h after last

dose of 1 g of metformin; at visit 9, 3776 ng/ml 13 h after

last dose of 1 g of metformin; and at visit 10, 4133 ng/ml

13 h after last dose of 1 g of metformin. Patients (n = 7)

with only one single high measurement, and the rest

within the ordinary range, could have been noncompliant

at the visit coupled to the high measurement and thus

could have taken metformin just before blood sampling.

However, these few individuals did not affect the above

calculation of variation noticeably (r= 0.57, without the

seven patients).

All of the included patients had their blood samples taken

in the interval of 10–20 h after evening dosage of

metformin; the mean time since the last tablet was

13.9 h (95% CI: 13.7–14.0). The absolute decrease in

HbA1c (DHbA1c) for the metformin-treated patients

from visit 1 to 9 (over initial 6 months) and from visit 1 to

15 (over the entire 24 months of the study) averaged

between – 1.5 to – 2.0% and – 1.3 to – 2.0% (Table 1

shows medians, percentiles, and range). From the cohort,

365 patients were genotyped and allele frequencies are

tabulated in Table 2.

OCT1

Seven genetic variations were analyzed: rs12208357

(R61C), rs461473, rs34104736 (S189L), rs34130495

(G401S), rs72552763 (M420del), and rs622342 and

rs34059508 (G465R). No genetic variation was found in

rs34104736. All of the OCT1 variations were in HWE.

Nine haplotypes were estimated in the cohort; five

haplotypes had a frequency above 5.0%. The SNP

rs622342 was in complete LD (D0= 1, lower limit of

detection > 3) with three other SNPs: rs461473

(r2 = 0.08), rs34130495 (r2 = 0.08), and rs34059508

(r2 = 0.03). The minor allele in rs461473 was in complete

LD with the major allele in rs622342; the minor alleles in

rs34059508 and rs34130495 were linked to the minor

allele in rs622342. However, none of the SNPs were in

perfect LD (r2 = 1), that is, able to predict the genotype

Table 3 (a–d) Metformin trough steady-state concentration and absolute decrease in HbA1c in relation to the number of reduced functionalleles in OCT1

(a) Haplotypes for RF OCT1 n (%)

H1 CG(NoDel)G 512 (70.1)H2 CG(Del)G 111 (15.2)H3 CG(Del)A 15 (2.1)H4 CA(NoDel)G 32 (4.4)H5 TG(NoDel)G 60 (8.2)

(b) Diplotypes for RF OCT1 n

H1/H1 179 WT/WT [n = 179 (49.0%)]H1/H2 83

WT/RF [n = 154 (42.2%)]H1/H3 8H1/H4 20H1/H5 43H2/H2 4

RF/RF [n = 32 (8.8%)]

H2/H3 4H2/H4 6H2/H5 10H3/H3 1H3/H5 1H4/H5 6

(c) Impact of OCT1 RF diplotypes at Css metformin, trend test

Diplotype WT/WT (n = 72) WT/RF (n = 65) RF/RF (n = 10) P

642 (555–743) 542 (465–632) 397 (266–594) 0.001

(d) Impact of OCT1 RF diplotypes at DHbA1c, trend test

Diplotype WT/WT (Reference, n) WT/RF RF/RF P

6 months 0% 74 0.2%* ( – 0.2–0.6, n, 64) 1.1%*, w (0.4 –1.9; n, 11) 0.02424 months 64 0.5%z, } (0.0–1.0, n, 60) 0.8%z ( – 0.1–1.7; n, 10) 0.043

