Identification of Short-Chain Fatty Acids for Predicting Preterm Birth in Cervicovaginal Fluid Using Mass Spectrometry.

Preterm birth (PTB) refers to delivery before 37 weeks of gestation. Premature neonates exhibit higher neonatal morbidity and mortality rates than term neonates; therefore, predicting and preventing PTB are important. In this study, we investigated the potential of using short-chain fatty acid (SCFA) levels, specific vaginal microbiota-derived metabolites, as a biomarker in predicting PTB using gas chromatography/mass spectrometry. Cervicovaginal fluid (CVF) was collected from 89 pregnant women (29 cases of PTB vs. 60 controls) without evidence of other clinical infections, and SCFA levels were measured. Furthermore, the PTB group was divided into two subgroups based on birth timing after CVF sampling: delivery ≤ 2 days after sampling (n = 10) and ≥2 days after sampling (n = 19). The concentrations of propionic acid, isobutyric acid, butyric acid, valeric acid, hexanoic acid, and heptanoic acid were significantly higher in the PTB group than in the term birth (TB) group (p < 0.05). In particular, the concentrations of propionic acid, isobutyric acid, hexanoic acid, and heptanoic acid were continuously higher in the PTB group than in the TB group (p < 0.05). In the delivery ≤ 2 days after sampling group, the propionic acid, isobutyric acid, hexanoic acid, and heptanoic acid levels were significantly higher than those in the other groups (p < 0.05). This study demonstrated a significant association between specific SCFAs and PTB. We propose these SCFAs as potential biomarkers for the prediction of PTB.

Correction: Park et al. Effect of Particulate Matter 2.5 on Fetal Growth in Male and Preterm Infants through Oxidative Stress. Antioxidants 2023, 12, 1916.

In the original publication [...].

Urinary metabolite biomarkers of pregnancy complications associated with maternal exposure to particulate matter.

Particulate matter 2.5 (PM2.5) is associated with reproductive health and adverse pregnancy outcomes. However, studies evaluating biological markers of PM2.5 are lacking, and identifying biomarkers for estimating prenatal exposure to prevent pregnancy complications is essential. Therefore, we aimed to explore urine metabolites that are easy to measure as biomarkers of exposure. In this matched case-control study based on the PM2.5 exposure, 30 high PM2.5 group (>15 µg/m3) and 30 low PM2.5 group (<15 µg/m3) were selected from air pollution on pregnancy outcome (APPO) cohort study. We used a time-weighted average model to estimate individual PM exposure, which used indoor PM2.5 and outdoor PM2.5 concentrations by atmospheric measurement network based on residential addresses. Clinical characteristics and urine samples were collected from participants during the second trimester of pregnancy. Urine metabolites were quantitatively measured using gas chromatography-mass spectrometry following multistep chemical derivatization. Statistical analyses were conducted using SPSS version 21 and MetaboAnalyst 5.0. Small for gestational age and gestational diabetes (GDM) were significantly increased in the high PM2.5 group, respectively (P = 0.042, and 0.022). Fifteen metabolites showed significant differences between the two groups (P < 0.05). Subsequent pathway enrichment revealed that four pathways, including pentose and glucuronate interconversion with three pentose sugars (ribose, arabinose, and xylose; P < 0.05). The concentration of ribose increased preterm births (PTB) and GDM (P = 0.044 and 0.049, respectively), and the arabinose concentration showed a tendency to increase in PTB (P = 0.044). Therefore, we identified urinary pentose metabolites as biomarkers of PM2.5 and confirmed the possibility of their relationship with pregnancy complications.

Maternal PM2.5 exposure is associated with preterm birth and gestational diabetes mellitus, and mitochondrial OXPHOS dysfunction in cord blood.

