IMR Press / CEOG / Volume 52 / Issue 1 / DOI: 10.31083/CEOG26710
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1. Introduction
2. Materials and Methods
3. Results
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Open Access Systematic Review
Studies on the Association of GCK and GCKR Polymorphisms with Susceptibility to Gestational Diabetes Mellitus: A Meta-Analysis
Hongxia Tu1, Youyi Zhang1, Mingyi Wang1,*
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Affiliation
1 Department of Gynaecology and Obstetrics, The General Hospital of Western Theater Command PLA, 610083 Chengdu, Sichuan, China
*Correspondence: wangmingyidr@163.com (Mingyi Wang)
Clin. Exp. Obstet. Gynecol. 2025, 52(1), 26710; https://doi.org/10.31083/CEOG26710
Submitted: 24 September 2024 | Revised: 28 November 2024 | Accepted: 5 December 2024 | Published: 16 January 2025
Copyright: © 2025 The Author(s). Published by IMR Press.
This is an open access article under the CC BY 4.0 license.
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Abstract
Background:

A prevalent condition during pregnancy, gestational diabetes mellitus (GDM) affects a significant proportion of pregnancies worldwide and poses substantial risks to maternal as well as fetal health. Polymorphisms in the glucokinase (GCK) and glucokinase regulatory protein (GCKR) genes, which are crucial for glucose homeostasis, may modulate susceptibility to GDM. Hence, this meta-analysis aimed to assess the relationship between GDM and polymorphisms in GCK (rs1799884, rs4607517) and GCKR (rs780094, rs1260326).

Methods:

In this systematic review, we retrieved data from PubMed, EMBASE, Medline, EBSCO, Cochrane Library, and Chinese National Knowledge Infrastructure (CNKI) databases. Studies were critically appraised using the Newcastle-Ottawa Scale, and meta-analyses were performed using STATA 12.0. The odds ratios (ORs) were calculated with 95% confidence intervals (CIs) and heterogeneity was assessed with Cochran’s Q test as well as I2 statistical tests, respectively. Moreover, Begg’s test helped in evaluating publication bias.

Results:

We included 20 studies, comprising 9745 GDM women and 15,830 controls. All genetic models showed a strong correlation between the GCK rs1799884 polymorphism and GDM, with carriers of the A allele exhibiting an increased risk. Conversely, GCK rs4607517, GCKR rs780094, and rs1260326 were not significantly associated. However, heterogeneity was influenced by ethnicity and diagnostic criteria.

Conclusions:

The GCK rs1799884 polymorphism can be a potential predictive marker because it is significantly associated with an increased risk of GDM.

Keywords
gestational diabetes mellitus
GCK gene
GCKR gene
polymorphisms
meta-analysis
1. Introduction

A significant public health concern, gestational diabetes mellitus (GDM) affects approximately 7%–18% of pregnancies worldwide [1, 2, 3]. GDM is characterized by the development of pregnancy-related glucose intolerance [4] and is substantially risky for maternal and fetal health. GDM increases the risk of cesarean delivery, hypertensive disorders, and long-term metabolic complications for both the mother and child [5, 6, 7]. Increased obesity and sedentary lifestyles exacerbated the incidence of GDM [8, 9], necessitating a comprehensive understanding of its pathophysiology and genetic underpinnings to enhance prevention and treatment strategies. The glucokinase (GCK) and glucokinase regulatory protein (GCKR) genes, essential for glucose metabolism and homeostasis, are linked to GDM [10, 11]. Primarily expressed in pancreatic beta cells, GCK acts as a glucose sensor that regulates insulin secretion [12, 13]. Conversely, GCKR modulates GCK activity, thereby influencing glucose metabolism and insulin sensitivity [14, 15, 16]. Thus, variations in these genes can disrupt glucose regulation and lead to GDM [17, 18].

Several studies have implicated GCK and GCKR gene polymorphisms in GDM susceptibility [19, 20]. These genetic variants may affect glucose metabolism and insulin response. She et al. [19] suggested that the GCK gene rs1799884 (–30G > A) polymorphism’s AA genotype is a risk factor for GDM. Moreover, GCK rs4607517, and GCKR (rs780094 and rs1260326) polymorphisms were not significantly associated with GDM susceptibility. However, Zhu et al. [20] found that GCKR rs1260326 polymorphism was significantly correlated with elevated GDM risk. Varying results from different studies highlight the necessity of a meta-analysis to assemble data and provide a more definitive conclusion.

