Plasma sFlt-1/PlGF ratio of 11.5 multiples of median predicts preeclampsia with severe features within two weeks of testing.

BACKGROUND: Angiogenic imbalances, characterized by an excess of antiangiogenic factors (soluble fms-like tyrosine kinase 1 [sFlt-1]) and reduced angiogenic factors (VEGF and placental growth factor [PlGF]), contribute to the mechanisms of disease in preeclampsia. The ratio of sFlt-1 to PlGF has been used as a biomarker for preeclampsia, but cut-off values may vary with gestational age and assay platform. OBJECTIVES: To compare multiples of the median (MoM) of maternal plasma sFlt-1/PlGF ratio, sFlt-1, PlGF, and conventional clinical and laboratory values to predict preeclampsia with severe features. STUDY DESIGN: We conducted a cohort study across 18 U.S. centers involving hospitalized hypertensive individuals between 23-35 weeks' gestation. Receiver operating characteristic curve (ROC) analyses of maternal plasma biomarkers, highest systolic or diastolic blood pressures, and laboratory values at enrollment were performed for the prediction of preeclampsia with severe features. Their areas under the curve (AUC) were compared, and quasi-Poisson regression models were fitted to estimate relative risks. The primary outcome was preeclampsia with severe features within two weeks of enrollment. Secondary outcomes were a composite of severe adverse maternal outcomes (elevated liver enzymes, low platelets count, placental abruption, eclampsia, disseminated intravascular coagulation, and pulmonary edema) and a composite of severe adverse perinatal outcomes (birthweight <3rd percentile, very preterm birth [<32 weeks] and fetal/neonatal death). RESULTS: Out of 543 individuals included in the study, preeclampsia with severe features within two weeks was observed in 33.1% (n=180) of them. A ROC-derived cut-off of 11.5 MoM for sFlt-1/PlGF plasma ratio provided sensitivity (90.6%), specificity (76.9%), positive predictive value (66.0%), negative predictive value (94.3%), positive likelihood ratio (3.91), negative likelihood ratio (0.12), and accuracy (81.4%) for preeclampsia with severe features within two weeks. This cut-off was used to compare test positive cases (≥ cut-off) and test negative cases (< cut-off). Preeclampsia with severe features (66.0% vs. 5.7%; <0.001), and composites of severe adverse maternal (8.11% vs. 2.7%; p=0.006) or perinatal outcomes (41.3% vs. 10.14%; p=0.001) within two weeks were more frequent in test positive cases than test negative cases. sFlt-1/PlGF plasma ratio ≥11.5 MoM was independently associated with preeclampsia with severe features (adjusted incidence rate ratio [aIRR]: 9.08, 95% CI: 6.11 to 14.06; p<0.001) and a composite of severe adverse perinatal outcomes (aIRR: 9.42, 95% CI: 6.36 to 14.53; p<0.001), but not with a composite of severe adverse maternal outcomes (aIRR: 2.20, 95% CI: 0.95 to 5.54; p=0.08).The AUC of sFlt-1/PlGF plasma ratio in MoM (0.91; 95% CI: 0.89-0.94) for preeclampsia with severe features within two weeks was significantly higher (p<0.001 for all comparisons) than either plasma biomarker alone or any other parameter, with the exception of absolute sFlt-1/PlGF plasma ratio values. CONCLUSIONS: SFlt-1/PlGF plasma ratio ≥11.5 MoM among hospitalized, hypertensive patients between 23- and 35-week's gestation predicts progression to preeclampsia with severe features and severe adverse perinatal outcomes within two weeks.

Culture-independent identification of bloodstream infections from whole blood: prospective evaluation in specimens of known infection status.

