Association of Single Nucleotide Polymorphism in OCT1 and OCT3 Genes with the Efficacy of Metformin Response in North Indian Type 2 Diabetes Mellitus Patients.

doi:10.21203/rs.3.rs-3227562/v1

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Research Article

Association of Single Nucleotide Polymorphism in OCT1 and OCT3 Genes with the Efficacy of Metformin Response in North Indian Type 2 Diabetes Mellitus Patients.

https://doi.org/10.21203/rs.3.rs-3227562/v1

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Purpose

Interindividual variation in efficacy of metformin among type 2 diabetes mellitus (T2DM) patients have been associated with several genetic variants. Understanding the genetic pathways involved in pharmacodynamics of metformin can affect personalized treatment of T2DM. Our study aimed to find the implication of genetic polymorphism in organic cation transporters (OCT1 and OCT3) genes on drug efficacy of metformin therapy in T2DM patients from North India.

Methods

This study evaluated the role of OCT1 (rs628031) and OCT3 (rs2292334) polymorphism in metformin response on T2DM patients. Response to metformin was defined by HbA1c levels based on which patients (n = 177) were divided into two groups: responders (HbA1C < 7% ; n = 127) and non-responders (HbA1C ≥ 7%; n = 50). The responders were further subcategorized as T2DM patients on monotherapy (n = 55) and on combination therapy (n = 72). Genotyping was done using PCR-RFLP approach.

Results

No significant association was found between OCT1 (rs628031) polymorphism and metformin response in T2DM patients. On the other hand, significant association of OCT3 (rs rs2292334) polymorphism was observed with metformin response where AA genotype carriers showed higher efficacy of metformin both in mono [OR (CI) = 0.29(0.11–0.72) and p = 0.007] and combination therapy [OR (CI) = 0.41(0.16-1.0) and p = 0.047]. Also, A allele was more prevalent in responders [OR (CI) = 0.48(0.28–0.84) and p = 0.010] while G allele was found to be associated with inefficacy of metformin in T2DM patients [OR (CI) = 2.07(1.19–3.61) and p = 0.010].

Conclusion

Genotyping of OCT3 (rs2292334) might be useful in predicting the response to metformin in T2DM patients.

Anti-diabetic drugs

Metformin

Organic Cation Transporter

Pharmacogenetics

Single Nucleotide Polymorphism

Type 2 Diabetes Mellitus

Type 2 Diabetes Mellitus (T2DM) is a complex metabolic disorder with multiple etiologies including genetic and environmental risk factors. It has emerged as a global epidemic in the past few decades where the number of T2DM cases in 2021 were 536.6 million and the figures are expected to rise to 783.2 million by 2045[1]. There are different treatment options available for T2DM management with metformin monotherapy being generally preferred as the first line of treatment. However if it fails to control the blood glucose levels, then a dual or combination therapy of metformin and sulfonylureas is usually prescribed to patients together. Research conducted globally have shown a better effect of combination therapy in lowering blood glucose levels compared to monotherapy [2].

The clinical etiology, presentation and complications of T2DM vary from person to person which make the management of T2DM a much more challenging task for clinicians even after having several treatment options. Long-standing uncontrolled glucose levels have been found to be associated with various micro vascular damage and complications in the eyes, kidneys, nerves and heart thus affecting the quality of life ultimately resulting in death in most cases. The difference in response to oral hypoglycemic drugs amongst T2DM patients pose a major hurdle in disease management and interestingly up to 40% of these inter-individual variabilities are affected by genetic factors [3]. Based on the contribution of multiple genetic factors in the etiology of T2DM and variable response to different anti-diabetic agents, genetic architecture of each individual alter their treatment efficacy and drug response. Using genetic information and calculating a genetic risk score, based on different genetic variants, may enable us to reclassify patients with T2DM in discrete pathophysiological subgroups in order to apply targeted preventive or therapeutic interventions, which will eventually increase drug efficacy and decrease adverse drug reactions.

