Front. Genet. Frontiers in Genetics Front. Genet. 1664-8021 Frontiers Media S.A. 783078 10.3389/fgene.2021.783078 Genetics Systematic Review Different Associations Between CDKAL1 Variants and Type 2 Diabetes Mellitus Susceptibility: A Meta-analysis Zeng et al. CDKALl Variants and T2DM Susceptibility Zeng Qiaoli 1 2 3 † Zou Dehua 2 3 4 † Gu Shanshan 3 5 † Han Fengqiong 6 Cao Shilin 7 * Wei Yue 8 * Guo Runmin 1 2 3 9 * 1 Department of Internal Medicine, Shunde Women and Children’s Hospital (Maternity and Child Healthcare Hospital of Shunde Foshan), Guangdong Medical University, Foshan, China 2 Key Laboratory of Research in Maternal and Child Medicine and Birth Defects, Guangdong Medical University, Foshan, China 3 Matenal and Child Research Institute, Shunde Women and Children’s Hospital (Maternity and Child Healthcare Hospital of Shunde Foshan), Guangdong Medical University, Foshan, China 4 State Key Laboratory for Quality Research of Chinese Medicines, Macau University of Science and Technology, Taipa, Macau SAR, China 5 Institute of Neurology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China 6 Department of Obstetric, Shunde Women and Children’s Hospital (Maternity and Child Healthcare Hospital of Shunde Foshan), Guangdong Medical University, Foshan, China 7 Department of Medical, Shunde Women and Children’s Hospital (Maternity and Child Healthcare Hospital of Shunde Foshan), Guangdong Medical University, Foshan, China 8 Department of Ultrasound, Shunde Women and Children’s Hospital (Maternity and Child Healthcare Hospital of Shunde Foshan), Guangdong Medical University, Foshan, China 9 Department of Endocrinology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China

Edited by: Liangcai Zhang, Janssen Research and Development, United States

Reviewed by: Dalin Li, Cedars Sinai Medical Center, United States

Huaizhen Qin, University of Florida, United States

 *Correspondence: Shilin Cao, 33033950@qq.com; Yue Wei, weiyue138@163.com; Runmin Guo, runmin.guo@gdmu.edu.cn

These authors have contributed equally to this work

This article was submitted to Statistical Genetics and Methodology, a section of the journal Frontiers in Genetics

 05 01 2022 2021 12 783078 25 09 2021 13 12 2021 Copyright © 2022 Zeng, Zou, Gu, Han, Cao, Wei and Guo. 2022 Zeng, Zou, Gu, Han, Cao, Wei and Guo

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Background: CDK5 regulatory subunit associated protein 1 like 1 (CDKAL1) is a major pathogenesis-related protein for type 2 diabetes mellitus (T2DM). Recently, some studies have investigated the association of CDKAL1 susceptibility variants, including rs4712523, rs4712524, and rs9460546 with T2DM. However, the results were inconsistent. This study aimed to evaluate the association of CDKAL1 variants and T2DM patients.

Methods: A comprehensive meta-analysis was performed to assess the association between CDKAL1 SNPs and T2DM among dominant, recessive, additive, and allele models.

Results: We investigated these three CDKAL1 variants to identify T2DM risk. Our findings were as follows: rs4712523 was associated with an increased risk of T2DM for the allele model (G vs A: OR = 1.172; 95% CI: 1.103–1.244; p < 0.001) and dominant model (GG + AG vs AA: OR = 1.464; 95% CI: 1.073–1.996; p = 0.016); rs4712524 was significantly associated with an increased risk of T2DM for the allele model (G vs A: OR = 1.146; 95% CI: 1.056–1.245; p = 0.001), additive model (GG vs AA: OR = 1.455; 95% CI: 1.265–1.673; p < 0.001) recessive model (GG vs AA + AG: OR = 1.343; 95% CI: 1.187–1.518; p < 0.001) and dominant model (GG + AG vs AA: OR = 1.221; 95% CI: 1.155–1.292; p < 0.001); and rs9460546 was associated with an increased risk of T2DM for the allele model (G vs T: OR = 1.215; 95% CI: 1.167–1.264; p = 0.023). The same results were found in the East Asian subgroup for the allele model.

