scholarly journals Metabolic Effect and Mechanism of Gestational Diabetes Mellitus on Offspring of Different Sexes

2021 ◽  
Author(s):  
Ke-Ying Fang ◽  
Zi-Qi Liu ◽  
Qi-Lin Hu ◽  
Zhi-Hao Chen ◽  
Yuan Cai ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Gestational Diabetes ◽  
Metabolic Diseases ◽  
Sample Pretreatment ◽  
Enrichment Analysis ◽  
Female Offspring ◽  
Gene Set Enrichment Analysis ◽  
Related Complication ◽  
Chronic Inflammatory Response ◽  
String Database

Abstract IntroductionGestational diabetes mellitus (GDM) is a common pregnancy-related complication that can seriously endanger the health of the mother and child. Studies have reported that offspring have varying sensitivities to high blood sugar in utero based on their sex. However, the underlying pathogenesis of metabolic diseases is still largely unknown. Therefore, this study aims to study the metabolic influence and mechanism of gestational diabetes on male and female offspring, which is beneficial in preventing or reducing the possibility of metabolic diseases among the offspring of mothers with GDM through long-term medical monitoring.MethodsResearch samples meeting the experimental ideas were evaluated and selected from GEO database. After sample pretreatment, enrichment analysis was performed using R software to further enrich the differentially expressed genes (DEGs), and further research on the biological processes and molecular pathways related to these genes was conducted through GO analysis and KEGG analysis. Following this, a protein–protein interaction (PPI) network of the DEGs in the STRING database was constructed and then refined using Cytoscape software. The CytoHubba software was then used to screen out the top 10 hub genes. At last, Gene set enrichment analysis (GSEA) was performed using GSEA software (v. 4.0) to further understand the molecular mechanism of the disease.ResultsA total of 718 different genes were selected from GSE150621, including 454 and 264 genes with up-regulated and down-regulated expressions, which were statistically significant. Based on the data from the STRING database, the top 10 genes with the highest degree of connectivity, including OAS1, OAS2, OAS3, RSAD2, MX1, IFIT1, IFIT2, IFIT3, XAF1, and ISG15, were selected. The relative expression levels of IFIT1, OSA1, and ISG15 are relevant to the prognosis of GDM patients and the potential occurrence of some metabolic diseases in their offspring.ConclusionsThe accumulation of OAS1, IFIT1, and ISG15 genes suggests that a chronic inflammatory response is a requisite part of the GDM process. However, this is not clearly related to the metabolic mechanisms of different gender offspring of mothers with GDM; therefore, this is subject to further research.

Diet and Nutritional Interventions with the Special Role of Myo-Inositol in Gestational Diabetes Mellitus Management. An Evidence-Based Critical Appraisal

2019 ◽  
Vol 25 (22) ◽  
pp. 2467-2473 ◽  
Author(s):  
Enrique Reyes-Muñoz ◽  
Federica Di Guardo ◽  
Michal Ciebiera ◽  
Ilker Kahramanoglu ◽  
Thozhukat Sathyapalan ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus ◽  
Metabolic Diseases ◽  
Nutritional Intervention ◽  
Attractive Alternative ◽  
Special Role ◽  
Dose Frequency ◽  
Nutritional Interventions

Background: Gestational Diabetes Mellitus (GDM), defined as glucose intolerance with onset or first recognition during pregnancy, represents one of the most common maternal-fetal complications during pregnancy and it is associated with poor perinatal outcomes. To date, GDM is a rising condition over the last decades coinciding with the ongoing epidemic of obesity and Type 2 Diabetes Mellitus (T2DM). Objective: The aim of this review is to discuss the role of diet and nutritional interventions in preventing GDM with the explanation of the special role of myo-inositol (MI) in this matter. Methods: We performed an overview of the most recent literature data on the subject with particular attention to the effectiveness of diet and nutritional interventions in the prevention of GDM with the special role of MI. Results: Nutritional intervention and physical activity before and during pregnancy are mandatory in women affected by GDM. Moreover, the availability of insulin-sensitizers such as different forms of inositol has dramatically changed the scenario, allowing the treatment of several metabolic diseases, such as those related to glucose dysbalance. Although the optimal dose, frequency, and form of MI administration need to be further investigated, diet supplementation with MI appears to be an attractive alternative for the GDM prevention as well as for the reduction of GDM-related complications. Conclusion: More studies should be conducted to prove the most effective nutritional intervention in GDM. Regarding the potential effectiveness of MI, further evidence in multicenter, randomized controlled trials is needed to draw firm conclusions.