Metformin is the first line of treatment for T2DM. This biguanide derivative helps to decrease blood glucose levels by reducing hepatic glucose production, enhancing glucose uptake in skeletal tissue and suppressing the intestinal absorption of glucose. It is a hydrophilic organic cation that acts as a substrate for organic cation transporters (OCT1 and OCT3) which function in either hepatic distribution or renal excretion of metformin. Uptake of metformin from blood into hepatocytes is mediated by OCT1 and OCT3 [4]. Functional polymorphisms in the corresponding gene such as rs628031 (Met408Val) have been shown to affect its liver uptake, and consequently influence its efficacy [5]. According to the in vitro and in silico analysis, Met408Val missense polymorphism reduce OCT1 mRNA expression [6], alters the structure of OCT-1 protein and thus affects disposition of metformin [7]. On the other hand, the OCT3 rs2292334 (1233G > A) is a variant in which G > A displacement converts one alanine codon into another codon for alanine (A411A) resulting in altered mRNA expression levels of OCT3 gene [8].

The importance and role of the OCT1 and OCT3 genes and their genetic variations in metformin pharmacokinetics suggests the need to examine the genetic variations and their relationships with drug response and efficacy in different ethnic populations. Consequently, in this study we aimed to analyse the association between rs628031 polymorphism of OCT1 and rs2292334 of OCT3 genes, and glycemic response to metformin in North Indian population of T2DM patients.

Study design

This observational cross-sectional study was carried out as a collaborative effort between the Department of Personalized and Molecular Medicine and Department of Medicine in Era’s Lucknow Medical College & Hospital (Lucknow, India). This study was approved by the Institutional Ethical Committee, and the study strictly followed good clinical practice guidelines and the Helsinki declaration.

Patient selection

This study involved 177 North Indian T2DM patients divided into two groups, responders with HbA1C < 7% (n = 127) and non-responders with HbA1C ≥ 7% (n = 50). The responders were further subcategorized as T2DM patients on monotherapy (n = 55) and combination therapy (n = 72) of metformin.T2DM patients, diagnosed according to the International Diabetes Federation criteria, were consecutively recruited from the Department of Medicine and provided informed consent prior to study participation. The patients received metformin either as monotherapy or in combination with sulphonylurea (glimepiride) as combination therapy for at least 3 months to ensure that the patient reached maximum glycated hemoglobin (HbA1C) reduction (< 7%). Metformin was prescribed as 500mg daily, and up to a maximum dose of 2000 mg/day, and sulphonylurea (glimepiride) in combination with metformin was prescribed as 1–2 mg/day dose. Doses were titrated based on tolerability and on glycemic control during each monthly visit. HbA1C was used to assess therapeutic and glycemic response for T2DM patients, to classify them as responders/non-responders, and a cut-off of 7% was recommended by ADA [9]. Responders were T2DM patients with their HbA1C < 7% while non-responders had HbA1C levels ≥ 7% post 3 months of treatment. We excluded patients on insulin therapy, those suffering from renal or hepatic impairments and having other metabolic or endocrine disorders. Patients with type 1 diabetes mellitus and those taking drugs that cause secondary diabetes mellitus were also excluded. Data including full medical and medication history, treatment duration, drug dosage, family history, blood pressure, was collected from each patient through patient interviewing during appointment checkup.

Biochemical estimations:

Blood samples were collected for biochemical and molecular assays. BMI was calculated by the Quetelet equation (weight in kilograms/ height in meter square). Fasting plasma glucose (via the glucose oxidase-peroxidase method), serum cholesterol (via the cholesterol oxidase-peroxidase method), serum triglyceride (via the glycerol phosphate oxidase-peroxidase amidopyrine method), high-density lipoprotein (HDL) and cholesterol (both via immunoinhibition assay) were assessed by the XL-300 Fully-Automated Analyzer (Transasia, Mannheim, Germany). Low-density lipoprotein (LDL) cholesterol levels were calculated using the Friedewald formula. HbA1C was measured using the Transasia Semi-Autoanalyzer. All the assays were performed according to the manufacturer’s protocols. Characteristics of all subjects participating in this study are shown in Table 1.

