Influence of GLUT2 rs8192675, MATE1 rs2289669, and OCT2 rs316019 Genetic Polymorphism on Metformin Efficacy and Glycemic Control in Type 2 Diabetes Mellitus Patients

doi:10.21203/rs.3.rs-3947421/v1

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Research Article

Influence of GLUT2 rs8192675, MATE1 rs2289669, and OCT2 rs316019 Genetic Polymorphism on Metformin Efficacy and Glycemic Control in Type 2 Diabetes Mellitus Patients

https://doi.org/10.21203/rs.3.rs-3947421/v1

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Metformin, being the gold standard drug of choice in type 2 diabetes mellitus (T2DM) shows differential therapeutic response in patients due to gene polymorphism. The objective of this study was to investigate the influence of GLUT2 rs8192675, MATE1 rs2289669, and OCT2 rs316019 being hotspot single nucleotide polymorphisms (SNPs) on metformin efficacy and glycemic control in T2DM. In current research work, 417 subjects were enrolled, of which 200 were healthy control, and 217 newly diagnosed T2DM patients, involving 60 metformin non-responding and 157 metformin responding individuals. The patients were subjected to three months of metformin monotherapy and their initial and final HbA1c, BMI, fasting glucose, and lipid profiles were determined. Genotyping was performed through real-time PCR with melt curve analysis followed by agarose gel electrophoresis and Sanger sequencing. GLUT2 rs8192675 CC genotype (OR 0.24, CI 95% 0.06–0.84, p = 0.02) and MATE1 rs2289669 A allele (OR 0.14, CI 95% 0.05–0.33, p < 0.0001) were significantly associated with metformin response and glucose-lowering effect. No significant association ( p > 0.05) was observed for OCT2 rs316019. GLUT2 rs8192675 CC genotype and MATE1 rs2289669 A allele are significantly associated with low glucose and HbA1c levels, positively altering metformin efficacy in newly diagnosed T2DM responsive individuals.

Type 2 diabetes mellitus. Metformin efficacy. Glycemic control. Single nucleotide polymorphisms. Association study

Diabetes, a complex, multifactorial disease is a global challenge with currently half a billion patients (Saeedi et al. 2019). Clinical consequences of T2DM involve developing blindness, kidney failure, and a higher risk of lower limb imputation. Patients with T2DM are at higher risk (2–3 times) of cardiovascular diseases like cardiac arrest and stroke (Alwan 2011). Generally, the mortality rate in diabetics is two times greater than in their non-diabetic peers. As per WHO by 2030 diabetes mellitus will be the seventh leading cause of death (Collins and Ke 2012).

Metformin is the first-line oral drug for the treatment of T2DM belonging to the class of biguanides and the drug of choice as per American and European diabetic associations (Amin 2018; Davies et al. 2018; Baker et al. 2021). Metformin is an anti-hyperglycemic drug that has been approved by the Federal Drug Authority (FDA) being the cheapest, highly efficacious in T2DM in controlling blood glucose levels, and has the greatest safety profile (Foretz, Guigas and Viollet 2019; Kuhlmann et al. 2021). The absolute pathway of metformin action is still elusive although various interpretations have been proposed. It is considered that metformin decreases the hepatic production of glucose, lowers intestinal absorption of glucose, and sensitizes insulin along with paving the way for greater utilization of glucose in peripheral tissues (LaMoia and Shulman 2021).

Globally 120 million T2DM patients use metformin. Besides the great efficacy of metformin, it has been observed that one-third of the patients do not respond well to its monotherapy. Recent pharmacogenetic studies revealed that varying genotypes or gene polymorphisms of drug-metabolizing enzymes and transporters are involved in metformin response (Damanhouri et al. 2023). The phenomenon of gene polymorphism in such a case has great importance when greater population size and different ethnicities exist. Metformin is mainly excreted through kidneys and is not metabolized by the hepatic routine redox process; therefore, metformin transporters gene polymorphism is of prime interest to researchers (Al-Eitan et al. 2019).