This meta-analysis aimed to systematically evaluate the association between GCK (rs1799884 and rs4607517) and GCKR (rs780094 and rs1260326) polymorphisms as well as GDM susceptibility. By integrating data from multiple studies, we sought to clarify the genetic components of GDM and provide relavant insights for future research and clinical practice.

2. Materials and Methods
2.1 Research Selection

We used databases like PubMed (https://pubmed.ncbi.nlm.nih.gov/), EMBASE (https://www.embase.com/), Medline (https://www.nlm.nih.gov/medline/medline_home.html), EBSCO (http://search.ebscohost.com/), Cochrane Library (https://www.cochranelibrary.com/), and the Chinese National Knowledge Infrastructure (CNKI) (https://www.cnki.net/) for literature search. Keywords like “glucokinase” or “GCK”, “glucokinase regulatory protein” or “GCKR”, “gestational diabetes mellitus” or “gestational diabetes” or “GDM”, along with terms for genetic variations such as “polymorphisms”, “mutations”, or “variants”, as well as “risk” and “susceptibility” were used. Reference lists from identified articles were screened to ensure a comprehensive collection of relevant studies.

2.2 Inclusion and Exclusion Criteria

Our inclusion criteria were: (1) case-control studies comprising both a GDM group and a control group of pregnant women without GDM, and (2) those with adequate data on the genotypes of the GCK gene variants rs1799884 and rs4607517, as well as the GCKR gene variants rs780094 and rs1260326. Our exclusion criteria were: (1) studies lacking publication details; (2) case reports, reviews, abstracts, or meta-analyses; (3) those not on GCK or GCKR polymorphisms or GDM susceptibility; (4) research without case-control design; (5) those with incomplete odds ratio (OR) calculation data, and (6) those with genotype distributions that deviated from the control group’s Hardy-Weinberg equilibrium (HWE).

2.3 Data Extraction

Using eligible publications, we meticulously extracted data like the first author’s name, publication year, the participants’ ethnicity, the genotype methodology, sample size, gestational age, and the number of genotypes as well as alleles in both study and control groups, respectively.

2.4 Statistical Analysis

Each study was rigorously assessed using the Newcastle-Ottawa Scale (NOS) score [21]. Our analysis only included studies that met the NOS score of ≥5. Meta-analysis was performed by STATA 12.0 software (StataCorp, College Station, TX, USA). Heterogeneity among studies was calculated by Cochran’s Q and I2 statistical tests, respectively [22]. We employed a random-effects model to calculate pooled effects in significant heterogeneity (p < 0.05 or I2 >50%) [23]; otherwise, the fixed-effects model was used [24]. ORs with 95% confidence intervals (CIs) were calculated using the Z test to evaluate the association between the polymorphisms and GDM susceptibility. Subgroup analyses helped to explore potential heterogeneity sources. While publication bias was assessed by Begg’s test [25]. GPower 3.1 (University of Duesseldorf, Duesseldorf, Germany) conducted power analysis for individual single nucleotide polymorphism (SNP).

3. Results
3.1 Characteristics of Enrolled Studies

Our literature search yielded 368 articles from Embase, Medline, EBSCO, PubMed, CNKI, and Wanfang databases. After removing 53 duplicates, we excluded 69 articles that did not fit our inclusion criteria, like being non-human studies, lacking full texts, or being meta-analyses. Additionally, 203 articles were disqualified because they were irrelevant to GDM, did not employ a case-control design, or did not investigate the specified GCK or GCKR polymorphisms. This resulted in 43 full-text articles for further analysis. Our final analysis included 20 articles [19, 20, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43] comprising 9745 GDM patients and 15,830 healthy controls after excluding those without investigations on polymorphisms of interest (rs1799884, rs4607517, rs780094, or rs1260326) or lacked sufficient data (Fig. 1; Table 1, Ref. [19, 20, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43]). All included trials had NOS scores >6 (Supplementary Table 1).

Fig. 1.

Flow diagram for eligible article selection. CNKI, China National Knowledge Infrastructure; GDM, gestational diabetes mellitus; GCK, glucokinase; GCKR, glucokinase regulatory protein.