Sepsis caused by bloodstream infection (BSI) is a major healthcare burden and a leading cause of morbidity and mortality worldwide. Timely diagnosis is critical to optimize clinical outcome, as mortality rates rise every hour treatment is delayed. Blood culture remains the "gold standard" for diagnosis but is limited by its long turnaround time (1-7 days depending on the organism) and its potential to provide false-negative results due to interference by antimicrobial therapy or the presence of mixed (i.e., polymicrobial) infections. In this paper, we evaluated the performance of resistance and pathogen ID/BSI, a direct-from-specimen molecular assay. To reduce the false-positivity rate common with molecular methods, this assay isolates and detects genomic material only from viable microorganisms in the blood by incorporating a novel precursor step to selectively lyse host and non-viable microbial cells and remove cell-free genomic material prior to lysis and analysis of microbial cells. Here, we demonstrate that the assay is free of interference from host immune cells and common antimicrobial agents at elevated concentrations. We also demonstrate the accuracy of this technology in a prospective cohort pilot study of individuals with known sepsis/BSI status, including samples from both positive and negative individuals. IMPORTANCE: Blood culture remains the "gold standard" for the diagnosis of sepsis/bloodstream infection (BSI) but has many limitations which may lead to a delay in appropriate and accurate treatment in patients. Molecular diagnostic methods have the potential for markedly improving the management of such patients through faster turnaround times and increased accuracy. But molecular diagnostic methods have not been widely adopted for the identification of BSIs. By incorporating a precursor step of selective lysis of host and non-viable microorganisms, our resistance and pathogen ID (RaPID)/BSI molecular assay addresses many limitations of blood culture and other molecular assay. The RaPID/BSI assay has an approximate turnaround time of 4 hours, thereby significantly reducing the time to appropriate and accurate diagnosis of causative microorganisms in such patients. The short turnaround time also allows for close to real-time tracking of pathogenic clearance of microorganisms from the blood of these patients or if a change of antimicrobial regimen is required. Thus, the RaPID/BSI molecular assay helps with optimization of antimicrobial stewardship; prompt and accurate diagnosis of sepsis/BSI could help target timely treatment and reduce mortality and morbidity in such patients.

Placental expression quantitative trait loci in an East Asian population.

Expression quantitative trait loci (eQTL) analysis measures the contribution of genetic variation in gene expression on complex traits. Although this methodology has been used to examine gene regulation in numerous human tissues, eQTL research in solid tissues is relatively lacking. We conducted eQTL analysis on placentas collected from an East Asian population in an effort to identify gene regulatory mechanisms in this tissue. Placentas (n = 102) were collected at the time of cesarean delivery. mRNA was extracted, sequenced with NGS, and compared with matched maternal and fetal DNA arrays performed using maternal and neonatal cord blood. Linear regression modeling was performed using tensorQTL. Fine-mapping along with epigenomic annotation was used to select putative functional variants. We identified 2,703 coding genes that contained at least one eQTL with statistical significance (false discovery rate <0.05). After fine-mapping, we found 108 previously unreported eQTL variants with posterior inclusion probability >0.1. Of these, 19% were located in genomic regions with evidence from public placental epigenome suggesting that they may be functionally relevant. For example, variant rs28379289 located in the placenta-specific regulatory region changes the binding affinity of transcription factor leading to higher expression of LGALS3, which is known to affect placental function. This study expands the knowledge base of regulatory elements within the human placenta and identifies 108 previously unreported placenta eQTL signals, which are listed in our publicly available GMI eQTL database. Further studies are needed to identify and characterize genetic regulatory mechanisms that affect placental function in normal pregnancy and placenta-related diseases.

Increased ferroptosis in leukocytes from preeclamptic women involving the long non-coding taurine upregulated gene 1 (TUG1).