CI, confidence interval; HbA1c, hemoglobin A1c; n, number of patients/diplotypes; NS, not significant; RF, reduced function; WT, wild-type, the haplotype with only activealleles.(a) Haplotype for RF OCT1 (the haplotypes consist of rs12208357, rs34130495, rs72552763, rs34059508); (b) diplotypes for RF OCT1; (c) impact of OCT1 RFdiplotypes at plasma metformin trough steady-state concentration, evaluated by the Cuzick nonparametric test (95% CI); and (d) impact of OCT1 RF diplotypes atDHbA1c for patients treated with metformin (95% CI) the first initial drop (6 months) and the entire period (24 months). RF: reduced function alleles: rs12208357 C > T.rs34130495 G > A, rs72552763 NoDel > Del and rs34059508 G > A.*Wald test P = 0.016 and wP = 0.004 for RF/RF versus WT/WT, both tests were adjusted for randomization. zWald test P = 0.07 and }P = 0.05 for RF/WT versusWT/WT, both tests were adjusted for randomization.

9>>>>=>>>>;

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in both directions. There was complete LD between

rs72552763 and rs34059508 (r2 = 0.1) implicating the

minor allele variant in rs34059508 predicted having the

deletion in rs72552763.

The deletion rs72552763 was associated with a statisti-

cally significant trend to decrease in trough metformin

steady-state concentration with an increasing number of

deletions 624 ng/ml (95% CI: 550–710), 499 ng/ml (95%

CI: 418–596), and 346 ng/ml (95% CI: 76–1567), P value

equal to 0.02 in the trend test. Only one patient was

homozygous for rs72552763 and thus statistical signifi-

cance using mixed-effect modeling could only be

obtained between the patient homozygous for the

wild-type and the patient heterozygous for the alleles

(P = 0.027). Thus, in the adjusted Wald test for

rs72552763 in which all groups were compared, the

significance was the P value equal to 0.06. For rs34130495

wild-type versus heterozygous trough metformin steady-

state concentration was 600 ng/ml (95% CI: 539–668)

versus 380 ng/ml (95% CI: 266–541) and the adjusted

Wald test was near significant P value equal to 0.08. For

the four known reduced function alleles (RF)

rs12208357, rs34130495, rs72552763, and rs34059508,

the five haplotypes H1–5 resulted in 12 diplotypes

(Table 3a,b). Diplotype H2/H4 had a significantly smaller

trough steady-state concentration of metformin than

patients with wild-type H1/H1 (146 ng/ml (95% CI:

62–348) versus 642 ng/ml (95% CI: 556–742; P = 0.001).

The trend test across the categorized diplotypes (WT/

WT, WT/RF, RF/RF) showed a significant decrease in the

trough steady-state concentration with an increasing

number of reduced function haplotypes [WT/WT:

642 ng/ml (95% CI: 555–743), WT/RF: 542 ng/ml (95%

CI: 465–632), and RF/RF: 397 ng/ml (95% CI: 266–594;

P = 0.001; Fig. 3 and Table 3c]. The impact of OCT1reduced functional diplotypes at the initial decrease in

HbA1c (%) over the first 6 months was evaluated using

the Cuzick nonparametric trend test. The change in

absolute decrease in HbA1c for zero (n = 74), one

(n = 64), or two (n = 11) reduced function alleles were:

WT/WT: 0%, WT/RF: 0.2% (95% CI:0.2–0.6), RF/RF:

1.1% (95% CI: 0.4–1.9; P = 0.024; Table 3d and Fig. 4)

The Wald test was also significant (P = 0.016). The

difference between the homozygous wild-type and the

heterozygous was insignificant (P = 0.32); however, there

was a significant difference in response between the

homozygous wild-type and the homozygous variant

(P = 0.004). A significant change in trend was further-

more seen in the decrease in HbA1c from visit 1 to 15

[reference WT/WT: 0%, WT/RF: 0.5% (95% CI:

0.00003–1.0), RF/RF: 0.8% (95% CI: – 0.1 to 1.7);

P = 0.043; Table 3d]. The Wald test was almost

significant (P = 0.07).

