Lipidomic signatures in patients with early-onset and late-onset Preeclampsia.

BACKGROUND: Preeclampsia is a pregnancy-specific clinical syndrome and can be subdivided into early-onset preeclampsia (EOPE) and late-onset preeclampsia (LOPE) according to the gestational age of delivery. Patients with preeclampsia have aberrant lipid metabolism. This study aims to compare serum lipid profiles of normal pregnant women with EOPE or LOPE and screening potential biomarkers to diagnose EOPE or LOPE. METHODS: Twenty normal pregnant controls (NC), 19 EOPE, and 19 LOPE were recruited in this study. Untargeted lipidomics based on ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was used to compare their serum lipid profiles. RESULTS: The lipid metabolism profiles significantly differ among the NC, EOPE, and LOPE. Compared to the NC, there were 256 and 275 distinct lipids in the EOPE and LOPE, respectively. Furthermore, there were 42 different lipids between the LOPE and EOPE, of which eight were significantly associated with fetal birth weight and maternal urine protein. The five lipids that both differed in the EOPE and LOPE were DGTS (16:3/16:3), LPC (20:3), LPC (22:6), LPE (22:6), PC (18:5e/4:0), and a combination of them were a potential biomarker for predicting EOPE or LOPE. The receiver operating characteristic analysis revealed that the diagnostic power of the combination for distinguishing the EOPE from the NC and for distinguishing the LOPE from the NC can reach 1.000 and 0.992, respectively. The association between the lipid modules and clinical characteristics of EOPE and LOPE was investigated by the weighted gene co-expression network analysis (WGCNA). The results demonstrated that the main different metabolism pathway between the EOPE and LOPE was enriched in glycerophospholipid metabolism. CONCLUSIONS: Lipid metabolism disorders may be a potential mechanism of the pathogenesis of preeclampsia. Lipid metabolites have the potential to serve as biomarkers in patients with EOPE or LOPE. Furthermore, lipid metabolites correlate with clinical severity indicators for patients with EOPE and LOPE, including fetal birth weight and maternal urine protein levels.

Role of the Gut Microbiota in the Increased Infant Body Mass Index Induced by Gestational Diabetes Mellitus.

The connection between gestational diabetes mellitus (GDM) and the offspring's development, such as obesity, is well established. Emerging evidence indicates that the microbiota of the neonate's meconium is associated with maternal GDM status. To explore whether the association between GDM and infant body mass index (BMI) in early childhood is affected by the meconium microbiota, we recruited 120 mothers (60 healthy women and 60 with GDM) and their newborns from the Women's Hospital of Nanjing Medical University. Meconium of 120 neonates was collected within a few hours after birth and sequenced using 16S rRNA sequencing analysis. Children's BMI was measured at 12 months of age. The results revealed that infants born to mothers with GDM had increased BMI Z-scores at 12 months old and that the ß-diversity of their meconium microbiota was reduced. Several genera were observed to be significantly different between the GDM and control groups. The genus Burkholderia-Caballeronia-Paraburkholderia and an untitled genus in the family Enterobacteriaceae enriched in neonates born to healthy mothers were found to be negatively associated with infant BMI by using regression analysis. A coabundance group depleted in the GDM group was correlated negatively with 12-month BMI and mediated 21.65% of the association between GDM and infant BMI by mediation analyses. This study provided evidence for the associations among maternal GDM, the meconium microbiota, and infant BMI. Maternal GDM was demonstrated to affect infant BMI, mediated by the gut microbiome. Gut microbiome interventions might represent a novel technique to decrease the risk of GDM-induced childhood obesity. IMPORTANCE Using 16S rRNA sequencing analysis, regression analysis and mediation analysis were used to explore whether maternal gestational diabetes mellitus (GDM) changed the function and composition of the meconium microbiota and whether this explained the GDM-induced alterations of infant body mass index (BMI). This study showed that gut microbiome dysbiosis induced by maternal GDM might play an important role in the increased infant BMI during the first 12 months of life. Therefore, gut microbiome interventions might represent a novel technique to decrease the risk of GDM-induced childhood obesity.