BACKGROUND: Ferroptosis plays a key role in placental development and physiology, and abnormal ferroptosis has been implicated in trophoblast injury leading to preeclampsia (PE). We hypothesize that leukocytes isolated from PE exhibit increased ferroptosis and that extracellular vesicles contain long non-coding (lnc) RNA/mRNAs that modulate oxidative stress and iron toxicity in vascular endothelial cells. METHODS: We measured the expression of key regulators of ferroptosis in leukocytes and extracellular vesicles as well as circulating biomarkers of iron homeostasis and oxidative stress in plasma from women with/without PE at different timepoints during pregnancy. For markers that were dysregulated, we assessed their temporal correlation with established markers of disease activity and marker of endothelial activation. For markers dysregulated in early pregnancy, we assessed their ability to predict the development of PE. RESULTS: We found decreased lncRNA/mRNAs in leukocytes, but not extracellular vesicles, in PE that may modulate oxidative stress and iron toxicity. This decrease in anti-ferroptotic markers does not appear to be related to maternal disease activity or plasma oxidative stress status but rather to attenuated anti-inflammatory expression in these cells. Circulating ferritin was elevated in PE, supporting the hypothesis that PE represents a disbalance in iron homeostasis. Low lncRNA taurine upregulated gene 1 RNA levels in leukocytes at 22-24 weeks were strongly associated with the development of PE. CONCLUSIONS: Our findings suggest that maternal leukocytes in PE show decreased anti-ferroptotic activity that correlates with anti-inflammatory expression. Moreover, some of these changes in ferroptotic activity appear to precede the development of PE.

Platelet and mitochondrial RNA is decreased in plasma-derived extracellular vesicles in women with preeclampsia-an exploratory study.

BACKGROUND: Circulating extracellular vesicles (EVs) are increased in preeclampsia (PE) and are associated with severity and progression. We examined in this exploratory cohort study if the mRNAs and long noncoding RNAs (lncRNAs) in plasma-derived EVs were dysregulated in PE compared to normal pregnancy and display different temporal patterns during gestation. METHODS: We isolated EVs from plasma at weeks 22-24 and 36-38 in women with and without PE (n=7 in each group) and performed RNA-seq, focusing on mRNAs and lncRNAs. We validated highly expressed mitochondrial and platelet-derived RNAs discovered from central pathways in 60 women with/without PE. We examined further one of the regulated RNAs, noncoding mitochondrially encoded tRNA alanine (MT-TA), in leukocytes and plasma to investigate its biomarker potential and association with clinical markers of PE. RESULTS: We found abundant levels of platelet-derived and mitochondrial RNAs in EVs. Expression of these RNAs were decreased and lncRNAs increased in EVs from PE compared to without PE. These findings were further validated by qPCR for mitochondrial RNAs MT-TA, MT-ND2, MT-CYB and platelet-derived RNAs PPBP, PF4, CLU in EVs. Decreased expression of mitochondrial tRNA MT-TA in leukocytes at 22-24 weeks was strongly associated with the subsequent development of PE. CONCLUSIONS: Platelet-derived and mitochondrial RNA were highly expressed in plasma EVs and were decreased in EVs isolated from women with PE compared to without PE. LncRNAs were mostly increased in PE. The MT-TA in leukocytes may be a useful biomarker for prediction and/or early detection of PE.

Ferroportin expression and regulation in human placenta/fetal membranes: Implications for ferroptosis and adverse pregnancy outcomes.

Iron overload is associated with pregnancy complications. Ferroportin (FPN) is the only known iron exporter in mammalian cells. We hypothesize that FPN is functionally important in ferrotopsis, a process of iron-dependent non-apoptotic programmed cell death, and may have a critical role to play in pregnancy success. We investigated the expression of FPN in placenta/fetal membranes by immunohistochemistry in tissues collected from pregnancies with/without preeclampsia (PE) and spontaneous preterm birth (SPTB). FPN was highly expressed in both trophoblasts and decidual cells found in placenta/fetal membranes. Staining was significantly reduced in fetal membranes from SPTB versus healthy pregnancies (P = 0.046). FPN expression in immortalized human endometrial stromal cells (HESC) increased with in vitro decidualization induction using 1 µM of medroxyprogesterone acetate and 0.5 mM of dibutyryl-cAMP. In addition, both HESC cells and immortalized extravillous trophoblast SW71 cells with FPN knockdown showed significant sensitivity to ferroptosis inducer, erastin (P < 0.001 and P = 0.009, respectively). The survival of both HESC and SW71 cells was not negatively affected by iron supplementation with ferric ammonium citrate in the medium. However, SW71 cells were more sensitive than HESC cells to physiologic iron in the presence of a non-lethal dose of erastin (P < 0.001). Taken together, our data demonstrating increased sensitivity of FPN knockdown HESC and SW71 cells to erastin and increased sensitivity of trophoblasts to iron overload under ferroptotic stress support the hypothesis that FPN protects against ferroptosis during pregnancy.