Maternal exposure to fine particulate matter (PM2.5) is associated with adverse pregnancy and neonatal health outcomes. To explore the mechanism, we performed mRNA sequencing of neonatal cord blood. From an ongoing prospective cohort, Air Pollution on Pregnancy Outcome (APPO) study, 454 pregnant women from six centers between January 2021 and June 2022 were recruited. Individual PM2.5 exposure was calculated using a time-weighted average model. In the APPO study, age-matched cord blood samples from the High PM2.5 (˃15 ug/m3; n = 10) and Low PM2.5 (≤ 15 ug/m3; n = 30) groups were randomly selected for mRNA sequencing. After selecting genes with differential expression in the two groups (p-value < 0.05 and log2 fold change > 1.5), pathway enrichment analysis was performed, and the mitochondrial pathway was analyzed using MitoCarta3.0. The risk of preterm birth (PTB) increased with every 5 µg/m3 increase of PM2.5 in the second trimester (odds ratio 1.391, p = 0.019) after adjusting for confounding variables. The risk of gestational diabetes mellitus (GDM) increased in the second (odds ratio 1.238, p = 0.041) and third trimester (odds ratio 1.290, p = 0.029), and entire pregnancy (odds ratio 1.295, p = 0.029). The mRNA-sequencing of cord blood showed that genes related to mitochondrial activity (FAM210B, KRT1, FOXO4, TRIM58, and FBXO7) and PTB-related genes (ADIPOR1, YBX1, OPTN, NFkB1, HBG2) were upregulated in the High PM2.5 group. In addition, exposure to high PM2.5 affected mitochondrial oxidative phosphorylation (OXPHOS) and proteins in the electron transport chain, a subunit of OXPHOS. These results suggest that exposure to high PM2.5 during pregnancy may increase the risk of PTB and GDM, and dysregulate PTB-related genes. Alterations in mitochondrial OXPHOS by high PM2.5 exposure may occur not only in preterm infants but also in normal newborns. Further studies with larger sample sizes are required.

Effect of Particulate Matter 2.5 on Fetal Growth in Male and Preterm Infants through Oxidative Stress.

Particulate matter 2.5 (PM2.5) levels are associated with adverse pregnancy outcomes. In this retrospective cohort study, we examined whether the concentration of indoor PM2.5 affected pregnancy outcomes. Additionally, we evaluated biomarkers of pregnancy-related complications caused by fine dust. We collected clinical information and data based on residential addresses from the Air Korea database to assess PM2.5 exposure levels. As a multicenter prospective cohort study, we measured the indoor PM2.5 concentration and inflammatory and oxidative stress markers. The PM2.5 concentration of the low-birth-weight (LBW) delivery group was 27.21 µg/m3, which was significantly higher than that of the normal-birth-weight (NBW) group (26.23 µg/m3) (p = 0.02). When the newborns were divided by sex, the PM2.5 concentration of the LBW group was 27.89 µg/m3 in male infants, which was significantly higher than that of the NBW group (26.26 µg/m3) (p = 0.01). In the prospective study, 8-hydroxy-2-deoxyguanosine significantly increased in the high-concentration group (113.55 ng/mL, compared with 92.20 ng/mL in the low-concentration group); in the high-concentration group, the rates of preterm birth (PTB) and small size for gestational age significantly increased (p < 0.01, p = 0.01). This study showed an association between PM2.5, oxidative stress, and fetal growth, with the PTB group being more vulnerable.

Personal exposure of PM2.5 and metabolic syndrome markers of pregnant women in South Korea: APPO study.

We examined the association between exposure to PM2.5, focused on individual exposure level, and metabolic dysfunction during pregnancy. APPO study (Air Pollution on Pregnancy Outcome) was a prospective, multicenter, observational cohort study conducted from January 2021 to March 2023. Individual PM2.5 concentrations were calculated using a time-weighted average model. Metabolic dysfunction during pregnancy was assessed based on a modified definition of metabolic syndrome and its components, accounting for pregnancy-specific criteria. Exposure to PM2.5 during pregnancy was associated with worsened metabolic parameters especially glucose metabolism. In comparison to participants exposed to the low PM2.5 group, those exposed to high PM2.5 levels exhibited increased odds of gestational diabetes mellitus (GDM) after adjusting for confounding variables in different adjusted models. Specifically, in model 1, the adjusted odds ratio (aOR) was 3.117 with a 95% confidence interval (CI) of 1.234-7.870; in model 2, the aOR was 3.855 with a 95% CI of 1.255-11.844; in model 3, the aOR was 3.404 with a 95% CI of 1.206-9.607; and in model 4, the aOR was 2.741 with a 95% CI of 0.712-10.547. Exposure to higher levels of PM2.5 during pregnancy was associated with a tendency to worsen metabolic dysfunction markers specifically in glucose homeostasis. Further research is needed to investigate the mechanisms underlying the effects of ambient PM2.5 on metabolic dysfunction during pregnancy.

