Laura Jelliffe-Pawlowski

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Abstract

Mental health conditions are the leading cause of preventable maternal mortality and morbidity, yet few investigations have examined perinatal bipolar disorders. This study sought to examine racial differences in the odds of having a bipolar disorder diagnosis in perinatal women across self-reported racial groups in a large sample in California, USA.

Abstract

Purpose: Mental health conditions are the leading cause of preventable maternal mortality and morbidity, yet few investigations have examined perinatal bipolar disorders. This study sought to examine racial differences in the odds of having a bipolar disorder diagnosis in perinatal women across self-reported racial groups in a large sample in California, USA. Method: This cross-sectional study uses data from 3,831,593 women who had singleton live births in California, USA from 2011 to 2019 existing in a linked dataset which included hospital discharge records and birth certificates. International Classification of Diseases codes were used to identify women with a bipolar disorder diagnosis code on the hospital discharge record. Medical charts and birth certificate data was used to extract information on clinical and demographic covariate characteristics. Multivariable logistic regression was used to estimate the odds of having a bipolar disorder diagnosis across different self-reported racial groups. Results: We identified 19,262 women with bipolar disorder diagnoses. Differences in the presence of a bipolar disorder diagnosis emerged by self-reported race. In the fully adjusted model, Multiracial (selection of two races self-reported) women, compared to single-race White women had the highest odds of having a bipolar disorder diagnosis. Further examination of the all-inclusive Multiracial category revealed differences across subgroups where White/Black, White/American Indian Alaskan Native, and Black/American Indian Alaskan Native women had increased odds for bipolar disorder compared to single race White women. Conclusions: Differences in bipolar disorder diagnoses exist across racial categories and when compared to White women, Multiracial women had the highest odds of bipolar disorder and thus represent a perinatal population of focus for future intervention studies. The increased burden of mental health problems among Multiracial women is consistent with recent research that employs disaggregated race data. More studies of Multiracial women are needed to determine how self-reported racial categories are related to increased risk for perinatal bipolar disorder.

Abstract

Background: Previous findings related to the association of adverse pregnancy outcomes with anorexia nervosa are mixed. Objective: This study aimed to investigate the association of adverse live-born pregnancy outcomes with anorexia nervosa using adjustment modeling accounting for confounding factors, and a mediation analysis addressing the contribution of underweight prepregnancy body mass index and gestational weight gain to those outcomes. Study Design: The sample included California live-born singletons with births between 2007 and 2021. The administrative data set contained birth certificates linked to hospital discharge records. Anorexia nervosa diagnosis during pregnancy was obtained from International Classification of Diseases codes on hospital discharge records. Adverse pregnancy outcomes examined included gestational diabetes, gestational hypertension, preeclampsia, anemia, antepartum hemorrhage, premature rupture of membranes, premature labor, cesarean delivery, oligohydramnios, placenta previa, chorioamnionitis, placental abruption, severe maternal morbidity, small for gestational age, large for gestational age, low birthweight, and preterm birth (by timing and indication). Risk of each adverse outcome was calculated using Poisson regression models. Unadjusted risk of each adverse outcome was calculated, and then the risks were adjusted for demographic factors. The final adjusted model included demographic factors, anxiety, depression, substance use, and smoking. A mediation analysis was performed to estimate the excess risk of adverse outcomes mediated by underweight prepregnancy body mass index and gestational weight gain below the American College of Obstetricians and Gynecologists recommendation. Results: The sample included 241 pregnant people with a diagnosis of anorexia nervosa and 6,418,236 pregnant people without an eating disorder diagnosis. An anorexia nervosa diagnosis during pregnancy was associated with many adverse pregnancy outcomes in unadjusted models (relative risks ranged from 1.65 [preeclampsia] to 3.56 [antepartum hemorrhage]) in comparison with people without an eating disorder diagnosis. In the final adjusted models, birthing people with an anorexia nervosa diagnosis were more likely to have anemia, preterm labor, oligohydramnios, severe maternal morbidity, a small for gestational age or low-birthweight infant, and preterm birth between 32 and 36 weeks with spontaneous preterm labor (adjusted relative risks ranged from 1.43 to 2.55). Underweight prepregnancy body mass index mediated 7.78% of the excess in preterm births and 18.00% of the excess in small for gestational age infants. Gestational weight gain below the recommendation mediated 38.89% of the excess in preterm births and 40.44% of the excess in low-birthweight infants. Conclusion: Anorexia nervosa diagnosis during pregnancy was associated with a number of clinically important adverse pregnancy outcomes in comparison with people without an eating disorder diagnosis. Adjusting for anxiety, depression, substance use, and smoking during pregnancy decreased this risk. A substantial percentage of the excess risk of adverse outcomes was mediated by an underweight prepregnancy body mass index, and an even larger proportion of excess risk was mediated by gestational weight gain below the recommendation. This information is important for clinicians to consider when caring for patients with anorexia nervosa. Considering and treating anorexia nervosa and comorbid conditions and counseling patients about mediating factors such as preconception weight and gestational weight gain may improve live-born pregnancy outcomes among people with anorexia nervosa.

