BACKGROUND:Prenatal depression is associated with offspring behavioral problems, but heterogeneity in the strength of this association is not well understood. Maternal vitamin D concentration during pregnancy is important for fetal brain development and may help explain this variability, with potential differences by timing of exposure and maternal race. METHODS:Using data from 1,451 mother-child pairs in the Environmental influences on Child Health Outcomes cohort, linear mixed-effects models examined associations between prenatal depressive symptom severity, gestational 25-hydroxy-vitamin D (25[OH]D) concentrations, and internalizing and externalizing behaviors in preschool-aged children. Analyses were stratified by common 25(OH)D deficiency thresholds, prenatal timing, and race. RESULTS:Prenatal depressive symptom severity was associated with greater child internalizing ( β  = 0.18, 95% CI = 0.11, 0.25) and externalizing ( β  = 0.21, 95% CI = 0.14, 0.28) behaviors. Gestational 25(OH)D concentration did not moderate depression effect estimates in adjusted models. In stratified analyses, the association between prenatal depressive symptoms and child externalizing behaviors persisted regardless of 25(OH)D threshold levels, but the association with internalizing behaviors attenuated at 25(OH)D < 20 ng/mL. Timing of 25(OH)D measurement (early/late pregnancy) did not modify relationships. Higher gestational 25(OH)D was associated with fewer externalizing problems among offspring of Black mothers only. CONCLUSIONS:Prenatal depressive symptoms showed robust associations with child behavioral problems, largely independent of gestational 25(OH)D. However, attenuated risk for internalizing behaviors with low vitamin D levels warrants investigation of social-environmental factors.

Many research findings are based on a nested subset of an original cohort followed over time, but results are rarely generalized back to the original population. Here, we conduct a "proof of concept" analysis demonstrating the application of simple methods to generalize the association between prepregnancy obesity and preeclampsia in women from the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers to Be (nuMoM2b, target sample), using a subset of women whose follow-up was extended to 3 years postpartum in the Heart Health Study (nuMoM2b-HHS, source sample). We constructed inverse probability of selection weights and estimated three risk ratios for the association between obesity and preeclampsia: in the target nuMoM2b sample (N = 9,920), in the source nuMoM2b-HHS sample (N = 4,486), and in the weighted source nuMoM2b-HHS sample generalized back to the target sample using inverse probability of selection weights (pseudo N = 4,468). In the target, source, and weighted samples, the estimated risk ratios (95% confidence intervals) were 2.0 (1.7, 2.3), 2.2 (1.8, 2.6), and 2.0 (1.6, 2.4), respectively. We discuss the assumptions involved in generalizing study findings and methods available for doing so. When relevant, researchers should deploy methods for generalizing study nested cohort findings back to a target sample.

Introduction Gestational weight gain (GWG) is an important indicator of maternal nutrition to be monitored during pregnancy. However, there is no evidence-based tool that can be used to monitor it across all geographic locations and pre-pregnancy body mass index (BMI) categories. The WHO is undertaking a project to develop GWG charts by pre-pregnancy BMI category, and to identify GWG ranges associated with the lowest risks of adverse maternal and infant outcomes. This protocol describes all the steps that will be used to accomplish the development of these GWG charts.Methods and analysis This project will involve the analysis of individual participant data (researcher-collected or administrative). To identify eligible datasets with GWG data, a literature review will be conducted and a global call for data will be launched by the WHO. Eligible individual datasets obtained from multiple sources will be harmonised into a pooled database. The database will undergo steps of cleaning, data quality assessment and application of individual-level inclusion criteria. Heterogeneity of maternal weight and GWG will be assessed to verify the possibility of combining datasets from multiple sources and regions into a single database. Generalized Additive Models for Location, Scale and Shape will be applied for the construction of the centile curves. Diagnostic measures, internal and external validation procedures will also be performed.Ethics and dissemination This project will include an analysis of existing study de-identified data. To be included in the pooled database, each included study should have received ethics approvals from relevant committees. Manuscripts will be submitted to open-access journals and a WHO document will be published, including the GWG charts and cut-offs for application in antenatal care.

