Abstract
Background:
Most existing research on gestational weight gain and pregnancy outcomes has not accounted for timing of weight gain. The area under the weight gain curve (AUC) provides a single measure that incorporates both timing of weight gain and total amount gained. This study evaluated predictors and outcomes associated with second- and third-trimester weight gain AUC from the second and third trimester using time-to-event analysis to account for the correlation between gestional weight gain and gestational duration.
Methods:
Our prospective cohort study used data from the LifeCodes study at Brigham and Women’s Hospital. Maternal weights were available from all prenatal and study visits. We used log–poisson models with empirical variance estimation to identify predictors of total AUC from 14 weeks to delivery and Cox proportional hazards models to estimate hazard ratios and 95% confidence intervals for the association between AUC quintile and adverse pregnancy outcomes.
Results:
Compared to the middle quintile, the highest quintile of accumulated pound–days was associated with a decreased hazard of spontaneous preterm birth among multigravid women [Hazard Ratio (HR): 0.44, 95% Confidence Interval (CI) 0.23, 0.84], a decreased hazard of small-for-gestational age (SGA) births (HR 0.65, 95% CI 0.45, 0.92) overall and an increased hazard of large for gestational age (LGA) births among normal and underweight women (HR 3.21, 95% CI 1.50, 6.89)
Conclusion:
In our study, a pattern of gestational weight gain characterized by more rapid gains earlier in pregnancy was associated with improved pregnancy outcomes in some subgroups of pregnant women.
Keywords: pregnancy, gestational weight gain, preterm birth, area under the curve, small-for-gestational age, large-for-gestational age
INTRODUCTION
Evidence suggests that the amount of weight gained by most women during pregnancy is considered either excessive or inadequate relative to current Institute of Medicine recommendations.1 Data from the National Vital Statistics System show that in 2015, only 32% of women giving birth to full-term, singleton infants gained weight within the recommended range by the end of pregnancy, while 48% gained more weight and 21% gained less weight than recommended.1 Consequently, a large proportion of the pregnant population may be at increased risk for adverse pregnancy outcomes associated with inadequate and excessive weight gain. Numerous studies have linked inadequate gestational weight gain to preterm birth and small-for-gestational age births (SGA) and excessive gestational weight gain to large-for-gestational age births (LGA).2–14 Promoting adequate gestational weight gain therefore constitutes an important public health issue.
At the same time, the use of total weight gain as a single measure to summarize pregnancy weight gain does not account for an individual’s weight gain pattern.15 For example, low weight gain in early pregnancy followed by rapid weight gain later may influence the risk of adverse outcomes differently than constant weight gain.16 The comparatively small number of studies to examine the timing of weight gain point toward potentially critical periods in pregnancy during which weight gain may have a larger impact on outcomes. One study found that low weight gain by 24 weeks of gestation was associated with an increased risk of low birth weight, even if total weight gain was normal.17 Other studies have shown positive associations between gestational weight and infant birth weight restricted to the first18 and second trimesters,18–20 and an inverse association between weight gain in the second and third trimesters and intrauterine growth restriction.21 High weight gain solely in the third trimester has been associated with an increased risk for LGA22 whereas low weight gain during this trimester alone has been associated with an increased risk for preterm birth.23, 24
To simultaneously address both the timing of weight gain and the total amount gained, Kleinman et al.25 proposed an area under the curve (AUC) approach. Calculating the area under the gestational weight gain curve provides a single measure which is interpretable as the additional pound–days carried due to pregnancy. Higher accumulated pound–days reflect more weight gained earlier in gestation. Despite the utility of this method, few studies thus far have employed it to assess the relation between gestational weight gain and birth outcomes.25,26 In addition, while several predictors of overall gestational weight gain have been identified including pre-pregnancy BMI,27, 28 parity,28 and socio-demographic factors,29–32 to our knowledge no information is known regarding predictors of accumulation of pound–days during pregnancy.