The mobility of metformin inside the body in terms of absorption, uniform distribution, and excretion from the body involves many genes including organic cation transporters (OCTs), multidrug and toxin extruders (MATEs), and plasma membrane monoamine transporter (PMAT). Absorption of metformin occurs through PMAT, which is encoded by the SLC29A4 gene, and OCT3 encoded by the SLC22A3 gene present on the surface of enterocytes. Similarly, OCT1 is encoded by the SLC22A1 gene and is present on the basolateral side of enterocytes, which carries metformin to the general blood current (Liang and Giacomini 2017). In the liver, metformin is taken up and transported by OCT1 and possibly by OCT3, where metformin is excreted through a renal mechanism by MATE1 and MATE2 encoded by the SLC47A2 gene (Jensen et al. 2016).

Previous studies show an association of gene polymorphism of metformin transporters with the altered metformin pharmacokinetics (Liang and Giacomini 2017). Pharmacogenomics and Genome-wide association studies (GWAS) have highly associated glucose transporter 2 gene (GLUT2 encoded by SLC2A2) rs8192675 polymorphism with a greater glycemic response to metformin (Zhou et al. 2016). A GWAS study showed that rs11212617 located near the ATM gene is considerably associated with metformin pharmacokinetics (Harries et al. 2011). Other candidate genes of metformin pharmacokinetics are LKB1/STK11 and AMPK (Jablonski et al. 2010). However, there exist contradictory results of metformin pharmacokinetics in patients with type 2 diabetes mellitus (Todd and Florez 2014). Studies suggest that inter-individual differences in metformin response exist based on ethnic disparities (Williams et al. 2014). Due to the complex nature of T2DM and the different genetic makeup of various ethnicities of large populations, further work is needed to ascertain a more explicit role of associated genes and their effects due to gene polymorphism. This study was performed to find out inter-patient variability in T2DM in the clinical efficacity of metformin. Variability in outcomes may arise due to genetic variations in specific hot spot loci, such as GLUT2 rs8192675, MATE1 rs2289669 and OCT2 rs316019. This investigation aims to assess the genotypes of T2DM patients at these specific loci in the selected genes, examining allele frequencies concerning metformin efficacy and glycemic control. This research will add to pharmacogenomic approaches toward personalized precision medicine in T2DM patients.

Patients and Sample Collection

A total of 417 subjects including 217 T2DM patients (103 males and 114 females) along with 200 healthy control individuals (108 males and 92 females) were enrolled in this study. The enrolled patients were unrelated, newly diagnosed with T2DM as per WHO criteria 1999 (Lemeshow et al. 1990), and aged 20 to 60 years belonging to Khyber Pakhtunkhwa Pashtun ethnicity (Zhang et al. 2018). Patients with confirmed severe liver and kidney dysfunction, stroke, myocardial infarction, cancer, pregnant women, and individuals with a mixed ethnic background were excluded from the study. All subjects were recruited from Hayat Abad Medical Complex Hospital Peshawar, Pakistan. The subjects were divided into three groups: Control, T2DM patients responding to metformin monotherapy, and T2DM patients not responding to metformin monotherapy. The metformin response was assessed during a 3-month follow-up period. Prior written consent and other relevant information including age, gender, body mass index, family history, etc. were collected from patients and controls on a pre-designed proforma. The study was approved by the ethical review board of the University of Peshawar, Pakistan (316/EC/F.LIFE/UOP-2020).

Determination of biochemical parameters

The peripheral whole blood of subjects was collected in BD vacutainers, especially 5 mL EDTA tubes. The blood was stored at − 20 ⁰C for further investigations. Various biochemical parameters, including glycosylated hemoglobin (HbA1c) and blood glucose levels, were determined for newly diagnosed T2DM patients at the time of recruitment and after a 3-month follow-up period. The daily metformin dose was adjusted within the range of 500 to 2000 mg. Patients who exhibited a decrease in their HbA1c level greater than 0.5 percent from the baseline were classified as responders, while those with a decrease of less than 0.5 percent in their HbA1c level were grouped as non-responders (Umamaheswaran et al. 2015, Mahrooz et al. 2015). Similarly, total cholesterol, high-density lipoprotein (HDL), and low-density lipoproteins (LDL) along with HbA1c and glucose were determined using Cobas® 6000 (Roche Diagnostics, Germany) clinical analyzer.