The Healthy Eating Index (HEI) is widely used to assess diet quality, but certain contexts (e.g. pregnancy) may benefit from tailored versions. We evaluated whether the HEI's current approach of assigning approximately equal weights to all components to compute the total score is appropriate when studying diet quality around conception. Data were from a US prospective cohort of individuals who had not delivered a previous pregnancy past 20 weeks' gestation (2010-2013, n 7882). Usual dietary intake around conception was estimated from FFQ. Select adverse pregnancy outcomes (gestational diabetes, pre-eclampsia, preterm delivery and small-for-gestational age birth) were abstracted from the medical record. We regressed each outcome on the thirteen HEI-2015 component scores using SuperLearner, an ensemble machine learning method that combines predictions from multiple algorithms and avoids relying on parametric assumptions that characterise standard regression. We assessed the relative importance of each component using two permutation-based metrics: change in negative log likelihood (global influence) and absolute difference in the predicted probabilities (individual-level influence). Six of the thirteen components (Greens and Beans, Saturated Fats, Total Protein Foods, Seafood and Plant Proteins, Fatty Acids and Added Sugars) were important according to at least one metric for at least two of the four outcomes. In contrast, the Refined Grains component was not appreciably important for any outcome. These findings suggest that equal weighting of the HEI components may not be appropriate when evaluating diet quality for studies of pregnancy.

BACKGROUND:We aimed to establish how weight gain patterns across successive pregnancies relate to longer-term maternal cardiometabolic health. METHODS:Obstetric records of all nulliparous pregnancies in Stockholm and Gotland (Sweden, 2008-2015) were linked with hospital discharges, outpatient visits, and prescription dispensations until 2019. Total pregnancy weight gain (kg) was standardized for gestational age and early pregnancy body mass index and classified as ≤ -1, > -1 and < +1 (reference), and ≥ +1 Z scores. Postpartum cardiometabolic conditions (type 2 diabetes, hypertension, cardiovascular diseases) were identified using International Classification of Diseases, Tenth Revision codes and medications. Hazard ratios were estimated using a Cox proportional hazards model. RESULTS:Among 58 333 individuals, 5.9% (n=3440) developed a cardiometabolic condition, with a median onset of 4 years [interquartile range, 2-6]. Hazard ratios were higher for those gaining ≥ +1 Z score (19.4 kg at 40 weeks in normal-weight individuals) in the first pregnancy (hazard ratio, 1.29 [95% CI, 1.19-1.40]). Among individuals developing conditions after the second pregnancy, risks were increased for those with high weight gain in the first pregnancy, but not the second (hazard ratios, 1.30 [95% CI, 1.13-1.50] versus 1.05 [95% CI, 0.88-1.26], respectively), compared with those gaining > -1 and < +1 Z score in both pregnancies. CONCLUSIONS:Individuals with high weight gain in their first pregnancy were 30% more likely to develop a cardiometabolic condition than those with lower weight gain. Preventing excessive weight gain in the first pregnancy may be key to reducing maternal cardiometabolic risk.

The population-level increase in prepregnancy obesity affects pregnancy weight gain and postpartum weight retention, contributing to an intergenerational cycle of obesity. This study evaluated age, period, and cohort effects in prepregnancy body mass index (BMI), excessive pregnancy weight gain, and postpartum weight retention among 811 111 primiparous Pennsylvania births from 2003 to 2020. We used hierarchical age-period-cohort cross-classified random effects models to estimate age-period-cohort effects in prepregnancy BMI, as well as excessive weight gain and postpartum weight retention stratified by BMI category. The 1.8-kg/m2  acceleration of prepregnancy BMI from 2003 to 2020 occurred to a similar degree in all age groups and birth cohorts. There were no main effects of delivery period or birth cohort on excessive weight gain beyond age-related variation in any BMI group. Over the same period, weight retention between the first and second pregnancies (n = 382 328) increased faster with each younger generation of women who were in normal weight during their first pregnancies, but to a comparable extent in all generations of women in all other BMI groups. Our data suggest that the acceleration in prepregnancy BMI and postpartum weight retention will continue unless structural and institutional changes are made to systems and policies necessary to promote healthy nutrition for all women.

