Self-reported Pre-pregnancy Weight Research Articles

Maternal pre-pregnancy diet and prenatal depression: the mediating role of pre-pregnancy weight status and prenatal inflammation.

Depression is a common prenatal psychological complication. We aimed to investigate if maternal pre-pregnancy diet can impact prenatal depressive symptoms and the mediating role of pre-pregnancy BMI and inflammation. We used data (N 1141) from the Alberta Pregnancy Outcomes and Nutrition cohort study. We calculated Mediterranean diet adherence (MED) and dietary inflammatory index (DII) scores using data from pre-pregnancy FFQ. In the third-trimester, we assessed depressive symptoms using Edinburgh Postpartum Depression Scale (EPDS) and inflammation through serum C-reactive protein (CRP) levels. BMI was calculated from self-reported pre-pregnancy weight. Race-stratified analyses (white and people of colour) were run. We observed no association between MED or DII tertiles and depressive symptoms. However, white participants in the MED tertile-3 had lower risk of depression (EPDS < 10) compared with tertile-1 (OR = 0·56, 95 % CI, 0·33, 0·95). White individuals in MED tertile-3 had lower BMI (MD = -1·08; 95 % CI, -1·77, -0·39) and CRP (MD = -0·53; 95 % CI, -0·95, -0·11) than tertile-1, and those in DII tertile-2 (MD = 0·44; 95 % CI, 0·03, 0·84) and tertile-3 (MD = 0·42; 95 % CI, 0·01, 0·83) had higher CRP than tertile-1. Among people of colour, neither MED nor DII was associated with BMI or CRP, but BMI was negatively associated with depressive symptoms (β = -0·25, 95 % CI, -0·43, -0·06). We found no association between diet and depressive symptoms through BMI or CRP, in either race. Pre-pregnancy diet might affect the risk of prenatal depression in a race-specific way. Further research is required to explore the racial differences in the association between maternal diet and prenatal depressive symptoms/depression risk.

Pre-pregnancy body mass index and caesarean section in Andean women in Peru: a prospective cohort study

BackgroundDuring the last two decades, Caesarean section rates (C-sections), overweight and obesity rates increased in rural Peru. We examined the association between pre-pregnancy body mass index (BMI) and C-section in the province of San Marcos, Northern Andes-Peru.MethodsThis is a prospective cohort study. Participants were women receiving antenatal care in public health establishments from February 2020 to January 2022, who were recruited and interviewed during pregnancy or shortly after childbirth. They answered a questionnaire, underwent a physical examination and gave access to their antenatal care card information. BMI was calculated using maternal height, measured by the study team and self-reported pre-pregnancy weight measured at the first antenatal care visit. For 348/965 (36%) women, weight information was completed using self-reported data collected during the cohort baseline. Information about birth was obtained from the health centre’s pregnancy surveillance system. Regression models were used to assess associations between C-section and BMI. Covariates that changed BMI estimates by at least 5% were included in the multivariable model.ResultsThis study found that 121/965 (12.5%) women gave birth by C-section. Out of 495 women with pre-pregnancy normal weight, 46 (9.3%) had C-sections. Among the 335 women with pre-pregnancy overweight, 53 (15.5%) underwent C-sections, while 23 (18.5%) of the 124 with pre-pregnancy obesity had C-sections. After adjusting for age, parity, altitude, food and participation in a cash transfer programme pre-pregnancy overweight and obesity increased the odds of C-section by more than 80% (aOR 1.82; 95% CI 1.16–2.87 and aOR 1.85; 95% CI 1.02–3.38) compared to women with a normal BMI.ConclusionsHigh pre-pregnancy BMI is associated with an increased odds of having a C-section. Furthermore, our results suggest that high BMI is a major risk factor for C-section in this population. The effect of obesity on C-section was partially mediated by the development of preeclampsia, suggesting that C-sections are being performed due to medical reasons.