Fig. 3

Number of reduced function alleles in the OCT1 gene

0

5000

30

Trou

gh m

etfo

rmin

ste

ady-

stat

e pl

asm

a co

ncen

trat

ion

(ng/

ml)

1

P = 0.001 in trend test

2

Impact of OCT1 reduced function diplotypes at trough steady-state metformin, evaluated using Cuzick’s nonparametric trend test. The box displaysthe median and interquartile range (the 25th–75th percentile). The whiskers display lower and upper values within 1.5 times the interquartile rangebeyond the 25th and 75th percentile.



The rs34130495 affected the initial DHbA1c significantly

(Table 4). For wild-type versus heterozygous patients, the

drop in HbA1c over the initial 6 months was antagonized;

hence, the heterozygous patients showed a 1.1% (95% CI:

0.4–1.8; P = 0.003) lower decrease than the wild-type

patients. No patients homozygous for the minor allele

were found. Furthermore, the homozygous variant in

rs461473 affected the initial DHbA1c significantly and

showed a – 1.7% (95% CI: – 3.4 to – 0.03; P = 0.046)

additional decrease than in the wild-type patients.

OCT2

The SNP rs316019 was genotyped in OCT2. There was no

statistically significant LD to SNPs in OCT1. Neither

trough steady-state metformin concentration nor

DHbA1c was affected significantly by rs316019 genotype

(Tables 4 and 5).

MATE1

Two SNPs, rs2252281and rs2289669, were genotyped in

the MATE1. Both SNPs were in HWE. No LD was found

between the SNPs. Neither trough steady-state metfor-

min concentration nor DHbA1c was significantly affected

by any of the SNPs. The interaction between rs622342

and rs2289669 reported by Becker et al. [21] was not

reproduced.

MATE2

The SNP rs34399035 was examined in MATE2 and in

HWE. No LD was found between this SNP and the two

SNPs in MATE1. The rs34399035 genotype did not affect

the trough steady-state metformin concentration signifi-

cantly; however, it could perhaps affect the long-term

DHbA1c (Tables 4 and 5). For wild-type versus hetero-

zygous patients, the drop in HbA1c over 24 months was

antagonized; hence, the heterozogous patients showed a

1.1% (95% CI: – 0.1 to 2.2; P = 0.06) lower decrease than

the wild-type patients. No patients homozygous for the

minor allele were found.

PMAT

The gene was tagged and most of the SNPs were in

HWE. However, for rs2685753 and rs6971788, the

observed versus predicted heterozygosites in percent

were found to be 29.9 versus 35.9% (P = 0.003) and 27.7

versus 32.6%.(P = 0.009) Six haplotypes had a frequency

above 5.0% and together they represent 69.5% of the

PMAT haplotypes in the dataset. Neither the trough

Fig. 4

−6

−4

−2

0

2D

elta

HbA

1c (%

)

0 1 2

Impact of OCT1 reduced functional diplotypes at the initial (6 months) absolute decrease in HbA1c

Impact of OCT1 reduced functional diplotypes at the initial absolute decrease in hemoglobin A1c (HbA1c, %), evaluated using the Cuzick’snonparametric trend test. The change in absolute decrease in HbA1c for zero (n = 74), one (n = 64), or two (n = 11) reduced function alleles(P = 0.024). The Wald test was significant P value equal to 0.016. The box displays the median and interquartile range (the 25th–75th percentile).The whiskers display lower and upper values within 1.5 times the interquartile range beyond the 25th and 75th percentile.



Table 4 Impact of genotype at the absolute decrease in HbA1c

Absolute decrease over 24 months Absolute decrease over the initial 6 months

Genotype (n) DHbA1c(%) Genotype (n) DHbA1c#(%)

Gene dbSNP ID wt/wt wt/v v/v wt/wt wt/v v/v Pa wt/wt wt/var v/v wt/wt wt/v v/v Pa

