Meta-analysis of genetic association studies on gestational diabetes mellitus

doi:10.21203/rs.3.rs-1127993/v1

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Systematic Review

Meta-analysis of genetic association studies on gestational diabetes mellitus

https://doi.org/10.21203/rs.3.rs-1127993/v1

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Background

Several molecular epidemiological studies have analyzed the associations between genetic variants and the risk of gestational diabetes mellitus (GDM). However, all these studies suffer from inconsistent and conflicting results owing to relatively smaller sample sizes, fewer genetic variants included in the research, and limited statistical power. Hence, a coherent review and meta-analysis were carried out to provide a quantitative summary related to the associations of commonly studied SNPs with GDM risk.

Methods

Eligible studies were retrieved from PubMed,updated on Dec. 2019. Based on several inclusion and exclusion criteria, 71 articles with 42928 GDM patients and 77793 controls were finally considered for meta-analysis. The genotype data from 23 variants of sixteen genes were statistically analyzed using RevMan v 5.2 software. Newcastle-Ottawa Scale (NOS) was used to assess the quality of the research article. Heterogeneity among studies was tested by I² and odds ratio with 95% confidence interval (CI) was carried out for all five genetic models.

Results

The overall combined odds ratio reveals that variants like MTNR1B (rs1083963, rs1387153), GCK (rs1799884), CANP10 (rs3792267), and GCKR (rs780094) are significantly associated with GDM in all genetic models while CANP10 (rs5030952), ADRB (rs4994) and FTO (rs8050136) are not significantly associated with GDM in any genetic models. Variants MTNR1B (rs1083963, rs1387153) and GCK (rs1799884) are associated with increased risk (OR>1, p<0.05) of GDM, and all these are related to insulin secretion. Other variants related to insulin secretion like TCF7L2 (rs7903146) and SLC30A8 (rs1326634) are also associated with increased risk (OR>1, p<0.05) of GDM. On the contrary, CANP10 (rs3792267) and GCKR (rs780094) are found associated with decreased risk (OR<1, p<0.05) of GDM. Other variants are significantly associated with the GDM in at least one or more genetic models.

Conclusion

Our study identified that most of the variants related to insulin secretions like MTNR1B (rs1083963), GCK (rs1799884), TCF7L2 (rs7903146), GCKR (rs780094), and SLC30A8 (rs1326634) are more strongly associated (p<0.005) with GDM as compared to the variants related to the insulin resistance like PPARG (rs1801282), IRS1 (rs1801278) and ADIPOQ (rs266729).

Medical Genetics

Molecular Genetics

Genetic variants

Gestational Diabetes Mellitus

Insulin secretion

SNPs

Gestational diabetes mellitus (GDM), a complex metabolic disorder, is defined as any degree of carbohydrate intolerance with onset or first recognition during pregnancy (American Diabetes Association, 2010). Risk factors include maternal age, pre-pregnancy obesity, and previous delivery of a newborn with congenital malformations such as macrosomia, previous/prior history of GDM, cesarean section, and a family history of diabetes in first degree relatives (Reece et al., 2009).However, intrinsic factors like environmental interaction and genetic predisposition can’t remain unaddressed (Shaat and Groop, 2007). Women with GDM had an increased risk of developing diabetes type 2 which accounts for 90% of cases of diabetes. GDM can progress when a genetic susceptibility to pancreatic islets cells are exposed to incremental insulin resistance during pregnancy. GDM is often associated with adverse pregnancy outcomes, including fetal macrosomia, stillbirth, neonatal metabolic disturbances, and related problems, and causes short- and long-term complications in women and their offspring (Bellamy et al., 2009; “Gestational Diabetes Mellitus,” 2004; Reece et al., 2009). GDM women are at over seven-fold higher risk of developing type 2 diabetes mellitus (T2DM) later in life (Bellamy et al., 2009).

The Burden of GDM is growing at a much higher rate in developing and low-to-middle income countries than in developed countries. The prevalence of GDM varies widely from 1.8–25.1% of all pregnancies. It is higher among the Middle East and North Africa, South Asia, and Western Pacific regions while lowest in Europe (Zhu and Zhang, 2016). In the western world, the incidence of GDM is about 1–3% of all pregnancies while 5–10% in Asian pregnancies (Shaat and Groop, 2007). In India, the incidence of GDM is estimated to be 10-14.3% which is much higher than in developed countries(Lowe et al., 2016). This significant variation in its prevalence is attributed to racial and ethnic differences a social-economic variations (Zhu and Zhang, 2016). The Asian/Pacific Islander women have a higher incidence of GDM than non-Hispanic white, Black, or Hispanic women (Chu et al., 2009; Kim et al., 2012). Due to racial and regional differences in GDM prevalence, exploring the relationship of susceptible gene polymorphism in GDM women of different racial backgrounds will be quite informative.

In this study, we systematically analyzed all the current evidence regarding the genetic associations of GDM to quantitatively summarize the effect size of replicated single nucleotide polymorphisms (SNPs) on GDM risk and identify the possible sources of heterogeneity among the eligible researchers. A total of 23 different SNPs related to 16 genes were included for meta-analysis. These sixteen genes are namely TCF7L2 (Transcription factor 7-like 2), MTNR1B (Melatonin receptor 1B), FTO (Alpha-ketoglutarate-dependent dioxygenase), PPARG (Peroxisome proliferator-activated receptor-gamma), GCK (Glucokinase), GCKR (Glucokinase Regulator), ADIPOQ (Adiponectin), TNF (Tumor necrosis factor), IRS1 (Insulin receptor substrate 1), KCNJ11 (Potassium inwardly-rectifying channel, subfamily J, member 11), IGF2BP2 (Insulin-like growth factor 2 mRNA-binding protein 2), ADRB3 (Adrenoceptor beta 3), CDKAL1 (CDK5 regulatory subunit associated protein 1-like 1), HNF1A (Hepatocyte nuclear factor 1-alpha), CANP10 (Calpain-10), and SLC30A8 (Solute carrier family 30 member 8). These genes and their respective SNPs were involved in pathways like type 1 diabetes mellitus, type1 diabetes mellitus, insulin signalling pathway, Maturity onset diabetes of the young, PPAR signalling pathway, Adipocytokine signalling pathway, glycolysis, and amino sugar metabolism. To the best of our knowledge, several studies reporting the association of different SNPs to GDM are present. However, a single study including all polymorphisms is lacking. Herein, we present a meta-analysis including all polymorphisms associated with GDM studied so far.

Search strategy

A systematic literature search in the PubMed, Scopus and Google Scholar databases (Canese et al., n.d.) was carried out for each Single Nucleotide Polymorphism (SNPs) studied so far from 1994 to 2019 for their association with GDM. The keywords used for article search were “SNPs” OR “Polymorphism” OR “Variant” OR “Genotype” OR “SNPs” AND Diabetes, Gestational” [MESH] OR “Diabetes, Pregnancy-Induced” OR “Pregnancy-Induced Diabetes” OR “Gestational Diabetes” OR “Diabetes Mellitus, Gestational” OR “Gestational Diabetes Mellitus.” Cross-references were also screened for the literature retrieved.

Study selection

The inclusion criteria considered for selection of eligible studies were as follows: (1) English publication (2) Studies with case-control design (3) proper diagnostic criteria for Gestational Diabetes Mellitus (GDM) (4) studies with adequate data for genotyping in case and control (5) literature with sufficient data to estimate the odds ratio (ORs) with 95% confidence interval (CI). The Criteria included for exclusion were as follows: (1) Abstract, reviews, and meta-analysis (2) studies with duplicate data (3) studies without control design (4) Irrelevant studies with insufficient data. All identified studies were critically reviewed by two investigators independently to determine their eligibility for inclusion or exclusion in meta-analysis. Screening based on inclusion and exclusion criteria led to the identification of a total of 71 potential studies containing 42928 GDM patients and 77793 controls to be included in the meta-analysis.

Methodological quality appraisal

The modified Newcastle-Ottawa Scale (NOS) (Cook and Reed, 2015) was employed to identify high-quality research. The methodological quality of each study was assessed for three parameters: selection (0-4 points), comparability (0-2 points), and exposure (0-3 points). Each meeting point was given a score, and thus each study was scored from 0 to 9. According to the modified NOS, articles with no less than five scores were defined as high quality.

Data extraction

All 71 eligible studies were independently reviewed by two reviewers, and the following information was extracted from each study: first author, publication year, ethnicity, country, mean age, genotyping method, gene, genetic variants, size of the sample, number of cases and controls, study design, genotype distribution in case and control groups, allele frequency and NOS quality score. Disagreements were resolved through discussion with all authors.

Statistical analysis

All statistical analysis was performed using REVMAN software version 5.2 (Schmidt et al., 2019). The P-values <0.05 were considered statistically significant unless otherwise emphasized. To explore the significant deviation from HWE among controls in each study, the Chi-square test was calculated (Wigginton et al., 2005). OR and 95% CI were used to calculate the strength of associations between different polymorphisms and GDM susceptibility. I² tests were utilized to assess the heterogeneity of ORs (Higgins, 2003). If I²< 50%, the heterogeneity was regarded as not significant. The associations between genetic polymorphisms and GDM were examined under the allele model (A vs B, where A is the risk allele), the recessive model (AA vs AB + BB), the dominant model (AB + AA vs. BB), the homozygous contrast model (AA vs. BB), and the heterozygote contrast model (AA vs. AB).

