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CDKAL1 gene rs7756992 A/G and rs7754840 G/C polymorphisms are associated with gestational diabetes mellitus in a sample of Bangladeshi population: implication for future T2DM prophylaxis

Diabetology & Metabolic Syndrome volume 14, Article number: 18 (2022) Cite this article

Abstract

Background

Association of single nucleotide polymorphisms (SNP) rs7756992 A/G and rs7754840 G/C of cyclin-dependent kinase 5 regulatory subunit-associated protein 1-like 1 (CDKAL1) gene with the susceptibility of gestational diabetes mellitus (GDM) has been studied in a group of Bangladeshi women.

Methods

In this case–control study, 212 GDM patients and 256 control subjects were genotyped for rs7756992 and rs7754840 by PCR-RFLP and TaqMan™ allelic discrimination assay method respectively. Genotyping results were confirmed by DNA sequencing and replicated TaqMan™ assay. The odds ratios and their 95% confidence interval were calculated by logistic regression to determine the associations between genotypes and GDM.

Results

The genotype frequencies of rs7756992-AA/AG/GG in the GDM group and the control group were 37%/48%, 53%/45%, 10%/7% and those of rs7754840-CC/CG/GG were 51%/55%, 40.1%/39.8%, 9%/5% respectively. Under dominant and log additive models rs7756992 was revealed significantly associated with GDM after being adjusted for family history of diabetes (FHD) and gravidity. Conversely, rs7754840 was significantly associated (P = 0.047) with GDM only under the recessive model after the same adjustment. The risk allele frequency of both SNPs was higher in the GDM group but significantly (P = 0.029) increased prevalence was observed in the rs7756992 G allele. When positive FHD and risk alleles of these SNPs were synergistically present in any pregnant woman, the chance of developing GDM was augmented by many folds. The codominant model revealed 2.5 and 2.1 folds increase in odds by AG (rs7756992) and GC (rs7754840) genotypes and 3.7 and 4.5 folds by GG (rs7756992) and CC (rs7754840) genotypes respectively. A significant 2.7 folds (P = 0.038) increase in odds of GDM resulted from the interaction of rs7756992 and family history of diabetes under the dominant model. The cumulative effect of multigravidity and risk alleles of these SNPs increased the odds of GDM more than 1.5 folds in different genotypes.

Conclusion

This study not only revealed a significant association between rs7756992 and rs7754840 with GDM but also provided the possibility as potential markers for foretelling about GDM and type 2 diabetes mellitus in Bangladeshi women.

Introduction

Gestational diabetes mellitus (GDM) is varying degree of carbohydrate intolerance that is first recognized during pregnancy and is a common obstetric complication. An impaired compensatory increase in insulin secretion to overcome the pregnancy-induced insulin resistance characterizes this disease. Various reports indicate the risk of developing Type 2 Diabetes Mellitus (T2DM) later in the life of individuals having GDM and it notably influences the metabolic health of their offspring both in the short and long run [1]. Yet, the metabolic pathways involved with this complication need to be better understood.

This complication (GDM) is rising rapidly in the prevalence of 36.6% of total pregnancies in Bangladesh [2, 3]. This prevalence varies from 0.7 to 51% in Asia [4,5,6,7]. Differences in ethnicity, diagnostic criteria, screening procedures, and population characteristics may account for the large discrepancy in prevalence rates [7]. This disease has been known to have a genetic basis [8, 9] but remarkably few susceptibility genes with strong and reproducible effects have been identified so far. Compared to T2DM, the genetics of GDM has been less studied [10, 11]. However, a concordance of risk alleles, as well as the direction of their effect has been reported. Up to date, only one genetic marker of GDM has been reported in Bangladesh [12, 13].

CDKAL1 gene is located on chromosome 6p22.3 and encodes a 65-kD protein CDKAL1 which may be involved in beta cell dysfunction and T2DM susceptibility [14]. Evidence of this protein functioning also as a tRNA modification enzyme has been shown in some recent studies and activity of which is associated with ATP generation [15,16,17]. Downregulation of CDKAL1 expression was assumed to increase the activity of cyclin-dependent kinase 5 (CDK5) [18]. So CDK5 induced insulin secretion might be regulated by binding of CDKAL1 to the CDK5 activator p35 [19,20,21]. However, further investigations need to state exactly how CDKAL1 alters insulin release in pancreatic beta cells and the susceptibility to T2DM by interactions between these proteins [22]. As T2DM and GDM share similar pathophysiological backgrounds [23] there must be some common genetic variants between these two disease entities. However, variations in insulin release, pancreatic cell functions, hemoglobin A1C level, and/or response to pancreatic KATP channel agonists [21, 24, 25] as well as T2DM, GDM, ulcerative colitis, Crohn’s disease, obesity, and/or birth weight [19, 26,27,28] have been reported to be associated with some intron variants; rs7754840, rs7756992, and rs10946398 of this gene. The major allele G of rs7756992 of CDKAL1 is conferred a higher risk of T2DM [20]. This SNP was also found to be associated with impaired insulin secretion [19, 29] and diabetic retinopathy [30]. In pregnant women, rs7754840 conferred variations in glucose induced GIP (glucose-dependent insulinotropic polypeptide) response [31].

In the context of the situation, the present study aims to investigate the association of CDKAL1 gene polymorphism rs7756992 A/G and rs7754840 G/C with GDM in a group of Bangladeshi pregnant women. This is the first study to evaluate the genomic variation of these SNPs of GDM candidate genes for the Bangladeshi population compared with other ethnic groups.

Methods and materials

Study subjects

This case control study encompassed 468 unrelated pregnant women irrespective of trimester attending an antenatal clinic for their routine treatment and checkup at the Department of Gynecology and Obstetrics, Bangabandhu Sheikh Mujib Medical University (BSMMU). Individuals with known diabetes mellitus, acute critical illness, steroid treatment due to any cause, established organ dysfunction e.g., chronic kidney disease, chronic liver disease, heart failure, known case of thyroid dysfunction, were excluded. Informed consent was taken from participants and the research ethics committee (REC) of the National Institute of Biotechnology (NIB) approved the study protocol (NIBREC 2016-04).