scholarly journals Studies on the Mechanism of Gegen Qinlian Decoction in Treating Diabetes Mellitus Based on Network Pharmacology

2021 ◽  
Vol 16 (1) ◽  
pp. 1934578X2098213
Author(s):  
Xiaodong Deng ◽  
Yuhua Liang ◽  
Jianmei Hu ◽  
Yuhui Yang
Keyword(s):  
Diabetes Mellitus ◽  
Molecular Docking ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Network Pharmacology ◽  
Hub Genes ◽  
Topological Parameters

Diabetes mellitus (DM) is a chronic disease that is very common and seriously threatens patient health. Gegen Qinlian decoction (GQD) has long been applied clinically, but its mechanism in pharmacology has not been extensively and systematically studied. A GQD protein interaction network and diabetes protein interaction network were constructed based on the methods of system biology. Functional module analysis, Kyoto Encyclopedia of Genes and Genomes pathway analysis, and Gene Ontology (GO) enrichment analysis were carried out on the 2 networks. The hub nodes were filtered by comparative analysis. The topological parameters, interactions, and biological functions of the 2 networks were analyzed in multiple ways. By applying GEO-based external datasets to verify the results of our analysis that the Gene Set Enrichment Analysis (GSEA) displayed metabolic pathways in which hub genes played roles in regulating different expression states. Molecular docking is used to verify the effective components that can be combined with hub nodes. By comparing the 2 networks, 24 hub targets were filtered. There were 7 complex relationships between the networks. The results showed 4 topological parameters of the 24 selected hub targets that were much higher than the median values, suggesting that these hub targets show specific involvement in the network. The hub genes were verified in the GEO database, and these genes were closely related to the biological processes involved in glucose metabolism. Molecular docking results showed that 5,7,2', 6'-tetrahydroxyflavone, magnograndiolide, gancaonin I, isoglycyrol, gancaonin A, worenine, and glyzaglabrin produced the strongest binding effect with 10 hub nodes. This compound–target mode of interaction may be the main mechanism of action of GQD. This study reflected the synergistic characteristics of multiple targets and multiple pathways of traditional Chinese medicine and discussed the mechanism of GQD in the treatment of DM at the molecular pharmacological level.

The effects of gut microbiota on metabolic outcomes in pregnant women and their offspring

2018 ◽  
Vol 9 (9) ◽  
pp. 4537-4547 ◽  
Author(s):  
You Lv ◽  
Zi Yan ◽  
Xue Zhao ◽  
Xiaokun Gang ◽  
Guangyu He ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Gestational Diabetes ◽  
Gut Microbiota ◽  
Gestational Diabetes Mellitus ◽  
Pregnant Women ◽  
Metabolic Diseases ◽  
Health Issues ◽  
Metabolic Outcomes

Metabolic diseases such as gestational diabetes mellitus and obesity during pregnancy have become severe health issues due to adverse pregnant outcomes in recent years.

scholarly journals Gestational Diabetes Mellitus Epigenetically Predominantly Affects Genes Involved in Metabolic Diseases

2013 ◽  
Vol 37 ◽  
pp. S241 ◽  
Author(s):  
Stephanie-May Ruchat ◽  
Andrée-Anne Houde ◽  
Julie St-Pierre ◽  
Patrice Perron ◽  
Jean-Partice Baillargeon ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus ◽  
Metabolic Diseases

scholarly journals Novel Biomolecules in the Pathogenesis of Gestational Diabetes Mellitus

2021 ◽  
Vol 22 (21) ◽  
pp. 11578
Author(s):  
Monika Ruszała ◽  
Magdalena Niebrzydowska ◽  
Aleksandra Pilszyk ◽  
Żaneta Kimber-Trojnar ◽  
Marcin Trojnar ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus ◽  
Pregnant Women ◽  
Metabolic Diseases ◽  
Fibroblast Growth Factor 21 ◽  
Lipocalin 2 ◽  
Fatty Acid Binding ◽  
Clear Cut