Epidemiologists increasingly use machine learning to adjust for high-dimensional confounding. Augmented inverse probability weighting (AIPW) and targeted maximum likelihood estimation (TMLE) are most widely used but may yield different results and both can become unstable under weak positivity violations. Residual-on-residual regression is a stable alternative that estimates an exposure effect encoded in a partially linear model by fitting confounder adjusted models for the outcome and exposure, then regressing outcome residuals against exposure residuals using ordinary least squares. We illustrate the approach using data from the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be (nuMoM2b; n = 7,923), estimating the association between high vegetable intake density and preeclampsia. Residual-on-residual regression, AIPW, and TMLE yielded concordant estimates, indicating a modest reduction in preeclampsia risk. In simulations, residual-on-residual regression was unbiased with near-nominal confidence interval coverage, performing comparably to AIPW and TMLE and substantially better than a misspecified parametric model when the exposure effect is approximately constant. However, in simulation settings with positivity violations, residual on residual regression outperformed AIPW and TMLE when the true effect was coded in a partially linear model. When the exposure effect is approximately constant, residual-on-residual regression is interpretable, computationally simple, and provides a triangulation strategy for observational causal inference.

BACKGROUND:Little is known about diet quality changes during the transition from periconception to postpartum. OBJECTIVES:We aimed to describe within-person changes in diet quality and related factors from the periconceptional period to 3 y postpartum in a socioeconomically and geographically diverse cohort of United States pregnant individuals. METHODS:We analyzed data from 4423 participants in the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-be Heart Health Study, followed from 6 to 13 wk of gestation to ∼3 y postpartum. Usual dietary intake in the 3 mo around conception and the 3 mo before the postpartum visit was estimated using a food frequency questionnaire. We calculated the proportion of participants adhering to food group recommendations from the 2020-2025 Dietary Guidelines for Americans. For each food group, we estimated the proportion of participants with meaningful increases or decreases (defined as an increase or decrease by ≥20% of the recommended intake) in intake density and determined differences by maternal characteristics. RESULTS:For all food groups, adherence to recommendations was consistently low during both the periconceptional and postpartum periods, with few individuals' diets changing over time. Across food groups, the proportions of participants who improved adherence to recommendations ranged from 4% to 19%, whereas 7%-15% of participants experienced declines in adherence. Although few participants met recommendations, meaningful increases were observed in intake densities of vegetables, protein foods, oils, added sugars, and saturated fats (30%-49%), whereas decreases were common for fruits, grains, dairy, and added sugars (27%-47%). These changes primarily varied according to race and ethnicity and education. CONCLUSIONS:Improving the current food environment and providing sustained, accessible nutritional support that extends into the postpartum period may help individuals of reproductive age meet the dietary guidelines, which is crucial for improving maternal and child health outcomes and reducing related inequities.

When estimating causal effects, it is important to assess external validity, i.e., determine how useful a given study is to inform a practical question for a specific target population. One challenge is that the covariate distribution in the population underlying a study may be different from that in the target population. If some covariates are effect modifiers, the average treatment effect (ATE) may not generalize to the target population. To tackle this problem, we propose new methods to generalize or transport the ATE from a source population to a target population, in the case where the source and target populations have different sets of covariates. When the ATE in the target population is identified, we propose new doubly robust estimators and establish their rates of convergence and limiting distributions. Under regularity conditions, the doubly robust estimators provably achieve the efficiency bound and are locally asymptotic minimax optimal. A sensitivity analysis is provided when the identification assumptions fail. Simulation studies show the advantages of the proposed doubly robust estimator over simple plug-in estimators. Importantly, we also provide minimax lower bounds and higher-order estimators of the target functionals. The proposed methods are applied in transporting causal effects of dietary intake on adverse pregnancy outcomes from an observational study to the whole U.S. female population.