Prediction of postpartum hemorrhage (PPH) using machine learning algorithms in a Kenyan population.

Introduction: Postpartum hemorrhage (PPH) is a significant cause of maternal mortality worldwide, particularly in low- and middle-income countries. It is essential to develop effective prediction models to identify women at risk of PPH and implement appropriate interventions to reduce maternal morbidity and mortality. This study aims to predict the occurrence of postpartum hemorrhage using machine learning models based on antenatal, intrapartum, and postnatal visit data obtained from the Kenya Antenatal and Postnatal Care Research Collective cohort. Method: Four machine learning models - logistic regression, naïve Bayes, decision tree, and random forest - were constructed using 67% training data (1,056/1,576). The training data was further split into 67% for model building and 33% cross validation. Once the models are built, the remaining 33% (520/1,576) independent test data was used for external validation to confirm the models' performance. Models were fine-tuned using feature selection through extra tree classifier technique. Model performance was assessed using accuracy, sensitivity, and area under the curve (AUC) of the receiver operating characteristics (ROC) curve. Result: The naïve Bayes model performed best with 0.95 accuracy, 0.97 specificity, and 0.76 AUC. Seven factors (anemia, limited prenatal care, hemoglobin concentrations, signs of pallor at intrapartum, intrapartum systolic blood pressure, intrapartum diastolic blood pressure, and intrapartum respiratory rate) were associated with PPH prediction in Kenyan population. Discussion: This study demonstrates the potential of machine learning models in predicting PPH in the Kenyan population. Future studies with larger datasets and more PPH cases should be conducted to improve prediction performance of machine learning model. Such prediction algorithms would immensely help to construct a personalized obstetric path for each pregnant patient, improve resource allocation, and reduce maternal mortality and morbidity.

The serotonin reuptake inhibitor fluoxetine induces human fetal membrane sterile inflammation through p38 MAPK activation.

Serotonin Reuptake Inhibitors (SRIs) are often used as first line therapy for depression and other psychiatric disorders. SRI use during pregnancy is associated with preterm premature rupture of membranes (PPROM) and subsequent preterm birth. The objective of this study was to investigate the mechanism(s) responsible for SRI-associated PPROM. Putative mechanisms underlying PPROM include fetal membrane (FM) inflammation, increased apoptosis, and/or accelerated senescence, the later which may be reversed by statins. Human FM explants from normal term deliveries without labor, infection, or antidepressant use were treated with or without the SRI, fluoxetine (FLX), either alone or in the presence of a p38 MAPK inhibitor or the statins, simvastatin or rosuvastatin. FMs were also collected from women either unexposed or exposed to FLX during pregnancy. FLX significantly increased FM p38 MAPK activity and secretion of inflammatory IL-6. Inhibition of p38 MAPK reduced FM IL-6 secretion in response to FLX. Statins did not reduce the SRI-induced FM IL-6 production. FMs from women exposed to FLX during pregnancy expressed elevated levels of p38 MAPK activity compared to matched unexposed women. FMs exposed to FLX did not exhibit signs of increased apoptosis and/or accelerated senescence. These results indicate that the SRI, FLX, may induce sterile FM inflammation during pregnancy through activation of the p38 MAPK pathway, and in the absence of apoptosis and senescence. These findings may better inform clinicians and patients as they weigh the risks and benefits of SRI antidepressant treatment during pregnancy.

