Risk of type 2 diabetes mellitus and cardiovascular complications in KCNJ11, HHEX and SLC30A8 genetic polymorphisms carriers: A case-control study.

BACKGROUND: Type 2 diabetes mellitus (T2DM) and cardiovascular disease (CVD) are two deadly diseases caused by the complex interaction of multiple genetic loci, lifestyle and environmental factors. Genome-wide association studies described hundreds of susceptibility loci for T2DM and T2DM-related CVD, but it remains uncertain due to geographic and ethnic variations. The objective of this study was to evaluate the associations of KCNJ11 rs5219, SLC30A8 rs13266634 and HHEX rs1111875 polymorphisms with T2DM and related CVD.
METHODS: Genotyping of all three polymorphisms was performed using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method on 250 T2DM cases and 246 healthy controls. Both descriptive and inferential statistical methods were applied using MedCalc and IBM SPSS software programs for statistical analyses.
RESULTS: A significantly increased association of KCNJ11 rs5219 (p<0.05) with T2DM was found in dominant, recessive, heterozygote, homozygote, and allele model (aOR = 2.23, 2.03, 1.90, 3.09, and 1.80, respectively). For SLC30A8 rs13266634, only dominant, heterozygote, and allele model (aOR = 3.37, 3.59, and 1.79, respectively) showed significantly increased association with T2DM. SNP rs1111875 (HHEX) also revealed 2.08, 4.18, 5.93, and 2.08-times significant association in dominant, recessive, homozygote, and allele models. Besides, a significantly reduced correlation of KCNJ11 rs5219 was found with T2DM-related CVD in the recessive and allele model (aOR = 0.40 and 0.65, respectively). Again, a significant difference was observed between T2DM-related CVD and non-CVD patients in terms of gender distribution, fasting blood glucose (FBG), systolic blood pressure (SBP), diastolic blood pressure (DBP), total cholesterol (TC), and triglycerides (TG).
CONCLUSIONS: Our investigation indicates that KCNJ11 rs5219, SLC30A8 rs13266634 and HHEX rs1111875 polymorphisms are associated with T2DM. Moreover, KCNJ11 rs5219 polymorphism is correlated with the risk of T2DM-related CVD.

Introduction

Type 2 diabetes mellitus (T2DM) is a global chronic and lifelong health problem mainly caused by genetic factors, deficiency or ineffectiveness of pancreatic insulin production and secretion, peripheral insulin resistance, and environmental factors [1, 2, 3]. The prevalence of T2DM patients has been drastically increasing around the world, more specifically, in low to middle economic countries. In 2017, more than 69 million patients were Bangladeshis out of 425 million diagnosed patients with diabetes worldwide, with a prevalence of 6.9%, and surprisingly 90% of them had T2DM [4, 5]. It has recently turned into a shocking epidemic in South East Asia, where the Indian subcontinent, including Bangladesh and China, has the highest incidence rate and the number is rapidly rising [6, 7, 8].

Different environmental and genetic factors as well as their complex interactions are involved in T2DM development [9, 10, 11]. T2DM creates a significant burden on public health care by producing a variety of complications that can damage the heart, kidney, eyes, and others. Genome-wide association studies (GWAS) have recently identified more than 100 loci for susceptibility to T2DM and identified different genetic markers that are significantly associated with obesity, diabetes, and cardiovascular disease (CVD) [12, 13, 14, 15, 16, 17]. Moreover, many other studies have shown that different genes are involved in the metabolism of glucose, beta-cell function, and insulin secretion pathways in a single and combined way, which ultimately results in T2DM [18, 19].

The electrical activity of insulin secretion can be limited by different ion channels of plasma membrane-like ATP-sensitive potassium (KATP) channel [20]. Potassium inwardly-rectifying channel (KCNJ11)-11th member of J subfamily gene belongs to the potassium ion channel. KCNJ11 makes up the compartment of the KATP channel, up-regulating insulin secretion in their inhibition condition [21, 22]. After point mutation in the rs5219 locus of KCNJ11, thymine (T) takes the place of cytosine (C) by substitution at the NH2- terminal of Kir6.2 and 15th intron, respectively that attenuate the sensitivity of the channel to ATP [3, 22]. Previous studies have evaluated the role KJNJ11 polymorphisms and the risk of T2DM and associated diseases [3, 8, 15].