Gestational diabetes mellitus (GDM) is one of the most common metabolic diseases in pregnant women. Its early diagnosis seems to have a significant impact on the developing fetus, the course of delivery, and the neonatal period. It may also affect the later stages of child development and subsequent complications in the mother. Therefore, the crux of the matter is to find a biopredictor capable of singling out women at risk of developing GDM as early as the very start of pregnancy. Apart from the well-known molecules with a proven and clear-cut role in the pathogenesis of GDM, e.g., adiponectin and leptin, a potential role of newer biomolecules is also emphasized. Less popular and less known factors with different mechanisms of action include: galectins, growth differentiation factor-15, chemerin, omentin-1, osteocalcin, resistin, visfatin, vaspin, irisin, apelin, fatty acid-binding protein 4 (FABP4), fibroblast growth factor 21, and lipocalin-2. The aim of this review is to present the potential and significance of these 13 less known biomolecules in the pathogenesis of GDM. It seems that high levels of FABP4, low levels of irisin, and high levels of under-carboxylated osteocalcin in the serum of pregnant women can be used as predictive markers in the diagnosis of GDM. Hopefully, future clinical trials will be able to determine which biomolecules have the most potential to predict GDM.

Increased Fetal Cardiovascular Disease Risk: Potential Synergy Between Gestational Diabetes Mellitus and Maternal Hypercholesterolemia

2021 ◽  
Vol 19 ◽  
Author(s):  
Cristian Espinoza ◽  
Barbara Fuenzalida ◽  
Andrea Leiva
Keyword(s):  
Diabetes Mellitus ◽  
Endothelial Dysfunction ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus ◽  
Metabolic Diseases ◽  
Disease Risk ◽  
Cardiovascular Disease Risk ◽  
Nutrient Transport ◽  
Specific Treatment ◽  
Cvd Risk

: Cardiovascular diseases (CVD) remain a major cause of death worldwide. Evidence suggests that the risk for CVD can increase at fetal stages due to maternal metabolic diseases such as gestational diabetes mellitus (GDM) and maternal supraphysiological hypercholesterolemia (MSPH). GDM is a hyperglycemic, inflammatory and insulin-resistant state that increases plasma levels of free fatty acids and triglycerides, impairs endothelial vascular tone regulation and, due to increased nutrient transport, exposes the fetus to the altered metabolic conditions of the mother. MSPH involves increased levels of cholesterol (mainly as low-density lipoprotein cholesterol) which also causes endothelial dysfunction and alters nutrient transport to the fetus. Despite that an association has already been established between MSPH and increased CVD risk, little is known about the cellular processes underlying this relationship. Our knowledge is further obscured when simultaneous presentation of MSPH and GDM takes place. In this context, GDM and MSPH may substantially increase fetal CVD risk due to synergistic impairment of placental nutrient transport and endothelial dysfunction. More studies on the separate and/or cumulative role of both processes are warranted to suggest specific treatment options.

scholarly journals Research Advances in the Roles of Circular RNAs in Pathophysiology and Early Diagnosis of Gestational Diabetes Mellitus

2022 ◽  
Vol 9 ◽  
Author(s):  
Yan-ping Zhang ◽  
Sha-zhou Ye ◽  
Ying-xue Li ◽  
Jia-li Chen ◽  
Yi-sheng Zhang
Keyword(s):  
Diabetes Mellitus ◽  
Molecular Markers ◽  
Early Diagnosis ◽  
Gestational Diabetes ◽  
Glucose Tolerance ◽  
Gestational Diabetes Mellitus ◽  
Metabolic Diseases ◽  
Circular Rna ◽  
Circular Rnas ◽  
Oral Glucose

Gestational diabetes mellitus (GDM) refers to different degrees of glucose tolerance abnormalities that occur during pregnancy or are discovered for the first time, which can have a serious impact on the mother and the offspring. The screening of GDM mainly relies on the oral glucose tolerance test (OGTT) at 24–28 weeks of gestation. The early diagnosis and intervention of GDM can greatly improve adverse pregnancy outcomes. However, molecular markers for early prediction and diagnosis of GDM are currently lacking. Therefore, looking for GDM-specific early diagnostic markers has important clinical significance for the prevention and treatment of GDM and the management of subsequent maternal health. Circular RNA (circRNA) is a new type of non-coding RNA. Recent studies have found that circRNAs were involved in the occurrence and development of malignant tumors, metabolic diseases, cardiovascular and cerebrovascular diseases, etc., and could be used as the molecular marker for early diagnosis. Our previous research showed that circRNAs are differentially expressed in serum of GDM pregnant women in the second and third trimester, placental tissues during cesarean delivery, and cord blood. However, the mechanism of circular RNA in GDM still remains unclear. This article focuses on related circRNAs involved in insulin resistance and β-cell dysfunction, speculating on the possible role of circRNAs in the pathophysiology of GDM under the current research context, and has the potential to serve as early molecular markers for the diagnosis of GDM.

