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AimsTo evaluate the clinical utility of first and second trimester prenatal screening biomarkers for early pregnancy prediction of gestational diabetes mellitus (GDM) risk in nulliparous women.MethodsWe conducted a population-based cohort study of nulliparous women participating in the California Prenatal Screening Program from 2009 to 2011 (n = 105,379). GDM was ascertained from hospital discharge records or birth certificates. Models including maternal characteristics and prenatal screening biomarkers were developed and validated. Risk stratification and reclassification were performed to assess clinical utility of the biomarkers.ResultsDecreased levels of first trimester pregnancy-associated plasma protein A (PAPP-A) and increased levels of second trimester unconjugated estriol (uE3) and dimeric inhibin A (INH) were associated with GDM. The addition of PAPP-A only and PAPP-A, uE3, and INH to maternal characteristics resulted in small, yet significant, increases in area under the receiver operating characteristic curve (AUC) (maternal characteristics only: AUC 0.714 (95% CI 0.703-0.724), maternal characteristics + PAPP-A: AUC 0.718 (95% CI 0.707-0.728), maternal characteristics + PAPP-A, uE3, and INH: AUC 0.722 (0.712-0.733)); however, no net improvement in classification was observed.ConclusionsPAPP-A, uE3, and INH have limited clinical utility for prediction of GDM risk in nulliparous women. Utility of other readily accessible clinical biomarkers in predicting GDM risk warrants further investigation.

Implementation of dietary and lifestyle interventions prior to and early in pregnancy in high risk women has been shown to reduce the risk of gestational diabetes mellitus (GDM) development later in pregnancy. Although numerous risk factors for GDM have been identified, the ability to accurately identify women before or early in pregnancy who could benefit most from these interventions remains limited. As nulliparous women are an under-screened population with risk profiles that differ from their multiparous counterparts, development of a prediction model tailored to nulliparous women may facilitate timely preventive intervention and improve maternal and infant outcomes. We aimed to develop and validate a model for preconception and early pregnancy prediction of gestational diabetes mellitus based on clinical risk factors for nulliparous women. A risk prediction model was built within a large California birth cohort including singleton live birth records from 2007-2012. Model accuracy was assessed both internally and externally, within a cohort of women who delivered at University of Iowa Hospitals and Clinics between 2009-2017, using discrimination and calibration. Differences in predictive accuracy of the model were assessed within specific racial/ethnic groups. The prediction model included five risk factors: race/ethnicity, age at delivery, pre-pregnancy body mass index, family history of diabetes, and pre-existing hypertension. The area under the curve (AUC) for the California internal validation cohort was 0.732 (95% confidence interval (CI) 0.728, 0.735), and 0.710 (95% CI 0.672, 0.749) for the Iowa external validation cohort. The model performed particularly well in Hispanic (AUC 0.739) and Black women (AUC 0.719). Our findings suggest that estimation of a woman's risk for GDM through model-based incorporation of risk factors accurately identifies those at high risk (i.e., predicted risk >6%) who could benefit from preventive intervention encouraging prompt incorporation of this tool into preconception and prenatal care.

Maternal lipid profiles during pregnancy are associated with risk for preterm birth. This study investigates the association between maternal dyslipidemia and subsequent preterm birth among pregnant women in the state of California. Births were identified from California birth certificate and hospital discharge records from 2007-2012 (N = 2,865,987). Preterm birth was defined as

Objective: Leukemia and lymphoma are top cancers affecting children, adolescents and young adults with high five-year survival rates. Late effects of these cancers are a concern in reproductive-age patients, including pregnancy outcomes such as preterm birth. Our study aimed to evaluate whether diagnosis of leukemia or lymphoma prior to pregnancy was associated with preterm birth (<37 weeks gestation). Methods: We conducted a cross-sectional study using a population-based dataset from California with linked birth certificates to hospital discharge records and an Iowa-based sample that linked birth certificates to Surveillance, Epidemiology, and End Results (SEER) cancer registry data. Preterm birth was defined using birth certificates. We ascertained history of leukemia and lymphoma using discharge diagnosis data in California and SEER registry in Iowa. Results: Prevalence of preterm birth in California and Iowa was 14.6% and 12.0%, respectively, in women with a history of leukemia/lymphoma compared to 7.8% and 8.2%, respectively, in women without a cancer history. After adjusting for maternal age, race, education, smoking, and plurality, Women with history of leukemia/lymphoma were at an increased risk of having a preterm birth in California (odds ratio (OR) 1.89; 95% confidence interval (CI) 1.56–2.28) and Iowa (OR 1.61; 95% CI 1.10–2.37) compared to those with no cancer history. Conclusion: In both California and Iowa, women with a history of leukemia or lymphoma were at increased risk for preterm birth. This suggests the importance of counseling with a history of leukemia/lymphoma prior to pregnancy and increased monitoring of women during pregnancy. [ABSTRACT FROM AUTHOR]

