Hindawi Journal of Diabetes Research Volume 2019, Article ID 3618103, 7 pages https://doi.org/10.1155/2019/3618103

Research Article Single Nucleotide Polymorphisms in CDKAL1 Are Associated with Risk of Gestational Diabetes Mellitus in Chinese Population

Keke Wang ,1 Qiong Chen ,1,2 Yongliang Feng,1 Hailan Yang,3 Weiwei Wu,1 Ping Zhang,1 Ying Wang,1 Jamie Ko,4 Feng Zhao,1 Wenqiong Du,1 Feifei Yang,1 Tianbi Han,1 Suping Wang ,1 and Yawei Zhang 1,5,6

1Department of Epidemiology, Shanxi Medical University School of Public Health, Taiyuan 030001, China 2Office for Cancer Prevention and Research, Affiliated Cancer Hospital of Zhengzhou University/Henan Cancer Hospital, Zhengzhou 450008, China 3Department of Obstetrics, the First Affiliated Hospital, Shanxi Medical University, Taiyuan 030001, China 4Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, 06520 CT, USA 5Department of Surgery, Yale University School of Medicine, New Haven 06520, USA 6Department of Environmental Health Sciences, Yale School of Public Health, New Haven, 06520 CT, USA

Correspondence should be addressed to Suping Wang; [email protected] and Yawei Zhang; [email protected]

Received 22 October 2018; Revised 11 February 2019; Accepted 4 March 2019; Published 14 April 2019

Academic Editor: Daniela Foti

Copyright © 2019 Keke Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Gestational diabetes mellitus (GDM) is a growing public health concern for many reasons, and its etiology remains unclear. Due to the similarity of its pathophysiology with (T2DM), we evaluated the relationship between published T2DM susceptibility and the risk of GDM. A total of 303 SNPs from genes including IRS1, IGF2BP2, CDKAL1, GCK, TCF7L2, KCNQ1, and KCNJ11 and the risk of GDM were examined in a nested case-control study with 321 GDM cases and 316 controls. The odds ratios (ORs) and their 95% confidence interval (95% CI) were estimated by unconditional logistical regression as a measure of the associations between genotypes and GDM in additive, recessive, dominant, and codominant models adjusting for maternal age, maternal BMI, parity, and family history of diabetes. At the gene level, CDKAL1 was associated with GDM risk. SNPs in the CDKAL1 gene including rs4712527, rs7748720, rs9350276, and rs6938256 were associated with reduced GDM risk. However, SNPs including rs9295478, rs6935599, and rs7747752 were associated with elevated GDM risk. After adjusting for multiple comparisons, rs9295478 and rs6935599 were still significant across the additive, recessive, and codominant models; rs7748720 and rs6938256 were significant in dominant and codominant models; and rs4712527 was only significant in the codominant model. Our study provides evidence for an association between the CDKAL1 gene and risk of GDM. However, its role in the GDM pathogenesis still needs to be verified by further studies.

1. Introduction trend is expected to continue due to a rising number of overweight or obese women of childbearing age [2, 3]. Gestational diabetes mellitus (GDM) is a public health Although the pathogenesis of GDM remains unclear, it is concern due to its large disease burden and its short- and considered as a complex disease caused by multiple factors long-term adverse health impact on pregnant women and including genetic, environment, lifestyle, and other random their offspring including , metabolic syndrome, and factors that related to reduction of insulin sensitivity and type 2 diabetes mellitus (T2DM) [1]. GDM affects approxi- insulin resistance. mately 5-17% of all pregnancies worldwide [2]. The preva- GDM shares common risk factors with T2DM, includ- lence has increased over the past 20 years, and this upward ing family history of diabetes, obesity, high maternal age, 2 Journal of Diabetes Research abnormal glucose tolerance, and specific ethnicity [4]. The 2.2. Case and Control Selection. Blood glucose was tested similarities of pathophysiology between GDM and T2DM using a 75 g oral glucose tolerance test (OGTT) during may suggest that the genetic factors of T2DM may also 24-28 weeks of gestation. Subjects were diagnosed as hav- be involved in the development of GDM. GDM has been ing GDM if they met at least one of the following criteria: suggested as precursor for T2DM by some researchers (1) fasting blood glucose > 5 1 mmol/L, (2) 1-hour blood [5]. Genome-wide association studies and candidate gene glucose > 10 0 mmol/L, and/or (3) 2-hour blood glucose > studies had already identified several genes that associated 8 5 mmol/L. A total of 334 women had GDM (cases), with risk of T2DM, and some of them were also verified and 334 control subjects who had no GDM were randomly to be associated with GDM risk by candidate gene studies selected through frequency matching to cases by age, month and one GWA study including TCF7L2, GCK, KCNJ11, of conception, and residence. 13 cases and 18 controls KCNQ1, CDKAL1, IGF2BP2, and IRS1 genes [6]. However, were excluded due to missing genotyping per person or there are still many genes that were previously reported to per SNP. Finally, 321 cases and 316 controls were included be associated with T2DM but were not associated with in the analysis. GDM risk. It is possible that the T2DM susceptibility genes were mostly found in GWA studies or meta- 2.3. Genotyping. DNA was extracted, isolated, and purified analysis normally with large sample size and weak effect, from whole blood samples according to a standard phenol- but those found in GDM studies often have small sample chloroform extraction method. Genotyping was conducted size resulting in insufficient statistical power to identify using an Illumina GoldenGate Platform. Duplicate samples weak effects [7]. (5%) were interspersed throughout the plates used for Current evidence suggested that the genetic polymor- genotype analysis for quality control purposes. A total of phisms often had different GDM risks in different ethnic 303 single-nucleotide polymorphisms (SNPs) from genes populations [5, 8]. Genetic polymorphisms in TCF7L2 and including IRS1, IGF2BP2, CDKAL1, GCK, TCF7L2, KCNQ1, GCKR genes were found to be associated with GDM risk in and KCNJ11 were considered for this study. The completion European women, and polymorphisms in TSPAN8 were rate for all SNPs was over 99%. The Hardy-Weinberg associated with increased GDM risk in African American equilibrium (HWE) was assessed in controls for each women [9]. TCF7L2 and KCNQ1 polymorphisms were found SNP using a chi-square test. SNPs with a P value >0.05 to be associated with GDM risk in Mexican women [10]. from the chi-square test were considered to be in HWE. In Asia, polymorphisms in TCF7L2, KCNQ1, KCNJ11, Of the 303 SNPs tested, 8 SNPs were not in HWE and were IGF2BP2, and CDKAL1 genes were associated with GDM excluded from the final analysis (as shown in Supplementary risk in the Korean population [8], while CDKAL1 and GCK Table 1). gene polymorphisms were associated with GDM in Indian and Thai women [8]. However, to our knowledge, the associ- 2.4. Statistical Analysis. All statistical analyses were carried ation with GDM risk was only evaluated in IGF2BP2 and out using R statistical software version 3.3.1. The odds ratios KCNQ1 genes in Chinese women [7], and other genes like (OR) and their 95% confidence intervals (95% CI) were TCF7L2, GCK, KCNJ11, CDKAL1, and IRS1 still need to estimated by unconditional logistical regression as a measure be verified by additional studies. Thus, we examined the of the associations between genotypes and GDM adjusting associations between these genes and risk of GDM using for maternal age, maternal BMI, parity, and family history a nested case-control study design in the Taiyuan birth of diabetes. The associations were estimated in additive, cohort population. dominant, codominant, and recessive models. The minimum P (minP) tests were conducted to examine an association at 2. Materials and Methods the gene level using “minPtest” R package [12]. The minP test was based on permutation resampling and was conducted to 2.1. Subjects. The subjects were enrolled from an ongoing assess the true statistical significance of the smallest P trend birth cohort at the First Affiliated Hospital of Shanxi Medical within each gene region. Haplotype analyses were conducted University during March 1, 2012, and July 30, 2014, in for all genes in which more than 1 SNP was genotyped. Taiyuan, China [11]. Eligible women included pregnant Haplotype block structures were evaluated with HaploView women who came to the hospital for delivery with gestational version 4 using the method of Four Gamete Rule. Logistic age of 20 weeks or more, who had no mental illness, and who regression was used to calculate the ORs of the haplotypes were aged 18 years or older. Eligible women were informed of using the method implemented in the “haplo.ccs” package the study procedure upon their arrival at the hospital for [13]. Only major haplotypes with an estimated frequency of delivery. After obtaining written consent, an in-person over 5% are considered in this report. The ORs were com- interview was conducted at the hospital by trained study puted compared to haplotype with the highest estimated interviewers using a standardized and structured question- population frequency. An additive model was tested adjust- naire. The questionnaire collected information regarding ing for maternal age, maternal BMI, parity, and family demographic factors, reproductive and medical history, history of diabetes. The false discovery rate (FDR) method smoking, alcohol and tea consumption, occupational and with a significance level of 0.2 was applied for multiple residential histories, physical activity, and diet. Informa- comparisons. Gene-by-gene interactions were detected using tion on birth outcomes and pregnancy complications was the method of generalized multivariate dimensionality abstracted from medical records. reduction (GMDR) [14]. Journal of Diabetes Research 3