A genome-wide and candidate gene association study of preterm birth in Korean pregnant women.

Preterm birth (PTB) refers to delivery before 37 weeks of gestation. Premature neonates exhibit higher neonatal morbidity and mortality rates than term neonates; therefore, it is crucial to predict and prevent PTB. Advancements enable the prediction and prevention of PTB using genetic approaches, especially by investigating its correlation with single nucleotide polymorphisms (SNPs). We aimed to identify impactive and relevant SNPs for the prediction of PTB via whole-genome sequencing analyses of the blood of 31 pregnant women with PTB (n = 13) and term birth (n = 18) who visited the Ewha Womans University Mokdong Hospital from November 1, 2018 to February 29, 2020. A genome-wide association study was performed using PLINK 1.9 software and 256 SNPs were selected and traced through protein-protein interactions. Moreover, a validation study by genotyping was performed on 60 other participants (preterm birth, n = 30; term birth, n = 30) for 25 SNPs related to ion channel binding and receptor complex pathways. Odds ratios were calculated using additive, dominant, and recessive genetic models. The risk of PTB in women with the AG allele of rs2485579 (gene name: RYR2) was significantly 4.82-fold increase, and the risk of PTB in women with the AG allele of rs7903957 (gene name: TBX5) was significantly 0.25-fold reduce. Our results suggest that rs2485579 (in RYR2) can be a genetic marker of PTB, which is considered through the association with abnormal cytoplasmic Ca2+ concentration and dysfunctional uterine contraction due to differences of RYR2 in the sarcoplasmic reticulum.

Lactobacillus Probiotics Improve Vaginal Dysbiosis in Asymptomatic Women.

Vaginal dysbiosis can lead to serious infections in asymptomatic women. Lactobacillus probiotics (LBPs) are being investigated as a promising therapy for reversing vaginal microbiota dysbiosis. This study aimed to investigate whether administering LBPs could improve vaginal dysbiosis and facilitate the colonization of Lactobacillus species in asymptomatic women. 36 asymptomatic women were classified based on the Nugent score as Low-NS (n = 26) and High-NS (n = 10) groups. A combination of Lactobacillus acidophilus CBT LA1, Lactobacillus rhamnosus CBT LR5, and Lactobacillus reuteri CBT LU4 was administered orally for 6 weeks. The study found that among women with a High-NS, 60% showed improved vaginal dysbiosis with a Low-NS after LBP intake, while four retained a High-NS. Among women with a Low-NS, 11.5 % switched to a High-NS. Genera associated with vaginal dysbiosis were positively correlated with the alpha diversity or NS, while a negative correlation was observed between Lactobacillus and the alpha diversity and with the NS. Vaginal dysbiosis in asymptomatic women with an HNS improved after 6 weeks of LBP intake, and qRT-PCR revealed the colonization of Lactobacillus spp. in the vagina. These results suggested that oral administration of this LBP could improve vaginal health in asymptomatic women with an HNS.

The introduction to air pollution on pregnancy outcome (APPO) study: a multicenter cohort study.

OBJECTIVE: The air pollution on pregnancy outcome (APPO) study is a prospective hospital-based cohort study designed to investigate the maternal and fetal effects of a particulate matter with an aerodynamic below 10 µm (PM10) and PM2.5 (below 2.5 µm) exposure. This study aims to analyze a relationship between particulate matter and adverse pregnancy outcomes and to find related biomarkers and develop management guidelines. METHODS: About 1,200 pregnant women are recruited for 3 years (from January 2021 to December 2023) from seven university hospitals to investigate the effects of particulate matter on pregnancy complications and adverse pregnancy outcomes. We collect biological samples by 5 mL of maternal venous blood and 15 mL of urine in each trimester of pregnancy, and 5 mL of umbilical cord blood and 2×2×2 cm of placental tissue are collected after delivery. In addition, by applying PM10 and PM2.5 concentration values and time-activity patterns from the time weighted average model, the individual predicted exposure of air pollution for the pregnant women are obtained. RESULTS: The average exposure of PM10 and PM2.5 of the participants in the entire period of pregnancy, was exceeded the World Health Organization air quality guidelines (an annual level, PM10 >15 µg/m3, PM2.5 >5 µg/m3). Moreover, it was revealed that the PM concentration was increasing toward the 3rd trimester of pregnancy. CONCLUSION: The APPO study will be able to identify the degree of exposure to air pollution in pregnant women and use it as basic data for estimating individual exposure to particulate matter. And the results of the APPO study will facilitate in the development of health management for pregnant women against air pollution.

