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

The socioeconomic landscape of the exposome during pregnancy

Background: While socioeconomic position (SEP) is consistently related to pregnancy and birth outcome dis-parities, relevant biological mechanisms are manifold, thus necessitating more comprehensive characterization of SEP-exposome associations during pregnancy. Objectives: We implemented an exposomic approach to systematically characterize the socioeconomic landscape of prenatal exposures in a setting where social segregation was less distinct in a hypotheses-generating manner. Methods: We described the correlation structure of 134 prenatal exogenous and endogenous sources (e.g., micronutrients, hormones, immunomodulatory metabolites, environmental pollutants) collected in a diverse, population-representative, urban, high-income longitudinal mother-offspring cohort (N = 1341; 2009-2011). We examined the associations between maternal, paternal, household, and areal level SEP indicators and 134 ex-posures using multiple regressions adjusted for precision variables, as well as potential effect measure modifi-cation by ethnicity and nativity. Finally, we generated summary SEP indices using Multiple Correspondence Analysis to further explore possible curved relationships. Results: Individual and household SEP were associated with anthropometric/adiposity measures, folate, omega-3 fatty acids, insulin-like growth factor-II, fasting glucose, and neopterin, an inflammatory marker. We observed paternal education was more strongly and consistently related to maternal exposures than maternal education. This was most apparent amongst couples discordant on education. Analyses revealed additional non-linear as-sociations between areal composite SEP and particulate matter. Environmental contaminants (e.g., per-and polyfluoroalkyl substances) and micronutrients (e.g., folate and copper) showed opposing associations by ethnicity and nativity, respectively. Discussion: SEP-exposome relationships are complex, non-linear, and context specific. Our findings reinforce the potential role of paternal contributions and context-specific modifiers of associations, such as between ethnicity and maternal diet-related exposures. Despite weak presumed areal clustering of individual exposures in our context, our approach reinforces subtle non-linearities in areal-level exposures.Peer reviewe

Gestational diabetes alters functions in offspring's umbilical cord cells with implications for cardiovascular health

10.1210/en.2016-1889Endocrinology15872102-2112ENDOAGUSTO (Growing up towards Healthy Outcomes

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

10.3390/ijerph19116792International Journal of Environmental Research and Public Health191

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

10.3390/ijerph19116792International Journal of Environmental Research and Public Health191

Umbilical Cord Plasma Lysophospholipids and Triacylglycerols Associated with Birthweight Percentiles

10.3390/nu16020274Nutrients16227