Influence of gestational diabetes mellitus on lipid signatures in breast milk and association with fetal physical development.

Gestational diabetes mellitus (GDM) commonly leads to adverse pregnancy outcomes and long-term metabolic complications in offspring. Breastfeeding has been shown to rewrite the fetal "metabolic programming" resulting from maternal diabetes and finally lead to a lower risk of future metabolic disease. Lipids in breast milk act like hormones to promote infant growth and development, but there is minimal information invested thus far in constitution changes of lipids in breast milk, especially in the context of GDM. In the present study, we performed a lipidomics analysis to compare the lipid composition in breast milk collected from women with or without GDM. We further revealed the correlations of dysregulated lipids in breast milk with maternal glucose and infant physical development. A total of 833 lipid species from 15 classes were identified, 60 of which were found to be significantly altered in response to the high glucose, suggesting a remarkable lipid profiling change in breast milk induced by GDM. Our results showed significant associations between dysregulated lipids (e.g., neutral lipids, phospholipids, sphingolipids) and maternal glucose. Furthermore, correction analysis demonstrated that GDM related lipids were also associated with indicators of infant physical development, including body weight, length, and head circumference. These findings may help to understand the protective effects of breastfeeding especially during GDM pregnancy.

[Influence of umbilical cord milking versus delayed cord clamping on the early prognosis of preterm infants with a gestational age of <34 weeks: a Meta analysis]. / 脐带挤压与延迟断脐对胎龄<34周早产儿早期预后影响的Meta分析.

OBJECTIVES: To study the influence of umbilical cord milking versus delayed cord clamping on the early prognosis of preterm infants with a gestational age of <34 weeks. METHODS: PubMed, Web of Science, Embase, the Cochrane Library, CINAHL, China National Knowledge Infrastructure, Wanfang Data, Weipu Database, and SinoMed were searched for randomized controlled trials on umbilical cord milking versus delayed cord clamping in preterm infants with a gestational age of <34 weeks published up to November 2021. According to the inclusion and exclusion criteria, two researchers independently performed literature screening, quality evaluation, and data extraction. Review Manger 5.4 was used for Meta analysis. RESULTS: A total of 11 articles were included in the analysis, with 1 621 preterm infants in total, among whom there were 809 infants in the umbilical cord milking group and 812 in the delayed cord clamping group. The Meta analysis showed that compared with delayed cord clamping, umbilical cord milking increased the mean blood pressure after birth (weighted mean difference=3.61, 95%CI: 0.73-6.50, P=0.01), but it also increased the incidence rate of severe intraventricular hemorrhage (RR=1.83, 95%CI: 1.08-3.09, P=0.02). There were no significant differences between the two groups in hemoglobin, hematocrit, blood transfusion rate, proportion of infants undergoing phototherapy, bilirubin peak, and incidence rates of complications such as periventricular leukomalacia and necrotizing enterocolitis (P>0.05). CONCLUSIONS: Compared with delayed cord clamping, umbilical cord milking may increase the risk of severe intraventricular hemorrhage in preterm infants with a gestational age of <34 weeks; however, more high-quality large-sample randomized controlled trials are needed for further confirmation.

A comparative lipidomic study of the human placenta from women with or without gestational diabetes mellitus.

