Biomarkers commonly assessed in prenatal screening have been associated with a number of adverse perinatal and birth outcomes. However, it is not clear whether first trimester measurements of prenatal screening biomarkers are associated with subsequent risk of gestational diabetes mellitus (GDM). We aimed to systematically review and statistically summarize studies assessing the relationship between first trimester prenatal screening biomarker levels and GDM development. We comprehensively searched PubMed/MEDLINE, EMBASE, CINAHL, and Scopus (from inception through January 2018) and manually searched the reference lists of all relevant articles. We included original, published, observational studies examining the association of first trimester pregnancy associated plasma protein-A (PAPP-A) and/or free β-human chorionic gonadotropin (free β-hCG) levels with GDM diagnosis. Mean differences were calculated comparing PAPP-A and free β-hCG multiples of median (MoM) levels between women who developed GDM and those who did not and were subsequently pooled using two-sided random-effects models. Our meta-analysis of 13 studies on PAPP-A and nine studies on free β-hCG indicated that first trimester MoM levels for both biomarkers were lower in women who later developed GDM compared to women who remained normoglycemic throughout pregnancy (MD -0.17; 95% CI -0.24, -0.10; MD -0.04; 95% CI -0.07–0.01). There was no evidence for between-study heterogeneity among studies on free β-hCG (I2 = 0%). A high level of between-study heterogeneity was detected among the studies reporting on PAPP-A (I2 = 90%), but was reduced after stratifying by geographic location, biomarker assay method, and timing of GDM diagnosis. Our meta-analysis indicates that women who are diagnosed with GDM have lower first trimester levels of both PAPP-A and free β-hCG than women who remain normoglycemic throughout pregnancy. Further assessment of the predictive capacity of these biomarkers within large, diverse populations is needed.

Background A growing amount of evidence indicates in utero and early life growth has profound, long-term consequences for an individual’s health throughout the life course; however, there is limited data in preterm infants, a vulnerable population at risk for growth abnormalities. Objective To address the gap in knowledge concerning early growth and its determinants in preterm infants. Methods A retrospective cohort study was performed using a population of preterm (< 37 weeks gestation) infants obtained from an electronic medical record database. Weight z-scores were acquired from discharge until roughly two years corrected age. Linear mixed effects modeling, with random slopes and intercepts, was employed to estimate growth trajectories. Results Thirteen variables, including maternal race, hypertension during pregnancy, preeclampsia, first trimester body mass index, multiple status, gestational age, birth weight, birth length, head circumference, year of birth, length of birth hospitalization stay, total parenteral nutrition, and dextrose treatment, were significantly associated with growth rates of preterm infants in univariate analyses. A small percentage (1.32% - 2.07%) of the variation in the growth of preterm infants can be explained in a joint model of these perinatal factors. In extremely preterm infants, additional variation in growth trajectories can be explained by conditions whose risk differs by degree of prematurity. Specifically, infants with periventricular leukomalacia or retinopathy of prematurity experienced decelerated rates of growth compared to infants without such conditions. Conclusions Factors found to influence growth over time in children born at term also affect growth of preterm infants. The strength of association and the magnitude of the effect varied by gestational age, revealing that significant heterogeneity in growth and its determinants exists within the preterm population.

Objective: To conduct a comprehensive review of racial and ethnic health disparities in infertility care and treatment. Evidence Review: Systematic literature searches were performed in PubMed and Embase from inception to April 2021. Studies were eligible for inclusion if they were original research performed in humans, observational study design, focused on circumstances contributing to infertility, access to infertility care, or outcomes of infertility treatment, and provided relevant information on racial or ethnic groups. Titles and abstracts were reviewed independently by two reviewers to identify pertinent articles. In addition, references of included articles were screened. Result(s): The PubMed search yielded 2,113 articles. An additional 2,301 articles were found in the Embase search. In total, 4,414 articles were screened on the basis of title and, where necessary, abstract. Thirty-four were found to meet the inclusion criteria and included in this review. Three additional studies were found from searching references of the included articles, resulting in 37 articles for discussion: 26 retrospective cohort studies, 2 prospective cohort studies, and 9 cross-sectional studies. The overall consensus in the literature is that reproductive health disparities on the basis of race and ethnicity impact fertility, access to care, and fertility treatment outcomes. Conclusion(s): Racial and ethnic differences in access to full-spectrum reproductive care, including infertility evaluation and treatment, remain. Despite access to infertility treatment, disparate treatment outcomes persist. Intrinsic and extrinsic factors, such as the institutionalization of racism and discrimination within medicine, remain influential in the diagnosis, care, and treatment outcomes of individuals with infertility. To address these inequities, we should mitigate provider bias, fund high-quality health disparity research, improve patient reproductive health knowledge, and advocate for increased access to treatment for all. (Fertil Steril Rev Ò 2021;2:169-88. Ó2021 by American Society for Reproductive Medicine.