Effect of Maternal Obesity on Pregnancy Outcomes and Metabolic Consequences

Background: Maternal obesity has emerged as a critical public health issue with profound implications for both maternal and neonatal health. The increasing prevalence of obesity among women of reproductive age underscores the urgency of examining its impact on pregnancy outcomes and the metabolic health of offspring. Previous studies have identified associations between maternal obesity and adverse pregnancy outcomes, including gestational diabetes mellitus (GDM), hypertensive disorders of pregnancy (HDP), and cesarean delivery, as well as long-term metabolic disturbances in offspring. Objective: This study aimed to investigate the effects of maternal obesity on pregnancy outcomes and to elucidate the potential metabolic consequences for offspring, contributing to the understanding of intergenerational transmission of metabolic risk. Methods: Conducted at the Gyne B Unit of Hayatabad Medical Complex, Peshawar, this retrospective cohort study included 6,910 obstetric admissions from January to December of the previous year. Pregnant women aged 18-45 years with singleton pregnancies were included, excluding those with pre-existing medical conditions. BMI was calculated from self-reported pre-pregnancy weight and height. Outcomes analyzed included GDM, HDP, mode of delivery, macrosomia, neonatal hypoglycemia, stillbirth, and neonatal mortality. Data analysis utilized SPSS version 25, employing descriptive statistics, chi-square tests, t-tests or Mann-Whitney U tests, and multivariable logistic regression models. Results: Among the participants, 55.7% were of normal weight, 20.6% overweight, and 18.4% obese. Obese pregnant women demonstrated significantly higher rates of GDM (8.6% vs. 23.7%, p=0.001), HDP (18.3% vs. 6.4%, p=0.011), and cesarean delivery (55.7% vs. 30.1%, p=0.003) compared to their normal-weight counterparts. Offspring of obese mothers had a higher incidence of fetal macrosomia (10.2% vs. 5.4%, p=0.001) and neonatal hypoglycemia (10.4% vs. 6%, p&lt;0.001). Conclusion: The study confirms the significant association between maternal obesity and adverse pregnancy outcomes, including increased risks of GDM, HDP, and cesarean delivery. Furthermore, offspring of obese mothers are at an elevated risk of metabolic disturbances, indicating the necessity of addressing maternal obesity to improve health outcomes for both mothers and their children.

Prepregnancy weight loss and maternal metabolic and inflammatory biomarkers during pregnancy: An analysis of National Health and Nutrition Examination Survey.

Women with overweight or obesity are recommended to lose weight before conception to optimize pregnancy outcomes. However, the obstetrical implications of prepregnancy weight loss have been minimally examined. The objective of this study was to investigate the association between prepregnancy weight loss and maternal metabolic and inflammatory profiles during a subsequent pregnancy. This study was a retrospective analysis of National Health and Nutrition Examination Survey data (2003-2018). Participants were women who were pregnant at the time of assessment. Prepregnancy weight loss was described as percent weight change based on self-reported baseline (1 year before pregnancy) and prepregnancy weight. Metabolic (e.g., blood pressure [BP]) and inflammatory biomarkers (i.e., high-sensitivity C-reactive protein [hs-CRP]) were determined by standard medical tests. Statistical analyses included linear regressions with appropriate imputation, weighting, and variance estimation techniques. Participants (N = 236) reported a mean percent weight loss of 4.6% (standard error [SE] = 0.3%) during the year before pregnancy. Regression models showed that prepregnancy weight loss was inversely associated with levels of total cholesterol (β = -1.24, p = 0.01), low-density lipoprotein-cholesterol (β = -0.79, p < 0.01), and high-density lipoprotein-cholesterol (β = -0.18, p < 0.01). The effect of prepregnancy weight loss on BP, insulin sensitivity, and hs-CRP was not significant, although there was a trend toward higher levels of diastolic BP (β = 0.24, p = 0.07) and hs-CRP (β = 0.10, p = 0.08). This study found favorable changes in lipid profiles following prepregnancy weight loss. Due to limitations such as a relatively small sample size, self-reported weight measures, and missing data on several outcome variables, future studies are needed to confirm study findings.

The association between multidimensional sleep health and gestational weight gain.

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 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). 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). 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. Future research should determine whether sleep health is an intervention target for healthy GWG.

Abstract MP76: The Association Between Sleep Health and Gestational Weight Gain

Background: Poor sleep health is associated with weight gain and obesity outside of pregnancy. Still, there is little research regarding the impact of sleep health on weight among pregnant populations, particularly using a multidimensional sleep-health framework. This study examined associations between mid-pregnancy sleep health indicators, multidimensional sleep health, and gestational weight gain (GWG). Methods: This was 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 health domains (i.e., regularity, nap duration, timing, efficiency, and duration) were assessed via actigraphy between 16 and 21 weeks of gestation. We defined “good” sleep health 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 (&lt;-1 SD), moderate (-1 or +1 SD), and high (&gt;+1 SD). Results: We identified four distinct sleep profiles (Figure 1). While indicators of individual sleep domains were not associated with GWG, multidimensional sleep health was associated with higher risk of low GWG. Compared to people with a good sleep profile (Class 1), people with a low efficiency and long sleep duration profile (Class 4) had a 1.88 (95% CI 1.04 to 3.39) higher risk of low GWG (vs. moderate GWG) in models adjusting for education, depressive symptoms, race, smoking status. Discussion: Multidimensional sleep health was more strongly associated with GWG than were individual sleep domains. Future research should determine whether sleep health is a useful intervention target for optimizing GWG. Figure 1- Probabilities of each sleep characteristic by sleep profile, derived from latent class analysis

Large maternal waist circumference in relation to height is associated with high glucose concentrations in an early-pregnancy oral glucose tolerance test: A population-based study.

