Developmental Effects of (Pre-)Gestational Diabetes on Offspring: Systematic Screening Using Omics Approaches

Proteins and metabolites fingerprints of gestational diabetes mellitus forming protein-metabolite interactomes are its potential biomarkers

Gestational diabetes mellitus (GDM) is a consequence of glucose intolerance with an inadequate production of insulin that happens during pregnancy and leads to adverse health consequences for both mother and fetus. GDM patients are at higher risk for preeclampsia, and developing diabetes mellitus type 2 in later life, while the child born to GDM mothers are more prone to macrosomia, and hypoglycemia. The universally accepted diagnostic criteria for GDM are lacking, therefore there is a need for a diagnosis of GDM that can identify GDM at its early stage (first trimester). We have reviewed the literature on proteins and metabolites fingerprints of GDM. Further, we have performed protein-protein, metabolite-metabolite, and protein-metabolite interaction network studies on GDM proteins and metabolites fingerprints. Notably, some proteins and metabolites fingerprints are forming strong interaction networks at high confidence scores. Therefore, we have suggested that those proteins and metabolites that are forming protein-metabolite interactomes are the potential biomarkers of GDM. The protein-metabolite biomarkers interactome may help in a deep understanding of the prognosis, pathogenesis of GDM, and also detection of GDM. The protein-metabolites interactome may be further applied in planning future therapeutic strategies to promote long-term health benefits in GDM mothers and their children.

Differential effects of diet and physical activity interventions in pregnancy to prevent gestational diabetes mellitus and reduce gestational weight gain by level of maternal adiposity: a protocol for an individual patient data (IPD) meta-analysis of randomised controlled trials

INTRODUCTION

Women and their infants are at increased risk of complications if gestational diabetes mellitus (GDM) or excessive gestational weight gain (GWG) occurs in pregnancy. Weight management interventions in pregnancy, consisting of diet and physical activity components are targeted based on maternal body mass index (BMI). However, the relative effectiveness of interventions targeted based on alternative measures of adiposity to BMI is unclear. This individual patient data (IPD) meta-analysis aims to explore whether interventions are more effective at preventing GDM and reducing GWG in women according to their level of adiposity.

METHODS

The International Weight Management in Pregnancy Collaborative Network has a living database of IPD from randomised trials of diet and/or physical activity interventions in pregnancy. This IPD meta-analysis will use IPD from trials identified from systematic literature searches up until March 2021, where maternal adiposity measures (eg, waist circumference) were collected prior to 20 weeks' gestation. A two-stage random effects IPD meta-analysis approach will be taken for each outcome (GDM and GWG) to understand the effect of early pregnancy adiposity measures on the effect of weight management interventions for GDM prevention and GWG reduction. Summary intervention effects with 95% CIs) will be derived along with treatment covariate interactions. Between-study heterogeneity will be summarised by I² and tau² statistics. Potential sources of bias will be evaluated, and the nature of any missing data will be explored and appropriate imputation methods adopted.

ETHICS AND DISSEMINATION

Ethics approval is not required. The study is registered on the International Prospective Register of Systematic Reviews (CRD42021282036). Results will be submitted to peer-reviewed journals.

PROSPERO REGISTRATION NUMBER

CRD42021282036.

Multi-omics and machine learning for the prevention and management of female reproductive health

Females typically carry most of the burden of reproduction in mammals. In humans, this burden is exacerbated further, as the evolutionary advantage of a large and complex human brain came at a great cost of women's reproductive health. Pregnancy thus became a highly demanding phase in a woman's life cycle both physically and emotionally and therefore needs monitoring to assure an optimal outcome. Moreover, an increasing societal trend towards reproductive complications partly due to the increasing maternal age and global obesity pandemic demands closer monitoring of female reproductive health. This review first provides an overview of female reproductive biology and further explores utilization of large-scale data analysis and -omics techniques (genomics, transcriptomics, proteomics, and metabolomics) towards diagnosis, prognosis, and management of female reproductive disorders. In addition, we explore machine learning approaches for predictive models towards prevention and management. Furthermore, mobile apps and wearable devices provide a promise of continuous monitoring of health. These complementary technologies can be combined towards monitoring female (fertility-related) health and detection of any early complications to provide intervention solutions. In summary, technological advances (e.g., omics and wearables) have shown a promise towards diagnosis, prognosis, and management of female reproductive disorders. Systematic integration of these technologies is needed urgently in female reproductive healthcare to be further implemented in the national healthcare systems for societal benefit.

