Validation of long-term recall of pregnancy-related weight in the Life-course Experiences And Pregnancy (LEAP) study.

BACKGROUND: Pre-pregnancy body mass index (BMI) and gestational weight gain (GWG) are determinants of maternal and child health. However, many studies of these factors rely on error-prone self-reported measures. METHODS: Using data from Life-course Experiences And Pregnancy (LEAP), a US-based cohort, we assessed the validity of pre-pregnancy BMI and GWG recalled on average 8 years postpartum against medical record data treated as alloyed gold standard ("true") values. We calculated probabilities of being classified into a self-reported pre-pregnancy BMI or GWG category conditional on one's true category (analogous to sensitivities and specificities), and probabilities of truly being in each pre-pregnancy BMI or GWG category conditional on one's self-reported category (analogous to positive and negative predictive values). RESULTS: There was a tendency toward under-reporting pre-pregnancy BMI. Self-report misclassified 32% (95% CI: 19%, 48%) of those in LEAP with truly overweight and 13% (5%, 27%) with obesity into a lower BMI category. Self-report correctly predicted the truth for 72% (55%, 84%) with self-reported overweight to 100% (90%, 100%) with self-reported obesity. For GWG, both under- and over-reporting were common; self-report misclassified 32% (15%, 55%) with truly low GWG as having moderate GWG and 50% (28%, 72%) with truly high GWG as moderate or low GWG. Self-report correctly predicted the truth for 45% (25%, 67%) with self-reported high GWG to 85% (76%, 91%) with self-reported moderate GWG. Misclassification of BMI and GWG varied across maternal characteristics. CONCLUSIONS: Findings can be used in quantitative bias analyses to estimate bias-adjusted associations with pre-pregnancy BMI and GWG.

Should gestational weight gain charts exclude individuals with excess postpartum weight retention?

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.

Safety of low weight gain or weight loss in pregnancies with class 1, 2, and 3 obesity: a population-based cohort study.

BACKGROUND: There are concerns that current gestational weight gain recommendations for women with obesity are too high and that guidelines should differ on the basis of severity of obesity. In this study we investigated the safety of gestational weight gain below current recommendations or weight loss in pregnancies with obesity, and evaluated whether separate guidelines are needed for different obesity classes. METHODS: In this population-based cohort study, we used electronic medical records from the Stockholm-Gotland Perinatal Cohort study to identify pregnancies with obesity (early pregnancy BMI before 14 weeks' gestation ≥30 kg/m2) among singleton pregnancies that delivered between Jan 1, 2008, and Dec 31, 2015. The pregnancy records were linked with Swedish national health-care register data up to Dec 31, 2019. Gestational weight gain was calculated as the last measured weight before or at delivery minus early pregnancy weight (at <14 weeks' gestation), and standardised for gestational age into z-scores. We used Poisson regression to assess the association of gestational weight gain z-score with a composite outcome of: stillbirth, infant death, large for gestational age and small for gestational age at birth, preterm birth, unplanned caesarean delivery, gestational diabetes, pre-eclampsia, excess postpartum weight retention, and new-onset longer-term maternal cardiometabolic disease after pregnancy, weighted to account for event severity. We calculated rate ratios (RRs) for our composite adverse outcome along the weight gain z-score continuum, compared with a reference of the current lower limit for gestational weight gain recommended by the US Institute of Medicine (IOM; 5 kg at term). RRs were adjusted for confounding factors (maternal age, height, parity, early pregnancy BMI, early pregnancy smoking status, prepregnancy cardiovascular disease or diabetes, education, cohabitation status, and Nordic country of birth). FINDINGS: Our cohort comprised 15 760 pregnancies with obesity, followed up for a median of 7·9 years (IQR 5·8-9·4). 11 667 (74·0%) pregnancies had class 1 obesity, 3160 (20·1%) had class 2 obesity, and 933 (5·9%) had class 3 obesity. Among these pregnancies, 1623 (13·9%), 786 (24·9%), and 310 (33·2%), respectively, had weight gain during pregnancy below the lower limit of the IOM recommendation (5 kg). In pregnancies with class 1 or 2 obesity, gestational weight gain values below the lower limit of the IOM recommendation or weight loss did not increase risk of the adverse composite outcome (eg, at weight gain z-score -2·4, corresponding to 0 kg at 40 weeks: adjusted RR 0·97 [95% CI 0·89-1·06] in obesity class 1 and 0·96 [0·86-1·08] in obesity class 2). In pregnancies with class 3 obesity, weight gain values below the IOM limit or weight loss were associated with reduced risk of the adverse composite outcome (eg, adjusted RR 0·81 [0·71-0·89] at weight gain z-score -2·4, or 0 kg). INTERPRETATION: Our findings support calls to lower or remove the lower limit of current IOM recommendations for pregnant women with obesity, and suggest that separate guidelines for class 3 obesity might be warranted. FUNDING: Karolinska Institutet and the Eunice Kennedy Shriver National Institute of Child Health and Human Development.

