Background: Published data on the association between the MTNR1B rs1387153 polymorphism and gestational diabetes mellitus (GDM) risk are controversial. Objective: A meta-analysis was performed to assess whether the polymorphism of MTNR1B rs1387153 is associated with GDM risk. Method: Medline, Embase, China National Knowledge Infrastructure, and Chinese Biomedicine Databases were searched to identify eligible studies. Pooled odds ratios (ORs) and 95% confidence intervals (CIs) for MTNR1B rs1387153 polymorphism and GDM were appropriately derived from fixed-effects or random effects models. Results: A total of 8 studies were enrolled in this meta-analysis. The pooled analyses revealed that MTNR1B rs1387153 polymorphism significantly increased the risk of GDM in all models (allele contrast (C vs. T): OR, 0.78; 95% CI, 0.73–0.83; homozygote (CC vs. TT): OR, 0.61; 95% CI, 0.53–0.69; heterozygote (CT vs. TT): OR, 0.78; 95% CI, 0.69–0.89; dominant model (CC + CT vs. TT): OR, 0.71; 95% CI, 0.63–0.80; recessive model (CC vs. CT + TT): OR, 0.73; 95% CI, 0.67–0.81). Further subgroup analyses by ethnicity of participants yielded similar positive results. Conclusions: Present meta-analysis reveals that MTNR1B rs1387153 variant may serve as genetic biomarkers of GDM.

Gestational diabetes mellitus (GDM), which is characterized by glucose intolerance and hyperglycemia during the period of pregnancy [1], is estimated to affect 10–25% of all pregnancies worldwide [2]. GDM not only presents risks to the pregnant individual but is also associated with adverse outcomes for the baby, including a predisposition to chronic metabolic diseases later in life. The etiology and mechanism of GDM have not been fully understood. It has been identified that GDM is a multifactorial disorder influenced by both genetic and environmental factors [3, 4]. Among these, genetic susceptibility has emerged as a significant risk factor for GDM [5].

The MTNR1B gene, situated in chromosome region 11q14.3, encodes the G protein-coupled melatonin receptor 1B [6]. This receptor is instrumental in the function of melatonin, an indole hormone, which plays a role in regulating insulin secretion, glucose metabolism, and circadian rhythms. Genetic variations in MTNR1B may cause increased expression of this receptor in related tissue cells. When combined with melatonin, this can result in a reduction of insulin secretion [7, 8]. The activity and expression of MTNR1B may serve as indicators of active glucose homeostasis, characterized by enriched glucose production. Polymorphisms in MTNR1B, specifically rs1387153 and rs10830963, are associated with impaired β-cell function and contribute to an elevated risk of various GDM phenotypes [9].

In the past two decades, numerous studies have probed the potential link between the MTNR1B rs1387153 variant and the risk of GDM, but the results have been inconsistent and unclear [10‒14]. In light of this ambiguity, a single meta-analysis of published case-control studies was undertaken to investigate the relationship between the MTNR1B rs1387153 variant and GDM risk.

Publication Search

A comprehensive search was conducted for articles exploring the association between MTNR1B polymorphisms and the risk of GDM. The databases consulted included PubMed, Embase, the China Biomedical Database, and CNKI (China National Knowledge Infrastructure). The search employed keywords such as “MTNR1B rs1387153,” “variant,” “polymorphism,” and “GDM.” The most recent update to this search was made on October 10, 2022.

Inclusion and Exclusion Criteria

To be considered for inclusion, all studies, regardless of sample size, had to meet the following criteria: (i) evaluate the association between the MTNR1B rs1387153 polymorphism and the risk of GDM; (ii) be case-control research; and (iii) provide sufficient data to calculate a 95% confidence interval (95% CI) for the odds ratio (OR). Studies were excluded if they fell within the following categories: (i) abstracts, reviews, overviews, or editorials; (ii) lacking sufficient data.

Data Extraction

In accordance with the aforementioned inclusion criteria, two reviewers (Shan Dan and Wang Ao) independently extracted information from all eligible and qualified publications. Any discrepancies encountered were resolved through consultation with an arbitrator (Yi Ke).