BACKGROUND:The Super Learner is an ensemble learning method that has been widely used with doubly robust causal effect estimators. It is recommended to deploy the Super Learner with a diverse library of algorithms. To our knowledge, however, the magnitude of the improvements gained by including many algorithms has not yet been systematically evaluated in common epidemiologic research settings. METHODS:We applied Super Learning with two doubly robust estimators, augmented inverse probability weighting (AIPW) and targeted minimum loss-based estimation (TMLE), to estimate the average treatment effect (ATE) of high periconceptional dietary fruit and vegetable density on the risk of preeclampsia among 7,923 women from the nuMoM2b study. Using a reference ensemble with a diverse library of algorithms, we compared estimates under different sets of algorithms included in the Super Learner to evaluate whether ATE estimates were sensitive to library choices. RESULTS:The doubly robust estimators fitted with the reference Super Learner ensemble suggested ≥2.5 cups/1,000 kcal of total fruit and vegetable density was associated with a lower risk of preeclampsia. ATE estimated on the risk difference scale by AIPW was -0.019 (95% confidence interval = -0.036, -0.003) and by TMLE was -0.023 (95% confidence interval = -0.039, -0.007). Excluding any individual algorithm from the reference ensemble had little impact on estimates from either AIPW or TMLE. However, relying on a single algorithm (e.g., extreme gradient boosting) yielded results that were much more variable. CONCLUSION:Our empirical findings support recommendations to build ensemble learners for doubly robust estimators using a diverse array of flexible machine learning algorithms.

BACKGROUND:"Precision nutrition" aims to recognize variation in response to dietary patterns to inform tailored advice based on behavioral, social, environmental, genetic, and metabolic factors. OBJECTIVES:We sought to identify characteristics of pregnant individuals that modify the associations between a high fat, sugar, and sodium diet and poor perinatal outcomes. METHODS:We used data from 8054 participants in the Nulliparous Pregnancy Outcomes Study: monitoring mothers-to-be (8 United States medical centers, 2010‒2013), a prospective cohort study. Usual periconceptional dietary intake was assessed at 6‒13 wk of gestation using a food frequency questionnaire. The exposure was a high fat, sugar, and sodium dietary pattern compared with all other diet patterns. The outcome was a composite of 1 or more perinatal outcomes: preeclampsia, gestational diabetes, preterm birth, or small-for-gestational-age birth. We used the doubly robust learner, which enables the use of machine learning to identify maternal characteristics that modify the effect of the dietary pattern on the composite outcome. RESULTS:Approximately 29% had a dietary pattern that was high in fat, sugar, and sodium. One quarter had any adverse pregnancy outcome. The confounder-adjusted association between a high fat, sugar, and sodium dietary pattern and risk of the adverse composite pregnancy outcome was stronger among certain subgroups of the cohort than others, including individuals with a higher BMI, lower socioeconomic status, and non-Hispanic Black race/ethnicity. For instance, compared with other diet patterns, intake of a diet high in fat, sugar, and sodium was associated with 5.9 excess cases per 100 pregnancies {adjusted risk difference 0.059 [95% confidence interval (CI): 0.012, 0.11]} among individuals living in a high-poverty neighborhood, but 2.3 excess cases per 100 pregnancies (0.023; 95% CI: -0.011, 0.057) among those residing in a low-poverty neighborhood. CONCLUSIONS:This work may provide clues that contribute to a deeper understanding of the heterogeneity in dietary responses in pregnancy.

There is a growing focus on better understanding the complexity of dietary patterns and how they relate to health and other factors. Approaches that have not traditionally been applied to characterize dietary patterns, such as machine learning algorithms and latent class analysis methods, may offer opportunities to measure and characterize dietary patterns in greater depth than previously considered. However, there has not been a formal examination of how this wide range of approaches has been applied to characterize dietary patterns. This scoping review synthesized literature from 2005-2022 applying methods not traditionally used to characterize dietary patterns, referred to as novel methods. MEDLINE, CINAHL, and Scopus were searched using keywords including machine learning, latent class analysis, and least absolute shrinkage and selection operator (LASSO). Of 5274 records identified, 24 met the inclusion criteria. Twelve of 24 articles were published since 2020. Studies were conducted across 17 countries. Nine studies used approaches that have applications in machine learning to identify dietary patterns. Fourteen studies assessed associations between dietary patterns that were characterized using novel methods and health outcomes, including cancer, cardiovascular disease, and asthma. There was wide variation in the methods applied to characterize dietary patterns and in how these methods were described. The extension of reporting guidelines and quality appraisal tools relevant to nutrition research to consider specific features of novel methods may facilitate complete and consistent reporting and enable evidence synthesis to inform policies and programs aimed at supporting healthy dietary patterns.

