Resistin role in development of gestational diabetes mellitus

Air pollution exposure during preconception and first trimester of pregnancy and gestational diabetes mellitus in a large pregnancy cohort, Hebei Province, China

Objective

To explore the relationship between the exposure level of particulate matter 2.5 (PM2.5) and particulate matter 10 (PM10) in the air of pregnant women during preconception and first trimester of pregnancy and the risk of gestational diabetes mellitus (GDM).

Methods

The data of pregnant women delivered in 22 monitoring hospitals in Hebei Province from 2019 to 2021 were collected, and the daily air quality data of their cities were used to calculate the exposure levels of PM2.5 and PM10 in different pregnancy stages, and logistic regression model was used to analyze the impact of exposure levels of PM2.5 and PM10 on GDM during preconception and first trimester of pregnancy.

Results

108,429 singleton live deliveries were included in the study, of which 12,967 (12.0%) women had a GDM diagnosis. The prevalence of GDM increased over the course of the study from 10.2% (2019) to 14.9% (2021). From 2019 to 2021, the average exposure of PM2.5 and PM10 was relatively 56.67 and 103.08μg/m3 during the period of preconception and first trimester of pregnancy in Hebei Province. Handan, Shijiazhuang, and Xingtai regions had the most severe exposure to PM2.5 and PM10, while Zhangjiakou, Chengde, and Qinhuangdao had significantly lower exposure levels than other regions. The GDM group had statistically higher exposure concentrations of PM2.5 and PM10 during the period of preconception, first trimester, preconception and first trimester (P<0.05). Multivariate logistic regression analysis showed that the risk of GDM increases by 4.5%, 6.0%, and 10.6% for every 10ug/m3 increase in the average exposure value of PM2.5 in preconception, first trimester, preconception and first trimester, and 1.7%, 2.1%, and 3.9% for PM10. Moreover, High exposure to PM2.5 in the first, second, and third months of preconception and first trimester is associated with the risk of GDM. And high exposure to PM10 in the first, second, and third months of first trimester and the first, and third months of preconception is associated with the risk of GDM.

Conclusion

Exposure to high concentrations of PM2.5 and PM10 during preconception and first trimester of pregnancy can significantly increase the risk of GDM. It is important to take precautions to prevent exposure to pollutants, reduce the risk of GDM, and improve maternal and fetal outcomes.

The role of machine learning algorithms in detection of gestational diabetes; a narrative review of current evidence

Gestational Diabetes Mellitus (GDM) poses significant health risks to mothers and infants. Early prediction and effective management are crucial to improving outcomes. Machine learning techniques have emerged as powerful tools for GDM prediction. This review compiles and analyses the available studies to highlight key findings and trends in the application of machine learning for GDM prediction. A comprehensive search of relevant studies published between 2000 and September 2023 was conducted. Fourteen studies were selected based on their focus on machine learning for GDM prediction. These studies were subjected to rigorous analysis to identify common themes and trends. The review revealed several key themes. Models capable of predicting GDM risk during the early stages of pregnancy were identified from the studies reviewed. Several studies underscored the necessity of tailoring predictive models to specific populations and demographic groups. These findings highlighted the limitations of uniform guidelines for diverse populations. Moreover, studies emphasised the value of integrating clinical data into GDM prediction models. This integration improved the treatment and care delivery for individuals diagnosed with GDM. While different machine learning models showed promise, selecting and weighing variables remains complex. The reviewed studies offer valuable insights into the complexities and potential solutions in GDM prediction using machine learning. The pursuit of accurate, early prediction models, the consideration of diverse populations, clinical data, and emerging data sources underscore the commitment of researchers to improve healthcare outcomes for pregnant individuals at risk of GDM.

Progress in Research on Biomarkers of Gestational Diabetes Mellitus and Preeclampsia

Gestational diabetes mellitus (GDM) and preeclampsia (PE) are common complications in pregnancy, with incidence rates of 1-5% and 9.4%, respectively, in China. Both these phenomena can cause adverse pregnancy outcomes and are extremely harmful to the mother and fetus. In this study, we observed that several predictive factors have important value in GDM and PE. Among the GDM group, abnormal levels of adiponectin (APN), C-reactive protein (CRP), and Leptin were observed. The coexistence of PE and GDM in the pregnant population is not uncommon. Ultimately, we discovered abnormal levels of factors such as Visfatin, Advanced oxidative protein product (AOPP), Fibroblast growth factor 21 (FGF21), and resistin in both GDM and PE groups. Particularly, the FGF21 factor holds significant importance in our research. Therefore, we need to complete the analysis and discussion of relevant predictive factors to enable early prediction and disease monitoring of GDM, PE, and other pregnancy-related disorders, ultimately contributing to the long-term health of pregnant women.