Role of Adipose Tissue microRNAs in the Onset of Metabolic Diseases and Implications in the Context of the DOHaD

The worldwide epidemic of obesity is associated with numerous comorbid conditions, including metabolic diseases such as insulin resistance and diabetes, in particular. The situation is likely to worsen, as the increase in obesity rates among children will probably lead to an earlier onset and more severe course for metabolic diseases. The origin of this earlier development of obesity may lie in both behavior (changes in nutrition, physical activity, etc.) and in children's history, as it appears to be at least partly programmed by the fetal/neonatal environment. The concept of the developmental origin of health and diseases (DOHaD), involving both organogenesis and epigenetic mechanisms, encompasses such programming. Epigenetic mechanisms include the action of microRNAs, which seem to play an important role in adipocyte functions. Interestingly, microRNAs seem to play a particular role in propagating local insulin resistance to other key organs, thereby inducing global insulin resistance and type 2 diabetes. This propagation involves the active secretion of exosomes containing microRNAs by adipocytes and adipose tissue-resident macrophages, as well as long-distance communication targeting the muscles and liver, for example. Circulating microRNAs may also be useful as biomarkers for the identification of populations at risk of subsequently developing obesity and metabolic diseases.

Transcriptomic Profiling of Rectus Abdominis Muscle in Women with Gestational Diabetes-Induced Myopathy: Characterization of Pathophysiology and Potential Muscle Biomarkers of Pregnancy-Specific Urinary Incontinence

Gestational diabetes mellitus (GDM) is recognized as a "window of opportunity" for the future prediction of such complications as type 2 diabetes mellitus and pelvic floor muscle disorders, including urinary incontinence and genitourinary dysfunction. Translational studies have reported that pelvic floor muscle disorders are due to a GDM-induced-myopathy (GDiM) of the pelvic floor muscle and rectus abdominis muscle (RAM). We now describe the transcriptome profiling of the RAM obtained by Cesarean section from GDM and non-GDM women with and without pregnancy-specific urinary incontinence (PSUI). We identified 650 genes in total, and the differentially expressed genes were defined by comparing three control groups to the GDM with PSUI group (GDiM). Enrichment analysis showed that GDM with PSUI was associated with decreased gene expression related to muscle structure and muscle protein synthesis, the reduced ability of muscle fibers to ameliorate muscle damage, and the altered the maintenance and generation of energy through glycogenesis. Potential genetic muscle biomarkers were validated by RT-PCR, and their relationship to the pathophysiology of the disease was verified. These findings help elucidate the molecular mechanisms of GDiM and will promote the development of innovative interventions to prevent and treat complications such as post-GDM urinary incontinence.

Sleep Disturbances Before Pregnancy and Subsequent Risk of Gestational Diabetes Mellitus

PURPOSE

To investigate the relationship between sleep disturbances before pregnancy and the subsequent risk for gestational diabetes mellitus (GDM).

PATIENTS AND METHODS

Pregnant women who attended antenatal clinic before the 12th gestational week between September 2019 and June 2020 were enrolled. The sleep status at the month before the last menstrual period was collected by filling the Pittsburgh Sleep Quality Index (PSQI) and Berlin Questionnaire (BQ) to evaluate the sleep duration, quality and the risk of obstructive sleep apnea (OSA). With monthly antenatal care, the oral glucose tolerance test (OGTT) was performed during 24-28 gestational weeks. According to the results, GDM and non-GDM group were classified. The sleep status and baseline characters were compared between the two groups.

RESULTS

A total of 355 pregnant women were enrolled in this study, and 63 of them (17.7%) were diagnosed with GDM. Univariate analysis showed that maternal age, body mass index (BMI), family history of diabetes, PSQI score and positive BQ were associated with GDM (p < 0.05). Maternal age (aOR 1.10, 95% CI, 1.01-1.17), BMI before pregnancy (aOR 1.12, 95% CI, 1.02-1.23), family history of diabetes (aOR 2.35, 95% CI, 1.33-4.17), positive BQ (aOR 4.03, 95% CI, 1.04-15.63) were independent risk factors for GDM in multivariate analysis. The decision tree indicated that among the pregnant women with BMI >20.6 kg/m² and age >28.5, the risk for GDM with positive BQ increased from 27.5% to 66.7%.

CONCLUSION

The high risk of OSA before pregnancy may increase the risk for GDM during pregnancy.