KCNQ1 rs2237895 polymorphism is associated with the therapeutic response to sulfonylureas in Iranian type 2 diabetes mellitus patients

Abstract

Background and Aims

Sulfonylureas are the most secondary prescribed oral anti-diabetic drug. Understanding its genetic role in pharmacodynamics can elucidate a considerable knowledge about personalized treatment in type 2 diabetes patients. This study aimed to assess the impact of KCNQ1 variants on sulfonylureas response among type 2 diabetes Iranian patients.

Methods and Results

100 patients were recruited who were under sulfonylureas therapy for six months. 50 responder and 50 non-responder patients were selected. KCNQ1 variants were determined by the RFLP method, and their role in treatment response was assessed retrospectively. Patients with rs2237895 CC and AC genotypes demonstrated a significant decrement in FBS and HbA1c after treatment over patients with AA genotypes (All P < 0.001). Compared to the A allele, the odds ratio for treatment success between carriers with rs2237895 C allele was 4.22-fold (P < 0.001). Patients with rs2237892 CT heterozygous genotype exhibit a higher reduction rate in HbA1c and FBS than CC homozygotes (P=0.064 and P=0.079, respectively). The rs2237892 T allele carriers showed an odds ratio equals to 2.83-fold over C allele carriers in the responder group compared to the non-responder group (p=0.081).

Conclusion

Findings suggest that the KCNQ1 rs2237895 polymorphism is associated with the sulfonylureas response on Iranian type 2 diabetes patients.

Introduction

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disease that shows high heterogeneity. The most common traits among patients are insulin resistance besides β-cells dysfunction, which leads to hyperglycemia (1). Based on the International Diabetes Federation, about 415 million people had diabetes in 2015, and it is anticipated that by 2030, diabetes will be the seventh most life-threatening disease in the world (2). In addition, It has been approximated that by 2030 approximately 9.2 million Iranians are likely to have diabetes (3). Sulfonylureas (SUs) are oral anti-diabetic drugs that act at the pancreatic β-cell membrane by blocking the KATP channel, which results in increased insulin secretion. Next to metformin, sulfonylureas (SUs) are the widely prescribed drugs for type 2 diabetes patients, and usually, they have been used as the second-line treatment. These drugs have shown a significant decreasing effect on glucose levels, and even some studies have suggested those to be used as the initial prescription for patients with negative glycemic control (4). The Dutch type 2 diabetes guideline suggested gliclazide as the second-line treatment among all the SUs groups (5). In order to find out which of these drugs would be efficient or caused adverse side effects, proper knowledge from T2D population diversity and variations will be required. These variations are related to physiological traits such as BMI, sex, and age, plus pathological conditions including nephropathy or hepatopathy accompanied by various lifestyle factors (Smoking, taking drugs, and alcohol consumption) (6). Gliclazide is a second-generation sulfonylurea oral hypoglycemic agent that enhances insulin secretion through pancreatic sulfonylureas receptor 1 (SUR1) by β-cell stimulation (7). Glibenclamide is another sulfonylurea, despite the gliclazide depicted non-specific affinity to various sulfonylurea receptors, such as SUR1, SUR2A, and SUR2B (8). Although sulfonylureas mainly have shown a beneficial response due to attenuating the glycemic profiles in type 2 diabetes patients, there have been observed adverse situations regards the response of different individuals to these types of drugs, which inter-individual genetic structure differences can explain. Inter-individual genetic variation, to some extent, can affect the absorption, distribution, metabolism, targeting, and efficacy of the drug (9). To a limited degree, a new field in the genetics of diabetes has recently been developed, pharmacogenomics, and is addressing the mentioned issues. Moreover, this field has been trying to, through the help of surveying interindividual variations in DNA sequence associated with the drug treatment consequences, develop effective personalized T2D management. Approximately 20%–40% of differences between patients in metabolism and their therapeutic response toward pharmacological drugs have resulted from genetic factors (10). Several gene variants have shown an association with SUs treatment outcomes, such as ABCC8, KCNJ11, TCF7L2, IRS1, CDKAL1, CDKN2A/2B, and KCNQ1 (11). It is noteworthy that most of these genes are type 2 diabetes risk factors (12); besides, in recent years, even more studies have shown that various polymorphisms of these genes are associated with susceptibility to type 2 diabetes mellitus (13). Moreover, a recent study in Iran has demonstrated that the voltage-gated potassium channel KQT-like sub-family, member one gene (KCNQ1) polymorphism, is associated with T2DM development (14). KCNQ1 channels might have an important role in regulating insulin secretion and regulate cell volume, which is crucially essential for regulating metabolism via insulin (15–20). The KCNQ1 gene is located at 11p15.5 and encodes subunits of the voltage-dependent potassium channel (KvLQ1), which plays a crucial role in repolarizing the cardiac action potential as the transport of water and salts in the epithelial cells (21). This channel is expressed in various tissues such as the heart, skeletal muscle, liver, and epithelium. In addition, it has been found in other tissues, including pancreatic islets, as well as in cultured insulin-secreting INS-1 cells. Considering that, shreds of evidence have shown that KCNQ1 is associated with the β-cell function (18, 22). Recent studies have revealed a significant association between KCNQ1 gene polymorphisms and the sulfonylureas therapeutic response, such as rs163184, rs2237897, rs2237892, and rs2237895. Remarkably, these research types have been conducted primarily on East Asian and European populations (23, 24). For instance, a study in Slovakia showed a significant correlation between KCNQ1 rs163184 (T>G) and sulfonylurea efficacy (25). Moreover, a study in China found an association between two KCNQ1 polymorphisms, including rs2237892 and rs2237895 and Gliclazide response status (26). Although KCNQ1 SNPs have shown correlation with several SUs groups in East Asia and Europe, it remains elusive whether KCNQ1 variants have the same effect on SUs treatment outcome among T2DM patients in Iranians. Therefore, we decided to manage the current study to assess the correlation of KCNQ1 polymorphisms and the therapeutic response to both Gliclizide and Glibenclamide in type 2 diabetes patients in Iran.