To explore the role of maternal anthropometric characteristics in early-pregnancy glycemia, we analyzed the associations and interactions of maternal early-pregnancy waist circumference (WC), height and pre-pregnancy body mass index (BMI) with plasma glucose concentrations in an oral glucose tolerance test (OGTT) at 12-16 weeks' gestation. A population-based cohort of 1361 pregnant women was recruited in South Karelia, Finland, from March 2013 to December 2016. All participants had their WC, weight, height, HbA1c , and blood pressure measured at 8-14 weeks' gestation and subsequently underwent a 2-h 75-g OGTT, including assessment of fasting insulin concentrations, at 12-16 weeks' gestation. BMI (kg/m2 ) was calculated using self-reported pre-pregnancy weight. Maternal WC ≥80 cm was defined as large. Maternal height ≥166 cm was defined as tall. Data on gestational diabetes treatment was extracted from hospital records. In the total cohort, 901 (66%) of women had an early-pregnancy WC ≥80 cm, which was associated with higher early-pregnancy HbA1c, higherconcentrationsoffasting plasma glucose and serum insulin, higherpost-load plasma glucose concentrations, higherHOMA-IR indices, higher blood pressure levels, and higherfrequencies of pharmacologically treated gestational diabetes, than early-pregnancy WC <80 cm. Maternal height ≥166 cm was negatively associated with 1- and 2-h post-load plasma glucose concentrations. Waist-to-height ratio (WHtR) >0.5 was positively associated with both fasting and post-load plasma glucose concentrations at 12-16 weeks' gestation, even when adjusted for age, smoking, nulliparity, and family history of type 2 diabetes. The best cut-offs for WHtR (0.58 for 1-h plasma glucose, and 0.54 for 2-h plasma glucose) were better predictors of post-load glucose concentrations >90th percentile than the best cut-offs for BMI (28.1kg/m2 for 1-h plasma glucose, and 26.6kg/m2 for 2-h plasma glucose), with areas-under-the-curve (95% confidence interval) 0.73 (0.68-0.79) and 0.73 (0.69-0.77), respectively, for WHtR, and 0.68 (0.63-0.74) and 0.69 (0.65-0.74), respectively, for BMI. In our population-based cohort, early-pregnancy WHtR >0.5 waspositively associatedwith both fasting and post-load glucose concentrations at 12-16 weeks' gestation and performed better than BMI in the prediction of post-load glucose concentrations >90th percentile. Overall, our results underline the importance of evaluating maternal abdominal adiposity in gestational diabetes risk assessment.

Adverse Childbirth and Perinatal Outcomes Among Healthy, Low-Risk Pregnant Women with Abnormal Total Gestational Weight Gain.

Purpose: Little is known on the potential effects of abnormal gestational weight gain (GWG) among low-risk, healthy pregnant women with no comorbidities or gestational complications. We investigated perinatal outcomes of these pregnancies according to GWG as per the 2009 National Academy of Medicine (NAM) recommendations. Materials and Methods: A retrospective analysis of prospectively collected data of low-risk pregnant women giving birth at term between January 2016 and December 2020. Inclusion criteria were normal pregestational body mass index (pBMI) (18.5-24.9 kg/m2) and no pregestational or gestational complication. Self-reported prepregnancy weight was used to calculate pBMI; GWG was the difference between maternal weight at childbirth and prepregnancy weight. Women were classified according to the 2009 NAM guidelines for GWG: insufficient (iGWG, <11.5 kg), adequate (aGWG, 11.5-16 kg), and excessive (eGWG, >16 kg). Logistic regression analysis with aGWG as referent was performed to independently estimate dose-response associations. Results: During the study period, there were 4,127 (33.1%) births fulfilling the inclusion criteria. Fifty-two percent of women gained outside the recommended range: 33.5% had iGWG and 18.7% had eGWG. iGWG women were 40% more likely to have early-term births and small for gestational age neonates. In turn, eGWG women displayed increased odds of prolonged pregnancy (adjusted odds ratio [aOR] 1.32), cesarean section in labor (aOR 1.50), high-degree perineal tears (aOR 2.04), postpartum hemorrhage ≥1,000 mL (aOR 1.54), and large for gestational age newborns (aOR 1.83). Conclusion: Our data show that abnormal GWG independently associates with heightened risk of adverse outcomes among healthy, low-risk pregnant women with normal pBMI and no comorbidity or gestational complication.