Gestational diabetes mellitus (GDM) is always accompanied by lipid disorders. The placenta serves as a center for lipid synthesis and transport and plays a critical role in establishing GDM. Thus, the changes in the type and content of lipids in the placenta may contribute to the development of GDM. Here, we performed an untargeted lipidomic analysis to profile the alterations of lipids in the placenta induced by GDM. Principal component analysis (PCA) was used to reduce the dimensionality of lipid data, and orthogonal projections to latent structures-discriminate analysis (OPLS-DA) was launched to show the differences in the lipid profile between the GDM group and normal controls. Additional multivariate data processing was carried out, including classification, pathway analysis and correlation analysis between dysregulated lipids and maternal blood glucose levels. We finally identified 1202 lipids in positive mode and 924 lipids in negative mode, of which 63 lipids were strongly associated with GDM. Notably, most dysregulated lipids were clustered in two major subtypes: glycerophospholipids and glycerolipids. Consistently, a significant down-regulation of glycerophospholipid metabolism was observed from pathway analysis. In addition, we found that SHexCer(d50:1), TAG(15:0/20:6/20:6) and PE(18:1e/21:2) were positively correlated with blood glucose levels, while PC(12:0/22:3), PC(22:4e/18:5) and PE(18:1e/26:4) showed negative correlations. Combining these lipids with fasting blood glucose showed high accuracy in the discrimination of women with GDM. In general, we explored the placental lipidomic abnormalities induced by GDM, and these findings may help us understand the pathological mechanisms of GDM.

Src family kinases involved in the differentiation of human preadipocytes.

BACKGROUND: Obesity is characterized by the excess accumulation of white adipose tissue (WAT). Src family kinases (SFKs) are non-receptor tyrosine kinases consisting of eight members (SRC, FYN, YES1, HCK, LCK, LYN, FGR and BLK) that have been studied extensively in mammalian cells. Although individual members in murine cells provide some clues that are associated with the regulation of adipogenesis, the specific role of this family in adipocyte differentiation has rarely been assessed, especially in human adipocytes. METHODS: Herein, we first explored the expression profiles of SFKs during human preadipocyte differentiation. Then, we used the pyrazolo-pyrimidinyl-amine compound PP1, a potent SFK inhibitor, to evaluate the function of SFKs during adipocyte differentiation. Furthermore, we adopted a loss-of-function strategy with siRNAs to determine the role of FGR in adipocyte differentiation. RESULTS: Here, we found that SRC, FYN, YES1, LYN and FGR were expressed in human preadipocytes and induced after the initiation of differentiation. Furthermore, the SFK inhibitor PP1 suppressed adipocyte differentiation. We also found that PP1 significantly suppressed the SFK activity in preadipocytes and decreased the expression of adipogenic genes in early and late differentiation. Given that FGR exhibited the most expression enhancement in mature adipocytes, we focused on FGR and found that its knockdown reduced lipid accumulation and adipogenic gene expression. CONCLUSIONS: Collectively, these findings suggest that SFKs, especially FGR, are involved in the differentiation of human preadipocytes. Our results lay a foundation for further understanding the role of SFKs in adipocyte differentiation and provide new clues for anti-obesity therapies.

High expression of an unknown long noncoding RNA RP11-290L1.3 from GDM macrosomia and its effect on preadipocyte differentiation.

AIMS: Gestational diabetes mellitus (GDM)-induced macrosomia is predominantly characterized by fat accumulation, which is closely related to adipocyte differentiation. An unknown long noncoding RNA RP11-290L1.3, referred to as RP11, was identified to be dramatically upregulated in the umbilical cord blood of women with GDM-induced macrosomia in our previous study. We conducted this study to identify the function of RP11 in GDM-induced macrosomia. METHODS: The effects of RP11 gain- and loss-of-function on HPA-v (human preadipocytes-visceral) adipogenesis were determined with lentivirus mediated cell transduction. The mRNA and protein expression levels of adipogenesis makers were evaluated by qPCR/Western blot. Then, we performed the microarray and pathway analysis to explore the possible mechanisms by which RP11 regulates adipogenesis. RESULTS: Overexpression of RP11 significantly enhanced adipocyte differentiation and increased the mRNA and protein expression levels of adipogenesis makers, such as PPARγ, SREBP1c, and FASN by qPCR/Western blot. Knockdown of RP11 showed opposite effects. Microarray and pathway analysis showed, after RP11 knockdown, 1612 genes were upregulated, and 583 genes were down-regulated which were found to be mainly involved in metabolic pathways, insulin signaling pathway and MAPK signaling pathway. CONCLUSION: In conclusion, the unknown lncRNA RP11 serves as a positive factor on preadipocyte differentiation which could shed light on fetal fat accumulation in GDM.