Table 1

Demographic and biochemical characteristics of study subjects
Anthropometric/ Biochemical Parameters	Responders (n = 127)		Non-Responders(n = 50)	P-value
	Patients on Metformin Monotherapy (n = 55)	Patients on Metformin Combination Therapy (n = 72)	Non-Responders(n = 50)
Age (Years)	49.64 ± 10.12	49.89 ± 12.56	50.66 ± 10.73	0.891
Sex (M/F)	(19/12)	(26/31)	(8/7)	-
BMI	25.74 ± 9.18	28.85 ± 7.21	29.13 ± 8.63	0.058
FBS(mg/ dl)	117.93 ± 20.11	115.67 ± 24.52	167.4 ± 32.78	< 0.001^*
HbA1c (%)	6.2 ± 0.80	6.3 ± 0.72	7.6 ± 0.91	< 0.001*
TC(mg/ dl)	168.64 ± 30.75	175.78 ± 18.75	180.50 ± 31.29	0.073
TG(mg/dl)	150.75 ± 45.99	159.22 ± 40.69	177.38 ± 77.5	0.043*
HDL(mg/dl)	49.83 ± 11.88	48.31 ± 9.65	48.04 ± 12.45	0.664
LDL(mg/dl)	98.64 ± 22.78	97.88 ± 19.21	102.84 ± 18.21	0.38
SBP: systolic blood pressure; DBP: diastolic blood pressure; BMI: body mass index; FBS: fasting blood sugar; TG1: triglyceride fraction 1; TC: total cholesterol; HDL: high-density lipoprotein; LDL: low-density lipoprotein.
*Statistically significant

DNA isolation

Genomic DNA was isolated from whole blood using NucleoSpin 96 Blood Quick Pure kit (Takara, India), following manufacturer’s instructions. The quantity of DNA was estimated using Nanodrop spectrophotometer, and the quality of DNA was checked by measuring A260/A280. Finally, the isolated DNA was stored at -20°C

Genetic Analysis

The genetic polymorphisms of OCT1 and OCT3 genes were genotyped using polymerase chain reaction and restriction fragment length polymorphism (PCR-RFLP). Details of primers used for each SNP and Restriction digestion details are given in Table 2.

Table 2

**List of primers, PCR product size, restriction enzymes and digestion conditions**
Gene	SNP (rs)	Primer sequences (5’-3’)	PCR Product (bp)	Restriction Endonucleases	Digestion Temp. (Incubation time)	Digested Band Sizes
OCT1	rs628031	Forward Primer: 5’- AGGTTCACGGACTCTGTGCT − 3’ Reverse Primer: 5’- AAGCTGGAGTGTGCGATCT-3’	600	BspHI	37°C (overnight)	AA-600 bp Aa- 600-, 197- and 403-bp aa-197- and 403-bp
OCT3	rs2292334	Forward Primer: 5’- GTGGTGGAACTGCCAGGA-3’ Reverse Primer: 5’- CTACAGAACCAATCTCTTACTTCG − 3’	293	AciI	37°C (overnight)	AA-307 bp GG-70 and 237 bp AG-70, 237 and 307 bp

PCR amplification was carried out in a reaction mixture of 20 µl containing 0.5U Taq DNA Polymerase, 20 mM Tris HCl, 80 mM KCl, 4 mM MgCl2, 10 pmol/l of each primer, approximately 100–150 ng of genomic DNA and nuclease free water in T100 Thermal Cycler (Biorad,U.S.A.). The PCR conditions for each gene included a denaturation, annealing, extension and a final extension step. The details are mentioned in Table 3.

Table 3

**Details of primers and PCR conditions for Studied Genes**
Gene	Steps	Temperature	Duration	Cycles
OCT1 gene	Initial denaturation	93ºC	3 min
	Denaturation	93ºC	45 sec	35
	Annealing	58ºC	35 sec
	Extension	72ºC	35 sec
	Final extension	72ºC	5 min
	End/Hold	4ºC	∞
OCT3 gene	Denaturation	93ºC	50 sec	35
	Annealing	52ºC	40 sec
	Extension	72ºC	40 sec
	Final extension	72ºC	5 min
	End/Hold	4ºC	∞

After successful amplification, each gene was digested with specific restriction enzyme in a 20µl reaction mixture consisting of 7–10µl PCR product, 10X restriction enzyme digestion buffer, 1-5U of restriction enzymes (New England Biolabs, UK) and nuclease free water (for final volume makeup). Samples were later incubated at 37°C. The RFLP fragments thus generated were resolved on 3% agarose gel stained with ethidium bromide and visualized in gel documentation system (UVP Bioimaging system, Cambridge, U.K).