Networking and KEGG pathway enrichment

All sixteen genes were uploaded in STRING v 11.0 (Mering, 2003), and a PPI network was constructed. All these genes were also enriched for their biological process, molecular function, and KEGG pathways using DAVID v 6.7 (Huang et al., 2009).

Literature search and Characteristics of eligible Studies

According to the search strategy, a total of 243 articles were initially retrieved from PubMed, Scopus and Google Scholar(Fig. 1). After excluding 15 duplicate articles, a total of 228 articles were considered for full-text review. Among these, meta-analysis (n=13), review (n=4), and articles related to other disease and non-clinical data (n=97) were excluded, leaving 114 articles for eligibility check. Further, thirty-seven articles were excluded due to insufficient data, and four abstracts were also excluded. Finally, 75 articles with a total of 42928 GDM cases and 77793 controls were included in the meta-analysis study. The characteristics of the studies included in the meta-analysis are summarized in Table 1. All included studies were published from 1994 to 2019 and were of moderate to high quality, with NOS scores of more than five stars. The studies had a heterogeneous population with all three races. These 71 studies include a total of 23 polymorphisms related to 16 genes. The genotype and allele distribution for each of these polymorphisms is listed in Table 2. The genotype distribution of the control group was in accordance with HWE in all studies (P > 0.05).

Overall meta-analysis

In the present study, a total of 23 SNPs related to 16 different genes were analyzed. Characteristic features of genes and their related alleles are provided in Table 3. Of these, CANP10 and TCF7L2 have three polymorphisms; MTNR1B, FTO, and GCKR have two polymorphisms, while the rest genes have only one SNPs included in the study (Fig. 2A). Association between all twenty-three polymorphism and GDM risk were assessed in five genetic models, and detailed results have been shown in Table 4. Out of 23 SNPs analyzed, the results showed that only 17 SNPs were significantly associated with GDM risk in at least one genetic model (Fig. 2B and D, Table 4). A total of 13 polymorphisms regarding genotypes in the dominant model, 11 polymorphisms regarding genotypes in the recessive model, 14 polymorphisms regarding genotypes in the homozygous model, ten polymorphisms regarding genotypes in the heterozygote model and ten polymorphisms regarding genotypes in the allele model were found to be significantly (p<0.05) associated with GDM risk (Fig. 2D). The four polymorphisms, namely rs780094 (GCKR), rs1387153 (MTNR1B), rs1799884 (GCK), rs1083963 (MTNR1B) were showing significant association (p<0.05) with GDM risk in all five genetic models (Fig. 2C). On the other hand, two polymorphisms (rs3792267, rs9939609) exclusively present the dominant, recessive, homozygote, and heterozygote model each while another two polymorphisms (rs2975760, rs12255572) exclusively present in the dominant, recessive, homozygote, and allele models were found to be significantly (p<0.05) associated with GDM risk (Fig. 2C).

Association between polymorphisms and GDM

In all five genetic models analyzed, the number of polymorphisms associated with increased risk (OR>1) of GDM is larger than those polymorphisms which have protective effects (OR<1) (Fig 3A). Analysis of all 23 SNPs in the dominant genetic model revealed significant heterogeneity (p<0.05) in only seven polymorphisms. Thus, a random-effect model was conducted to pool results for total OR estimation. Genotype analysis indicates that all these seven polymorphisms are significantly associated with increased risk (OR>1, p<0.05) of GDM (Supplementary file 1). In recessive models (Fig. 3B), significant heterogeneity (p<0.05) was found in 10 polymorphisms out of 23 analyzed. Random effect model showed significant protective association (OR<1) of eight polymorphisms while two polymorphisms namely GCK rs1799884 (OR = 1.77(1.56-2.00); I² = 86%, p<0.00001) and TCF7L2 rs12255572 (OR = 2.24(1.81-2.77); I² = 87%, p<0.00001) are significantly associated with increased risk (OR>1) of GDM. In the homozygote model (Fig. 3B), significant heterogeneity was observed in total eleven polymorphisms, of which ten were strongly associated with increased risk (OR>1) of GDM while only one, i.e., GCKR rs780094 was showing protective association (OR = 0.52(0.38-0.70); I² = 83%, p<0.00001). A significant heterogeneity (p<0.05) was observed in six polymorphisms out of 23 in the analysis of the heterozygote model (Fig. 3B). Genetic analysis through the random effect model revealed that five polymorphisms are significantly associated with increased risk (OR<1) of GDM while GCKR rs780094 showed protective association (OR = 0.72(0.53-0.97); p = 0.03). Genotypes in the allele model (Fig. 3B) revealed a total of nine significant (p<0.05) heterogeneity, of which six were significantly associated with increased risk (OR>1) while three were showing protective association (OR<1) for GDM. The overall results revealed the significant heterogeneity (p<0.05) of five polymorphisms, namely rs1326634 (SLC30A8), rs780094 (GCKR) rs1083963 (MTNR1B), rs7903146 (TCF7L2), rs9939609 (FTO) in all five genetic models analyzed. Among these five, rs780094 (GCKR) rs1083963 (MTNR1B) were significant for overall effect in all five genetic models analyzed. Polymorphisms rs1799884 (GCK) and rs1387153 (MTNR1B) were also significantly associated with the disease. However, they are not significant at the heterogeneity level in all five genetic models analyzed.

Association of GDM with genetic variants related to insulin secretion

Transcription factor 7-like 2 (TCF7L2)

This meta-analysis studied three variants of TCF7L2, namely rs7903146, rs12255372, and rs7901695, which were studied in this meta-analysis rs7903146 was the most widely studied variant in the association with GDM. A meta-analysis of twenty-five studies (Cho et al., 2009; de Melo et al., 2015; Ekelund et al., 2012; Franzago et al., 2018; Freathy et al., 2010; Huerta-Chagoya et al., 2015; Khan et al., 2019; Lauenborg et al., 2009; Pagán et al., 2014; Papadopoulou et al., 2011; Pappa et al., 2011; Reyes-López et al., 2014; Rizk et al., n.d.; Shaat et al., 2007; Thomas et al., 2014; Včelák et al., 2012) including a total of 16672 controls and 6692 GDM cases, showed the significantly (p<0.0001 except Recessive Model) increased susceptibility for the rs7903146 allele or genetic models (Fig. 4) associated with an increased risk of GDM (Dominant Model: OR-1.59; Recessive model: OR-1.03; Homozygote Model: OR-2.03; Heterozygote Model: OR-1.46; Allelic model: OR-1.60). Overall heterogeneity was substantial under all comparisons (I²: 43%-92%).

For rs12255372, a total of eleven studies (Cho et al., 2009; de Melo et al., 2015; Pagán et al., 2014; Papadopoulou et al., 2011; Popova et al., 2017; Reyes-López et al., 2019, 2014; Rizk et al., n.d.; Thomas et al., 2014; Včelák et al., 2012) involving 2923 controls and 2842 GDM cases, while only five studies involving 1233 controls and 2093 GDM cases for rs7901695 (Gorczyca-Siudak et al., 2016; Pagán et al., 2014; Papadopoulou et al., 2011; Stuebe et al., 2013) were included for the data synthesis. Overall-effect analysis showed significantly increased susceptibility for the rs12255372 allele or genetic models (Supplementary fig. 1A) with increased GDM risk (Dominant Model: OR-1.37; Recessive model: OR-2.24; Homozygote Model: OR-1.69; Heterozygote Model: OR-1.10; Allelic model: OR-1.80). In the case of rs7901695, significantly increased susceptibility with increased GDM risk was found only in the dominant (OR-1.41; p=0.01) and homozygote model (OR-1.69; p=0.0005) (Supplementary fig. 2A). Overall heterogeneity for rs12255372 ranged from moderate to considerable (20%-87%), while for rs7901695, it was substantial (41%-95%).

Glucokinase (GCK)

The rs1799884 variant in the GCK gene has been widely investigated in GDM risk (Chiu et al., 2000; Freathy et al., 2010; Popova et al., 2017; Shaat et al., 2006; Tarnowski et al., 2017; Zaidi et al., 1997). In the present meta-analysis, for rs1799884, a total of eight studies involving 7923 controls and 2416 GDM cases were analyzed for five genetic models. There was significantly (p<0.00001) increased susceptibility for the rs1799884 allele or genetic models (Fig. 5) with increased GDM risk (Dominant Model: OR-1.88; Recessive model: OR-1.77; Homozygote Model: OR-1.98; Heterozygote Model: OR-1.62; Allelic model: OR-1.52). Overall heterogeneity for rs1799884 was very less (0-40%) except recessive models (86%).