Bibliometric Analysis of Global Research on Perinatal Palliative Care.

OBJECTIVES: The aim of this study was to perform a bibliometric analysis of publications related to perinatal palliative care to identify scientific output and research trends at a global level. METHODS: The Web of Science Core Collection database was searched to retrieve publications focusing on perinatal palliative care published between 2001 and 2020. All retrieved publications were identified by title and abstract for their relevance to perinatal palliative care. These eligible publications were extracted from the following data: title, abstract, year, keywords, author, organization, journal and cited literature. VOSviewer software was used to conduct bibliographic coupling, coauthorship, and cooccurrence analyses and to detect publication trends in perinatal palliative care research. RESULTS: A total of 114 publications concerning perinatal palliative care were included. The annual number of publications has increased dramatically in recent years. The United States has made the largest contribution to this field with the majority of publications (68, 59.6%) and citations (1,091, 70.5%) and with close collaborations with researchers in Canada, Portugal and Australia. Wool C and her institution, York College of Pennsylvania, are the respectively, most prolific author and institution in this field, publishing 18 papers (15.8%). Journal of Palliative Medicine is the leading and main journal in this field. According to the cooccurrence network analysis, five main research topics were identified: the candidates for PPC, service models and forms, framework components, parental perspectives and satisfaction, and challenges and needs of health care providers. CONCLUSION: The findings of this bibliometric study illustrate the current state and global trends of perinatal palliative care for the past two decades, which will help researchers determine areas of research focus and explore new directions for future research in this field.

Relationships between gut microbiota, plasma glucose and gestational diabetes mellitus.

AIMS/INTRODUCTION: To investigate the changes in the gut microbiome in the second trimester of pregnancy associated with later-diagnosed gestational diabetes mellitus (GDM) and their relationship with fasting serum levels of metabolites, especially glucose. MATERIALS AND METHODS: We carried out a case-control study with 110 GDM patients and 220 healthy pregnant women who provided fecal samples for 16S ribosomal ribonucleic acid sequencing in the second trimester of pregnancy. RESULTS: Our results showed that GDM patients had lower α-diversity that was significantly associated with glycemic traits. Principal coordinates analysis showed significantly different microbial communities, as within GDM patients, seven genera within the phylum Firmicutes and two within the phylum Actinobacteria were significantly decreased, and four genera within phylum Bacteroidetes were increased. In addition, microbiota co-occurrence network analysis was carried out, and decreased genera within the phylum Firmicutes in GDM patients showed a significant negative correlation with oral glucose tolerance test values. Finally, microbial gene functions related to glycan biosynthesis and metabolism were found to be enriched in GDM patients. CONCLUSIONS: Our results show the relationship between changed gut microbiota composition in the second trimester of pregnancy before the diagnosis of GDM and fasting serum levels of metabolites, which might inform the diagnosis, prevention and treatment of GDM.

Early Recognition of the Preference for Exclusive Breastfeeding in Current China: A Prediction Model based on Decision Trees.

Exclusive breastfeeding (EBF) is affected by multiple risk factors. Therefore, it is difficult for clinical professionals to identify women who will not practice EBF well and provide subsequent medical suggestions and treatments. This study aimed to apply a decision tree (DT) model to predict EBF at two months postpartum. The socio-demographic, clinical and breastfeeding parameters of 1,141 breastfeeding women from Nanjing were evaluated. Decision tree modelling was used to analyse and screen EBF factors and establish a risk assessment model of EBF. The Chinese version of the Breastfeeding Self-Efficacy Scale (CV-BSES) score, early formula supplementation, abnormal nipples, mastitis, neonatal jaundice, cracked or sore nipples and intended duration of breastfeeding were significant risk factors associated with EBF in the DT model. The accuracy, sensitivity and specificity of the DT model were 73.1%, 75.5% and 66.3%, respectively. The DT model showed similar or better performance than the logistic regression model in assessing the risk of early cessation of EBF before two months postpartum. The DT model has potential for application in clinical practice and identifies high-risk subpopulations that need specific prevention.