Statistical Analysis

Statistical analyses were performed with SPSS version 21 software (IBM Corp., Chicago, Illinois, U.S.A.). Variables were tested for normality using the Kolmogorov–Smirnov test. Variables with a normal distribution were expressed as mean with standard deviation. The genotyping data were analyzed using Chi-square test. P-values ≤ 0.05 were considered as significant. Odds ratios (OR) and 95% confidence intervals (CI) were calculated to test the relative risk for association.

This study included 177 North Indian T2DM patients divided into two groups, responders (n = 127) and non-responders (n = 50). The responders were further subcategorized as T2DM patients on monotherapy (n = 55) and combination therapy (n = 72) of metformin. All included patients showed good knowledge and compliance to their therapy regimen and medication dosing. The groups did not differ significantly in age 49.64 ± 10.12; 49.89 ± 12.56 in the responder group (monotherapy and combination therapy respectively) and 50.66 ± 10.73 in the non-responder group (p = 0.891). Patient’s characteristics and demographics were all found to be statistically insignificant between the two groups. However, FBS, HbA1C and TG levels were significantly lower in the responders group compared to the non-responder group and nearly significant difference was observed in BMI too among the groups.

OCT1 (rs628031) polymorphism analysis

The frequencies of aa, AA and Aa genotypes were 10.91%, 72.73%, and 16.36% in monotherapy responders while they were 6%, 72% and 22% in non-responders (p = 0.369, 0.934 and 0.463 respectively). In responders on combination therapy, the frequencies of aa, AA and Aa genotypes were 4.17%; 70.83%; 25% respectively with no significant observed with frequencies observed in non-responders (p = 0.104, 0.889 and 0.702 respectively). The frequencies of a and A alleles in monotherapy responders and non-responders were 19.09%, 80.91 (p = 0.514) and 17%’ 83% (p = 0.395) respectively. Whereas, the frequencies of a and A alleles in combination therapy responders were 16.67% and 83.33% (p = 0.945) respectively.

The frequency of occurrence of mutant aa genotype and a allele was higher in monotherapy responders than non-responders but the difference was found to be non-significant (p > 0.05). On the other hand in case of combination therapy the prevalence of Aa genotype was marginally higher in responders than non-responders but again the difference was found to be non-significant (p > 0.05).

OCT3 (rs2292334) polymorphism analysis

The frequencies of AA, GG and AG genotypes were 40%, 34.55%, and 25.45% in monotherapy responders while they were 16%, 46% and 38% in non-responders (p = 0.007, 0.231 and 0.167 respectively). In responders on combination therapy, the frequencies of AA, GG and AG genotypes were 31.94%; 38.89%; 29.17% respectively with no significant observed with frequencies observed in non-responders (p = 0.047, 0.434 and 0.307 respectively). The frequencies of A and G alleles in monotherapy responders and non-responders were 52.73%, 35% (p = 0.01) and 47.27%’ 65% (p = 0.01) respectively. Whereas, the frequencies of A and G alleles in combination therapy responders were 46.53% and 53.47% (p = 0.073) respectively.

In case of patients on monotherapy, frequency of occurrence of AA genotype and A allele was significantly higher in responders than non-responders (p = 0.007 and p = 0.01 respectively). On the other hand frequency of occurrence of G allele was significantly lower in responders than non-responders (p = 0.01).

In case of patients on combination therapy, the prevalence of AA genotype was again significantly higher in responders than non-responders (p = 0.047) and the frequency of A and G allele were different between the two groups but the difference was found to be non-significant (p = 0.073).

Detailed results along with the genotype and allele frequencies for responders and non-responders are shown in Tables 4 and 5.

Table 4

Allele frequencies and genotype distribution of *OCT1 (rs628031) and OCT3 (rs2292334)* polymorphism in Metformin monotherapy responders and non-responders
Gene	Genotype/Alleles	Responders (n = 55)		Non-Responders (n = 50)		OR (CI)	P-Value
Gene	Genotype/Alleles	Count	%	Count	%	OR (CI)	P-Value
OCT1 (rs628031)	aa	6	10.91	3	6	0.52 (0.12–2.21)	0.369
	AA	40	72.73	36	72	0.96 (0.41–2.27)	0.934
	Aa	9	16.36	11	22	1.44 (0.54–3.84)	0.463
	a	21	19.09	17	17	0.79 (0.39–1.61)	0.514
	A	89	80.91	83	83	0.71 (0.33–1.56)	0.395
OCT3 (rs2292334)	AA	22	40	8	16	0.29(0.11–0.72)	0.007*
	GG	19	34.55	23	46	1.61 (0.74–3.54)	0.231
	AG	14	25.45	19	38	1.79(0.78–4.13)	0.167
	A	58	52.73	35	35	0.48(0.28–0.84)	0.010*
	G	52	47.27	65	65	2.07(1.19–3.61)	0.010*
*Statistically significant