Table 1. Characteristics of eligible studies.
Author Year Country Ethnicity Diagnosis GDM group Control group p HWE Genotype method SNPs
Procedure Criteria Size Gestational age Age Size Gestational age Age
(mean ± SD) (mean ± SD)
Chiu et al. [26] 1994 USA African OGTT WHO 174 / 28.2 ± 5.8 99 / 22.1 ± 4.6 0.286 PCR-SSCP GCK rs1799884
Zaidi et al. [30] 1997 UK Caucasian 75 g OGTT WHO 92 28–32 31 ± 5.5 45 28–32 / 0.414 PCR-SSCP GCK rs1799884
Shaat et al. [29] 2006 Sweden Caucasian 75 g OGTT DPSG-EASD 642 27–28 32.3 ± 0.2 1229 27–28 30.5 ± 0.1 0.504 PCR-RFLP GCK rs1799884
Freathy et al. [27] 2010 UK, Australia, Thailand Caucasian, Asian 75 g OGTT IADPSG 998 24–32 / 5587 24–32 / 0.91 Illumina GCK rs1799884
Santos et al. [28] 2010 Brazil Caucasian 75 g OGTT ADA 150 24–28, 32–36 31.9 ± 6.2 600 24–28, 32–36 25.2 ± 6.5 0.371 PCR-RFLP GCK rs1799884
Li W [31] 2011 China Asian 100 g OGTT ADA 668 28.2 ± 2.2 32.5 ± 3.9 758 27.7 ± 2.6 31.2 ± 3.8 0.558 TaqMan GCK rs1799884, GCKR rs780094
Han et al. [34] 2015 China Asian 75 g OGTT / 948 2–32 / 975 / / 0.985 PCR-based invader assay GCK rs1799884
Tarnowski et al. [36] 2017 Poland Caucasian 75 g OGTT IADPSG 207 24–28 31.7 ± 4.5 204 / 29.2 ± 5.0 0.875 TaqMan GCK rs1799884, GCKR rs780094
Zhou [43] 2020 China Asian 75 g OGTT National 835 24–28 30.97 ± 4.56 870 / 28.84 ± 4.21 0.246 MassARRAY GCK rs1799884 and rs4607517, GCKR rs780094 and rs1260326
Popova et al. [41] 2021 Russia Caucasian 75 g OGTT IADPSG 688 24–28 31.9 ± 4.5 454 24–28 29.5 ± 4.7 0.141 TaqMan GCK rs1799884
She et al. [19] 2022 China Asian 75 g OGTT IADPSG 835 24–28 30.97 ± 4.56 870 / 28.84 ± 4.21 0.246 MassARRAY GCK rs1799884 and rs4607517, GCKR rs780094 and rs1260326
Wang et al. [32] 2011 China Asian 100 g OGTT ADA 1701 / 30 (30, 35) 1023 / 32 (28, 33) 0.804 Taqman GCK rs4607517
Stuebe et al. [33] 2014 USA Caucasian, African–American 100 g OGTT / 80 24–29 28.3 ± 6.1 1138 24–29 / 0.324 Sequenom iPLEX GCK rs4607517, GCKR rs780094 and rs1260326
Ao et al. [40] 2021 China Asian 75 g OGTT National 562 / 30.18 ± 2.64 452 / 29.50 ± 2.68 0.28 MassARRAY GCK rs4607517
Anghebem-Oliveira et al. [35] 2017 Brazil Caucasian / / 127 / 31.9 ± 6.4 125 / 30.6 ± 4.7 0.094 RT-PCR GCKR rs780094
Jamalpour et al. [37] 2018 Malaysia Asian OGTT / 186 / / 588 / 29.9 ± 4.4 0.512 Sequenom iPLEX GCKR rs780094
Li et al. [42] 2018 China Asian 75 g OGTT ADA 127 24–32 31.9 ± 6.4 125 / 30.6 ± 4.7 0.094 RT-PCR GCKR rs780094
Franzago et al. [38] 2017 Italy Caucasian 75 g OGTT IADPSG 102 24–28 34.6 ± 5.4 66 24–28 31.9 ± 5.1 0.46 High resolution melting GCKR rs1260326
Franzago et al. [39] 2018 Italy Caucasian 75 g OGTT IADPSG 104 24–28 34.6 ± 5.4 124 24–28 32.5 ± 5.2 0.276 RT-PCR GCKR rs1260326
Zhu et al. [20] 2023 China Asian 75 g OGTT IADPSG 519 24–28 31 (28–34) 498 24–28 29 (27–32) 0.737 Illumina GCKR rs1260326