Extraction of DNA, genotyping through RT-PCR, and sequencing

Genomic DNA was extracted from participants using the phenol-chloroform method (Russell and Sambrook 2001). DNA was quantified using a nanophotometer NP80 ® (Implen, Germany). For rs316019 the allele-specific internal primers along with glucose-6-phosphate isomerase (GPI) as internal control primers and outer sequencing primers were used as reported previously (Collins and Ke 2012). While for rs2289669, and rs8192675 allele-specific internal confronting primers using amplification-refractory mutation system (ARMS)-PCR (Table 1), in which outer product was used for downstream analysis employing primer-1 (Collins and Ke 2012). The genotyping was performed through real-time polymerase chain reaction (RT-PCR) using BIO-RAD CFX connect™ (Optics module, Singapore).

Table 1

Primers used for genotyping and sequencing.
Primer name	Primer sequence (5′→3′)	Size (bp)
rs316019 Forward (C)	GGTGGTTGCAGTTCACAGTCG	439
rs316019 Reverse (C)	ATGCTCTGCTTTCTCCACCC	439
rs316019 Forward (A)	GGTGGTTGCAGTTCACAGTCT	439
rs316019 Reverse (A)	ATGCTCTGCTTTCTCCACCC	439
rs316019 Forward/Seq;	TGTTTGCTTATGTTGGCAGGC	798
rs316019 Reverse/Seq;	ATGCTCTGCTTTCTCCACCC	798
GPI Forward	GGTACACAGGCAAGACCATC	255
GPI Reverse	ACCTCCTGAAGAGTATGGCTT	255
rs8192675 FO	CTAATTTCAGGCCTGGTTCCTATG	278
rs8192675 RO	AATTGGCTTTTCTCTCTGTGCTGGT	278
rs8192675 FIT	CACCCATCTACTTCATCCTCTACA	134 191
rs8192675 RIC	CGATGCAATAGTAGTAGTGGTACC	134 191
rs2289669 FO	GACAAAACCTGGGCTCCTGGTGAGTC	339
rs2289669 RO	CACACTGAAGCCTGCAGGGACCTAAAAT	339
rs2289669 FIA	GGGCTCAGTTTCCACAGTAGCGTTGA	204 186
rs2289669 RIG	CATCCCCTTTGTCTAGCCGGGAAATC	204 186

The analysis was carried out through BIO-RAD CFX Maestro software 2.3 (version 5.03.022.1030). The melting curve analysis was performed for allele discrimination and non-specific amplification. For further confirmation corresponding allele-specific amplification products were run on 1.5% agarose gel electrophoresis and visualized using BIO-RAD imaging system (chemiDoc™ Singapore). Sanger sequencing (capillary sequencing) of random samples was carried out using Applied Biosystems 3730xl DNA Analyzer (Thermo Fisher Scientific, USA). Bioedit sequence alignment editor (BioEdit version 7.7.1) was used for sequencing data analysis.

Statistical Analysis

SPSS18.0 (Chicago, Illinois, USA) and MedCalc Software Ltd (https://www.medcalc.org Version 22.016; accessed December 18, 2023) were used for statistical analysis. The two–sample t-test and Mann–Whitney U test were used for the comparison of T2DM patients and normal individuals. The association of SNPs under study in the context of their alleles and genotypes with T2DM and metformin efficacy was evaluated utilizing logistic regression. p < 0.05 with a 95% confidence interval was considered as significant. The odds ratio (OR) was also calculated and used for the correlation of metformin response in metformin responders and non-responders. All values for biochemical parameters were expressed as mean ± SD.

Demographic information of the subjects

In total, 250 T2DM patients were initially enrolled for the study under the criteria of being newly diagnosed and on metformin monotherapy. During the study period, 33 patients were either lost, or data about them was not sufficient, resulting in a net dataset of 217 patients. As per routine clinical practice by the physicians, the patients were also advised on lifestyle interventions. The number of controlled healthy individuals was 200. Both the control and patients had a sufficient representation of each gender, as mentioned in Table 2.