Adipokines in Pregnancy: A Systematic Review of Clinical Data

Adipokines are signaling proteins involved in metabolic, endocrinological, vascular and immunogenic processes. Associations of various adipokines with not only insulin resistance but also with increased insulin sensitivity, increased systolic blood pressure, and atherosclerosis highlight the significance of adipokines in several components of metabolic syndrome and metabolic diseases in general. As pregnancy presents a unique metabolic state, the role of adipokines in pregnancy, and even in various pregnancy complications, appears to be key to elucidating these metabolic processes. Many studies in recent years have attempted to clarify the role of adipokines in pregnancy and gestational pathologies. In this review, we aim to investigate the changes in maternal adipokine levels in physiological gestation, as well as the association of adipokines with pregnancy pathologies, such as gestational diabetes mellitus (GDM) and preeclampsia (PE). Furthermore, we will analyze the association of adipokines in both maternal serum and cord blood with parameters of intrauterine growth and various pregnancy outcomes.

Novel Biomolecules in the Pathogenesis of Gestational Diabetes Mellitus

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.

GDM-complicated pregnancies: focus on adipokines

Gestational diabetes mellitus (GDM) is a serious complication of pregnancy and is defined as a state of glucose intolerance that is first diagnosed and arises during gestation. Although the pathophysiology of GDM has not yet been thoroughly clarified, insulin resistance and pancreatic β-cell dysfunction are considered critical components of its etiopathogenesis. To sustain fetus growth and guarantee mother health, many significant changes in maternal metabolism are required in normal and high-risk pregnancy accompanied by potential complications. Adipokines, adipose tissue-derived hormones, are proteins with pleiotropic functions including a strong metabolic influence in physiological conditions and during pregnancy too. A growing number of studies suggest that various adipokines including adiponectin, leptin, visfatin, resistin and tumor necrosis factor α (TNF-α) are dysregulated in GDM and might have pathological significance and a prognostic value in this pregnancy disorder. In this review, we will focus on the current knowledge on the role that the aforementioned adipokines play in the development and progression of GDM.

Gestational diabetes mellitus - A metabolic and reproductive disorder

Maternal health associated with Gestational Diabetes Mellitus (GDM) has been gaining significant research attention due to its severe risk and adverse health effects. GDM is the leading health disease in pregnant women. It is the most common metabolic disease and it can affect up to 25% of women during pregnancy. Pregnancy is a sensitive period that impacts both pregnant women and their unborn children's long-term health. It is a well-known fact that the leading causes of disease and mortality worldwide are diabetes mellitus and cancer, and specifically, women with diabetes mellitus are at a higher risk of developing breast cancer (BC). Women who have diabetes are equally vulnerable to reproductive diseases. Reproductive dysfunctions with diabetes are mainly attributed to coexisting polycystic ovarian syndrome (PCOS), obesity, and hyperinsulinemia, etc. Moreover, India has long been recognized as the world's diabetic capital, and it is widely acknowledged that particularly pregnant and lactating women are among the most affected by diabetes. In India, one-third (33%) of women with GDM had a history of maternal diabetes. Nevertheless, the latest research suggests that gestational diabetes is also a risk factor for cardiometabolic diseases of the mother and offspring. Therefore, in the 21st century, GDM imposes a major challenge for healthcare professionals. We intend to explore the role of diabetes on female reproductive function throughout various stages of life in the perspective of the changing prognosis, prevalence, and prevention of GDM.

Serum adipokines play different roles in type I and II ketosis

Objective

This study was conducted to investigate the differences in several serum adipokines in perinatal dairy cows with type I and II ketosis, and the correlations between these adipokines and the two types of ketosis.