2.5. Results. The characteristics of the cases and controls have gene interaction analysis using GMDR did not find sig- already been described elsewhere [11]. In brief, there were nificant results. more GDM cases with a higher BMI and family history of diabetes than control subjects (P =00002, P =0005). None 3. Discussion of the cases or controls consumed alcohol during pregnancy. The distributions of maternal age, weight gain during preg- We verified associations between polymorphisms in genes nancy, parity, high blood pressure during pregnancy, passive including TCF7L2, GCK, KCNJ11, KCNQ1, CDKAL1, smoking, and gestational weeks between cases and controls IGF2BP2, and IRS1 and risk of GDM in a Chinese popula- were similar. tion. At the gene level, CDKAL1 was significantly associated All associations between genotypes in the seven genes with GDM risk. The CDKAL1 gene located in the short and GDM risk are shown in Supplementary Table 2. arm of human 6 could affect the function of Significant associations with GDM risk in the CDKAL1 β-cells through inhibiting the activity of CDK5 and acting gene are shown in Table 1; risk alleles of A, G, and G in as a tRNA-modifying enzyme [15], and risk alleles in this rs9295478, rs6935599, and rs7747752 were found to be gene could affect the process of proinsulin conversion to associated with elevated GDM risk in additive, dominant, insulin through protein translation. In our study, several codominant, and recessive models. These alleles could SNPs in the CDKAL1 gene were found to be associated with increase the GDM risk for around 2 times in the GDM risk. Among these SNPs, the G allele in rs4712527 was additive model, 1.45 times in the dominant model, 1.4 reported to be associated with reduced risk of T2DM in a times in the codominant model, and 1.8 times in the cohort study from Argentina [16], but not in a Chinese recessive model. These associations remained statistically population [17]. CDKAL1 gene polymorphisms including significant after adjusting for multiple comparisons in rs7754840 and rs10946398 were reported to be associated additive, recessive, and codominant models for rs9295478 with risk of T2DM in Asian populations [18, 19]. Results and rs6935599. from a GWA study in Korean women suggested that G alleles in rs4712527, rs7748720, rs9350276, and rs7754840 in CDKAL1 is strongly associated with GDM rs6938256 were found to be associated with reduced GDM [20], and subsequent meta-analysis also confirmed that the risk compared with A alleles in all four models as mentioned C allele of rs7754840 was associated with elevated risk of above. GDM risks in women who carried G alleles in GDM [6]. A study from India did not find a significant rs4712527 and rs7748720 were approximately 0.20 times in association between CDKAL1 polymorphisms and GDM risk the additive model, 0.50 times in dominant and codominant [21]. Another study from Russia examined whether CDKAL1 models, and 0.20 times in the recessive model compared with polymorphisms modified the relationship between lifestyles women who carried A alleles. GDM risks in women who including food consumption, physical activity, and smoking carried G alleles in rs9350276 and rs6938256 were around as well as GDM risk and it did not find significant inter- 0.40 times in the additive model, 0.60 times in dominant actions; however, results from this study suggest that the and codominant models, and 0.50 times in the recessive association of sausage consumption with GDM risk can model compared with women who carried A alleles. After be determined based on the number of risk alleles of adjusting for multiple comparisons, the associations for rs1799884 in GCK [22]. The CDKAL1 rs10946398 CC rs7748720 and rs6938256 remained significant in dominant genotype was reported to be associated with the need for and codominant models, and rs4712527 remained significant insulin therapy in a study conducted in Poland; however, in the codominant model; however, the nonsignificant results the association did not pass the statistical significance thresh- were found in rs9350276. old after correction for multiple testing [23]. Rs7748720 in As shown in Table 2, CDKAL1 gene polymorphisms CDKAL1 was found to be significantly associated with were significantly associated with risk of GDM at the gene GDM risk; however, it was never reported in previous level, and this gene was still statistically significant after studies, and it still has to be verified by further studies in a adjusting for multiple comparisons. Nonsignificant results different population. were found in IRS1, IGF2BP2, GCK, TCF7L2, KCNQ1, Although the precise pathology of GDM remains unclear, and KCNJ11 genes. it was reported to be related to increased insulin resistance We observed strong linkage disequilibrium of 16 and/or decreased insulin sensitivity during pregnancy [1]. blocks in 111 SNPs in the CDKAL1 gene (Figure 1). SNPs Therefore, the association between GDM risk and CDKAL1 significantly associated with GDM that are listed in Table 1 polymorphisms could be supported by its relation to these in the manuscript including negative SNPs (rs4712527 and metabolic characteristics. In a Cardiometabolic Risk in rs7748720) and positive SNPs (rs9295478, rs6935599, and Chinese (CRC) study, rs10946398 of CDKAL1 was associated rs7747752) belong to the same block 4, another SNP with markers of impaired insulin secretion, suggesting rs9350276 belong to block 5, and rs6938256 does not that its effect on glucose-related traits might have a role belong to any block constructed by HaploView. The in the development of GDM [24]. Among Greek children, GACGGACG haplotype in block 4 and the AG haplotype rs9356744 and rs2206734 were related to insulin resistance, a in block 5 were associated with reduced risk of GDM. clinical marker related to T2DM in a cross-sectional cohort Haplotype analyses were consistent with the results of of Greek children and adolescents of European descent the individual SNP analyses and did not provide addi- [25]. In a meta-analysis, rs9460557 of CDKAL1 showed tional insight into these associations (Table 3). Gene and evidence with insulin clearance and suggested it may have a 4