Conclusions: Our findings suggest that CDKAL1 polymorphisms (rs4712523, rs4712524, and rs9460546) are significantly associated with T2DM.

 type 2 diabetes mellitus CDKAL1 polymorphisms susceptibility meta-analysis 1 Introduction

Type 2 diabetes mellitus (T2DM) is a complex disease characterized by insulin resistance in peripheral tissues and dysregulated insulin secretion by pancreatic β-cells (Li et al., 2020). The incidence of T2DM in adults has been increasing over recent decades (Yang et al., 2010; Tian et al., 2019) and is estimated to increase to over 700 million by 2045 (Saeedi et al., 2019; Li et al., 2020). T2DM is caused by genetic and environmental factors (Tian et al., 2019; Wu et al., 2014). Genetic variants are thought to be involved in the development of T2DM. Genome-wide association studies have indicated that some single nucleotide polymorphisms (SNPs) are critical risk factors for T2DM (Tian et al., 2019).

CDK5 regulatory subunit associated protein 1 like 1 (CDKAL1) is a crucial pathogenesis-related protein for T2DM. The CDKAL1 gene encodes cyclin-dependent kinase 5 regulatory subunit-associated protein 1 (CDK5RAP1)-like 1. Cyclin-dependent kinase 5 (CDK5) is a serine/threonine protein kinase that contributes to the glucose-dependent regulation of insulin secretion (Li et al., 2020); therefore, it plays a critical role in the pathophysiology of β-cell dysfunction and predisposition to T2DM (Li et al., 2020; Wei et al., 2005; Ubeda et al., 2006). The associations of many SNPs in CDKAL1 with T2DM have been examined in some meta-analyses, but no published meta-analysis has evaluated the role of CDKAL1 rs4712523, rs4712524 and rs9460546 variants in the susceptibility to T2DM. Several studies have examined the association between CDKAL1 polymorphisms (rs4712523, rs4712524 and rs9460546) and T2DM risk, but some findings were failed to replicate. Therefore, performing a meta-analysis is needed to evaluate the association between CDKAL1 polymorphisms (rs4712523, rs4712524, and rs9460546) and T2DM.

 2 Materials and Methods

This meta-analysis was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines.

2.1 Literature Search

The Google Scholar, PubMed and Chinese National Knowledge Infrastructure databases were systematically searched for relevant studies using the following terms:

1 “CDKAL1” or “rs4712523” or “polymorphism” and “T2DM”;

2 “CDKAL1” or “rs4712524” or “polymorphism” and “T2DM”;

3 “CDKAL1”, or “rs9460546” or “polymorphism” and “T2DM”, respectively.

The search was performed with no date or language restrictions. All the studies were evaluated by reading the title and abstract and excluding irrelevant studies. The full texts of eligible studies were then assessed by reading the full text to confirm inclusion in the study.

 2.2 Inclusion and Exclusion Criteria

The inclusion criteria of the studies were as follows: 1) case-control/cohort studies; 2) studies that evaluated the association between CDKAL1 SNPs (rs4712523, rs4712524, and rs9460546) and T2DM; 3) adequate raw data or sufficient data to calculate odds ratios (ORs) with corresponding 95% confidence intervals (CIs); 4) a T2DM diagnosis based on the clinical criteria of the World Health Organization.

The exclusion criteria were as follows: 1) not a case-control/cohort study; 2) not related to CDKAL1 SNPs (rs4712523, rs4712524, and rs9460546) and T2DM; 3) insufficient data; 4) NDM data not in Hardy-Weinberg equilibrium (HWE).

 2.3 Data Extraction

Two authors independently extracted the following data from the included studies: first author, ethnicity, year of publication, numbers of T2DM patients and NDM controls, distribution of alleles and genotypes, and ORs with 95% CIs of the allele distribution.

2.4 Statistical Analysis

Four genetic models were evaluated in rs4712523 and rs4712524: the dominant model (GG + AG vs AA), recessive model (GG vs AA + AG), additive model (GG vs AA) and allele model (G vs A). Additionally, the allele model (G vs T) was evaluated in rs9460546. Genetic heterogeneity was estimated using Q-test and I2 test. Lower heterogeneity was defined as I2 <50% and p > 0.01, using the fixed effects model (Mantel–Haenszel) to calculate ORs with corresponding 95% CIs. Otherwise, the random effects model (Mantel–Haenszel) was used. The significance of the ORs was evaluated using the Z test. Begg’s and Egger’s tests were used to determine publication bias. STATA v.14.0 software (Stata Corporation, Texas, United States) was used to perform all statistical analyses.