Transition in vaginal Lactobacillus species during pregnancy and prediction of preterm birth in Korean women.

The predominance of vaginal Lactobacillus species, specifically L. crispatus, is important for pregnancy maintenance, but varies by race. The composition of the vaginal microbiome can affect susceptibility to adverse pregnancy outcomes. We performed 16S rRNA gene amplicon sequencing on vaginal swabs taken from Korean pregnant women. Here, we report the transition of Lactobacillus spp. in samples of full-term birth (FTB) collected longitudinally in the second and third trimesters of pregnancy in a cohort study (n = 23) and their association with Lactobacillus abundance and preterm birth (PTB) in a case-control study (n = 200). Lactobacillus species, which was dominant in FTB samples including those that received interventions in the second trimester, did not change until 37 weeks of gestation. However, L. crispatus was replaced by other Lactobacillus species after 37 weeks. The PTB risk showed a closer association with the Lactobacillus abundance than with community state type determined by Lactobacillus species. PTB was associated with less than 90% of Lactobacillus abundance and an increase in Ureplasma parvum in the second trimester. Thus, the vaginal microbiome may change in preparation for childbirth in response to multiple intrinsic factors after 37 weeks of gestation. Monitoring the Lactobacillus abundance may help improve the reliability of microbial PTB biomarkers.

Predicting preterm birth through vaginal microbiota, cervical length, and WBC using a machine learning model.

An association between the vaginal microbiome and preterm birth has been reported. However, in practice, it is difficult to predict premature birth using the microbiome because the vaginal microbial community varies highly among samples depending on the individual, and the prediction rate is very low. The purpose of this study was to select markers that improve predictive power through machine learning among various vaginal microbiota and develop a prediction algorithm with better predictive power that combines clinical information. As a multicenter case-control study with 150 Korean pregnant women with 54 preterm delivery group and 96 full-term delivery group, cervicovaginal fluid was collected from pregnant women during mid-pregnancy. Their demographic profiles (age, BMI, education level, and PTB history), white blood cell count, and cervical length were recorded, and the microbiome profiles of the cervicovaginal fluid were analyzed. The subjects were randomly divided into a training (n = 101) and a test set (n = 49) in a two-to-one ratio. When training ML models using selected markers, five-fold cross-validation was performed on the training set. A univariate analysis was performed to select markers using seven statistical tests, including the Wilcoxon rank-sum test. Using the selected markers, including Lactobacillus spp., Gardnerella vaginalis, Ureaplasma parvum, Atopobium vaginae, Prevotella timonensis, and Peptoniphilus grossensis, machine learning models (logistic regression, random forest, extreme gradient boosting, support vector machine, and GUIDE) were used to build prediction models. The test area under the curve of the logistic regression model was 0.72 when it was trained with the 17 selected markers. When analyzed by combining white blood cell count and cervical length with the seven vaginal microbiome markers, the random forest model showed the highest test area under the curve of 0.84. The GUIDE, the single tree model, provided a more reasonable biological interpretation, using the 10 selected markers (A. vaginae, G. vaginalis, Lactobacillus crispatus, Lactobacillus fornicalis, Lactobacillus gasseri, Lactobacillus iners, Lactobacillus jensenii, Peptoniphilus grossensis, P. timonensis, and U. parvum), and the covariates produced a tree with a test area under the curve of 0.77. It was confirmed that the association with preterm birth increased when P. timonensis and U. parvum increased (AUC = 0.77), which could also be explained by the fact that as the number of Peptoniphilus lacrimalis increased, the association with preterm birth was high (AUC = 0.77). Our study demonstrates that several candidate bacteria could be used as potential predictors for preterm birth, and that the predictive rate can be increased through a machine learning model employing a combination of cervical length and white blood cell count information.