Body mass index

Anthropometric measurements of weight and height were obtained by standardized techniques. The body mass index (BMI) was calculated as the weight in kilograms divided by the square of the height in meters. BMI was measured in both GDM patients and normoglycemic pregnant controls.

Oral glucose tolerance test (OGTT)

Participants underwent 75 gm OGTT on the appointed day after 08–10 h overnight fasting and glycemic status determined using WHO 2013 criterion for GDM [fasting plasma glucose (FPG) 5.1–6.9 mmol/L and/or 1-h plasma glucose (OPG) ≥ 10.0 mmol/L and/or 2-h plasma glucose (TPG) 8.5–11.0 mmol/L]. Plasma glucose was assayed by the glucose-oxidase method using commercial kits in an automated analyzer (Dade Behring, Germany). If the glycemic status was found normal when tested before the 24th week of gestation, the mother was advised to repeat the OGTT during 24th to 28th week of gestation, and glycemic status was reconsidered. Otherwise, they were excluded from this study.

DNA extraction

Genomic DNA was extracted from peripheral blood samples of patients and controls using PureLink® Genomic DNA extraction kit (Invitrogen) according to the manufacturer’s protocol. The purity and concentration of the extracted DNAs were tested with NanoDrop 2000 UV Vis Spectrophotometer. DNA samples with an OD260/OD280 ratio between 1.8–2.0 and concentrations more than 80 ng/mL were used for genotyping.

Polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP)

Briefly, the region comprising rs7756992 polymorphism was amplified with the following primers: Forward 5ʹ-TTGATTGTAAAGACTGGGTCTCA-3ʹ; Reverse 5ʹ-GAACGAAGGCAAATAAATTCAA-3ʹ. The PCR cycles were as follows: 5 min at 95 °C, followed by 35 cycles of 1 min at 95 °C, 1 min at 50 °C, and 1 min at 72 °C. The final extension was for 7 min at 72 °C. Subsequently, 7 µL of the 684 bp amplified product was digested with the restriction enzyme BglII (neb) for 1 h at 37 °C. After digestion, amplicons were subjected to 2% agarose gel electrophoresis. The appearance of two bands of 388 bp and 296 bp was indicative of the AA genotype, whereas 684 bp digestion products indicated the GG genotype. When heterozygous genotypes exist, three bands were observed (Fig. 1).

Fig. 1
figure 1

Genotyping of samples by RFLP analysis. Lane 1(M): 1 kb plus DNA ladder, lanes 2, 4, 6, 8, 10, 12, 14 and 16; Undigested PCR product (C = control); lane 3, 5, 7 and 15; AA Homozygous genotype, lanes 9, 11 and 13; AG Heterozygous genotypes and lane 17; GG Homozygous genotype

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DNA sequencing

PCR RFLP genotyping results were validated by sequencing randomly selected samples (Fig. 2). The amplified CDKAL1 locus was sequenced by Sanger’s di-deoxy chain terminating method using a sequencing kit (BigDye Terminator v3.1, Applied Biosystems, Foster City, CA, USA). Sequences were analyzed to determine the genotype using sequencing analysis software, version 5.2 (Applied Biosystems, Foster City, CA, USA).

Fig. 2
figure 2

Confirmation of the RFLP results by sequencing

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TaqMan™ allelic discrimination assay

All samples were genotyped for rs7754840 using allelic discrimination assay on a QuantStudio™ 5 Real-Time PCR System for human identification (Applied Biosystems, USA) with predesigned TaqMan™ SNP Genotyping Assay, human (assay ID: C_29246232_10). As a quality standard previously sequenced three positives have been randomly included (one homozygous wild-type allele carrier, one heterozygous, and one homozygous risk allele carrier) and three negative (all components excluding DNA) controls in each run. QuantStudio Design and analysis software v 1.4.3 was used to analyze real-time data (Fig. 3). 20% samples were repeated and no differences were found.

Fig. 3
figure 3

An allele discrimination plot from a single run of 30 samples containing both cases and controls and representative amplification plots for each genotype, as well as a negative control. Blue, green and red dots denoted GG, GC and CC genotypes respectively; black squares denoted negative controls

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Statistical analyses

Normality of the random variables was checked by visual inspection of histograms using R statistical software version 4.0.3. Student’s t-tests were used to compare the ages, BMI, systolic blood pressure, diastolic blood pressure, and plasma glucose levels (FPG, OPG, and TPG) between the GDM and control groups while the family history of diabetes and gravidity between these groups were tested using the Chi-square test. Numerical variables were expressed as mean ± standard deviation of the mean (mean ± SD). On the other hand, categorical variables were expressed in numbers (n) and percentages (%). Hardy–Weinberg equilibrium (HWE) was tested using Pearson’s chi-squared (χ2) test with a threshold of P > 0.05 in cases and controls separately. Risk factors of GDM were detected by multivariate logistic regression. The general association of genotypes with GDM was assessed with multivariate logistic regression analysis under codominant, dominant, recessive, overdominant, and log-additive models and adjusted for gravidity and family history of diabetes by using SNPStats [32]. The best-fitting model with multiple variables was chosen by stepwise addition of prospective confounding variables using Akaike’s Information Criterion (AIC) and Bayesian Information Criterion (BIC) values which are criteria of goodness of fit. The association of haplotypes of rs7756992 and rs7754840 with GDM was carried out SNPStats [32]. The statistical power was calculated using the GAS power calculator [33].

