Decreased Innate Migration of Pro-Inflammatory M1 Macrophages through the Mesothelial Membrane Is Affected by Ceramide Kinase and Ceramide 1-P

The retrograde flow of endometrial tissues deposited into the peritoneal cavity occurs in women during menstruation. Classically (M1) or alternatively (M2) activated macrophages partake in the removal of regurgitated menstrual tissue. The failure of macrophage egress from the peritoneal cavity through the mesothelium leads to chronic inflammation in endometriosis. To study the migration differences of macrophage phenotypes across mesothelial cells, an in vitro model of macrophage egress across a peritoneal mesothelial cell monolayer membrane was developed. M1 macrophages were more sessile, emigrating 2.9-fold less than M2 macrophages. The M1 macrophages displayed a pro-inflammatory cytokine signature, including IL-1α, IL-1β, TNF-α, TNF-β, and IL-12p70. Mass spectrometry sphingolipidomics revealed decreased levels of ceramide-1-phosphate (C1P), an inducer of migration in M1 macrophages, which correlated with its poor migration behavior. C1P is generated by ceramide kinase (CERK) from ceramide, and blocking C1P synthesis via the action of NVP231, a specific CERK chemical inhibitor, prohibited the emigration of M1 and M2 macrophages up to 6.7-fold. Incubation with exogenously added C1P rescued this effect. These results suggest that M1 macrophages are less mobile and have higher retention in the peritoneum due to lower C1P levels, which contributes to an altered peritoneal environment in endometriosis by generating a predominant pro-inflammatory cytokine environment

Exploring preconception signatures of metabolites in mothers with gestational diabetes mellitus using a non-targeted approach

BackgroundMetabolomic changes during pregnancy have been suggested to underlie the etiology of gestational diabetes mellitus (GDM). However, research on metabolites during preconception is lacking. Therefore, this study aimed to investigate distinctive metabolites during the preconception phase between GDM and non-GDM controls in a nested case-control study in Singapore.MethodsWithin a Singapore preconception cohort, we included 33 Chinese pregnant women diagnosed with GDM according to the IADPSG criteria between 24 and 28 weeks of gestation. We then matched them with 33 non-GDM Chinese women by age and pre-pregnancy body mass index (ppBMI) within the same cohort. We performed a non-targeted metabolomics approach using fasting serum samples collected within 12 months prior to conception. We used generalized linear mixed model to identify metabolites associated with GDM at preconception after adjusting for maternal age and ppBMI. After annotation and multiple testing, we explored the additional predictive value of novel signatures of preconception metabolites in terms of GDM diagnosis.ResultsA total of 57 metabolites were significantly associated with GDM, and eight phosphatidylethanolamines were annotated using HMDB. After multiple testing corrections and sensitivity analysis, phosphatidylethanolamines 36:4 (mean difference beta: 0.07; 95% CI: 0.02, 0.11) and 38:6 (beta: 0.06; 0.004, 0.11) remained significantly higher in GDM subjects, compared with non-GDM controls. With all preconception signals of phosphatidylethanolamines in addition to traditional risk factors (e.g., maternal age and ppBMI), the predictive value measured by area under the curve (AUC) increased from 0.620 to 0.843.ConclusionsOur data identified distinctive signatures of GDM-associated preconception phosphatidylethanolamines, which is of potential value to understand the etiology of GDM as early as in the preconception phase. Future studies with larger sample sizes among alternative populations are warranted to validate the associations of these signatures of metabolites and their predictive value in GDM.Peer reviewe

Machine Learning-Derived Prenatal Predictive Risk Model to Guide Intervention and Prevent the Progression of Gestational Diabetes Mellitus to Type 2 Diabetes : Prediction Model Development Study