Table 1: SNPs in CDKAL1 gene and risk of gestational diabetes mellitus#.

Additive Dominant Recessive Codominant SNPs Genotypes Cases Controls OR (95% CI) P OR (95% CI) P OR (95% CI) P OR (95% CI) P ∗ ∗ ∗∗ AA 245 208 0.21 (0.05-0.69) 0.019 0.59 (0.41-0.84) 0.003 0.24 (0.05-0.77) 0.029 0.59 (0.43-0.81) 0.001 rs4712527 AG 73 96 GG 3 12 ∗ ∗∗ ∗∗ AA 241 193 0.15 (0.03-0.47) 0.003 0.51 (0.36-0.72) 0.0002 0.18 (0.04-0.55) 0.007 0.51 (0.37-0.70) 0.00003 rs7748720 AG 75 107 GG 3 15 ∗ ∗ ∗∗ GG 72 93 2.08 (1.33-3.27) 0.001 1.46 (1.01-2.10) 0.042 1.83 (1.26-2.66) 0.001 1.44 (1.15-1.81) 0.002 rs9295478 GA 149 160 AA 100 63 ∗ ∗ ∗∗ AA 72 93 2.05 (1.31-3.22) 0.002 1.45 (1.01-2.10) 0.044 1.80 (1.24-2.62) 0.002 1.43 (1.14-1.79) 0.002 rs6935599 AG 149 160 GG 99 63 ∗ ∗ ∗∗ CC 76 96 1.96 (1.25-3.09) 0.004 1.45 (1.01-2.08) 0.042 1.69 (1.16-2.48) 0.006 1.39 (1.11-1.75) 0.004 rs7747752 CG 153 159 GG 92 60 ∗ ∗∗ AA 188 155 0.46 (0.26-0.81) 0.008 0.67 (0.49-0.92) 0.014 0.52 (0.30-0.90) 0.021 0.70 (0.55-0.89) 0.004 rs9350276 AG 110 122 GG 23 39 ∗ ∗∗ ∗∗ AA 207 165 0.39 (0.20-0.74) 0.005 0.58 (0.42-0.81) 0.001 0.46 (0.24-0.87) 0.019 0.63 (0.48-0.81) 0.0005

rs6938256 AG 96 118 Research Diabetes of Journal GG 16 30 ∗ and ∗∗ indicate that the associations were statistically significant at the 0.20 and 0.05 levels after adjusting for multiple comparisons using the FDR method. #ORs were adjusted for maternal age, maternal BMI, parity, and family history of diabetes. Journal of Diabetes Research 5

Table 2: Gene polymorphisms and risk of gestational diabetes mellitus.