Results

General characteristics of the study subjects and risk factors of GDM

The study included 468 subjects classified into 212 pregnant women with GDM and 256 control subjects. Since it was a time-bound study, sample collection was stopped after the stipulated time period. Their age ranged from 18 to 44 (years). Participants with GDM had significantly higher age (27.58 ± 4.59 vs. 25.42 ± 4.58, P < 0.0001), BMI (26.64 ± 4.15 vs. 25.29 ± 3.92, P = 0.0003), plasma glucose levels (FPG: 5.15 ± 0.47 vs. 4.31 ± 0.48, P < 0.0001; OPG: 9.77 ± 1.65 vs. 7.49 ± 1.26, P < 0.0001 and TPG: 8.25 ± 1.49 vs. 6.4 ± 1.06, P < 0.0001); higher percentage of positive family history of diabetes (46.23% vs. 30. 0.85%, P = 0.0006), multigravida (62.74% vs. 53.52%, P = 0.04) and lower percentage of primigravida (35.85% vs. 46. 48%, P = 0.02) than those in controls. There is no significant difference between systolic (109.27 ± 11.75 vs. 108.94 ± 11.91, P = 0.69) and diastolic (70.51 ± 9.18 vs. 69.25 ± 9.05, P = 0.12) blood pressures between these two groups. As odds of age (OR = 1.1, 95% CI 1.06–1.15, P < 0.001) and BMI (OR = 1.08, 95% CI 1.03–1.14, P = 0.002) were very close to 1 they could not be risk factors of GDM. On the other hand risk of GDM was higher in gravidity (OR = 1.53, 95% CI 1.05–2.23, P = 0.03) and family history of diabetes (OR = 1.93, 95% CI 1.32–2.83, P < 0.001), so they are the confounders for which adjustments were carried out.

Genotype frequency and association of CDKAL1 gene variants with GDM

Genotyping for rs7756992 and rs7754840 polymorphisms of CDKAL1 gene revealed that genotype distribution of these two SNPs differed in individuals with and without GDM (Table 1). Frequencies of AG (53% vs. 45%) and GG (10% vs. 7%) genotype of rs7756992 were higher in the GDM group whereas the control group comprised a higher proportion of AA (48% vs. 37%) genotypes. In case of rs7754840 polymorphism CC (9% vs. 5%) genotype is higher in the GDM group and GG (55% vs. 51%) in the control group whereas the frequency of CG genotype is almost equal (GDM-40.1% vs. control-39.8%) in both groups. Genotype distributions of cases and controls of the last-mentioned and controls of former were consistent with Hardy–Weinberg equilibrium (HWE) (Table 1) and could be used for the following analysis. Departure from HWE resulted in genotype distribution of rs7756992 in cases which indicates that there may be an association of this SNP with GDM.

Table 1 Genotype and allele frequency of CDKAL1 gene variants (rs7756992 and rs7754840) in the study subjects
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Association of these genetic variants with GDM was tested under codominant, dominant, recessive, overdominant and log additive models (Additional file 1: Table S1, S2). Our analyses revealed significant (P < 0.05) association of rs7756992 with GDM patients under codominant [AA vs. AG (OR = 1.56, 95% CI 1.06 to 2.30), AA vs. GG (OR = 1.74, 95% CI 0.88 to 3.45), P = 0.047] dominant (OR = 1.59, 95% CI 1.10 to 2.30, P = 0.014) and log additive (OR = 1.42, 95% CI 1.06 to 1.90, P = 0.019) models. Odds of having GDM is also higher in both recessive (OR = 1.37, 95% CI 0.72 to 2.62, P = 0.34) and over dominant (OR = 1.42, 95% CI 0.99 to 2.05, P = 0.059) models though P value is > 0.05 in both (Additional file 1: Table S1). Adjustment for gravidity and family history of diabetes results significant associations of this variant with GDM under dominant (P = 0.02) and log additive (P = 0.022) models but significance of codominant model increased from P = 0.047 to P = 0.062 (Additional file 1: Table S1) even though the odd remained considerably higher (OR = 1.81) than that of reference genotype (Fig. 4).

Fig. 4
figure 4

Associations of rs7756992 (AA/AG/GG) and rs7754840 (GG/GC/CC) with GDM under different genetic models adjusted for family history of diabetes and gravidity with odds ratios shown by closed circles and whiskers representing the 95% confidence intervals

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On the other hand no significant association was found between rs7754840 and GDM under any genetic model [co-dominant model {(GG vs. CG): OR = 1.10, 95% CI 0.75 to 1.61; (GG vs. CC): OR = 2.03, 95% CI 0.97 to 4.26} P = 0.17; dominant model: OR = 1.20, 95% CI 0.84 to 1.73, P = 0.32; recessive model: OR = 1.95, 95% CI 0.94 to 4.01, P = 0.067; overdominant: OR = 1.01, 95% CI 0.70 to 1.47, P = 0.96 and log-additive: OR = 1.26, 95% CI 0.94 to 1.69, P = 0.12] (Additional file 1:Table S2). When adjusted for confounding covariates the odds of having GDM with CC genotype increased to 2.23 with a significance level of 95% CI from 1.04 to 4.75 though P value remained greater than 0.05. Impressively this adjustment results significant (P = 0.047) association between this SNP with GDM under recessive model with an OR of 2.09 (95% CI 1.00 to 4.36) and an increase in odds under overdominant model and vice versa under log additive model with a tiny change in P values. There was no notable change observed under dominant model (Fig. 4).

Association of risk alleles of studied SNPs with GDM

The frequency of the G allele of rs7756992 was higher in GDM whereas that of the A allele was higher in control (GDM vs. control, G-allele: 37% vs. 30%; A-allele 63% vs. 70%) (Table 1). In presence of the G allele, the odds of having GDM increase significantly (P = 0.029) by 1.36-fold with a 95% CI of 1.03 to 1.78. Though the frequency of the C allele of rs7754840 was also higher in the GDM group (29% vs. 25%) the effect of this allele on the susceptibility of GDM is insignificant (OR = 1.25, 95% CI 0.94 to 1.67, P = 0.125).

