Effect of maternal body mass index on the steroid profile in women with gestational diabetes mellitus

Uncoupling protein 1 deficiency leads to transcriptomic differences in livers of pregnancy female mice and aggravates hepatic steatosis

Pregnancy requires the coordination of metabolically active organs to support maternal nutrition and fetal growth. However, the metabolic cross-talk between adipose tissue and liver in females during pregnancy is still less clear. In this study, we evaluated the metabolic adaptations and phenotypes of liver in response to pregnancy-associated metabolic stress, particularly in the context of genetic ablation of Uncoupling protein 1 (Ucp1)-mediated catabolic circuit. Our results revealed that Ucp1 deficiency (UCP1 knockout, KO) mice during late pregnancy exhibited significantly deteriorated metabolic phenotypes, including hepatic steatosis and whole-body glucose and lipid homeostasis, as compared to Ucp1 deficiency or normal pregnancy mice. However, non-pregnant Ucp1 deficiency mice displayed nearly normal metabolic phenotypes and structure alterations similar to those of littermate controls. Moreover, transcriptomic analyses by RNA sequencing (RNA-seq) clearly revealed that Ucp1 deficiency led to a significant liver metabolic remodeling of differentially express genes (DEGs) before and especially during pregnancy. Consistently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses demonstrated the potential altered functions and signaling pathways, including metabolic dysfunctions in ribosome, oxidative phosphorylation, etc. Importantly, as derived from trend analyses of DEGs, our results further revealed the distinct expression pattern of each subcluster, which coincided with potential biological functions and relevant signaling pathways. The findings in the present study might provide valuable insights into the molecular mechanism of metabolic dysfunction-associated fatty liver disease (MAFLD) during pregnancy. Additionally, our data may provide a novel animal model of MAFLD, thus facilitating its potential therapies. NEW & NOTEWORTHY: Genetic ablation of Ucp1 during pregnancy increases hepatic steatosis and deteriorated whole-body glucose and lipid homeostasis. Moreover, changes in hepatic gene expression are closely associated with metabolic dysfunctions in ribosome and oxidative phosphorylation. This work highlights the therapeutic potential of targeting UCP1- mediated catabolic circuit between adipose and liver during pregnancy, and the utility of RNA-seq analysis to reveal valuable information for the distinct expression pattern of each subcluster that contribute to pregnancy-dependent MASLD progression.

A prediction model for gestational diabetes mellitus based on steroid hormonal changes in early and mid-down syndrome screening: A multicenter longitudinal study

BACKGROUND

Steroid hormones (SH) during pregnancy are associated with the development of gestational diabetes mellitus (GDM). Early and mid-Down syndrome screening is used to assess the risk of Down syndrome in the fetus. It is unclear whether changes in SH during this period can be used as an early predictor of GDM.

METHODS

This study was a multicenter, longitudinal cohort study. GDM is diagnosed by an oral glucose tolerance test (OGTT) between 24 and 28 weeks of gestation. We measured SH levels at early and mid-Down syndrome screening, respectively. Based on the SH changes, logistic regression analysis was used to construct a prediction model for GDM. Finally, evaluated the model's predictive performance by creating a receiver operating characteristic curve (ROC) and performing external validation.

RESULTS

This study enrolled 193 pregnant women (discovery cohort, n = 157; validation cohort, n = 36). SH changes occur dynamically after pregnancy. At early Down syndrome screening, only cortisol (F) (p < 0.05, 95 % CI 4780.95-46083.68) was elevated in GDM. At mid-Down syndrome screening, free testosterone (FT) (p < 0.01, 95 % CI 0.10-0.55) and estradiol (E2) (p < 0.05, 95 % CI 203.55-1784.78) were also significantly elevated. There were significant differences in the rates of change in E2 (Fold change (FC) = 1.3425, p = 0.0072), albumin (ALB) (FC=1.5759, p = 0.0117), and dihydrotestosterone (DHT) (FC=-2.1234, p = 0.0165) between GDM and no-GDM. Stepwise logistic regression analysis resulted in the best predictive model, including six variables (Δweight, ΔF, Δcortisone (E), ΔE2, Δprogesterone (P), ΔDHT). The area under the curve for this model was 0.791, and for the external validation cohort, it was 0.799.

CONCLUSIONS

A GDM prediction model can be constructed using SH measures during early and mid-Down syndrome screening.

Using metabolomics and proteomics to identify the potential urine biomarkers for prediction and diagnosis of gestational diabetes

Gestational diabetes mellitus (GDM) is one of the most common metabolic complications during pregnancy, threatening both maternal and fetal health. Prediction and diagnosis of GDM is not unified. Finding effective biomarkers for GDM is particularly important for achieving early prediction, accurate diagnosis and timely intervention. Urine, due to its accessibility in large quantities, noninvasive collection and easy preparation, has become a good sample for biomarker identification. In recent years, a number of studies using metabolomics and proteomics approaches have identified differential expressed urine metabolites and proteins in GDM patients. In this review, we summarized these potential urine biomarkers for GDM prediction and diagnosis and elucidated their role in development of GDM.