Notes: GDM, gestational diabetes mellitus; OGTT, oral glucose tolerance test; WHO, World Health Organization; DPSG-EASD, Diabetic Pregnancy Study Group of the European Association for the Study of Diabetes; IADPSG, new International Association of Diabetes and Pregnancy Study Groups; ADA, American Diabetes Association; PCR-SSCP, polymerase chain reaction–single strand conformation polymorphism; PCR-RFLP, polymerase chain reaction restriction fragment-length polymorphism; RT-PCR, real time-polymerase chain reaction; SD, standard deviation; p HWE, p value of Hardy-Weinberg Equilibrium in control group; SNPs, single nucleotide polymorphisms; GCK, glucokinase; GCKR, glucokinase regulatory protein; MassARRAY, Sequenom MassARRAY iPLEX system.

3.2 The Combined Analyses of GCK and GCKR Polymorphisms with GDM Susceptibility

Eleven articles (encompassing 12 studies) focused on the GCK rs1799884 polymorphism with a power of 1.0, while five articles on GCK rs4607517 had a power of 0.812. Eight articles (including 11 studies) and six articles (including 7 studies) concentrated on GCKR rs780094 with a power of 0.829 and GCKR rs1260326 with a power of 1.0, respectively. Table 2 (Ref. [19, 20, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43]) shows the genotype distributions for these SNPs.

Table 2. Genotype distributions of GCK and GCKR polymorphisms.
Author Year Country Ethnicity GDM group Control group
11 12 22 11 12 22
rs1799884 GG GA AA GG GA AA
Chiu et al. [26] 1994 USA African 56 37 4 63 34 2
Zaidi et al. [30] 1997 UK Caucasian 47 42 3 25 20 2
Shaat et al. [29] 2006 Sweden Caucasian 435 181 26 889 316 24
Freathy et al. [27] 2010 UK, Australia Caucasian 388 194 32 2575 1114 122
Freathy et al. [27] 2010 Thailand Asian 288 91 5 1375 311 20
Santos et al. [28] 2010 Brazil Caucasian 86 56 8 387 186 27
Li W [31] 2011 China Asian 632 349 42 552 315 40
Han et al. [34] 2015 China Asian 705 226 17 787 178 10
Tarnowski et al. [36] 2017 Poland Caucasian 163 42 2 147 52 5
Zhou [43] 2020 China Asian 506 277 47 556 280 27
Popova et al. [41] 2021 Russia Caucasian 488 173 27 343 99 12
She et al. [19] 2022 China Asian 506 277 47 556 280 27
rs4607517 GG GA AA GG GA AA
Wang et al. [32] 2011 China Asian 1420 244 37 618 356 49
Stuebe et al. [33] 2014 USA Caucasian 49 3 0 731 53 2
Ao et al. [40] 2021 China Asian 316 200 46 283 154 15
Zhou [43] 2020 China Asian 702 112 18 735 124 7
She et al. [19] 2022 China Asian 702 112 18 735 124 7
rs780094 TT TC CC TT TC CC
Li W [31] 2011 China Asian 275 502 247 265 453 225
Stuebe et al. [33] 2014 USA Caucasian 24 23 5 266 376 150
Stuebe et al. [33] 2014 USA African–American 16 6 0 255 87 4
Anghebem-Oliveira et al. [35] 2017 Brazil Caucasian 15 48 64 14 68 43
Jamalpour et al. [37] 2018 Malaysia Asian 18 69 95 84 284 214
Jamalpour et al. [37] 2018 Malaysia Asian 5 30 13 23 76 64
Jamalpour et al. [37] 2018 Malaysia Asian 3 13 16 16 47 39
Tarnowski et al. [36] 2017 Poland Caucasian 33 101 73 28 99 77
Li et al. [42] 2018 China Asian 64 48 15 43 68 14
Zhou [43] 2020 China Asian 200 371 213 227 401 212
She et al. [19] 2022 China Asian 200 371 213 227 401 212
rs1260326 TT TC CC TT TC CC
Stuebe et al. [33] 2014 USA Caucasian 5 26 25 154 395 291
Stuebe et al. [33] 2014 USA African–American 1 7 16 7 107 248
Franzago et al. [38] 2017 Italy Caucasian 21 58 23 15 36 15
Franzago et al. [39] 2018 Italy Caucasian 21 58 25 30 68 26
Zhou [43] 2020 China Asian 220 404 182 238 424 195
She et al. [19] 2022 China Asian 220 404 182 238 424 195
Zhu et al. [20] 2023 China Asian 142 241 122 164 245 86

Notes: GDM, gestational diabetes mellitus; GCK, glucokinase; GCKR, glucokinase regulatory protein.