Metabolic dysfunction-associated fatty liver disease as a risk factor for adverse outcomes in subsequent pregnancy: a nationwide cohort study.

BACKGROUND AND PURPOSE: A recent international expert consensus opinion suggested that metabolic dysfunction-associated fatty liver disease (MAFLD) replaces nonalcoholic fatty liver disease (NAFLD), since MAFLD is a better predictor of cardiovascular disease. We estimated the prevalence of FLD in fertile females and evaluated the clinical impact of either NAFLD or MAFLD on maternal and fetal outcomes during subsequent pregnancy. METHODS: The study population included fertile females who underwent health examinations and became pregnant within 1 year of health examination. Hepatic steatosis was defined as a fatty liver index of ≥ 30. The fertile females were divided into four groups: neither-FLD, NAFLD-only, MAFLD-only, and both-FLDs. During subsequent pregnancy, the risks of adverse pregnancy outcomes, including gestational diabetes, pregnancy-associated hypertension, preterm birth, and low birthweight, were compared among the four groups. RESULTS: The study population comprised 762,401 females, including 720,606 with neither-FLD, 318 with NAFLD-only, 14,371 with MAFLD-only, and 27,106 with both-FLDs. Compared to females with neither-FLD, the risk of adverse pregnancy outcomes was higher in females with any FLD, with an adjusted OR of 1.73 (95% CI 1.25-2.41) in the NALFD-only group, 2.65 (2.53-2.77) in the MAFLD-only group, and 2.39 (2.31-2.48) in the both-FLDs group. Pregnancy outcomes (cesarean delivery, gestational diabetes, and low birthweight) were worse in females with MAFLD compared with NAFLD. CONCLUSION: Any form of FLD is a risk factor for adverse pregnancy outcomes. These data suggest that MAFLD is associated with a higher risk of adverse pregnancy outcomes for both mother and fetus than NAFLD.

Development of early prediction model for pregnancy-associated hypertension with graph-based semi-supervised learning.

Clinical guidelines recommend several risk factors to identify women in early pregnancy at high risk of developing pregnancy-associated hypertension. However, these variables result in low predictive accuracy. Here, we developed a prediction model for pregnancy-associated hypertension using graph-based semi-supervised learning. This is a secondary analysis of a prospective study of healthy pregnant women. To develop the prediction model, we compared the prediction performances across five machine learning methods (semi-supervised learning with both labeled and unlabeled data, semi-supervised learning with labeled data only, logistic regression, support vector machine, and random forest) using three different variable sets: [a] variables from clinical guidelines, [b] selected important variables from the feature selection, and [c] all routine variables. Additionally, the proposed prediction model was compared with placental growth factor, a predictive biomarker for pregnancy-associated hypertension. The study population consisted of 1404 women, including 1347 women with complete follow-up (labeled data) and 57 women with incomplete follow-up (unlabeled data). Among the 1347 with complete follow-up, 2.4% (33/1347) developed pregnancy-associated HTN. Graph-based semi-supervised learning using top 11 variables achieved the best average prediction performance (mean area under the curve (AUC) of 0.89 in training set and 0.81 in test set), with higher sensitivity (72.7% vs 45.5% in test set) and similar specificity (80.0% vs 80.5% in test set) compared to risk factors from clinical guidelines. In addition, our proposed model with graph-based SSL had a higher performance than that of placental growth factor for total study population (AUC, 0.71 vs. 0.80, p < 0.001). In conclusion, we could accurately predict the development pregnancy-associated hypertension in early pregnancy through the use of routine clinical variables with the help of graph-based SSL.

Nonalcoholic fatty liver disease and early prediction of gestational diabetes mellitus using machine learning methods.