Glucokinase Receptor (GCKR)

Two variants of GCKR, namely rs780094 and rs1260326, have been investigated in the present study. A total of seven studies involving 2317 controls and 667 GDM cases for the rs780094 variant (Anghebem-Oliveira et al., 2017; Jamalpour et al., 2018; Stuebe et al., 2013; Tarnowski et al., 2017) (Fig. 6A) while four studies involving 1230 controls and 462 GDM cases for the rs1260326 variant (de Melo et al., 2015; Franzago et al., 2018; Stuebe et al., 2013) (Fig. 6B) were assessed and analyzed in the present study. Overall-effect analysis indicated the significantly (p<0.05, except recessive model in both variant) decreased susceptibility for both variants in homozygote (rs780094: rs1260326, OR=0.52:0.51), heterozygote (rs780094: rs1260326, OR=0.72:0.66) and allelic model (rs780094: rs1260326, OR=0.51:0.54).

Melatonin receptor 1B (MTNR1B)

Kim et al. (2011) first studied the two variants of MTNR1B, namely rs10830963 and rs1387153. For rs10830963, a total of fourteen studies (Alharbi et al., 2019; Ao et al., 2015; Grotenfelt et al., 2016; Junior et al., 2015; Kim et al., 2011; Liu et al., 2010; Popova et al., 2017; Tarnowski et al., 2017; Vejrazkova et al., 2014; Vlassi et al., 2012; Wang et al., 2011) involving 5121 controls and 4564 GDM cases were analyzed, while for rs1387153, a total of five studies (Alharbi et al., 2019; Kim et al., 2011; Liu et al., 2010; Popova et al., 2017; Vlassi et al., 2012) involving 2139 controls and 2138 GDM cases were included in the present study. In the case of rs10830963, the overall-effect analysis revealed the significantly (p<0.00001) increased susceptibility with increased GDM risk in all genetic models except recessive model (Dominant Model: OR-1.81; Homozygote Model: OR-2.82; Heterozygote Model: OR-1.82; Allelic model: OR-1.85) (Fig. 7A). Similarly, for the rs1387153, the significantly increased susceptibility with increased GDM risk was found in all genetic models except the recessive model (Dominant Model: OR-1.68; Homozygote Model: OR-3.42; Heterozygote Model: OR-1.73; Allelic model: OR-2.0) (Fig. 7B). Overall heterogeneity for rs10830963 was considerable (64%-88%) while for rs1387153 it was highly variable (0%-90%).

Zinc transporter 8 (SLC30A8)

The rs1326634 variant in SLC30A8 has recently gained much interest in GDM risk. In the present meta-analysis, a total of six studies (Cho et al., 2009; Dereke et al., 2012; Khan et al., 2019; Lauenborg et al., 2009; Teleginski et al.,,, 2017) having 3861 controls and 1946 GDM cases were analyzed. Overall-effect analysis showed the significantly (p<0.00001) increased susceptibility with increased GDM risk only in the dominant (OR-1.91) and heterozygote model (OR-2.90), while in the allelic models, there was significantly (p<0.05) decreased susceptibility (OR-0.76) associated with GDM risk (Fig. 8).

Gene-interaction, functional and pathway enrichment

The protein-protein interaction network reveals a high degree of interaction among these sixteen genes (Fig. 9A). Gene ontology enrichment analysis highlights the role of these sixteen genes in significant biological processes like regulation of glucose metabolism and insulin secretion, cellular response to insulin stimulus, and fatty acid oxidation (Fig. 9B). Significant pathways being regulated by these genes are the PPAR signalling pathway, mTOR signalling pathway, Maturity onset of diabetes in young (MODI), and type II diabetes mellitus pathway (Fig. 9C).

Heterogeneity

Heterogeneity was measured in all comparisons. Both Cochrane`s Q test and I-square statistic suggest different levels of heterogeneity (from less/no to very severe) across all studies or for each subgroup. Hence, both the fixed effect model and random effect model were employed to pool all studies. Moderate (I²<50%) or no heterogeneity (I²=0%) was found in seven SNPs namely rs3792267 (CANP10), rs5030952 (CANP10), rs4994 (ADRB3), rs1801278 (IRS1), rs1800629 (TNF), rs1260326 (GCKR), and rs8050136 (FTO), out of 23 SNPs analyzed. The remaining sixteen SNPs show considerable (I², 50% - 90%) and substantial (I², 75% - 100%) heterogeneity.

Sensitivity analysis

For maternal genotype sensitivity analysis, the overall OR after exclusion of any individual study showed no change, ranging from 0.71 to 0.81, including the robust at-risk effect of the rs1326634, rs7754840 rs1083963, rs9939609 genotype against gestational diabetes.

Publication bias analysis

Egger’s test was performed to assess the publication bias. No statistically significant evidence of publication bias was observed for studies included in the analyses.

In the present meta-analysis, all studied genetic variants related to type1 and type2 diabetes was investigated for their association with the GDM risk. Several previous studies have included only a few variants or have missed some variants (Ref.). Moreover, the pathophysiology of GDM shares similarities with type1 (insufficient insulin secretion) and type2 diabetes (insulin resistance). Both insulin insufficiency and insulin resistance play a significant role in the development and progression of GDM. Besides, these two pathways, glucose and lipid metabolism pathway, also play an essential role in the pathophysiology of GDM. Hence in this meta-analysis, we have used rigorous statistical analysis to rule out the most significantly associated variants with increased GDM risk and related pathways. Further, we also tried to identify those pathways whose genetic variants are strongly associated with the increased GDM risk. Thus, our replication study provides a more comprehensive and concise summary of the currently available evidence regarding GDM genetic variants.

Pregnancy is accompanied by a number of changes in metabolic activity, which helps in dwelling the interaction between mothers and growing fetuses to meet their energy needs. There is a slight enhancement of insulin sensitivity seen during early gestation; however, this insulin sensitivity declines during 12-14 weeks. Moreover, in the third trimester, this insulin sensitivity increases, and these values, as reported in some cases, approach the values of T2DM. This condition is termed GDM; evidence has reported that GDM develops when a genetic predisposition of pancreatic islet B- cells impairment is unmasked by the increased insulin resistance during pregnancy. A GWAS study confirmed the association of various SNPs with impaired b-cell function (MTNR1), insulin resistance, and abnormal utilization of glucose (GCK, CANP10). We came across many studies with heterogeneous results, and this variation is liable to ethnicity, study design, and tissue is taken. Overall, we observed that variants related to insulin secretions pathways like MTNR1B (rs1083963), GCK (rs1799884), TCF7L2 (rs7903146), GCKR (rs780094), and SLC30A8 (rs1326634) are more strongly associated (p<0.005) with increased GDM compared to the variants related to the insulin resistance like PPARG (rs1801282), IRS1 (rs1801278) and ADIPOQ (rs266729).

The MTNR1B polymorphisms

The MTNR is reported to modulate pancreatic islet B-cells function. Our study coincides with the result of Kim et al., who first reported a significant association of GDM with MTNR1B rs1387153. A study conducted by Zheng et al. in 2013 observed the T allele of rs1387153 associated with increased risk of GDM, Vlassi et al. (2012) studies supported. However, the study on Chinese women conducted by Wang et al. disregarded the findings.

The GCK polymorphisms

The Glucokinase (GCK) with the rs179884 has been widely studied in different ethnic populations with conflicting results. Chiu et al. and Zaidi et al. reported no association between GDM and rs179884. However, Shaat et al., Freathy et al., Santas et al. found significant association when they conducted research on a relatively larger population. Our replication study also aligned with their findings and the meta-analysis also presented the significant association with no significant heterogeneity.

The CANP10 polymorphism

The CAPN10 gene belongs to the calpain family and is a Ca2+ dependent intracellular cysteine protease. CAPN10 is found to be involved in glucose homeostasis as it regulates the activity of pancreatic B islet-cells, liver, skeletal muscle, and adipocytes. However, the direct association between GDM and rs3792267 remains elusive due to reported contradictory results (Heinz et al., Luo et al., N Shaat et al., Thomas et al.)

The superiority of this study was that multiple databases were included to search the literature as thoroughly as possible. The subgroup and random effect analysis were utilized to decline heterogeneity, and the comprehensive assessment of publication bias was done, which identified the results of our study effectively and reliably. These findings suggest insulin resistance or defects in insulin secretion have major implications in aetiology in GDM however ethnicity plays a crucial role in it.

Conflict of Interest

Authors declare no any conflict of interest.