Method and material

Patients

The current study assessed 100 patients with type 2 diabetes mellitus through a retrospective approach from outpatient clinics in Ahvaz Judishapur University of medical sciences affiliated hospitals in Ahvaz, Iran. All cases were diagnosed based on World Health Organization criteria (27). Over 500 patients’ records were evaluated, and patients matched with our criteria were selected for the study. Patients were qualified for the analysis if only they have been under treatment with metformin for the last three months and failed to maintain their HbA1c at the controlled level (HbA1c < 6.5%). Inclusion criteria for this study were the age range between 20 to 65 years old with an uncontrolled HbA1c (HbA1c > 6.5%) and BMI > 20 kg / m2 who were under treatment of Gliclazide or Glibenclamide. The exclusion criteria were as follows: 1- Allergy to Gliclazide/Glibenclamide 2- Type 1 diabetes mellitus, gestational diabetes mellitus, or any other types of diabetes 3- Clinical hepatic or renal impairment 4- Cardiac abnormalities Such as myocardial infarction, etc. 5. Any history of diabetic ketoacidosis or non-ketotic hyperosmolar coma or chronic diabetic complications 6- Consumption of non-diabetic drugs that affect glucose metabolism 7- Hepatitis virus infection 8- Malignant diseases 9. Hematological diseases 10. Psychosis 11. Autoimmune disease 12. Pregnant and lactating women. This study was authorized by the ethical committee of Ahvaz Jundishapur University of Medical Sciences. Moreover, written informed consent was obtained from all participants or their guardians.

Anthropometric and biochemical analysis

All cases were subjected to a physical examination; meanwhile, their medical history, demographic parameter, and medication use were gathered through the questionnaire and patients’ records. Anthropometric parameters, including weight and height, were recorded at the baseline besides their serum lipid profiles such as LDL, HDL, triglyceride, and total cholesterol, as well as the FBS. Meanwhile, the BMI was assessed using the weight divided by the height squared formula. Patients were given gliclazide or glibenclamide as treatment, whereas their medication dosage could have been adjusted based on their glycemic status after each referral in a 3-months interval. Furthermore, HbA1c was analyzed at the baseline using the HPLC technique (Bio-Rad Laboratories, Hercules, CA, USA). Subsequently, all the mentioned variables were measured six months after initiation of the sulphonylurea treatment.

Determination of sulfonylurea response

The current study showed a 1.02% reduction of HbA1c on average. Several studies have demonstrated various ranges of glycated hemoglobin reduction from 0.43% to 1.2% (24, 28–31), whereas other studies determined the range of HbA1c reduction from 0.9%–2.5% (32, 33). Considering the HbA1c reduction rate of our study, we decided to divide the subjects into two groups based on their response to sulfonylurea treatment in order to distinguish different responses to the sulfonylureas. ∆HbA1c >1% was considered the responder group, whereas ∆HbA1c < 1% was considered the non-responder group.