BACKGROUND/AIMS: To develop an early prediction model for gestational diabetes mellitus (GDM) using machine learning and to evaluate whether the inclusion of nonalcoholic fatty liver disease (NAFLD)-associated variables increases the performance of model. METHODS: This prospective cohort study evaluated pregnant women for NAFLD using ultrasound at 10-14 weeks and screened them for GDM at 24-28 weeks of gestation. The clinical variables before 14 weeks were used to develop prediction models for GDM (setting 1, conventional risk factors; setting 2, addition of new risk factors in recent guidelines; setting 3, addition of routine clinical variables; setting 4, addition of NALFD-associated variables, including the presence of NAFLD and laboratory results; and setting 5, top 11 variables identified from a stepwise variable selection method). The predictive models were constructed using machine learning methods, including logistic regression, random forest, support vector machine, and deep neural networks. RESULTS: Among 1,443 women, 86 (6.0%) were diagnosed with GDM. The highest performing prediction model among settings 1-4 was setting 4, which included both clinical and NAFLD-associated variables (area under the receiver operating characteristic curve [AUC] 0.563-0.697 in settings 1-3 vs. 0.740-0.781 in setting 4). Setting 5, with top 11 variables (which included NAFLD and hepatic steatosis index), showed similar predictive power to setting 4 (AUC 0.719-0.819 in setting 5, P=not significant between settings 4 and 5). CONCLUSION: We developed an early prediction model for GDM using machine learning. The inclusion of NAFLDassociated variables significantly improved the performance of GDM prediction. (ClinicalTrials.gov Identifier: NCT02276144).

Metabolic Dysfunction-Associated Fatty Liver Disease and Subsequent Development of Adverse Pregnancy Outcomes.

BACKGROUND & AIMS: Recently, metabolic dysfunction-associated fatty liver disease (MAFLD), rather than nonalcoholic fatty liver disease (NAFLD), was proposed to better describe liver disease associated with metabolic dysfunction (MD). In this study, we attempted to investigate the impact of MAFLD on pregnancy complications. METHODS: The current study is a secondary analysis of a multicenter prospective cohort designed to examine the risk of NAFLD during pregnancy. In the first trimester, enrolled pregnant women were evaluated for hepatic steatosis by liver ultrasonography, and blood samples were collected for biochemical measurements. The study population was divided into 3 groups: no NAFLD, hepatic steatosis but without metabolic dysfunction (non-MD NAFLD), and MAFLD. The primary outcome was the subsequent development of adverse pregnancy outcomes, including gestational diabetes mellitus, pregnancy-associated hypertension, preterm birth, and fetal growth abnormalities. RESULTS: The study population consisted of 1744 pregnant women, including 1523 with no NAFLD, 43 with non-MD NAFLD, and 178 with MAFLD. The risk of subsequent development of adverse pregnancy outcomes was higher in MAFLD than in non-MD NAFLD (adjusted odds ratio, 4.03; 95% CI, 1.68-9.67), whereas the risk was not significantly different between no NAFLD and non-MD NAFLD. Among women with no NAFLD, the presence of MD increased the risk of adverse pregnancy outcomes. However, women with MAFLD were at higher risk for adverse pregnancy outcomes than women with no NAFLD without MD or those with no NAFLD with MD. CONCLUSIONS: In pregnant women, MAFLD may be associated with an increased risk of subsequent adverse pregnancy outcomes.

Circulating Angiogenic Factor Levels in Hypertensive Disorders of Pregnancy.