Development of a New Biomarker Model for Predicting Preterm Birth in Cervicovaginal Fluid.

Preterm birth (PTB) is a social problem that adversely affects not only the survival rate of the fetus, but also the premature babies and families, so there is an urgent need to find accurate biomarkers. We noted that among causes, eubiosis of the vaginal microbial community to dysbiosis leads to changes in metabolite composition. In this study, short chain fatty acids (SCFAs) representing dysbiosis were derivatized using (N-tert-butyldimethylsilyl-N-methyltrifluoroacetamide, MTBSTFA) and targeted analysis was conducted in extracted organic phases of cervicovaginal fluid (CVF). In residual aqueous CVF, polar metabolites produced biochemistry process were derivatized using methoxyamine and N,O-bis(trimethylsilyl)trifluoroacetamide (BSTFA), and non-targeted analysis were conducted. Nine SCFAs were quantified, and 58 polar metabolites were detected in 90 clinical samples using gas chromatography/mass spectrometry (GC/MS). The criteria of statistical analysis and detection rate of clinical sample for development of PTB biomarkers were presented, and 19 biomarkers were selected based on it, consisting of 1 SCFA, 2 organic acids, 4 amine compounds, and 12 amino acids. In addition, the model was evaluated as a suitable indicator for predicting PTB without distinction between sample collection time. We hope that the developed biomarkers based on microbiota-derived metabolites could provide useful diagnostic biomarkers for actual patients and pre-pregnancy.

Prenatal maternal alcohol exposure: diagnosis and prevention of fetal alcohol syndrome.

Fetal alcohol syndrome (FAS) is a developmental and congenital disorder characterized by neurocognitive impairment, structural defects, and growth restriction due to prenatal alcohol exposure. The estimated global prevalence of alcohol use during pregnancy is 9.8%, and the estimated prevalence of FAS in the general population is 14.6 per 10,000 people. In Korea, the estimated prevalence of alcohol use during pregnancy is 16%, and the prevalence of FAS is 18-51 per 10,000 women, which is higher than the global prevalence. Women's alcohol consumption rates have increased, especially in women of childbearing age. This could increase the incidence of FAS, leading to higher medical expenses and burden on society. Alcohol is the single most important teratogen that causes FAS, and there is no safe trimester to drink alcohol and no known safe amount of alcohol consumption during pregnancy. Thus, physicians should assess women's drinking patterns in detail and provide education on FAS to women by understanding its pathophysiology. Moreover, the prevention of FAS requires long-term care with a multidisciplinary approach.

Ureaplasma and Prevotella colonization with Lactobacillus abundance during pregnancy facilitates term birth.

Ureaplasma and Prevotella infections are well-known bacteria associated with preterm birth. However, with the development of metagenome sequencing techniques, it has been found that not all Ureaplasma and Prevotella colonizations cause preterm birth. The purpose of this study was to determine the association between Ureaplasma and Prevotella colonization with the induction of preterm birth even in the presence of Lactobacillus. In this matched case-control study, a total of 203 pregnant Korean women were selected and their cervicovaginal fluid samples were collected during mid-pregnancy. The microbiome profiles of the cervicovaginal fluid were analyzed using 16S rRNA gene amplification. Sequencing data were processed using QIIME1.9.1. Statistical analyses were performed using R software, and microbiome analysis was performed using the MicrobiomeAnalyst and Calypso software. A positive correlation between Ureaplasma and other genera was highly related to preterm birth, but interestingly, there was a negative correlation with Lactobacillus and term birth, with the same pattern observed with Prevotella. Ureaplasma and Prevotella colonization with Lactobacillus abundance during pregnancy facilitates term birth, although Ureaplasma and Prevotella are associated with preterm birth. Balanced colonization between Lactobacillus and Ureaplasma and Prevotella is important to prevent preterm birth.

Identification of Indicators for Preterm Birth Using Retinoid Metabolites.