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Table 1: Characteristics of the studies included in the meta-analysis

Sl. No.	Author (Year)	Study Design	Ethnicity	Country	No. of Controls	No. of Cases	Mean Age cases/controls	GDM Criteria	Genotyping Method	NOS Score
1	N. Shaat et al. (2004)	Case–control	Arabian	Sweden	122	100	31.9/NA	OGTT-2 hour	RFLP–PCR	6
2	N. Shaat et al. (2005)	Case–control	Caucasian	Sweden	1189	587	32.2/30.5	EASD-DPSG criteria	TaqMan allelic discrimination assay	6
3	N. Shaat et al. (2006)	Case–control	Caucasian	Sweden	1229	642	32.3/30.5	EASD-DPSG criteria	RFLP–PCR	6
4	N. Shaat et al. (2007)	Case–control	Caucasian	Sweden	1111	585	32.3/30.5	EASD-DPSG criteria	TaqMan allelic discrimination assay	6
5	Popova et al. (2017)	Case–control	Caucasian	Russia	179	278	31.8/29.4	IADPSG	NA	6
6	Cho et al. (2009)	Case–control	Asian	Korea	627	868	32/64.7	Third IWCGDM criteria	TaqMan allelic discrimination assay	6
7	Lauenborg et al. (2009)	Case–control	Caucasian	Denmark	2353	276	43.1/45.2	WHO criteria 1999	TaqMan allelic discrimination assay	7
8	Freathy et al. (2010)	Case–control	Caucasian	Australia and UK	3811	614	NA	IADPSG 2010 criteria	TaqMan allelic discrimination assay	5
9	SF de Melo et al. (2015)	Case–control	Caucasian	Brazil	200	200	33.0 ± 6.4	ADA	Taq-Man assay	7
10	M Franzago et al. (2018)	Case–control	Caucasian	Italy	124	104	26.0 ± 8.4	IADPSG	HRM	5
11	RR Lopez et al. (2014)	Case–control	Hispanic/Latino	Mexico	108	90	29/31	ADA	PCR	6
12	Thomas et al. (2014)	Case–control	Asian	India	49	117	NA	NA	PCR	5
13	Thomas et al. (2013)	Case–control	Caucasian	Germany	297	204	NA	NA	PCR	6
14	Beysel et al. (2019)	Case–control	Caucasian	Turkey	145	160	29.16/28.01	OGTT-2 hour	RT-PCR	5
15	Pappa et al. (2011)	Case–control	Caucasian	Greece	107	148	32.5/26.67	Fourth IWCGDM criteria	RFLP–PCR	6
16	S. Jamalpour et al. (2017)	Case–control	Asian	Malaysia	582	182	31.31/29.89	75-g mOGTT	Sequenom MassARRAY	6
17	A Pagan et al. (2014)	Case–control	Caucasian	Spain	24	45	31.2/34.31	OM/NDDG	Sequencing	6
18	A.Papadppoulou et al. (2011)	Case–control	White	Sweden	1110	803	NA	IADPSG	Taq-Man assay	6
19	Wang et al. (2011)	Case–control	Asian	China	1029	700	32.0/30.0	ADA criteria	TaqMan allelic discrimination assay	6
20	Stuebe et al. (2014)	Case–control	Caucasian	US, African	792	52	NA	Other criteria	Sequenom iPLEX platform	6
21	Chao Li et al. (2013)	Case–control	Asia	China	480	350	NA	NA	Sequencing	6
22	Chao Li et al. (2018)	Case–control	Asia	China	243	215	NA	NA	RFLP–PCR	4
23	Rizk et al. (2011)	Case–control	Caucasian	Qatar	74	40	NA	NA	TaqMan allelic discrimination assay	5
24	Shi et al. (2014)	Case–control	Asian	China	100	100	27.4/24.2	IADPSG	Allelespecific PCR	6
25	DG Siudak et al. (2016)	Case–control	Caucasian	Poland	26	50	30.36/30.88	NA	TaqMan SNP Genotyping Assay	5
26	IA Khan et al. (2018)	Case–control	Asian	India	150	137	26.7/27.6	OGTT-2 hour	RFLP–PCR	6
27	Vlassi et al. (2012)	Case–control	Caucasian	Greece	98	77	35.45/31.39	ADA criteria	RFLP–PCR	6
28	Vcelak et al. (2012)	Case–control	Caucasian	Greece	376	261	35.45/31.39	ADA criteria	RFLP–PCR	5
29	Aris et al. (2012)	Case–control	Asian	Malay	114	173	NA	ADA criteria	Illumina	5
30	M. Ekelund et al. (2012)	Case–control	Caucasian	Sweeden	476	125	32.2/32.8	NA		6
31	KK Alharbi et al. (2019)	Case–control		Saudi Arabia	200	200	32.43/31.36	OGTT	RFLP–PCR	5
32	Liu et al. (2015)	Case–control	Asian	China	674	674	31.88/28.78	OGTT	Mass spectrometry	6
33	JY Kim et al. (2011)	Case–control	Asian	Korea	966	908	33.17/32.24	Carpenter and Coustan criteria	TaqMan allelic discrimination assay	3
34	Saucedo et al. (2017)	Case–control	Caucasian	Mexico	80	80	30 (26.7–32.8)	ADA	Taq-Man assay	5
35	Tok et al. (2006)	Case–control	Caucasian	Turkey	100	62	NA	NDDG criteria	RFLP–PCR	6
36	S Chon et al. (2013)	Case–control	East Asian	Korea	41	94	29.2/26.7	NA	TaqMan	6
37	M. Tarnowski et al (2017)	Case–control	Caucasian	Poland	207	204	29.3 ± 5.9	IADPSG	Taq-Man assay	5
38	Z. Liang et al. (2010)	Case–control	Asia	China	79	50	NA	NA	PCR	7
39	G Silva et al. (2011)	Case–control	Caucasian	Brazil	168	79	NA	NA	RFLP–PCR	7
40	Fallucca et al. (2006)	Case–control	Caucasian	Italy	277	309	34.1/32.7	Carpenter and Coustan criteria	NA	6
41	Zhang X. et al. (2019)	Case–control	Asia	China	152	138	28.2/27.5	NA	RFLP–PCR	5
42	Heinz et al. (2014)	Case–control	Caucasian	Austria	40	43	31.0/33.6	NA	RFLP–PCR	6
43	Luo et al. (2009)	Case–control	Asian	China	120	42	28.5/29.1	NA	NA	5
44	Deng et al. (2011)	Case–control	Asian	China	91	87	29.7/31.8	OGTT	Sequencing	5
45	Tarnowski et al. (2017)	Case–control	Europe	Poland	207	99	NA	NA	RT-PCR	5
46	Vejrazkova et al. (2014)	Case–control	Europe	Czech	422	458	NA	NA	TaqMan	7
47	Junior et al. (2017)	Case–control	NA	NA	183	183	NA	NA	PCR	NA
48	NE. Grotenfelt et al. (2016)	Case–control	Caucasian	Finland	106	120	NA	OGTT	Sequenom iPLEX	5
49	Cheng et al. (2010)	Case–control	Asian	China	173	55	27/29.6	OGTT (not specified	PCR–denaturing HPLC	6
50	Yan et al. (2014)	Case–control	East Asian	China	180	156	NA/NA	NA	RFLP	6
51	Du et al. (2012)	Case–control	East Asian	China	69	66	NA/NA	NA	RFLP	6
52	Papa et al. (2011)	Case–control	Caucasian	Greece	107	148	32.5/26.67	Fourth IWCGDM criteria	RFLP–PCR	6
53	Heude et al. (2011)	Case–control	Caucasian	France	1587	109	NA	50-g glucose load	RFLP–PCR or TaqMan allelic	5
54	Chiu et al. (1994)	Case–control	Caucasian	USA	99	97	NA	OGTT 2 h glucose	PCR-SSCP	6
55	Zaidi et al. (1997)	Case–control	Caucasian	UK	92	47	NA	OGTT 2 h glucose	RFLP–PCR	5
56	Santos et al. (2010)	Case–control	Caucasian	Brazil	600	150	NA	ADA 2009 criteria	RFLP–PCR	6
57	Kan et al. (2014)	Case–control	Asian	China	100	100	30.7/30.9	OGTT	TaqMan Allelic discrimination assay	5
58	Huerta-Chagoya et al. (2015)	Case–control	Latino	Mexico Hispanic/	342	408	NA	Carpenter and Coustan	NA	5
59	Klein et al. (2012)	Case–control	Caucasian	Australia	125	125	NA	IADPSG	NA	6
60	J. Dereke et al. (2016)	Case–control	Arabian	Sweden	536	511	NA	EASD	PCR-RFLP	7
61	A. Teleginski et al. (2017)	Case–control	Caucasian	Brazil	180	134	NA	SBD	TaqMan	5
62	Festa et al. (1997)	Case–control	Caucasian	Austria	109	70	NA	OGTT 1 h	RFLP–PCR	6
63	Alevizavaki et al. (2000)	Case–control	Caucasian	Greek	130	176	NA	ADA criteria	RFLP–PCR	5
64	Tsai et al. (2004)	Case–control	Asian	China	258	41	NA	OGTT (not specified)	RFLP–PCR	6
65	Noury et al. (2018)	Case–control	Caucasian	Egypt	51	47	NA	ADA criteria	TaqMan Allelic discrimination assay	5
66	Wu et al (2015)	Case–control	Asian	China	180	153	23.3 ± 2.1	IADPSG	PCR–RFLP	5
67	Kanthimathi et al (2015)	Case–control	Asian	India	910	495	27.5 ± 2.4	IADPSG system	MassARRAY	7
68	Beltcheva et al. (2014)	Case–control	Caucasian	America	259	130	NA	NA	TaqMan	6
69	Pawlik et al. (2017)	Case–control	Europe	Poland	207	204	NA	NA	TaqMan	7
70	Chang et al. (2005)	Case–control	Asian	China	35	35	30/28	OGTT (not specified	RFLP–PCR	6
71	Montazeri et al. (2010)	Case–control	Asian	Malaysia	102	110	NA	WHO criteria 1999	RFLP–PCR	5
72	Flores et al. (2013)	Case–control	NA	NA	44	51	NA	NA	NA
73	A Oliveira et al. (2016)	Case–control	Caucasian	Brazil	125	127	32.7 ± 6.3	ADA criteria	Taq-Man assay	5
74	A. Pagan et al. (2014)	Case–control	Caucasian	Spain	25	45	30.95 ± 0.86	NDDG	Direct sequencing	6
75	Imran et al. (2014)	Case–control	Asian	India	150	137	24/26.7	NA	RFLP–PCR	6