In human chromosome 10, many polymorphisms of the 350 kb linkage disequilibrium (LD) block are inextricably associated with insulin secretion, T2DM development, and increased glucagon secretion. Haematopoietically expressed homeobox or HHEX is one of the three genes of LD block, and it contains an insulin-degrading enzyme [23, 24, 25]. The rs1111875 is a single nucleotide polymorphism (SNP) in the 3′ flanking regions of the HHEX gene and after polymorphism, it causes T2DM [26]. The eighth member of solute carrier family 30 (SLC30A8) or Zinc transporter 8 (ZNT8), mainly expressed in beta cells of pancreatic islets, a novel ZNT family member which responsibly transports zinc into insulin-containing granules from the cytoplasm and subsequently forms insulin crystal for secretion, maturation and storage [27]. At 325 position, rs13266634 of SLC30A8 gene forms tryptophan (W) in the place of arginine (R) (Arg325Trp) after polymorphism that may be correlated with impaired glucose regulation [28]. Investigations have provided evidence of the association between SLC30A8 gene variants and the risk of T2DM and cardiovascular complications [28].

Unfortunately, there is a scarcity in a genetic association of T2DM and related cardiovascular complications related research in Bangladesh. To date, there is no single study that evaluated the impact of genetic variants on the risk of T2DM and T2DM-related CVD. Therefore, we have identified three single nucleotide polymorphisms (SNPs) in three candidate genes, namely KCNJ11 rs5219, HHEX rs1111875, and SLC30A8 rs13266634 for the present study, which were previously known to be involved with T2DM and T2DM-related CVD among different ethnic groups. Our present study aimed to provide evidence of the possible link of these SNPs with T2DM and T2DM-related CVD in the Bangladeshi population.

Methods

Statistical analysis

Different demographic data of the T2DM patients including, age, BMI, FBG level, 2-hour postprandial blood glucose level, blood pressure, total cholesterol, triglycerides, and serum creatinine and related data of healthy controls (age, BMI and FBG level), and T2DM patients with T2DM-related CVD and without CVD were presented as average ± standard deviations and compared with a one way ANOVA test.

Hardy-Weinberg equilibrium (HWE) values were found from the Hardy-Weinberg equilibrium test. To find the genotypic associations of all SNPs with type 2 diabetes, we have tested different genetic models such as heterozygote model (Aa vs. AA), homozygote model (aa vs. AA), dominant model (Aa + aa vs. AA), recessive model (AA + Aa vs. aa), and allele model (a vs. A). Association of risk of T2DM and T2DM-related CVD was presented as adjusted odds ratio (aOR) that were adjusted based on the age, gender and BMI, and 95% confidence intervals (95% CI) based on χ2-test, which were evaluated by MedCalc software. All other calculations including HWE were performed by SPSS (Version 25, IBM). p <0.05 was considered statistically significant for all of the analyses in this study. Again, statistical power was estimated for three SNPs using OSSE online tool (http://osse.bii.a-star.edu.sg/).

Ethical statement

The study protocol and questionnaire were approved by the ethical committee of the Noakhali Science and Technology University (ID# 03/2018). Furthermore, written informed consent was obtained prior to the investigation from all the cases and healthy controls.

Clinical parameters

T2DM-related CVD has been diagnosed in 250 patients with T2DM according to clinical features. Among them, the presence of at least one of the following pathological conditions has been found: diabetic cardiomyopathy, heart failure and coronary heart disease. T2DM-related CVD was found in a total of 116 patients (46.40%). Based on the presence or absence of hypertension, 250 patients with T2DM were divided into two groups (116 with T2DM-related CVD and 134 without CVD). Hypertension was defined based on the presence of ≥140/90 mmHg blood pressure or the use of antihypertensive drugs.

All clinical features were documented in a questionnaire form, and our current analysis has been conducted in accordance with the Helsinki Declaration and its subsequent amendments [30]. The full genetic analysis was conducted in the Laboratory of Pharmacogenomics and Molecular Biology, Department of Pharmacy, Noakhali Science and Technology University, Bangladesh.