Monitoring gestational weight gain: setting up a regional surveillance system in Italy

BackgroundIn many countries, including Italy, there are few national data on pre-pregnancy Body Mass Index (BMI) and gestational weight gain (GWG), despite these being important predictors of maternal and neonatal health outcomes. This dearth of information makes it difficult to develop and monitor intervention policies to reduce the burden of disease linked to inadequate BMI status and/or GWG in pregnant women. This study describes the setting up and initial implementation of a regional surveillance system on pre-pregnancy BMI and GWG.MethodsBetween 1 January 2017 and 31 December 2018, anthropometric data were collected from all pregnant women accessing public health services in the Friuli Venezia Giulia region (Italy) for first ultrasound check (T1) and at delivery (T2). Anthropometric data collected at T1 (self-reported pre-pregnancy weight and measured weight and height) and T2 (measured weight and self-reported pre-pregnancy weight and height) were compared.ResultsThe system was able to reach 43.8% of all the women who gave birth in the region, and provided complete data for 6400 women of the 7188 who accessed the services at T1. At the beginning of pregnancy 447 (7.0%) women were underweight, 4297 (67.1%) had normal weight, 1131 (17.7%) were overweight and 525 (8.2%) had obesity. At delivery, 2306 (36.0%) women were within the appropriate weight gain range, while for 2021 (31.6%) weight gain was insufficient and for 2073 (32.4%) excessive. Only minor differences were observed between measured and self-reported anthropometric data.ConclusionsThe surveillance system offers an overview of the weight status of women during pregnancy. About 1/3 of women entered pregnancy with unsatisfactory BMI and 2/3 did not achieve the recommended weight gain. This surveillance system can be an effective tool to guide public health interventions.

RF16 | PSUN177 Predictors of Postpartum Depressive Symptoms in Women with Recent GDM

Abstract Background Patients with gestational diabetes mellitus (GDM) have an increased risk of developing type 2 diabetes mellitus (T2DM) after delivery and are encouraged to practice behaviors, such as healthy nutrition, physical activity and weight loss to attenuate future risk of T2DM. GDM has been shown to significantly increase the risk for post-partum depressive symptoms, which can impair a woman's ability to engage in and adhere to diabetes prevention efforts. To further inform the development and adapation of lifestyle interventions to prevent T2DM, we sought to identify factors associated with depressive symptoms in the early postpartum period among women with recent GDM. Methods Participants are from the baseline cohort of the Balance after Baby Intervention study, a two-year randomized clinical trial of a lifestyle intervention for women with recent history GDM conducted in Boston, MA and Denver, CO between 2016-2019. Prior to randomization, a study visit was scheduled at approximately 6 weeks post-partum. At this visit, weight and height were measured for determination of BMI. We administered the Edinburgh Postpartum Depression Scale (EPDS) and Perceived Stress Scale (PSS-10). We used an EPDS score of ≥9 as depressive symptoms and a PSS score of ≥14 as moderate to high perceived stress. We collected self-reported pre-pregnancy weight, and reviewed medical records to obtain medical history, including GDM diagnosis, mode of delivery, gestational weight gain and insulin use during pregnancy. We conducted bivariate analyses to identify predictors of postpartum depressive symptoms, then modeled the odds of post-partum depressive symptoms using multivariate logistic regression. Results Our study included 181 women (mean age, SD 32.6 ± 5.3 years, 51.4% White/14.4% Black/33.7% Latinx, mean pre-pregnancy BMI 29.8 ± 6.0 kg/m2). 19% of women scored ≥9 on EPDS and 53% of women scored ≥14 on PSS at the post-partum visit. Variables included in the bivariate analysis were age, Black/Latinx-identifying, college completion, perceived stress, primiparity, having a partner, pre-pregnancy BMI, gestational weight gain, C-section, insulin use during pregnancy, breastfeeding, 1st GDM pregnancy, exceeding IOM weight gain guidelines and income level. Of those variables, the best fit multivariate model with the lowest Akaike information criterion (AIC) included perceived stress, C-section and 1st GDM pregnancy. Using this model, the regression analysis showed that only perceived stress (4.5 OR, 95% CI [2.6-7.7], p was predictive of post-partum depressive symptoms. Conclusion Perceived stress was predictive of post-partum depressive symptoms in women with recent GDM. Addressing perceived stress in the early post-partum period may be an important target for development or adaptations of future lifestyle interventions to prevent T2DM in women with recent GDM. Presentation: Sunday, June 12, 2022 12:30 p.m. - 2:30 p.m., Sunday, June 12, 2022 1:18 p.m. - 1:23 p.m.