OCT1 rs12208357 115 19 — Reference 0.4 – 0.3; 1.1 — 0.22 128 21 — Reference 0.3 – 0.3; 0.8 — 0.34rs34130495 120 13 — 0.6 – 0.2; 1.4 — 0.16 135 13 — 1.1 0.4; 1.8 — 0.003rs72552763 86 46 1 0.3 – 0.2; 0.8 0.3 – 2.5; 3.1 0.48 97 50 1 0.2 – 0.2; 0.6 – 0.2 – 2.6; 2.2 0.67rs34059508 131 3 — 0.9 – 0.7; 2,5 — 0.25 145 3 — 0.7 – 0.7; 2.1 — 0.32rs461473 102 30 2 0.3 – 0.3; 0.9 – 1.0 – 3.0; 1.0 0.34 115 32 2 0.01 – 0.5; 0.5 – 1.7w – 3.4; – 0.03 0.13rs622342 45 72 16 0.2 – 0.3; 0.7 0.5 – 0.3; 1.3 0.47 51 79 18 0.3 – 0.1; 0.8 0.3 – 0.4; 1.0 0.33

OCT2 rs316019 105 29 — – 0.2 – 0.8; 0.4 — 0.47 119 30 — 0.1 – 0.4; 0.6 — 0.73MATE1 rs2289669 40 72 22 – 0.4 – 1.0; 0.1 – 0.4 – 1.2; 0.3 0.27 45 81 23 – 0.1 – 0.6; 0.3 – 0.002 – 0.6; 0.6 0.82

rs2252281 47 63 21 0.1 – 0.5; 0.6 – 0.5 – 1.2; 0.3 0.31 53 67 26 0.2 – 0.2; 0.7 – 0.1 – 0.7;0.5 0.44MATE2 rs34399035 128 6 — 1.1* – 0.1; 2.2 — 0.06 143 6 — 0.4 – 0.6; 1.4 — 0.44PMAT rs11760365 43 62 26 – 0.1 – 0.6; 0.5 0.1 – 0.6; 0.8 0.89 46 72 28 0.2 – 0.3; 0.6 0.2 – 0.4; 0.8 0.68

rs4724512 81 39 11 0.2 – 0.3; 0.7 0.5 – 0.4; 1.4 0.43 87 47 12 0.4z 0.02; 0.9 0.4 – 0.3; 1.1 0.10rs6959643 37 64 33 0.1 – 0.5; 0.7 – 0.3 – 0.9; 0.4 0.45 43 68 38 – 0.2 – 0.6; 0.3 – 0.4 – 0.9; 0.1 0.32rs6958502 96 36 2 0.1 – 0.5; 0.6 1.0 – 1.0; 3.0 0.61 105 41 3 0.03 – 0.4; 0.5 0.7 – 0.7; 2.1 0.60rs6963810 57 60 17 – 0.01 – 0.5; 0.5 0.1 – 0.7; 0.9 0.96 65 64 20 – 0.1 – 0.5; 0.3 0.1 – 0.6; 0.7 0.85rs6965716 36 61 35 0.1 – 0.5; 0.6 0.3 – 0.4; 1.0 0.63 38 73 35 0.2 – 0.3; 0.6 0.2 – 0.4; 0.8 0.77rs2685753 79 40 14 0.1 – 0.5; 0.6 0.4 – 0.4; 1.2 0.61 84 50 14 0.1 – 0.4; 0.5 0.5 – 0.2; 1.2 0.43rs3889348 69 49 16 0.03 – 0.5; 0.6 0.3 – 0.4; 1.1 0.69 74 59 16 0.1 – 0.4; 0.5 0.4 – 0.3; 1.0 0.54rs4720572 63 49 21 – 0,1 – 0.6; 0.4 – 0.03 – 0.7; 0.7 0.93 66 59 23 – 0.1 – 0.5; 0.3 – 0.1 – 0.7; 0.5 0.84rs4299914 39 57 38 – 0.07 – 0.7; 0.5 – 0.02 – 0.7; 0.6 0.96 44 67 38 – 0.1 – 0.5; 0.4 – 0.002 – 0.6; 0.5 0.96rs6971788 83 40 10 – 0.01 – 0.6;1.1 0.1 – 0.8; 1.1 0.96 89 49 10 0.1 – 0.4; 0.5 0.2 – 0.6; 1.3 0.84