BACKGROUND:Risk factors during adolescence appear to shape adult health, but little is known about how they are associated with pregnancy health. OBJECTIVES:We aimed to assess whether a variety of adolescent risk factors with links to adult overweight or obesity are associated with pre-pregnancy obesity (Body Mass Index [BMI] ≥ 30 kg/m2) and high gestational weight gain (GWG; > 0.5 SD for pre-pregnancy BMI category and gestational age) in a cohort of women participating since adolescence in a longitudinal cohort. METHODS:At age 11-18 years participants reported on adolescent risk factors (overweight or obesity, healthy and unhealthy home food availability, food insufficiency, family meals, depressive symptoms, body dissatisfaction, weight teasing, binge eating, unhealthy weight control behaviours and dieting). Twenty years later, participants reporting a live birth (n = 656) recalled their pre-pregnancy weight and total GWG. Modified Poisson regression models were used to estimate associations of each factor with pre-pregnancy obesity and high GWG, adjusting for sociodemographics. We used Multivariate Imputation by Chained Equations to account for outcome misclassification using internal validation data. RESULTS:Eighteen percent of the sample had pre-pregnancy obesity and 26% had high GWG. Adolescent overweight or obesity (RR = 4.98, 95% CI 3.27, 7.57), body dissatisfaction (RR = 1.99; 95% CI: 1.31, 3.03) and unhealthy weight control behaviours (RR = 1.70; 95% CI: 1.06, 2.74), among other factors, were associated with pre-pregnancy obesity risk. For high GWG, there were imprecise associations with adolescent overweight or obesity (RR = 1.57; 95% CI: 1.06, 2.31), binge eating (RR = 1.36; 95% CI: 0.77, 2.39) and unhealthy weight control behaviours (RR = 1.38; 95% CI: 0.84, 2.25), among others. CONCLUSIONS:Findings suggest that some risk markers for pre-pregnancy obesity (and possibly high GWG) may be apparent as early as adolescence. Supporting adolescent health and well-being might have a role in improving weight-related health in the perinatal period.

BACKGROUND:High gestational weight gain is associated with excess postpartum weight retention, yet excess postpartum weight retention is not an exclusion criterion for current gestational weight gain charts. We aimed to assess the impact of excluding individuals with high interpregnancy weight change (a proxy for excess postpartum weight retention) on gestational weight gain distributions. METHODS:We included individuals with an index birth from 2008 to 2014 and a subsequent birth before 2019, in the population-based Stockholm-Gotland Perinatal Cohort. We estimated gestational weight gain (kg) at 25 and 37 weeks, using weight at first prenatal visit (<14 weeks) as the reference. We calculated high interpregnancy weight change (≥10 kg and ≥5 kg) using the difference between weight at the start of an index and subsequent pregnancy. We compared gestational weight gain distributions and percentiles (stratified by early-pregnancy body mass index) before and after excluding participants with high interpregnancy weight change. RESULTS:Among 55,723 participants, 17% had ≥10 kg and 34% had ≥5 kg interpregnancy weight change. The third, tenth, 50th, 90th and 97th percentiles of gestational weight gain were similar (largely within 1 kg) before versus after excluding participants with high interpregnancy weight change, at both 25 and 37 weeks. For example, among normal weight participants at 37 weeks, the 50th and 97th percentiles were 14 kg and 23 kg including versus 13 kg and 23 kg excluding participants with ≥5 kg interpregnancy weight change. CONCLUSIONS:Excluding individuals with excess postpartum weight retention from normative gestational weight gain charts may not meaningfully impact the charts' percentiles.