The following information was gathered from all eligible publications: first author’s last name, date of publication, participants’ country, case- and control-sample size, sources of controls (population-based or hospital-based control), races, genotyping methods, minor allele frequencies. The hospital-based case-control study (HCC) was derived from data from hospital patients, whereas the population-based case-controlled study was conducted on healthy persons. The ethnic groupings have been classified as Asian, Caucasian, or Mixed.

Statistical Analysis

ORs and corresponding 95% CIs were used to evaluate the strength of the association between the MTNR1B rs1387153 polymorphism and the risk of GDM. Additionally, we conducted stratified analyses based on ethnicity and the source of the control group.

We employed the Cochran Q statistic and the I2 index to assess and validate the heterogeneity among the studies. A p value greater than 0.10 for the Q statistic indicates a lack of significant between-study heterogeneity [15]. In such cases, a fixed-effects model (utilizing the Mantel-Haenszel method) is appropriate [16]. Conversely, if there’s evidence of heterogeneity, the random-effects model (employing the DerSimonian and Laird method) is employed [17].

Publication bias was investigated through visual inspection of funnel plots, utilizing Egger’s power-weighted regression method and Begg’s rank correlation method. A p value of less than 0.05 was considered to indicate statistical significance [18, 19]. All statistical analyses were conducted using STATA software, version 13.0 (STATA Corp., College Station, TX, USA).

Trial Sequential Analysis

We utilized Trial Sequential Analysis (TSA) to assess the Required Information Size (RIS) and the reliability of our findings. The RIS was determined based on a 5% risk of type I error (α = 5%), an 80% power of study (β = 20%), and a two-sided boundary type was applied. TSA was conducted using TSA software (version 0.9.5.10 beta) from the Copenhagen Trial Unit, Denmark.

Characteristics of Studies

After literature review, we shortlisted 14 publications deemed worthy of thorough examination. On assessing the titles and abstracts, we found three papers to be outside our criteria and subsequently excluded them. This led us to a detailed review of the full texts of the remaining 11 articles. We excluded one article as it primarily centered on a literature review [20], and two articles due to their insufficient data [21, 22]. In the end, we gathered eight case-control studies concerning the rs1387153 polymorphism and GDM [10‒14, 23‒25], all of which adhered to the MOOSE (Meta-analysis Of Observational Studies in Epidemiology) guides [26]. The process of document retrieval and research selection is illustrated in Figure 1.

Fig. 1.

Literature search and study selection procedures used for a meta-analysis of MTNR1B rs1387153 genetic polymorphism and GDM.

Fig. 1.

Literature search and study selection procedures used for a meta-analysis of MTNR1B rs1387153 genetic polymorphism and GDM.

Close modal

The detailed characteristics of the selected studies are presented in Table 1. Three of these studies focused on individuals of Caucasian descent, while five targeted those of Asian descent. The research was conducted across China, Korea, Greece, and Saudi Arabia. Control groups primarily consisted of healthy subjects, matched based on age and/or geographical region. Among these, six studies were population-based, while two were hospital-based.

Table 1.

Characteristics of studies included in this meta-analysis

AuthorYearCountryEthnicitySource of controlsGDM criteriaSampleGenotyping methodsMAF in controlsHWE
Kim et al. [232011 Korea Asian PCC IADPSG 928/990 Taqman 0.44 0.11 
Vlassi et al. [242012 Greece Caucasian PCC ADA 78/98 MPCR 0.29 0.18 
Wang et al. [252014 China Asian PCC WHO 184/235 PCR-RFLP 0.34 0.58 
Liu et al. [132016 China Asian PCC IADPSG 674/690 Taqman 0.29 <0.01 
Popova et al. [142017 Russia Caucasian PCC IADPSG 278/179 PCR-RFLP 0.27 0.42 
Alharbi et al. [102019 Sandi Caucasian PCC IADPSG 200/200 PCR-RFLP 0.34 0.15 
Jia et al. [112020 China Asian HCC IADPSG 753/676 Taqman 0.39 0.34 
Liu et al. [122022 China Asian HCC IADPSG 818/861 Taqman 0.43 0.15 
AuthorYearCountryEthnicitySource of controlsGDM criteriaSampleGenotyping methodsMAF in controlsHWE
Kim et al. [232011 Korea Asian PCC IADPSG 928/990 Taqman 0.44 0.11 
Vlassi et al. [242012 Greece Caucasian PCC ADA 78/98 MPCR 0.29 0.18 
Wang et al. [252014 China Asian PCC WHO 184/235 PCR-RFLP 0.34 0.58 
Liu et al. [132016 China Asian PCC IADPSG 674/690 Taqman 0.29 <0.01 
Popova et al. [142017 Russia Caucasian PCC IADPSG 278/179 PCR-RFLP 0.27 0.42 
Alharbi et al. [102019 Sandi Caucasian PCC IADPSG 200/200 PCR-RFLP 0.34 0.15 
Jia et al. [112020 China Asian HCC IADPSG 753/676 Taqman 0.39 0.34 
Liu et al. [122022 China Asian HCC IADPSG 818/861 Taqman 0.43 0.15 