Genes Location Start End Number of SNPs minP minP∗ IRS1 2q36.3 227,596,033 227,664,485 4 0.362 0.656 IGF2BP2 3q27.2 185,361,527 185,542,844 14 0.515 0.709 CDKAL1 6p22.3 20,534,688 21,232,635 111 0.001 0.007 GCK 7p13 44,183,870 44,237,769 15 0.157 0.550 TCF7L2 10q25.2 114,710,009 114,927,437 43 0.608 0.709 KCNQ1 11p15.5 2,465,914 2,870,340 107 0.375 0.656 KCNJ11 11p15.1 17,406,795 17,410,878 1 0.923 0.923 minP∗ after adjusting for multiple comparisons using the method of FDR. ″ ″ ″ s13210277 s12215519 s12664336 ″ s898165 ″ ″ rs415446 ″ rs4580857 ″ rs989969 r ″ r ″ r ″ r ″ ″ rs9358372 ″ ″ rs10806925 ″ ″ rs7753109 ″ ″ rs4395717 ″ ″ rs7753271 ″ ″ rs4712556 ″ ″ rs9366363 ″ ″ rs4077405 ″ ″ rs10946415 ″ ″ rs10946416 ″ ″ rs67816138 ″ ″ rs6938256 ″ ″ rs6456382 ″ ″ rs9358377 ″ ″ rs4710958 ″ ″ rs72834308 ″ ″ rs7765765 ″ ″ rs72657619 ″ ″ rs12196595 ″ ″ rs1004172 ″ ″ rs4712573 ″ ″ rs1980458 ″ ″ rs6456389 ″ ″ rs9358383 ″ ″ rs6932393 ″ ″ rs2494728 ″ ″ rs12192642 ″ ″ rs6905258 ″ ″ rs10946425 ″ ″ rs76376935 ″ ″ rs12526927 ″ ″ ″ ″ rs73386546 ″ ″ rs9366354 ″ ″ rs9350258 ″ ″ rs2064320 ″ ″ rs16883932 ″ ″ rs9368204 ″ ″ rs6926658 ″ ″ rs12209806 ″ ″ rs9465831 ″ ″ rs9350268 ″ ″ rs6917599 ″ ″ rs10484633 ″ ″ rs9465850 ″ ″ rs9465851 ″ ″ rs9348440 ″ ″ rs9460540 ″ ″ rs16884072 ″ ″ rs6456369 ″ ″ rs4712527 ″ ″ rs9356744 ″ ″ rs9368222 ″ ″ rs7748720 ″ ″ rs9295478 ″ ″ rs6935599 ″ ″ rs7747752 ″ ″ rs6928012 ″ ″ rs13217519 ″ ″ rs9350276 ″ ″ rs72832315 ″ ″ rs4710944 ″ ″ rs7747724 ″ ″ rs2819996 ″ ″ rs10946406 ″ rs4585542 ″ ″ ″ rs12201217 ″ ″ rs62404549 ″ ″ rs1563726 ″ ″ rs2328574 ″ ″ rs9466002 ″ ″ rs9466004 ″ ″ rs7770752 ″ ″ rs1137970 ″ ″ rs201312 ″ ″ rs9350314 ″ ″ rs201327 ″ ″ rs1001310 ″ ″ rs2068072 ″ ″ rs9366375 ″ ″ rs6904348 ″ ″ rs77373471 ″ ″ rs79034658 ″ ″ rs9350319 ″ ″ rs4712585 ″ ″ rs6456397 ″ ″ rs62404499 ″ ″ rs10946430 ″ ″ rs6937439 ″ ″ rs9295494 ″ ″ rs10498701 ″ ″ rs1466340 ″ ″ rs9368283 ″ ″ rs9356770 ″ ″ rs11965768 ″ ″ rs12526391 ″ ″ rs9465982 ″ ″ rs9295501 ″ ″ rs9465985 ″ ″ rs9465989 ″ ″ rs35013686 ″ ″ rs16884616 ″ ″ rs9465991 ″ ″ rs1531303 ″ ″ rs10456242 ″