Study design and subject recruitment

This case-control study consisted of 250 patients with T2DM and 246 healthy volunteers matching age and sex with the patients. T2DM patients were recruited from Al-Haj Sirajul Islam Diabetic and General Hospital, Maijdee, Noakhali, Bangladesh. According to the WHO criteria (fasting blood glucose or FBG level >7.0 mmol/l or random plasma glucose level >11.1 mmol/l), these patients were diagnosed. In the presence of expert physicians, the detailed physical (sex, age, body mass index or BMI) and clinical (fasting blood glucose level, 2-hour postprandial blood glucose level, blood pressure, total cholesterol, triglycerides and serum creatinine) history of all cases were taken by a trained nurse from the personal interview and medical records between the period of August 2018 to April 2019 [29]. By matching sex, age, and BMI, healthy non-diabetic controls with a <6.2 mmol/L of FBG level were selected from different parts of the Noakhali region, and the FBG levels of controls were detected with a portable Quick Check Glucometer. Normal glucose tolerance, no family history of severe diseases like kidney disease, ocular disease, cancer, and heart disease were present in their body. Subjects with other chronic illnesses were excluded during recruitment.

Genotyping of single nucleotide polymorphisms (SNPs) of candidate genes

From all the cases and healthy controls, 3 ml venous blood samples were collected in ethylenediaminetetraacetic acid (EDTA)-Na2 containing sterile tubes, and until DNA extraction, it was stored at -80 °C. After extraction of all genomic DNA following the previously established method [31], DNA amplification was done using the primers as described earlier [32, 33]. To genotype KCNJ11 rs5219, SLC30A8 rs13266634 and HHEX rs1111875 polymorphisms, the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method was used. By running on 1% agarose gel, PCR products (210 bp, 256 bp and 161 bp for rs5219, rs13266634 and rs1111875 polymorphisms, respectively) were confirmed. Then with the restriction endonucleases (BanII, MspI and XbaI, respectively) successively for rs5219 (Figure 1), rs13266634 (Figure 2) and rs1111875 (Figure 3) SNPs, five microliters of confirmed PCR products were digested at proper conditions, and after ethidium bromide staining, fragments were visualized on 2% agarose gel. We have reanalyzed all mutant homozygotes and 20% of heterozygotes twice that confirmed our findings. PCR conditions, number of cycles and the number of fragments for wild-type homozygote (AA), heterozygote (Aa) and mutant homozygote (aa) of all SNPs are described in Table 1.

Figure 1

Restriction endonuclease (BanII) digestion fragments of KCNJ11 rs5219 polymorphism (2% agarose gel). Lane-1: 50 bp molecular marker; Lanes 2 and 8: heterozygous (CT) form (28, 36, 146 and 174 bp); Lanes 3 and 7: mutant (TT) form (36 and 174 bp) and lanes 4, 5, and 6: wild-type (CC) form (28, 36 and 146 bp).

Figure 2

Restriction Endonuclease (MspI) digestion fragments of rs13266634 allele of SLC30A8 gene (1% agarose gel). Lane-1: 50 bp molecular ruler; Lanes- 3-6 and 8-12: heterozygous (CT) form (46, 210 and 256 bp); Lane- 7: mutant (TT) form (256 bp); lane- 2: wild-type (CC) form (46 and 210 bp).

Figure 3

Restriction endonuclease (XbaI) digestion fragments of HHEX rs1111875 polymorphism (2% agarose gel). Lane-1: 50 bp molecular marker; Lanes- 3, 4, 5 and 7: heterozygous (CT) form (54, 107 and 161 bp), Lane- 6: homozygous (TT) form (54, 107 bp), and Lanes- 2, 8, 9, and 10: wild-type (CC) form (161 bp).

Table 1

Primers, conditions of PCR, restriction enzymes, digestion condition and estimated DNA fragments on digestion to the genotype of selected SNPs.

SNPs	Primers (5’-3’)	PCR condition	No. of cycles	SAF (bp)	RE	Digestion condition	Fragments of DNA
KCNJ11rs5219	F: ACGTTGCAGTTGCCTTTCTTR: GACTCTGCAGTGAGGCCCTA	95 °C 30 s62 °C 30 s72 °C 30 s	35	210	BanII (Takara, Japan)	37 °C (incubated 4 h)	AA: 28, 36, 146Aa: 28, 36, 146, 174 aa: 36, 174
SLC30A8rs13266634	F: GAAGTTGGAGTCAGAGCAGTCR: TGGCCTGTCAAATTTGGGAA	94 °C 30 s59 °C 27 s72 °C 40 s	35	256	MspI (NEB, England)	37 °C (incubated overnight)	AA: 46, 210Aa: 46, 210, 256 aa: 256
HHEXrs1111875	F: GCTGCTTATGGAAACTGCATTACTR: CATCATAACTTCTCACTCCCTTCC	94 °C 1 min61 °C 1 min72 °C 1 min	35	161	XbaI (Takara, Japan)	37 °C (incubated overnight)	AA: 161Aa: 54, 107, 161 aa: 54, 107

size of amplification fragment; restriction endonuclease; wild-type homozygote; heterozygote; mutant homozygote; forward; reverse.