 3 Results 3.1 Study Inclusion and Characteristics

A total of 179 potential studies were searched using the inclusion and exclusion criteria. Figure 1 shows a flow chart of the study selection process. Twelve articles, including 7 in English and 5 in Chinese, had rs4712523 data. Eight articles, including 5 in English, 2 in Chinese and 1 in Russian, had rs4712524 data. Five articles, including 5 in English, had rs9460546 data. The characteristics of each included study are shown in Tables 1−3.

Flow diagram of the literature search and selection.

Characteristics of each study included in rs4712523 of meta-analysis.

Author Year Ethnic T2DM/NDM ORs with 95% CI (G vs A) Allele distribution Genotype distribution
T2DM, n NDM, n T2DM, n NDM, n
A G A G AA AG GG AA AG GG
Liju et al. 2020 India 1183/1188 1.077 (0.893–1.300) 1640 726 1684 692 — — — — — —
Tian et al. 2019 Chinese 510/503 1.420 (1.190–1.690) 508 512 588 418 131 246 133 175 238 90
Qian et al. 2019 Chinese 526/526 1.027 (0.956–1.103) 590 462 556 496 164 262 100 149 258 119
Rao et al. 2016 Chinese 458/429 0.924 (0.766–1.114) 525 391 475 383 154 217 87 138 199 92
Ren et al. 2013 Chinese 98/97 1.521 (1.018–2.273) 99 97 118 76 9 81 8 26 66 5
Li et al. 2013 Chinese 192/190 1.654 (1.237–2.212) 202 182 246 134 22 158 12 62 122 6
Lu et al. 2012 Chinese 2897/3259 1.223 (1.139–1.314) 3105 2689 3816 2702 848 1409 640 1120 1576 563
Gong et al. 2016 Chinese 91/186 1.380 (1.250–1.520) — — — — — — — — — —
Long et al. 2012 African Americans 1549/2722 0.960 (0.870–1.070) — — — — — — — — — —
Takeuchi et al. 2009 Japanese 5629/6406 1.270 (1.210–1.330) — — — — — — — — — —
Takeuchi et al. 2009 Europeans 14586/17968 1.120 (1.080–1.160) — — — — — — — — — —
Rung et al. 2009 Caucasian 180/165 1.200 (1.140–1.260) — — — — — — — — — —
Scott et al. 2007 Finnish 1161/1174 1.123 (1.032–1.222) — — — — — — — — — —

n, Number; T2DM, type 2 diabetes mellitus; NDM, Non-diabetic subject; OR, odds ratio; CI, confidence interval.

Characteristics of each study included in rs4712524 of meta-analysis.

Author Year Ethnic T2DM/NDM Allele distribution Genotype distribution
T2DM, n NDM, n T2DM, n NDM, n
A G A G AA AG GG AA AG GG
Liju et al. 2020 India 1183/1188 658 1708 624 1752 — — — — — —
Li et al. 2020 Chinese 1169/1277 1324 1014 1551 1003 375 574 220 470 611 196
Azarova et al. 2020 Russian 1579/1627 1988 1170 2204 1050 636 716 227 721 762 144
Tian et al. 2019 Chinese 508/493 506 510 570 416 130 246 132 171 228 94
Li et al. 2018 Chinese 123/311 128 118 327 295 34 60 29 94 139 78
Rao et al. 2016 Chinese 456/417 521 391 457 377 150 221 85 125 207 85
Unoki et al. 2008 Japanese 4795/3441 5119 4471 4019 2863 1431 2257 1107 1176 1667 598
Lu et al. 2012 Chinese 2899/3260 3157 2641 3868 2652 880 1397 622 1156 1556 548

n, Number; T2DM, type 2 diabetes mellitus; NDM, Non-diabetic subject (-), not applicable.

Characteristics of each study included in rs9460546 of meta-analysis.