HCC, hospital-based case-control; PCC, population-based case-control; PCR-RFLP, polymerase chain reaction-restriction fragment length polymorphism; MPCR, multiplex polymerase chain reaction; MAF, minor allele frequency; ADA, American Diabetes Association; WHO, 1999 World Health Organization recommendation; IADPSG, International Association of Diabetes and Pregnancy Study Groups; HWE, Hardy-Weinberg Equilibrium.

Quantitative Synthesis

We incorporated eight case-control studies comprising 3,913 patients and 3,929 controls. The results of the meta-analysis are presented in Table 2. The forest plots evaluating the association between the MTNR1B rs1387153 polymorphism and GDM risk are depicted in Figure 2.

Table 2.

Quantitative analyses of the MTNR1B rs1387153 polymorphism on the GDM risk

Genetic model
Allele contrastHomozygoteHeterozygoteDominant ModelRecessive Model
VariablesSample sizeC versus TCC versus TTCT versus TTCC + CT versus TTCC versus CT + TT
Nacase/controlOR (95% CI)pbOR (95% CI)pbOR (95% CI)pbOR (95% CI)pbOR (95% CI)pb
Total 3,913/3,929 0.76 (0.72, 0.81) 0.188 0.59 (0.52, 0.67) 0.192 0.77 (0.68, 0.87) 0.325 0.69 (0.62, 0.78) 0.201 0.72 (0.66, 0.79) 0.212 
Ethnicity 
 Caucasian 556/477 0.64 (0.53, 0.77) 0.376 0.42 (0.28, 0.62) 0.323 0.61 (0.41, 0.91) 0.571 0.51 (0.35, 0.74) 0.436 0.61 (0.47, 0.78) 0.349 
 Asia 3,357/3,452 0.78 (0.73, 0.84) 0.368 0.62 (0.54, 0.71) 0.321 0.79 (0.69, 0.90) 0.225 0.72 (0.63, 0.81) 0.237 0.74 (0.67, 0.82) 0.243 
Source of control 
 PCCc 2,342/2,392 0.75 (0.69, 0.82) 0.116 0.57 (0.48, 0.68) 0.158 0.72 (0.61, 0.85) 0.674 0.66 (0.56, 0.76) 0.363 0.73 (0.65, 0.83) 0.095 
 HCCc 1,571/1,537 0.78 (0.70, 0.86) 0.317 0.61 (0.50, 0.75) 0.183 0.83 (0.69, 1.01) 0.052 0.74 (0.62, 0.88) 0.066 0.70 (0.60, 0.82) 0.997 
Genetic model
Allele contrastHomozygoteHeterozygoteDominant ModelRecessive Model
VariablesSample sizeC versus TCC versus TTCT versus TTCC + CT versus TTCC versus CT + TT
Nacase/controlOR (95% CI)pbOR (95% CI)pbOR (95% CI)pbOR (95% CI)pbOR (95% CI)pb
Total 3,913/3,929 0.76 (0.72, 0.81) 0.188 0.59 (0.52, 0.67) 0.192 0.77 (0.68, 0.87) 0.325 0.69 (0.62, 0.78) 0.201 0.72 (0.66, 0.79) 0.212 
Ethnicity 
 Caucasian 556/477 0.64 (0.53, 0.77) 0.376 0.42 (0.28, 0.62) 0.323 0.61 (0.41, 0.91) 0.571 0.51 (0.35, 0.74) 0.436 0.61 (0.47, 0.78) 0.349 
 Asia 3,357/3,452 0.78 (0.73, 0.84) 0.368 0.62 (0.54, 0.71) 0.321 0.79 (0.69, 0.90) 0.225 0.72 (0.63, 0.81) 0.237 0.74 (0.67, 0.82) 0.243 
Source of control 
 PCCc 2,342/2,392 0.75 (0.69, 0.82) 0.116 0.57 (0.48, 0.68) 0.158 0.72 (0.61, 0.85) 0.674 0.66 (0.56, 0.76) 0.363 0.73 (0.65, 0.83) 0.095 
 HCCc 1,571/1,537 0.78 (0.70, 0.86) 0.317 0.61 (0.50, 0.75) 0.183 0.83 (0.69, 1.01) 0.052 0.74 (0.62, 0.88) 0.066 0.70 (0.60, 0.82) 0.997 