Genotyping

As described in the manufacture’s instruction, the genomic DNA was extracted by SinaPure TM DNA kit (Sinaclon, Tehran, Iran). Each SNP sequence was downloaded from (www.ensembl.org), and specific primers were designed for each SNP using (https://primer3.ut.ee/) (Fig. 1). In the current study, all primers were manufactured by Sinaclon Co. One primer set were designed to amplify the specific segment which contains the rs2237892 variant (forward primer: 5’-AGTGTGCATCCTAAGGTGGT-3′; reverse primer: 5’-GCTGGTAGGGAACAACTGGA-3′). Next, another primer set for replicating rs2237895 was designed as follows: forward primer: 5’-TGGGGCAGGGGTGTCTTTA-3′; reverse primer: 5’-TCTGCCTCTTGGTCTCATCTTTAC-3′. The PCR (polymerase chain reaction) was performed in a 25 μl final reaction containing 100 ng template DNA, 8 μl of 2XRed Master Mix 1.5 mM, 0.5 μl from each primer(10 μM), and up to final reaction volume filled by distilled H₂O. Amplification was carried out with the initial denaturation step at 95 °C for 5 min, followed by 35 cycles of denaturation at 95 °C for 1 min, annealing at 59 °C and 56 °C for 45 s in cases of rs2237892 and rs2237895, respectively. Subsequently, the extension was at 72 °C for 1 min, then a final extension at 72 °C for 7 min. Both amplification products were digested via Rapid Digest Sma I (Sinaclon, Tehran, Iran) at 25 °C for 1 h. Regarding rs2237892, CC homozygote samples illustrated two bands each composed of 127 bp whereas TT homozygote samples showed a band composed of 254 bp. Moreover, CT heterozygotes demonstrated three bands which including two 127 bp bands and a 254 bp band. In the case of rs2237895, CC homozygote samples displayed two bands post-digestion including 166 bp and 275 bp. In addition, AA homozygote samples illustrated a single 441 bp band whereas the AC heterozygote samples displayed three bands including 166 bp, 275 bp and 441 bp on the gel electrophoresis post-digestion (Fig. 2). Ultimately, Sanger sequencing was performed on 20 randomly selected samples to confirm the resolution of PCR-RFLP results. As well in the case of partial digestion or any other unclarity, the same method was conducted on those specific samples to confirm PCR-RFLP results (Supplemental, Figs. 1 and 2).

Fig. 1.

PCR products on 2% gel electrophoresis. A) KCNQ1 rs2237892 polymorphism PCR product. B) KCNQ1 rs2237895 polymorphism PCR product (Rasband, W.S., ImageJ software, U. S. National Institutes of Health, Bethesda, Maryland, USA)

Fig. 2.

PCR-RFLP results on 2% gel electrophoresis. A) Separated PCR-RFLP products of targeted gene (KCNQ1 rs2237892 polymorphism). B) Separated PCR-RFLP products of targeted gene (KCNQ1 rs2237895 polymorphism) (Rasband, W.S., ImageJ software, U. S. National Institutes of Health, Bethesda, Maryland, USA)

Statistical analysis

All variables are presented as the Mean±SEM or n (%). Paired t-test was used to compare the baseline values versus the after 6-month values of sulfonylurea treatment. Hardy-Weinberg equilibrium and allelic frequencies were analyzed with Pearson’s χ2-test. Genotype distribution differences between the responder and the non-responder groups were assessed by chi-squared (Fisher’s exact) test. The Δ rates were calculated as the rates at the 6th month minus the rates of the baseline. ANOVA followed by the Bonferroni multiple range tests were performed to analyze the differences among the three genotypes. After adjusting for age, gender, BMI, and ∆ values adjusting for their baseline values, general linear models and multiple linear regression were performed to assess the differences in quantitative traits at baseline, six months, and Δ values. The odds ratio (OR) values were calculated with 95% confidence intervals (CIs). A two-tailed test with a type error level (α) set at 5% was applied in all statistical analyses. P < 0.05 was taken into account as significant. Statistical analyses were performed using SPSS for Windows software (version 26; SPSS Statistics, IBM Corporation, Armonk, NY, USA).

