Post on 14-May-2023
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: mmchristensen@health.sdu.dk
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
Supplemental digital content is available for this article. Direct URL citationsappear in the printed text and are provided in the HTML and PDF versions of thisarticle on the journal’s website (www.pharmacogeneticsandgenomics.com).
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.
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
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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
Metformingroup 4 n = 46
Metformingroup 6 n = 45
Metformingroupe 8 n = 48
Placebogroup 3 n = 46
Placebogroup 5 n = 48
Placebogroup 7 n = 47
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
Copyright © Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
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%.
840 Pharmacogenetics and Genomics 2011, Vol 21 No 12
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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].
Metformin pharmacogenetics, new insights Christensen et al. 841
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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.
842 Pharmacogenetics and Genomics 2011, Vol 21 No 12
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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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Metformin pharmacogenetics, new insights Christensen et al. 843
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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.
844 Pharmacogenetics and Genomics 2011, Vol 21 No 12
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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.
Metformin pharmacogenetics, new insights Christensen et al. 845
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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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ilkins. Unauthorized reproduction of this article is prohibited.
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.
Metformin pharmacogenetics, new insights Christensen et al. 847
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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.
848 Pharmacogenetics and Genomics 2011, Vol 21 No 12
Copyright © Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
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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