First-Trimester Serum Acylcarnitine Levels to Predict Preeclampsia: A Metabolomics Approach

Objective. To expand the search for preeclampsia (PE) metabolomics biomarkers through the analysis of acylcarnitines in first-trimester maternal serum.Methods. This was a nested case-control study using serum from pregnant women, drawn between 8 and 14 weeks of gestational age. Metabolites were measured using an UPLC-MS/MS based method. Concentrations were compared between controls (n=500) and early-onset- (EO-) PE (n=68) or late-onset- (LO-) PE (n=99) women. Metabolites with a false discovery rate <10% for both EO-PE and LO-PE were selected and added to prediction models based on maternal characteristics (MC), mean arterial pressure (MAP), and previously established biomarkers (PAPPA, PLGF, and taurine).Results. Twelve metabolites were significantly different between EO-PE women and controls, with effect levels between −18% and 29%. For LO-PE, 11 metabolites were significantly different with effect sizes between −8% and 24%. Nine metabolites were significantly different for both comparisons. The best prediction model for EO-PE consisted of MC, MAP, PAPPA, PLGF, taurine, and stearoylcarnitine (AUC = 0.784). The best prediction model for LO-PE consisted of MC, MAP, PAPPA, PLGF, and stearoylcarnitine (AUC = 0.700).Conclusion. This study identified stearoylcarnitine as a novel metabolomics biomarker for EO-PE and LO-PE. Nevertheless, metabolomics-based assays for predicting PE are not yet suitable for clinical implementation.

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Putrescine as a Novel Biomarker of Maternal Serum in First Trimester for the Prediction of Gestational Diabetes Mellitus: A Nested Case-Control Study

Frontiers in Endocrinology ◽

10.3389/fendo.2021.759893 ◽

2021 ◽

Vol 12 ◽

Author(s):

Cheng Liu ◽

Yuanyuan Wang ◽

Wei Zheng ◽

Jia Wang ◽

Ya Zhang ◽

...

Keyword(s):

Diabetes Mellitus ◽

Gestational Diabetes ◽

Gestational Diabetes Mellitus ◽

Case Control Study ◽

Prediction Models ◽

Fasting Blood Glucose ◽

First Trimester ◽

Case Control ◽

Nested Case Control ◽

Control Study

AimsEarly identification of gestational diabetes mellitus (GDM) aims to reduce the risk of adverse maternal and perinatal outcomes. Currently, no acknowledged biomarker has proven clinically useful for the accurate prediction of GDM. In this study, we tested whether serum putrescine level changed in the first trimester and could improve the prediction of GDM.MethodsThis study is a nested case-control study conducted in Beijing Obstetrics and Gynecology Hospital. We examined serum putrescine at 8-12 weeks pregnancy in 47 women with GDM and 47 age- and body mass index (BMI)-matched normoglycaemic women. Anthropometric, clinical and laboratory variables were obtained during the same period. The receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to assess the discrimination and calibration of the prediction models.ResultsSerum putrescine in the first trimester was significantly higher in women who later developed GDM. When using putrescine alone to predict the risk of GDM, the AUC of the nomogram was 0.904 (sensitivity of 100% and specificity of 83%, 95% CI=0.832–0.976, P<0.001). When combined with traditional risk factors (prepregnant BMI and fasting blood glucose), the AUC was 0.951 (sensitivity of 89.4% and specificity of 91.5%, 95% CI=0.906-0.995, P<0.001).ConclusionThis study revealed that GDM women had an elevated level of serum putrescine in the first trimester. Circulating putrescine may serve as a valuable predictive biomarker for GDM.

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Increased secretory sphingomyelinase activity in the first trimester of pregnancy in women later developing preeclampsia: a nested case-control study

Biological Chemistry ◽

10.1515/hsz-2015-0266 ◽

2016 ◽

Vol 397 (3) ◽

pp. 269-279 ◽

Cited By ~ 3

Author(s):

Víctor Rodríguez-Sureda ◽

Francesca Crovetto ◽

Stefania Triunfo ◽

Olga Sánchez ◽

Fátima Crispi ◽

...