Results

In the current study, we have enrolled 100 individuals with type 2 diabetes mellitus (68 females and 32 Male) with a mean age of (53.51 ± 0.70) and assessed them retrospectively. The clinical characteristics after six months of treatment by sulfonylureas are summarized in Table 1. After the treatment, FBS, LDL, and HbA1c levels declined significantly compared to their baseline level (all P < 0.001). Meanwhile, other variables such as body weight, BMI, HDL, TG, and TC have not shown any significant changes after sulfonylurea treatment. To evaluate the effects of KCNQ1 SNPs (rs2237892, rs2237895) on indices of glycemic control and other clinical characteristics before and after treatment, we analyzed the variances of each index among different genotype groups (Fig. 2). The distribution of all SNPs genotypes followed the Hardy-Weinberg equilibrium. The mean values of these parameters at the baseline, post-treatment, and ∆ level are illustrated in Tables 2 and 3.

Table 1.

Clinical characteristics of patients before and after sulfonylurea treatment

	Pretreatment	Posttreatment	P Value
Weight(Kg)	73.160 ± 1.03	73.340 ± 1.04	0.165
BMI (Kg/m2)	27.67 ± 0.36	27.74 ± 0.34	0.187
HbA1c (%)	8.64 ± 0.12	7.62 ± 0.13	<0.001
HDL (mmol/L)	1.15 ± 0.17	1.14 ± 0.16	0.122
LDL (mmol/L)	2.96 ± 0.10	2.56 ± 0.09	<0.001
TG (mmol/L)	1.90 ± 0.44	1.86 ± 0.42	0.143
TC (mmol/L)	4.64 ± 0.12	4.49 ± 0.13	0.58
FBS (mmol/L)	9.78 ± 0.13	8.02 ± 0.15	<0.001

Data are presented as mean ± SEM. BMI, Body mass index; HbA1c, glycated hemoglobin; HDL, High-density lipoprotein; LDL, Low-density lipoprotein; TG, Triglyceride; TC, Total cholesterol; FBS, Fast blood sugar

Table 2.

Clinical and biochemical characteristics of the subjects according to KCNQ1 rs2237892 genotypes

Parameter	CC	CT	P Value
Gender(Male/Female)	26/56	6/12	0.893
Age(Years)	53.65 ± 0.78	52.87 ± 1.66	0.672
Weight(Kg) - Baseline	73.28 ± 1.07	72.58 ± 2.28	0.781
Weight(Kg)-Posttreatment	73.41 ± 1.09	72.98 ± 2.32	0.866
∆Weight(Kg)	0.13 ± 0.14	0.40 ± 0.30	0.413
BMI – Baseline(Kg/m2)	27.83 ± 0.39	26.98 ± 0.84	0.365
BMI – Posttreatment(Kg/m2)	27.87 ± 0.40	27.14 ± 0.85	0.441
∆BMI(Kg/m2)	0.04 ± 0.05	0.16 ± 0.11	0.344
HbA1c – Baseline (%)	8.64 ± 0.13	8.63 ± 0.28	0.976
HbA1c – Posttreatment (%)	7.67 ± 0.15	7.35 ± 0.32	0.379
∆HbA1c (%)	−0.97 ± 0.07	−1.28 ± 0.15	0.064
HDL-Baseline(mmol/L)	1.15 ± 0.01	1.19 ± 0.04	0.369
HDL-Posttreatment(mmol/L)	1.13 ± 0.018	1.19 ± 0.03	0.167
∆HDL(mmol/L)	−0.011 ± 0.005	0.012 ± 0.01	0.061
LDL-Baseline(mmol/L)	2.99 ± 0.12	2.86 ± 0.26	0.657
LDL-Posttreatment(mmol/L)	2.55 ± 0.10	2.57 ± 0.21	0.927
∆LDL(mmol/L)	−0.42 ± 0.06	−0.32 ± 0.14	0.527
TG-Baseline(mmol/L)	1.91 ± 0.05	1.83 ± 0.10	0.533
TG-Posttreatment(mmol/L)	1.85 ± 0.04	1.90 ± 0.10	0.628
∆TG(mmol/L)	−0.06 ± 0.02	0.05 ± 0.059	0.081
TC-Baseline(mmol/L)	4.60 ± 0.13	4.61 ± 0.29	0.969
TC Posttreatment(mmol/L)	4.44 ± 0.13	4.48 ± 0.29	0.907
∆TC(mmol/L)	−0.15 ± 0.08	−0.13 ± 0.18	0.889
FBS-Baseline(mmol/L)	9.71 ± 0.14	10.02 ± 0.31	0.370
FBS-Posttreatment(mmol/L)	8.06 ± 0.16	7.77 ± 0.36	0.460
∆FBS(mmol/L)	−1.65 ± 0.10	−2.11 ± 0.23	0.079