Cohort profile: Life-course experiences and pregnancy (LEAP)-A retrospective pregnancy cohort for life-course reproductive health research.

BACKGROUND: Life course factors may be associated with pre-pregnancy body mass index and gestational weight gain; however, collecting information on pre-pregnancy exposures and pregnancy health in the same cohort is challenging. OBJECTIVES: The Life-course Experiences And Pregnancy (LEAP) study aims to identify adolescent and young adult risk factors for pre-pregnancy weight and gestational weight gain (GWG). We built upon an existing cohort study to overcome challenges inherent to studying life course determinants of pregnancy health. POPULATION: Participants in an ongoing prospective cohort study of weight-related health who identified as women. DESIGN: Retrospective cohort study. METHODS: In 2019-2020, 1,252 women participating since adolescence in a cohort study of weight-related health were invited to complete an online reproductive history survey. Participants who reported a live birth were invited to release their prenatal, delivery, and postpartum medical records for validation of survey reports. Descriptive analyses were conducted to assess the characteristics of the overall cohort and the medical record validation subsample, and to describe adolescent and young adult characteristics of those with high (>80th percentile), moderate (20th-80th percentile), and low (<20th percentile) GWG z-score for gestational age and pre-pregnancy weight status. PRELIMINARY RESULTS: Nine hundred seventy-seven women (78%) completed the LEAP survey and 656 reported a live birth. Of these, 379 (58%) agreed to release medical records, and 250 records were abstracted (66% of the 379). Of the 977 survey respondents 769 (79%) reported attempting a pregnancy, and 656 (67%) reported at least one live birth. The validation subsample was similar to the overall cohort. Women with a high GWG had a higher adolescent BMI percentile and prevalence of unhealthy weight control behaviors than those with moderate or low GWG. CONCLUSIONS: LEAP offers a valuable resource for identifying life course factors that may influence the health of pregnant people and their offspring.

Application of a Web-based Tool for Quantitative Bias Analysis: The Example of Misclassification Due to Self-reported Body Mass Index.

BACKGROUND: We describe the use of Apisensr, a web-based application that can be used to implement quantitative bias analysis for misclassification, selection bias, and unmeasured confounding. We apply Apisensr using an example of exposure misclassification bias due to use of self-reported body mass index (BMI) to define obesity status in an analysis of the relationship between obesity and diabetes. METHODS: We used publicly available data from the National Health and Nutrition Examination Survey. The analysis consisted of: (1) estimating bias parameter values (sensitivity, specificity, negative predictive value, and positive predictive value) for self-reported obesity by sex, age, and race-ethnicity compared to obesity defined by measured BMI, and (2) using Apisensr to adjust for exposure misclassification. RESULTS: The discrepancy between self-reported and measured obesity varied by demographic group (sensitivity range: 75%-89%; specificity range: 91%-99%). Using Apisensr for quantitative bias analysis, there was a clear pattern in the results: the relationship between obesity and diabetes was underestimated using self-report in all age, sex, and race-ethnicity categories compared to measured obesity. For example, in non-Hispanic White men aged 40-59 years, prevalence odds ratios for diabetes were 3.06 (95% confidence inerval = 1.78, 5.30) using self-reported BMI and 4.11 (95% confidence interval = 2.56, 6.75) after bias analysis adjusting for misclassification. CONCLUSION: Apisensr is an easy-to-use, web-based Shiny app designed to facilitate quantitative bias analysis. Our results also provide estimates of bias parameter values that can be used by other researchers interested in examining obesity defined by self-reported BMI.

Do current pregnancy weight gain guidelines balance risks of adverse maternal and child health in a United States cohort?