Impact of genotype at the absolute decrease in HbA1c (with 95% confidence intervals) between visit 1 and 9 and 1 and 15 with the homozygote wild-type as reference for the patients randomized to metformin.dbSNP ID, single nucleotide polymorphism database identification; Reference: homozygote wild-type; n, number genotyped; v, genetic variant; wt, wild-type.Wald tests have been performed as postestimations for each genotype.*P = 0.064.wP = 0.046.zP = 0.038.aThe significances have been adjusted for randomization group, i.e. the difference in antidiabetic treatment.

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steady-state metformin concentration nor initial or long-

term DHbA1c were affected at the 0.0045 level

(P < 0.05/11, correction for multiple testing in the

explorative analysis) of any of the SNPs in PMAT(Tables 4 and 5). However, the SNPs rs2685753

rs3889348, rs4720572, rs4299914, and rs6971788 reached

significance at the 0.05 level for trough steady-state

metformin concentration for the wild-type versus the

heterozygous/homozygous variant. (P = 0.027, 0.015,

0.029, 0.025, 0.049, and 0.047, respectively). The SNP

rs2685753 also reached a significance level of 0.05 in the

Wald test (P = 0.05; Table 5).

DiscussionWe have determined the trough steady-state plasma

concentrations of metformin in the largest sample of

patients with type 2 diabetes studied so far in this

context. Several measures were taken to avoid or

minimize the risk of noncompliance. Thus, we are

convinced that the very large interindividual variability

in metformin pharmacokinetics characterized by a mean

trough steady-state plasma concentration of 576 ng/ml

(95% CI: 520–637) and a nearly 80-fold range from 54 to

4133 ng/ml in fact is a true interindividual variability. The

interindividual differences in the trough steady-state

pharmacokinetics of metformin greatly exceed what has

previously been reported in smaller samples of pa-

tients [8,41–43]. The interindividual differences in the

plasma trough steady-state concentrations of metformin

reported here most likely reflect a combination of

variation in the renal excretion of metformin, volume of

distribution, and in the bioavailability of the drug.

The trough steady-state plasma concentration of metfor-

min was statistically significantly lower in patients

heterozygous in OCT1 for the minor allele in

rs72552763. The reduced function alleles in OCT1,

rs12208357, rs34130495, rs72552763, and rs34059508

resulted in five haplotypes. When categorized as diplo-

types, the trend test revealed a significant and additive

decrease in the trough steady-state plasma concentration

with increasing number of reduced function haplotypes.

The same reduced function alleles have previously been

shown to have an additive effect on renal metformin

clearance [20]. However, in contrast, an unchanged

metformin renal clearance but a significant difference in

the oral clearance, volume of distribution (Vd/F), and a

larger fraction excreted in the urine has been demon-

strated for the same reduced alleles [19]. The important

role of the OCT1 as the main gatekeeper of hepatic

metformin transport has elegantly been illustrated by

Wang et al. [44] in knockout mice. Thus, in patients with

reduced function OCT 1 variants less metformin is

transported into the hepatocytes, and the volume of

distribution decreases with a shortening of half-life and

lower trough plasma concentration of metformin as a

consequence. The hepatic contribution to metformin

clearance is negligible [9]. Hence, the extra renal

clearance reflects only the incomplete bioavailability of

metformin. OCT1 has been localized in both the apical

membrane of the kidney and in the intestine [20,39], and

the decrease in trough steady-state concentration could

be a combined result of reduced intestinal absorption, an

increased renal clearance, and a decreased volume of

distribution. In our study, none of the other SNPs

Table 5 The impact of genotype at trough steady-state metformin concentration

Genotype (n) Trough steady-state metformin concentration, Css

Gene dbSNP ID wt/wt wt/v v/v wt/wt wt/v v/v Adjusted Pa

OCT1 rs12208357 118 21 — 569 508–636 632 480–832 0.23rs34130495 126 12 — 600 539–668 380 266–541 — 0.08rs72552763 90 47 1 624 550–710 499 * 418–596 346 76–1567 0.06rs34059508 133 4 — 577 519–642 497 255–967 — 0.15rs461473 104 32 3 556 494–627 665 536–826 450 209–969 0.21rs622342 48 71 19 629 529–749 571 494–660 467 353–618 0.16