Data are presented as mean ± SEM. P < 0.05 compared CC vs. CT (ANOVA). All Values were adjusted for age, gender, BMI at baseline, and ∆ level adjusted for their baseline values (Comparison CC vs CT- Bonferroni test)

Table 3.

Clinical and biochemical characteristics of the subjects according to KCNQ1 rs2237895 genotypes

Parameter	AA	AC	CC	P Value
Gender(Male/Female)	10/20	17/35	5/13	0.912
Age(Years)	54.08 ± 1.28	53.95 ± 0.97	51.27 ± 1.65	0.334
Weight(Kg) - Baseline	74.28 ± 1.74	71.50 ± 1.32	76.08 ± 2.27	0.169
Weight(Kg)-Posttreatment	74.15 ± 1.77	71.67 ± 1.35	76.77 ± 2.31	0.147
∆Weight(Kg)	−0.13 ± 0.23	0.19 ± 0.17	0.66 ± 0.30	0.119
BMI – Baseline(Kg/m2)	27.95 ± 0.66	27.51 ± 0.50	27.68 ± 0.85	0.871
BMI – Posttreatment(Kg/m2)	27.89 ± 0.66	27.59 ± 0.50	27.92 ± 0.87	0.910
∆BMI(Kg/m2)	−0.05 ± 0.08	0.07 ± 0.06	0.24 ± 0.11	0.125
HbA1c – Baseline (%)	8.63 ± 0.21	8.55 ± 0.16	8.93 ± 0.28	0.517
HbA1c – Posttreatment (%)	8.01 ± 0.25	7.45 ± 0.19	7.42 ± 0.32	0.178
∆HbA1c (%)	−0.62 ± 0.10	−1.09 ± 0.07	−1.53 ± 0.13	<0.001**
HDL-Baseline(mmol/L)	1.15 ± 0.03	1.16 ± 0.02	1.15 ± 0.04	0.985
HDL-Posttreatment(mmol/L)	1.14 ± 0.03	1.15 ± 0.02	1.13 ± 0.04	0.931
∆HDL(mmol/L)	−0.007 ± 0.009	−0.005 ± 0.007	−0.01 ± 0.01	0.704
LDL-Baseline(mmol/L)	3.08 ± 0.20	2.97 ± 0.15	2.78 ± 0.26	0.674
LDL-Posttreatment(mmol/L)	2.82 ± 0.16	2.47 ± 0.12	2.37 ± 0.21	0.149
∆LDL(mmol/L)	−0.21 ± 0.10	−0.49 ± 0.08	−0.48 ± 0.14	0.103
TG-Baseline(mmol/L)	1.83 ± 0.08	1.88 ± 0.06	2.05 ± 0.10	0.245
TG-Posttreatment(mmol/L)	1.84 ± 0.08	1.83 ± 0.06	1.96 ± 0.10	0.574
∆TG(mmol/L)	−0.004 ± 0.04	−0.05 ± 0.03	−0.06 ± 0.06	0.666
TC-Baseline(mmol/L)	4.39 ± 0.22	4.71 ± 0.17	4.64 ± 0.29	0.535
TC Posttreatment(mmol/L)	4.25 ± 0.22	4.46 ± 0.17	4.73 ± 0.29	0.444
∆TC(mmol/L)	−0.18 ± 0.14	−0.22 ± 0.10	0.09 ± 0.18	0.318
FBS-Baseline(mmol/L)	9.60 ± 0.24	9.69 ± 0.18	10.23 ± 0.31	0.251
FBS-Posttreatment(mmol/L)	8.43 ± 0.27	7.91 ± 0.20	7.59 ± 0.35	0.149
∆FBS(mmol/L)	−1.18 ± 0.16	−1.76 ± 0.12	−2.55 ± 0.21	0.017,<0.001**