Results

Characteristic features of cases and healthy controls

In this case-control study, a total of 250 T2DM patients and 246 healthy controls were recruited for the analysis. Among them, 124 (49.60%) were males, and 126 (50.40%) were females in the patient group, whereas 140 (56.91%) were males and 106 (43.09%) were females in the control group. All clinical data and demographic characteristics of study populations are summarized in Table 2. Again, significant differences were observed between T2DM with CVD patients and T2DM without CVD in terms of gender distribution, fasting blood glucose (FBG), systolic blood pressure (SBP), diastolic blood pressure (DBP), total cholesterol (TC), and triglycerides (TG) were found (p-value: <0.001, 0.020, 0.011, 0.034, 0.009, 0.039, respectively) that are presented briefly in Table 3.

Table 2

Sociodemographic and clinical characteristics of T2DM patients and healthy controls.

Variables	T2DM Patients(n = 250)	Healthy Controls(n = 246)	Normal Range
Age (years) (±SD)	53.83 ± 12.03	52.30 ± 10.84	NA
Age range (years)	25–80	25–77	NA
Sex (male/female)	124/126	140/106	NA
BMI (kg/m2) (±SD)	25.33 ± 3.90	23.54 ± 2.64	18.5–24.9
FBG (mmol/l) (±SD)	10.06 ± 1.95	5.76 ± 0.56	3.9–5.6
2 h PBG (mmol/l) (±SD)	14.90 ± 3.89	NA	<7.8
SBP (mmHg) (±SD)	124.52 ± 12.71	NA	120–129
DBP (mmHg) (±SD)	81.25 ± 10.04	NA	80–84
TC (mg/dl) (±SD)	200.96 ± 38.34	NA	<200
TG (mg/dl) (±SD)	192.46 ± 31.30	NA	<150
SC (mg/dl) (±SD)	1.03 ± 0.19	NA	0.7–1.2

Data are expressed as mean ± SD, median (interquartile range), or percentage values (%); type 2 diabetes mellitus; body mass index; fasting blood glucose; 2 h postprandial blood glucose; systolic blood pressure; diastolic blood pressure; total cholesterol; triglycerides; serum creatinine; not available.

Table 3

Selected characteristics of T2DM patients with T2DM-related CVD and without CVD.

Variables	T2DM with CVD(n = 116) (%)	T2DM without CVD(n = 134) (%)	p-value
Gender
Male	68 (58.62)	56 (41.79)	0.059
Female	48 (41.38)	78 (58.21)	<0.001
Age (years)
Mean age, n (±SD)	53.74 ± 11.42	53.92 ± 12.59	0.284
Range	28–80	25–80
BMI (kg/m2) (±SD)	26.04 ± 3.66	24.72 ± 4.01	0.872
FBG (mmol/l) (±SD)	10.73 ± 1.76	9.48 ± 1.92	0.020
2 h PBG (mmol/l) (±SD)	15.48 ± 4.17	14.41 ± 3.58	0.069
SBP (mmHg) (±SD)	132.01 ± 11.65	118.10 ± 9.75	0.011
DBP (mmHg) (±SD)	86.25 ± 8.46	76.96 ± 9.31	0.034
TC (mg/dl) (±SD)	223.38 ± 37.16	181.75 ± 27.48	0.009
TG (mg/dl) (±SD)	210.29 ± 24.37	177.18 ± 28.45	0.039
SC (mg/dl) (±SD)	1.11 ± 0.20	0.95 ± 0.14	0.146

Data are expressed as mean ± SD, median (interquartile range), or percentage values (%); type 2 diabetes mellitus; body mass index; fasting blood glucose; 2 h postprandial blood glucose; systolic blood pressure; diastolic blood pressure; total cholesterol; triglycerides; serum creatinine. p-value <0.05 was considered significant (Bold).