Association of CDKAL1 gene variants with the family history of diabetes and gravidity

Logistic regression analysis was carried out to check the relationship between the variants with the family history of diabetes under two genetic models (Additional file 1: Table S3). This cross-classification interaction with rs7756992 and rs7754840 results 1.18 (95% CI 0.65 to 2.14) to 4.02 (95% CI 1.33 to 12.09) (Fig. 5) and 1.72 (95% CI 1.02 to 2.89) to 7.42 (95% CI 1.55 to 35.57) (Fig. 6) folds increased in odds of having GDM with a positive family history of T2DM respectively. A significant P value (0.038) was obtained only from the interaction of rs7756992 with GDM (Fig. 5).

Fig. 5
figure 5

Correlation between GDM risk with cumulation of CDKAL1 rs7756992 polymorphism and family history of diabetes

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Fig. 6
figure 6

Correlation between GDM risk with cumulation of CDKAL1 rs7754840 polymorphism and family history of diabetes

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In multigravida women, there was a noticeable increase observed in the odds of having GDM compared to primigravida in the case of rs7754840 with odds varied from 1.39 (95% CI 0.83–2.32) to 3.05 (95% CI 1.12–8.30) under the codominant and dominant model (Fig. 8). For AA and AG genotype of rs7756992 odd ratios increased in multigravida than that of primigravida under both models whereas for GG genotype risk of having GDM increased by more than two folds [OR = 2.09, (95% CI 0.79–5.49) and 2.11 (0.77–5.77)] in both strata (Fig. 7 and Additional file 1: Table S4).

Fig. 7
figure 7

Correlation between GDM risk with cumulation of CDKAL1 rs7756992 polymorphism and gravidity

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The association of haplotype of rs7756992 and rs7754840 with GDM

Taking the common rs7754840/rs7756992 GA haplotype as reference (OR = 1.00), multivariate analysis adjusted for gravidity and family history of diabetes confirmed the association of rs7754840 C- and rs7756992 G-allele containing (CG) haplotype with GDM, thus conferring significant (P = 0.032) disease susceptibility with an odd of 1.43 (1.03–1.98) (Table 2).

Table 2 The association of haplotype of rs7756992 and rs7754840 with GDM
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Discussion

Among others, CDKAL1 is one of the first set of T2DM susceptibility genes identified by genome-wide association (GWA) and other studies [19, 20, 34,35,36,37,38]. Variants of this gene rs7756992 A/G and rs7754840 C/G included in our study were previously found to be associated with T2DM among different Asian, European, and American populations [19, 20, 26, 28, 29, 39,40,41,42,43]. The association of these genetic variants with GDM was also studied in a number of populations as this disease may have a common genetic background with T2DM [27, 44, 45]. Recently some studies on rs7756992 and rs7754840 reported significant association in Asian populations [26, 28, 46, 47] whereas in some populations there was no association found [45, 48]. Nevertheless, no report is available on the association of CDKAL1 rs7756992 and rs7754840 SNPs with GDM in Bangladeshi women.

In our study confirmation of the GDM cases was crucial and challenging. Insulin resistance mediated by placental hormones increases GDM as the pregnancy advances so testing too early for GDM may not be of help in some patients. During the second trimester (13–28 weeks of gestation) of pregnancy insulin resistance increases and glucose levels rise in women unable to produce enough insulin to adopt this resistance. Also, performing tests too late in the third trimester limits the time of metabolic interventions; hence GDM screening is usually preferred at 24–28 weeks of gestation [49]. Taking these into consideration case samples were collected irrespective of the trimester, and individuals having normal plasma glucose levels at the earlier stage of pregnancy were further tested for confirmation at 24–28th weeks of gestation.

GAS power calculator [33] was used to detect the power of study and considering the total sample size (both cases and controls) studied (n = 468), genotype relative risks (1.74 and 2.03) for rs7756992 and rs7754840, the prevalence of GDM (0.366) in Bangladeshi population [3], disease allele frequency obtained (0.36 and 0.29), the significance level equal to 0.05 we had almost 98% power for both SNPs which indicates that the study sample size had sufficient power to detect the association.

The genotype distributions of the target SNPs (rs7756992 and rs7754840) were in concordance with HWE in the control group which indicates that there is no selection bias, population stratification, and genotyping errors [50] in the study population. In addition to the control, cases should also be evaluated to avoid eliminating important SNPs that could potentially be causal SNPs of a common disease [51]. Therefore, the distribution of genotypes of both target SNP cases has been assessed for HWE and rs7756992 showed significant (P = 0.03) departure from the equilibrium which is indicative of a strong association of this SNP with GDM. In accordance with the positive associations that have been reported in the Asian population [26, 52] our data also shows a significant association of rs7756992 with GDM. The effect size of the rs7756992 G allele (OR = 1.36), is close in magnitude to those reported for Asians (OR = 1.41), and south Indians (OR = 1.45). In our study, association analyses were performed under several genetic models namely codominant, dominant, recessive, overdominant, and log additive to avoid possible biases in finding and reporting significant associations [53] and three of them-codominant (P = 0.047), dominant (P = 0.014) and log additive (P = 0.019) models revealed significant (P < 0.05) association (Additional file 1: Table S1) between this SNP with GDM. Adjustments for gravidity and family history of diabetes result in no change in the significant association of rs7756992 with GDM under dominant and log additive models. After adjustment, GG genotype showed a higher odd ratio (1.81) whereas the odd of AG genotype remained comparable with an insignificant P value (Fig. 4).

Though there is no significant association observed between rs7754840 and GDM risk in our study, the homozygous risk allele of this SNP results in higher odds of having GDM compared to that of rs7756992. Our findings are consistent with a Chinese and Egyptian study where no significant association was found [45, 54], while studies in Caucasian, Korean and South Indian populations showed significant associations [26, 52, 55] indicating that the common susceptibility loci rs7754840 in CDKAL1 may be not associated with GDM in our population. These variations in association could be due to differences in ethnicities, sample sizes, and diagnostic criteria for GDM. However, after adjusted for gravidity and family history of diabetes, the recessive model revealed a significant association (P = 0.047) of this variant with GDM (Fig. 4).