Cesarean Section Delivery Does Not Impact Postpartum Weight Loss And Recovery

Inability to lose weight gained during pregnancy increases a woman’s risk of becoming overweight or obese. Epidemiological studies have suggested that obesity is strongly associated with history of cesarean section (c-section) delivery. This association may be related to postpartum changes in physiological (e.g. physical activity) and behavioral (e.g. sleep) factors associated with weight loss. PURPOSE: To examine differences in postpartum body mass and factors related to weight loss between women who gave birth via c-section vs. vaginally. METHODS: Body mass was measured in 37 women (11 c-section [CD] and 26 vaginal [VD] delivery), at 3, 6, 9, and 12 months postpartum. Additionally, physiological factors (body composition (DXA), resting energy expenditure, and physical activity) and behavioral factors (sleep quality, fatigue, depression, and body image) related to weight loss were assessed at each time point. Self-reported pre-pregnancy weight and pregnancy weight gain were recorded and differences between CD and VD were assessed with student’s t-tests. Differences between CD and VD postpartum factors were assessed using two-way ANOVA with repeated measures. RESULTS: There was no significant difference between CD and VD groups in pre-pregnancy BMI (26.5 ± 5.9 kg/m2 vs. 25.4 ± 5.4 kg/m2, p = 0.59, CD vs VD respectively), pregnancy weight gain (16.2 ± 5.2 kg vs. 14.4 ± 4.8 kg, p = 0.32, CD vs VD respectively), and body mass at delivery (86.3 ± 17.8 kg vs. 83.9 ± 13.8 kg, p = 0.67, CD vs VD respectively). At 12 months postpartum, similar weight loss occurred in both groups (14.3 ± 6.1 kg vs 13.8 ± 5.4 kg, p = 0.77, CD vs VD respectively). ANOVA analysis of the changes in physiological and behavioral factors related to weight loss over the postpartum period revealed no significant difference between CD and VD women. CONCLUSION: Despite potentially longer recovery time associated with c-section delivery, weight loss in the postpartum period was not impacted and therefore c-section delivery does not appear to be a risk factor for obesity.

Exploring weight bias internalization in pregnancy

BackgroundRecent research has shown that pregnant individuals experience weight stigma throughout gestation, including negative comments and judgement associated with gestational weight gain (GWG). Weight bias internalization (WBI) is often a result of exposure to weight stigma and is detrimental to biopsychological health outcomes. The purpose of this study was to explore WBI in pregnancy and compare scores based on maternal weight-related factors including pre-pregnancy body mass index (BMI), obesity diagnosis and excessive GWG.MethodsPregnant individuals in Canada and USA completed a modified version of the Adult Weight Bias Internalization Scale. Self-reported pre-pregnancy height and weight were collected to calculate and classify pre-pregnancy BMI. Current weight was also reported to calculate GWG, which was then classified as excessive or not based on Institute of Medicine (2009) guidelines. Participants indicated if they were diagnosed with obesity by a healthcare provider. Inferential analyses were performed comparing WBI scores according to pre-pregnancy BMI, excessive GWG, and obesity diagnosis. Significance was accepted as p < 0.05 and effect sizes accompanied all analyses.Result336 pregnant individuals completed the survey, with an average WBI score of 3.9 ± 1.2. WBI was higher among those who had a pre-pregnancy BMI of obese than normal weight (p = 0.04, η2 = 0.03), diagnosed with obesity than not diagnosed (p < 0.001, Cohen’s d = 1.3), and gained excessively versus not (p < 0.001, Cohen’s d = 1.2).ConclusionsPregnant individuals who have a higher BMI, obesity and gain excessively may experience WBI. Given that weight stigma frequently occurs in pregnancy, effective person-oriented strategies are needed to mitigate stigma and prevent and care for WBI.

Association Between Gestational Weight Gain in Each Trimester and Weight Retention at 6 and 12 Months Postpartum: Results From a National Administrative Dataset in Brazil

ObjectivesTo estimate the association between gestational weight gain (GWG) in the first, second, and third pregnancy trimesters and weight retention at 6 and 12 mo postpartum.MethodsIndividual data from the Brazilian National Food and Nutrition Surveillance System from 2008–2020 were used. Cumulative GWG was calculated as the difference between the last weight measured in each trimester and self-reported pre-pregnancy weight, standardized for gestational age and pre-pregnancy body mass index (BMI) into z-scores using the Brazilian charts. Postpartum weight retention (PWR) was calculated as the difference between weight measured close to (±1 month) 6 and 12 months postpartum and self-reported pre-pregnancy weight and categorized as < 5 or ≥ 5 kg. Median, interquartile ranges (IQR), frequencies, and proportions were calculated. Maternal age (y), participation in the Bolsa Familia conditional cash transfer program (yes/no), and pre-pregnancy BMI (kg/m2) were used to adjust linear regression models.ResultsAmong the 31,846 women included, 88.5% were < 35 y old; 77.2% participated in the Bolsa Familia program, and 52.7% were classified as normal weight before pregnancy. The median (IQR) cumulative GWG was 1.2 (0–2.8), 4.0 (1.2–7.0), and 9.0 (5.3–12.7) kg in the first, second, and third pregnancy trimesters, respectively. At 6 and 12 mo postpartum, 32.7% and 31.4% of the women had PWR ≥ 5 kg, respectively. The prevalence of PWR ≥ 5 kg at 6 and 12 mo was higher among women who gained weight > 90th percentile of the Brazilian charts than those who gained below those values (73.2 v. 29.6% for 6; 63.9 v. 28.7% for 12 mo, respectively). Each 1 z-score increase in the cumulative GWG in the first, second, and third trimesters was associated with an increase of 2.0, 2.3, and 2.4 kg, respectively, in the PWR at 6 mo. Similar trends were observed for PWR at 12 mo (P < 0.001 for all cases).ConclusionsGWG in all pregnancy trimesters was associated with PWR at 6 and 12 mo postpartum. The association between GWG in the first trimester and PWR highlights the importance of monitoring weight gain since the beginning of pregnancy to prevent the occurrence of adverse outcomes, such as maternal obesity, during the postpartum period.Funding SourcesBill & Melinda Gates Foundation.