Table 2

Demographic and clinical features of healthy controls and T2DM patients.
Characteristics	Healthy individuals (n = 200)	T2DM patients (n = 217)	p-Value
Gender (M/F)	108/92	103/114	---
Age (years)	44.5 ± 13.50	46.51 ± 11.33	0.1
BMI	25.21 ± 3.45	26.15 ± 4.92	0.02
Fasting Glucose (mg/dL)	83.53 ± 5.30	188.10 ± 4.21	< 0.0001
HbA1c	4.5 ± 0.5	8.02 ± 0.67	< 0.0001
Cholesterol (mg/dL)	175.50 ± 35.50	211.83 ± 30.71	< 0.0001
Triglycerides (mg/dL)	145.54 ± 30.50	177.94 ± 99.59	< 0.0001
LDL (mg/dL)	119.54 ± 50.45	131.84 ± 65.66	0.03
HDL (mg/dL)	35.13 ± 3.15	38.29 ± 7.84	< 0.0001

Allele and genotype frequency distribution

Genotyping through real time PCR was subjected to sanger sequencing for further evaluation of the specific amplifications and absolute DNA sequence. The sequencing data confirmed the presence of all various alleles present in previous literature and databases. The outer primer employed for sequencing gave a comprehensive sequence of the larger amplicon around the SNPs (Fig. 1,2,3).

The allele and genotype frequency of both healthy individuals and T2DM patients is shown in Table 2. Both groups fulfilled the criteria of Hardy-Weinberg Equilibrium. There was no significant deviation in the expected allele frequency distribution of all three genetic polymorphisms under study. As depicted in Table 3, no significant statistical difference has been found between the allele and genotype frequency distribution of T2DM and controls (p > 0.05) indicating that these SNPs do not influence the occurrence of T2DM in the study population.

Table 3

Allele and genotype frequency distribution among patients and controls for rs8192675, rs2289669 and rs316019 and their association with T2DM.
Genotype	Controls	T2DM	OR	CI (95%)	p-Value
	n = 200 (%)	n = 217 (%)
rs8192675
TT	109 (54.5)	114 (52.53)	Reference
TC	71 (35.5)	73 (33.64)	0.98	0.64–1.49	0.93
CC	20 (10)	30 (13.82)	1.43	0.76–2.67	0.25
Allele Frequencies
T	301 (0.75)	301 (69.35)	Reference
C	99 (0.25)	133 (30.64)	1.34	0.99–1.82	0.06
rs2289669
GG	44 (21.9)	43 (19.81)	Reference
AA	56 (28.1)	68 (31.33)	1.24	0.71–2.15	0.43
GA	100 (50)	106 (48.84)	1.08	0.65–1.79	0.75
Allele Frequencies
G	188 (0.47)	192 (44.23)	Reference
A	212 (0.53)	242 (55.76)	1.11	0.85–1.46	0.42
rs316019
CC	152 (76)	159 (73.27)	Reference
AC	46 (23)	55 (25.34)	1.14	0.72–1.79	0.56
AA	2 (1)	3 (1.38)	1.43	0.23–8.70	0.69
Allele Frequencies
C	175 (87.5)	187 (86.17)	Reference
A	25 (12.5)	30 (13.82)	1.12	0.63–1.98	0.68

Comparative analysis of biochemical characteristics between metformin responders and non-responders

Out of a total of 217 T2DM patients enrolled in metformin monotherapy, 60 patients were categorized as metformin non-responders, while the remaining 157 patients were classified as metformin responders. The primary criteria for this categorization were a decrease in their HbA1c (%) by at least 0.5% for responders and either no change or an increase in HbA1c (%) for non-responders. Table 4 represents different clinical, biochemical, and biophysical characteristics of both groups before and after metformin monotherapy for the newly diagnosed T2DM patients under study. A suitable baseline was set. The net change was expressed as the mean percent difference after metformin therapy compared to the baseline. Characteristics with significant differences (p < 0.05) between responders and non-responders were BMI (0.90 ± 2.50 vs. -1.51 ± 3.10, p < 0.0001), HbA1c (13.95 ± 8.85 vs. -13.74 ± 5.32, p < 0.0001), FBG (30.45 ± 27.64 vs. -40.32 ± 14.53, p < 0.0001), LDL (5.03 ± 17.32 vs. -10.78 ± 16.45, p = 0.0242), and TC (-1.35 ± 13.23 vs. -10.34 ± 14.53, p < 0.0001). For the remaining characteristics like age, gender, HDL, and TG showed no significant differences in their mean percent change.