Influences of the variants in T2DM

For all SNPs, we tested the Hardy-Weinberg equilibrium (HWE) in cases and healthy controls. We evaluated the association between risk of T2DM and all selected SNPs (Table 4). Genotype distributions for KCNJ11 rs5219, SLC30A8 rs13266634 and HHEX rs1111875 for T2DM group and healthy control group are presented in Supplementary Table 1. Among the three variants from three different loci, rs5219 of KCNJ11 showed significant association in heterozygote model, homozygote model, allele model, dominant model, and recessive model (p = 0.004, <0.0001, <0.0001, <0.0001, and 0.001; aOR = 1.90, 3.09, 1.80, 2.23, and 2.03, respectively). On the other hand, SLC30A8 rs13266634 demonstrated a significant relationship with T2DM in heterozygote, allele, and dominant models (p <0.0001 for all models; aOR = 3.59, 1.79, and 3.37, respectively). Another variant, rs1111875 of HHEX, showed significant correlation in allele model, homozygote model, dominant, and recessive model (p <0.0001, <0.0001, 0.001, and <0.0001; aOR = 2.08, 5.93, 2.08, and 4.18, respectively).

Table 4

Association of candidate polymorphisms with T2DM cases and healthy controls.

SNP		rs5219	rs13266634	rs1111875
Chromosome		11p15.1	8q24.11	10q23.33
Position		17388025	117172544	92703125
Gene		KCNJ11	SLC30A8	HHEX
N (Patient/Control)		250/246
Major/Minor allele		C/T	C/T	C/T
HWE p-value	Patient	0.010	0.000	0.218
	Control	0.005	0.456	0.009
aOR (95% CI)	Heterozygote model	1.90 (1.23–2.95)	3.59 (2.39–5.37)	1.38 (0.88–2.17)
	Homozygote model	3.09 (1.86–5.13)	2.40 (1.12–5.11)	5.93 (3.25–10.79)
	Allele model	1.80 (1.40–2.32)	1.79 (1.37–2.34)	2.08 (1.62–2.68)
	Dominant model	2.23 (1.50–3.31)	3.37 (2.28–4.98)	2.08 (1.36–3.19)
	Recessive model	2.03 (1.33–3.10)	1.12 (0.56–2.22)	4.18 (2.60–6.73)
p-value	Heterozygote model	0.004	<0.0001	0.157
	Homozygote model	<0.0001	0.024	<0.0001
	Allele model	<0.0001	<0.0001	<0.0001
	Dominant model	<0.0001	<0.0001	0.001
	Recessive model	0.001	0.747	<0.0001

p-value <0.05 was considered significant (Bold); aOR = adjusted odds ratio.

Association with CVD in T2DM patients

We also examined the link of T2DM-related CVD in KCNJ11 rs5219 and SLC30A8 rs13266634 SNPs in T2DM patients (Table 5). Genotype distributions for KCNJ11 rs5219, and SLC30A8 rs13266634 for T2DM-without-CVD group and T2DM-with-CVD group in Supplementary Table 2. Between them, only rs5219 of KCNJ11 showed significant correlation in recessive and allele model (p = 0.002 and 0.017; aOR = 0.40 and 0.65, respectively). SLC30A8 rs13266634 did not show any significant association to T2DM-related CVD risk in any evaluated genetic association models.

Table 5

Association of alleles or genotypes with risk of T2DM-related CVD in Bangladeshi T2DM patients.

SNPs	Genes		Association models
			Heterozygote model	Homozygote model	Dominant model	Recessive model	Allele model
rs5219	KCNJ11	p-value	0.240	0.066	0.799	0.002	0.017
		aOR (95% CI)	1.48 (0.77–2.87)	0.51 (0.25–1.05)	0.93 (0.51–1.68)	0.40 (0.22–0.71)	0.65 (0.45–0.93)
rs13266634	SLC30A8	p-value	0.509	0.708	0.617	0.467	0.981
		aOR (95% CI)	1.22 (0.68–2.20)	0.80 (0.24–2.63)	1.16 (0.65–2.08)	0.68 (0.24–1.94)	1.00 (0.70–1.44)

Bold values indicate statistically significant (p < 0.05); aOR = adjusted odds ratio.

Statistical power analysis

After completing the laboratory-based genetic analysis using the online sample size estimator (OSSE), the statistical power was estimated, setting a 5% significance level. The outcome of the analysis revealed that KCNJ11 rs5219 had 88.10%, SLC30A8 rs13266634 had 87.30%, and HHEX rs1111875 had 98.00% statistical power.

