Maternal history of childhood maltreatment and pregnancy weight outcomes.

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 pre-pregnancy BMI and gestational weight gain among women who had participated for >20 years in a longitudinal cohort.At age 26-35 participants reported childhood maltreatment (physical, sexual, and emotional abuse; emotional neglect) and, 5 years later, about pre-pregnancy weight and gestational weight gain for previous pregnancies (n=656). Modified Poisson regression models were used to estimate associations of maltreatment history with pre-pregnancy 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 pre-pregnancy BMI ≥30 kg/m2 in women with certain types of maltreatment (e.g., emotional abuse RR=2.4; 95% CI: 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 RR=1.7; 95% CI: 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 pre-pregnancy BMI ≥30 kg/m2 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.

Periconceptional Dietary Quality and Metabolic Syndrome at 3 Years Postpartum.

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.

Pseudo-Random Number Generator Influences on Average Treatment Effect Estimates Obtained with Machine Learning.

BACKGROUND: 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 = 1,004) 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 5,000 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 (IQRw) of the risk differences in the full sample with the simple algorithm was 13 per 1000. However, all other IQRs 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 warrants 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.

Advances in methods for characterizing dietary patterns: A scoping review.

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.

Gestational weight gain below recommendations and adverse maternal and child health outcomes for pregnancies with overweight or obesity: a United States cohort study.

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.

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.

Validation of Long-term Recall of Pregnancy-related Weight in the Life-course Experiences And Pregnancy Study.

BACKGROUND: Prepregnancy 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 prepregnancy 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 prepregnancy BMI or GWG category conditional on one's true category (analogous to sensitivities and specificities) and probabilities of truly being in each prepregnancy 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 prepregnancy BMI. Self-report misclassified 32% (95% confidence interval [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. CONCLUSION: Findings can be used in quantitative bias analyses to estimate bias-adjusted associations with prepregnancy BMI and GWG.

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.

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.

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.