BACKGROUND: The Institute of Medicine pregnancy weight gain guidelines were developed without evidence linking high weight gain to maternal cardiometabolic disease and child obesity. The upper limit of current recommendations may be too high for the health of the pregnant individual and child. OBJECTIVES: The aim of this study was to identify the range of pregnancy weight gain for pregnancies within a normal body mass index (BMI) range that balances the risks of high and low weight gain by simultaneously considering 10 different health conditions. METHODS: We used data from an United States prospective cohort study of nulliparae followed until 2 to 7 y postpartum (N = 2344 participants with a normal BMI). Pregnancy weight gain z-score was the main exposure. The outcome was a composite consisting of the occurrence of ≥1 of 10 adverse health conditions that were weighted for their seriousness. We used multivariable Poisson regression to relate weight gain z-scores with the weighted composite outcome. RESULTS: The lowest risk of the composite outcome was at a pregnancy weight gain z-score of -0.6 SD (standard deviation) (equivalent to 13.1 kg at 40 wk). The weight gain ranges associated with no more than 5%, 10%, and 20% increase in risks were -1.0 to -0.2 SD (11.2-15.3 kg), -1.4 to 0 SD (9.4-16.4 kg), and -2.0 to 0.4 SD (7.0-18.9 kg). When we used a lower threshold to define postpartum weight increase in the composite outcome (>5 kg compared with >10 kg), the ranges were 1.6 to -0.7 SD (8.9-12.6 kg), -2.2 to -0.3 SD (6.3-14.7 kg), and ≤0.2 SD (≤17.6 kg). Compared with the ranges of the current weight gain guidelines (-0.9 to -0.1 SD, 11.5-16 kg), the lower limits from our data tended to be lower while upper limits were similar or lower. CONCLUSIONS: If replicated, our results suggest that policy makers should revisit the recommended pregnancy weight gain range for individuals within a normal BMI range.

Periconceptional Dietary Patterns and Adverse Pregnancy and Birth Outcomes.

BACKGROUND: The periconceptional period is a critical window for the origins of adverse pregnancy and birth outcomes, yet little is known about the dietary patterns that promote perinatal health. OBJECTIVE: We used machine learning methods to determine the effect of periconceptional dietary patterns on risk of preeclampsia, gestational diabetes, preterm birth, small-for-gestational-age (SGA) birth, and a composite of these outcomes. METHODS: We used data from 8259 participants in the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be (8 US medical centers, 2010‒2013). Usual daily periconceptional intake of 82 food groups was estimated from a food frequency questionnaire. We used k-means clustering with a Euclidean distance metric to identify dietary patterns. We estimated the effect of dietary patterns on each perinatal outcome using targeted maximum likelihood estimation and an ensemble of machine learning algorithms, adjusting for confounders including health behaviors and psychological, neighborhood, and sociodemographic factors. RESULTS: The 4 dietary patterns that emerged from our data were identified as "Sandwiches and snacks" (34% of the sample); "High fat, sugar, and sodium" (29%); "Beverages, refined grains, and mixed dishes" (21%); and "High fruits, vegetables, whole grains, and plant proteins" (16%). One-quarter of pregnancies had preeclampsia (8% incidence), gestational diabetes (5%), preterm birth (8%), or SGA birth (8%). Compared with the "High fat, sugar, and sodium" pattern, there were 3.3 to 4.3 fewer cases of the composite adverse outcome per 100 pregnancies among participants following the "Beverages, refined grains and mixed dishes" pattern (risk difference -0.043; 95% confidence interval -0.078, -0.009), "High fruits, vegetables, whole grains and plant proteins" pattern (-0.041; 95% confidence interval -0.078, -0.004), and "Sandwiches and snacks" pattern (-0.033; 95% confidence interval -0.065, -0.002). CONCLUSIONS: Our results highlight that there are a variety of periconceptional dietary patterns that are associated with perinatal health and reinforce the negative health implications of diets high in fat, sugars, and sodium.

The association between multidimensional sleep health and gestational weight gain.