Discussion

T2DM is a multifactorial disease along with various genetic and environmental factors. Previous studies have shown that 30–70% of T2DM patients are genetically at higher to moderate risk, the reason for which are different genes and their numerous combinations leading to the development of T2DM [11, 12]. For the development of T2DM, South Asian populations, i.e., especially people of Bangladesh, India, Sri Lanka, Bhutan, seem to be at high risk. In South Asia, almost 120.9 million people will be diabetic patients by 2030. Among them, T2DM patients will be 90–95%, which will be more than 50% of all affected European or North American [34]. However, in 2011, the prevalence of T2DM in the Bangladeshi population was higher (9.6%) than in other countries of South Asia. Previous GWAS in the South Asian population (except Bangladesh) found 20 independent SNPs in type 2 diabetes patients, and so we took this initiative to find out the susceptible SNPs for developing T2DM in Bangladeshi populations [34].

To take the candidate SNPs, we selected those genes which are- 1) found from the previous GWAS due to the relation with insulin resistance or T2DM development and 2) related to the pathway of insulin secretion or other different diabetic-related complications like hypertension and/or CVD. Under different genetic models like recessive, dominant, homozygote, and heterozygote, our present study was performed as suggested by Salanti et al. [35] to eliminate strong biases in searching and reporting the level of association. For the very first time in the Bangladeshi population, a significant association was identified between T2DM and the studied SNPs, including KCNJ11 rs5219, SLC30A8 rs13266634 and HHEX rs1111875.

Polymorphism in the rs5219 of KCNJ11 gene causes unregulated insulin secretion as well as congenital hyperinsulinism. It may also be associated with autosomal dominant T2DM through the polymorphism of rs5219 (E23K), where the T allele or lysine (K) instead of C allele or glutamate (E) is suppressed insulin secretion through decreasing the ATP sensitivity of the KATP channel [36]. As stated earlier in this study, we found a strong association of KCNJ11 (p < 0.0001, <0.0001, 0.001, 0.004, and <0.0001; aOR = 1.80, 2.23, 2.03, 1.90, and 3.09; by turn, in allele, dominant, recessive, heterozygote, and homozygote model) with T2DM patients of Bangladesh as like Chinese (p < 0.05, OR = 1.72. 95% CI = 1.12–2.63 in the dominant model) and Iranian (p = 0.048, OR = 2.50. 95% CI = 1.01–6.14 in allele model) populations, though no such association was found in the case of the South Indian population [21, 32, 37].

On the other hand, through the activation of hepatocyte nuclear factor 1a, the HHEX gene regulates pancreatic beta-cell development and function. It is reliably assumed that the risk allele reduces cell mass and decreases the secretory capacity of beta-cell to raise T2DM [38, 39]. Our study showed a strong association of HHEX rs1111875 polymorphism with T2DM (p < 0.0001 for all models; aOR = 1.79, 3.37, and 3.59, in allele, dominant, and heterozygote model, respectively) like the population of the Dutch Breda cohort (mutant homozygosity is 39.6% and 37% for cases and healthy controls, respectively) [25]. However, our results are not consistent with some other studies; for example, in the Indian and European populations, no association of HHEX rs1111875 polymorphism was found with T2DM development [40, 41].

Moreover, zinc is essential for the process of insulin maturation to secretion and SLC30A8 gene- located in secretory granules of insulin, plays an important role in zinc transportation to the container vesicles of insulin. After non-synonymous polymorphism in SLC30A8 rs13266634, a newly formed amino acid (arginine) affects its regular function and puts down the formation and secretion of insulin, which lowers the amount of insulin in the body as a whole [27, 28]. From our study, we observed a strong association of rs13266634 in our population to develop T2DM (p < 0.0001, 0.001, <0.0001, and <0.0001; aOR = 2.08, 2.08, 4.18, and 5.97, respectively in allele, dominant, recessive, and homozygote model) and a similar association was reported in several meta-analyses for Asian and European population [42, 43]. Moreover, most of the SLC30A8 carriers in our studied population are taking either combined medicine or insulin due to the extreme reduction of insulin in the body.