Table 5

Allele frequencies and genotype distribution of *OCT1 (rs628031) and OCT3 (rs2292334)* polymorphism in Metformin combination therapy responders and non-responders
Gene	Genotype/Alleles	Responders (n = 72)		Non-Responders (n = 50)		OR (CI)	P-Value
Gene	Genotype/Alleles	Count	%	Count	%	OR (CI)	P-Value
OCT1 (rs628031)	aa	3	4.17	3	6	3.14 (0.75–13.19)	0.104
	AA	51	70.83	36	72	1.06 (0.48–2.36)	0.889
	Aa	18	25	11	22	0.85 (0.36–1.99)	0.702
	a	24	16.67	17	17	1.02 (0.52–2.02)	0.945
	A	120	83.33	83	83	0.98(0.49–1.93)	0.945
OCT3 (rs2292334)	AA	23	31.94	8	16	0.41(0.16-1.0)	0.047*
	GG	28	38.89	23	46	1.34(0.64–2.78)	0.434
	AG	21	29.17	19	38	1.49(0.69–3.2)	0.307
	A	67	46.53	35	35	0.62(0.37–1.05)	0.073
	G	77	53.47	65	65	1.62(0.96–2.73)	0.073
*Statistically significant

Metformin is mostly the first line of treatment and most commonly used drugs for T2DM. However, T2DM patients show considerable interindividual variations in metformin response, with approximately 35–40% of patients taking this drug fail to achieve initial glycemic control [10]. The phenotypic differences in drug disposition and action could result from polymorphisms in genes that regulate pharmacokinetics/pharmacodynamics of metformin. In this context, it was found that OCTs play a critical role in glycemic response and renal elimination of metformin. Thus, it is crucial to understand the role of pharmacogenomics of OCT genes and their association with altered response in T2DM patients to design a more suitable personalized treatment strategy with maximum efficacy. The rs628031 polymorphism of OCT1 gene and rs2292334 of OCT3 has been widely studied in different ethnicities but no study is reported in North Indians.

The OCT1 (SLC22A1) gene consists of 11 exons and is present at 6q26 location on human chromosome spanning about 37 kb [10]. OCT1 is highly polymorphic resulting in differences in transporter function leading to interindividual variations in the metformin responses [11]. A study on Iranian T2DM patients showed non-significantly higher frequency of occurrence of Aa and mutant aa genotype in non-responders (44.1% and 6.8%) than responders (29.2% and 6.2%) which is in accordance with findings from our study where Aa and aa genotype were more prevalent in non-responders (22% and 6%) when compared with responders (16.36% and 4.17%) of monotherapy and combination therapy, respectively [12]. A study reported by Becker et al. showed that Caucasian T2DM patients carrying the mutant allele of OCT1 (rs628031) variant showed less efficacy of metformin in lowering HbA1c and glucose levels, which could be a reason of their higher prevalence in non-responders [13]. A study conducted on Han Chinese T2DM population reported a higher efficacy of metformin with a significant reduction in HbA1c levels (P < 0.02) in subjects carrying the wild type AA genotype compared to those with the heterozygous genotype (Aa) [14]. In another study, mutant allele significantly reduced the efficacy of metformin in Saudi Arabian population (p < < 0.05) [15]. On the contrary, a recent follow-up study in North Indian newly diagnosed diabetics on metformin monotherapy reported increase in metformin efficacy by two times among mutant allele carriers. The frequency of aa genotype and a allele was higher (48.8% and 67.7%) in responders than in non-responders (20.9% and 49.1%) [16]. Our study did not find significant difference in the frequency of AA genotype among responders and non-responders. However, it was marginally higher in monotherapy responders as compared to non-responders.