Background Babies born early and/or small for gestational age in Low and Middle-income countries (LMICs) contribute substantially to global neonatal and infant mortality. Tracking this metric is critical at a population level for informed policy, advocacy, resources allocation and program evaluation and at an individual level for targeted care. Early prenatal ultrasound examination is not available in these settings, gestational age (GA) is estimated using new-born assessment, last menstrual period (LMP) recalls and birth weight, which are unreliable. Algorithms in developed settings, using metabolic screen data, provided GA estimates within 1–2 weeks of ultrasonography-based GA. We sought to leverage machine learning algorithms to improve accuracy and applicability of this approach to LMICs settings. Methods This study uses data from AMANHI-ACT, a prospective pregnancy cohorts in Asia and Africa where early pregnancy ultrasonography estimated GA and birth weight are available and metabolite screening data in a subset of 1318 new-borns were also available. We utilized this opportunity to develop machine learning (ML) algorithms. Random Forest Regressor was used where data was randomly split into model-building and model-testing dataset. Mean absolute error (MAE) and root mean square error (RMSE) were used to evaluate performance. Bootstrap procedures were used to estimate confidence intervals (CI) for RMSE and MAE. For pre-term birth identification ROC analysis with bootstrap and exact estimation of CI for area under curve (AUC) were performed. Results Overall model estimated GA had MAE of 5.2 days (95% CI 4.6–6.8), which was similar to performance in SGA, MAE 5.3 days (95% CI 4.6–6.2). GA was correctly estimated to within 1 week for 85.21% (95% CI 72.31–94.65). For preterm birth classification, AUC in ROC analysis was 98.1% (95% CI 96.0–99.0; p < 0.001). This model performed better than Iowa regression, AUC Difference 14.4% (95% CI 5–23.7; p = 0.002). Conclusions Machine learning algorithms and models applied to metabolomic gestational age dating offer a ladder of opportunity for providing accurate population-level gestational age estimates in LMICs settings. These findings also point to an opportunity for investigation of region-specific models, more focused feasible analyte models, and broad untargeted metabolome investigation.

Deregulation of the circadian system in humans and animals can lead to various adverse reproductive outcomes due to genetic mutations and environmental factors. In addition to the clock, lipid metabolism may also play an important role in influencing reproductive outcomes. Despite the importance of the circadian clock and lipid metabolism in regulating birth timing few studies have examined the relationship between circadian genetics with lipid levels during pregnancy and their relationship with preterm birth (PTB). In this study we aimed to determine if single nucleotide polymorphisms (SNPs) in genes from the circadian clock and lipid metabolism influence 2 nd trimester maternal lipid levels and if this is associated with an increased risk for PTB. We genotyped 72 SNPs across 40 genes previously associated with various metabolic abnormalities on 930 women with 2 nd trimester serum lipid measurements. SNPs were analyzed for their relationship to levels of total cholesterol, high density lipoprotein (HDL), low density lipoprotein (LDL) and triglycerides (TG) using linear regression. SNPs were also evaluated for their relationship to PTB using logistic regression. Five SNPs in four genes met statistical significance after Bonferroni correction ( p < 1.8 × 10 -4 ) with one or more lipid levels. Of these, four SNPs were in lipid related metabolism genes: rs7412 in APOE with total cholesterol, HDL and LDL, rs646776 and rs599839 in C ELSR2-PSRC1-SORT1 gene cluster with total cholesterol, HDL and LDL and rs738409 in PNPLA3 with HDL and TG and one was in a circadian clock gene: rs228669 in PER3 with TG. Of these SNPs only PER3 rs228669 was marginally associated with PTB ( p = 0.02). In addition, PER3 rs228669 acts as an effect modifier on the relationship between TG and PTB.