For these SNPs, 2 referes risk allele and 1 defiene as reference allele. The combined analyses for GCK (rs1799884 and rs4607517) and GCKR (rs780094 and rs1260326) polymorphisms indicated that these SNPs were significantly correlated with elevated GDM susceptibility under 22 vs. 11 (OR = 1.28, 95% CI = 1.08–1.51, Fig. 2A); decreased GDM susceptibility under 12 vs. 11 (OR = 0.65, 95% CI = 0.52–0.81, Fig. 2B) and 22 vs. 11 + 12 (OR = 0.80, 95% CI = 0.70–0.92, Fig. 2C) genetic models. However, no significant association was discovered in 22 + 12 vs. 11 (OR = 1.05, 95% CI = 0.91–1.21, Fig. 2D) and 2 vs. 1 (OR = 1.07, 95% CI = 0.96–1.20, Fig. 2E) genetic models, respectively.

Fig. 2.

Forest plot for merged odds ratios (ORs) with 95% confidence intervals (CIs) for GCK and GCKR polymorphisms. (A) Forest plot under 22 vs. 11 model. (B) Forest plot under 12 vs. 11 model. (C) Forest plot under 22 vs. 11 + 12 model. (D) Forest plot under 22 + 12 vs. 11 model. (E) Forest plot under 2 vs. 1 model.

3.3 Pooled Association of GCK rs1799884 and rs4607517 Polymorphisms with GDM Susceptibility

Regarding the rs1799884 polymorphism, significant heterogeneity was observed in the GA vs. GG model (p < 0.05, Fig. 2B), but not in the other four genetic models. Consequently, we used a random-effects model to assess the pooled association of rs1799884 with GDM susceptibility; however, a fixed-effects model was used for other models. Additionally, rs1799884 and GDM susceptibility were positively correlated in the AA vs. GG (OR = 1.55, 95% CI = 1.29–1.86, Fig. 3A) and AA + GA vs. GG (OR = 1.19, 95% CI = 1.11–1.27, Fig. 3B) genetic models, negatively correlated in the AA vs. GG + GA (OR = 0.67, 95% CI = 0.56–0.80, Fig. 3C) and A vs. G (OR = 1.19, 95% CI = 1.12–1.26, Fig. 3D) genetic models, respectively. No significant association was discovered between rs1799884 and GDM susceptibility uncer GA vs. GG model (Fig. 3E). The rs4607517 polymorphism showed significant heterogeneity across all five genetic models (Supplementary Fig. 1), and the random-effects model revealed a significant association with GDM susceptibility under AA + GA vs. GG model (OR = 0.66, 95% CI = 0.59–0.74) (Supplementary Fig. 1C), but not in other genetic models.

Fig. 3.

Forest plot for merged odds ratios (ORs) with 95% confidence intervals (CIs) for GCK rs1799884 polymorphism. (A) Forest plot for subgroup analysis based on ethnicity under AA vs. GG model. (B) Forest plot for subgroup analysis based on ethnicity under AA + GA vs. GG model. (C) Forest plot for subgroup analysis based on ethnicity under AA vs. GG + GA model. (D) Forest plot for subgroup analysis based on ethnicity under A vs. G model. (E) Forest plot for subgroup analysis based on ethnicity under AG vs. GG model.

3.4 Pooled Associations of GCKR rs780094 and rs1260326 Polymorphisms with GDM Susceptibility

The rs780094 polymorphism showed significant heterogeneity in the TC vs. TT, CC vs. TT+TC, and C vs. T genetic models (p < 0.05), leading to the usage of a random-effects model. The results indicated a significant correlation between the rs780094 polymorphism and reduced GDM susceptibility in the TC vs. TT model (OR = 0.47, 95% CI = 0.30–0.73, Fig. 4A); however, no significant associations were found in the other models (Fig. 4B–D). For the rs1260326 polymorphism, the TC vs. TT model (p < 0.05) displayed significant heterogeneity. The random-effects model was used to analyze the association under this model, fixed-effects model analyzed for other genetic models. The pooled results revealed no significant association in any of the genetic models (Supplementary Fig. 2).