BACKGROUND: Among women with hypertensive disorders of pregnancy, biomarkers may stratify risk for developing preeclampsia with severe features (sPE). METHODS: Across 18 U.S. centers, we prospectively measured the ratio of serum soluble fms-like tyrosine kinase 1 (sFlt-1) to placental growth factor (PlGF) in pregnant women hospitalized between 23 and 35 weeks of gestation. The primary outcome was predicting sPE, and secondary outcomes included predicting adverse outcomes within 2 weeks. The prognostic performance of the sFlt-1:PlGF ratio was assessed by using a derivation/validation design. RESULTS: A total of 1014 pregnant women were evaluated; 299 were included in the derivation cohort and 715 in the validation cohort. In the derivation cohort, the median sFlt-1:PlGF ratio was 200 (interquartile range, 53 to 458) among women who developed sPE compared with 6 (interquartile range, 3 to 26) in those who did not (P<0.001). The discriminatory ratio of ≥40 was then tested in the validation cohort and yielded a 65% positive (95% confidence interval [CI], 59 to 71) and a 96% negative (95% CI, 93 to 98) predictive value for the primary outcome. The ratio performed better than standard clinical measures (area under the receiver-operating characteristic curve, 0.92 versus <0.75 for standard-of-care tests). Compared with women with a ratio <40, women with a ratio ≥40 were at higher risk for adverse maternal outcomes (16.1% versus 2.8%; relative risk, 5.8; 95% CI, 2.8 to 12.2). CONCLUSIONS: In women with a hypertensive disorder of pregnancy presenting between 23 and 35 weeks of gestation, measurement of serum sFlt-1:PlGF provided stratification of the risk of progressing to sPE within the coming fortnight. (Funded by Cedars-Sinai Medical Center and Thermo Fisher Scientific; ClinicalTrials.gov NCT03815110.)

Maternal dyslipidemia and altered cholesterol metabolism in early pregnancy as a risk factor for small for gestational age neonates.

We evaluated the relationship between maternal cholesterol levels and its biologically active precursors and metabolites in the first trimester and subsequent risk for small-for-gestational-age birthweight (SGA). This is a secondary analysis of a prospective cohort study which enrolled healthy singleton pregnancies (n = 1337). Maternal fasting blood was taken in the first trimester and followed up till delivery. The lipid parameters were compared between women who delivered SGA neonates (SGA-group, birthweight < 10th percentile, n = 107) and women who did not (non-SGA-group, n = 1230). In addition, metabolic signatures of cholesterol were evaluated in a subset consisting of propensity-score matched SGA (n = 56) and control group (n = 56). Among lipid parameters, maternal high-density lipoprotein cholesterol (HDL-C) levels were significantly lower in SGA-group than in non-SGA-group (p = 0.022). The risk for SGA was negatively correlated with maternal serum HDL-C quartiles (p = 0.003), and this association remained significant after adjustment for confounding variables. In metabolic signatures of cholesterol, the cholesterol/lathosterol ratio in SGA-group was significantly higher than non-SGA-group [(2.7 (1.6-3.7) vs. 2.1 (1.5-2.9), respectively; p = 0.034)], suggesting increased endogenous cholesterol biosynthesis. We demonstrated that dyslipidemia and increased cholesterol biosynthesis led to delivery of SGA neonates even in early pregnancy.

Dysregulated non-coding telomerase RNA component and associated exonuclease XRN1 in leucocytes from women developing preeclampsia-possible link to enhanced senescence.

Senescence in placenta/fetal membranes is a normal phenomenon linked to term parturition. However, excessive senescence which may be induced by telomere attrition, has been associated with preeclampsia (PE). We hypothesized that the telomerase complex in peripheral blood mononuclear cells (PBMC) and circulating telomere associated senescence markers would be dysregulated in women with PE. We measured long non-coding (nc) RNA telomerase RNA component (TERC) and RNAs involved in the maturation of TERC in PBMC, and the expression of TERC and 5'-3' Exoribonuclease 1 (XRN1) in extracellular vesicles at 22-24 weeks, 36-38 weeks and, 5-year follow-up in controls and PE. We also measured telomere length at 22-24 weeks and 5-year follow-up. The circulating senescence markers cathelicidin antimicrobial peptide (CAMP), ß-galactosidase, stathmin 1 (STMN1) and chitotriosidase/CHIT1 were measured at 14-16, 22-24, 36-38 weeks and at 5-year follow-up in the STORK study and before delivery and 6 months post-partum in the ACUTE PE study. We found decreased expression of TERC in PBMC early in pregnant women who subsequently developed PE. XRN1 involved in the maturation of TERC was also reduced in pregnancy and 5-year follow-up. Further, we found that the senescence markers CAMP and ß-galactosidase were increased in PE pregnancies, and CAMP remained higher at 5-year follow-up. ß-galactosidase was associated with atherogenic lipid ratios during pregnancy and at 5-year follow-up, in PE particularly. This study suggests a potential involvement of dysfunctional telomerase biology in the pathophysiology of PE, which is not restricted to the placenta.