Other studies on T2DM subjects observed that the frequency of occurrence of rs628031 wild type allele was 80.3% in South Indians [17], 74% in Koreans [18], 81% in Japanese [19], 76.2% in Asians living in the USA and 73.5% in African-Americans [20], which is similar to results reported in our study where it was 72.73% and 70.83% in responders (mono and combination therapy, respectively) and 72% in non-responders. However, it was significantly higher than European Caucasians from the USA (59.8%) [20], Germany (57.4%) [21] and the Netherlands (60.3%) [13]. Our study is consistent with studies conducted by Iranian [12] South Indian [22] Caucasian [13] and Japanese populations that showed no association between OCT1 (rs628031) polymorphism and metformin response [23].

The OCT3 gene (SLC22A3) consists of 15 exons and is present at 6q25.3 location on human chromosome. It is an organic cation transporter that is specifically expressed in liver, kidney and intestine. The principle role of OCT3 is transport of various small endogenous molecules, drugs and environmental toxins. The OCT3-1233G > A (rs2292334) is a synonymous variant in which the displacement G > A converts an alanine codon into another codon of alanine (A411A) [24]. Owing to its role as a drug transporter, number of studies had been conducted on OCT 3 gene reporting its relationship with metformin response. A study conducted on Iranian T2DM patients reported a higher mean reduction in HbA1c levels in patients carrying A allele than in those with the homozygous G allele [8]. Similarly another study on Pakistani population, showed the protective role of the A allele and its association with clinical efficacy of metformin [25]. In our study also, we reported a significantly higher frequency of AA genotype in responders, both in monotherapy (40%) and combination therapy (31.94%) as compared to non-responders (16%) respectively. Also, the frequency of occurrence of A allele was significantly higher in our monotherapy responders (52.73%) when compared with non-responders (35%, p = 0.01) which is in agreement with the above mentioned studies.

The frequency of occurrence of wild type G and mutant A alleles in our study were found to be 65% and 35% in T2DM non-responders subjects respectively which is consistent with the frequencies reported in Iranian (65% and 35%) T2DM subjects [8]. Chen et al reported that the frequency of mutant A allele was 35% in Mexican-American population and 36% in a European-American population [26] whereas, Lazar et al reported the frequency of A allele in a healthy Caucasian population as 36% which is closely similar to the results reported in the present study [24]. However, our study is non in agreement with other studies by Tzvetkov et al. in 2009 and AL-Eitan LN et al in 2019 that showed no significant association between OCT3 (rs2292334) and glycemic control or mean HbA1c levels [21, 27]. The limitation of this study is the lack of lifestyle information and the weak compliance and adherence assessment of the patients, which could affect the glycemic control and response to hypoglycemic agents.

The present study revealed that OCT3 (rs2292334) polymorphism was associated with better metformin response in T2DM patients both on mono and combination therapy whereas OCT1 (rs628031) failed to show any association with metformin efficacy in our study population. The AA genotype and A allele were found to enhance the effectiveness of metformin treatment in T2DM patients whereas GG genotype was found be associated with decreased efficacy of metformin in our study. The results of this study might provide great benefits for patients upon the introduction of personalized T2DM therapy and more focused treatment regimens in North India. Additionally, data from larger cohorts with multiomics approaches along with necessary functional validation would be significantly beneficial to explain comprehensively interindividual variability in the pharmacokinetic, pharmacodynamics, effectiveness and toxicity profiles of drugs, including nephrotoxicity.

Acknowledgement: We would like to acknowledge the entire Medical and Research staff and our colleagues at Era's Lucknow Medical College for their support and contributions in this study.

Authors’ contribution statement: Conceptualization: FM; Supervision and Review: STR; Experimental work, Writing and Editing: SR.

Statements and Declarations

Competing Interests: The authors have no relevant financial or non-financial interests to disclose.
Funding: No funding was received for conducting this study.

Compliance with Ethical Standards

Conflict of interest: The authors declare that they have no conflict of interest in the publication.
Research involving Human participants: This study involved blood samples of subjects who willingly participated.
Informed Consent: Informed consent was taken from the subjects before sample collection.

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Association of Single Nucleotide Polymorphism in OCT1 and OCT3 Genes with the Efficacy of Metformin Response in North Indian Type 2 Diabetes Mellitus Patients.

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