The relationships between HLA class II alleles and antigens with gestational diabetes mellitus: A meta-analysis

Is there a causal association between gestational diabetes mellitus and immune mediators? A bidirectional Mendelian randomization analysis

Background

Diabetes that only appears or is diagnosed during pregnancy is referred to as gestational diabetes mellitus (GDM). The maternal physiological immune profile is essential for a positive pregnancy outcome. However, the causal relationship between GDM and immunophenotypes is not fully defined.

Methods

Based on the high-density genetic variation data at the genome-wide level, we evaluated the logical associations between 731 specific immune mediators and GDM using bidirectional Mendelian randomization (MR). The inverse variance weighted (IVW) was the main method employed for MR analysis. We performed multiple methods to verify the robustness and dependability of the MR results, and sensitivity measures were applied to rule out potential heterogeneity and horizontal pleiotropy.

Results

A substantial causal association between several immune mediators and GDM was detected. After FDR testing, HLA DR++ monocyte %leukocyte and HLA DR on plasmacytoid DC were shown to increase the risk of GDM; in contrast, CD127 on CD28+ CD45RA+ CD8br and CD19 on PB/PC were shown to attenuate the effect of GDM. Moreover, the progression of GDM has been shown to decrease the maternal levels of CD39+ activated Treg AC, CD39+ activated Treg %CD4 Treg, CD39+ resting Treg AC, CD39+ resting Treg %CD4 Treg, and CD39+ CD8BR %T cell.

Conclusions

Our findings support a possible causal association between GDM and various immunophenotypes, thus facilitating the provision of multiple options for preventive recognition as well as for the diagnostic and therapeutic management of GDM in clinical practice.

Identification of potential gene markers in gestational diabetes mellitus

This study aims to investigate underlying mechanisms of gestational diabetes mellitus (GDM). In this work, the GSE70493 dataset from GDM and control samples was acquired from Gene Expression Omnibus (GEO) database. Afterward, differentially expressed genes (DEGs) were screened between GDM and control samples. Subsequently, functional enrichment analysis and protein–protein interaction (PPI) network analysis of these DEGs were carried out. Furthermore, significant sub‐modules were identified, and the functional analysis was also performed. Finally, we undertook a quantitative real‐time polymerase chain reaction (qRT‐PCR) with the purpose of confirming several key genes in GDM development. There were totally 528 up‐regulated and 684 down‐regulated DEGs between GDM and healthy samples. The functional analyses suggested that the above genes were dramatically enriched in type 1 diabetes mellitus (T1DM) process and immune‐related pathways. Moreover, PPI analysis revealed that several members of human leukocyte antigen (HLA) superfamily, including down‐regulated HLA‐DQA1, HLA‐DRB1, HLA‐DPA1, and HLA‐DQB1 served as hub genes. In addition, six significant sub‐clusters were extracted and functional analysis suggested that these four genes in sub‐module 1 were also associated with immune and T1DM‐related pathways. Finally, they were also confirmed by qRT‐PCR array. Besides, the four members of HLA superfamily might be implicated with molecular mechanisms of GDM, contributing to a deeper understanding of GDM development.

HLA Class II Allele Analyses Implicate Common Genetic Components in Type 1 and Non-Insulin-Treated Type 2 Diabetes

CONTEXT

Common genetic susceptibility may underlie the frequently observed co-occurrence of type 1 and type 2 diabetes in families. Given the role of HLA class II genes in the pathophysiology of type 1 diabetes, the aim of the present study was to test the association of high density imputed human leukocyte antigen (HLA) genotypes with type 2 diabetes.

OBJECTIVES AND DESIGN

Three cohorts (Ntotal = 10 413) from Leipzig, Germany were included in this study: LIFE-Adult (N = 4649), LIFE-Heart (N = 4815) and the Sorbs (N = 949) cohort. Detailed metabolic phenotyping and genome-wide single nucleotide polymorphism (SNP) data were available for all subjects. Using 1000 Genome imputation data, HLA genotypes were imputed on 4-digit level and association tests for type 2 diabetes, and related metabolic traits were conducted.

RESULTS

In a meta-analysis including all 3 cohorts, the absence of HLA-DRB5 was associated with increased risk of type 2 diabetes (P = 0.001). In contrast, HLA-DQB*06:02 and HLA-DQA*01:02 had a protective effect on type 2 diabetes (P = 0.005 and 0.003, respectively). Both alleles are part of the well-established type 1 diabetes protective haplotype DRB1*15:01~DQA1*01:02~DQB1*06:02, which was also associated with reduced risk of type 2 diabetes (OR 0.84; P = 0.005). On the contrary, the DRB1*07:01~DQA1*02:01~DQB1*03:03 was identified as a risk haplotype in non-insulin-treated diabetes (OR 1.37; P = 0.002).

CONCLUSIONS

Genetic variation in the HLA class II locus exerts risk and protective effects on non-insulin-treated type 2 diabetes. Our data suggest that the genetic architecture of type 1 diabetes and type 2 diabetes might share common components on the HLA class II locus.