Impact of Progesterone on Molecular Mechanisms of Preterm Premature Rupture of Membranes.

The role and mechanisms of progesterone in preterm premature rupture of membranes (PPROM) remains unclear. This study aims to investigate the molecular mechanisms of action of progesterone in pre-labor full-term fetal amniotic membrane cells with and without stimulation by microbial, pro-inflammatory, or thrombogenic agents. Fetal amniotic membranes were collected from 30 women with a normal singleton pregnancy undergoing elective cesarean section at term prior to the onset of labor. The human amniotic epithelial cells isolated were pretreated with and without medroxyprogesterone acetate for 24 h. Then, cells were treated with and without TLR/NLR agonists, pro-inflammatory cytokines, or thrombin for 48 h. Semi-quantitative RT-PCR, Western blot, and caspase-3 activity measurement were performed. Progesterone stimulation decreased the expression of TLR2, TLR5, and Nod2 genes (alone and/or in combination with TLR/NLR agonists) and decreased the expression of IL-1ß and IL-8 genes increased by stimulation with specific agonists for TLR2, TLR4, TLR5, Nod1, and Nod2. Moreover, progesterone decreased thrombin-induced IL-8 gene expression. Progesterone also decreased expression of Bax and Bid proteins (pro-apoptotic factors) increased by stimulation with pro-inflammatory cytokines (TNF-α, NGAL, IL-18, and IL-1ß) and thrombin. Progesterone stimulation alone as well as co-stimulation with TNF-α, NGAL, IL-18, IL-1ß, or thrombin with progesterone either increased, decreased, or did not change the expression of Bcl-2, Bcl-XL, or XIAP genes (anti-apoptotic factors). These data suggest progesterone plays protective roles against PPROM through anti-microbial, anti-inflammatory, and anti-thrombogenic actions on human-term fetal amniotic membrane cells. Progesterone alters pro-inflammatory cytokine- and thrombin-induced apoptosis by controlling the expression of pro-apoptotic and anti-apoptotic factors.

Characterization of miR-200 family members as blood biomarkers for human and laying hen ovarian cancer.

MicroRNA-200 (miR-200) family is highly expressed in ovarian cancer. We evaluated the levels of family members relative to the internal control miR-103a in ovarian cancer and control blood specimens collected from American and Hong Kong Chinese institutions, as well as from a laying hen spontaneous ovarian cancer model. The levels of miR-200a, miR-200b and miR-200c were significantly elevated in all human cancer versus all control blood samples. Further analyses showed significantly higher miR-200 levels in Chinese control (except miR-429) and cancer (except miR-200a and miR141) samples than their respective American counterparts. Subtype-specific analysis showed that miR-200b had an overall elevated level in serous cancer compared with controls, whereas miR-429 was significantly elevated in clear cell and endometrioid cancer versus controls. MiR-429 was also significantly elevated in cancer versus control in laying hen plasma samples, consistent with the fact that endometrioid tumor is the prevalent type in this species. A neural network model consisting of miR-200a/200b/429/141 showed an area under the curve (AUC) value of 0.904 for American ovarian cancer prediction, whereas a model consisting of miR-200b/200c/429/141 showed an AUC value of 0.901 for Chinese women. Hence, miR-200 is informative as blood biomarkers for both human and laying hen ovarian cancer.