Keyword(s):

Plasma Cholesterol ◽

First Trimester ◽

Endothelial Damage ◽

Biologically Active ◽

Control Groups ◽

Potential Biomarker ◽

Circulating Markers ◽

First Trimester Of Pregnancy ◽

Nested Case Control ◽

Control Study

Abstract The pathogenic basis of abnormal placentation and dysfunction in preeclampsia (PE) is highly complex and incompletely understood. Secretory sphyngomyelinase activity (S-ASM) was analyzed in plasma samples from 158 pregnant women developing PE and 112 healthy pregnant controls. Serum PlGF, sFlt-1, s-Endoglin and sVCAM were measured. Results showed S-ASM activity to be higher in women who later developed PE than in those with uncomplicated pregnancies (40.6% and 28.8% higher in the late- and early-onset groups, respectively). Plasma S-ASM activity correlated significantly with circulating markers of endothelial damage in the late-PE group (endoglin and sVCAM-1), with plasma cholesterol and total lipid levels. However, these significant associations were not observed in the early-PE or control groups. This work provides the first evidence of significantly elevated circulating S-ASM activity in the first trimester of pregnancy in women who go on to develop PE; thus, it may be deduced that the circulating form of ASM is biologically active in PE and could contribute to promoting endothelial dysfunction and cardiovascular programming. Plasma S-ASM measurement may have clinical relevance as a further potential biomarker contributing to the earliest identification of women at risk of developing preeclampsia.

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Role of Maternal Serum Glycodelin as Predictor of Ectopic Pregnancy in First Trimester

International Journal of Women s Health and Reproduction Sciences ◽

10.15296/ijwhr.2019.77 ◽

2018 ◽

Vol 7 (4) ◽

pp. 467-470

Author(s):

Wasan Wajdi Ibrahim ◽

Afraa Mahjoob Al-Naddawi ◽

Hayder A. Fawzi

Keyword(s):

Ectopic Pregnancy ◽

Predictive Value ◽

First Trimester ◽

Maternal Serum ◽

Intrauterine Pregnancy ◽

Receiver Operator Curve Analysis ◽

Medical City ◽

First Trimester Of Pregnancy ◽

Control Study

Objectives: Assessment of glycodelin (GD) as a marker for unruptured ectopic pregnancy (EP) in the first trimester of pregnancy. Materials and Methods: This case-control study was conducted during June 2016 to May 2017 in the Obstetrics and Gynecological Department of Baghdad University at Baghdad teaching hospital/medical city complex. In this study, 100 pregnant women in their first trimester of pregnancy were included after clinical and ultrasonic findings. Results: Based on the results, GD levels in EP were significantly lower than those with normal intrauterine pregnancy (1.58 ± 1.18 vs. 30.1 ± 11.9). In addition, using receiver operator curve analysis, the cut-off GD level of 9.5 and less had acceptable validity results (100% sensitivity, 100% specificity, 95% positive predictive value, 100% negative predictive value, and accuracy 100%) to predict EP. Conclusions: In general, serum GD is considered as an excellent predictor of unruptured EP.

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Serum placental growth factor in late first trimester of pregnancy for prediction of preeclampsia in primigravida - a case control study

International Journal of Reproduction Contraception Obstetrics and Gynecology ◽

10.18203/2320-1770.ijrcog20164641 ◽

2016 ◽

Vol 6 (1) ◽

pp. 106

Author(s):

Rachna Agarwal ◽

Shweta Chaudhary ◽

Rajarshi Kar ◽

Gita Radhakrishnan ◽

Richa Sharma

Keyword(s):

Case Control Study ◽

Gestational Hypertension ◽

Final Analysis ◽

First Trimester ◽

Case Control ◽

Placental Growth Factor ◽

Serum Samples ◽

First Trimester Of Pregnancy ◽

Nested Case Control ◽

Control Study

Background: We studied the correlation of serum PLGF levels at 11-14 weeks in primigravida for prediction of future preeclampsia in a prospective nested case control study and estimated the critical levels of PLGF for possible use as screening test.Methods: Subjects with preeclampsia/gestational hypertension were taken as cases with an equal number of controls.Results: Out of 300 participants, final analysis was possible in 291 subjects. Thirty five were cases; two had early PE, 15 late PE and 18 had GH. PLGF level was lower in cases (20 pg/ml) compared to controls (79 pg/ml). PLGF was significantly lower in PE cases (15 pg/ml) compared to GH cases (34 pg/ml). PLGF had maximum area under the ROC curve (AUC) for PE with value of 0.867. Further, late PE had more AUC (0.853) as compared to GH (0.759). The cut off value for prediction of PE was found to be <30 pg/ml with 88.2% sensitivity and 71.4% specificity.Conclusions: PLGF levels were significantly lower in first trimester serum samples of subjects who later developed either preeclampsia or gestational hypertension. PLGF had better detection rate for PE and late PE as compared to GH.