Data are presented as mean ± SEM. *P < 0.05 pairwise comparison AA vs. CC (Bonferroni test), ^** P < 0.05 General linear model – pairwise comparison AA vs. AC and AA vs. CC, respectively (Bonferroni test). All Values were adjusted for age, gender, BMI at baseline and in case of ∆ level adjusted for their baseline values as well

Regarding rs2237892, there were no significant differences between the clinical characteristics. It is noteworthy that among all of the 100 patients, no TT genotype was detected. In the case of rs2237892, the reduction rate of FBS (∆FBS) and HbA1c (∆HbA1c) after six months of treatment showed a unique relationship but not significant between genotype groups, and also analysis revealed that CT genotype exhibited a lower mean value compared to CC genotype (P = 0.079, P = 0.064, respectively). Furthermore, no apparent variation was found between clinical characteristics in rs2237895 except for FBS and HbA1c ∆ values, displayed in Table 3. For instance, reduction rates (∆) of HbA1c and FBS have demonstrated a significant difference among patients with various genotypes. In ∆HbA1c and ∆FBS, subjects with the C allele demonstrated a lower mean value than patients with AA genotype (P < 0.001). In addition, after adjusting for age, gender, BMI at baseline, there was a significant linear relationship between the C allele and the reduction rate of both FBS and HbA1c (P < 0.001). Multiple linear regression analysis showed that the predictors with a significant linear relationship with ∆FBS were the genotype groups and BMI at the baseline level (R² = 0.290, P < 0.001, and P=0.012, respectively).

On the other hand, the current study also showed two predictors with a significant relationship with ∆HbA1c; rs2237895 genotypes and BMI-baseline (P < 0.001, and P=0.045, respectively). As illustrated in Table 3, a direct linear relationship between units of rs2237895 C alleles and the decline rate of ∆HbA1c (R² = 0.282, P < 0.001). In order to expand our exploration, we decided to evaluate the relevance of concomitant occurrence of both SNPs genotypes and the clinical characteristics. According to the current analysis, only ∆ values of HbA1c and FBS showed significant differences among various bivariate genotypes (P < 0.001). However, no other clinical characteristics showed significant differences between other bivariate genotype groups (Supplemental, Table 1).

Furthermore, to assess the association of mentioned KCNQ1 gene polymorphisms with the response status of SUs drugs, the genotype distribution based on therapeutic response is illustrated in Table 4. According to the response criteria, rs2237892 had no significant association with SUs treatment (P = 0.066); however, the rs2237892 CT heterozygotes demonstrated an impressive response to SUs treatment. Meanwhile, following rs2237895, a higher number of CC homozygotes among responders, CC; 30% of the CC homozygous patients included as responders, compared with just 8% AA homozygotes among responders (P < 0.001). Similarly, AC heterozygotes showed more responders (62%) than AA homozygotes (8%).

Table 4.

Genotype and allele distributions of responders and non-responders carrying the KCNQ1 rs2237892 and rs2237895 variants

rs2237892	CC	CT	TT	P Value
Responder (%)	37 (74)	13 (26)	0	0.066
Non-responder (%)	45 (90)	5 (10)	0
OR (95% CI)	0.31 (0.10–0.96)	3.16 (1.03–9.68)	0
rs2237895	AA	AC	CC
Responder (%)	4 (8)	31 (62)	15 (30)	<0.001
Non-responder (%)	26 (52)	21 (42)	3 (6)
OR (95% CI)	0.061a (0.019–0.192)	2.25b (1.01–5.01)	6.71c (1.80–24.99)