Abstract

In neonatal, symptomatic tetralogy of Fallot (sTOF), data are lacking on whether high-risk groups would benefit from staged (SR) or complete repair (CR). We studied the association of gestational age (GA) at birth and z-score for birth weight (BWz), with management strategy and outcomes in sTOF. California population-based cohort study (2011–2017) of infants with sTOF (defined as catheter or surgical intervention prior to 44 weeks corrected GA) was performed, comparing management strategy and timing by GA and BWz categories. Multivariable models evaluated composite outcomes and days alive and out of hospital (DAOOH) in the first year of life. Among 345 patients (SR = 194; CR = 151), management strategy did not differ by GA or BWz with complete repair defined as prior to 44 weeks corrected gestational age; however, did differ by GA with regard to complete/timely repair (defined as complete repair within first 30 days of life). Full-term and early-term neonates underwent CR 20 (95%CI: − 27.1, − 14.1; p < 0.001) and 15 days (95%CI: − 22.1, − 8.2; p < 0.001) sooner than preterm neonates. Prematurity and major anomaly were associated with mortality or non-cardiac morbidity, while only major anomaly was associated with mortality or cardiac morbidity (OR = 3.5, 95%CI: 1.8,6.7, p < .0001). Full-term infants had greater DAOOH compared to preterm infants (35.2 days, 95%CI: 4.0, 66.5, p = 0.03). LGA infants and those with major anomaly had significantly lower DAOOH. In sTOF, patient specific risk factors such as prematurity and major anomaly were more associated with outcomes than management strategy.

Abstract

Background: Copper (Cu), an essential trace mineral regulating multiple actions of inflammation and oxidative stress, has been implicated in risk for preterm birth (PTB). Objectives: This study aimed to determine the association of maternal Cu concentration during pregnancy with PTB risk and gestational duration in a large multicohort study including diverse populations. Methods: Maternal plasma or serum samples of 10,449 singleton live births were obtained from 18 geographically diverse study cohorts. Maternal Cu concentrations were determined using inductively coupled plasma mass spectrometry. The associations of maternal Cu with PTB and gestational duration were analyzed using logistic and linear regressions for each cohort. The estimates were then combined using meta-analysis. Associations between maternal Cu and acute-phase reactants (APRs) and infection status were analyzed in 1239 samples from the Malawi cohort. Results: The maternal prenatal Cu concentration in our study samples followed normal distribution with mean of 1.92 μg/mL and standard deviation of 0.43 μg/mL, and Cu concentrations increased with gestational age up to 20 wk. The random-effect meta-analysis across 18 cohorts revealed that 1 μg/mL increase in maternal Cu concentration was associated with higher risk of PTB with odds ratio of 1.30 (95% confidence interval [CI]: 1.08, 1.57) and shorter gestational duration of 1.64 d (95% CI: 0.56, 2.73). In the Malawi cohort, higher maternal Cu concentration, concentrations of multiple APRs, and infections (malaria and HIV) were correlated and associated with greater risk of PTB and shorter gestational duration. Conclusions: Our study supports robust negative association between maternal Cu and gestational duration and positive association with risk for PTB. Cu concentration was strongly correlated with APRs and infection status suggesting its potential role in inflammation, a pathway implicated in the mechanisms of PTB. Therefore, maternal Cu could be used as potential marker of integrated inflammatory pathways during pregnancy and risk for PTB.

Abstract

Importance: Sudden infant death syndrome (SIDS) is a major cause of infant death in the US. Previous research suggests that inborn errors of metabolism may contribute to SIDS, yet the relationship between SIDS and biomarkers of metabolism remains unclear. Objective: To evaluate and model the association between routinely measured newborn metabolic markers and SIDS in combination with established risk factors for SIDS. Design, Setting, and Participants: This was a case-control study nested within a retrospective cohort using data from the California Office of Statewide Health Planning and Development and the California Department of Public Health. The study population included infants born in California between 2005 and 2011 with full metabolic data collected as part of routine newborn screening (NBS). SIDS cases were matched to controls at a ratio of 1:4 by gestational age and birth weight z score. Matched data were split into training (2/3) and testing (1/3) subsets. Data were analyzed from January 2005 to December 2011. Exposures: Metabolites measured by NBS and established risk factors for SIDS. Main Outcomes and Measures: The primary outcome was SIDS. Logistic regression was used to evaluate the association between metabolic markers combined with known risk factors and SIDS. Results: Of 2276578 eligible infants, 354 SIDS (0.016%) cases (mean [SD] gestational age, 38.3 [2.3] weeks; 220 male [62.1%]) and 1416 controls (mean [SD] gestational age, 38.3 [2.3] weeks; 723 male [51.1%]) were identified. In multivariable analysis, 14 NBS metabolites were significantly associated with SIDS in a univariate analysis: 17-hydroxyprogesterone, alanine, methionine, proline, tyrosine, valine, free carnitine, acetyl-L-carnitine, malonyl carnitine, glutarylcarnitine, lauroyl-L-carnitine, dodecenoylcarnitine, 3-hydroxytetradecanoylcarnitine, and linoleoylcarnitine. The area under the receiver operating characteristic curve for a 14-marker SIDS model, which included 8 metabolites, was 0.75 (95% CI, 0.72-0.79) in the training set and was 0.70 (95% CI, 0.65-0.76) in the test set. Of 32 infants in the test set with model-predicted probability greater than 0.5, a total of 20 (62.5%) had SIDS. These infants had 14.4 times the odds (95% CI, 6.0-34.5) of having SIDS compared with those with a model-predicted probability less than 0.1. Conclusions and Relevance: Results from this case-control study showed an association between aberrant metabolic analytes at birth and SIDS. These findings suggest that we may be able to identify infants at increased risk for SIDS soon after birth, which could inform further mechanistic research and clinical efforts focused on monitoring and prevention.