Hypertension, high cholesterol, and triglyceride level in T2DM patients highly elevate the risk of cardiovascular complications. Again, a diabetic patient with T2DM-related CVD tends to lead to chronic kidney disease [44]. Therefore, here we studied KCNJ11 rs5219 and SLC30A8 rs13266634 SNPs for the risk of cardiovascular complications in our T2DM patient. Between them, rs5219 of KCNJ11 showed significant association in recessive and allele model (p = 0.002 and 0.017; aOR = 0.40 and 0.65, respectively). However, we did not observe any significant link of SLC30A8 with cardiovascular complications in our population, which is further needed to be evaluated. Furthermore, we have observed significant differences between T2DM with CVD patients and T2DM without CVD in terms of gender distribution, FBG, SBP, DBP, TC, and TG status (p-value: <0.001, 0.020, 0.011, 0.034, 0.009, 0.039, respectively).

It is worth mentioning that we carried out this study on a comparatively small scale population from the Noakhali district of Bangladesh, which might affect the significance level to some extent. Besides, the inclusion of more clinicopathological information of patients may provide a more practical outcome. For a better conclusion and concrete outcome, a large prospective study is required from the different regions of Bangladesh along with the gene-gene and gene-environment interactions in the future.

Conclusion

In conclusion, our investigation indicates that KCNJ11 rs5219, SLC30A8 rs13266634 and HHEX rs1111875 polymorphisms are associated with T2DM. Moreover, KCNJ11 rs5219 polymorphism is correlated with the risk of T2DM-related CVD. Large-scale investigations are warranted to validate the results of our study.

Declarations

Author contribution statement

Tutun Das Aka: Performed the experiments; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Urmi Saha, Sayara Akter Shati: Performed the experiments; Contributed reagents, materials, analysis tools or data.

Md. Abdul Aziz, Mobashera Begum: Performed the experiments; Wrote the paper.

Md. Saddam Hussain, Md. Shalahuddin Millat, Mohammad Sarowar Uddin: Analyzed and interpreted the data; Wrote the paper.

Mohammad Safiqul Islam: Conceived and designed the experiments; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Funding statement

This work was supported by the Research Cell of Noakhali Science and Technology University.

Data availability statement

Data will be made available on request.

Declaration of interests statement

The authors declare no conflict of interest.

Additional information

No additional information is available for this paper.Appendix

Supplementary Table 1

Genotype distributions for KCNJ11 rs5219, SLC30A8 rs13266634 and HHEX rs1111875 for T2DM group and healthy control group

Genotypes	Cases(n = 250) (%)	HWE		Healthy Controls(n = 246) (%)	HWE
		χ2	p-value		χ2	p-value
KCNJ11 rs5219
CC	64 (13.64)			102 (16.67)
CT	104 (44.54)	6.70	0.010	96 (41.67)	7.97	0.005
TT	82 (41.82)			48 (41.67)
C	232 (46.40)			300 (60.98)
T	268 (53.60)			192 (39.02)
SLC30A8 rs13266634
CC	70 (10.91)			134 (13.89)
CT	162 (82.73)	31.44	0.00	92 (77.78)	0.55	0.456
TT	18 (6.36)			20 (8.33)
C	302 (60.40)			360 (73.17)
T	198 (39.60)			132 (26.83)
HHEX rs1111875
CC	48 (10.91)			78 (13.89)
CT	112 (82.73)	1.52	0.218	138 (77.78)	6.81	0.009
TT	90 (6.36)			30 (8.33)
C	208 (41.60)			294 (59.76)
T	292 (58.40)			198 (40.24)

Supplementary Table 2

Genotype distributions for KCNJ11 rs5219, and SLC30A8 rs13266634 for T2DM-without-CVD group and T2DM-with-CVD group

Genotypes	T2DM with CVD(n = 116) (%)	HWE		T2DM without CVD(n = 134) (%)	HWE
		χ2	p-value		χ2	p-value
KCNJ11 rs5219
CC	30 (10.91)			34 (13.89)
CT	60 (82.73)	0.148	0.70	42 (77.78)	16.64	0.00
TT	26 (6.36)			58 (8.33)
C	120 (51.72)			110 (41.04)
T	112 (48.28)			158 (58.96)
SLC30A8 rs13266634
CC	30 (10.91)			40 (13.89)
CT	80 (82.73)	22.56	0.00	82 (77.78)	10.49	0.001
TT	6 (6.36)			12 (8.33)
C	140 (60.34)			162 (60.45)
T	92 (39.66)			106 (39.55)