Table 4

Biochemical and biophysical characteristics of T2DM patients (metformin responders and non-responders) during monotherapy.
characteristics	Non-responders (n = 60)	Metformin responders (n = 157)	p-Value
Age (years)	45.79 ± 12.13	47.23 ± 10.53	0.38
Gender (M/F)	38 /22	65 /92	---
BMI (kg/m2)
Baseline (before therapy)	24.82 ± 5.02	27.49 ± 4.83	0.0004
Net mean % change after therapy	0.90 ± 2.50	-1.51 ± 3.10	< 0.0001
HbA1c (%)
Baseline (before therapy)	7.98 ± 0.85	8.06 ± 0.50	0.39
Net mean % change after therapy	13.95 ± 8.85	-13.74 ± 5.32	< 0.0001
FBG (mg/dL)
Baseline (before therapy)	190.56 ± 45.55	185.65 ± 47.64	0.49
Net mean % change after therapy	30.45 ± 27.64	-40.32 ± 14.53	< 0.0001
LDL (mg/dL)
Baseline (before therapy)	130.54 ± 32.34	133.14 ± 33.32	0.60
Net mean % change after therapy	-5.03 ± 17.32	-10.78 ± 16.45	0.02
HDL (mg/dL)
Baseline (before therapy)	37.34 ± 8.56	39.25 ± 7.13	0.09
Net mean % change after therapy	3.44 ± 21.34	3.83 ± 19.23	0.89
TG (mg/dL)
Baseline (before therapy)	179.53 ± 108.43	176.36 ± 90.76	0.82
Net mean % change after therapy	-3.89 ± 27.13	-7.95 ± 25.35	0.30
TC (mg/dL)
Baseline (before therapy)	208.35 ± 30.88	215.31 ± 30.55	0.13
Net mean % change after therapy	-1.35 ± 13.23	-10.34 ± 14.53	< 0.0001

Influence of GLUT2 rs8192675, MATE1 rs2289669, and OCT2 rs316019 genetic polymorphism on therapeutic efficacy of metformin

The analysis of allele and genotype frequency distribution in terms of mean percent change in HbA1c between the metformin responders and non-responders revealed significant differences for GLUT2 rs8192675 and MATE1 rs2289669 (Table 5). A significant difference in genotype CC of rs8192675 between responders and non-responders was observed (OR 0.24, CI 95% 0.06–0.84, p = 0.02) and allele C over T (OR 0.56, CI 95% 0.34–0.92, p = 0.02). Similarly, for rs2289669 significant difference was observed concerning genotype GA (OR 0.17, CI 95% 0.07–0.37, p < 0.0001) and AA (OR 0.14, CI 95% 0.05–0.33, p < 0.0001).

The allele frequency of rs2289669 A compared to G was also significant (OR 0.37, CI 95% 0.24–0.57, p < 0.0001). For rs316019 no significant difference of genotypes and alleles was observed between responders and non-responders (p > 0.05). Thus, CC genotype of rs8192675 and A allele (AA and GA) of rs2289669 are significantly associated with metformin response in metformin responders.

Table 5

Allele and genotype frequency distribution in rs8192675, rs2289669 and rs316019 in metformin responders and non-responders.
	Controls	T2DM	Metformin non-responders	Metformin Responders	OR	CI (95%)	p-Value
	n = 200 (%)	n = 217 (%)	n = 60(%)	n = 157(%)
rs8192675
TT	109 (54.5)	114(52.53)	36(60)	78(49.68)	Reference
TC	71 (35.5)	73(33.64)	21(35)	52 (33.12)	0.87	0.46 to 1.66	0.68
CC	20 (10)	30(13.82)	3(5)	27 (17.19)	0.24	0.06 to 0.84	0.02
Alleles
T	301 (75.25)	301(69.35)	93(77.5)	208 (66.24)	Reference
C	99 (24.75)	133 (30.64)	27(22.5)	106 (33.75)	0.56	0.34 to 0.92	0.02
rs2289669
GG	44 (21.9)	43 (19.81)	26 (43.33)	17 (10.82)	Reference
GA	100 (50)	106 (48.84)	22 (36.66)	84 (53.50)	0.17	0.07 to 0.37	< 0.0001
AA	56 (28.1)	68 (31.33)	12 (20)	56 (35.66)	0.14	0.05 to 0.33	< 0.0001
Alleles
G	188 (0.47)	192 (44.23)	74 (61.66)	118 (37.57)	Reference
A	212 (0.53)	242 (55.76)	46 (38.33)	196 (62.42)	0.37	0.24 to 0.57	< 0.0001
rs316019
CC	152 (76)	159 (73.27)	45 (75)	114 (72.61)	Reference
AC	46 (23)	55 (25.34)	14 (23.33)	41 (26.11)	0.86	0.43 to 1.73	0.68
AA	2 (1)	3 (1.38)	1 (1.66)	2 (1.27)	1.26	0.11 to 14.31	0.84
Alleles
C	175 (87.5)	187 (86.17)	104 (86.66)	269 (85.66)	Reference
A	25 (12.5)	30 (13.82)	16 (13.33)	45 (14.33)	0.91	0.49 to 1.69	0.78