Metabolites reflect the biochemical dynamics for the maintenance of pregnancy and parturition. UPLC-Q/TOF-MS and LC-MS/MS metabolomics were performed to identify and validate the plasma metabolomic signatures of preterm birth (PTB). We recruited pregnant women between 16 and 40 weeks 5 days gestational age at Ewha Womans Mokdong Hospital for a nested case-control study. In untargeted UPLC-Q/TOF-MS, score plots of partial least-squares discriminant analysis clearly separated the PTB group from the term birth (TB, n = 10; PTB, n = 11). Fifteen metabolites were significantly different between the two groups, as indicated by a variable importance in projection >1 and p < 0.05. Metabolic pathways involving retinol, linoleic acid, D-arginine, and D-ornithine were associated with PTB. Verification by LC-MS/MS focused on retinol metabolism (TB, n = 39; PTB, n = 20). Retinol levels were significantly reduced in PTB compared to TB, while retinal palmitate, all-trans-retinal, and 13-cis-retinoic acid (13cis-RA) significantly increased (p < 0.05). Retinol-binding protein levels were also elevated in PTB. Additionally, all-trans-retinal (AUC 0.808, 95% CI: 0.683-0.933) and 13cis-RA (AUC 0.826, 95% CI: 0.723-0.930) showed improved predictions for PTB-related retinol metabolites. This study suggests that retinoid metabolism improves the accuracy of PTB predictions and plays an important role in maintaining pregnancy and inducing early parturition.

Molecular Mechanism of Microbiota Metabolites in Preterm Birth: Pathological and Therapeutic Insights.

Preterm birth (PTB) refers to the birth of infants before 37 weeks of gestation and is a challenging issue worldwide. Evidence reveals that PTB is a multifactorial dysregulation mediated by a complex molecular mechanism. Thus, a better understanding of the complex molecular mechanisms underlying PTB is a prerequisite to explore effective therapeutic approaches. During early pregnancy, various physiological and metabolic changes occur as a result of endocrine and immune metabolism. The microbiota controls the physiological and metabolic mechanism of the host homeostasis, and dysbiosis of maternal microbial homeostasis dysregulates the mechanistic of fetal developmental processes and directly affects the birth outcome. Accumulating evidence indicates that metabolic dysregulation in the maternal or fetal membranes stimulates the inflammatory cytokines, which may positively progress the PTB. Although labour is regarded as an inflammatory process, it is still unclear how microbial dysbiosis could regulate the molecular mechanism of PTB. In this review based on recent research, we focused on both the pathological and therapeutic contribution of microbiota-generated metabolites to PTB and the possible molecular mechanisms.

Predictability of Macrosomic Birth based on Maternal Factors and Fetal Aneuploidy Screening Biochemical Markers in Hyperglycemic Mothers.

Background: Macrosomic birth weight has been implicated as a significant risk factor for developing various adult metabolic diseases such as diabetes mellitus and coronary heart diseases; it has also been associated with higher incidences of complicated births. This study aimed to examine the predictability of macrosomic births in hyperglycemic pregnant women using maternal clinical characteristics and serum biomarkers of aneuploidy screening performed in the first half of pregnancy. Methods: A retrospective observational study was performed on a cohort of 1,668 pregnant women who 1) had positive outcomes after undergoing 50-g oral glucose challenge test (OGCT) at two university-based hospitals and 2) underwent any one of the following maternal biomarker screening tests for fetal aneuploidy: triple test, quadruple test, and integrated test. Logistic regression-based models for predicting macrosomic births using maternal characteristics and serum biomarkers were developed and evaluated for prediction power. A nomogram, which is a graphical display of the best predictable model, was then generated. Results: The study cohort included 157 macrosomic birth cases defined as birth weight ≥3,820 g, which was equivalent to the top 10 percentile of the modeling cohort. Three primary models solely based on serum biomarkers achieved area under curves (AUCs) of 0.55-0.62. Expanded models, including maternal demographic and clinical factors, demonstrated an improved performance by 25% (AUCs, 0.69-0.73). Conclusion: Our prediction models will help to identify pregnancies with an elevated risk of macrosomic births in hyperglycemic mothers using maternal clinical factors and serum markers from routine antenatal screening tests. Prediction of macrosomic birth at mid-pregnancy may allow customized antenatal care to reduce the risk of macrosomic births.

Prenatal Exposure to Alcohol, Tobacco, and Coffee: Associated Congenital Complications and Adverse Birth Outcomes.