Author Year Ethnic T2DM/NDM ORs with 95% CI (G vs T)
Li et al. 2020 Chinese 1169/1277 1.133 (1.011–1.270)
Hu et al. 2009 Chinese 1849/1785 1.145 (1.041–1.260)
Herder et al. 2008 German 433/1438 1.410 (1.190–1.680)
Unoki et al. 2008 Japanese 4775/3442 1.226 (1.152–1.305)
Maller et al. 2012 European 632/677 1.250 (1.150–1.350)

T2DM, type 2 diabetes mellitus; NDM, Non-diabetic subject; OR, odds ratio; CI, confidence interval.

 3.2 Heterogeneity Analysis 3.2.1 rs4712523

High heterogeneity among studies (Scott et al., 2007; Rung et al., 2009; Takeuchi et al., 2009; Long et al., 2012; Lu et al., 2012; Gong, 2016; Li et al., 2013; Ren et al., 2013; Rao et al., 2016; Qian, 2019; Tian et al., 2019; Liju et al., 2020) was detected in the allele model (G vs A: I2 = 84.4%; p < 0.001), additive model (GG vs AA: I2 = 84.6%; p < 0.001), recessive model (GG vs AA + AG: I2 = 73.8%; p = 0.002), and dominant model (GG + AG vs AA: I2 = 86.1%; p < 0.001) (Figure 2).

Meta-analysis using a random effects model for the association between the CDKALl rs4712523 polymorphism and T2DM susceptibility (A) Allele model, G vs A (B) Additive model, GG vs AA (C) Recessive model, GG vs AA + AG (D) Dominant model, GG + AG vs AA. OR: odds ratio, CI: confidence interval, I-squared: measure to quantify the degree of heterogeneity in meta-analyses.

 3.2.2 rs4712524

High heterogeneity among studies (Unoki et al., 2008; Lu et al., 2012; Rao et al., 2016; Li, 2018; Tian et al., 2019; Azarova, 2020; Li et al., 2020; Liju et al., 2020) was detected in the allele model (G vs A: I2 = 75.1%; p < 0.001). A moderate degree of heterogeneity among studies was detected under the additive model (GG vs AA: I2 = 58.7%; p = 0.024) and recessive model (GG vs AA + AG: I2 = 57.8%; p = 0.027). Low heterogeneity among studies was detected under the dominant model (GG + AG vs AA: I2 = 31.8%; p = 0.185) (Figure 3).

Meta-analysis for the association between the CDKALl rs4712524 polymorphism and T2DM susceptibility (A) Allele model, G vs A (random effects model) (B) Additive model, GG vs AA (random effects model) (C) Recessive model, GG vs AA + AG (random effects model) (D) Dominant model, GG + AG vs AA (fixed effects model). OR: odds ratio, CI: confidence interval, I-squared: measure to quantify the degree of heterogeneity in meta-analyses.

 3.2.3 rs9460546

Low heterogeneity among studies (Herder et al., 2008; Unoki et al., 2008; Hu et al., 2009; Maller et al., 2012; Li et al., 2020) was detected in the allele model (G vs T: I2 = 37.0%; p = 0.174) (Figure 4).

Meta-analysis using a fixed effects model for the association between the CDKAL1 rs9460546 polymorphism and T2DM susceptibility (Allele model, G vs T). OR: odds ratio, CI: confidence interval, I-squared: measure to quantify the degree of heterogeneity in meta-analyses.

 3.3 Meta-Analysis Results 3.3.1 rs4712523

A significant difference was found between T2DM patients and NDM controls for the allele model (G vs A: OR = 1.172; 95% CI: 1.103–1.245; p < 0.001) and dominant model (GG + AG vs AA: OR = 1.464; 95% CI: 1.073–1.996; p = 0.016). No significant associations were found under the additive model (GG vs AA: OR = 1.495; 95% CI: 0.990–2.257; p = 0.056) and recessive model (GG vs AA + AG: OR = 1.188; 95% CI: 0.900–1.568; p = 0.223) using a random effects model (Figure 2).