Background: Leukemia and lymphoma are cancers affecting children, adolescents, and young adults and may affect reproductive outcomes and maternal metabolism. We evaluated for metabolic changes in newborns of mothers with a history of these cancers. Methods: A cross-sectional study was conducted on California births from 2007 to 2011 with linked maternal hospital discharge records, birth certificate, and newborn screening metabolites. History of leukemia or lymphoma was determined using ICD-9-CM codes from hospital discharge data and newborn metabolite data from the newborn screening program. Results: A total of 2,068,038 women without cancer history and 906 with history of leukemia or lymphoma were included. After adjusting for differences in maternal age, infant sex, age at metabolite collection, gestational age, and birthweight, among newborns born to women with history of leukemia/lymphoma, several acylcarnitines were significantly (p < .001 – based on Bonferroni correction for multiple testing) higher compared to newborns of mothers without cancer history: C3-DC (mean difference (MD) = 0.006), C5-DC (MD = 0.009), C8:1 (MD = 0.008) C14 (MD = 0.010), and C16:1 (MD = 0.011), whereas citrulline levels were significantly lower (MD = 0.581) among newborns born to mothers with history of leukemia or lymphoma compared to newborns of mothers without a history of cancer. Conclusion: The varied metabolite levels suggest history of leukemia or lymphoma has metabolic impact on newborn offspring, which may have implications for future metabolic consequences such as necrotizing enterocolitis and urea cycle enzyme disorders in children born to mothers with a history of leukemia or lymphoma. [ABSTRACT FROM AUTHOR]

The extent to which ligand-binding affinities are modulated by isomerization between Itk () SH2 (Src homology) conformers is revealed by the analysis of NMR data. Itk SH2 domain is the first example of dual ligand recognition governed by prolyl cis/trans imide bond within a folded protein.

Penalized regression methods are an attractive tool for high-dimensional data analysis, but their widespread adoption has been hampered by the difficulty of applying inferential tools. In particular, the question "How reliable is the selection of those features?" has proved difficult to address. In part, this difficulty arises from defining false discoveries in the classical, fully conditional sense, which is possible in low dimensions but does not scale well to high-dimensional settings. Here, we consider the analysis of marginal false discovery rates (mFDRs) for penalized regression methods. Restricting attention to the mFDR permits straightforward estimation of the number of selections that would likely have occurred by chance alone, and therefore provides a useful summary of selection reliability. Theoretical analysis and simulation studies demonstrate that this approach is quite accurate when the correlation among predictors is mild, and only slightly conservative when the correlation is stronger. Finally, the practical utility of the proposed method and its considerable advantages over other approaches are illustrated using gene expression data from The Cancer Genome Atlas and genome-wide association study data from the Myocardial Applied Genomics Network. [ABSTRACT FROM AUTHOR]

Objective: To examine the association between genetic predisposition to elevated C-reactive protein (CRP)and risk for preeclampsia using validated genetic loci for C-reactive protein.Methods: Preeclampsia cases (n = 177) and normotensive controls (n = 116) were selected from live birth certificates to nulliparous Iowa women during the period August 2002-May 2005. Disease status was verified by the medical chart review. Genetic predisposition to CRP was estimated by a genetic risk score on the basis of established loci for CRP levels. Logistic regression analyses were used to evaluate the relationships between the genotype score and preeclampsia. Replication analyses were performed in an independent, US population of preeclampsia cases (n = 516) and controls (n = 1,097) of European ancestry.Results: The genetic risk score (GRS) related to higher levels of CRP demonstrated a significantly decreased risk of preeclampsia (OR 0.89, 95% CI 0.82-0.96). When the GRS was analyzed by quartile, an inverse linear trend was observed (p = 0.0006). The results were similar after adjustments for the body mass index (BMI), smoking, and leisure-time physical activity. In the independent replication population, the association with the CRP GRS was also marginally significant (OR 0.97, 95% CI 0.92, 1.02). Meta-analysis of the two studies was statistically significant (OR 0.95, 95% CI 0.90, 0.99).Conclusion: Our data suggest an inverse, counterintuitive association between the genetic predisposition to elevated levels of CRP and a decreased risk of preeclampsia. This suggests that the blood CRP level is a marker of preeclampsia, but it does not appear to be a factor on the causal pathway. [ABSTRACT FROM AUTHOR]