Publisher Copyright: © Mukkesh Kumar, Li Ting Ang, Cindy Ho, Shu E Soh, Kok Hian Tan, Jerry Kok Yen Chan, Keith M Godfrey, Shiao-Yng Chan, Yap Seng Chong, Johan G Eriksson, Mengling Feng, Neerja KarnaniBackground: The increasing prevalence of gestational diabetes mellitus (GDM) is concerning as women with GDM are at high risk of type 2 diabetes (T2D) later in life. The magnitude of this risk highlights the importance of early intervention to prevent the progression of GDM to T2D. Rates of postpartum screening are suboptimal, often as low as 13% in Asian countries. The lack of preventive care through structured postpartum screening in several health care systems and low public awareness are key barriers to postpartum diabetes screening. Objective: In this study, we developed a machine learning model for early prediction of postpartum T2D following routine antenatal GDM screening. The early prediction of postpartum T2D during prenatal care would enable the implementation of effective strategies for diabetes prevention interventions. To our best knowledge, this is the first study that uses machine learning for postpartum T2D risk assessment in antenatal populations of Asian origin. Methods: Prospective multiethnic data (Chinese, Malay, and Indian ethnicities) from 561 pregnancies in Singapore's most deeply phenotyped mother-offspring cohort study-Growing Up in Singapore Towards healthy Outcomes-were used for predictive modeling. The feature variables included were demographics, medical or obstetric history, physical measures, lifestyle information, and GDM diagnosis. Shapley values were combined with CatBoost tree ensembles to perform feature selection. Our game theoretical approach for predictive analytics enables population subtyping and pattern discovery for data-driven precision care. The predictive models were trained using 4 machine learning algorithms: logistic regression, support vector machine, CatBoost gradient boosting, and artificial neural network. We used 5-fold stratified cross-validation to preserve the same proportion of T2D cases in each fold. Grid search pipelines were built to evaluate the best performing hyperparameters. Results: A high performance prediction model for postpartum T2D comprising of 2 midgestation features-midpregnancy BMI after gestational weight gain and diagnosis of GDM-was developed (BMI_GDM CatBoost model: AUC=0.86, 95% CI 0.72-0.99). Prepregnancy BMI alone was inadequate in predicting postpartum T2D risk (ppBMI CatBoost model: AUC=0.62, 95% CI 0.39-0.86). A 2-hour postprandial glucose test (BMI_2hour CatBoost model: AUC=0.86, 95% CI 0.76-0.96) showed a stronger postpartum T2D risk prediction effect compared to fasting glucose test (BMI_Fasting CatBoost model: AUC=0.76, 95% CI 0.61-0.91). The BMI_GDM model was also robust when using a modified 2-point International Association of the Diabetes and Pregnancy Study Groups (IADPSG) 2018 criteria for GDM diagnosis (BMI_GDM2 CatBoost model: AUC=0.84, 95% CI 0.72-0.97). Total gestational weight gain was inversely associated with postpartum T2D outcome, independent of prepregnancy BMI and diagnosis of GDM (P = .02; OR 0.88, 95% CI 0.79-0.98). Conclusions: Midgestation weight gain effects, combined with the metabolic derangements underlying GDM during pregnancy, signal future T2D risk in Singaporean women. Further studies will be required to examine the influence of metabolic adaptations in pregnancy on postpartum maternal metabolic health outcomes. The state-of-the-art machine learning model can be leveraged as a rapid risk stratification tool during prenatal care.Peer reviewe

Automated Machine Learning (AutoML)-Derived Preconception Predictive Risk Model to Guide Early Intervention for Gestational Diabetes Mellitus

The increasing prevalence of gestational diabetes mellitus (GDM) is contributing to the rising global burden of type 2 diabetes (T2D) and intergenerational cycle of chronic metabolic disorders. Primary lifestyle interventions to manage GDM, including second trimester dietary and exercise guidance, have met with limited success due to late implementation, poor adherence and generic guidelines. In this study, we aimed to build a preconception-based GDM predictor to enable early intervention. We also assessed the associations of top predictors with GDM and adverse birth outcomes. Our evolutionary algorithm-based automated machine learning (AutoML) model was implemented with data from 222 Asian multi-ethnic women in a preconception cohort study, Singapore Preconception Study of Long-Term Maternal and Child Outcomes (S-PRESTO). A stacked ensemble model with a gradient boosting classifier and linear support vector machine classifier (stochastic gradient descent training) was derived using genetic programming, achieving an excellent AUC of 0.93 based on four features (glycated hemoglobin A(1c) (HbA(1c)), mean arterial blood pressure, fasting insulin, triglycerides/HDL ratio). The results of multivariate logistic regression model showed that each 1 mmol/mol increase in preconception HbA(1c) was positively associated with increased risks of GDM (p = 0.001, odds ratio (95% CI) 1.34 (1.13-1.60)) and preterm birth (p = 0.011, odds ratio 1.63 (1.12-2.38)). Optimal control of preconception HbA(1c) may aid in preventing GDM and reducing the incidence of preterm birth. Our trained predictor has been deployed as a web application that can be easily employed in GDM intervention programs, prior to conception.Peer reviewe

