Background: To evaluate the interaction of depression and anxiety with the development of recurrent pregnancy loss (RPL).Methods: A nested case–control study involving 2558 participants was conducted with data from the prospective Miscarriage Woman Cohort study between 2017 and 2019 in the province of Gansu, China. The questionnaire data, self-rating anxiety scale and self-rating depression scale were collected after each participant’s first miscarriage. Information on RPL outcomes was obtained from the medical records within the subsequent 2 years. All patients diagosed RPL were recruited as cases whilst a randomly selected group of women with only one miscarriage in the past were recruited as controls. The logistic regression and the interaction effects between anxiety and depression and RPL were analysed.Results: The prevalence of anxiety (n = 325, 28.7% vs. n = 278, 19.5%) and depression symptoms (n = 550, 48.6% vs. n = 589, 41.3%) for the 1132 RPL cases were higher than 1426 non-RPL controls (P < 0.001). After adjusting for possible confounding variables, the odds ratio (OR) value, reflecting the multiplicative interaction, was 1.91 (95% CI 1.50–2.44,P < 0.001) for cases with both anxiety and depression symptoms compared with the non-RPL group. The relative excess risk of interaction value, reflecting the additive interaction between anxiety and depression to RPL was 1.15 (95% CI 0.32–4.21). Moreover, the adjusted OR for RPL cases with mild anxiety and severe depression was 2.77 (95% CI 1.07–44.14,P < 0.001), for RPL cases with severe anxiety and mild depression was 4.23 (95% CI 1.01–22.21,P < 0.001), for RPL cases with severe anxiety and moderate depression was 4.34 (95% CI 1.03–21.28,P < 0.001) and for RPL cases with severe anxiety and severe depression was 5.95 (95% CI 1.09–45.09,P < 0.05).Conclusions: Either depression or anxiety alone could increase the risk of subsequent RPL. Anxiety and depression had a synergistic effect after the first miscarriage which increased the development of subsequent RPL disease.

Background: The relationship between maternal body compositions and birth weight was not definite. Fat Mass (FM) and Fat Free Mass (FFM) can accurately reflect the maternal body fat compositions and have been considered as better predictors of birth weight. Despite its potential role, no studies have been described the maternal compositions during pregnancy in East Asian women previously. We investigated the correlation between birth weight and Maternal body composition including fat mass (FM) and fat free mass (FFM). To determine whether birth weight is associated with maternal body fat FM and FFM during pregnancy and, if so, which trimester and parameter is more critical in determining birth weight. Methods: A longitudinal prospective observational study performed, 348, 481 and 321 non-diabetics Han Chinese women with a singleton live birth attending a routine visit in their first, second and third trimesters were recruited. Maternal body composition was measured using segmental multi-frequency bioelectrical impedance analysis. Data of the pre-pregnancy body mass index (BMI), maternal BMI, the gestational weight gain (GWG), and placental and birth weight were collected. Results: A significant correlation exists between maternal FFM in the process of pregnancy, placental weight, GWG at delivery, and birth weight ( P < 0.05). On stepwise multiple linear regression analysis, material’s FFM was the most important factor associated with the birth weight. After adjustment, there was significantly associated with 2.47-fold increase in risk for birth weight more than 4 kg when FFM ≥ 40.76 kg (Upper quartile of participants). The increased maternal age became a protective factor (OR = 0.69) while the increased pre-pregnancy BMI (OR = 1.50) remained predictors to birth weight more than 4 kg. Conclusions: The change of maternal FFM during pregnancy is independently affected the birth weight.

This study aims to analyze the pathways and the placental brain axis genes of gestational diabetes mellitus (GDM) affecting offspring neurodevelopment. Differentially expressed genes (DEGs) were identified through transcriptome sequencing of placental tissues. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed on DEGs. A protein-protein interaction (PPI) network was constructed and annotated using the STRING online software. The expression of neurodevelopment-related genes was analyzed by qPCR. Hubgenes were analyzed using Cytoscape 3.7.1 software. The correlation between Hubgenes and placental brain axis genes was analyzed through literatures alignment. The pathways of GDM affecting offspring neural development were predicted using the KEGG database. The placental transcriptome revealed that there were 404 DEGs between GDM and Normal groups. Among these DEGs, 125 were upregulated and 279 were downregulated. GO analysis indicated that DEGs were mainly involved in intracellular calcium activated chloride channel activity, anion channel activity, G protein-coupled peptide receptors, etc. Additionally, KEGG analysis revealed that DEGs were predominantly involved in neuroactive ligand receptor interaction pathways. STRING online software analysis revealed that the DLGAP1, NXNL2, SCG2, SLC18A2, LYNX1, GRM1, DLGAP1, BIRC7 genes were associated with neurodevelopment. PCR validation of these 8 genes was consistent with transcriptome results (P<0.05). Literatures alignment showed that DLGAP1, GRM1 and SLC18A2 are placental brain axis genes that influence offspring neurodevelopment. The placental brain axis genes DLGAP1, GRM1, SLC18A2 have been found to influence GDM offspring neurodevelopment through the regulation of the Gq/PLC/PKC pathway.