An identified shortcoming of the AUC approach is its inherent correlation with gestational duration.15 Longer pregnancies provide more opportunity to accumulate pound–days. Without accounting for this correlation, studies of the association between the AUC and gestational age-dependent pregnancy outcomes such as preterm birth may be biased. Treating the AUC as a time-varying exposure in a time-to-event analysis will mitigate this source of bias because it ensures that an individual’s AUC at the time of her pregnancy outcome is compared to the AUC among only those individuals with ongoing pregnancies at that gestational age.15,33 Since maternal weight is rarely measured during each week of gestation, however, this method does necessitate the interpolation of weight values for the weeks in between weight measurements, but simulations have shown that the use of interpolated weights during the intervals between actual measurements can nonetheless yield unbiased estimates.33
Using data from the ongoing LifeCodes prospective birth cohort at Brigham and Women’s Hospital, we aim in this study determine predictors of the weight gain AUC and to evaluate the association between accumulated pound–days during pregnancy as a time-varying exposure in a time-to-event analysis. We then compare these results to those obtained using gestational-age-standardized total weight gain z-scores as a measure of gestational weight gain, which is similarly uncorrelated with gestational duration15 but does not account for weight gain pattern.
METHODS
Study Cohort
Participants in this study were pregnant women from the Boston area who attended Brigham and Women’s Hospital for prenatal care and enrolled in the LifeCodes cohort during the period between its initiation in 2006 and September 2015.34 Participation in this cohort involved the completion of study visits that coincide with clinical care at approximately 10 weeks, 24 weeks, and 35 weeks of gestation in addition to attending regular prenatal visits. At the first visit, participants completed a questionnaire that ascertained sociodemographic and health information. First trimester ultrasound was used to validate and/or establish gestational age as needed. Participants were followed until delivery, at which time complications and neonatal anthropometric measurements were recorded. All participants provided written informed consent. The Institutional Review Board at Brigham and Women’s Hospital approved the study. This secondary analysis was limited to singleton pregnancies lasting more than 22 weeks with known dates of delivery, at least 2 recorded weight measurements, an initial weight measurement taken at or before 12 weeks of gestation, and plausible values (± 5 standard deviations (SD) from the cohort mean) for total gestational weight gain at the end of pregnancy.
Assessment of gestational weight gain
Weight measurements were taken at both regular prenatal visits and study visits. Participants’ first available weight measurements are referred to as their initial weights, and BMI calculated from this is referred to as initial BMI. We characterized the AUC as a time-varying exposure, which requires weight measurements for each week of gestation in order for all participants with ongoing pregnancies at the time of a pregnancy outcome to contribute to the risk set. We interpolated weight values for weeks in which weight measurements were unavailable using a linear repeated-measures mixed-effects model with gestational age as the independent variable, weight as the dependent variable, fixed and random effects for the intercept and gestational age terms, and an unstructured variance–covariance matrix. This model produced subject specific slopes and intercepts which were used to interpolate weight values. Each participant therefore had a real or interpolated weight value for each week of gestation starting from the week of their first weight measurement. In a validation study, this interpolation method showed an average difference in real and interpolated weight values of 0.13 lbs at 28 weeks and −0.30 lbs at 40 weeks.35
Starting with measured or interpolated weight at 12 weeks of gestation, we calculated the total accumulated pound–days for each week by summing the areas of trapezoids formed by connecting successive measures of weight gain in pounds perpendicularly with the time axis of the weight curve (gestational age in weeks). If participants had weight values that were smaller than their initial weight measurement, we replaced these values with the pre-pregnancy weight to avoid negative pound–days in accordance with the methodology used by Kleinman et al.25 We then classified accumulated pound–days for each week of gestation into quintiles based on the distribution of the study population for that specific week. In stratified analyses (BMI < 25 vs ≥25, primigravid vs. multigravid), we calculated quintile distributions for each stratum separately.
We also classified total gestational weight gain according to gestational-age standardized z-scores using reference charts specifically developed for normal, overweight, and obese pregnant women.36,37 We calculated total weight gain as the difference between participants’ measured or interpolated weight at the time of delivery and their initial weight. We categorized total weight gain z-scores as <−1 SD, −1 SD to +1 SD, and >+1 SD to facilitate interpretability.