Long-Term Mortality Implications of Maternal Weight Change in Pregnancy

ObjectivesHigh pregnancy weight gain is associated with greater postpartum weight retention; yet the long-term implications remain unknown. We examined associations of pregnancy weight change with mortality after ∼50 years of follow-up.MethodsThe Collaborative Perinatal Project (CPP) Mortality Linkage Study linked maternal participants in the CPP, a prospective pregnancy cohort (1959–1965), to the National Death Index and Social Security Death Master File for vital status through 2016 (n = 46,042). Total gestational weight change (gain or loss) in the index pregnancy (last CPP singleton pregnancy) was the difference in recorded delivery weight and self-reported pre-pregnancy weight. All-cause and cause-specific mortality associations with quintiles of gestational weight change were estimated using Cox regression adjusting for index pregnancy age, pre-pregnancy body mass index (BMI), race, parity, smoking, marital status, income, education, site, study year, prior chronic conditions, and gestational week at delivery.ResultsMajority of women (69%) had a healthy pre-pregnancy BMI and 46% were White and 45% were Black. Median (interquartile range) total gestational weight change was 9.5 kg (6.4–12.2), respectively; 2.5% lost weight in pregnancy. Over a median follow-up of 52 years (IQR 45–54), 38.9% of women died. Adjusted hazard ratios (HRs) and 95% confidence intervals (CI) for mortality across quintiles 1 to 5 were 1.07 (95% CI 1.01–1.12), 1.03 (0.98–1.09), 1.00 (Reference), 1.01 (0.96–1.07), and 1.09 (1.03- 1.14), respectively. Compared to the middle quintile, HRs for the top four causes of death, cancer, cardiovascular disease, respiratory disease, and diabetes for the first quintile were 1.20 (0.99–1.46), 1.05 (0.96–1.15), 1.14 (0.96–1.37), and 0.92 (0.72–1.17), respectively; HRs for the fifth quintile were 1.19 (0.98–1.45), 1.14 (1.03–1.27), 0.95 (0.78–1.14), and 1.52 (1.20–1.92) respectively.ConclusionsThis study's novel findings extend the importance of healthy pregnancy weight gain beyond the pregnancy window to women's long mortality risk, particularly from cancer, cardiovascular disease, and diabetes.Funding SourcesThis work was funded by the Intramural Research Program of the NICHD.

Sleep Quality Relates to Metabolic Health in Second Trimester of Pregnancy

ObjectivesExamine the relationships between self-reported sleep quality parameters and markers of metabolic health during pregnancy.MethodsBaseline data collected at 12–20 weeks gestation from pregnant women enrolled in a high-fiber RCT (SPROUT; NCT04868110) were analyzed. Exclusion criteria included any known metabolic disturbance (e.g., history of diabetes, preeclampsia, hypertension). At the visit, a fasted blood draw, height, and weight were performed. Self-reported pre-pregnancy weight was collected. Sleep quality was assessed using the global Pittsburg Sleep Quality Index score (PSQI; >5 indicates poorer overall sleep quality) and sleep efficiency from the PSQI (100-point scale, higher indicates better efficiency). Total cholesterol (TC), low-density lipoprotein (LDL-C), high-density lipoprotein (HDL-C), triglycerides (TG), serum glucose, serum insulin, and hemoglobin A1c were analyzed using standardized protocols. Pearson's correlations, means, and standard deviations were performed.ResultsThirty-three participants were included in the analyses. Participants had a pre-pregnancy BMI of 25.4 ± 4.3 kg/m2, PSQI of 4.61 ± 2.4, and sleep efficiency of 89.8 ± 10.7. Sleep efficiency was related to TC (r = −0.41, p = 0.018) and LDL-C (r = −0.418, p = 0.016); and PSQI was related to LDL-C (r = 0.372, p = 0.033). No relationships were found between sleep parameters and other lipid or glycemic markers.ConclusionsIn a group of low-risk pregnant women, lower sleep efficiency and overall sleep quality were related to higher levels of blood lipids.Funding SourcesInternal funding received from School of Health Professions at presenting author's academic institution.