A few studies to date have examined the association between prenatal exposure to alcohol, tobacco, and coffee, and congenital complications/adverse birth outcomes among South Korean populations. Thus, this study analyzed the data of 1675 Korean women with birth experience within the last 3 years for pregnancy-related health and nutritional behaviors and relative outcomes. During their pregnancies, 11.58% of the study population consumed alcohol at least once, 1.43% drank throughout all three trimesters, 1.13% smoked, 25.43% were exposed to secondhand smoking, and 28.18% consumed 3 coffees or more every day. Prenatal alcohol exposure was associated with 11.24 times increased risk of birth defects/disabilities [Odds Ratio (OR): 11.24, 95% Confidence Interval (CI) 1.07-117.86] and 10.66 times increased risk of inherited metabolic diseases (OR: 10.66, 95% CI: 1.08-104.82). Prenatal secondhand smoke exposure (OR: 1.62, 95% CI: 1.01-2.62) and coffee consumption (OR: 1.92, 95% CI: 1.22-3.03) was associated with increased risk of low birth weight. Such results were in alignment with that of previous studies and confirmed that prenatal alcohol, tobacco, and coffee exposure can have detrimental neonatal and maternal consequences.

Prediction of preterm birth based on machine learning using bacterial risk score in cervicovaginal fluid.

PROBLEM: Preterm birth (PTB) is a major cause of increased morbidity and mortality in newborns. The main cause of spontaneous PTB (sPTB) is the activation of an inflammatory response as a result of ascending genital tract infection. Despite various studies on the effects of the vaginal microbiome on PTB, a practical method for its clinical application has yet to be developed. METHOD OF STUDY: In this case-control study, 94 Korean pregnant women with PTB (n = 38) and term birth (TB; n = 56) were enrolled. Their cervicovaginal fluid (CVF) was sampled, and a total of 10 bacteria were analyzed using multiplex quantitative real-time PCR (qPCR). The PTB and TB groups were compared, and a PTB prediction model was created using bacterial risk scores using machine learning techniques (decision tree and support vector machine). The predictive performance of the model was validated using random subsampling. RESULTS: Bacterial risk scoring model showed significant differences (P < 0.001). The PTB risk was low when the Lactobacillus iners ratio was 0.812 or more. In groups with a ratio under 0.812, moderate and high risk was classified as a U. parvum ratio of 4.6 × 10-3 . The sensitivity and specificity of the PTB prediction model using bacteria risk score were 71% and 59%, respectively, and 77% and 67%, respectively, when white blood cell (WBC) data were included. CONCLUSION: Using machine learning, the bacterial risk score in CVF can be used to predict PTB.

Identification of Potential Biomarkers in the Cervicovaginal Fluid by Metabolic Profiling for Preterm Birth.

During pregnancy, dysbiosis in the vaginal microbiota directly affects the metabolic profiles, which might impact preterm birth (PTB). In this study, we performed cervicovaginal fluid (CVF) metabolic profiling using nuclear magnetic resonance (NMR) spectroscopy and identified the metabolic markers for predicting PTB. In this nested case-control study, 43 South Korean pregnant women with PTB (n = 22), and term birth (TB; n = 21) were enrolled with their demographic profiles, and CVF samples were collected by vaginal swabs. The PTB group had two subgroups based on post-CVF sampling birth: PTB less than (PTB < 7 d) and more than 7 days (PTB ≥ 7 d). We observed significant differences in the gestational age at birth (GAB), cervical length (CL), and neonatal birth weight among the groups. The principal component analysis (PCA), and partial least square discriminant analysis (PLS-DA) scatter plot showed the separation between the PTB < 7 d group, and the TB group. Out of 28 identified metabolites, acetone, ethanol, ethylene glycol, formate, glycolate, isopropanol, methanol, and trimethylamine N-oxide (TMAO) were significantly increased in the PTB group compared with the TB group. The ROC curve analysis revealed that the acetone, ethylene glycol, formate, glycolate, isopropanol, methanol, and TMAO had the best predictive values for PTB. Additionally, the correlation analysis of these metabolites showed a strong negative correlation with GAB and CL. These metabolites could be beneficial markers for the clinical application of PTB prediction.