Block 1 (76 kb) Block 2 (4 kb) Block 3 (20 kb) Block 4 (65 kb) Block 5 (5 kb) Block 6 (105 kb) Block 7 (27 kb) Block 8 (20 kb) Block 9 (49 kb) Block 10 (18 kb) Block 11 (26 kb) Block 12 (11 kb) Block 13 (36 kb) Block 14 (27 kb) Block 15 (3 kb) Block 16 (2 kb) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 98 96 99 12 98 99 2 91 98 96 99 38 94 98 98 1 96 98 97 99 98 98 98 98 23 96 91 98 56 98 95 90 94 59 86 93 96 0 97 98 47 97 96 89 40 87 25 94 99 77 99 99 85 89 85 98 93 91 97 98 96 98 39 2 98 99 96 78 69 98 69 95 68 86 96 97 79 73 98 53 54 80 29 96 64 90 39 81 98 99 86 85 64 82 94 98 83 88 95 51 32 86 98 89 99 36 83 97 98 99 63 91 13 84 99 87 99 92 14 93 93 97 97 95 97 40 67 74 97 97 99 78 98 98 98 98 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52 71 10 26 33 71 48 22 3 11 44 32 2 25 12 15 3 63 41 46 87 4 13 9 66 51 77 67 9 23 82 74 82 64 60 42 33 25 37 29 12 12 47 6 28 51 26 27 25 94 36 93 57 79 44 18 95 97 81 13 96 81 67 20 76 56 6 5 83 71 53 53 49 74 18 1 43 6 33 26 83 43 61 31 36 36 94 3 39 10 0 61 51 27 55 3 33 6 18 14 34 34 20 63 42 66 50 24 35 8 8 60 71 50 32 82 86 38 64 19 8 24 9 73 61 25 76 56 14 77 28 79 28 47 36 52 39 79 39 60 20 96 65 55 89 94 93 82 1 79 21 74 89 59 77 5 22 63 51 45 42 2 19 46 48 30 12 42 26 57 26 4 0 60 5 18 58 83 49 61 66 30 21 7 56 24 45 22 22 33 37 19 56 27 76 15 74 24 53 24 2 55 73 5 63 37 38 63 15 8 6 20 40 79 12 31 50 77 13 79 23 52 36 93 39 60 61 15 95 20 89 76 92 78 72 61 82 69 56 55 70 82 21 66 32 42 78 65 28 27 30 42 14 22 40 93 25 38 68 43 3 22 7 39 15 45 40 16 5 41 9 0 27 13 3 3 43 39 45 4 89 53 55 4 66 60 36 83 38 43 76 39 7 26 21 25 25 55 76 11 29 14 30 28 90 37 53 29 60 63 86 11 98 65 12 96 8 47 65 60 84 57 59 77 75 24 6 36 55 76 57 52 28 10 42 20 23 7 54 31 49 49 92 16 55 55 48 37 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22 39 13 10 22 18 18 53 40 22 23 44 1 40 4 32 27 32 27 79 58 27 93 66 21 29 19 80 68 77 40 12 53 59 48 55 68 21 65 60 29 51 34 5 16 3 18 41 61 71 7 2 7 38 21 7 7 16 43 47 17 38 22 9 15 9 17 57 13 41 4 12 28 17 21 35 11 45 3 58 0 13 58 24 12 76 19 38 54 86 4 23 55 21 3 60 54 48 27 47 14 61 43 66 24 2 19 77 77 0 3 38 29 15 63 6 46 74 42 66 29 56 45 11 62 5 10 41 68 39 52 50 10 17 33 6 72 47 5 41 39 25 40 0 15 7 17 22 37 13 34 31 49 18 38 31 29 15 1 43 45 70 24 52 24 10 37 20 18 27 48 20 21 52 40 76 32 29 28 8 99 77 51 24 26 13 77 74 10 2 62 57 76 48 55 75 44 70 0 34 56 35 64 63 1 24 0 9 41 54 90 11 5 3 48 73 15 42 72 44 41 6 17 1 5 15 16 27 46 17 83 35 17 24 40 20 6 27 1 20 45 7 2 63 67 4 4 19 38 16 2 27 21 19 45 1 81 61 48 16 57 9 26 62 31 26 6 83 74 13 61 2 41 39 46 45 46 83 21 61 77 47 35 13 50 38 9 6 24 15 56 38 10 15 19 24 26 17 9 34 20 47 22 29 13 8 17 2 17 5 32 66 22 3 17 3 36 19 16 27 29 19 21 50 0 42 33 77 37 1 16 36 2 19 55 20 28 52 76 32 6 61 24 14 24 2 7 43 82 13 63 12 3 0 55 3 26 44 22 54 33 46 12 62 13 18 0 27 6 9 38 25 13 53 4 1 55 7 5 38 23 19 2 12 2 17 17 18 6 20 42 30 30 5 85 23 5 13 20 25 10 17 18 70 1 5 4 2 12 41 1 55 20 70 15 80 54 55 9 77 0 9 18 18 44 41 12 63 14 22 4 63 28 71 19 21 66 43 53 69 63 38 84 43 23 5 9 10 26 14 17 52 75 5 55 4 38 38 29 4 5 6 38 10 22 3 22 3 21 16 19 67 18 13 28 9 11 19 13 44 45 12 52 13 38 16 30 21 8 36 27 40 6 59 11 63 22 21 15 82 42 0 36 14 24 56 19 53 27 78 39 54 34 38 35 34 53 38 18 57 10 8 22 1 1 20 64 29 23 23 37 38 39 3 5 18 39 9 2 16 62 28 30 21 72 4 37 15 26 2 13 9 19 23 12 0 41 0 17 35 2 8 0 72 1 27 8 81 1 24 14 10 44 81 1 64 11 24 58 18 0 10 23 24 5 46 1 71 11 4 28 18 43 7 15 7 15 9 13 62 38 6 21 18 14 35 38 2 13 19 41 96 0 14 17 8 12 20 26 17 21 4 22 15 16 19 0 11 10 7 9 5 1 1 43 21 1 7 19 38 27 7 62 22 17 4 47 42 20 64 0 39 56 13 5 4 20 16 63 71 39 45 52 61 19 85 43 10 76 9 1 12 9 8 43 30 35 59 7 15 38 69 11 9 19 23 28 10 16 6 17 21 20 14 20 10 11 26 8 26 6 4 11 12 4 49 0 40 16 53 13 8 20 21 40 61 0 26 13 15 21 84 13 24 0 15 32 13 9 53 15 75 18 33 74 4 55 38 62 49 10 15 56 11 9 9 26 10 27 61 3 27 28 17 39 75 2 14 16 4 19 8 14 63 27 16 19 93 26 12 31 11 16 20 15 8 2 8 9 49 43 0 56 35 20 22 9 21 46 37 53 21 4 44 20 88 44 13 15 59 15 9 54 61 2 15 26 2 54 63 40 19 3 15 9 0 7 27 1 15 59 64 76 24 28 29 20 2 5 15 85 10 17 20 35 42 17 13 0 21 8 10 30 0 87 10 18 19 6 14 3 1 22 27 52 4 8 10 42 41 43 44 13 24 42 10 12 7 36 58 12 8 53 62 12 1 33 24 43 38 54 3 33 41 16 74 9 10 15 15 25 12 79 2 51 9 6 27 4 8 6 62 12 30 3 25 7 10 14 15 0 21 42 42 6 81 18 35 16 26 7 8 50 20 12 56 38 10 56 39 6 21 31 45 22 11 8 14 16 20 12 4 31 63 9 48 25 55 52 4 8 40 8 36 11 20 56 11 0 2 8 3 81 15 48 16 30 7 37 73 3 25 61 53 9 8 15 32 37 12 21 42 14 17 27 29 3 18 22 20 12 16 77 10 21 14 83 40 57 0 27 8 5 74 44 12 45 8 35 40 7 3 56 28 9 48 25 10 29 1 57 22 34 1 63 16 55 0 15 20 10 84 27 24 7 33 34 1 25 5 5 19 58 67 1 24 74 34 20 26 16 16 33 9 29 38 28 5 17 87 