Gestational diabetes mellitus (GDM) significantly impacts maternal and neonatal health, yet its influence on the neonatal intestinal microbiota remains poorly characterized. This study investigated the effects of maternal GDM on offspring microbiota by analyzing meconium samples from newborns of GDM-affected mothers (n = 16) and healthy controls (n = 16). Microbiome analysis revealed distinct composition alterations in the intestinal microbiota of GDM-exposed neonates. Significant differences in microbial abundance were observed between groups: at the phylum level, the GDM group exhibited a significant increase in Bacteroidetes but significant reductions in Actinobacteria and Proteobacteria. At the genus level, opportunistic pathogens including Bacteroides and Escherichia-Shigella were significantly enriched in the GDM group. These findings demonstrate that maternal GDM perturbs the initial colonization of the neonatal intestinal microbiota. Interventions targeting maternal glycemic control during gestation may mitigate offspring microbiota dysbiosis and its potential clinical implications.

Major public health emergencies would have a negative influence on the psychology of the public, and an effective psychological intervention can help them to relieve some emotions, such as tension and panic. However, differences in individual environments affect people’s psychological intervention demands and intervention mode choices. Therefore, it is of great theoretical and practical value to analyze and identify the key factors affecting these demands and choices. Based on a nationwide sample of 24,188 respondents from the “Internet Survey of Residents’ Behavioral Changes and Psychological Conditions during the Epidemic”, the different characteristics of public psychological intervention demands and choices under different factors are explored in this paper. The results demonstrate that: (1) the psychological status of Chinese people was relatively stable during the epidemic period, and there were 1016 respondents who had subjective demands for a psychological intervention, (2) age, gender, occupation type, residence, family size, risk perception, psychological status, education level, and fixed expenditure all significantly affect public psychological intervention demands, and (3) risk perception, psychological status, age, gender, and family size will impact the choice of psychological intervention methods. The above results can provide a decision-making basis for the construction of a psychological intervention system in psychological crisis management during the post-epidemic prevention and control period, as well as reference and suggestions for handling psychological stress of similar sudden crisis events in the future.

To investigate the predictive value of the hepatic steatosis index (HSI) in the first trimester for gestational diabetes mellitus (GDM), and explore the association between HSI and the risk of developing GDM. A total of 2795 pregnant women were included in this cohort study. Maternal baseline data and basic laboratory data, including alanine aminotransferase, aspartate aminotransferase, fasting plasma glucose (FPG), and others were collected during 8–13 weeks of gestation. The 75-g oral glucose tolerance test was conducted at 24–28 weeks of gestation, and according to the criteria of American Diabetes Association, the participant would be diagnosed as GDM if one of the thresholds was met: fasting blood glucose ≥ 5.1 mmol/L, 1-h blood glucose ≥ 10.0 mmol/L, or 2-h blood glucose ≥ 8.5 mmol/L. The relationship between first-trimester HSI and GDM outcomes was analyzed by using Logistic Regression, Restricted Cubic Spline and Subgroup Analyses. The nomogram model including HSI, FPG and other factors was established, and diagnostic efficacies of the nomogram model, FPG, HSI, and white blood cell count (WBC) were analysed by using the receiver operating characteristic curve. 212 pregnant women were diagnosed with GDM among 2,795.After adjusting for the covariates, compared to women in the lowest quartile (Q1), those in the highest quartile group (Q4) of first-trimester HSI had a 2.328-fold risk of developing GDM (95% CI 1.262–4.295, p < 0.05). There was a positive nonlinear dose–response relationship between the first-trimester HSI and GDM (p for overall < 0.001; p for nonlinear = 0.010). Subgroup analyses found that HSI didn’t interact with covariates such as age, gravidity, parity, etc. (p > 0.05). The diagnose efficacies of the nomogram model, HSI, FPG and WBC were 0.737 (95% CI 0.702–0.773), 0.696 (95% CI 0.658–0.734), 0.631 (95% CI 0.593–0.670), and 0.569 (95% CI 0.526–0.611), respectively. First-trimester HSI, FPG, WBC, and age may jointly predict the occurrence of GDM.