A similar trend was observed after analyzing the data in terms of different genetic models (Table 6). In the case of rs8192675, a significant difference was observed for recessive (OR 0.25, CI 95% 0.07–0.86, p = 0.02) and additive genetic model (OR 0.55, CI 95% 0.30–0.98, p = 0.04) respectively. For rs2289669 dominant model (OR 0.15, CI 95% 0.07–0.32, p < 0.0001), recessive (OR 0.45, CI 95% 0.22–0.91, p = 0.02), over-dominant (OR 1.98, CI 95% 1.07–3.66, p = 0.02) and additive model (OR 0.1535, CI 95% 0.07–0.30, p = 0.15) shows the relative influence of genotype distribution. For rs316019 no significant correlation or association has been observed between responders and non-responders (p > 0.05). These genetic models further strengthen the results of Table 5, comprehensively depicting that the CC genotype of rs8192675 and A allele of rs2289669 are significantly associated with metformin response in metformin responders.

Table 6

Evaluation of different genetic models for rs8192675, rs2289669 and rs316019 in metformin responders and non-responders.
Genetic model	Genotype	OR	CI (95%)	p-Value
rs8192675
Dominant	TT Vs TC + CC	0.65	0.36–1.20	0.17
Recessive	CC Vs TT + TC	0.25	0.07–0.86	0.02
Over-dominant	TT + CC Vs TC	0.91	0.49–1.72	0.79
Additive	C Vs TT	0.55	0.30–0.98	0.04
rs2289669
Dominant	GG Vs GA + AA	0.15	0.07–0.32	< 0.0001
Recessive	AA Vs GG + GA	0.45	0.22–0.92	0.02
Over-dominant	GG + AA Vs GA	1.98	1.07–3.66	0.02
Additive	A Vs GG	0.15	0.07–0.30	< 0.0001
rs316019
Dominant	CC Vs AC + AA	0.88	0.44–1.74	0.72
Recessive	AA Vs CC + AC	1.31	0.11–14.75	0.82
Over-dominant	CC + AA Vs AC	1.16	0.57–2.32	0.67
Additive	A Vs CC	0.90	0.46–1.75	0.75

Diabetes, being a complex multifactorial global disease, affects all populations and all ethnicities worldwide. The net trend in T2DM especially is on the rise. Diabetes is mainly characterized by persistent hyperglycemia due to abnormalities in insulin secretion or resistance to insulin action (Petersmann et al. 2019). Metformin is the gold standard choice of drug against hyperglycemia, is widely used, and has the highest safety profile. Besides the high efficacy of metformin, one third of patients do not respond well to metformin. Metformin is not metabolized by routine hepatic metabolism and is excreted through kidneys unchanged. Previous studies show that gene polymorphism in the transporter genes may affect the metformin concentration and thus efficacy in the body (Damanhouri et al. 2023; Al-Eitan et al. 2019). In our current research work we tried to associate three potential hot spot SNPs to metformin efficacy in the Khyber Pakhtunkhwa population in a three-month case-control follow-up strategy.

Our data analysis shows that the GLUT2 rs8192675 CC genotype has been associated more with decreasing HbA1c levels than TC and TT genotypes in newly diagnosed T2DM patients in the metformin-responsive group. It has been demonstrated that the C allele of the variant rs8192675 in the gene SLC2A2 (GLUT2) is crucial for controlling the metformin action. It was revealed by the MetGen Consortium that the C allele of the variant rs8192675 in the gene SLC2A2 is involved in the metformin action (Zhou et al. 2016). The specific mechanism through which this SNP exerts its action is still elusive. Similarly, Rathmann et al. (2019) also demonstrated that the variant rs8192675 in the SLC2A2 gene (C allele) is associated with an improved glucose response to metformin monotherapy during the first year after diagnosis in type 2 diabetes.