 3.3.2 rs4712524

A random effects model was used to analyze the allele, additive and recessive models, and the dominant model was analyzed using a fixed effects model. A significant difference was found between T2DM patients and NDM controls for the allele model (G vs A: OR = 1.146; 95% CI: 1.056–1.245; p = 0.001), additive model (GG vs AA: OR = 1.455; 95% CI: 1.265–1.673; p < 0.001) recessive model (GG vs AA + AG: OR = 1.343; 95% CI: 1.187–1.518; p < 0.001) and dominant model (GG + AG vs AA: OR = 1.221; 95% CI: 1.155–1.292; p < 0.001) (Figure 3).

 3.3.3 rs9460546

A significant difference was found between T2DM patients and NDM controls for the allele model (G vs T: OR = 1.215; 95% CI: 1.167–1.264; p = 0.023) using a fixed effects model (Figure 4).

 3.4 Subgroup Analyses 3.4.1 rs4712523

We performed subgroup analysis according to ethnicity to evaluate the association between rs4712523 and T2DM susceptibility in the allele model. Rs35767 was significantly related to the risk of T2DM in the East Asian (G vs A: OR = 1.241; 95% CI: 1.123–1.371; p < 0.001) and others subgroup (G vs A: OR = 1.108; 95% CI: 1.039–1.180; p = 0.002) using a random effects model (Figure 5A).

Association between the CDKALl variants and T2DM susceptibility in the subgroup for the allele model (A) rs4712523: G vs A (random effects model) (B) rs4712524: G vs A (random effects model) (C) rs9460546: G vs T (fixed effects model). OR: odds ratio, CI: confidence interval, I-squared: measure to quantify the degree of heterogeneity in meta-analyses.

 3.4.2 rs4712524

We performed subgroup analysis according to ethnicity to evaluate the association between rs4712524 and T2DM susceptibility in the allele model. Rs4712524 was significantly related to the risk of T2DM in the East Asian (G vs A: OR = 1.182; 95% CI: 1.095–1.277; p < 0.001), but no significant associations were found in others subgroup (G vs A: OR = 1.071; 95% CI: 0.807–1.423; p = 0.634) using a random effects model (Figure 5B).

 3.4.3 rs9460546

We performed subgroup analysis according to ethnicity to evaluate the association between rs9460546 and T2DM susceptibility in the allele model. Rs9460546 was significantly related to the risk of T2DM in the East Asian (G vs T: OR = 1.189; 95% CI: 1.134–1.247; p < 0.001) and others subgroup (G vs T: OR = 1.277; 95% CI: 1.188–1.373; p < 0.001) using a fixed effects model (Figure 5C).

 3.5 Publication Bias

According to Begg’s and Egger’s tests, no significant publication bias was found in each of the genetic models (all p > 0.05, data not shown), and the funnel plots are shown in Figures 6–9.

Funnel plot of the odds ratios in the CDKALl rs4712523 meta-analysis (A) Allele model, G vs A (B) Additive model, GG vs AA (C) Recessive model, GG vs AA + AG (D) Dominant model, GG + AG vs AA.

Funnel plot of the odds ratios in the CDKALl rs4712524 meta-analysis (A) Allele model, G vs A (B) Additive model, GG vs AA (C) Recessive model, GG vs AA + AG (D) Dominant model, GG + AG vs A.

Funnel plot of the odds ratios in the CDKALl rs9460546 meta-analysis for the allele model (G vs T).

Funnel plot of the odds ratios in the CDKALl variants in the subgroup meta-analysis for the allele model (A) rs4712523: G vs A (B) rs4712524: G vs A (C) rs9460546: G vs T.

 4 Discussion

CDKAL1 is a key pathogenesis-related protein for T2DM (Tian et al., 2019). Genetic variants may play an essential role in T2DM susceptibility. In this meta-analysis, three SNPs (rs4712523, rs4712524, and rs9460546) from previous studies were evaluated to determine the association of CDKAL1 polymorphisms with T2DM. CDKAL1 polymorphisms (rs4712523, rs4712524, and rs9460546) showed a significant association with T2DM. Our results were consistent with some previous study findings.