BACKGROUND: Although poor sleep health is associated with weight gain and obesity in the non-pregnant population, research on the impact of sleep health on weight change among pregnant people using a multidimensional sleep health framework is needed. OBJECTIVES: This secondary data analysis of the Nulliparous Pregnancy Outcome Study: Monitoring Mothers-to-be Sleep Duration and Continuity Study (n = 745) examined associations between mid-pregnancy sleep health indicators, multidimensional sleep health and gestational weight gain (GWG). METHODS: Sleep domains (i.e. regularity, nap duration, timing, efficiency and duration) were assessed via actigraphy between 16 and 21 weeks of gestation. We defined 'healthy' sleep in each domain with empirical thresholds. Multidimensional sleep health was based on sleep profiles derived from latent class analysis and composite score defined as the sum of healthy sleep domains. Total GWG, the difference between self-reported pre-pregnancy weight and the last measured weight before delivery, was converted to z-scores using gestational age- and BMI-specific charts. GWG was defined as low (<-1 SD), moderate (-1 or +1 SD) and high (>+1 SD). RESULTS: Nearly 50% of the participants had a healthy sleep profile (i.e. healthy sleep in most domains), whereas others had a sleep profile defined as having varying degrees of unhealthy sleep in each domain. The individual sleep domains were associated with a 20%-30% lower risk of low or high GWG. Each additional healthy sleep indicator was associated with a 10% lower risk of low (vs. moderate), but not high, GWG. Participants with late timing, long duration and low efficiency (vs. healthy) profiles had the strongest risk of low GWG (relative risk 1.5, 95% confidence interval 0.9, 2.4). Probabilistic bias analysis suggested that most associations between individual sleep health indicators, sleep health profiles and GWG were biased towards the null. CONCLUSIONS: Future research should determine whether sleep health is an intervention target for healthy GWG.

Is the Association Between Fruits and Vegetables and Preeclampsia Due to Higher Dietary Vitamin C and Carotenoid Intakes?

BACKGROUND: Diets dense in fruits and vegetables are associated with a reduced risk of preeclampsia, but pathways underlying this relationship are unclear. Dietary antioxidants may contribute to the protective effect. OBJECTIVE: We determined the extent to which the effect of dietary fruit and vegetable density on preeclampsia is because of high intakes of dietary vitamin C and carotenoids. METHODS: We used data from 7572 participants in the Nulliparous Pregnancy Outcomes Study: monitoring mothers-to-be (8 United States medical centers, 2010‒2013). Usual daily periconceptional intake of total fruits and total vegetables was estimated from a food frequency questionnaire. We estimated the indirect effect of ≥2.5 cups/1000 kcal of fruits and vegetables through vitamin C and carotenoid on the risk of preeclampsia. We estimated these effects using targeted maximum likelihood estimation and an ensemble of machine learning algorithms, adjusting for confounders, including other dietary components, health behaviors, and psychological, neighborhood, and sociodemographic factors. RESULTS: Participants who consumed ≥2.5 cups of fruits and vegetables per 1000 kcal were less likely than those who consumed <2.5 cups/1000 kcal to develop preeclampsia (6.4% compared with 8.6%). After confounder adjustment, we observed that higher fruit and vegetable density was associated with 2 fewer cases of preeclampsia (risk difference: -2.0; 95% CI: -3.9, -0.1)/100 pregnancies compared with lower density diets. High dietary vitamin C and carotenoid intake was not associated with preeclampsia. The protective effect of high fruit and vegetable density on the risk of preeclampsia and late-onset preeclampsia was not mediated through dietary vitamin C and carotenoids. CONCLUSIONS: Evaluating other nutrients and bioactives in fruits and vegetables and their synergy is worthwhile, along with characterizing the effect of individual fruits or vegetables on preeclampsia risk.

Experiences of Racial Discrimination and Periconceptional Diet Quality.

BACKGROUND: Racism is a key determinant of perinatal health disparities. Poor diet may contribute to this effect, but research on racism and dietary patterns is limited. OBJECTIVE: We aimed to describe the relation between experiences of racial discrimination and adherence to the 2015‒2020 Dietary Guidelines for Americans. METHODS: We used data from a prospective pregnancy cohort study conducted at 8 United States medical centers (2010‒2013). At 6‒13 weeks of gestation, 10,038 nulliparous people with singleton pregnancies were enrolled. Participants completed a Block food frequency questionnaire, assessing usual diet in the 3 mo around conception, and the Krieger Experiences of Discrimination Scale, assessing the number of situational domains (e.g., at school and on the street) in which participants ever experienced racial discrimination. Alignment of dietary intake with the 2015-2020 Dietary Guidelines for Americans was assessed using the Healthy Eating Index (HEI)-2015. RESULTS: The study showed that 49%, 44%, 35%, and 17% of the Asian, Black, Hispanic, and White participants reported experiences of racial discrimination in any domain. Most participants experienced discrimination in 1 or 2 situational domains. There were no meaningful differences in HEI-2015 total or component scores in any racial or ethnic group according to count of self-reported domains in which individuals experienced discrimination. For example, mean total scores were 57‒59 among Black, 61‒66 among White, 61‒63 among Hispanic, and 66‒69 among Asian participants across the count of racial discrimination domains. CONCLUSIONS: This null association stresses the importance of going beyond interpersonal racial discrimination to consider the institutions, systems, and practices affecting racialized people to eliminate persistent inequalities in diet and perinatal health.