Pregnancy Outcomes
We defined spontaneous preterm births as delivery before 37 weeks with presentation of spontaneous labor or preterm premature rupture of the membranes.38 All cases of preterm birth were independently validated by a panel of board-certified maternal–fetal medicine physicians. Infant weights were recorded in their medical records immediately after birth. We considered infants weighing below the 10th percentile for gestational age at delivery according to population reference standards37 to be SGA and those weighing above the 90th percentile to be LGA. Weight for gestational-age z-scores were calculated according to U.S. national reference data.39
Covariates
We chose the following as potential predictors of pound–days accumulated by the end of pregnancy and potential confounders in the analyses of pound–days and pregnancy outcomes: age (< 25, 25 – <30, 30 – <35, ≥ 35 years), initial BMI (continuous), maternal height (continuous), gravidity (0, 1, 2, ≥ 3), current smoking reported at baseline (yes/no), race/ethnicity (Caucasian, African American, Asian, Hispanic, Other), insurance status (private/self-pay, public/none), education (high school or less, some college, college graduate, graduate school), history of diabetes (yes/no), history of chronic hypertension (yes/no), history of thyroid disease (yes/no), gestational diabetes (yes/no), gestational hypertension (yes/no), and pre-eclampsia (yes/no).
Statistical Analysis
We used generalized linear models with a normal distribution and identity link function to estimate crude and multivariable adjusted beta coefficients and 95% confidence intervals for the associations between each potential predictor and both total accumulated pound–days by the end of pregnancy and gestational weight gain z-score. Gestational age at delivery was also included as a covariate in multivariable models to account for correlated gestational age.
We used Cox proportional hazards regression models with the Andersen-Gill counting method and week of gestation as the time scale to estimate crude and adjusted hazard ratios and 95% confidence intervals for the association between time-varying quintile of accumulated pound–days and time to each pregnancy outcome. In this modeling approach, risk sets are formed that include all participants with equal or longer pregnancy durations, thereby eliminating the dependency of gestational weight gain on gestational length. We used log–poisson regression with empirical variance estimation to estimate crude and adjusted risk ratios and 95% confidence intervals for the associations between total weight gain z-score category and each birth outcome among participants with pregnancies lasting at least 28 weeks (n=1,898).
These models were further stratified by initial BMI category (< 25 kg/m2, ≥ 25 kg/m2) and gravidity (primigravid, multigravid), since both have shown to modify associations between gestational weight gain and birth outcomes.40–42 Medically indicated preterm births are censored at the time of birth in the analysis of AUC quintiles and grouped with full term births in the analysis of gestational weight gain z-scores.
We conducted several post-hoc sensitivity analyses. First, we assessed the impact of excluding women who lost weight during pregnancy, since potential pathologies underlying this weight loss, such as hyperemesis gravidarum, may limit their comparability to the full cohort. Second, we investigated the impact of the interpolated values on the results. In this analysis, we examined the association between each outcome and the weight gain AUC calculated from weight measurements taken at two time-points, 18 and 26 weeks of gestation, which were chosen due to the large number of measured weights available for these weeks. We modeled these associations both among participants with measured weight values at these time points (n=395) and among all participants, and compared the results. Third, we repeated the analyses excluding participants with who were diagnosed with gestational diabetes, pre-eclampsia, and gestational hypertension (n=117). Last, we repeated the analysis among participants who had higher than the median number of weight measurements (n=1299).
RESULTS
Pregnancy weight information was available for N=2,624 (69%) of the N=3,813 participants enrolled in LifeCodes up to September 2015. We excluded 236 twin pregnancies, five pregnancies for implausible weight gain (± 5 SD), and 12 pregnancies that ended earlier than 22 weeks gestation. All 2,370 final participants had at least two weight measurements. A flow diagram for inclusion into the study is shown in Figure 1. Participants in the final analytic cohort were similar to the full cohort with respect to baseline characteristics (eAppendix 1).
Figure 1.