Fig. 4.

Forest plot for merged odds ratios (ORs) with 95% confidence intervals (CIs) for GCKR rs780094 polymorphism. (A) Forest plot for subgroup analysis based on ethnicity under TC vs. TT model. (B) Forest plot for subgroup analysis based on ethnicity under CC vs. TT + TC model. (C) Forest plot for subgroup analysis based on ethnicity under C vs. T model. (D) Forest plot for subgroup analysis based on ethnicity under CC + TC vs. TT model.

3.5 Subgroup Analysis

Stratified by ethnicity and diagnostic criteria, subgroup analyses were conducted to fine heterogeneity sources. The heterogeneity observed in the rs1799884 polymorphism’s GA vs. GG model might have stemmed from variable ethnic and diagnostic criteria. Due to fewer studies for rs4607517, rs780094, and rs1260326 polymorphisms and multiple diagnostic criteria, diagnostic criteria-based subgroup analysis was not feasible. The heterogeneity in these polymorphisms was attributed to varying ethnicity.

In the ethnicity-based subgroup analysis, a positive association between rs1799884 polymorphism and GDM susceptibility was evident in the AA vs. GG model in both Asian (OR = 1.48, 95% CI = 1.16–1.90) and Caucasian (OR = 1.63, 95% CI = 1.24–2.15) subgroups, respectively (Fig. 3A). Analysis by diagnostic criteria revealed a positive association in the AA vs. GG model in subgroups following the Diabetic Pregnancy Study Group of the European Association for the Study of Diabetes (DPSG-EASD) (OR = 2.21, 95% CI = 1.26–3.90), International Association of Diabetes and Pregnancy Study Groups (IADPSG) (OR = 1.63, 95% CI = 1.25–2.13), and National criteria (OR = 1.91, 95% CI = 1.17–3.12) (Supplementary Fig. 3A). We also observed positive associations in the AA+GA vs. GG (Fig. 3B), AA vs. GG+GA (Fig. 3C), and A vs. G (Fig. 3D) models in the specified ethnic and diagnostic subgroups (Supplementary Fig. 3B–D). Conversely, the ethnicity-based subgroup analysis revealed a negative correlation between the rs780094 polymorphism and GDM susceptibility in the African-American subgroup under the TC vs. TT model (OR = 0.07, 95% CI = 0.02–0.20, Fig. 4A), but not in other for genetic models (Fig. 4B–D). Only one Caucasian study was conducted on the rs4607517 (Supplementary Fig. 1) and one African-American study was conducted on rs1260326 (Supplementary Fig. 2) polymorphism. Small number of studies, especially only one study in the subgroup, usually lead to unreliable estimation of heterogeneity, such as false positive result. Therefore, the ethnicity-based subgroup analysis was not performed in five genetic models of rs4607517 and rs1260326.

Hence, these findings suggest a significant association between the rs1799884 as well as rs780094 polymorphisms and GDM susceptibility. However, the rs4607517 and rs1260326 polymorphisms were not be significantly associated with the overall population.

3.6 Publication Bias

Begg’s test helped to assess publication bias across the combined analysis of GCK and GCKR genetic polymorphisms. None of the publications showed any significant publication bias (Begg’s test: p = 0.063 for 22 vs. 11, Fig. 5A; p = 0.386 for 12 vs. 11; p = 0.806 for 22 vs. 11+12, Fig. 5B; p = 0.088 for 22+12 vs. 11; p = 0.629 for 2 vs. 1).

Fig. 5.

Publication bias for eligible studies of GCK and GCKR polymorphisms. (A) Publication bias under 22 vs. 11 model. (B) Publication bias under 22 vs. 11 + 12 model. 22 is risk genotype, 12 is heterozygote genotype and 11 is the reference genotype.

4. Discussion

We systematically reviewed 20 eligible studies in this extensive meta-analysis that examined the relationship between GDM susceptibility and GCK (rs1799884 and rs4607517) and GCKR genes (rs780094 and rs1260326). Our results indicated that these SNPs were significantly correlated with GDM susceptibility. Meanwhile, GCK rs1799884 polymorphism was significantly associated with GDM susceptibility in five genetic models; the A allele carriers displayed a higher risk for GDM. This is consistent with previous meta-analytical results that identified a positive association between the rs1799884 polymorphism and GDM susceptibility [20, 34, 44]. Moreover, a positive association between rs1799884 polymorphism and GDM susceptibility was also noted. We found that GCK rs4607517 was a risk factor for GDM susceptibility in the AA vs. GG genetic model. Mao et al. [45] also reported a positive correlation between the GCK rs4607517 A allele and GDM risk, suggesting that GCK polymorphisms might lead to GDM.