BACKGROUND:The current Institute of Medicine (IOM) pregnancy weight gain guidelines were developed using the best available evidence but were limited by substantial knowledge gaps. Some have raised concern that the guidelines for individuals affected by overweight or obesity are too high and contribute to short- and long-term complications for the mother and child. OBJECTIVES:To determine the association between pregnancy weight gain below the lower limit of the current IOM recommendations and risk of 10 adverse maternal and child health outcomes among individuals with overweight and obesity. METHODS:We used data from a prospective cohort study of United States nulliparae with prepregnancy overweight (n = 955) or obesity (n = 897) followed from the first trimester to 2-7 y postpartum. We used multivariable Poisson regression to relate pregnancy weight gain z-scores with a severity-weighted composite outcome consisting of ≥1 of 10 adverse outcomes (gestational diabetes, preeclampsia, unplanned cesarean delivery, maternal postpartum weight increase >10 kg, maternal postpartum metabolic syndrome, infant death, stillbirth, preterm birth, small-for-gestational age birth, and childhood obesity). RESULTS:Pregnancy weight gain z-scores below, within, and above the IOM-recommended ranges occurred in 5%, 13%, and 80% of pregnancies with overweight and 17%, 13%, and 70% of pregnancies with obesity. There was a positive association between pregnancy weight gain z-scores and all adverse maternal outcomes, childhood obesity, and the composite outcome. Pregnancy weight gain z-scores below the lower limit of the recommended ranges (<6.8 kg for overweight, <5 kg for obesity) were not associated with the severity-weighted composite outcome. For example, compared with the lower limit, adjusted rate ratios (95% confidence interval) for z-scores of -2 standard deviations in pregnancies with overweight (equivalent to 3.6 kg at 40 wk) and obesity (-2.8 kg at 40 wk) were 0.99 (95% confidence interval [CI]: 0.91, 1.06) and 0.97 (95% CI: 0.87, 1.07). CONCLUSIONS:These findings support arguments to decrease the lower limit of recommended weight gain ranges in these prepregnancy body mass index groups.

BACKGROUND:The use of machine learning to estimate exposure effects introduces a dependence between the results of an empirical study and the value of the seed used to fix the pseudo-random number generator. METHODS:We used data from 10,038 pregnant women and a 10% subsample (N = 1004) to examine the extent to which the risk difference for the relation between fruit and vegetable consumption and preeclampsia risk changes under different seed values. We fit an augmented inverse probability weighted estimator with two Super Learner algorithms: a simple algorithm including random forests and single-layer neural networks and a more complex algorithm with a mix of tree-based, regression-based, penalized, and simple algorithms. We evaluated the distributions of risk differences, standard errors, and P values that result from 5000 different seed value selections. RESULTS:Our findings suggest important variability in the risk difference estimates, as well as an important effect of the stacking algorithm used. The interquartile range width of the risk differences in the full sample with the simple algorithm was 13 per 1000. However, all other interquartile ranges were roughly an order of magnitude lower. The medians of the distributions of risk differences differed according to the sample size and the algorithm used. CONCLUSIONS:Our findings add another dimension of concern regarding the potential for "p-hacking," and further warrant the need to move away from simplistic evidentiary thresholds in empirical research. When empirical results depend on pseudo-random number generator seed values, caution is warranted in interpreting these results.