15 6 63 20 1 18 74 23 0 6 0 15 78 75 10 3 7 9 22 9 9 55 66 48 49 26 2 38 3 26 0 66 36 23 40 8 56 11 14 8 17 14 16 6 9 36 39 0 1 7 29 14 67 7 17 3 31 24 23 50 20 43 21 8 16 19 10 12 18 31 18 77 2 35 18 36 35 6 9 15 21 78 23 42 7 16 6 54 1 21 65 20 27 26 5 35 11 4 20 9 63 8 47 11 22 7 8 11 0 2 49 17 15 10 37 26 15 17 11 21 9 8 63 75 32 14 35 90 23 19 23 23 97 98 2 18 3 23 15 52 35 38 40 25 6 10 1 70 20 8 14 6 6 43 11 17 41 20 20 54 36 5 35 2 1 41 34 1 13 41 6 35 1 71 14 10 18 33 48 2 11 16 4 25 10 11 10 62 14 1 63 64 34 24 19 31 22 14 8 26 21 17 28 8 29 12 4 1 38 34 32 22 7 1 10 69 9 42 15 14 0 9 11 23 53 62 53 24 9 35 3 43 2 26 14 21 28 47 0 20 13 82 17 2 2 7 16 12 13 11 33 22 17 41 15 16 66 6 16 25 0 35 28 18 5 22 57 8 46 19 15 1 0 38 36 22 31 15 10 11 15 11 6 2 40 10 1 7 15 12 20 24 7 42 3 41 3 10 34 29 5 56 36 6 16 9 28 1 2 36 70 10 17 84 0 34 36 52 6 10 6 55 7 61 17 79 19 25 27 13 69 93 22 11 99 9 18 7 37 4 2 3 14 11 8 23 7 18 11 13 9 43 7 28 42 35 6 30 15 42 9 1 11 11 33 5 61 22 7 55 10 35 39 49 8 5 11 85 4 2 44 18 34 2 40 32 39 61 64 19 25 26 28 29 52 25 25 27 17 54 8 2 3 7 14 90 13 8 55 10 8 36 24 0 1 53 5 6 26 9 31 5 27 9 3 1 17 31 1 0 24 17 23 39 17 82 12 7 10 5 21 83 9 8 21 18 11 3 7 29 12 15 63 36 17 35 10 24 12 0 7 37 57 10 10 56 8 14 59 94 12 56 9 29 14 10 17 39 9 11 6 7 2 46 5 51 9 3 5 15 5 9 30 6 71 55 3 26 1 44 76 10 10 16 17 2 13 8 19 14 13 20 32 8 7 15 41 67 39 31 7 38 88 2 12 92 35 3 41 15 15 58 20 3 9 21 18 0 3 9 50 0 42 25 10 32 12 51 6 30 4 17 5 5 11 0 7 60 40 78 56 30 2 10 20 3 2 7 17 71 31 13 2 7 19 39 3 21 67 5 11 33 68 20 41 15 25 46 9 20 14 14 16 29 6 20 49 0 6 34 7 12 6 24 18 23 2 19 7 3 11 18 10 2 2 6 7 21 33 26 14 51 7 29 5 21 16 19 3 9 75 38 14 2 72 3 7 35 4 16 4 62 8 13 26 11 42 0 9 6 73 13 11 20 8 6 45 2 8 13 28 44 39 24 3 33 19 52 14 3 3 31 10 2 4 8 12 39 2 32 56 8 14 10 36 1 17 1 9 18 71 20 13 16 75 31 20 4 51 4 39 59 58 74 2 7 88 21 7 75 12 4 10 50 5 2 8 20 22 43 15 26 16 33 12 15 7 28 3 21 31 2 11 2 2 7 31 78 27 51 3 31 10 53 0 1 2 20 22 36 18 28 68 31 1 21 20 39 16 9 56 34 23 42 13 20 50 3 8 8 5 26 36 0 9 52 19 5 41 18 3 32 2 3 5 14 23 16 3 11 5 2 0 10 25 12 44 16 16 34 3 54 16 3 17 12 29 12 13 76 9 1 18 4 5 2 6 36 2 30 26 1 24 11 6 5 18 5 48 14 45 2 24 13 32 9 50 0 3 3 40 16 0 28 5 0 12 6 13 19 35 24 32 16 52 15 53 12 18 23 33 14 24 43 1 18 16 28 21 31 27 4 64 32 23 11 73 10 7 5 6 7 1 6 5 13 1 18 23 5 20 0 0 6 5 20 36 0 8 15 0 12 8 51 9 57 3 39 35 27 0 73 22 24 29 1 14 76 3 28 16 26 16 2 18 25 57 21 21 40 23 30 2 4 10 0 9 49 14 1 61 37 1 30 5 48 4 4 8 6 17 1 8 7 1 11 16 60 6 12 13 27 52 85 2 3 49 29 16 19 68 0 13 16 8 16 39 28 17 32 5 4 38 36 11 24 8 0 5 19 48 6 60 14 6 32 7 10 3 5 2 19 9 4 34 8 5 34 16 68 11 43 8 15 14 53 27 0 31 20 1 32 14 14 8 10 24 1 64 18 41 13 30 5 38 8 10 32 41 16 24 4 7 16 29 5 15 19 51 5 37 3 1 10 20 0 49 9 35 4 9 33 65 10 21 6 8 17 51 2 5 46 24 61 3 14 67 3 15 12 73 20 15 11 12 6 21 7 6 21 40 13 8 10 16 25 24 24 19 20 3 8 7 10 0 15 17 2 19 0 3 8 11 6 10 32 3 5 9 67 86 55 31 9 20 29 40 19 68 10 19 12 57 2 3 13 10 24 51 9 40 5 10 12 5 19 1 16 6 1 21 20 21 0 17 2 19 0 4 2 52 7 38 11 63 8 13 1 5 3 45 30 0 55 46 74 10 12 57 7 10 15 18 2 17 3 44 15 47 0 60 19 38 11 6 4 2 7 33 29 29 19 50 6 2 7 1 14 2 8 4 19 9 36 62 1 70 1 8 6 40 9 4 6 19 37 15 15 3 15 12 88 14 4 22 44 53 5 4 33 21 42 4 43 1 22 9 1 8 51 3 12 7 18 7 3 2 0 9 22 8 11 2 28 24 8 9 50 8 12 7 0 10 13 2 68 1 19 12 5 18 17 46 21 9 6 6 51 22 5 12 23 3 40 22 1 4 14 6 23 19 4 6 9 9 3 16 45 4 15 0 16 9 72 4 0 7 73 65 14 26 72 11 57 20 3 15 2 11 45 5 14 20 0 40 8 26 3 27 2 31 6 13 16 46 7 2 18 0 8 9 5 3 2 10 0 1 16 75 6 4 5 17 36 43 2 76 30 16 15 16 24 14 0 19 2 6 16 46 1 26 8 21 58 2 15 15 26 1 22 3 11 1 7 23 5 23 23 2 2 10 75 7 6 12 27 1 13 61 72 0 1 13 0 19 16 14 29 74 6 7 1 9 19 1 20 7 4 10 27 1 14 8 3 0 13 9 9 4 48 1 12 1 13 2 10 8 1 24 52 20 8 23 30 26 16 5 5 11 17 19 9 20 1 5 3 0 5 9 3 15 27 8 8 15 3 0 2 4 25 2 9 2 0 15 74 3 1 24 67 17 13 2 78 2 1 13 27 16 4 29 14 77 55 14 1 0 0 11 7 3 2 9 12 8 8 10 37 13 16 26 51 2 49 7 5 0 17 74 9 1 24 72 27 21 11 19 3 61 14 64 14 15 31 77 11 55 2 6 4 8 4 1 2 49 1 5 9 3 14 15 27 5 2 30 0 11 3 76 13 9 0 7 72 29 18 16 42 6 24 26 17 34 11 6 10 14 26 9 0 2 11 1 23 28 1 16 47 3 24 4 48 7 1 11 1 57 6 1 22 12 29 13 23 18 10 1 10 0 12 6 11 13 2 50 13 3 55 3 9 7 0 12 25 0 9 3 12 0 43 83 17 21 70 14 12 25 49 20 12 4 65 34 44 11 55 63 3 10 33 10 9 11 3 29 13 4 10 31 71 18 1 26 53 3 16 8 24 14 1 12 43 5 8 26 31 9 3 39 12 3 2 18 0 6 53 7 23 6 10 3 9 11 7 14 86 50 4 10 0 15 17 6 25 7 40 9 5 43 30 6 3 3 38 9 4 25 9 62 7 7 13 43 2 7 21 16 7 94 44 6 9 70 9 6 8 12 2 1 8 4 66 70 11 5 5 8 16 6 26 16 6 15 10 12 2 21 5 24 23 22 92 8 0 1 2 22 21 19 0 10 1 14 9 22 24 42 16 10 14 6 99 1 23 55 18 4 3 24 0 67 55 12 15 0 19 8 19 16 5 7 18 13 5 19