Methods

Serum adiponectin (ADP), leptin (LEP), resistin, tumor necrosis factor-α (TNF-α), and interleukin-6 (IL-6) levels, and energy balance indicators related to ketosis were measured. Type I and II ketosis were distinguished by serum glucose (Glu) and Y values and the correlations between adipokines in the two types of ketosis were analyzed.

Results

β-Hydroxybutyric acid of type I ketosis cows was significantly negatively correlated with insulin (INS) and LEP and had a significant positive correlation with serum ADP. In type II ketosis cows, ADP and LEP were significantly negatively correlated, and INS and resistin were significantly positively correlated. Revised quantitative INS sensitivity check index (RQUICKI) values had a significantly positive correlation with ADP and had a very significant and significant negative correlation with resistin, TNF-α, and IL-6. ADP was significantly negatively correlated with resistin and TNF-α, LEP had a significantly positive correlation with TNF-α, and a significantly positive correlation was shown among resistin, IL-6, and TNF-α. There was also a significant positive correlation between IL-6 and TNF-α.

Conclusion

INS, ADP, and LEP might exert biological influences to help the body recover from negative energy balance, whereas resistin, TNF-α, and IL-6 in type II ketosis cows exacerbated INS resistance and inhibited the production and secretion of ADP, weakened INS sensitivity, and liver protection function, and aggravated ketosis.

MTNR1B genetic polymorphisms as risk factors for gestational diabetes mellitus: a case-control study in a single tertiary care center

BACKGROUND

Gestational diabetes mellitus (GDM) is a metabolic disease in pregnancy that causes carbohydrate intolerance and hyper-glycemia. Genome-wide association studies and meta-analyses have found that the single nucleotide polymorphisms (SNPs) rs1387153 and rs10830963 of the melatonin receptor 1B ( MTNR1B) gene are associated with GDM. No studies on the MTNR1B gene effect on GDM have been performed in Saudis, other Arabs, or other Middle Eastern populations.

OBJECTIVES

Investigate the association of genotype or allele frequencies of the two SNPs with GDM and with clinical parameters related to GDM.

DESIGN

Case-control study.

SETTINGS

Tertiary care center, Riyadh.

PATIENTS AND METHODS

We recruited 400 pregnant Saudi women ages 18-45 years (200 were diagnosed with GDM, and 200 were healthy controls). Biochemical assays were performed, and rs1387153 and rs10830963 polymorphisms were analyzed by polymerase chain reaction-restriction fragment length polymorphism analysis and real-time polymerase chain reaction with TaqMan genotyping.

MAIN OUTCOME MEASURES

The association of MTNR1B gene (rs1387153 and rs10830963 polymorphisms) with GDM and with biochemical parameters related to GDM.

SAMPLE SIZE

200 GDM cases and 200 non-GDM controls.

RESULTS

Differences in allele frequencies for GDM vs non-GMD were statistically significant or nearly significant for both SNPs after adjustment for age and body mass index. In a logistic regression analysis, genotype TT was positively associated with post-prandial blood glucose (P=.018), but other associations were not statistically significant.

CONCLUSION

The odds ratios for the associations between the rs1387153 and rs10830963 SNPs and GDM exceeded 1.5-fold, which is higher than typically reported for diseases with complex genetic background. These effect sizes for GDM suggest pregnancy-specific factors related to the MTNR1B risk genotypes.

LIMITATIONS

Only two SNPs were studied.

CONFLICT OF INTEREST

None.

Immunometabolism, pregnancy, and nutrition

The emerging field of immunometabolism has substantially progressed over the last years and provided pivotal insights into distinct metabolic regulators and reprogramming pathways of immune cell populations in various immunological settings. However, insights into immunometabolic reprogramming in the context of reproduction are still enigmatic. During pregnancy, the maternal immune system needs to actively adapt to the presence of the fetal antigens, i.e., by functional modifications of distinct innate immune cell subsets, the generation of regulatory T cells, and the suppression of an anti-fetal effector T cell response. Considering that metabolic pathways have been shown to affect the functional role of such immune cells in a number of settings, we here review the potential role of immunometabolism with regard to the molecular and cellular mechanisms necessary for successful reproduction. Since immunometabolism holds the potential for a therapeutic approach to alter the course of immune diseases, we further highlight how a targeted metabolic reprogramming of immune cells may be triggered by maternal anthropometric or nutritional aspects.