Systolic and diastolic blood pressures were indistinguishable between control and GDM groups whereas significant differences were observed in age, BMI, gravidity, and family history of diabetes. Consequently, the latter were the prospective confounders that should be adjusted for in the subsequent analysis. To detect actual confounding factors multivariate logistic regression was carried out and from the value of odds ratios, risk factors were determined. Among the variables of this study age, BMI, gravidity, and family history of diabetes were selected as potential confounders [56]. Multivariate logistic regression analysis revealed gravidity (OR = 1.53) and family history of diabetes (OR = 1.93) as confounders in this study as they have a higher risk for GDM. In contrast to other types of biases, confounding can be controlled by adjusting for it after completion of a study using stratification [56], hence the population of this study was divided into strata or subgroups according to levels of the confounding factors. Relative risk analysis for each stratum was carried out for control and GDM groups by cross classification interactions under different models (Additional file 1: Table S3 and S4).

The cumulative effect of CDKAL1 rs7756992 polymorphism and positive family history of diabetes significantly increased the risk of GDM by 2.7-fold under the dominant genetic model (Additional file 1: Table S3). The AG and GG genotypes of this SNP increased the odds of GDM by 2.5 and 3.7 folds respectively whereas none of these genotypes provides any risks to individuals with a negative family history of diabetes (Fig. 5). Mansoori et al. suggested an association between the CC genotype of the rs7754840 polymorphism and people with late-onset T2DM [57]. In our analysis not only the CC genotype of this SNP increased the odds of having GDM 4.5 times but also the GG genotype increased it 1.72 times in women with a positive family history of diabetes (Fig. 6). These genetic predispositions of both variants may be a valuable marker for differentiating pregnant women with an elevated risk of GDM who could then be subjected to an earlier routine screening for GDM as well as lifestyle intervention before and after pregnancy to avoid the onset of GDM and T2DM respectively. In addition, new research avenues will open up in the search for cost-effective technology for early routine screening for GDM.

Multigravidity itself increased the risk of GDM compared to that in primigravida women i.e., increased odds of having GDM in presence of homozygous reference alleles of both SNPs (1.33-fold for AA genotype of rs7756992 and 1.39-fold for GG genotype of rs7754840). The presence of multigravidity and heterozygous risk alleles of rs7756992 (AG) and rs7754840 (GC) collectively increased the odds of GDM by 1.80 and 1.70 folds respectively whereas only the homozygous risk allele (CC) of rs7754840 increased the odds by 1.59-fold (Figs. 7 and 8). The homozygous risk allele of rs7756992 increased the risk of GDM by more than two folds in both primi and multigravida women (Fig. 7). Insignificant P values and wider CI values of different outcomes could be explained by the small sample size in each subgroup of both confounders (Additional file 1: Table S3, S4).

Fig. 8
figure 8

Correlation between GDM risk with cumulation of CDKAL1 rs7754840 polymorphism and gravidity

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The haplotype analysis of these SNPs revealed significant association of haplotype containing both alleles with GDM susceptibility that provides further evidence for the resulting association of this study.

The findings of the current study might be specific to GDM patients who lived in greater Dhaka city and used to be with the city lifestyle as they were recruited from Dhaka city and nearby regions such as Narayanganj and Gazipur. This may also explain the nature of the association between CDKAL1 rs7756992 and rs7754840 SNPs with GDM in this study. Though several risk factors are counted here in this study but other GDM associated factors such as food intake and physical activity could not be considered due to the unavailability of information. Therefore, interactions between lifestyle factors and these genetic variants in terms of GDM remain unknown. Furthermore, rs7756992 and rs7754840 are located in the intron of CDKAL1; thus, the relationships between these SNPs and genes and how they modulate GDM risk are largely unknown.

Conclusions

In conclusion, our study provides evidence that rs7756992 and rs7754840 are either significantly associated or increased the risk of GDM in pregnant Bangladeshi women, emphasizing the importance of these potentially functional variants in GDM development (Fig. 9). The rs7754840 is not only significantly associated with but also results in higher odds of having GDM compared to rs7756992. The underlying mechanisms are needed to be elucidated through functional investigations for the identification of causal loci and genes. The sample size in this study yielded power to identify significant associations between GDM and variants in the CDKAL1 gene, but stratification for confounding factors decreased the number of samples in each stratum that eventually resulted in insignificant associations with variants in some strata. Furthermore, large studies including other ethnic populations are needed to further replicate our findings.

Fig. 9
figure 9

Cumulation of genetic association of GDM with CDKAL1 rs7756992 and rs7754840 polymorphisms along with confounders

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Availability of data and materials

All data generated or used during the study are available from the corresponding author on reasonable request: Dr. Md. Salimullah, E-mail: salim2969@gmail.com.

References

  1. Noctor E, Dunne FP. Type 2 diabetes after gestational diabetes: The influence of changing diagnostic criteria. World J Diabetes. 2015;6(2):234–44.

    PubMed  PubMed Central  Google Scholar 

  2. Nusrat Sultana HM, Sharmin-Jahan HM, Panthi SYA, Fariduddin M. Alarming Frequency of Gestational Diabetes Mellitus (GDM) Attending a Tertiary Care Hospital in Bangladesh. J Clin Diabetol. 2016;2:1–5.

    Google Scholar 

  3. Sandesh-Panthi MA, Hasanat M-H, Yasmin-Aktar N-S, Sharmin-Jahan MFJJ. Frequency of gestational diabetes mellitus in Bangladesh impact of WHO 2013 screening criteria: Efficiency DIPSI & WHO 1999 criteria. JCD. 2015;2(2):13–9.

    Google Scholar 

  4. Alfadhli EM. Gestational diabetes mellitus. Saudi Med J. 2015;36(4):399.

    PubMed  PubMed Central  Google Scholar 

  5. Nguyen CL, Pham NM, Binns CW, Duong DV, Lee AHJ. Prevalence of gestational diabetes mellitus in eastern and southeastern Asia: a systematic review and meta-analysis. J diabetes res. 2018. https://doi.org/10.1155/2018/6536974.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Wahi P, Dogra V, Jandial K, Bhagat R, Gupta R, Gupta S, et al. Prevalence of gestational diabetes mellitus (GDM) and its outcomes in Jammu region. J Assoc Physicians India. 2011;59(4):227–30.