Association of high Body Mass Index and postdates pregnancy.

Background and Objective:Obesity with its growing prevalence is a major public health problem influencing gestational age at delivery. Raised Body Mass Index (BMI) has been shown to be associated with significantly increased risk of prolonged pregnancy; which is an important contributor to perinatal morbidity and mortality. Obesity needs modified antenatal, intrapartum and postpartum care by obstetrician. Limited data is found regarding association of obesity with prolonged pregnancy and the same fact led us to search for this association.Methods:This cohort study was carried out in Gynecology and Obstetrics department, MTI Lady Reading Hospital Peshawar from March 2020 to April 2021. Patients were enrolled in third trimester at 37 weeks of gestation with primary exposures of interest being either self-reported pre-pregnancy weight or obtained from first trimester antenatal record. Patients were divided into two classes based on BMI i.e., one with BMI <25 and other with BMI ≥ 25. Patients in both classes were followed till their delivery to determine outcome of gestational age at delivery.Results:Statistically significant difference between the two groups was seen at lower age range of 18-21 years(p-0.04) and higher age range of 39-42 years (p-0.0001). Statistically significant association was found between high pre pregnancy BMI and postdates pregnancy (OR: 4.93 ;95% CI: 1.98-12.26, p-0.001). Association of induction of labor with high pre pregnancy BMI was not significant. (OR 0.56, 95% CI: 0.21-1.48, P < 0.001). Higher rates of Instrumental deliveries(p-0.0005) and cesarean sections (p-0.0001) were seen in higher BMI group.Conclusion:Higher pre-pregnancy BMI is associated with increased risk of postdates pregnancy.

Compliance with gestational weight gain recommendations in a cross-sectional study of term pregnancies — how far reality falls from the standard?

During pregnancy, two aspects are critical in the context of adverse perinatal outcomes (APO): preconception obesity and gestational weight gain. This study aimed to assess compliance with the 2009 IOM guidelines, compare GWG with and without correcting for gestation duration, and observe the relationship between pre-pregnancy BMI and GWG and neonatal birth weight. This is a cross-sectional study conducted from 2015-2018 at the St. Sophia's Specialist Hospital in Warsaw, Poland. Self-reported pre-pregnancy and predelivery weight were collected. The presented data set amounts to 7820 records. Analysis of weight gain compliance with IOM recommendations showed that only 41-44% (depending on the calculation method) of women had weight gain in accordance with IOM guidelines (22-23% - below; 33-37% - above). Overweight and obese women with diabetes are more likely to comply with IOM than women without diabetes. In contrast, women with normal-weight and underweight with diabetes are less likely to achieve IOM weight gain in pregnancy than women without diabetes. Women who have GWG below recommendations significantly more often gave birth to SGA neonates, and women who exceeded GWG standards significantly more often gave birth to LGA neonates. Less than half of women had GWG within the recommended norms. Statistically significant differences were found in methods of calculation of GWG, but it was not found clinically significant. Correction for pregnancy duration when calculating GWG reclassifies two percent of patients. We underestimate the risk of crossing the line between overweight and obesity during pregnancy.

The Association of Maternal Dietary Patterns With Birth Weight and the Mediation of Gestational Weight Gain in a Prospective Birth Cohort

Abstract Objectives The aim of this study is to investigate the associations between maternal dietary patterns and birth weight, and further explore whether gestational weight gain (GWG) mediates these associations. Methods A total of 3334 pregnant women who completed a validated semi-quantitative food frequency questionnaire from the Tongji Maternal and Child Health Cohort were included. Dietary patterns were extracted by using principle component analysis. GWG was assessed by the last available weight measurement during pregnancy and self-reported pre-pregnancy weight at enrollment. Birth weight was collected according to medical records. Regression models and mediation analyses were performed to explore the associations between dietary patterns and birth weight and the effects of GWG on these associations. Results Five dietary patterns were identified: “Beans-vegetables”, “Fish-meat-eggs”, “Nuts-whole grains”, “Organ-poultry-seafood” and “Rice-wheat-fruits”. Women following “Beans-vegetables” pattern had significant lower GWG (β = −0.70; 95% CI: −1.15, −0.25) and had 16% lower risk of excessive GWG and 11% higher odd of adequate GWG. While as the score increased by one unit, GWG increased 0.18kg for “Fish-meat-eggs”, 0.23kg for “Nuts-whole grains” and 0.26kg for “Rice-wheat-fruits”, and the risks of insufficient GWG all decreased. Only women following “Beans-vegetables” pattern had heavier newborns (β = 47.39; 95% CI: 12.25 82.54). The association between “Beans-vegetables” pattern and birth weight was negatively mediated by GWG. Conclusions A dietary pattern enriched in beans and vegetables is beneficial for effectively controlling GWG and increasing birth weight. GWG serves as a mediator in the relationship between this pattern and birth weight helping to prevent excess birth weight. Funding Sources This work was supported by the National Program on Basic Research Project of China (NO.2013FY114200) and Fundamental Research Funds for the Central Universities (HUST2016YXZD040) for NY, and the National Natural Science Foundation of China (81973043) for XY.