Investigating the Shape and Strength of the Relationship Between Maternal Weight Gain and Gestational Age at Delivery in Twin and Singleton Pregnancies.

Both inadequate and excessive maternal weight gain are correlated with preterm delivery in singleton pregnancies, yet this relationship has not been adequately studied in twins. We investigated the relationship between time-varying maternal weight gain and gestational age at delivery in twin pregnancies and compared it with that in singletons delivered in the same study population. We used serial weight measurements abstracted from charts for twin and singleton pregnancies delivered during 1998-2013 in Pittsburgh, Pennsylvania. Our exposure was time-varying weight gain z score, calculated using gestational age-standardized and prepregnancy body mass index-stratified twin- and singleton-specific charts, and our outcome was gestational age at delivery. Our analyses used a flexible extension of the Cox proportional hazards model that allowed for nonlinear and time-dependent effects. We found a U-shaped relationship between weight gain z score and gestational age at delivery among twin pregnancies (lowest hazard of delivery observed at z score = 1.2), which we attributed to increased hazard of early preterm spontaneous delivery among pregnancies with low weight gain and increased hazard of late preterm delivery without labor among pregnancies with high weight gain. Our findings may be useful for updating provisional guidelines for maternal weight gain in twin pregnancies.

Does heterogeneity underlie differences in treatment effects estimated from SuperLearner versus logistic regression? An application in nutritional epidemiology.

PURPOSE: A strength of SuperLearner is that it may accommodate key interactions between model variables without a priori specification. In prior research, protective associations between fruit intake and preeclampsia were stronger when estimated using SuperLearner with targeted maximum likelihood estimation (TMLE) compared with multivariable logistic regression without any interaction terms. We explored whether heterogeneity (i.e., differences in the effect estimate due to interactions between fruit intake and covariates) may partly explain differences in estimates from these two models. METHODS: Using a U.S. prospective pregnancy cohort (2010-2013, n = 7781), we estimated preeclampsia risk differences (RDs) for higher versus lower fruit density using multivariable logistic regression and included two-way statistical interactions between fruit density and each of the 25 model covariates. We compared the RDs with those from SuperLearner with TMLE (gold standard) and logistic regression with no interaction. RESULTS: From the logistic regression models with two-way statistical interactions, 48% of the preeclampsia RDs were ≤-0.02 (closer to SuperLearner with TMLE estimate); 40% equaled -0.01 (same as logistic regression with no interaction estimate); the minority of RDs were at or crossed the null. CONCLUSIONS: Our exploratory analysis provided preliminary evidence that heterogeneity may partly explain differences in estimates from logistic regression versus SuperLearner with TMLE.

Association between gestational age at delivery and indicator-specific severe maternal morbidity.

OBJECTIVE: Individuals who deliver preterm are disproportionately affected by severe maternal morbidity. Limited data suggest that indicator-specific maternal morbidity varies by gestational age at delivery. We sought to evaluate the relationship between gestational age at delivery and the incidence of severe maternal morbidity and indicator-specific severe maternal morbidity. METHODS: We used a hospital administrative delivery database to identify all singleton deliveries between 16 and 42 weeks gestation from 2002 to 2018. We defined severe maternal morbidity as the presence of any International Classification of Disease diagnosis or procedure codes outlined by the Centers for Disease Control and Prevention, intensive care unit admission, and/or prolonged postpartum hospital length of stay. Indicator-specific severe maternal morbidity was based on the diagnosis and procedure codes and was characterized across gestational age epochs. We categorized gestational age into three epochs to capture extremely preterm birth (less than 28 weeks gestation), preterm birth (28-36 weeks gestation) and term birth (37 weeks gestation and above). Multivariable binomial regression was used to assess the association between categories of gestational age at delivery and severe maternal morbidity adjusting for confounders including age, race, body mass index (BMI), insurance status, and preexisting hypertension or diabetes. RESULTS: Severe maternal morbidity occurred in 2.5% of all deliveries. The unadjusted incidence of severe maternal morbidity by gestational age epoch was 12% at less than 28 weeks gestation, 8.4% at 28 to 36 weeks of gestation, and 1.7% at greater than or equal to 37 weeks gestation. After controlling for potential confounders the predicted probability of severe maternal morbidity was 16% (95% CI 14,17%) at 24 weeks compared to 2.2% (95% CI 2.1,2.3%) at 38 weeks. Sepsis, acute respiratory distress syndrome, mechanical ventilation, and shock were the most common diagnostic codes in deliveries less than 28 weeks gestation. Heart failure and cardiac arrhythmias were more common in patients with severe maternal morbidity delivering at term. CONCLUSION: A high proportion of severe maternal morbidity occurred in preterm patients, with the highest rates occurring at less than 28 weeks gestation. Individuals with severe maternal morbidity who deliver preterm had distinct indicators of morbidity compared to those who deliver at term.