Table 2: Genotype and allele distribution among cases and controls in included studies

			Number of Participants		Genotypes in Control			Genotypes in GDM				Minor allele frequency
Sl. No.	Author (Year)	Gene [Variants]	Control	Case	Wild	Hetrozygote	Mutant	wild	Hetrozygote	Mutant	Minor Allele	Control (%)	Case (%)
1	N. Shaat et al. (2004)	PPARG [rs1801282]Arabian	122	100	106	15	1	91	9	0	G	6.967213115	4.5
		PPARG [rs1801282]Scandinavian	428	400	317	105	6	286	111	3	G	13.6682243	14.625
		PPARG [rs1801282]	550	500	423	120	7	377	120	3	G	12.18181818	12.6
2	N. Shaat et al. (2005)	IRS1 [rs1801278]	1189	587	1078	111	0	534	49	4	A	4.667788057	4.855195911
		KCNJ11 [rs5219]	1180	588	440	576	164	185	310	93	T	38.30508475	42.17687075
		CANP10 [rs2975760]	1181	226	787	351	43	32	177	17	C	18.50127011	46.68141593
		CANP10 [rs3792267]	1181	577	620	476	85	305	220	52	A	27.34970364	28.0762565
3	N. Shaat et al. (2006)	GCK [rs1799884]	1229	642	889	316	24	435	181	26	A	14.80878763	18.14641745
		HNF1A [rs1169288]	1214	614	559	508	147	242	298	74	T	33.03130148	36.31921824
4	N. Shaat et al. (2007)	TCF7L2 [rs7903146]	1111	585	650	392	69	271	255	59	T	23.85238524	31.88034188
		PPARG [rs1801282]	1232	637	918	298	16	468	158	11	G	13.39285714	14.12872841
		ADRB3 [rs4994]	1227	639	1060	158	9	534	100	5	G	7.17196414	8.607198748
5	Popova et al. (2017)	TCF7L2 [rs7903146]	179	278	104	63	12	161	104	13	T	24.30167598	23.38129496
		TCF7L2 [rs12255372]	176	276	110	56	10	168	93	14	T	21.59090909	21.92028986
		MTNR1B [rs10830963]	243	215	87	121	35	54	102	59	G	39.30041152	51.1627907
		MTNR1B [rs10830963]	179	278	93	69	17	96	133	49	G	28.77094972	41.54676259
		MTNR1B [rs1387153]	179	278	93	75	11	104	131	43	T	27.09497207	39.02877698
		FTO [rs9939609]	275	176	79	136	60	61	87	28	A	46.54545455	40.625
		GCK [rs1799884]	179	278	142	37	0	185	81	12	A	10.33519553	18.88489209
		IRS1 [rs1801278]	179	278	160	19	0	257	21	0	A	5.30726257	3.776978417
		KCNJ11 [rs5219]	179	278	56	92	31	102	122	54	T	43.01675978	41.36690647
		IGFBP2 [rs4402960]	179	278	77	76	26	120	134	24	T	35.75418994	32.73381295
		CDKAL1 [rs7754840]	179	278	81	85	13	116	128	34	C	31.00558659	35.25179856
6	Cho et al. (2009)	TCF7L2 [rs7903146]	627	868	596	31	0	803	63	2	T	2.472089314	3.859447005
		TCF7L2 [rs12255372]	630	867	628	2	0	860	7	0	T	0.158730159	0.403690888
		FTO [rs8050136]	629	864	486	132	11	643	208	13	A	12.24165342	13.54166667
		PPARG [rs1801282]	632	865	567	63	2	793	71	1	G	5.300632911	4.219653179
		KCNJ11 [rs5219]	629	846	254	273	102	298	407	141	T	37.91732909	40.72104019
		IGFBP2 [rs4402960]	627	857	313	257	57	389	365	103	T	29.58532695	33.31388565
		CDKAL1 [rs7754840]	630	863	178	319	133	171	389	303	C	46.42857143	57.64774044
		SLC30A8 [rs13266634]	627	861	107	306	214	126	372	363	C	58.53269537	63.7630662
7	Lauenborg et al. (2009)	TCF7L2 [rs7903146]	2353	276	1292	863	198	118	125	33	T	26.75308117	34.60144928
		FTO [rs9939609]	2329	276	833	1101	395	82	133	61	A	40.59682267	46.19565217
		PPARG [rs1801282]	2383	265	1790	542	51	201	60	4	G	13.51237935	12.83018868
		KCNJ11 [rs5219]	2411	255	985	1101	325	91	124	40	T	36.31273331	40
		IGFBP2 [rs4402960]	2334	274	1138	972	224	115	132	27	T	30.41988003	33.94160584
		SLC30A8 [rs13266634]	2344	279	266	998	1080	22	119	138	C	67.36348123	70.78853047
8	Freathy et al. (2010)	TCF7L2 [rs7903146]	3811	614	1884	1557	370	293	246	75	T	30.13644713	32.247557
		TCF7L2 [rs7903146]	3197	614	1591	1311	295	293	246	75	T	29.73099781	32.247557
		TCF7L2 [rs7903146]	1706	384	1549	157	0	338	46	0	T	4.6014068	5.989583333
		GCK [rs1799884]	3811	614	2575	1114	122	388	194	32	A	17.81684597	21.00977199
		GCK [rs1799884]	1706	384	1375	311	20	288	91	5	A	10.28722157	13.15104167
9	SF de Melo et al. (2015)	TCF7L2 [rs7903146]	200	200	98	86	16	76	104	20	T	29.5	36
		TCF7L2 [rs12255372]	200	200	102	75	23	92	88	20	T	30.25	32
		FTO [rs9939609]	200	200	71	97	32	68	100	32	A	40.25	41
		FTO [rs8050136]	200	200	74	96	30	73	102	25	A	39	38
		GCKR [rs1260326]	200	200	74	96	30	73	102	25	T	39	38
10	M Franzago et al. (2018)	TCF7L2 [rs7903146]	124	104	59	48	17	38	38	28	T	33.06451613	45.19230769
		FTO [rs9939609]	124	104	38	60	26	33	42	29	A	45.16129032	48.07692308
		PPARG [rs1801282]	124	104	101	23	0	79	25	0	G	9.274193548	12.01923077
		GCKR [rs1260326]	124	104	26	68	30	25	58	21	T	51.61290323	48.07692308
11	RR López et al. (2014)	TCF7L2 [rs7903146]	108	90	81	23	4	55	29	6	T	14.35185185	22.77777778
		TCF7L2 [rs12255372]	108	90	101	5	2	60	23	7	T	4.166666667	20.55555556
		TCF7L2 [rs12255372]	83	47	62	18	3	29	11	7	T	14.45783133	26.59574468
12	Thomas et al. (2014)	TCF7L2 [rs7903146]	49	117	27	18	4	55	46	16	T	26.53061224	33.33333333
		TCF7L2 [rs12255372]	49	116	33	14	2	70	38	8	T	18.36734694	23.27586207
13	Thomas et al. (2013)	CANP10 [rs5030952]	297	204	253	42	2	180	23	1	T	7.744107744	6.12745098
		CANP10 [rs3792267]	297	204	152	122	23	103	78	23	A	28.28282828	30.39215686
14	Beysel et al. (2019)	FTO [rs9939609]	145	160	73	54	18	59	62	39	A	31.03448276	43.75
		FTO [rs9939609]	101	90	40	52	9	31	45	14	A	34.65346535	40.55555556
		HNF1A [rs1169288]	101	90	57	37	7	36	46	8	T	25.24752475	34.44444444
		HNF1A [rs1169288]	145	160	50	78	17	33	94	33	T	38.62068966	50
15	Pappa et al. (2011)	TCF7L2 [rs7903146]	107	148	62	38	7	49	81	18	T	24.29906542	39.52702703
		IRS1 [rs1801278]	107	148	60	40	7	58	73	17	A	25.23364486	36.14864865
		KCNJ11 [rs5219]	107	148	70	33	4	96	42	10	T	19.1588785	20.94594595
16	Jamalpour et al. (2017)	GCKR [rs780094]	582	182	84	284	214	18	69	95	A	61.16838488	71.15384615
		GCKR [rs780094]	163	48	23	76	64	5	30	13	A	62.57668712	58.33333333
		GCKR [rs780094]	102	32	16	47	39	3	13	16	A	61.2745098	70.3125
17	A Pagán et al. (2014)	TCF7L2 [rs7903146]	24	45	10	12	2	19	18	8	T	33.33333333	37.77777778
		TCF7L2 [rs12255372]	25	45	9	14	2	19	20	6	T	36	35.55555556
		TCF7L2 [rs7901695]	25	45	10	13	2	17	20	8	C	34	40
18	A.Papadppoulou et al. (2011)	TCF7L2 [rs7903146]	1110	803	644	384	82	363	352	88	T	24.68468468	32.87671233
		TCF7L2 [rs12255372]	1102	801	633	385	84	387	333	81	T	25.0907441	30.8988764
		TCF7L2 [rs7901695]	1102	794	607	405	90	343	356	95	C	26.54264973	34.38287154
19	Wang et al. (2011)	MTNR1B [rs10830963]	1029	700	329	509	191	199	364	137	G	43.29446064	45.57142857
		IGFBP2 [rs4402960]	1025	705	605	361	59	371	278	56	T	23.36585366	27.65957447
		CDKAL1 [rs7754840]	1020	697	197	512	311	159	339	199	C	55.58823529	52.86944046
20	Steube et al. (2014)	GCKR [rs780094]	792	52	266	376	150	24	23	5	A	42.67676768	31.73076923
		GCKR [rs780094]	346	22	255	87	4	16	6	0	A	13.7283237	13.63636364