Exploring how socioeconomic status affects neighbourhood environments? : effects on obesity risks : a longitudinal study in Singapore

Research on how socioeconomic status interacts with neighbourhood characteristics to influence disparities in obesity outcomes is currently limited by residential segregation-induced structural confounding, a lack of empirical studies outside the U.S. and other 'Western' contexts, and an over-reliance on cross-sectional analyses. This study addresses these challenges by examining how socioeconomic status modifies the effect of accumulated exposures to obesogenic neighbourhood environments on children and mothers' BMI, drawing from a longitudinal mother-child birth cohort study in Singapore, an Asian city-state with relatively little residential segregation. We find that increased access to park connectors was associated with a decrease in BMI outcomes for mothers with higher socioeconomic status, but an increase for those with lower socioeconomic status. We also find that increased access to bus stops was associated with an increase in BMIz of children with lower socioeconomic status, but with a decrease in BMIz of children with higher socioeconomic status, while increased access to rail stations was associated with a decrease in BMIz of children with lower socioeconomic status only. Our results suggest that urban interventions might have heterogeneous effects by socioeconomic status.Peer reviewe

Prepregnancy adherence to plant-based diet indices and exploratory dietary patterns in relation to fecundability

Background Modest associations have been reported between specific food groups or nutrients and fecundability [measured by time to pregnancy (TTP)]. Examining overall diets provides a more holistic approach towards understanding their associations with fecundability. It is not known whether plant-based diets indices or exploratory dietary patterns are associated with fecundability. Objectives We examine the associations between adherence to 1) plant-based diet indices; and 2) exploratory dietary patterns and fecundability among women planning pregnancy. Methods Data were analyzed from the Singapore Preconception Study of Long-Term Maternal and Child Outcomes (S-PRESTO) study. Prepregnancy diet was assessed using a semi-quantitative FFQ from which the overall, healthful, and unhealthful plant-based diet indices (oPDI, hPDI, and uPDI, respectively) were calculated. Exploratory dietary patterns were derived using factor analysis based on 44 predefined food groups. Participants were categorized into quintiles based on their dietary pattern scores. TTP (expressed in menstrual cycles) was ascertained within a year from the prepregnancy dietary assessment. Discrete-time proportional hazard models, adjusted for confounders, were used to estimate fecundability ratios (FRs) and 95% CIs, with FR > 1 indicating a shorter TTP. Results Among 805 women, 383 pregnancies were confirmed by ultrasound scans. Compared with women in the lowest quintile, those in the highest quintile of the uPDI had reduced fecundability (FR of Q5 compared with Q1, 0.65; 95% CI, 0.46-0.91; P trend, 0.009). Conversely, greater adherence to the hPDI was associated with increased fecundability (1.46; 95% CI, 1.02-2.07; P trend, 0.036). The oPDI was not associated with fecundability. Among the 3 exploratory dietary patterns, only greater adherence to the Fast Food and Sweetened Beverages (FFSB) pattern was associated with reduced fecundability (0.61; 95% CI, 0.40-0.91; P trend, 0.018). Conclusions Greater adherence to the uPDI or the FFSB dietary pattern was associated with reduced fecundability among Asian women. Greater adherence to the hPDI may be beneficial for fecundability, though this requires confirmation by future studies.Peer reviewe

Effect of fertility health awareness strategies on fertility knowledge and childbearing in young married couples (FertStart): study protocol for an effectiveness-implementation hybrid type I multicentre three-arm parallel group open-label randomised clinical trial