However, we could not find a significant association between GDM risk and the GCKR genes rs780094 as well as rs1260326. This was in contrast with a few meta-analyses that suggested a high risk of GDM for rs780094 G allele carriers [37, 46, 47]. This discrepancy may be attributed to variable ethnicities among the study populations.

Enhanced heterogeneity was discovered in the rs1799884 polymorphism’s GA vs. GG genetic model. We examined the rs1799884 polymorphism’s heterogeneity origin across different ethnicities and diagnostic criteria. The possible causes of heterogeneity include various ethnicities and diagnostic criteria. Due to limited studies on rs4607517, rs780094, and rs1260326 polymorphisms, we discussed their heterogeneities in different ethnic groups. Subsequently, we found that the GCK rs4607517 heterogeneities in five genetic models stemmed from different ethnicities. Heterogeneities were also discovered in the TC vs. TT model of GCKR rs780094 as well as the TC vs. TT model of rs1260326 polymorphism and were derived from different ethnicities. However, no significant heterogeneity was discovered in previous meta-analyses [45, 46, 47, 48]. Different diagnosis standards and ethnicities might be the cause of this discrepancy.

Ethnicity-based subgroup analysis revealed a positive correlation between rs1799884 polymorphism and GDM susceptibility in Asian and Caucasian populations. This is in line with the findings of Yang and Du [44] that A allele of rs1799884 is a risk factor for GDM in White and African populations. Notably, we also observed a negative correlation between the rs780094 polymorphism and GDM susceptibility in the African-American subgroup’s TC vs. TT genetic model. Conversely, Jamalpour et al. [37] observed that the C allele of rs780094 was positively correlated with GDM susceptibility in the Asian population. The rs1260326 polymorphism’s subgroup analysis was consistent with previous meta-analyses, indicating no significant association with GDM susceptibility across different populations [46, 47].

Furthermore, diagnostic criteria-based subgroup analysis confirmed their influence on the association between the rs1799884 polymorphism and GDM susceptibility. The DPSG-EASD and IADPSG subgroups were positively correlated. Yang and Du [44] demonstrated that the A allele of rs1799884 was not significantly associated with GDM susceptibility under World Health Organization (WHO) and American Diabetes Association (ADA) subgroups. This underscores the importance of standardized criteria for future studies and the possible effects of varying GDM diagnostic criteria on research outcomes.

Begg’s test revealed no significant publication bias, suggesting the reliability of observed associations. However, selection and information biases could potentially affect our results’ generalizability and statistical power. Our findings highlight the importance of targeted screening guidelines for Asian and Caucasian populations as well as focusing on nutritional and lifestyle interventions for pregnant women. Additional research with larger sample sizes and multicenter studies should be conducted to validate our findings and explore the biological mechanisms influencing the associations between GCK and GCKR polymorphisms as well as GDM susceptibility.

5. Conclusions

According to our meta-analysis, the GCK rs1799884 polymorphism’s A allele is associated with an increased risk of GDM. Although certain individuals with GCK rs4607517 and GCKR rs780094 polymorphisms may also be at higher risk for GDM, the overall association is less clear and warrants further investigation. These insights can help in identifying high-risk pregnant women early as well as emphasizing the necessity of genetic counseling and interdisciplinary collaboration for preventing and managing GDM.

Availability of Data and Materials

Corresponding authors may provide data and materials.

Author Contributions

HXT and MYW designed the research study. YYZ performed the research. HXT, YYZ and MYW analyzed the data. HXT and MYW wrote the manuscript. All authors contributed to editorial changes in the manuscript. All authors read and approved the final manuscript. All authors have participated sufficiently in the work and agreed to be accountable for all aspects of the work.

Ethics Approval and Consent to Participate

Not applicable.

Acknowledgment

Not applicable.

Funding

This research received no external funding.

Conflict of Interest

The authors declare no conflict of interest.

Supplementary Material

Supplementary material associated with this article can be found, in the online version, at https://doi.org/10.31083/CEOG26710.

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Clin. Exp. Obstet. Gynecol. Print ISSN 0390-6663 Electronic ISSN 2709-0094
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