^aAA versus AC+CC, ^bAC versus AA+CC, ^cCC versus AA+AC

Discussion

The current study has shown a significant relationship between a KCNQ1 SNP and SUs therapeutic effect in Iranian type 2 diabetes patients. These findings show that the rs2237895 A>C variant leads to better responses regards sulfonylurea therapy. In pancreatic β-cells, the Kv channels are mediators of repolarization, Ca²⁺ influx closure, and the insulin secretion restriction encoded by the KCNQ1 gene (34). The rs2237892 T allele is related to a higher second phase glucose-stimulated insulin secretion through the hyperglycemic clamp (35). Given the evidence that has been reported by Qing Li et al. toward the Chinese population, HbA1c reduction after sulfonylurea therapy might be caused by insulin secretion increases due to rs2237892 C>T variant (26). Therefore, we decided to assess the same subject in our population. Although the mentioned study has been performed on 100 individuals as the current study, rs2237892 CT heterozygous genotype displayed a slightly increased rate in treatment success compared to CC homozygous genotype; while it was not statistically significant. It is noteworthy that we have not come across any TT homozygotes throughout all our subjects. Nonetheless, the underlying mechanism of how the rs2237892 T allele would influence electrical alteration of the β-cell membrane after sulfonylurea treatment remains to be elucidated. On the subject of rs2237895, a study on the Scandinavian population’s findings confirmed that the rs2237895 C allele increases the risk of T2DM incident through β-cell impairment (19). Moreover, rs2237895 CC homozygous demonstrated an explicit enhancement in treatment success over AA homozygous genotype, and also there was a significant linear relationship between CC and AC genotypes with the reduction rate of both HbA1c and FPG. Following the previous studies on different populations, our results demonstrated that patients with CC rs2237895 homozygous genotype have a slightly higher rate of glycated hemoglobin at the baseline level. However, despite the other studies, there was no significant difference (P value=0.51) among different genotypes, which might have happened due to the genetic variations in different populations (25, 26). Furthermore, the reduction rate of glycated hemoglobin after SUs treatment in CC rs2237895 was significantly higher than AA homozygotes (P < 0.001), which caused the lower failure rate among the patients with the same genotype. However, our findings illustrated that rs2237895 AC heterozygotes also show a drastically higher rate of ∆HbA1c, which is not consistent with the previous studies; thus, we hypothesized that these discrepancies might have occurred due to the various genetic structure of different ethnicities (25, 26). Previous researches mainly have been conducted among the East Asian population, and in their prospective studies, they have performed experiments on newly diagnosed T2DM patients while patients were under monotherapy with a single drug such as gliclazide MR. Meanwhile, in this study, patients were prescribed gliclazide or glibenclamide and studied retrospectively, which could have caused the different rates in total. Thus far, to the best of our knowledge, no study has been conducted to assess the relationship between KCNQ1 SNPs and the SUs’ therapeutic effect in Iranians. Since both tested SNPs are located at the intron region, they cannot alter the amino acid sequence. However, both SNPs might play a much more critical role in SUs metabolism by affecting the gene’s function, which remains unknown and requires more investigations. Compared to a recent study by Qing Li et al. (26), we conducted adjustment for multiple comparisons, and therefore to some extent, the possibility of false-positive findings has been excluded. Furthermore, this study has investigated the simultaneous effect of KCNQ1 SNPs on participants’ clinical and biochemical characteristics, which given to our knowledge it has not been considered in the previous studies. There are some limitations to our approach. First, the sample size was relatively small, which might have resulted in insufficient statistical power and might also be the reason for the lack of TT homozygotes among the patients and could be responsible for the failure to detect a relationship between rs2237892 SNP and therapeutic response of sulfonylureas. However, the impact of the limitedness of follow-up duration cannot be overlooked. Second, various definitions of sulfonylurea response’s “cut-off” might have caused inconsistencies among different studies, which might affect the result of the current study. Also, different efficacies for sulfonylureas in the HbA1c or FBS baseline could be led to adverse results. Third, no placebo group was enrolled in the current study, so the lifestyle modification effect cannot be overlooked. In conclusion, the KCNQ1 rs2237895 polymorphism is associated with the sulfonylurea response in type 2 diabetes patients in Iranians. Consequently, these kinds of observations might result in developing personalized-based medicine strategies toward oral anti-diabetic drug treatment. Future large-scale studies are required for further assessments besides placebo control group enrollment, which is essential to replicate the same results in a more sophisticated study.

Abbreviations

T2DM

Type 2 diabetes mellitus

T2D

Type 2 diabetes

SUR1

Sulfonylurea receptor 1

SUR2A

Sulfonylurea receptor 2A

SUR2B

Sulfonylurea receptor 2B

RFLP

Restriction fragment length polymorphism

KCNQ1

Potassium Voltage-Gated Channel Subfamily Q Member 1

SU

Sulfonylurea

BMI

Body mass index

FBS

Fast blood sugar

HbA1c

Hemoglobin A1C

HDL

High-density lipid

LDL

Low-density lipid

TC

Total cholesterol

TG

Triglyceride

Funding

This work was supported by Ahvaz Jundishapur University Medical Sciences (research project no: D-9712).

Declarations

Competing Interest

We have no conflict of interest.