He et al. (2015) demonstrated that the AA genotype of MATE1 rs2289669 has a glucose-lowering effect of metformin in Chinese T2DM patients by delaying its excretion as compared to GG and GA genotypes. Kim et al. (2022) showed that SLC47A1 rs2289669 is associated with the glycemic response to metformin in drug-naive patients with type 2 diabetes. Our results further strengthen the effect of the A allele being prominent over the G allele, however, the combined effect of A with G in the AG genotype is still more significant than the GG genotype (p < 0.05). Tkáč et al. (2013) observed a net two-fold reduction in HbA1c level in patients carrying the AA genotype of SLC47A1 rs2289669 than patients with the GG genotype. However, they found no significant association between SLC22A2 rs316019 and metformin efficacy. Metformin is excreted mainly through kidneys utilizing OCT2/SLC22A2 transporter which has rs316019 (c.808G > T, p.270A > S) being the most common variant, assumed to alter its concentrations (Islam et al. 2019). Previous studies suggested that the AA homozygote of SLC22A2 rs316019 is involved in metformin clearance from the body (Li et al. 2010; Song et al. 2007; Song et al. 2008). In our present work, we did not find a significant (p > 0.05) association between metformin efficacy and SLC22A2 rs316019 in T2DM patients.

In summary, we conclude that GLUT2 rs8192675 CC genotype and MATE1 rs2289669 A allele are significantly associated with decreased HbA1c level thereby positively altering metformin pharmacokinetics in newly diagnosed T2DM responsive individuals. The relative effect of AA genotype in MATE1 rs2289669 is more than its heterozygous GA genotype although both contribute significantly to glycemic control.

Conflict of interest

The authors declare no conflict of interest.

Institutional Ethical Review Board Statement

The study was approved by the ethical review board of the University of Peshawar (316/EC/F.LIFE/UOP-2020).

Funding

The authors express gratitude to the Higher Education Commission of Pakistan for supporting this project under the Faculty Development Program, Shaheed Benazir Bhutto University Sheringal, and the University of Notre Dame USA for their generous financial support to this research work.

Author Contribution

Data curation, Aziz-ul-Hasan Aamir; Formal analysis, Zahid Khan and Durre Shahwar; Funding acquisition, Lamjed Mansour and Muhammad Imran; Investigation, Muhammad Kashif Raza, Zahid Khan and Durre Shahwar; Methodology, Muhammad Kashif Raza; Project administration, Aziz-ul-Hasan Aamir and Muhammad Imran; Resources, Aziz-ul-Hasan Aamir, Aktar Ali and Muhammad Imran; Supervision, Muhammad Imran; Validation, Lamjed Mansour and Aktar Ali; Writing – original draft, Muhammad Kashif Raza; Writing – review & editing, Lamjed Mansour and Muhammad Imran. All authors reviewed the manuscript and agreed to the published version of the manuscript.

Acknowledgments

The authors would like to extend their special thanks to the volunteers who willingly participated in this study. The authors also thank the Director and other staff of Hayat Abad Medical Complex, Peshawar, Pakistan, for their permission and help in the collection of blood samples from T2DM patients and healthy individuals. The authors extend their appreciation to the Researchers Supporting Project number (RSP 2024R75), King Saudi University, Riyadh, Saudi Arabia.

Data availability

All the relevant data is within the manuscript.

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Influence of GLUT2 rs8192675, MATE1 rs2289669, and OCT2 rs316019 Genetic Polymorphism on Metformin Efficacy and Glycemic Control in Type 2 Diabetes Mellitus Patients

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Abstract

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Introduction

Materials and Methods

Patients and Sample Collection

Determination of biochemical parameters

Extraction of DNA, genotyping through RT-PCR, and sequencing

Statistical Analysis

Results

Demographic information of the subjects

Allele and genotype frequency distribution

Comparative analysis of biochemical characteristics between metformin responders and non-responders

Discussion

Conclusion

Declarations

Institutional Ethical Review Board Statement

Funding

Author Contribution

Acknowledgments

Data availability

References

Additional Declarations

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