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Association between risk of preeclampsia and maternal plasma trimethylamine-N-oxide in second trimester and at the time of delivery

10.21203/rs.3.rs-18239/v1 ◽

2020 ◽

Author(s):

Xin Huang ◽

Zuodong Li ◽

Zhou Gao ◽

Dapeng Wang ◽

Xiaohui Li ◽

...

Keyword(s):

Case Control Study ◽

Late Onset ◽

Maternal Plasma ◽

Second Trimester ◽

Spearman Correlation ◽

The Mean ◽

Nested Case Control ◽

Control Study ◽

Risk Of Preeclampsia

Abstract Background: The data on the association between the microbiota-dependent metabolite trimethylamine-N-oxide (TMAO) during pregnancy and risk of preeclampsia (PE) is limited. Methods: We, therefore, conducted a prospective nested case control study during Sep 2017 to Dec 2018 to examine the association between plasma TMAO measured during pregnancy and the risk of PE. Total of 17 patients diagnosed with EOPE (early onset PE), 49 with LOPE (late onset PE) and 198 healthy controls were enrolled. Blood samples were collected at 15-23 gestational weeks and time at delivery. The Logistic regression model was used to assess the odds ratio (OR) and 95% confidence interval (CI) for TMAO and risk of PE, EOPE, LOPE, mild PE, and severe PE. Results: We found that the mean TMAO levels of overall subjects in the second trimester (T2) and at the time of delivery (TD) were 90.39 µg/m 3 (SD=45.91) and 175.01 µg/m 3 (SD=160.97), respectively. No significant spearman correlation was found between the TMAO in those two periods ( p > 0.05). T2 TMAO was not significantly associated with risk of PE or risk of any PE subtypes ( p >0.05). However, TD TMAO was significant associated with risk of PE, EOPE and severe PE (adjusted OR and 95%CI were 1.24(1.09, 1.40), 1.62(1.29, 2.03), and 1.41(1.17, 1.70)) per 50µg/m 3 increment, respectively). Conclusion: Our study found that plasma TMAO level would alter over the course of pregnancy. The major role of TMAO in PE development might be in the accelerating process not in the initiation.

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A first trimester prediction model for large for gestational age infants: a preliminary study

BMC Pregnancy and Childbirth ◽

10.1186/s12884-021-04127-3 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Francesca Monari ◽

Daniela Menichini ◽

Ludovica Spano’ Bascio ◽

Giovanni Grandi ◽

Federico Banchelli ◽

...

Keyword(s):

Prediction Model ◽

Gestational Age ◽

Predictive Accuracy ◽

First Trimester ◽

University Hospital ◽

Large For Gestational Age ◽

Aneuploidy Screening ◽

Maternal Characteristics ◽

Targeted Interventions ◽

Increased Risk

Abstract Background Large for gestational age infants (LGA) have increased risk of adverse short-term perinatal outcomes. This study aims to develop a multivariable prediction model for the risk of giving birth to a LGA baby, by using biochemical, biophysical, anamnestic, and clinical maternal characteristics available at first trimester. Methods Prospective study that included all singleton pregnancies attending the first trimester aneuploidy screening at the Obstetric Unit of the University Hospital of Modena, in Northern Italy, between June 2018 and December 2019. Results A total of 503 consecutive women were included in the analysis. The final prediction model for LGA, included multiparity (OR = 2.8, 95% CI: 1.6–4.9, p = 0.001), pre-pregnancy BMI (OR = 1.08, 95% CI: 1.03–1.14, p = 0.002) and PAPP-A MoM (OR = 1.43, 95% CI: 1.08–1.90, p = 0.013). The area under the ROC curve was 70.5%, indicating a satisfactory predictive accuracy. The best predictive cut-off for this score was equal to − 1.378, which corresponds to a 20.1% probability of having a LGA infant. By using such a cut-off, the risk of LGA can be predicted in our sample with sensitivity of 55.2% and specificity of 79.0%. Conclusion At first trimester, a model including multiparity, pre-pregnancy BMI and PAPP-A satisfactorily predicted the risk of giving birth to a LGA infant. This promising tool, once applied early in pregnancy, would identify women deserving targeted interventions. Trial registration ClinicalTrials.gov NCT04838431, 09/04/2021.