		GCKR [rs1260326]	840	56	291	395	154	25	26	5	T	41.8452381	32.14285714
21	Chao Li et al. (2013)	MTNR1B [rs10830963]	480	350	172	233	75	113	158	79	G	39.89583333	45.14285714
	Chao Li et al. (2018)	MTNR1B [rs10830963]	243	215	87	121	35	54	102	59	G	39.30041152	51.1627907
	Chao Li et al. (2014)	PPARG [rs1801282]	78	72	67	11	0	65	7	0	G	7.051282051	4.861111111
22	Rizk et al. (2011)	TCF7L2 [rs7903146]	74	40	29	37	8	16	18	6	T	35.81081081	37.5
		TCF7L2 [rs12255372]	74	40	25	38	11	6	28	6	T	40.54054054	50
23	Shi et al. (2014)	TCF7L2 [rs7903146]	100	100	55	38	7	40	36	24	T	26	42
24	DG Siudak et al. (2016)	TCF7L2 [rs7903146]	26	50	10	15	1	19	29	2	T	32.69230769	33
		TCF7L2 [rs7901695]	26	50	9	16	1	19	30	1	C	34.61538462	32
25	IA Khan et al. (2018)	TCF7L2 [rs7903146]	150	137	76	63	11	53	60	24	T	28.33333333	39.41605839
		SLC30A8 [rs13266634]	150	137	10	41	99	15	55	67	C	79.66666667	68.97810219
26	Vlassi et al. (2012)	MTNR1B [rs10830963]	98	77	56	30	12	30	31	16	G	27.55102041	40.90909091
		MTNR1B [rs1387153]	98	77	52	35	11	39	26	12	T	29.08163265	32.46753247
27	Vcelak et al. (2012)	TCF7L2 [rs7903146]	376	261	156	185	35	142	102	17	T	33.90957447	26.05363985
		TCF7L2 [rs12255372]	376	260	206	147	23	123	115	22	T	25.66489362	30.57692308
28	Aris et al. (2012)	TCF7L2 [rs7903146]	114	173	0	15	99	1	43	129	T	93.42105263	86.99421965
	Aris et al. (2011)	CDKAL1 [rs7754840]	113	169	64	37	12	64	81	24	C	26.99115044	38.16568047
29	M. Ekelund et al. (2012)	TCF7L2 [rs7903146]	476	125	239	195	42	49	56	20	T	29.30672269	38.4
		FTO [rs8050136]	480	126	180	223	77	39	62	25	A	39.27083333	44.44444444
30	KK Alharbi et al. (2019)	MTNR1B [rs10830963]	200	200	96	65	39	64	87	49	G	35.75	46.25
		MTNR1B [rs1387153]	200	200	91	81	28	64	92	44	T	34.25	45
31	Liu et al. (2015)	MTNR1B [rs10830963]	674	674	195	362	117	162	334	178	G	44.21364985	51.18694362
		MTNR1B [rs1387153]	690	674	367	246	77	341	228	105	T	28.98550725	32.4925816
32	JY Kim et al. (2011)	MTNR1B [rs10830963]	966	908	294	469	203	217	435	256	G	45.28985507	52.14757709
		MTNR1B [rs1387153]	972	909	313	455	204	235	433	241	T	44.39300412	50.330033
33	Saucedo et al. (2017)	FTO [rs9939609]	80	80	59	20	1	61	18	1	A	13.75	12.5
		FTO [rs8050136]	80	80	59	20	1	61	18	1	A	13.75	12.5
34	Tok et al. (2006)	PPARG [rs1801282]	100	62	84	16	0	50	12	0	G	8	9.677419355
		IRS1 [rs1801278]	100	62	89	11	0	53	9	0	A	5.5	7.258064516
35	S Chon et al. (2013)	PPARG [rs1801282]	41	94	34	7	0	89	5	0	G	8.536585366	2.659574468
		IGFBP2 [rs4402960]	41	94	15	24	2	57	30	7	T	34.14634146	23.40425532
36	M. Tarnowski et al (2017)	GCK [rs1799884]	207	204	163	42	2	147	52	5	A	11.11111111	15.19607843
		GCKR [rs780094]	207	204	73	101	33	77	99	28	C	40.33816425	37.99019608
37	Z. Liang et al. (2010)	ADIPOQ [rs266729]	79	50	49	22	8	22	21	7	G	24.05063291	35
		SLC30A8 [rs13266634]	24	24	0	16	8	2	3	19	C	66.66666667	85.41666667
38	G Silva et al. (2011)	ADIPOQ [rs266729]	168	79	105	50	13	54	20	5	G	22.61904762	18.98734177
		TNF [rs1800629]	168	79	133	31	4	59	18	2	A	11.60714286	13.92405063
39	Fallucca et al. (2006)	IRS1 [rs1801278]	277	309	255	22	0	271	34	4	A	3.971119134	6.796116505
		ADRB3 [rs4994]	277	346	248	29	0	309	35	2	C	5.23465704	5.63583815
40	Zhang X. et al. (2019)	CANP10 [rs2975760]	152	138	65	79	8	53	68	17	C	31.25	36.95652174
		CANP10 [rs3792267]	152	138	113	36	3	95	35	8	A	13.81578947	18.47826087
41	Heinz et al. (2014)	CANP10 [rs5030952]	40	43	20	17	3	29	5	9	T	28.75	26.74418605
		CANP10 [rs3792267]	40	40	22	15	3	24	15	1	A	26.25	21.25
42	Luo et al. (2009)	CANP10 [rs5030952]	120	42	70	43	7	22	16	4	T	23.75	28.57142857
		CANP10 [rs3792267]	120	42	103	17	0	40	2	0	A	7.083333333	2.380952381
43	Deng et al. (2011)	MTNR1B [rs10830963]	91	87	31	45	15	23	38	26	G	41.20879121	51.72413793
44	Tarnowski et al. (2017)	MTNR1B [rs10830963]	207	99	103	79	25	49	38	12	G	31.15942029	31.31313131
45	Vejrazkova et al. (2014)	MTNR1B [rs10830963]	422	458	206	184	32	169	227	62	G	29.38388626	38.31877729
46	Junior et al. (2017)	MTNR1B [rs10830963]	183	183	113	66	4	102	61	20	G	20.21857923	27.59562842
47	NE. Grotenfelt et al. (2016)	MTNR1B [rs10830963]	106	120	48	47	11	55	47	18	G	32.54716981	34.58333333
48	Cheng et al. (2010)	PPARG [rs1801282]	173	55	157	16	0	52	3	0	G	4.624277457	2.727272727
49	Yan et al. (2014)	PPARG [rs1801282]	180	156	153	24	3	144	12	0	G	8.333333333	3.846153846
50	Du et al. (2012)	PPARG [rs1801282]	69	66	57	12	0	59	7	0	G	8.695652174	5.303030303
51	Papa et al. (2011)	PPARG [rs1801282]	107	148	100	7	0	143	5	0	G	3.271028037	1.689189189
52	Heude et al. (2011)	PPARG [rs1801282]	1587	109	1265	305	17	92	17	0	G	10.6805293	7.798165138
53	Chiu et al. (1994)	GCK [rs1799884]	99	97	63	34	2	56	37	4	A	19.19191919	23.19587629
54	Zaidi et al. (1997)	GCK [rs1799884]	92	47	47	42	3	25	20	2	A	26.08695652	25.53191489
55	Santos et al. (2010)	GCK [rs1799884]	600	150	387	186	27	86	56	8	A	20	24
56	Kan et al. (2014)	TCF7L2 [rs7903146]	100	100	95	5	0	84	15	1	T	2.5	8.5
57	Huerta-Chagoya et al. (2015)	TCF7L2 [rs7903146]	342	408	265	67	10	265	124	19	T	12.71929825	19.85294118
58	Klein et al. (2012)	TCF7L2 [rs7903146]	125	125	11	106	8	10	110	5	T	48.8	48
59	J. Dereke et al. (2016)	SLC30A8 [rs13266634]	536	511	39	205	292	61	221	229	C	73.60074627	66.43835616
60	A. Teleginski et al. (2017)	SLC30A8 [rs13266634]	180	134	19	62	99	11	41	82	C	72.22222222	76.49253731
61	Festa et al. (1997)	ADRB3 [rs4994]	109	70	97	12	0	52	18	0	C	5.504587156	12.85714286
62	Alevizavaki et al. (2000)	ADRB3 [rs4994]	130	176	121	9	0	165	11	0	C	3.461538462	3.125
63	Tsai et al. (2004)	ADRB3 [rs4994]	258	41	189	63	6	34	6	1	C	14.53488372	9.756097561
64	Noury et al. (2018)	CDKAL1 [rs7754840]	51	47	8	23	20	3	26	18	C	61.76470588	65.95744681
65	Wu et al (2015)	CDKAL1 [rs7754840]	180	153	52	95	33	45	79	29	C	44.72222222	44.77124183
66	Kanthimathi et al (2015)	CDKAL1 [rs7754840]	910	495	46	306	558	49	172	274	C	78.13186813	72.72727273
67	Beltcheva et al. (2014)	ADIPOQ [rs266729]	259	130	126	103	30	80	44	6	G	31.46718147	21.53846154
68	Pawlik et al. (2017)	ADIPOQ [rs266729]	207	204	115	75	17	92	91	21	G	26.32850242	32.59803922
69	Chang et al. (2005)	TNF [rs1800629]	35	35	22	5	8	10	7	18	A	30	61.42857143
70	Montazeri et al. (2010)	TNF [rs1800629]	102	110	94	6	2	103	4	3	A	4.901960784	4.545454545
71	Flores et al. (2013)	TNF [rs1800629]	44	51	39	5	0	43	7	1	A	5.681818182	8.823529412
72	A Oliveira et al. (2016)	GCKR [rs780094]	125	127	43	68	14	64	48	15	C	38.4	30.70866142
73	A. Pagan et al. (2014)	FTO [rs9939609]	25	45	5	15	5	23	15	7	A	50	32.22222222
74	Imran et al. (2014)	CANP10 [rs2975760]	150	137	97	42	11	85	40	12	C	21.33333333	23.35766423