Figure 1: LD plot of 111 SNPs in the CDKAL1 gene.

pleiotropic effect on insulin secretion, insulin sensitivity, and Table insulin clearance [26]. rs7756992 was also validated in 3: Association of haplotypes in the CDKAL1 gene with relation to these intermediate traits related to T2DM in an gestational diabetes mellitus. Indian population [27]. SNP combinations Frequency (%) ORa 95% CI Pb GWA and candidate gene studies have increased our knowledge of loci associated with diabetes; however, apply- Block 4 ing these findings to infer pathophysiology and promote rs4712527 rs9356744 rs9368222 rs7748720 rs9295478 rs6935599 drug discovery remains challenging. CDKAL1 was revealed rs7747752 rs6928012 to be an inhibitor of CDK5 to suppress the β-cell differentia- AGAAAGGG 39.88 1 tion and further highlighted its importance in diabetes [28]. AACAGACA 31.24 0.82 0.62-1.07 0.145 T2DM-related alleles of SNPs located within the CDKAL1 GACGGACG 14.08 0.52 0.37-0.74 0.0003 gene region were reported to be associated with impaired AACAAGGG 6.72 0.98 0.62-1.55 0.931 β-cell function [29] and impaired insulin secretion [24, 30], Rare (<0.05) 8.08 0.86 0.56-1.33 0.505 which may suggest the role of CDKAL1 in the development of GDM. Mutation in CDKAL1 led to impaired glucose secre- Block 5 tion in vitro and vivo [31], and a high-content chemical rs13217519 rs9350276 screen identified a candidate drug that rescued CDKAL1- GA 36.73 1 specific defects by inhibiting the FOS/JUN pathway [31]. AA 35.32 1.03 0.79-1.33 0.845 This may help to the precise therapy of diabetes. AG 27.94 0.71 0.54-0.93 0.015 Strengths and limitations should be considered when interpreting the study results. Firstly, diagnosis of GDM in our study was acquired by investigating the well- 6 Journal of Diabetes Research documented medical records, which minimized potential References disease misclassification. Secondly, information on potential confounders was collected using a standardized and struc- [1] E. Chiefari, B. Arcidiacono, D. Foti, and A. Brunetti, “ ” tured questionnaire allowing for the control of potential Gestational diabetes mellitus: an updated overview, Journal – confounding effects. The current study was hospital-based, of Endocrinological Investigation, vol. 40, no. 9, pp. 899 which might have limited the generalizability of these study 909, 2017. [2] Y. Zhu and C. Zhang, “Prevalence of gestational diabetes and results. Thirdly, the BMI was not equally distributed between ” cases and control subjects; however, it was adjusted in each risk of progression to type 2 diabetes: a global perspective, Current Diabetes Reports, vol. 16, no. 1, p. 7, 2016. model analyzed. The results in our study will need to be fi [3] A. Ferrara, “Increasing prevalence of gestational diabetes veri ed by larger population-based studies. Lastly, the ” sample size in our study was moderate, and the weak effects mellitus: a public health perspective, Diabetes Care, vol. 30, Supplement 2, pp. S141–S146, 2007. of SNPs on GDM may not have been observed. “ To the best of our knowledge, our study for the first time [4] A. Ben-Haroush, Y. Yogev, and M. Hod, Epidemiology of gestational diabetes mellitus and its association with type 2 found a novel association between rs7748720 of CDKAL1 diabetes,” Diabetic Medicine, vol. 21, no. 2, pp. 103–113, 2004. and GDM risk. This would help increase our knowledge of [5] W. L. Lowe, D. M. Scholtens, V. Sandler, and M. G. Hayes, loci associated with GDM and infer the pathophysiology; “Genetics of gestational diabetes mellitus and maternal metab- however, additional studies still need to verify the results. olism,” Current Diabetes Reports, vol. 16, no. 2, p. 15, 2016. [6] C. Zhang, W. Bao, Y. Rong et al., “Genetic variants and the risk Data Availability of gestational diabetes mellitus: a systematic review,” Human Reproduction Update, vol. 19, no. 4, pp. 376–390, 2013. The data used to support the findings of this study are “ included within the article and supplementary information [7] J. Robitaille and A. M. Grant, The genetics of gestational files. diabetes mellitus: evidence for relationship with type 2 diabe- tes mellitus,” Genetics in Medicine, vol. 10, no. 4, pp. 240– Ethical Approval 250, 2008. [8] V. Radha, S. Kanthimathi, R. M. Anjana, and V. Mohan, “Genetics of gestational diabetes mellitus,” The Journal of the All study procedures were approved by the Human – Investigation Committee at the Shanxi Medical University. Pakistan Medical Association, vol. 66, no. 9, pp. S11 S14, 2016. [9] A. Stuebe, A. Wise, T. Nguyen, A. Herring, K. North, and Consent A. Siega-Riz, “Maternal genotype and gestational diabetes,” American Journal of Perinatology, vol. 31, no. 1, pp. 69– Informed consent to inclusion in the study was obtained 76, 2014. from all individual participants included in the study. [10] A. Huerta-Chagoya, P. Vazquez-Cardenas, H. Moreno-Macias et al., “Genetic determinants for gestational diabetes mellitus and related metabolic traits in Mexican women,” PLoS One, Conflicts of Interest vol. 10, no. 5, article e0126408, 2015. The authors declare that they have no conflict of interest. [11] Q. Chen, H. Yang, Y. Feng et al., “SOS1 gene polymorphisms are associated with gestational diabetes mellitus in a Chinese ’ population: results from a nested case-control study in Authors Contributions Taiyuan, China,” Diabetes and Vascular Disease Research, – Keke Wang, Qiong Chen, Suping Wang, and Yawei Zhang vol. 15, no. 2, pp. 158 161, 2018. “ contributed equally to this article. [12] C. Ober, K. S. Xiang, R. A. Thisted et al., Increased risk for gestational diabetes mellitus associated with insulin receptor and insulin-like growth factor II restriction fragment Acknowledgments length polymorphisms,” Genetic Epidemiology, vol. 6, no. 5, pp. 559–569, 1989. This work was supported by the National Natural Science [13] E. C. Tok, D. Ertunc, O. Bilgin, E. M. Erdal, M. Kaplanoglu, Foundation of China grants (No. 81473061), Natural Science and S. Dilek, “Association of insulin receptor substrate-1 Foundation of Shanxi Province grants (No. 2013021033-2), “ ” G972R variant with baseline characteristics of the patients 100 Talent Plan Award of Shanxi Province, Construction with gestational diabetes mellitus,” American Journal of Project of Characteristic Key Disciplines from Universities Obstetrics and Gynecology, vol. 194, no. 3, pp. 868–872, 2006. “ ” of Shanxi Province, and 10 Talent Plan Award of Shanxi [14] N. Shaat, M. Ekelund, A. Lernmark et al., “Association of the Medical University. The authors express their appreciation E23K polymorphism in the KCNJ11 gene with gestational to the participants in the Taiyuan Birth Cohort Study for diabetes mellitus,” Diabetologia, vol. 48, no. 12, pp. 2544– their enthusiastic support. 2551, 2005. [15] C. J. Palmer, R. J. Bruckner, J. A. Paulo et al., “Cdkal1, a type 2 Supplementary Materials diabetes susceptibility gene, regulates mitochondrial function in adipose tissue,” Molecular Metabolism, vol. 6, no. 10, Supplementary Table 1: genes and SNPs evaluated. Supple- pp. 1212–1225, 2017. mentary Table 2: genotypes and risk of gestational diabetes [16] J. D. L. Pinto, P. Yang, A. Sein et al., “The rs4712527 polymor- mellitus. (Supplementary Materials) phism in the CDKAL1 gene, as a protective predictor of Journal of Diabetes Research 7