aNumber of comparisons.

bp value of Q test for heterogeneity test. Random-effects model was used when p value for heterogeneity test <0.05; otherwise, fixed-effects model was used.

cHCC, hospital-based case-control; PCC, population-based case-control.

Fig. 2.

a Forest plots of ORs with 95% CIs for MTNR1B rs1387153 polymorphism and GDM risk stratified by ethnicity. b Forest plots of ORs with 95% CIs for MTNR1B rs1387153 polymorphism and GDM risk stratified by source of control.

Fig. 2.

a Forest plots of ORs with 95% CIs for MTNR1B rs1387153 polymorphism and GDM risk stratified by ethnicity. b Forest plots of ORs with 95% CIs for MTNR1B rs1387153 polymorphism and GDM risk stratified by source of control.

Close modal

Totally, we found the MTNR1B rs1387153 polymorphism is significantly associated with the risk of GDM in all models (allele contrast (C vs. T): OR, 0.78; 95% CI, 0.73–0.83; homozygote (CC vs. TT): OR, 0.61;95% CI, 0.53–0.69; heterozygote (CT vs. TT): OR, 0.78; 95% CI, 0.69–0.89; dominant model (CC + CT vs. TT): OR, 0.71; 95% CI, 0.63–0.80; recessive model (CC vs. CT + TT): OR, 0.73; 95% CI, 0.67–0.81). When stratified according to ethnicity, studies showed a notable elevation in GDM risk. However, when categorized based on the source of control, an increased GDM risk was evident in all models, with the exception of the heterozygote comparison within HCC studies: (OR, 0.83; 95% CI, 0.69–1.01) (Fig. 2b).

The NOS scores for the selected studies can be found in Table 3. The quality scores for the included studies ranged from 7 to 8 points.

Table 3.

Quality assessment of case-control studies included in this meta-analysisa

StudyAdequate definition of casesRepresentativeness of casesSelection of controlDefinition of controlControl for important factor or additional factorbExposure assessmentSame method of ascertainment for cases and controlsNonresponse ratecTotal quality scoresd
Kim et al. [23], 2011 ★ ★ ★ ★ ★★ ★ ★ 
Vlassi et al. [24], 2012 ★ ★ ★ ★ ★★ ★ ★ 
Wang et al. [25], 2014 ★ ★ ★ ★ ★★ ★ ★ 
Liu et al. [13], 2016 ★ ★ ★ ★ ★ ★ ★ 
Popova et al. [14], 2017 ★ ★ ★ ★ ★★ ★ ★ 
Alharbi et al. [10], 2019 ★ ★ ★ ★ ★★ ★ ★ 
Jia et al. [11], 2020 ★ ★ ★ ★★ ★ ★ 
Liu et al. [12], 2022 ★ ★ ★ ★★ ★ ★ 
StudyAdequate definition of casesRepresentativeness of casesSelection of controlDefinition of controlControl for important factor or additional factorbExposure assessmentSame method of ascertainment for cases and controlsNonresponse ratecTotal quality scoresd
Kim et al. [23], 2011 ★ ★ ★ ★ ★★ ★ ★ 
Vlassi et al. [24], 2012 ★ ★ ★ ★ ★★ ★ ★ 
Wang et al. [25], 2014 ★ ★ ★ ★ ★★ ★ ★ 
Liu et al. [13], 2016 ★ ★ ★ ★ ★ ★ ★ 
Popova et al. [14], 2017 ★ ★ ★ ★ ★★ ★ ★ 
Alharbi et al. [10], 2019 ★ ★ ★ ★ ★★ ★ ★ 
Jia et al. [11], 2020 ★ ★ ★ ★★ ★ ★ 
Liu et al. [12], 2022 ★ ★ ★ ★★ ★ ★ 