Flow diagram of study inclusion
The final analytic cohort had a total of 24,378 weight measurements available. The average number of weight measurements recorded per participant was 12 (minimum two, maximum 30). Another 34,495 weight measurements were interpolated. An average of 15 weight measurements were interpolated per participant. Based on the combination of real and interpolated weight measurements, participants in the cohort gained 24.7 on average pounds during pregnancy and accumulated 1143.9 pound–days. Mean weight gain for each week of gestation according to quintile of pound–days is shown in Figure 2, and eAppendix 2 provides ranges of pound days for each week of gestation by quintile.
Figure 2.
Mean pound-days accumulated in each quintile by week of gestation
Four-hundred sixty-seven women had at least one negative value for pound–days during follow-up. Approximately 25% of these women were overweight or obese and the other 75% were normal or underweight. Approximately 80% of participants contributed person–time to more than one quintile of pound–days, but over half (56% of these) changed quintiles only once or twice. As shown in eAppendix 3, most of this movement took place between adjacent quintiles.
Table 1 shows the percentages of participants with each baseline characteristic falling into quintiles of accumulated pound–days at the end of pregnancy. Participants differed most notably with respect to initial BMI. Mean initial BMI was 32.1 (SD 7.6) in the lowest quintile and 26.2 (5.9) in the highest quintile. In addition, more than twice the percentage of participants with three or more previous pregnancies were in the lowest quintile (29.8%) compared to primigravid women (13.7%). A notably smaller percentage of women with gestational diabetes (20.1%) were in the highest quintile compared to women without it (6.9%).
Table 1.
Sample Characteristics according to quintile of accumulated pound–days by the end of pregnancy (n=2,370)
| Quintile | |||||
|---|---|---|---|---|---|
| Characteristic | Lowest | Second-lowest | Middle | Second-highest | Highest |
| Pound–days, mean (SD) | 529.3 (270.6) | 941.1 (200.5) | 1131.1 (238.4) | 1354.9 (251.6) | 1866.4 (538.1) |
| Initial BMI (lbs/in.2), mean (SD) | 32.1 (7.6) | 27.6 (6.9) | 26.0 (5.8) | 25.4 (5.1) | 26.2 (5.9) |
| Maternal height (in.), mean (SD) | 64.2 (2.9) | 64.2 (2.8) | 64.2 (2.8) | 64.4 (2.6) | 64.7 (2.7) |
| Age in years, N (%) | |||||
| <25 | 73 (22) | 46 (14) | 62 (19) | 60 (18) | 87 (27) |
| 25 – < 30 | 116 (22) | 90 (17) | 108 (20) | 103 (19) | 121 (23) |
| 30 – < 35 | 156 (20) | 175 (23) | 150 (19) | 155 (20) | 140 (18) |
| ≥ 35 | 176 (34) | 158 (22) | 145 (20) | 132 (19) | 101 (14) |
| Gravidity, N (%) | |||||
| 0 | 77 (14) | 110 (20) | 125 (22) | 128 (28) | 124 (22) |
| 1 | 126 (20) | 131 (20) | 137 (21) | 121 (19) | 127 (20) |
| 2 | 103 (23) | 101 (23) | 87 (20) | 77 (17) | 78 (18) |
| ≥3 | 213 (30) | 131 (18) | 119 (17) | 126 (18) | 125 (18) |
| Smoking, N (%) | |||||