    PubMed  Google Scholar 

  7. Lee KW, Ching SM, Ramachandran V, Yee A, Hoo FK, Chia YC, et al. Prevalence and risk factors of gestational diabetes mellitus in Asia: a systematic review and meta-analysis. BMC Pregnancy Childbirth. 2018;18(1):1–20.

    CAS  Google Scholar 

  8. Lambrinoudaki I, Vlachou AS, Creatsas G. Genetics in gestational diabetes mellitus: association with incidence, severity, pregnancy outcome and response to treatment. Curr Diabetes Rev. 2010;6(6):393–9.

    PubMed  Google Scholar 

  9. Radha V, Kanthimathi S, Anjana RM, Mohan V. Genetics of gestational diabetes mellitus. J Pak Med Assoc. 2016;66(9 Suppl 1):s11-4.

    PubMed  Google Scholar 

  10. Cuschieri SJD, Research MSC, Reviews. The genetic side of type 2 diabetes—a review. Diabetes Metab Syndr Clin Res Rev. 2019;13(4):2503–6.

    Google Scholar 

  11. Moniruzzaman M, Ahmed I, Huq S, All Mahmud MS, Begum S, Mahzabin Amin U, et al. Association of polymorphism in heat shock protein 70 genes with type 2 diabetes in Bangladeshi population. Mol Genet Gen Med. 2020;8(2):e1073.

    CAS  Google Scholar 

  12. Hasan M, Hasanat M, Hasan K, Panthi S, Aktar Y, Sultana N, et al. TCF7L2 gene rs7903146 polymorphism is observed in gestational diabetes mellitus in Bangladesh. Integr Obes Diabetes. 2016. https://doi.org/10.15761/IOD.1000155.

    Article  Google Scholar 

  13. Mashfiqul-Hasan N-S, Sharmin-Jahan S-P, Yasmin-Aktar A-R, Fariduddin M, Hasanat M. TCF7L2 gene rs7903146 polymorphism may confer expression of gestational diabetes mellitus in relatively young and lean mothers. Diabet Obes. 2016;6:1–7.

    Google Scholar 

  14. El-Lebedy D, Ashmawy I. Common variants in TCF7L2 and CDKAL1 genes and risk of type 2 diabetes mellitus in Egyptians. J Genet Eng Biotechnol. 2016;14(2):247–51.

    PubMed  PubMed Central  Google Scholar 

  15. Xie P, Wei F-Y, Hirata S, Kaitsuka T, Suzuki T, Suzuki T, et al. Quantitative PCR measurement of tRNA 2-methylthio modification for assessing type 2 diabetes risk. Clin Chem. 2013;59(11):1604.

    CAS  PubMed  Google Scholar 

  16. Wei FY, Tomizawa K. Functional loss of Cdkal1, a novel tRNA modification enzyme, causes the development of type 2 diabetes. Endocr J. 2011;58(10):819–25.

    CAS  PubMed  Google Scholar 

  17. Wei F-Y, Suzuki T, Watanabe S, Kimura S, Kaitsuka T, Fujimura A, et al. Deficit of tRNALys modification by Cdkal1 causes the development of type 2 diabetes in mice. J Clin Investig. 2011;121(9):3598–608.

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Han X, Luo Y, Ren Q, Zhang X, Wang F, Sun X, et al. Implication of genetic variants near SLC30A8, HHEX, CDKAL1, CDKN2A/B, IGF2BP2, FTO, TCF2, KCNQ1, and WFS1 in Type 2 Diabetes in a Chinese population. BMC Med Genet. 2010;11(1):81.

    PubMed  PubMed Central  Google Scholar 

  19. Steinthorsdottir V, Thorleifsson G, Reynisdottir I, Benediktsson R, Jonsdottir T, Walters GB, et al. A variant in CDKAL1 influences insulin response and risk of type 2 diabetes. Nat Genet. 2007;39:770.

    CAS  PubMed  Google Scholar 

  20. Dehwah MAS, Wang M, Huang QY. CDKAL1 and type 2 diabetes: a global meta-analysis. Genet Mol Res. 2010;9(2):1109–20. https://doi.org/10.4238/vol9-2gmr802.

    Article  CAS  PubMed  Google Scholar 

  21. Ohara-Imaizumi M, Yoshida M, Aoyagi K, Saito T, Okamura T, Takenaka H, et al. Deletion of CDKAL1 affects mitochondrial ATP generation and first-phase insulin exocytosis. PLoS ONE. 2010;5(12):e15553.

    PubMed  PubMed Central  Google Scholar 

  22. Sun X-F, Xiao X-H, Zhang Z-X, Liu Y, Xu T, Zhu X-L, et al. Positive association between type 2 diabetes risk alleles near CDKAL1 and reduced birthweight in Chinese Han individuals. Chin Med J. 2015;128(14):1873–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Kwak SH, Jang HC, Park KS. Finding genetic risk factors of gestational diabetes. Genom Inf. 2012;10(4):239–43.

    Google Scholar 

  24. Pascoe L, Tura A, Patel SK, Ibrahim IM, Ferrannini E, Zeggini E, et al. Common variants of the novel type 2 diabetes genes CDKAL1 and HHEX/IDE are associated with decreased pancreatic beta-cell function. Diabetes. 2007;56(12):3101–4.

    CAS  PubMed  Google Scholar 

  25. Miyaki K, Oo T, Song Y, Lwin H, Tomita Y, Hoshino H, et al. association of a cyclin-dependent kinase 5 regulatory subunit-associated protein 1-like 1 (CDKAL1) polymorphism with elevated hemoglobin A1c levels and the prevalence of metabolic syndrome in Japanese men: interaction with dietary energy intake. Am J Epidemiol. 2010;172(9):985–91.