aA study can be awarded a maximum of one star for each numbered item except for the item Control for most important factor or second important factor.

bA maximum of two stars can be awarded for Control for most important factor or second important factor. Studies that controlled for maternal age received one star, whereas studies that controlled for high-risk factor (diabetes or pre-pregnancy body mass index or family history of hypertension) received one additional star.

cOne star was awarded if there was no significant difference in the response rate between control subjects and cases in the χ2 test (p > 0.05).

dThe studies are considered to be low-quality, when the scores were lower than six stars in quality assessment.

Heterogeneity Analysis

There was no notable heterogeneity observed across all models. Upon stratification by ethnicity and source of control, the studies still displayed no significant heterogeneity.

Sensitivity Analysis and Cumulative Analysis

The sensitivity analyses, as shown in Figure 3, and the cumulative meta-analysis, depicted in Figure 4, demonstrated the robustness and stability of the results.

Fig. 3.

Sensitivity analysis of associations between MTNR1B rs1387153 polymorphism and GDM risk. a–e show allelic, homozygous, heterozygous, dominant model, and recessive models, respectively.

Fig. 3.

Sensitivity analysis of associations between MTNR1B rs1387153 polymorphism and GDM risk. a–e show allelic, homozygous, heterozygous, dominant model, and recessive models, respectively.

Close modal
Fig. 4.

Cumulative meta-analysis of associations between MTNR1B rs1387153 polymorphism and GDM risk. a–e show allelic, homozygous, heterozygous, dominant model, and recessive models, respectively.

Fig. 4.

Cumulative meta-analysis of associations between MTNR1B rs1387153 polymorphism and GDM risk. a–e show allelic, homozygous, heterozygous, dominant model, and recessive models, respectively.

Close modal

Publication Bias

We conducted Begg’s and Egger’s tests to assess potential publication bias in the literature. The Begg’s funnel plot, as illustrated in Figure 5, displayed no signs of asymmetry. Furthermore, the statistical results indicated an absence of publication bias. Results of Begg’s and Egger’s test were available (allele contrast 0.27 and 0.14, homozygote 0.11 and 0.08, heterozygote 0.27 and 0.10, dominant model 0.17 and 0.09, recessive model 0.39 and 0.28).

Fig. 5.

Begg’s funnel plot for publication bias test. a–e show allelic, homozygous, heterozygous, dominant model, and recessive models, respectively.

Fig. 5.

Begg’s funnel plot for publication bias test. a–e show allelic, homozygous, heterozygous, dominant model, and recessive models, respectively.

Close modal

Trial Sequential Analysis

TSA was conducted to further assess the models between the two groups. The findings revealed that the cumulative Z value (depicted as the Z-curve in Fig. 6) surpassed the conventional boundary value, yet it did not intersect the TSA boundary. The accumulated information has not reached the Required Information Size (RIS), suggesting a potential risk of false positive results in the traditional meta-analysis. Additional studies are essential to solidify the correlation.

Fig. 6.

Trial sequential analyses for associations between MTNR1B rs1387153 polymorphism and GDM risk. a–e show allelic, homozygous, heterozygous, dominant model, and recessive models, respectively.

Fig. 6.

Trial sequential analyses for associations between MTNR1B rs1387153 polymorphism and GDM risk. a–e show allelic, homozygous, heterozygous, dominant model, and recessive models, respectively.