scholarly journals LIPEA: Lipid Pathway Enrichment Analysis

2018 ◽  
Author(s):  
Aldo Acevedo ◽  
Claudio Durán ◽  
Sara Ciucci ◽  
Mathias Gerl ◽  
Carlo Vittorio Cannistraci
Keyword(s):  
High Throughput ◽  
Metabolic Diseases ◽  
Enrichment Analysis ◽  
Absolute Quantification ◽  
Gene Set Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Web Tool ◽  
Gene Set Enrichment ◽  
Pathway Enrichment ◽  
Web Platform

AbstractMotivationAnalyzing associations among multiple omic variables to infer mechanisms that meaningfully link them is a crucial step in systems biology. Gene Set Enrichment Analysis (GSEA) was conceived to pursue this aim in computational genomics, unveiling significant pathways associated to certain gene signatures under investigation. Lipidomics is a rapidly growing omic field, and absolute quantification of lipid abundance by shotgun mass spectrometry is generating high-throughput datasets that depict lipid metabolism in a plethora of conditions and organisms. In addition, high-throughput lipidomics represents a new important ally to develop personalized medicine approaches, investigate the causes and predict effective biomarkers in metabolic diseases, and not only.ResultsHere, we present Lipid Pathway Enrichment Analysis (LIPEA), a web-tool for over-representation analysis of lipid signatures and detection of the biological pathways in which they are enriched. LIPEA is a new valid resource for biologists and physicians to mine pathways significantly associated to a set of lipids, helping them to discover whether common and collective mechanisms are hidden behind those lipids. LIPEA was extensively tested and we provide two examples where our system gave successfully results related with Major Depression Disease (MDD) and insulin re-sistance.AvailabilityThe tool is available as web platform at https://lipea.biotec.tu-dresden.de.

scholarly journals Analysis of Micro-RNAs Profile Identifies miR-3065-3p, miR-4650-3p, miR-29b-2-5p, and miR-3915 as Novel Biomarkers in Gestational Diabetes Mellitus

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Bhushan R ◽  
◽  
Gupta D ◽  
Rani A ◽  
Upadhyay S ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus ◽  
Insulin Signaling ◽  
Enrichment Analysis ◽  
Downstream Processing ◽  
Differentially Expressed ◽  
Regulatory Module ◽  
Micro Rnas ◽  
Mrna Gene

Background: Gestational Diabetes Mellitus (GDM) is a metabolic disorder characterized by carbohydrate intolerance. Complete mechanisms involved in pathophysiology of GDM are still not well known and hence makes its early diagnosis and treatment a difficult task. Micro-RNAs are non-coding RNAs and have been found to be associated with many diseases including GDM. Methods: Here, we analyzed the transcriptomic datasets (GSE98043) to unravel the role of miRNAs in GDM. We processed and analyzed the microarray datasets to find differentially expressed miRNAs followed by miRNA-mRNA gene regulatory module to have a better understanding of its regulation. Results: We identified a total of 128 Differentially Expressed (DE) miRNAs, of which the top 20 were selected for downstream processing. Four potential GDM miRNAs biomarkers namely miR-3065-3p, miR-4650-3p, miR-29b-2- 5p and miR-3915 were significantly altered in GDM. The micro-RNAs were linked to carbohydrate metabolism, insulin signaling, and cell proliferation and apoptosis. The pathways enrichment analysis shows that they are involved in insulin signaling and pathways related to cancer. Conclusions: Our study lead to the identification of four potential GDM miRNAs biomarkers namely miR-3065-3p, miR-4650-3p, miR-29b-2-5p and miR-3915 were significantly altered in GDM and can be used as diagnostic as well as therapeutic purpose.

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