| Yes | 78 (21) | 63 (18) | 62 (18) | 70 (20) | 82 (23) |
| No | 443 (22) | 409 (20) | 402 (20) | 382 (19) | 371 (19) |
| Ethnicity, N (%) | |||||
| Caucasian | 205 (17) | 252 (20) | 268 (22) | 279 (23) | 233 (19) |
| African American | 144 (35) | 73 (18) | 60 (15) | 57 (14) | 80 (19) |
| Asian | 22 (16) | 38 (28) | 32 (24) | 22 (16) | 22 (16) |
| Hispanic | 122 (26) | 92 (20) | 86 (19) | 74 (16) | 89 (19) |
| Other | 30 (25) | 18 (15) | 21 (18) | 20 (17) | 30 (25) |
| Insurance status, N (%) | |||||
| Private/Self-pay | 236 (31) | 121 (16) | 121 (16) | 121 (16) | 156 (21) |
| Public/None | 282 (18) | 350 (22) | 340 (21) | 328 (21) | 290 (18) |
| Education, N (%) | |||||
| High school or less | 120 (3) | 51 (13) | 72 (19) | 52 (13) | 95 (24) |
| Some college | 147 (29) | 95 (19) | 85 (17) | 90 (18) | 97 (19) |
| College graduate | 137 (20) | 142 (20) | 139 (19) | 145 (21) | 136 (19) |
| Graduate School | 116 (16) | 182 (24) | 168 (22) | 163 (22) | 121 (16) |
| History of diabetes, N (%) | |||||
| Yes | 18 (30) | 12 (20) | 3 (5) | 12 (20) | 15 (25) |
| No | 491 (21) | 455 (20) | 461 (20) | 436 (19) | 435 (19) |
| History of chronic hypertension, N (%) | |||||
| Yes | 42 (34) | 22 (18) | 21 (17) | 15 (12) | 22 (18) |
| No | 481 (21) | 451 (20) | 447 (20) | 437 (19) | 432 (19) |
| History of thyroid disease, N (%) | |||||
| Yes | 45 (21) | 36 (17) | 51 (24) | 48 (22) | 36 (17) |
| No | 478 (22) | 437 (20) | 417 (19) | 404 (19) | 418 (19) |
| Gestational diabetes, N (%) | |||||
| Yes | 68 (39) | 49 (28) | 28 (16) | 18 (10) | 12 (7) |
| No | 451 (21) | 422 (19) | 368 (21) | 430 (20) | 374 (20) |
SD=standard deviation, lb.=pounds, in.=inches, BMI=body mass index
Initial BMI was negatively associated with of accumulated pound–days at the end of pregnancy (multivariable β=−18 (95% CI −21, −15)) but positively associated with total weight gain z-score (multivariable β=0.02 (95% CI 0.01, 0.03)) (Table 2). Older maternal age was associated with both lower accumulated pound–days (multivariable β=−190 (95% CI −270, −100)) and lower total weight gain z-score (multivariable β=−0.25 (−0.42, −0.08)). While gestational diabetes was negatively associated with both pound–days and total weight gain z-score, pre-eclampsia and gestational hypertension were positively associated with these measures.
Table 2.
Factors associated with pound–days accumulated over gestation in generalized linear models (n=2,370) and weight gain z-score (n=1,898)
| Accumulated pound–days | Weight gain z-score | |||
|---|---|---|---|---|
| Characteristic | Unajusted Beta-coefficient (β) (95% CI) | Adjusteda Beta-coefficient (β) (95% CI) | Unajusted Beta-coefficient (β) (95% CI) | Adjusteda Beta-coefficient (β) (95% CI) |
| Age (years) | ||||
| <25 | 0.0 (Ref.) | 0.0 (Ref.) | 0.0 (Ref.) | 0.0 (Ref.) |
| 25 – < 30 | −120 (−190, −41) | −91 (−165, −16) | −0.01 (−0.15, 0.13) | −0.09 (−0.24, 0.06) |
| 30 – < 35 | −93. (−160, −22) | −130(−210, −47) | −0.12 (−0.25, 0.01) | −0.23 (−0.39, −0.08) |