OCT2 rs316019 112 27 — 585 521–657 546 431–691 — 0.29MATE1 rs2289669 45 74 24 587 487–707 560 485–647 618 476–802 0.94

rs2252281 46 67 24 606 508–724 546 470–636 582 451–751 0.59MATE2 rs34399035 132 6 — 573 515–636 705 417–1190 — 0.57PMAT rs11760365 44 63 31 554 463–664 538 462–627 705 564–880 0.11

rs4724512 78 47 11 589 513–676 541 451–649 595 409–866 0.88rs6959643 46 64 32 558 463–670 592 506–691 577 464–718 0.86rs6958502 97 38 3 606 536–684 516 422–630 472 229–976 0.37rs6963810 61 61 18 603 515–704 562 480–658 543 403–733 0.75rs6965716 38 69 35 562 460–688 531 457–616 686 556–846 0.22rs2685753 76 46 15 648 565–7743 501w 420–598 507 366–702 0.05rs3889348 66 57 18 643 554–747 525z 447–617 521 387–702 0.08rs4720572 62 52 22 636 545–743 501} 425–591 612 469–799 0.07rs4299914 42 58 38 519 429–627 575 491–673 6548 536–797 0.14rs6971788 83 44 10 636 557–726 496z 414–594 479 324–710 0.12

dbSNP ID, single nucleotide polymorphism database identification; HbA1c, hemoglobin A1c; n, number genotyped; v, the genetic variant; wt, wild-type.The concentrations are given with 95% confidence intervals. Wald tests were performed as postestimations for each genotype.Relevant results from the mixed-effect modeling where wild-type versus genotype has been tested for each genotype: *P = 0.027, wP = 0.015, zP = 0.029, }P = 0.025,8P = 0.049 and for zP = 0.047.aSignificance adjusted for the two independent variables: time of sampling and creatinine clearance.



evaluated in OCT1, OCT2, MATE1, or MATE2 had a

significant impact on the metformin trough steady-state

concentration. The SNPs in PMAT did not have

significant impact on the metformin trough steady-state

concentration at the 0.0045 level. However, additional and

repeated evaluation is relevant for the SNPs rs2685753

rs3889348, rs4720572, rs4299914, and rs6971788 to follow-

up the clear tendency for the cluster of SNPs to affect the

trough concentration level.

Savic et al. [45] have recently published an abstract of a

study using nonlinear mixed-effect analysis to develop

population a PK model for metformin and to evaluate the

between-subject variability explained by OCT genetic

variations. In the abstract, it is stated that metformin PK

was best described by a 2-compartment model as well as

suggested that SNPs in both OCT1 (rs34130495,

rs622342) and MATE1 (rs2289669, rs8065082) affected

the flow of metformin to peripheral compartments. They

confirmed their own earlier OCT2 kinetic findings:

individuals heterozygous for the SNP rs316019 have

higher renal clearance of metformin than wild-type

homozygotes [24]. The impact of rs316019 was found

to be the reverse in three other studies [20,25,26], and

we did not find that the SNP affected the through

metformin steady-state level. The effect of solute carrier

transporters on the distribution of metformin to periph-

eral compartments needs further studies; however, it

underscores that the many transporters, with different

tissue distribution and effects, most likely have complex

interactions.

Only minor differences in allele frequency were seen in

the previously evaluated gene variants, and Danish

diabetic patients did not appear genetically remarkably

different from Caucasians in general [21,22,27,39,40,46].