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Gut microbial-related metabolite trimethylamine-N-oxide and its precursor L-carnitine are associated with spontaneous preterm birth: a nested case-control study

10.21203/rs.2.19391/v1 ◽

2019 ◽

Author(s):

Xing Chen ◽

Ning Huang ◽

Chaoqun Liu ◽

Yue Chen ◽

Lulu Huang ◽

...

Keyword(s):

Preterm Birth ◽

Preterm Labor ◽

Case Control Study ◽

Case Control ◽

Normal Weight ◽

Maternal Serum ◽

Spontaneous Preterm Birth ◽

Nested Case Control ◽

Related Metabolite ◽

Control Study

Abstract Background: Gut microbiota has been proven to disease susceptibility and may lead to increased risk of preterm birth. To date, the link of gut microbial-related metabolite trimethylamine-N-oxide (TMAO), L-carnitine, and betaine, with spontaneous preterm birth (sPTB) has not been established. This study aimed to investigate the association of TMAO, L-carnitine and betaine, with sPTB risk. Methods: A nested case-control study was designed including 129 sPTB cases and 258 controls based on Guangxi Birth Cohort Study. TMAO, L-carnitine, and betaine level in maternal serum were determined by liquid chromatography with mass spectrometry. Conditional logistic regression analyses were used to examine the association between maternal serum metabolites and sPTB. Stratified analyses were further conducted according to BMI and preterm prelabor rupture of membranes. Spline analyses were performed to explore the dose-response relationship between the metabolites and sPTB.Results: Statistically significant association with decreased sPTB risk was observed for the highest L-carnitine (OR: 0.47; 95% CI: 0.23, 0.95). In risk analyses stratified by BMI, similar results were observed in normal weight gravida (BMI: 18.5~23.9 kg/cm2). The significant subtype-specific association with TMAO (OR: 0.43; 95% CI: 0.20, 0.93) and L-carnitine (OR: 0.45; 95% CI: 0.21, 0.97) were observed for preterm labor but not PPROM. Spline regression analysis indicated non-linear associations with TMAO and sPTB risk (P for nonlinearity: 0.057). Significant associations of TMAO with sPTB were observed in normal weight gravida (P = 0.028) and preterm labor subtype (P = 0.025). No statistically significant associations with sPTB risk were observed for betaine (P > 0.05).Conclusions: TMAO and L-carnitine levels in maternal serum are inversely linked with sPTB risk. Discovery of the association between gut-microbiota initiated TMAO metabolism and sPTB may open new avenues for diagnose and therapy.

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A Predictive Model Based on Machine Learning for the Early Detection of Late-Onset Neonatal Sepsis: Development and Observational Study (Preprint)

10.2196/preprints.15965 ◽

2019 ◽

Author(s):

Wongeun Song ◽

Se Young Jung ◽

Hyunyoung Baek ◽

Chang Won Choi ◽

Young Hwa Jung ◽

...

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Intensive Care ◽

High Resolution ◽

Prediction Model ◽

Neonatal Sepsis ◽

Vital Sign ◽

Prediction Models ◽

Late Onset ◽

Model Based

BACKGROUND Neonatal sepsis is associated with most cases of mortalities and morbidities in the neonatal intensive care unit (NICU). Many studies have developed prediction models for the early diagnosis of bloodstream infections in newborns, but there are limitations to data collection and management because these models are based on high-resolution waveform data. OBJECTIVE The aim of this study was to examine the feasibility of a prediction model by using noninvasive vital sign data and machine learning technology. METHODS We used electronic medical record data in intensive care units published in the Medical Information Mart for Intensive Care III clinical database. The late-onset neonatal sepsis (LONS) prediction algorithm using our proposed forward feature selection technique was based on NICU inpatient data and was designed to detect clinical sepsis 48 hours before occurrence. The performance of this prediction model was evaluated using various feature selection algorithms and machine learning models. RESULTS The performance of the LONS prediction model was found to be comparable to that of the prediction models that use invasive data such as high-resolution vital sign data, blood gas estimations, blood cell counts, and pH levels. The area under the receiver operating characteristic curve of the 48-hour prediction model was 0.861 and that of the onset detection model was 0.868. The main features that could be vital candidate markers for clinical neonatal sepsis were blood pressure, oxygen saturation, and body temperature. Feature generation using kurtosis and skewness of the features showed the highest performance. CONCLUSIONS The findings of our study confirmed that the LONS prediction model based on machine learning can be developed using vital sign data that are regularly measured in clinical settings. Future studies should conduct external validation by using different types of data sets and actual clinical verification of the developed model.