| ≥ 35 | −200. (−270, −120) | −190 (−270, −100) | −0.15 (−0.28, −0.01) | −0.25 (−0.42, −0.08) |
| Initial BMI (lbs/in.2) | −23 (−26, −20) | −17 (−21, −15) | 0.02 (0.01, 0.02) | 0.02 (0.01, 0.03) |
| Maternal height (in.) | 16 (8, 24) | 5.5 (−2.1, 13) | 0.01 (0, 0.03) | 0.01 (0, 0.02) |
| Gravidity | ||||
| 0 | 0.0 (Ref.) | 0.0 (Ref.) | 0.0 (Ref.) | 0.0 (Ref.) |
| 1 | −97 (−160, −35) | −53 (−110, 4.6) | −0.13 (−0.24, −0.02) | −0.09 (−0.2, 0.02) |
| 2 | −170 (−230, −98) | −80 (−140, −16) | −0.21 (−0.34, −0.09) | −0.19 (−0.31, −0.06) |
| ≥3 | −200 (−260, −140) | −62 (−120, −1.6) | −0.15 (−0.26, −0.04) | −0.06 (−0.17, 0.06) |
| Smoking | 37 (−25, 99) | 41 (−16, 99) | 0.1 (−0.01, 0.21) | 0.05 (−0.06, 0.17) |
| Ethnicity | ||||
| Caucasian | 0.0 (Ref.) | 0.0 (Ref.) | 0.0 (Ref.) | 0.0 (Ref.) |
| African American | −130 (−190, −64) | −15 (−84, 53) | 0.04 (−0.08, 0.15) | −0.07 (−0.2, 0.07) |
| Asian | −70 (−170, 27) | −53 (−140, 38) | −0.26 (−0.43, −0.08) | −0.15 (−0.32, 0.03) |
| Hispanic | −110 (−160, −47) | −42 (−110, 24) | −0.06 (−0.17, 0.04) | −0.08 (−0.2, 0.05) |
| Other | −75 (−180, 28) | −0.06 (−100, 100) | −0.19 (−0.38, 0.01) | −0.23 (−0.44, −0.03) |
| Insurance status | ||||
| Private/Self-pay | 0.0 (Ref.) | 0.0 (Ref.) | 0.00 (Ref.) | 0.00 (Ref.) |
| Public/None | 89 (42, 140) | 97 (35, 160) | 0.06 (−0.03, 0.15) | 0.17 (0.05, 0.3) |
| Education | ||||
| High school or less | 0.00 (Ref.) | 0.00 (Ref.) | 0.00 (Ref.) | 0.00 (Ref.) |
| Some college | −35 (−110, 37) | −36 (−100, 33) | 0.06 (−0.08, 0.19) | 0.08 (−0.05, 0.22) |
| College graduate | 32 (−36, 100) | −29 (−110, 49) | 0.07 (−0.05, 0.2) | 0.06 (−0.1, 0.21) |
| Graduate School | 57 (−10, 120) | −55 (−140, 29) | −0.03 (−0.15, 0.1) | 0.02 (−0.14, 0.19) |
| History of diabetes | −99 (−240, 42) | 33 (−100, 160) | 0.3 (0.06, 0.53) | 0.18 (−0.06, 0.42) |
| History of chronic hypertension | −190 (−290, −87) | −42 (−140, 56) | 0.16 (−0.02, 0.33) | 0.03 (−0.15, 0.21) |
| History of thyroid disease | 5.3 (−72, 82) | 18 (−53, 88) | 0.06 (−0.07, 0.2) | 0.06 (−0.08, 0.19) |
| Gestational diabetes | −310 (−390, −230) | −200 (−280, −120) | −0.26 (−0.42, −0.11) | −0.3 (−0.46, −0.14) |
| Gestational hypertension | 124 (11, 240) | 130 (27, 230) | 0.23 (0.04, 0.43) | 0.19 (0, 0.38) |
| Pre-eclampsia | −110 (−200, −24) | 140 (51, 230) | 0.42 (0.26, 0.58) | 0.39 (0.21, 0.56) |
CI=confidence interval, BMI=body mass index, lb.=pounds, in.=inches
Adjusted model controls for variables included in the table and gestational age at delivery
The highest quintile of accumulated pound–days was associated with a reduced hazard of preterm birth (HR 0.55, 95% CI 0.31, 0.99) and SGA (HR 0.65, 95% CI 0.45, 0.92) and an increased hazard of LGA (HR 1.6, 95% CI 1.0, 2.4) compared to the middle quintile (Table 3). Total weight gain z-scores below −1 were associated with an increased risk of SGA (RR 1.4, 95% CI 1.1, 1.9). We observed no association between total weight gain z-score and size for gestational age z-score.
Table 3.