    PubMed  Google Scholar 

  26. Cho YM, Kim TH, Lim S, Choi SH, Shin HD, Lee HK, et al. Type 2 diabetes-associated genetic variants discovered in the recent genome-wide association studies are related to gestational diabetes mellitus in the Korean population. Diabetologia. 2009;52(2):253–61.

    CAS  PubMed  Google Scholar 

  27. Lauenborg J, Grarup N, Damm P, Borch-Johnsen K, Jorgensen T, Pedersen O, et al. Common type 2 diabetes risk gene variants associate with gestational diabetes. J Clin Endocrinol Metab. 2009;94(1):145–50.

    CAS  PubMed  Google Scholar 

  28. Kwak SH, Kim SH, Cho YM, Go MJ, Cho YS, Choi SH, et al. A genome-wide association study of gestational diabetes mellitus in Korean women. Diabetes. 2012;61(2):531–41.

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Rong R, Hanson RL, Ortiz D, Wiedrich C, Kobes S, Knowler WC, et al. Association analysis of variation in/near FTO, CDKAL1, SLC30A8, HHEX, EXT2, IGF2BP2, LOC387761, and CDKN2B with type 2 diabetes and related quantitative traits in Pima Indians. Diabetes. 2009;58(2):478–88.

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Peng D, Wang J, Zhang R, Jiang F, Tam CHT, Jiang G, et al. CDKAL1 rs7756992 is associated with diabetic retinopathy in a Chinese population with type 2 diabetes. Sci Rep. 2017;7(1):8812.

    PubMed  PubMed Central  Google Scholar 

  31. Chang CL, Cai JJ, Huang SY, Cheng PJ, Chueh HY, Hsu SY. Adaptive human CDKAL1 variants underlie hormonal response variations at the enteroinsular axis. PLoS ONE. 2014;9(9):e105410.

    PubMed  PubMed Central  Google Scholar 

  32. Sole X, Guino E, Valls J, Iniesta R, Moreno V. SNPStats: a web tool for the analysis of association studies. Bioinformatics. 2006;22(15):1928–9. https://doi.org/10.1093/bioinformatics/btl268.

    Article  CAS  PubMed  Google Scholar 

  33. Johnson J, Abecasis G. GAS Power Calculator: web-based power calculator for genetic association studies bioRxiv. Cold Spring Harbor Laboratory. 2017;164343.

  34. Duesing K, Fatemifar G, Charpentier G, Marre M, Tichet J, Hercberg S, et al. Strong association of common variants in the CDKN2A/CDKN2B region with type 2 diabetes in French Europids. Diabetologia. 2008;51(5):821–6.

    CAS  PubMed  Google Scholar 

  35. Hertel J, Johansson S, Raeder H, Midthjell K, Lyssenko V, Groop L, et al. Genetic analysis of recently identified type 2 diabetes loci in 1,638 unselected patients with type 2 diabetes and 1,858 control participants from a Norwegian population-based cohort (the HUNT study). Diabetologia. 2008;51(6):971–7.

    CAS  PubMed  Google Scholar 

  36. Saxena R, Voight BF, Lyssenko V, Burtt NP, de Bakker PI, Chen H, Purcell S. Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science. 2007;316(5829):1331–6. https://doi.org/10.1126/science.1142358.

    Article  CAS  PubMed  Google Scholar 

  37. Scott LJ, Mohlke KL, Bonnycastle LL, Willer CJ, Li Y, Duren WL, et al. A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science. 2007;316(5829):1341–5.

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Takeuchi F, Serizawa M, Yamamoto K, Fujisawa T, Nakashima E, Ohnaka K, et al. Confirmation of multiple risk Loci and genetic impacts by a genome-wide association study of type 2 diabetes in the Japanese population. Diabetes. 2009;58(7):1690–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Hu C, Zhang R, Wang C, Wang J, Ma X, Lu J, et al. PPARG, KCNJ11, CDKAL1, CDKN2A-CDKN2B, IDE-KIF11-HHEX, IGF2BP2 and SLC30A8 are associated with type 2 diabetes in a Chinese population. PLoS ONE. 2009;4(10):e76440.

    Google Scholar 

  40. Ng MC, Park KS, Oh B, Tam CH, Cho YM, Shin HD, et al. Implication of genetic variants near TCF7L2, SLC30A8, HHEX, CDKAL1, CDKN2A/B, IGF2BP2, and FTO in type 2 diabetes and obesity in 6,719 Asians. Diabetes. 2008;57(8):2226–33.

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Omori S, Tanaka Y, Takahashi A, Hirose H, Kashiwagi A, Kaku K, et al. Association of CDKAL1, IGF2BP2, CDKN2A/B, HHEX, SLC30A8, and KCNJ11 with susceptibility to type 2 diabetes in a Japanese population. Diabetes. 2008;57(3):791–5.

    CAS  PubMed  Google Scholar 

  42. Wu Y, Li H, Loos RJ, Yu Z, Ye X, Chen L, et al. Common variants in CDKAL1, CDKN2A/B, IGF2BP2, SLC30A8, and HHEX/IDE genes are associated with type 2 diabetes and impaired fasting glucose in a Chinese Han population. Diabetes. 2008;57(10):2834–42.

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Zeggini E, Scott LJ, Saxena R, Voight BF, Marchini JL, Hu T, et al. Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nat Genet. 2008;40(5):638–45.

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Tarnowski M, Malinowski D, Pawlak K, Dziedziejko V, Safranow K, Pawlik A. GCK, GCKR, FADS1, DGKB/TMEM195 and CDKAL1 gene polymorphisms in women with gestational diabetes. Can J Diabetes. 2017;41(4):372–9.

    PubMed  Google Scholar 

  45. Wang Y, Nie M, Li W, Ping F, Hu Y, Ma L, et al. Association of six single nucleotide polymorphisms with gestational diabetes mellitus in a Chinese population. PLoS ONE. 2011;6(11):e26953.