Identification of hub-methylated differentially expressed genes in patients with gestational diabetes mellitus by multi-omic WGCNA basing epigenome-wide and transcriptome-wide profiling

Gestational diabetes mellitus (GDM), defined as dysglycaemia that is detected during pregnancy for the first time, has become a global health burden. GDM was found to be correlated to epigenetic changes, which would cause abnormal expression of placental genes. In the present study, we performed multi-omic weighted gene coexpression network analysis (WGCNA) to systematically identify the hub genes for GDM using both epigenome- and transcriptome-wide microarray data. Two microarray datasets (GSE70493 and GSE70494) were downloaded from the Gene Expression Omnibus (GEO) database. GEO2R was used to screen differentially expressed genes (DEGs) and differentially methylated genes (DMGs) between normal and GDM samples, separately. The results of WGCNA found that 15 modules were identified and the MEblack module had a significantly negative correlation with GDM (r = -.28, P = .03). GO enrichment analysis by BinGO of the MEblack module showed that genes were primarily enriched for the presentation of antigen processing, regulation of interferon-α production and interferon-γ-mediated signaling pathway. By comparing the DEGs, DMGs and hub genes in the coexpression network, we identified five hypermethylated, lowly expressed genes (ABLIM1, GRHL1, HLA-F, NDRG1, and SASH1) and one hypomethylated, highly expressed gene (EIF3F) as GDM-related hub DMGs. Moreover, the expression levels of ABLIM1, GRHL1, HLA-F, NDRG1, and SASH11 in the GDM patients and healthy controls were validated by a real-time quantitative polymerase chain reaction. Finally, gene set enrichment analysis showed that the biological function of cardiac muscle contraction was enriched for four GDM-related hub DMGs (ABLIM1, GRHL1, NDRG1, and SASH1). Analysis of this study revealed that dysmethylated hub genes in GDM placentas might affect the placental function and thus, take part in GDM pathogenesis and fetal cardiac development.

Risk factors for gestational diabetes: An umbrella review of meta-analyses of observational studies

BACKGROUND/OBJECTIVE

Gestational diabetes mellitus (GDM) is a common pregnancy complication, with complex disease mechanisms, and several risk factors may contribute to its onset. We performed an umbrella review to summarize the evidence from meta-analyses of observational studies on risk factors associated with GDM, evaluate whether there are indications of biases in this literature and identify which of the previously reported associations are supported by convincing evidence.

METHODS

We searched PubMed and ISI Web of Science from inception to December 2018 to identify meta-analyses examining associations between putative risk factors for GDM. For each meta-analysis we estimated the summary effect size, the 95% confidence interval, the 95% prediction interval, the between-study heterogeneity, evidence of small-study effects, and evidence of excess-significance bias.

RESULTS

Thirty eligible meta-analyses were identified, providing data on 61 associations. Fifty (82%) associations had nominally statistically significant findings (P<0.05), while only 15 (25%) were significant at P<10-6 under the random-effects model. Only four risk factors presented convincing evidence:, low vs. normal BMI (cohort studies), BMI ~30-35 kg/m2 vs. normal BMI, BMI >35 kg/m2 vs. normal BMI, and hypothyroidism.

CONCLUSIONS

The compilation of results from synthesis of observational studies suggests that increased BMI and hypothyroidism show the strongest consistent evidence for an association with GDM. Diet and lifestyle modifications in pregnancy should be tested in large randomized trials. Our findings suggest that women with known thyroid disease may be offered screening for GDM earlier in pregnancy.

Identification of diagnostic biomarker in patients with gestational diabetes mellitus based on transcriptome-wide gene expression and pattern recognition

Gestational diabetes mellitus (GDM) is becoming a growing threat for all pregnancies. In this study, we set up an automatic screening method combining both transcriptomic databases and support vector machine (SVM)-based pattern recognition to select biomarkers that can be used in predicting and preventing GDM for gravidas. We screened 63 samples (32 GDM samples and 31 normal controls) in GEO database for the GDM-specific biomarkers. Differentially expressed genes between patients with GDM and normal controls were picked out using edgeR package. Enrichment analysis was performed using database for annotation, visualization, and integrated discovery. The regulatory gene network was constructed based on the KEGG pathway database. Genes in the hub of the network were selected as specific biomarkers of GDM and further validated through document investigation. Finally, the GDM prediction model was verified using the SVMs. In total, 189 probes corresponding to 69 genes that differentially expressed between GDM and controls were screened out by edgeR package. Nineteen pathways were clustered by KEGG enrichment analysis and were integrated into a regulatory network containing 572 nodes and 1874 edges. The intersection of 50 hub genes extracted from the network and 69 differential genes picked out by edgeR was a collection of six genes, including members of HLA superfamily. In the SVM model, the six genes had a good capacity of predicting GDM in both the training data set (area under curve [AUC] is 0.781) and the testing data set (AUC is 0.710) and had been reported to be associated with GDM. We found that the collection of six genes can be potentially applied as a biomarker for GDM diagnosis.