proliferative diabetic retinopathy development in type 2 type 2 diabetic subjects and in a large sample of men with nor- diabetic patients,” Investigative Ophthalmology & Visual mal glucose tolerance,” The Journal of Clinical Endocrinology Science, vol. 58, no. 8, p. 1848, 2017. & Metabolism, vol. 93, no. 5, pp. 1924–1930, 2008. [17] L. L. Fu, Y. Lin, Z. L. Yang, and Y. B. Yin, “Association analysis [31] H. Zeng, M. Guo, T. Zhou et al., “An isogenic human ESC plat- of genetic polymorphisms of TCF7L2, CDKAL1, SLC30A8, form for functional evaluation of genome-wide-association- HHEX genes and microvascular complications of type 2 diabe- study-identified diabetes genes and drug discovery,” Cell Stem tes mellitus,” Zhonghua yi xue yi Chuan xue za zhi = Zhonghua Cell, vol. 19, no. 3, pp. 326–340, 2016. Yixue Yichuanxue Zazhi = Chinese Journal of Medical Genetics, vol. 29, no. 2, pp. 194–199, 2012. [18] Y. Mansoori, A. Daraei, M. M. Naghizadeh, and R. Salehi, “Significance of a common variant in the CDKAL1 gene with susceptibility to type 2 diabetes mellitus in Iranian population,” Advanced Biomedical Research, vol. 4, no. 1, p. 45, 2015. [19] Y. C. Chang, P. H. Liu, Y. H. Yu et al., “Validation of type 2 diabetes risk variants identified by genome-wide association studies in Han Chinese population: a replication study and meta-analysis,” PLoS One, vol. 9, no. 4, article e95045, 2014. [20] S. H. Kwak, S. H. Kim, Y. M. Cho et al., “A genome-wide asso- ciation study of gestational diabetes mellitus in Korean women,” Diabetes, vol. 61, no. 2, pp. 531–541, 2012. [21] A. E. Noury, O. Azmy, J. Alsharnoubi, S. Salama, A. Okasha, and W. Gouda, “Variants of CDKAL1 rs7754840 (G/C) and CDKN2A/2B rs10811661 (C/T) with gestational diabetes: insignificant association,” BMC Research Notes, vol. 11, no. 1, p. 181, 2018. [22] P. V. Popova, A. A. Klyushina, L. B. Vasilyeva et al., “Effect of gene-lifestyle interaction on gestational diabetes risk,” Oncotarget, vol. 8, no. 67, pp. 112024–112035, 2017. [23] M. Tarnowski, D. Malinowski, K. Pawlak, V. Dziedziejko, K. Safranow, and A. Pawlik, “GCK, GCKR, FADS1, DGKB/ TMEM195 and CDKAL1 gene polymorphisms in women with gestational diabetes,” Canadian Journal of Diabetes, vol. 41, no. 4, pp. 372–379, 2017. [24] J. Liang, Y. Pei, X. Liu et al., “The CDKAL1 gene is associated with impaired insulin secretion and glucose-related traits: the Cardiometabolic Risk in Chinese (CRC) study,” Clinical Endo- crinology, vol. 83, no. 5, pp. 651–655, 2015. [25] M. Rask-Andersen, G. Philippot, G. Moschonis et al., “CDKAL1-related single nucleotide polymorphisms are asso- ciated with insulin resistance in a cross-sectional cohort of Greek children,” PLoS One, vol. 9, no. 4, article e93193, 2014. [26] M. O. Goodarzi, X. Guo, J. Cui et al., “Systematic evaluation of validated type 2 diabetes and glycaemic trait loci for associa- tion with insulin clearance,” Diabetologia, vol. 56, no. 6, pp. 1282–1290, 2013. [27] V. Gupta, D. G. Vinay, S. Rafiq et al., “Association analysis of 31 common polymorphisms with type 2 diabetes and its related traits in Indian sib pairs,” Diabetologia, vol. 55, no. 2, pp. 349–357, 2012. [28] K. C. Liu, G. Leuckx, D. Sakano et al., “Inhibition of Cdk5 promotes β-cell differentiation from ductal progenitors,” Diabetes, vol. 67, no. 1, pp. 58–70, 2017. [29] L. Pascoe, A. Tura, S. K. Patel et al., “Common variants of the novel type 2 diabetes genes CDKAL1 and HHEX/IDE are asso- ciated with decreased pancreatic β-cell function,” Diabetes, vol. 56, no. 12, pp. 3101–3104, 2007. [30] A. Stancáková, J. Pihlajamäki, J. Kuusisto et al., “Single- nucleotide polymorphism rs7754840 of CDKAL1 is associated with impaired insulin secretion in nondiabetic offspring of M EDIATORSof INFLAMMATION

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