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Guo F, Long W, Zhou W, Zhang B, Liu J, Yu B, et al. FTO, GCKR, CDKAL1 and CDKN2A/B gene polymorphisms and the risk of gestational diabetes mellitus: a meta-analysis. Arch Gynecol Obstet. 2018;298(4):705–15.

    CAS  PubMed  Google Scholar 

  47. Zhang C, Bao W, Rong Y, Yang H, Bowers K, Yeung E, et al. Genetic variants and the risk of gestational diabetes mellitus: a systematic review. Hum Reprod Updat. 2013;19(4):376–90.

    Google Scholar 

  48. Xie K, Chen T, Zhang Y, Wen J, Cui X, You L, et al. Association of rs10830962 polymorphism with gestational diabetes mellitus risk in a Chinese population. Sci Rep. 2019;9(1):1–6.

    Google Scholar 

  49. International Association of Diabetes and Pregnancy Study Groups Consensus Panel. International association of diabetes and pregnancy study groups recommendations on the diagnosis and classification of hyperglycemia in pregnancy. Diabetes Care. 2010;33(3):676–82.

    PubMed Central  Google Scholar 

  50. Namipashaki A, Razaghi-Moghadam Z, Ansari-Pour NJCJ. The essentiality of reporting Hardy–Weinberg equilibrium calculations in population-based genetic association studies. Cell J (Yakhteh). 2015;17(2):187.

    Google Scholar 

  51. Wang J, Shete S. Using both cases and controls for testing hardy–weinberg proportions in a genetic association study. Hum Hered. 2010;69(3):212–8. https://doi.org/10.1159/000289597.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Kanthimathi S, Chidambaram M, Liju S, Bhavadharini B, Bodhini D, Prakash VG, et al. Identification of genetic variants of gestational diabetes in South Indians. Diabetes Technol Ther. 2015;17(7):462–7.

    CAS  PubMed  Google Scholar 

  53. Salanti G, Southam L, Altshuler D, et al. Underlying genetic models of inheritance in established type 2 diabetes associations. Am J Epidemiol. 2009;170:537–45.

    PubMed  PubMed Central  Google Scholar 

  54. El Noury A, Azmy O, Alsharnoubi J, Salama S, Okasha A, Gouda WJ. Variants of CDKAL1 rs7754840 (G/C) and CDKN2A/2B rs10811661 (C/T) with gestational diabetes: insignificant association. BMC Res Notes. 2018;11(1):1–6.

    Google Scholar 

  55. Rosta K, Al-Aissa Z, Hadarits O, Harreiter J, Nádasdi Á, Kelemen F, et al. Association study with 77 SNPs confirms the robust role for the rs10830963/G of MTNR1B variant and identifies two novel associations in gestational diabetes mellitus development. PLoS ONE. 2017;12(1):e0169781.

    PubMed  PubMed Central  Google Scholar 

  56. Jager KJ, Zoccali C, Macleod A, Dekker FW. Confounding: what it is and how to deal with it. Kidney Int. 2008;73(3):256–60. https://doi.org/10.1038/sj.ki.5002650 (Epub 2007 Oct 31).

    Article  CAS  PubMed  Google Scholar 

  57. Mansoori Y, Daraei A, Naghizadeh MM, Salehi R. Significance of a common variant in the CDKAL1 gene with susceptibility to type 2 diabetes mellitus in Iranian population. Adv Biomed Res. 2015;4:45.

    CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors are grateful to all the enrolled pregnant women and the Department of Gynecology and Obstetrics, BSMMU. All members of the Molecular Biotechnology Division, NIB, were highly appreciated for their unwavering and kind support.

Funding

The work has been supported by the fund of Special allocation of Ministry of Science and Technology, Government of the people’s Republic of Bangladesh in the fiscal year of 2014–15. The project ID was MEDI; S-19.

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Authors and Affiliations

  1. Molecular Biotechnology Division, National Institute of Biotechnology (NIB), Ganakbari, Ashulia, Savar, Dhaka, 1349, Bangladesh

    U. S. Mahzabin Amin, Nahid Parvez, Tahia Anan Rahman, Keshob Chandra Das & Md. Salimullah

  2. Department of Endocrinology and Metabolism, Bangabandhu Sheikh Mujib Medical University (BSMMU), Dhaka, Bangladesh

    Md. Rakibul Hasan, Sharmin Jahan & Muhammad Abul Hasanat

  3. Department of Biochemistry and Molecular Biology, University of Dhaka, Dhaka, Bangladesh

    Zeba I. Seraj

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Contributions

Conception and study design: SM, USMA; sample and data collection: USMA, MRH, SJ, MAH; genotyping and confirmation of genotypes: USMA, NP. Data analysis and interpreting the findings: USMA; manuscript writing and reviewing: USMA, SM; manuscript editing: TAR, USMA, SM; revising manuscript critically for important intellectual content and supervision of the study: SM, ZIS, MAH. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Md. Salimullah.

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Ethics approval and consent to participate

This study was approved (NIBREC 2016-04) by the research ethics committee (REC) of the National Institute of Biotechnology (NIB) and informed consent was taken from participants after explaining purpose and procedure of the study.

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Not applicable.

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The authors declare no competing interests.

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Supplementary Information

Additional file 1

: Table S1. Association of rs7756992 with GDM under different genetic models. Table S2. Association of rs7754840 with GDM under different genetic models. Table S3. Cross classification interaction table of CDKAL1 variants (rs7756992 and rs7754840) and family history of T2DM. Table S4. Cross classification interaction table of CDKAL1 variants (rs7756992 and rs7754840) and gravidity.

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Amin, U.S.M., Parvez, N., Rahman, T.A. et al. CDKAL1 gene rs7756992 A/G and rs7754840 G/C polymorphisms are associated with gestational diabetes mellitus in a sample of Bangladeshi population: implication for future T2DM prophylaxis. Diabetol Metab Syndr 14, 18 (2022). https://doi.org/10.1186/s13098-021-00782-w

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Keywords

Diabetology & Metabolic Syndrome

ISSN: 1758-5996

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