Weight gain patterns among pregnancies with obesity and small- and large-for-gestational-age births.

OBJECTIVE: This case-cohort study estimated associations between gestational weight gain (GWG) and small-for-gestational-age (SGA) and large-for-gestational-age (LGA) births stratified by obesity class (I: 30-34.9 kg/m2 ; II: 35-39.9 kg/m2 ; III: ≥40 kg/m2 ) (Magee-Womens Hospital, Pittsburgh, Pennsylvania, 1998-2011). METHODS: First-trimester GWG was categorized as being below (<0.2 kg), within (0.2-2.0 kg), or above (>2.0 kg) the Institute of Medicine recommendations. For second- and third-trimester GWG, four linear trajectories were derived: approximating maintenance (slope -0.05 ± 0.03 kg/wk), approximating the recommendations (0.27 ± 0.01 kg/wk; reference), higher than the recommendations (0.54 ± 0.01 kg/wk), and highest among those above the recommendations (0.91 ± 0.02 kg/wk). RESULTS: For classes I, II, and III, respectively, there were 1290, 1247, and 1198 pregnancies in the subcohort; 262, 171, and 123 SGA cases; and 353, 286, and 257 LGA cases. First-trimester GWG was not associated with SGA/LGA births. Second- and third-trimester weight maintenance was associated with potentially lower LGA risk (risk ratio [RR]: 0.80; 95% confidence interval [CI]: 0.55-1.1) but not higher SGA risk (RR: 0.98; 95% CI: 0.64-1.5) for class III. In addition, some sensitivity analyses supported no increased SGA risk with second- and third-trimester weight maintenance for classes I and II. CONCLUSIONS: Second- and third-trimester weight maintenance may be associated with more optimal birth weight for gestational age. However, how this could be achieved (e.g., through diet and exercise interventions) is unclear, given the observational design of our study.

Effect of antenatal milk expression education on lactation outcomes in birthing people with pre-pregnancy body mass index ≥25: protocol for a randomized, controlled trial.

BACKGROUND: Birthing people with pre-pregnancy body mass indices (BMIs) ≥ 25 kg/m2, particularly those without prior breastfeeding experience, are at increased risk for suboptimal lactation outcomes. Antenatal milk expression (AME) may be one way to counteract the negative effects of early infant formula supplementation common in this population. METHODS: This ongoing, randomized controlled trial in the United States evaluates the efficacy of a telelactation-delivered AME education intervention versus an attention control condition on lactation outcomes to 1 year postpartum among 280 nulliparous-to-primiparous, non-diabetic birthing people with pre-pregnancy BMI ≥ 25 kg/m2. The assigned study treatment is delivered via four weekly online video consultations between gestational weeks 37-40. Participants assigned to AME meet with study personnel and a lactation consultant to learn and practice AME. Instructions are provided for home practice of AME between study visits. Control group participants view videos on infant care/development at study visits. Participants complete emailed surveys at enrollment (340/7-366/7 gestational weeks) and 2 weeks, 6 weeks, 12 weeks, 6 months, and 12 months postpartum. Surveys assess lactation and infant feeding practices; breastfeeding self-efficacy, attitudes, and satisfaction; perception of insufficient milk; onset of lactogenesis-II; lactation support and problems; and reasons for breastfeeding cessation. Surveys also assess factors associated with lactation outcomes, including demographic characteristics, health problems, birth trauma, racial discrimination, and weight stigma. Health information and infant feeding data are abstracted from the pregnancy and birth center electronic health record. Milk samples are collected from the intervention group at each study visit and from both groups at each postpartum follow-up for future analyses. Qualitative interviews are conducted at 6 weeks postpartum to understand AME experiences. Primary outcomes of interest are breastfeeding exclusivity and breastfeeding self-efficacy scores at 2 weeks postpartum. Outcomes will be examined longitudinally with generalized linear mixed-effects modeling. DISCUSSION: This is the first adequately powered trial evaluating the effectiveness of AME among U.S. birthing people and within a non-diabetic population with pre-pregnancy BMI ≥ 25 kg/m2. This study will also provide the first evidence of acceptability and effectiveness of telelactation-delivered AME. TRIAL REGISTRATION: ClinicalTrials.gov: NCT04258709.