Close modal

From 8 case-control studies, our meta-analysis revealed that the MTNR1B rs1387153 polymorphism has a significant association with GDM. In a subgroup analysis stratified by ethnicity, all descendant trails examined exhibited a significant rise in GDM risk. Additionally, when segregating the analysis based on the source of control, all models indicated an amplified GDM risk, with the sole exception of the heterozygote model in HCC studies (OR, 0.83; 95% CI, 0.69–1.01). The cause of this disparity remains uncertain; however, the variance might stem from the selection bias inherent to HCC studies. Control subjects in HCC are often sourced from hospitals. Even though these control subjects aren’t diagnosed with GDM, they may have other medical conditions. Some of these conditions might share risk factors with GDM, meaning the control group in HCC does not necessarily mirror the broader population. Consequently, population-based case-control studies might offer a more effective approach in minimizing selection bias, especially in genetic research.

The findings pertaining to MTNR1B rs1387153 align with previous research. Jia et al. carried out a meta-analysis incorporating four studies with comprehensive data and two with limited data. Their analysis underscored a significant association between MTNR1B rs1387153 and an increased susceptibility to GDM [27]. In the two studies, only allele contrast data were available. Despite our efforts, we were unable to procure additional data from the authors. Consequently, these two studies with limited data were omitted from our meta-analysis.

In this meta-analysis, the studies included in the Asian population category are predominantly from China, with one study originating from Korea. We acknowledge that this limited representation of Asian populations, encompassing only two countries, could potentially restrict the generalizability of our findings. The rising incidence of GDM in Asia, coupled with the increased emphasis on genetic research in this region, might have resulted in more publications from these countries. The lack of diversity in the populations studied is a significant limitation. Due to the influence of genetic, lifestyle, and environmental factors, the risk of developing gestational diabetes and its influencing factors are significantly different in different populations. Populations from different regions of Asia, such as Southeast Asia, Central Asia, and West Asia, may have unique genetic markers or environmental risk factors that influence gestational diabetes, which did not show up in our analysis. Therefore, it is imperative to exercise caution when interpreting our results and take into account the possibility of regional variations when applying these findings to other Asian populations.

It is worth emphasizing the need for further research that incorporates a more diverse range of Asian populations. Expanding the scope of future studies to include a broader array of Asian ethnicities and regions would not only enhance the robustness of findings but also contribute to improving the generalizability of future meta-analyses within this field.

A fundamental concern in meta-analysis is the level of heterogeneity, as studies with high inconsistency can produce skewed results. In our meta-analysis, we employed the I2 statistics and Q-test to assess the significance of heterogeneity and observed that no notable heterogeneity existed across all models.

Another central concern in meta-analysis is the potential publication bias stemming from the selective reporting of studies. In our current meta-analysis, we applied both Egger’s and Begg’s tests to assess this bias. The statistical results and the funnel plot’s symmetry suggested no discernible publication bias. Notably, our sensitivity analysis further validated these findings, indicating the results are both consistent and robust. It’s important to note that one of the included studies did not conform to the Hardy-Weinberg Equilibrium [13]. After excluding the outlier study, we re-evaluated the association, and the findings remained consistent.

The current study does come with several limitations. (i) Due to the limited sample size in the studies and the small number of researches included in the meta-analysis, the results may not adequately reflect the true associations. (ii) Our analysis relied on unadjusted OR estimates as not all the included studies provided adjusted ORs. Moreover, where adjusted ORs were provided, the adjustments could vary based on factors like ethnicity, age, or smoking habits. (iii) The genotype distribution in the control group of one study did not align with the Hardy-Weinberg Equilibrium. (iv) In this meta-analysis, the included Asian population studies consist of four from China and one from Korea. Due to the lack of research data from other Asian populations, the generalizability of the findings may be limited.

In conclusion, our meta-analysis suggests that the MTNR1B rs1387153 variant holds potential as a genetic marker for GDM. Nonetheless, additional comprehensive, multicenter studies are essential to solidify and validate our findings.

An ethics statement is not applicable because this study is based exclusively on published literature.

The authors have no conflicts of interest to declare.

This study was supported by the Key Research and Development Program of Sichuan Province (Grant No. 2022YFS0079).

Yi Ke conceived and designed the meta-analysis. Shan Dan and Wang Ao performed the literature search. Wang Ao analyzed the data. Shan Dan wrote the paper.

All data generated or analyzed during this study are included in this article. Further inquiries can be directed to the corresponding author.

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