Introduction Birth rates have been declining in many advanced societies including Singapore. We designed two interventions with vastly different resource requirements, which include fertility education, personalised fertility information and a behavioural change component targeting modifiable psychological constructs to modify fertility awareness and childbearing intentions. We aim to evaluate the effect of these two interventions on knowledge, attitudes and practice around childbearing compared with a control group among young married couples in Singapore and understand the implementation factors in the setting of an effectiveness-implementation hybrid type 1 three-arm randomised trial. Methods and analysis We will randomise 1200 young married couples to no intervention (control), Fertility Health Screening group (FHS) or Fertility Awareness Tools (FAT) in a 7:5:5 ratio. Couples in FHS will undergo an anti-Mullerian hormone test and semen analysis, a doctor’s consultation to explain the results and standardised reproductive counselling by a trained nurse. Couples in FAT will watch a standardised video, complete an adapted fertility status awareness (FertiSTAT) tool and receive an educational brochure. The attitudes, fertility knowledge and efforts to achieve pregnancy of all couples will be assessed at baseline and 6 months post-randomisation. Birth statistics will be tracked using administrative records at 2 and 3 years. The primary outcome is the change in the woman’s self-reported intended age at first birth between baseline and 6 months post-randomisation. In addition, implementation outcomes and cost-effectiveness of the two interventions will be assessed

Comparing feature selection and machine learning approaches for predicting CYP2D6 methylation from genetic variation

IntroductionPharmacogenetics currently supports clinical decision-making on the basis of a limited number of variants in a few genes and may benefit paediatric prescribing where there is a need for more precise dosing. Integrating genomic information such as methylation into pharmacogenetic models holds the potential to improve their accuracy and consequently prescribing decisions. Cytochrome P450 2D6 (CYP2D6) is a highly polymorphic gene conventionally associated with the metabolism of commonly used drugs and endogenous substrates. We thus sought to predict epigenetic loci from single nucleotide polymorphisms (SNPs) related to CYP2D6 in children from the GUSTO cohort.MethodsBuffy coat DNA methylation was quantified using the Illumina Infinium Methylation EPIC beadchip. CpG sites associated with CYP2D6 were used as outcome variables in Linear Regression, Elastic Net and XGBoost models. We compared feature selection of SNPs from GWAS mQTLs, GTEx eQTLs and SNPs within 2 MB of the CYP2D6 gene and the impact of adding demographic data. The samples were split into training (75%) sets and test (25%) sets for validation. In Elastic Net model and XGBoost models, optimal hyperparameter search was done using 10-fold cross validation. Root Mean Square Error and R-squared values were obtained to investigate each models’ performance. When GWAS was performed to determine SNPs associated with CpG sites, a total of 15 SNPs were identified where several SNPs appeared to influence multiple CpG sites.ResultsOverall, Elastic Net models of genetic features appeared to perform marginally better than heritability estimates and substantially better than Linear Regression and XGBoost models. The addition of nongenetic features appeared to improve performance for some but not all feature sets and probes. The best feature set and Machine Learning (ML) approach differed substantially between CpG sites and a number of top variables were identified for each model.DiscussionThe development of SNP-based prediction models for CYP2D6 CpG methylation in Singaporean children of varying ethnicities in this study has clinical application. With further validation, they may add to the set of tools available to improve precision medicine and pharmacogenetics-based dosing

Metabolic health status and fecundability in a Singapore preconception cohort study