Table 3: Features of genes and genetic variants included in the meta-analysis

Sl. No.	Gene	Description	SNPs	Alleles	Variants	Biological Process
1	TCF7L2	Transcription factor 7-like 2	[rs7903146]	C>G / C>T	Intron Variant	positive regulation of insulin secretion
			[rs12255372]	G>A / G>T	Intron Variant
			[rs7901695]	T>C	Intron Variant
2	MTNR1B	Melatonin receptor 1B	[rs10830963]	C>G	Intron Variant	negative regulation of insulin secretion
			[rs1387153]	C>T	None	negative regulation of insulin secretion
3	FTO	Alpha-ketoglutarate-dependent dioxygenase	[rs9939609]	T>A	Intron Variant	regulation of lipid storage
			[rs8050136]	C>A	Intron Variant	regulation of lipid storage
4	PPARG	Peroxisome proliferator-activated receptor gamma	[rs1801282]	C>G	Missense Variant	cellular response to insulin stimulus
5	GCK	Glucokinase	[rs1799884]	G>A	2KB Upstream Variant	positive regulation of insulin secretion
6	GCKR	Glucokinase Regulator	[rs780094]	T>C	Intron Variant	negative regulation of glucokinase activity
			[rs1260326]	C>T	Missense Variant	negative regulation of glucokinase activity
7	ADIPOQ	Adiponectin	[rs266729]	C>A / C>G / C>T	2KB Upstream Variant	cellular response to insulin stimulus
8	TNF	Tumor necrosis factor	[rs1800629]	G>A	2KB Upstream Variant	negative regulation of glucose import
9	IRS1	Insulin receptor substrate 1	[rs1801278]	C>G / C>T	Missense Variant	insulin receptor signaling pathway
10	KCNJ11	Potassium inwardly rectifying channel, subfamily J, member 11	[rs5219]	C>T	Stop Gained	negative regulation of insulin secretion
11	IGF2BP2	Insulin-like growth factor 2 mRNA-binding protein 2	[rs4402960]	G>T	Intron Variant	regulation of cytokine biosynthetic process
12	ADRB3	Adrenoceptor beta 3	[rs4994]	T>C	Missense Variant	carbohydrate metabolic process
13	CDKAL1	CDK5 regulatory subunit associated protein 1-like 1	[rs7754840]	G>A / G>C / G>T	Intron Variant	maintenance of translational fidelity
14	HNF1A	Hepatocyte nuclear factor 1-alpha	[rs1169288]	G>T	Missense Variant	insulin secretion
15	CANP10	Calpain-10	[rs2975760]	T>C	Intron Variant	positive regulation of insulin secretion
			[rs5030952]	C>G / C>T	None
			[rs3792267]	G>A	Intron Variant
16	SLC30A8	Solute carrier family 30 member 8	[rs13266634]	C>A / C>T	Missense Variant	positive regulation of insulin secretion