Early Gestational Weight Gain and the Risk of Preeclampsia in Dichorionic Twin Pregnancies.

OBJECTIVE: The purpose of our study was to evaluate the body mass index (BMI)-specific association between early gestational weight gain (GWG) in dichorionic twin pregnancies and the risk of preeclampsia. STUDY DESIGN: We conducted a retrospective cohort study of all dichorionic twin pregnancies from 1998 to 2013. Data were obtained from a perinatal database and chart abstraction. Prepregnancy BMI was categorized as normal (18.5-24.9 kg/m2), overweight (25-29.9 kg/m2), and obese (≥30 kg/m2). Early GWG was defined as the last measured weight from 160/7 to 196/7weeks' gestation minus prepregnancy weight. GWG was standardized for gestational duration using BMI-specific z-score charts for dichorionic pregnancies. Preeclampsia was diagnosed using American College of Obstetricians and Gynecologists criteria and identified with International Classification of Diseases-9 coding. Early GWG z-score was modeled as a three-level categorical variable (≤ - 1 standard deviation [SD], 0, 3 +1 SD), where -1 to +1 was the referent group. We estimated risk differences and 95% confidence intervals (CIs) via marginal standardization. RESULTS: We included 1,693 dichorionic twin pregnancies in the cohort. In adjusted analysis, the incidence of preeclampsia increased with increasing early GWG among women with normal BMI. Women with normal BMI and a GWG z-score < - 1 (equivalent to 2.6 kg by 20 weeks) had 2.5 fewer cases of preeclampsia per 100 births (95% CI: -4.7 to - 0.3) compared with the referent; those with GWG z-score > +1 (equivalent to gaining 9.8 kg by 20 weeks) had 2.8 more cases of preeclampsia per 100 (95 % CI: 0.1-5.5) compared with the referent. In adjusted analyses, early GWG had minimal impact on the risk of preeclampsia in women with overweight or obesity. CONCLUSION: GWG of 2.6 kg or less by 20 weeks was associated with a decreased risk of preeclampsia among women pregnant with dichorionic twins and normal prepregnancy BMI. Current GWG guidelines focus on optimizing fetal weight and gestational length. Our findings demonstrate the importance of considering other outcomes when making GWG recommendations for twin pregnancy. KEY POINTS: · Early GWG decreased with increasing BMI category.. · Among women with normal weight, as early GWG increased so did the risk of preeclampsia.. · There was no association between early GWG and preeclampsia among women with overweight or obesity..

The association between multidimensional sleep health and gestational weight gain: nuMoM2b Sleep Duration and Continuity Study.

Background: Although poor sleep health is associated with weight gain and obesity in the non-pregnant population, research on the impact of sleep health on weight change among pregnant people using a multidimensional sleep-health framework is needed. This study examined associations among mid-pregnancy sleep health indicators, multidimensional sleep health, and gestational weight gain (GWG). Methods: We conducted a secondary data analysis of the Nulliparous Pregnancy Outcome Study: Monitoring Mothers-to-be Sleep Duration and Continuity Study (n=745). Indicators of individual sleep domains (i.e., regularity, nap duration, timing, efficiency, and duration) were assessed via actigraphy between 16 and 21 weeks of gestation. We defined "healthy" sleep in each domain based on empirical thresholds. Multidimensional sleep health was based on sleep profiles derived from latent class analysis. Total GWG, the difference between self-reported pre-pregnancy weight and the last measured weight before delivery, was converted to z-scores using gestational age- and BMI-specific charts. GWG was defined as low (<-1 SD), moderate (-1 or +1 SD), and high (>+1 SD). Results: Nearly 50% of the participants had a healthy sleep profile (i.e., healthy sleep in most domains), whereas others had a sleep profile defined as having varying degrees of poor health in each domain. While indicators of individual sleep domains were not associated with GWG, multidimensional sleep health was related to low and high GWG. Participants with a sleep profile characterized as having low efficiency, late timing, and long sleep duration (vs. healthy sleep profile) had a higher risk (RR 1.7; 95% CI 1.0, 3.1) of low GWG a lower risk of high GWG (RR 0.5 95% CI 0.2, 1.1) (vs. moderate GWG). Conclusions: Multidimensional sleep health was more strongly associated with GWG than individual sleep domains. Future research should determine whether sleep health is a valuable intervention target for optimizing GWG. Synopsis: Study question: What is the association between mid-pregnancy multidimensional sleep health and gestational weight gain?What's already known?: Sleep is associated with weight and weight gain outside of pregnancyWhat does this study add?: We identified patterns of sleep behaviors associated with an increased risk of low gestational weight gain.