Background: Obesity compromises metabolic health and female fertility, yet not all obese women are similar in metabolic status. The extent to which fecundability is influenced by the metabolic health status of women who are overweight or obese before conception is unknown. Objective: This study aimed to: (1) determine the metabolic health status, and (2) examine the association between metabolic health status and fecundability of overweight and obese women trying to conceive in the Singapore PREconception Study of long-Term maternal and child Outcomes cohort study. Study Design: We conducted a prospective preconception cohort study of Asian women (Chinese, Malay, and Indian) aged 18 to 45 years trying to conceive who were treated from 2015 to 2017 in KK Women's and Children's Hospital in Singapore (n=834). We defined women to have metabolically unhealthy status if they: (1) met 3 or more modified Joint Interim Statement metabolic syndrome criteria; or (2) had homeostasis model assessment-insulin resistance index ≥2.5. Body mass index was categorized as normal (18.5–22.9 kg/m2), overweight (23–27.4 kg/m2), or obese (≥27.5 kg/m2) on the basis of cutoff points for Asian populations. Fecundability was measured by time to pregnancy in menstrual cycles within a year of enrolment. Discrete-time proportional hazards models were used to estimate fecundability odds ratios, with adjustment for confounders and accounting for left truncation and right censoring. Results: Of 232 overweight women, 28 (12.1%) and 25 (10.8%) were metabolically unhealthy by metabolic syndrome ≥3 criteria and homeostasis model assessment-insulin resistance ≥2.5, respectively. Of 175 obese women, 54 (30.9%) and 93 (53.1%) were metabolically unhealthy by metabolic syndrome ≥3 criteria and homeostasis model assessment-insulin resistance ≥2.5, respectively. Compared with metabolically healthy normal-weight women, lower fecundability was observed in metabolically unhealthy overweight women on the basis of metabolic syndrome criteria (fecundability odds ratios, 0.38 [95% confidence interval, 0.15–0.92]) and homeostasis model assessment-insulin resistance (fecundability odds ratios, 0.68 [95% confidence interval, 0.33–1.39]), with metabolic syndrome criteria showing a stronger association. Metabolically unhealthy obese women showed lower fecundability than the healthy normal-weight reference group by both metabolic syndrome (fecundability odds ratios, 0.35; 95% confidence interval, 0.17–0.72) and homeostasis model assessment-insulin resistance criteria (fecundability odds ratios, 0.43; 95% confidence interval, 0.26–0.71). Reduced fecundability was not observed in overweight or obese women who showed healthy metabolic profiles by either definition. Conclusion: Overweight or obesity was not synonymous with having metabolic syndrome or insulin resistance. In our preconception cohort, metabolically unhealthy overweight and obese women showed reduced fecundability, unlike their counterparts who were metabolically healthy. These findings suggest that metabolic health status, rather than simply being overweight and obese per se, plays an important role in fecundability.acceptedVersionPeer reviewe

The Growing Up in Singapore Towards Healthy Outcomes Study

Objective Epidemiological studies relating maternal 25-hydroxyvitamin D (25OHD) with gestational diabetes mellitus (GDM) and mode of delivery have shown controversial results. We examined if maternal 25OHD status was associated with plasma glucose concentrations, risks of GDM and caesarean section in the Growing Up in Singapore Towards healthy Outcomes (GUSTO) study. Methods Plasma 25OHD concentrations, fasting glucose (FG) and 2-hour postprandial glucose (2HPPG) concentrations were measured in 940 women from a Singapore mother-offspring cohort study at 26–28 weeks’ gestation. 25OHD inadequacy and adequacy were defined based on concentrations of 25OHD ≤75nmol/l and >75nmol/l respectively. Mode of delivery was obtained from hospital records. Multiple linear regression was performed to examine the association between 25OHD status and glucose concentrations, while multiple logistic regression was performed to examine the association of 25OHD status with risks of GDM and caesarean section. Results In total, 388 (41.3%) women had 25OHD inadequacy. Of these, 131 (33.8%), 155 (39.9%) and 102 (26.3%) were Chinese, Malay and Indian respectively. After adjustment for confounders, maternal 25OHD inadequacy was associated with higher FG concentrations (β = 0.08mmol/l, 95% Confidence Interval (CI) = 0.01, 0.14), but not 2HPPG concentrations and risk of GDM. A trend between 25OHD inadequacy and higher likelihood of emergency caesarean section (Odds Ratio (OR) = 1.39, 95% CI = 0.95, 2.05) was observed. On stratification by ethnicity, the association with higher FG concentrations was significant in Malay women (β = 0.19mmol/l, 95% CI = 0.04, 0.33), while risk of emergency caesarean section was greater in Chinese (OR = 1.90, 95% CI = 1.06, 3.43) and Indian women (OR = 2.41, 95% CI = 1.01, 5.73). Conclusions 25OHD inadequacy is prevalent in pregnant Singaporean women, particularly among the Malay and Indian women. This is associated with higher FG concentrations in Malay women, and increased risk of emergency caesarean section in Chinese and Indian women