Table 4: Association between GDM risk and genetic variants

S.no.	Gene	rs ID	Genotype	Model	Heterogeneity		Overall effect
S.no.	Gene	rs ID	Genotype	Model	I² Value	p-value	OR (95% C.I.)	p-value
1	SLC30A8	rs1326634	CC vs.TT+ CT	Dominant	92%	<0.00001	1.91(1.57-2.32)	<0.00001
			TT vs. CC+CT	Recessive	77%	0.0006	0.99(0.81-1.19)	0.88
			TT vs. CC	Homozygote	86%	<0.00001	0.88(0.71-1.08)	0.22
			TT vs. CT	Heterozygote	96%	<0.00001	2.90(2.26-3.72)	<0.00001
			C vs. T allele	Allele	75%	0.001	0.76(0.58-1.00)	0.05
2	CANP10	rs3792267	GG vs. AA+ GA	Dominant	13%	0.33	1.40(1.04-1.88)	0.03
			AA vs. GG+GA	Recessive	83%	0.001	0.64(0.47-0.86)	0.003
			AA vs. GG	Homozygote	21%	0.28	0.72(0.53-0.98)	0.03
			AA vs. GA	Heterozygote	10%	0.34	0.66(0.48-0.91)	0.01
			G vs. A allele	Allele	6%	0.37	0.99(0.71-1.38)	0.95
3	CANP10	rs5030952	CC vs.TT+ CT	Dominant	0%	0.44	2.24(0.96-5.20)	0.06
			TT vs. CC+CT	Recessive	82%	0.004	0.71(0.45-1.11)	0.13
			TT vs. CC	Homozygote	0%	0.66	1.88(0.77-4.6)	0.16
			TT vs. CT	Heterozygote	0%	0.42	2.24(0.86-5.88)	0.1
			C vs. T allele	Allele	0%	0.48	1.09(0.7-1.68)	0.71
4	CANP10	rs2975760	TT vs.CC+ TC	Dominant	0%	0.39	0.49(0.32-0.75)	0.01
			CC vs. TT+TC	Recessive	93%	<0.00001	0.31(0.28-0.49)	<0.00001
			CC vs. TT	Homozygote	92%	<0.00001	3.84 (2.34-6.31)	<0.00001
			CC vs. TC	Heterozygote	67%	0.05	1.16(0.76-1.77)	0.5
			T vs. C allele	Allele	92%	<0.00001	2.92(1.96-4.35)	<0.00001
5	HNF1A	rs1169288	GG vs.TT+ GT	Dominant	83%	0.07	1.17(0.90-1.52)	0.23
			TT vs. GG+GT	Recessive	0%	0.39	0.34 (0.26-0.44)	0.00001
			TT vs. GG	Homozygote	84%	0.002	1.68( 1.25-2.26)	0.0005
			TT vs. GT	Heterozygote	61%	0.08	0.95(0.72-1.26)	0.74
			G vs. T allele	Allele	75%	0.02	2.42(1.55-3.77)	<0.00001
6	CDKAL1	rs7754840	GG vs.CC+ GC	Dominant	93%	<0.00001	1.78(1.55-2.04)	<0.00001
			CC vs. GG+GC	Recessive	89%	<0.00001	0.90(0.78-1.04)	0.14
			CC vs. GG	Homozygote	97%	<0.00001	2.70 (2.26-3.21)	<0.00001
			CC vs. GC	Heterozygote	93%	<0.00001	1.86(1.59-2.71)	<0.00001
			G vs. C allele	Allele	73%	0.001	1.11(0.83-1.48)	0.48
7	ADRB3	rs4994	TT vs.CC+ TC	Dominant	60%	0.08	0.68(0.30-1.56)	0.31
			CC vs. TT+TC	Recessive	48%	0.12	1.13(0.91-1.41)	0.28
			CC vs. TT	Homozygote	0%	0.7	0.79(0.32-1.94)	0.61
			CC vs. TC	Heterozygote	0%	0.56	0.83(0.33-2.09)	0.69
			T vs. C allele	Allele	0%	0.55	0.74(0.43-1.28)	0.28
8	IGFBP2	rs4402960	GG vs.TT+ GT	Dominant	0%	0.5	1.28(1.04-1.56)	0.02
			TT vs. GG+GT	Recessive	91%	<0.00001	0.25(0.20-0.32)	<0.00001
			TT vs. GG	Homozygote	65%	0.02	1.33(1.08-1.65)	0.008
			TT vs. GT	Heterozygote	70%	0.01	1.08(0.87-1.33)	0.5
			G vs. T allele	Allele	59%	0.04	1.05 (0.78-1.33	0.14
9	KCNJ11	rs5219	CC vs.TT+ CT	Dominant	31%	0.22	1.07(0.91-1.25)	0.43
			TT vs. CC+CT	Recessive	92%	<0.00001	1.05(0.91-1.22)	0.43
			TT vs. CC	Homozygote	95%	<0.00001	1.47(1.22-1.76)	<0.00001
			TT vs. CT	Heterozygote	0%	0.5	1.19(0.96-1.47)	0.12
			C vs. T allele	Allele	0%	0.83	1.23(0.88-1.73)	0.22
10	IRS1	rs1801278	GG vs. AA+ GA	Dominant	28%	0.25	3.10(1.38-6.94)	0.006
			AA vs. GG+GA	Recessive	56%	0.06	0.98(0.77-1.25)	0.87
			AA vs. GG	Homozygote	0%	0.45	4.50(1.92-10.56)	0.0006
			AA vs. GA	Heterozygote	47%	0.15	2.53(1.10-5.83)	0.03
			G vs. A allele	Allele	0%	0.42	1.66 (1.05-2.62)	0.03
11	TNF	rs1800629	GG vs. AA+ GA	Dominant	73%	0.01	2.49(1.19-5.22)	0.02
			AA vs. GG+GA	Recessive	13%	0.33	1.07 (0.67-1.79)	0.78
			AA vs. GG	Homozygote	0%	0.92	1.71(0.73-4.00)	0.21
			AA vs. GA	Heterozygote	2%	0.38	0.96(0.35-2.63)	0.94
			G vs. A allele	Allele	0%	0.54	1.64(1.02-2.51)	0.04
12	ADIPOQ	rs266729	CC vs.GG+ CG	Dominant	57%	0.07	0.85(0.56-1.29)	0.45
			GG vs. CC+CG	Recessive	89%	<0.00001	0.58(0.41-0.83)	0.003
			GG vs. CC	Homozygote	77%	0.005	0.88(0.57-1.36)	0.57
			GG vs. CG	Heterozygote	0%	0.43	0.75(0.47-1.20)	0.23
			C vs. G allele	Allele	81%	0.001	1.06(0.76-1.48)	0.73
13	GCKR	rs1260326	CC vs.TT+ CT	Dominant	0%	0.56	0.68(0.48-0.96)	0.03
			TT vs. CC+CT	Recessive	61%	0.05	1.25(0.91-1.72)	0.17
			TT vs. CC	Homozygote	34%	0.21	0.51(0.31-0.84)	0.008
			TT vs. CT	Heterozygote	0%	0.64	0.66(0.45-0.98)	0.04
			C vs. T allele	Allele	18%	0.3	0.54(0.33-0.87)	0.01
14	GCKR	rs780094	TT vs.CC+ TC	Dominant	87%	<0.00001	1.35(1.02-1.770)	0.03
			CC vs. TT+TC	Recessive	83%	<0.00001	0.69(0.53-0.90)	0.06
			CC vs. TT	Homozygote	51%	0.06	0.52(0.38-0.70)	<0.00001
			CC vs. TC	Heterozygote	65%	0.008	0.72(0.53-0.97)	0.03
			T vs. C allele	Allele	56%	0.04	0.51(0.39-0.67)	<0.00001
15	GCK	rs1799884	GG vs. AA+ GA	Dominant	22%	0.27	1.88(1.42-2.47)	<0.00001
			AA vs. GG+GA	Recessive	86%	<0.00001	1.77 (1.56-2.00)	<0.00001
			AA vs. GG	Homozygote	0%	0.41	1.98(1.50-2.62)	<0.00001
			AA vs. GA	Heterozygote	13%	0.33	1.62(1.22-2.17)	0.001
			G vs. A allele	Allele	0%	0.9	1.52(1.11-2.09)	0.001
16	FTO	rs8050136	CC vs. AA+ CA	Dominant	65%	0.04	1.30(0.92-1.83)	0.14
			AA vs. CC+CA	Recessive	69%	0.02	1.01(0.83-1.23)	0.89
			AA vs. CC	Homozygote	48%	0.12	1.18(0.78-1.77)	0.43
			AA vs. CA	Heterozygote	0%	0.53	0.92(0.64-1.35)	0.68
			C vs. A allele	Allele	81%	0.001	0.86(0.37-1.32)	0.5
17	MTNR1B	rs1387153	CC vs.TT+ CT	Dominant	8%	0.36	1.68(1.43-1.98)	<0.00001
			TT vs. CC+CT	Recessive	94%	<0.00001	0.69(0.57-0.83)	<0.00001
			TT vs. CC	Homozygote	90%	<0.00001	3.42(2.69-4.36)	<0.00001
			TT vs. CT	Heterozygote	0%	0.62	1.73(1.43-2.10)	<0.00001
			C vs. T allele	Allele	66%	0.02	2.0(1.64-3.24)	<0.00001
18	TCF7L2	rs7901695	TT vs.CC+ TC	Dominant	66%	0.02	1.41(1.07-1.85)	0.01
			CC vs. TT+TC	Recessive	95%	<0.00001	1.16(0.90-1.48)	0.25
			CC vs. TT	Homozygote	84%	<0.0001	1.69(1.26-2.27)	0.0005
			CC vs. TC	Heterozygote	41%	0.14	1.25(0.94-1.57)	0.13
			T vs. C allele	Allele	82%	0.0002	0.89(0.64-1.23)	0.48
19	TCF7L2	rs7903146	CC vs.TT+ CT	Dominant	43%	0.02	1.59(1.42-1.78)	<0.00001
			TT vs. CC+CT	Recessive	92%	<0.00001	1.03(0.92-1.15)	0.6
			TT vs. CC	Homozygote	63%	<0.00001	2.03(1.79-2.31)	<0.00001
			TT vs. CT	Heterozygote	45%	0.01	1.46(1.29-1.66)	<0.00001
			C vs. T allele	Allele	66%	<0.00001	1.60(1.38-1.86)	<0.00001
20	TCF7L2	rs12255572	GG vs.TT+ GT	Dominant	20%	0.27	1.37(1.09-1.72)	0.07
			TT vs. GG+GT	Recessive	87%	<0.00001	2.24(1.81-2.77)	<0.00001
			TT vs. GG	Homozygote	38%	0.11	1.69(1.33-2.15)	<0.00001
			TT vs. GT	Heterozygote	21%	0.25	1.10(0.86-1.41)	0.44
			G vs. T allele	Allele	56%	0.01	1.80(1.41-2.30)	<0.00001
21	MTNR1B	rs1083963	CC vs.GG+ CG	Dominant	64%	0.006	1.81(1.63-2.02)	<0.00001
			GG vs. CC+CG	Recessive	87%	<0.00001	0.55(0.49-0.61)	<0.00001
			GG vs. CC	Homozygote	88%	<0.00001	2.82(2.42-3.30)	<0.00001
			GG vs. CG	Heterozygote	72%	<0.00001	1.82(1.61-2.06)	<0.00001
			C vs. G allele	Allele	79%	<0.00001	1.85(1.50-2.29)	<0.00001
22	FTO	rs9939609	TT vs. AA+ TA	Dominant	66%	0.004	1.32(1.08-1.62)	0.007
			AA vs. TT+TA	Recessive	82%	<0.00001	0.72(0.60-0.86)	0.00004
			AA vs. TT	Homozygote	86%	<0.00001	1.86(1.42-2.43)	<0.00001
			AA vs. TA	Heterozygote	64%	0.007	1.40(1.11-1.75)	0.004
			T vs. A allele	Allele	77%	<0.00001	1.16(0.87-1.55)	0.3
23	PPARG	rs1801282	CC vs.GG+ CG	Dominant	0%	0.7	0.72(0.44-1.15)	0.17
			GG vs. CC+CG	Recessive	46%	0.03	0.95(0.85-1.07)	0.41
			GG vs. CC	Homozygote	13%	0.32	0.69(0.57-1.38)	0.59
			GG vs. CG	Heterozygote	0%	0.5	0.62(0.38-1.00)	0.05
			C vs. G allele	Allele	77%	<0.00001	0.55(0.43-0.70)	<0.00001

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Meta-analysis of genetic association studies on gestational diabetes mellitus

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Version 1

Abstract

Background

Methods

Results

Conclusion

Figures

Introduction

Materials And Methods

Search strategy

Study selection

Methodological quality appraisal

Data extraction

Statistical analysis

Networking and KEGG pathway enrichment

Results

Overall meta-analysis

Discussion

The MTNR1B polymorphisms

The GCK polymorphisms

The CANP10 polymorphism

Conclusion

Declarations

References

Tables

Supplementary Files

Status:

Version 1