The results revealed that the G allele and GG + AG genotypes of rs4712523 were associated with an increased risk of T2DM. Nine of the thirteen previous studies investigated rs4712523 showed an association between the G allele and T2DM (Scott et al., 2007; Rung et al., 2009; Takeuchi et al., 2009; Long et al., 2012; Lu et al., 2012; Gong, 2016; Li et al., 2013; Ren et al., 2013; Tian et al., 2019), and four studies found an association between the GG + AG genotypes and T2DM (Lu et al., 2012; Li et al., 2013; Ren et al., 2013; Tian et al., 2019). In addition, the rs4712524 G allele, GG and GG + AG genotypes were associated with an increased risk of T2DM susceptibility. That have been confirmed previous observations (Unoki et al., 2008; Lu et al., 2012; Tian et al., 2019; Azarova, 2020; Li et al., 2020). Additionally, the results showed that rs9460546 G allele was associated with T2DM susceptibility. Markedly, all five studies found that the rs9460546 G allele was associated with T2DM in various populations (Herder et al., 2008; Unoki et al., 2008; Hu et al., 2009; Maller et al., 2012; Li et al., 2020). Moreovr, rs4712523, rs4712524, and rs9460546 showed a significant association with T2DM in the East Asian subgroup for the allele model. In general, Our results have confirmed previous observations suggesting that CDKAL1 may play a role in T2DM. But it is worth noting that high heterogeneity among studies was detected in rs4712523 and rs4712524 likely because of the difference in country, ethnicity, genetic background and environmental factors. Subgroup analyses were performed by ethnicity in the allele model, and the subgroup still had high heterogeneity. Importantly, the high heterogeneity among studies might have affected our data.

CDKAL1 expression in human pancreatic β-cells increases insulin secretion by inhibiting CDK5 (Li et al., 2020; Wei et al., 2005; Ubeda et al., 2006; Ching et al., 2002). Subsequently, several studies have shown the association of genetic variants in CDKAL1 with defects in proinsulin conversion and the insulin response following glucose stimulation (Pascoe et al., 2007; Steinthorsdottir et al., 2007; Tian et al., 2019). Thus, CDKAL1 is involved in the development of T2DM. Genome-wide association studies have identified several SNPs in the CDKAL1 gene associated with T2D (Saxena et al., 2007; Scott et al., 2007; Tian et al., 2019). Our results confirmed the significant association between CDKAL1 SNPs and T2DM susceptibility. However, the mechanisms must be verified in functional studies. Our association results provide reference data to identify new biomarkers of T2DM that could contribute to the diagnosis of T2DM.

This meta-analysis has a few limitations. First, because of the limited examination of CDKAL1 variants in T2DM, the included studies had comparatively small sample sizes, which might affect the results of the meta-analysis because of insufficient statistical power. Thus, studies must be performed across different geographical and ethnic groups. Additionally, the factors of T2DM might be complex, with the contribution of genetic, environmental and dietary habits. Therefore, further study is required to evaluate whether other risk factors together with the CDKAL1 gene influence T2DM susceptibility.

 5 Conclusion

To our knowledge, this study is the first to assess the role of CDKAL1 polymorphisms (rs4712523, rs4712524, and rs9460546) in T2DM. Significant associations were found between the CDKAL1 rs4712523, rs4712524, and rs9460546 polymorphisms and susceptibility to T2DM.

 Data Availability Statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.

 Author Contributions

QZ, DZ and SG were responsible for the study design, statistical analysis, and manuscript preparation. QZ and FH managed the literature searches and analyses. The study was supervised by SC, YW and RG.

 Funding

Support for this work includes funding from the National Natural Science Foundation of China (81873649); Doctoral scientific research Initiate funding project of Shunde Women and Children’s Hospital of Guangdong Medical University (Maternity and Child Healthcare Hospital of Shunde Foshan) (2020BSQD007); Guangdong Medical University Research Foundation (GDMUM2020008 and GDMUM2020012); Medical Research Project of Foshan Health Bureau (20210188 and 20210289).

 Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

 Publisher’s Note

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 References Azarova L. (2020). POLYMORPHIC VARIANTS rs4712524 AND rs6931514 IN THE CDKAL1 GENE: ASSOCIATION WITH THE RISK OF DEVELOPMENT OF TYPE 2 DIABETES MELLITUS IN RUSSIAN POPULATIONE. Естественные и технические науки (in Russian) 9. 10.25633/ETN.2020.09.02 Ching Y.-P. Pang A. S. H. Lam W.-H. Qi R. Z. Wang J. H. (2002). Identification of a Neuronal Cdk5 Activator-Binding Protein as Cdk5 Inhibitor. J. Biol. Chem. 277 (18), 15237–15240. 10.1074/jbc.C200032200 Gong X. (2016). “The Genetic Diversity Analysis of Candidate Genes Associated with Type 2 Diabetes in Xinjiang Ethnic Minority Populations,” in A Dissertation Submitted to Xinjiang Medical University (Xinjiang: Xinjiang Medical University) (in Chinese). Herder C. Rathmann W. Strassburger K. Finner H. Grallert H. Huth C. (2008). Variants of thePPARG,IGF2BP2,CDKAL1,HHEX, andTCF7L2Genes Confer Risk of Type 2 Diabetes Independently of BMI in the German KORA Studies. Horm. Metab. Res. 40 (10), 722–726. 10.1055/s-2008-1078730 Hu C. Zhang R. Wang C. Wang J. Ma X. Lu J. (2009). PPARG, KCNJ11, CDKAL1, CDKN2A-Cdkn2b, IDE-KIF11-HHEX, IGF2BP2 and SLC30A8 Are Associated with Type 2 Diabetes in a Chinese Population. PLoS One 4 (10), e7643. 10.1371/journal.pone.0007643 Li C. Shen K. Yang M. Yang Y. Tao W. He S. (2020). Association between Single Nucleotide Polymorphisms in CDKAL1 and HHEX and Type 2 Diabetes in Chinese Population. Dmso 13, 5113–5123. 10.2147/DMSO.S288587 Li X. Su Y. Yan Z. Gu L. Li C. Li A. (2013). CDKALl Rs4712523 Polymorphism Is Associatedwith Type 2 Diabetes in Han Population in Inner Mongolia. Basic Clin. Med. 33, 3 (in Chinese). 10.16352/j.issn.1001-6325.2013.03.017 Li Y. (2018). “Association between Genetic Polymorphisms and Comorbidity of Coronary Heart Disease and Type 2 Diabetes in the Elderly,” in A Dissertation Submitted to Peking Union Medical College (Beijing: Peking Union Medical College) (in Chinese). Liju S. Chidambaram M. Mohan V. Radha V. (2020). Impact of Type 2 Diabetes Variants Identified through Genome-wide Association Studies in Early-Onset Type 2 Diabetes from South Indian Population. Genomics Inform. 18 (3), e27. 10.5808/GI.2020.18.3.e27 Long J. Edwards T. Signorello L. B. Cai Q. Zheng W. Shu X.-O. (2012). Evaluation of Genome-wide Association Study-Identified Type 2 Diabetes Loci in African Americans. Am. J. Epidemiol. 176 (11), 995–1001. 10.1093/aje/kws176 Lu F. Qian Y. Li H. Dong M. Lin Y. Du J. (2012). Genetic Variants on Chromosome 6p21.1 and 6p22.3 Are Associated with Type 2 Diabetes Risk: a Case-Control Study in Han Chinese. J. Hum. Genet. 57 (5), 320–325. 10.1038/jhg.2012.25 Maller J. B. McVean G. McVean G. Byrnes J. Vukcevic D. Palin K. (2012). Bayesian Refinement of Association Signals for 14 Loci in 3 Common Diseases. Nat. Genet. 44 (12), 1294–1301. 10.1038/ng.2435 Pascoe L. Tura A. Patel S. K. Ibrahim I. M. Ferrannini E. Zeggini E. (2007). Common Variants of the Novel Type 2 Diabetes Genes CDKAL1 and HHEX/IDE Are Associated with Decreased Pancreatic -Cell Function. Diabetes 56 (12), 3101–3104. 