However, complete LD was seen for several SNPs in

OCT1 and PMAT. This was illustrated by the complete

LD between deletion rs72552763 and cSNP rs34059508,

implicating that having the rare rs34059508 variant

predicted having the deletion in rs72552763. This was

in agreement with results reported by Shu et al. [19].

The initial absolute decrease in Hb1Ac, was significantly

associated to rs3413095 in OCT1. Instead of having the

expected decrease in Hb1Ac, 6 months after starting the

antidiabetic metformin treatment, patients with the

minor allele showed a 1.1% higher glycosylated hemoglo-

bin level compared with the patients who were homo-

zygous for the wild-type allele. However, the long-term

absolute decrease in Hb1Ac was insignificant and this

could indicate that the effect of the SNP will diminish

over time. The initial decrease in Hb1Ac was also

significantly associated to rs461473 in OCT1 (0.046). For

MATE2, data indicated (P = 0.06) that the rs34399035

could be associated with the long-term decrease in

Hb1Ac. To the best of our knowledge, this is the first

study to report a pharmacodynamic impact of MATE2

genotypes in a clinical setting with type 2 diabetic

patients. Although variation in rs34399035 has only been

seen in Caucasians, a loss of function alleles has been

demonstrated in vitro in MATE2 in the Japanese

population [28]. MATE2 is expressed in the kidney but

investigations of the expression in other organs are

lacking [13]. However, due to the fact only six patients

were heterozygous for the minor allele in rs34399035 and

only two patients were homozygous for the minor allele in

rs461473, we might have made type I errors for the

positive association to Hb1Ac. The impact of rs34399035

and rs461473 at renal and extra renal clearance and

Hb1Ac must be further investigated in an adequately

powered study.

None of the other evaluated alleles affected statistically

the absolute decrease in glycosylated hemoglobin initially

or over the period of 24 months. This is in accordance

with results from the observational Genetics of Diabetes

Audit and Research Tayside, Scotland study for

rs12208357 and rs72552763 [47]. The decrease in

glycosylated hemoglobin both initially and for the long-

term period was clearly associated with an increasing

number of reduced function alleles in OCT1. This

supports the biological explanation that metformin must

enter the hepatocyte to exert a pharmacodynamic effect

and this in line with the results found by Shu et al. [5].

The Wald test found that the difference in glycosylated

hemoglobin was insignificant over the first 6 months

among patients homozygous for the wild-type and

heterozygotes, but that the difference in response

between the homozygous wild-type and the homozygous

variant was significant. This supports the thesis by

Zolk [48] that the reduced function genetic variations

in OCT1 could be recessive; thus, we could not confirm

the association for the entire study period. Furthermore,

we could not confirm the additive decrease in HbA1c in

patients carrying the minor SNPs rs2289669, rs622342 or

the interaction between rs622342 and rs2289669 as seen

in the Rotterdam study [21–23].

ConclusionOur results demonstrate a huge interindividual variability in

trough steady-state metformin concentration in type 2

diabetics. The OCT1 activity affects metformin pharma-

cokinetics under trough steady-state conditions, which is in

keeping with previous findings, and furthermore the OCT1

activity is associated with a reduction in the absolute

decrease in Hb1Ac both during the initiation of the

treatment and over a long-term maintenance period. Finally,

a cluster of intron SNPs in PMAT could be associated to

decreased metformin absorption. Further studies on the

clinical relevance of these findings are warranted.

AcknowledgementsThe authors thank the assistance of laboratory techni-

cians Pernille Jordan and Birgitte Damby Sørensen.



Support: the work was funded by Grants from the A.J.

Andersen og Hustrus Fond (J. no. 01737–0005), the A.P.

Moeller Foundation for the Advancement of Medical

Science (J. no. 09034), and the Region of Southern

Denmark (J. no. 09/12913).

Conflicts of interest

There are no conflicts of interest.

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