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Early Pregnancy Peripheral Blood DNA Methylation and Risk of Gestational Diabetes Mellitus: A Nested Case-Control Study

10.21203/rs.3.rs-511407/v1 ◽

2021 ◽

Author(s):

Xiaolei Wang ◽

Jin Huang ◽

Sisi Long ◽

Huijun Lin ◽

Na Zhang ◽

...

Keyword(s):

Diabetes Mellitus ◽

Dna Methylation ◽

Peripheral Blood ◽

Case Control Study ◽

Methylation Status ◽

First Trimester ◽

Case Control ◽

Cpg Sites ◽

Nested Case Control ◽

Control Study

Abstract Introduction: Genome-wide DNA methylation profiling has been used to identify CpG sites relevant to gestational diabetes mellitus (GDM). However, these sites have not been verified in larger samples. Here, our aim was to evaluate the changes in target CpG sites in the peripheral blood of pregnant women with GDM in their first trimester. Research Design and Methods: This nested case-control study examined a large cohort of women with GDM in early pregnancy (10–15 weeks; n = 80). Target CpG sites were extracted from related published literature and bioinformatics analysis. The DNA methylation levels at 337 CpG sites located in 27 target genes were determined using MethylTarget™ sequencing. The best cut-off levels for methylation of CpG sites were determined using the generated ROC curve. The independent effect of CpG site methylation status on GDM was analyzed using conditional logistic regression. Results Methylation levels at 6 CpG sites were significantly higher in the GDM group than in controls, whereas those at 7 CpG sites were significantly lower (P < 0.05). The area under the ROC curve at each methylation level of the significant CpG sites ranged between 0.593 and 0.650 for GDM prediction. After adjusting for possible confounders, the hypermethylation status of candidate sites cg68167324 (OR = 3.168, 1.038–9.666) and cg24837915 (OR = 5.232, 1.659–16.506) was identified as more strongly associated with GDM; conversely, the hypermethylation of sites cg157130156 (OR = 0.361, 0.135–0.966) and cg89438648 (OR = 0.206, 0.065–0.655) might indicate lower risk of GDM. Conclusions The methylation status of target CpG sites in the peripheral blood of pregnant women during the first trimester is associated with GDM pathogenesis, and has potential as a predictor of GDM.

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Prediction of late-onset fetal growth restriction using a combined first- and second-trimester screening model in South Chinese infants: a retrospective study

10.21203/rs.2.17844/v2 ◽

2020 ◽

Author(s):

Haiqing Zheng ◽

Yan Feng ◽

Jiexin Zhang ◽

Kuanrong Li ◽

Huiying Liang ◽

...

Keyword(s):

Retrospective Study ◽

Birth Weight ◽

Prediction Model ◽

Fetal Growth ◽

Fetal Growth Restriction ◽

Detection Rate ◽

Prediction Models ◽

Late Onset ◽

Validation Dataset ◽

Second Trimester

Abstract Background: Prediction models for early and late fetal growth restriction (FGR) have been established in many high-income countries. However, prediction models for late FGR in China are limited. This study aimed to develop a simple combined first- and second-trimester prediction model for screening late-onset FGR in South Chinese infants. Methods: This retrospective study included 2258 women who had singleton pregnancies and received routine ultrasound scans as training dataset. A validation dataset including 565 pregnant women was used to evaluate the model in order to enable an unbiased estimation. Late-onset FGR was defined as a birth weight < the 10th percentile plus abnormal Doppler indices and/or a birth weight below the 3rd percentile after 32 weeks, regardless of the Doppler status. Multivariate logistic regression was used to develop a prediction model. The model included the a priori risk (maternal characteristics), the second-trimester head circumference (HC/AC) / abdomen circumference (HC) ratio and estimated fetal weight (EFW). Results: Ninety-three fetuses were identified as late-onset FGR. The significant predictors for late-onset FGR were maternal age, height, weight, and medical history; the second-trimester HC/ AC ratio; and the EFW. This model achieved a detection rate (DR) of 52.6% for late-onset FGR at a 10% false positive rate (FPR) (area under the curve (AUC): 0.80, 95%CI 0.76-0.85). The AUC of the validation dataset was 0.65 (95%CI 0.54-0.78). Conclusions: A multivariate model combining first- and second-trimester default tests can detect 52.6% of cases of late-onset FGR at a 10% FPR. Further studies with more screening markers are needed to improve the detection rate.

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