Meta-analysis of genetic association studies on gestational diabetes mellitus

Abstract

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).

Introduction

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.

Materials And Methods

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).

Results

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).

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.

Discussion

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).

Conclusion

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.

Declarations

Conflict of Interest

Authors declare no any conflict of interest.

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Tables

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

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

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

Table 4: Association between GDM risk and genetic variants