Are Detailed Behavioral, Psychosocial, and Environmental Variables Necessary to Control for Confounding in Pregnancy Weight Gain Research?

BACKGROUND: Associations between pregnancy weight gain and adverse outcomes may be spurious owing to confounding by factors not typically measured in cohort studies. We determined the extent to which the addition of detailed behavioral, psychosocial, and environmental measurements to commonly available covariates improved control of confounding. METHODS: We used data from a prospective US pregnancy cohort study (2010-2013, n = 8978). We calculated two propensity scores for low and high pregnancy weight gain (vs. adequate gain) using 11 standard confounders (e.g., age and education). We examined the balance of characteristics between weight gain groups before and after propensity score matching. We used negative binomial regression to estimate the association between weight gain and small- and large-for-gestational-age birth, preterm birth, and unplanned cesarean delivery, controlling for propensity score. To this model, we then added 17 detailed behavioral, psychosocial, and environmental measurements ("fully adjusted"). We calculated the risk ratio owing to confounding as the ratio of the standard confounder-adjusted risk ratio to the fully adjusted risk ratio. RESULTS: There were minimal imbalances between weight gain groups in detailed measures after matching for a propensity score of standard covariates. Accordingly, the inclusion of detailed covariates had minimal impact on estimated associations between low or high pregnancy weight gain and adverse pregnancy outcomes: risk ratios owing to confounding were null for all outcomes (e.g., 1.1 [95% CI = 1.0, 1.1] for low weight gain and preterm birth). CONCLUSIONS: Adjustment for detailed behavioral, psychosocial, and environmental measurements had minimal impact on estimated associations between pregnancy weight gain and adverse perinatal outcomes.

Severe Maternal Morbidity in Twins.

OBJECTIVE: While twin gestations are at increased risk of severe maternal morbidity (SMM), there is limited information about timing and causes of SMM in twins. Furthermore, existing data rely on screening definitions of SMM because a gold standard approach requires chart review. We sought to determine the timing and cause of SMM in twins using a gold standard definition outlined by the American College of Obstetricians and Gynecologists (ACOG). STUDY DESIGN: We used a perinatal database to identify all twin deliveries from 1998 to 2013 at a single academic medical center (n = 2,367). Deliveries were classified as screen positive for SMM if they met any of the following criteria: (1) one of the Centers for Disease Control and Prevention (CDC) International Classification of Diseases Ninth Revision diagnosis and procedure codes for SMM; (2) a prolonged postpartum length of stay (>3 standard deviations beyond mean length of stay by mode of delivery); or (3) maternal intensive care unit admission. We identified true cases of SMM through medical record review of all screen-positive deliveries using the definition of SMM outlined in the ACOG Obstetric Care Consensus. We also determined cause and timing of SMM. RESULTS: A total of 165 (7%) of twin deliveries screened positive for SMM. After chart review of all screen-positive cases, 2.4% (n = 56) were classified as a true case of SMM using the ACOG definition for a positive predictive value of 34%. The majority of SMM occurred postpartum (65%). Hemorrhage was the most common cause of SMM, followed by hypertensive and pulmonary etiologies. CONCLUSION: Commonly used approaches to screen for SMM perform poorly in twins. This has important implications for quality initiatives and epidemiologic studies that rely on screening definitions of maternal morbidity. Our study demonstrates that the immediate postpartum period is a critical time for maternal health among women with twin pregnancies. KEY POINTS: · Screening approaches for SMM have low positive predictive value in twins.. · Hemorrhage, hypertensive, and pulmonary complications were the most common morbidities.. · SMM was most common postpartum..