10.2337/db07-0634 Qian X. (2019). “Associations of Polymorphisms of CDKALI Gene with Type 2 Diabetes Mellitus and Diabetes-Related Traits,” in A Thesis Submitted to Zhengzhou University for the Degree of Master (Zhengzhou: Zhengzhou University) (in Chinese). Rao P. Yu X. Gai S. Wang H. Fang H. Wang Y. (2016). Association of IGF2BP2, CDKAL1 Gene Polymorphism and Gene Environment Interaction with Type 2 Diabetes Mellitus. Mod. instrument Med. Treat. 22, 2 (in Chinese). 10.11876/mimt201602008 Ren X. Yan Z. Li X. Su Y. Zhang S. (2013). Association of CDKAL1 Rs4712523 Polymorphism with Susceptibility to the Blood Lipid of Type 2 Diabetes in Han Population of Inner Mongolia. Chin. J. Clinicians 7, 17 (in Chinese). 10.3877/cma.j.issn.1674-0785.2013.17.013 Rung J. Cauchi S. Albrechtsen A. Shen L. Rocheleau G. Cavalcanti-Proença C. (2009). Genetic Variant Near IRS1 Is Associated with Type 2 Diabetes, Insulin Resistance and Hyperinsulinemia. Nat. Genet. 41 (10), 1110–1115. 10.1038/ng.443 Saeedi P. Petersohn I. Salpea P. Malanda B. Karuranga S. Unwin N. (2019). Global and Regional Diabetes Prevalence Estimates for 2019 and Projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9th Edition. Diabetes Res. Clin. Pract. 157, 107843. 10.1016/j.diabres.2019.107843 Saxena R. Voight B. F. Lyssenko V. Burtt N. P. de Bakker P. I. W. Chen H. (2007). Genome-Wide Association Analysis Identifies Loci for Type 2 Diabetes and Triglyceride Levels. Science 316 (5829), 1331–1336. 10.1126/science.1142358 Scott L. J. Mohlke K. L. Bonnycastle L. L. Willer C. J. Li Y. Duren W. L. (2007). A Genome-wide Association Study of Type 2 Diabetes in Finns Detects Multiple Susceptibility Variants. Science 316 (5829), 1341–1345. 10.1126/science.1142382 Steinthorsdottir V. Thorleifsson G. Reynisdottir I. Benediktsson R. Jonsdottir T. Walters G. B. (2007). A Variant in CDKAL1 Influences Insulin Response and Risk of Type 2 Diabetes. Nat. Genet. 39 (6), 770–775. 10.1038/ng2043 Takeuchi F. Serizawa M. Yamamoto K. Fujisawa T. Nakashima E. Ohnaka K. (2009). Confirmation of Multiple Risk Loci and Genetic Impacts by a Genome-wide Association Study of Type 2 Diabetes in the Japanese Population. Diabetes 58 (7), 1690–1699. 10.2337/db08-1494 Tian Y. Xu J. Huang T. Cui J. Zhang W. Song W. (2019). A Novel Polymorphism (Rs35612982) in CDKAL1 Is a Risk Factor of Type 2 Diabetes: A Case-Control Study. Kidney Blood Press. Res. 44 (6), 1313–1326. 10.1159/000503175 Ubeda M. Rukstalis J. M. Habener J. F. (2006). Inhibition of Cyclin-dependent Kinase 5 Activity Protects Pancreatic Beta Cells from Glucotoxicity. J. Biol. Chem. 281 (39), 28858–28864. 10.1074/jbc.M604690200 Unoki H. Takahashi A. Kawaguchi T. Hara K. Horikoshi M. Andersen G. (2008). SNPs in KCNQ1 Are Associated with Susceptibility to Type 2 Diabetes in East Asian and European Populations. Nat. Genet. 40 (9), 1098–1102. 10.1038/ng.208 Wei F.-Y. Nagashima K. Ohshima T. Saheki Y. Lu Y.-F. Matsushita M. (2005). Cdk5-dependent Regulation of Glucose-Stimulated Insulin Secretion. Nat. Med. 11 (10), 1104–1108. 10.1038/nm1299 Wu Y. Ding Y. Tanaka Y. Zhang W. (2014). Risk Factors Contributing to Type 2 Diabetes and Recent Advances in the Treatment and Prevention. Int. J. Med. Sci. 11 (11), 1185–1200. 10.7150/ijms.10001 Yang W. Lu J. Weng J. Jia W. Ji L. Xiao J. (2010). Prevalence of Diabetes Among Men and Women in China. N. Engl. J. Med. 362 (12), 1090–1101. 10.1056/NEJMoa0908292