Using Non-inferiority Margins to Define Optimal Gestational Weight Gain Ranges Based on Reducing the Risk of Occurrence of Adverse Neonatal Outcomes

ObjectivesTo identify optimal gestational weight gain (GWG) ranges to prevent adverse neonatal outcomes based on the new Brazilian GWG charts. MethodsData from 9,294 women from the Brazilian Maternal and Child Nutrition Consortium and Birth in Brazil study were used. Women aged ≥18 years, free of hypertensive disorders, diabetes in pregnancy, or diseases affecting GWG, were selected. Total GWG was calculated as last measured prenatal weight minus self-reported pre-pregnancy weight. Total GWG was standardized to gestational age-specific z scores according to the Brazilian GWG charts. A composite outcome was defined as the occurrence of any of small-for-gestational-age birth (SGA, birthweight < 10th percentile), large-for-gestational-age birth (LGA > 90th percentile) according to INTERGROWTH-21st charts, or preterm birth (birth < 37 weeks). We weighted each outcome in a composite index using previously-published weights to account for its relative seriousness. Logistic and Poisson regressions were performed with GWG z scores as exposure and independent outcomes and the composite outcome, respectively. Models were adjusted for maternal age, education, pre-pregnancy BMI, and smoking during pregnancy. GWG ranges associated with the lowest risk of the composite outcome were identified using the non-inferiority margins approach (20%). ResultsThe median total GWG was 12.5 kg (IQR 9–16), and 6.2% of the neonates were SGA, 16.6% LGA, and 10.5% were preterm. Higher GWG z scores were associated with an increase in LGA probabilities and preterm birth compared with neonates born with appropriate weight and ≥37 weeks, respectively. Lower z scores were associated with an increase in SGA probability. The non-inferiority margins analysis showed that to prevent the occurrence of these adverse outcomes, women with underweight, normal-weight, overweight, or obesity should gain between 6.5–14.1 kg, 6.4–13.8 kg, 2.2–12.1 kg, and –2.2–8.9 kg, respectively. ConclusionsTotal GWG ranges associated with lower risk of adverse neonatal outcomes were identified using non-inferiority margins. The next step must incorporate maternal outcomes in this analysis. Funding SourcesBrazilian National Research Council, Brazilian Ministry of Health, Bill and Melinda Gates Foundation.

Gestational weight gain charts: results from the Brazilian Maternal and Child Nutrition Consortium

ABSTRACTBackgroundMonitoring gestational weight gain (GWG) is fundamental to ensure a successful pregnancy for the mother and the offspring. There are several international GWG charts, but just a few for low- and middle-income countries.ObjectivesTo construct GWG charts according to pre-pregnancy BMI for Brazilian women.MethodsThis is an individual patient data analysis using the Brazilian Maternal and Child Nutrition Consortium data, comprising 21 cohort studies. External validation was performed using “Birth in Brazil,” a nationwide study. We selected adult women with singleton pregnancies who were free of infectious and chronic diseases, gestational diabetes, and hypertensive disorders; who delivered a live birth at term; and whose children were adequate for gestational age, and with a birth weight between 2500–4000 g. Maternal self-reported pre-pregnancy weight and weight measured between 10–40 weeks of gestation were used to calculate GWG. Generalized Additive Models for Location, Scale and Shape were fitted to create GWG charts according to gestational age, stratified by pre-pregnancy BMI.ResultsThe cohort included 7086 women with 29,323 weight gain measurements to construct the charts and 4711 women with 31,052 measurements in the external validation. The predicted medians for GWG at 40 weeks, according to pre-pregnancy BMI, were: underweight, 14.1 kg (IQR, 10.8–17.5 kg); normal weight, 13.8 kg (IQR, 10.7–17.2 kg); overweight, 12.1 kg (IQR, 8.5–15.7 kg); obesity, 8.9 kg (IQR, 4.8–13.2 kg). The 10th, 25th, 50th, 75th, and 90th percentiles were estimated. Results for internal and external validation showed that the percentages below the selected percentiles were close to those expected.ConclusionsThe charts proposed provide a description of GWG patterns according to gestational age and pre-pregnancy BMI among healthy Brazilian women with good neonatal outcomes. The external validation indicates that this new tool can be used to monitor GWG in the primary health-care setting and to test potential recommended values.