Abstract

Background: The US still has a high burden of preterm birth (PTB), with important disparities by race/ethnicity and poverty status. There is a large body of literature looking at the impact of pre-pregnancy obesity on PTB, but fewer studies have explored the association between underweight status on PTB, especially with a lens toward health disparities. Furthermore, little is known about how weight, specifically pre-pregnancy underweight status, and socio-economic-demographic factors such as race/ethnicity and insurance status, interact with each other to contribute to risks of PTB. Objectives: The objective of this study was to measure the association between pre-pregnancy underweight and PTB and small for gestational age (SGA) among a large sample of births in the US. Our secondary objective was to see if underweight status and two markers of health disparities–race/ethnicity and insurance status (public vs. other)–on PTB. Study design: We used data from all births in California from 2011 to 2017, which resulted in 3,070,241 singleton births with linked hospital discharge records. We ran regression models to estimate the relative risk of PTB by underweight status, by race/ethnicity, and by poverty (Medi-cal status). We then looked at the interaction between underweight status and race/ethnicity and underweight and poverty on PTB. Results: Black and Asian women were more likely to be underweight (aRR = 1.0, 95% CI: 1.01, 1.1 and aRR = 1.4, 95% CI: 1.4, 1.5, respectively), and Latina women were less likely to be underweight (aRR = 0.7, 95% CI: 0.7, 0.7). Being underweight was associated with increased odds of PTB (aRR = 1.3, 95% CI 1.3–1.3) and, after controlling for underweight, all nonwhite race/ethnic groups had increased odds of PTB compared to white women. In interaction models, the combined effect of being both underweight and Black, Indigenous and People of Color (BIPOC) statistically significantly reduced the relative risk of PTB (aRR = 0.9, 95% CI: 0.8, 0.9) and SGA (aRR = 1.0, 95% CI: 0.9, 1.0). The combined effect of being both underweight and on public insurance increased the relative risk of PTB (aRR = 1.1, 95% CI: 1.1, 1.2) but there was no additional effect of being both underweight and on public insurance on SGA (aRR = 1.0, 95% CI: 1.0, 1.0). Conclusions: We confirm and build upon previous findings that being underweight preconception is associated with increased risk of PTB and SGA–a fact often overlooked in the focus on overweight and adverse birth outcomes. Additionally, our findings suggest that the effect of being underweight on PTB and SGA differs by race/ethnicity and by insurance status, emphasizing that other factors related to inequities in access to health care and poverty are contributing to disparities in PTB.

Abstract

The US still has a high burden of preterm birth (PTB), with important disparities by race/ethnicity and poverty status. There is a large body of literature looking at the impact of pre-pregnancy obesity on PTB, but fewer studies have explored the association between underweight status on PTB, especially with a lens toward health disparities. Furthermore, little is known about how weight, specifically pre-pregnancy underweight status, and socio-economic-demographic factors such as race/ethnicity and insurance status, interact with each other to contribute to risks of PTB.

Abstract

Pregnancy is a vital period affecting both maternal and fetal health, with impacts on maternal metabolism, fetal growth, and long-term development. While the maternal metabolome undergoes significant changes during pregnancy, longitudinal shifts in maternal urine have been largely unexplored. In this study, we applied liquid chromatography-mass spectrometry-based untargeted metabolomics to analyze 346 maternal urine samples collected throughout pregnancy from 36 women with diverse backgrounds and clinical profiles. Key metabolite changes included glucocorticoids, lipids, and amino acid derivatives, indicating systematic pathway alterations. We also developed a machine learning model to accurately predict gestational age using urine metabolites, offering a non-invasive pregnancy dating method. Additionally, we demonstrated the ability of the urine metabolome to predict time-to-delivery, providing a complementary tool for prenatal care and delivery planning. This study highlights the clinical potential of urine untargeted metabolomics in obstetric care.