Introduction: Preeclampsia (PreE) remains a major source of maternal and newborn complications. Prenatal prediction of these complications could significantly improve pregnancy management. Objectives: Using metabolomic analysis we investigated the prenatal prediction of maternal and newborn complications in early and late PreE and investigated the pathogenesis of such complications. Methods: Serum samples from 76 cases of PreE (36 early-onset and 40 late-onset), and 40 unaffected controls were collected. Direct Injection Liquid Chromatography–Mass Spectrometry combined with Nuclear Magnetic Resonance (NMR) spectroscopy was performed. Logistic regression analysis was used to generate models for prediction of adverse maternal and neonatal outcomes in patients with PreE. Metabolite set enrichment analysis (MSEA) was used to identify the most dysregulated metabolites and pathways in PreE. Results: Forty-three metabolites were significantly altered (p < 0.05) in PreE cases with maternal complications and 162 metabolites were altered in PreE cases with newborn adverse outcomes. The top metabolite prediction model achieved an area under the receiver operating characteristic curve (AUC) = 0.806 (0.660–0.952) for predicting adverse maternal outcomes in early-onset PreE, while the AUC for late-onset PreE was 0.843 (0.712–0.974). For the prediction of adverse newborn outcomes, regression models achieved an AUC = 0.828 (0.674–0.982) in early-onset PreE and 0.911 (0.828–0.994) in late-onset PreE. Profound alterations of lipid metabolism were associated with adverse outcomes. Conclusion: Prenatal metabolomic markers achieved robust prediction, superior to conventional markers for the prediction of adverse maternal and newborn outcomes in patients with PreE. We report for the first-time the prediction and metabolomic basis of adverse maternal and newborn outcomes in patients with PreE.

Objective: The primary objective of this study is to evaluate the availability and duration of formal medical genetics and genomics (MGG) education during obstetrics and gynecology (OB/GYN) residency training in the United States compared to other noncore OB/GYN rotations. Methods: We performed a review of rotation schedules published in all American Council for Graduate Medical Education (ACGME)-accredited OB/GYN residency programs’ websites during the month of December 2016. Information regarding availability and duration of MGG rotation and other noncore OB/GYN rotations (ultrasound, breast health, and family planning) were collected. Results: Among 256 ACGME-accredited OB/GYN residency programs, rotation schedule was available for 238 (93%). Only 34 programs (14.3%) had some form of MGG rotations. In the GLM, when compared to other noncore OB/GYN rotations, the mean duration of MGG rotation was significantly less than ultrasound (0.07 versus 0.57 months, p < .05) and family planning (0.07 versus 0.42 months, p < .05). The number of residents was the only variable significantly correlated with the availability of an MGG rotation (OR 1.07, 95%CI 1.02–1.13). Conclusions: Despite the growing importance of MGG in day-to-day OB/GYN practice, only a limited number of ACGME-accredited OB/GYN residency programs offer an MGG rotation. When compared to other noncore OB/GYN rotations, such as, ultrasound and family planning, any MGG rotation was significantly shorter. With clear evidence that MGG will continue to radically change practice of OB/GYN in the future, it is imperative that steps need to be taken to address this deficiency in training.

Objectives: HBB-related significant hemoglobinopathies have been anecdotally associated with low fetal fraction on noninvasive prenatal screening (NIPS). We sought to compare the difference in fetal fraction using NIPS in women with HBB-related significant hemoglobinopathies (HSH) and women with normal hemoglobin. Study design: This is a retrospective case-control study. Cases were women with a diagnosis of HSH using NIPS from a commercial laboratory. The comparison group was women with hemoglobin AA from a tertiary care center database. We tested for differences in median fetal fraction using quantile regression analysis, adjusting for maternal body weight and gestational age. Results: This study includes 35 women with clinically significant HSH and a comparison group of 636 women with hemoglobin AA. Adjusting for gestational age and body weight, the median fetal fraction was 4.1 point lower in the HSH than in the comparison group (β − 4.1; 95% −5.7 to −2.5, p < .05). The rate of no-calls due to low fetal fraction was significantly higher in the clinically significant HSH group than in the comparison group [HSH: n = 9/35, 25.7% versus comparison: n = 32/636, 5.0% (p < .001)]. Conclusion: Women with HSH were more likely to have a lower fetal fraction and ultimately a five-fold higher no-call rate. What’s already known about this topic?

Background/Objectives: Metabolomics has emerged as a tool to gain insight into the body’s biological responses to therapeutic interventions. Bariatric surgery remains the most effective treatment for severe obesity and associated comorbidities, leading to significant weight and metabolic improvements. Using a multi-platform, multi-compartment metabolomics approach, this study systematically characterizes longitudinal changes in fecal and serum metabolomes of 45 patients following sleeve gastrectomy (SG). Participants were stratified by weight loss outcomes to identify metabolic signatures associated with differential responses, which may serve as predictors of weight loss and provide mechanistic insights in developing targeted therapeutic strategies. Methods: Metabolomic and lipidomic responses to SG were analyzed using multivariable linear mixed-effects models based on data collected pre-operatively and at 3 and 12 months post-operatively. Results: The percentage total weight loss for the highest versus lowest weight loss tertiles (T₃ vs. T₁) at twelve months was 35.81 + 5.4% and 19.49 + 2.62%, p < 0.001, respectively. Substantial alterations in fecal metabolites and lipid species were observed among T3 after twelve months, including aspartate, tyrosine, carnitine, glycine, PC.ae.C36.4, PC.aa.C38.0, PC.ae.C44.4, PC.ae.C40.2, and PC.aa.C40:5. Specifically, changes in serum lipid species including SM.OH.C22:1, PC.ae.C32:1, PC.aa.C34:4, PC.aa.C36:6, PC.ae.C34:3, PC.ae.C34:1, and PC.ae.C32:2, support serum lipidomics as a minimally invasive marker of gut remodeling and adaptation following SG. Sex-stratified analysis revealed unique fecal and serum metabolic changes, highlighting the significance of personalized metabolic monitoring and obesity treatment. Conclusions: Our findings identify metabolic alterations associated with response variability following SG and highlight the potential utility of machine learning to predict weight-loss trajectories and inform personalized interventions.