Background The period around pregnancy is a critical window in the primordial prevention of cardiovascular disease, but little is known about the role of dietary patterns in cardiometabolic health. Our objective was to determine the association between alignment of periconceptional diet with the 2020 to 2025 Dietary Guidelines for Americans and the risk of metabolic syndrome. Methods and Results We used data from the Nulliparous Pregnancy Outcomes Study: monitoring mothers‐to‐Be Heart Health Study, a pregnancy cohort study that followed pregnant participants to a median of 3 years postpartum (n=4423). Usual dietary intake in the 3 months around conception was estimated from a Food Frequency Questionnaire. Alignment with the Dietary Guidelines was measured using the Healthy Eating Index‐2020, where higher scores represent greater alignment. Postpartum metabolic syndrome was defined using the American Heart Association/National Heart, Lung, and Blood Institute guideline. The prevalence of metabolic syndrome at 3 years postpartum was 20%. After adjusting for confounders, the prevalence of metabolic syndrome was flat up to a periconceptional Healthy Eating Index‐2020 total score of ≈60, and then declined steeply as scores increased. Compared with a Healthy Eating Index‐2020 score of 60, having scores of 70, 80, and 90 were associated with 2, 4, and 7 fewer cases of metabolic syndrome per 100 individuals, respectively (prevalence differences: −0.02 [95% CI, −0.03, 0]; −0.04 [−0.08, −0.1]; −0.07 [−0.13, −0.02]). Conclusions Dietary interventions around conception and systems‐level changes to support high diet quality may be important for improving postpartum cardiometabolic health, and helping to reverse or slow the decline in women's cardiometabolic health.

Background: Childhood maltreatment is associated with elevated adult weight. It is unclear whether this association extends to pregnancy, a critical window for the development of obesity. Methods: We examined associations of childhood maltreatment histories with prepregnancy body mass index (BMI) and gestational weight gain among women who had participated for >20 years in a longitudinal cohort. At age 26-35 years, participants reported childhood maltreatment (physical, sexual, and emotional abuse; emotional neglect) and, 5 years later, about prepregnancy weight and gestational weight gain for previous pregnancies (n = 656). Modified Poisson regression models were used to estimate associations of maltreatment history with prepregnancy BMI and gestational weight gain z-scores, adjusting for sociodemographics. We used multivariate imputation by chained equations to adjust outcome measures for misclassification using data from an internal validation study. Results: Before misclassification adjustment, results indicated a higher risk of prepregnancy BMI >= 30 kg/m(2) in women with certain types of maltreatment (e.g., emotional abuse risk ratio = 2.4; 95% confidence interval: 1.5, 3.7) compared with women without that maltreatment type. After misclassification adjustment, estimates were attenuated but still modestly elevated (e.g., emotional abuse risk ratio = 1.7; 95% confidence interval: 1.1, 2.7). Misclassification-adjusted estimates for maltreatment associations with gestational weight gain z-scores were close to the null and imprecise. Conclusions: Findings suggest an association of maltreatment with prepregnancy BMI >= 30 kg/m(2) but not with high gestational weight gain. Results suggest a potential need for equitable interventions that can support all women, including those with maltreatment histories, as they enter pregnancy.

Background Low levels of vitamin D during pregnancy are associated with offspring behavioral problems but little is known about pre-pregnancy influences. Additionally, Black American individuals are underrepresented in studies, limiting translational impact. We tested independent and interactive effects of preconception and prenatal vitamin D in Black women in relation to positive behavioral and emotional outcomes in early childhood.Methods Black-identifying participants (N = 156) enrolled in the longitudinal Pittsburgh Girls Study (PGS) provided venous blood samples before and during pregnancy to measure 25-hydroxyvitamin D (25[OH]D) levels. Participants completed questionnaires assessing sociodemographic factors, depression severity and life stress, and later reported on child behavioral and emotional problems and prosocial behavior between 2 and 4 years.Results Mean serum 25(OH)D concentrations were 15.5 ng/ml (s.d. = 7.7) before pregnancy and 18.0 ng/ml (s.d. = 9.2) during pregnancy; below the sufficiency threshold according to commonly used dietary guidelines. After adjusting for covariates, prenatal 25(OH)D was negatively related to behavior problems and positively related to prosocial behavior in children, although the association attenuated for behavior problems after accounting for preconception 25(OH)D, which may reflect patterns of stability. Maternal 25(OH)D was unrelated to child emotional problems, and no synergistic effects of 25(OH)D timing were observed for any child outcome.Conclusions Findings have relevance for Black women living in the northeast U.S. Results suggest specific associations between maternal vitamin D and positive behaviors in early childhood, regardless of sufficiency levels and suggest potential opportunities for early interventions to support healthy child development.