Associations between gestational weight gain AUCa (n=2,370), total weight gain z-score (n=1,898) and birth outcomesb
| Outcome | Spontaneous preterm birth Hazard Ratio (95% CI) | Small for gestational age Hazard Ratio (95% CI) | Large for gestational age Hazard Ratio (95% CI) | Weight for gestational age z-score β (95% CI) |
|---|---|---|---|---|
| Cases/person -weeks | 167/54,275 | 387/58,873 | 218/58,873 | |
| Unadjusted model | ||||
| Quintile of pound–days | ||||
| Lowest | 0.98 (0.61, 1.6) | 1.2 (0.91, 1.6) | 1.2 (0.76, 1.9) | |
| Second lowest | 1.2 (0.8, 1.9) | 1.2 (0.9, 1.6) | 0.78 (0.47, 1.7) | |
| Middle | 1.0 (Ref.) | 1.00 (Ref.) | 1.00 (Ref.) | |
| Second highest | 1.1 (0.71, 1.8) | 0.84 (0.61, 1.2) | 1.5 (1.0, 2.4) | |
| Highest | 0.55 (0.31, 0.96) | 0.66 (0.47, 0.93) | 1.7 (1.1, 2.6) | |
| Adjusted model | ||||
| Quintile of pound–days | ||||
| Lowest | 0.94 (0.56, 1.6) | 1.3 (0.94, 1.8) | 0.55 (0.32, 0.93) | |
| Second lowest | 1.1 (0.72, 1.8) | 1.2 (0.85, 1.6) | 0.64 (0.38, 1.1) | |
| Middle | 1.00 (Ref.) | 1.00 (Ref.) | 1.00 (Ref.) | |
| Second highest | 1.2 (0.72, 1.8) | 0.83 (0.6, 1.2) | 1.4 (0.86, 2.1) | |
| Highest | 0.55 (0.31, 0.99) | 0.65 (0.45, 0.92) | 1.6 (1.0, 2.4) | |
| Unadjusted GWG z-score | ||||
| < −1 | 1.2 (0.74, 2.0) | 1.6 (1.2, 2.2) | 0.45 (0.25, 0.81) | −0.22 (−0.48, 0.05) |
| −1 to 1 | 1.00 (Ref.) | 1.00 (Ref.) | 1.00 (Ref.) | 0.00 (Ref.) |
| > 1 | 0.57 (0.14, 2.3) | 0.51 (0.19, 1.4) | 1.8 (0.95, 3.5) | 0.09 (−0.23, 0.43) |
| Adjusted GWG z-score | ||||
| < −1 | 1.4 (0.81, 2.3) | 1.4 (1.0, 1.9) | 0.57 (0.31, 1.0) | −0.21 (−0.48, 0.05) |
| −1 to 1 | 1.00 (Ref.) | 1.00 (Ref.) | 1.00 (Ref.) | 0.0 (Ref.) |
| > 1 | 1.1 (0.26, 5.0) | 0.44 (0.16, 1.2) | 1.8 (0.88, 3.5) | 0.06 (−0.30 0.42) |
CI=confidence interval, GWG=gestational weight gain, BMI=body mass index.
Pound–days treated as a time varying exposure in stratified Cox-proportional hazards models, with completed gestational weeks as the time scale
Adjusted for age, baseline BMI, maternal height, gravidity, race/ethnicity, education, insurance status, smoking status, history of thyroid disease, history of lupus, history of diabetes, gestational diabetes, gestational hypertension, and pre-eclampsia.
The direction of the associations between AUC quintile and spontaneous preterm birth, AUC quintile and SGA and total weight gain z-score did not change after stratification by initial BMI (Table 4) though the confidence intervals widened. Person–time in the highest AUC quintile was associated with 3.2-fold (95% CI 1.5, 6.9) increased risk of LGA births among normal weight and underweight women compared to the middle quintile; however, no association was observed for overweight or obese women.
Table 4.
Multivariable associations between time-varying quintile of AUC, total weight gain z-score, and birth outcomes, stratified by initial BMI (<25 (n=1,034) vs. ≥ 25 (n=1,327))a
| Outcome | Spontaneous preterm birth HR (95% CI) | Small for gestational age HR (95% CI) | Large for gestational age HR (95% CI) | Weight for gestational age z-score β (95% CI) |
|---|---|---|---|---|
| BMI < 25 | ||||
| Cases/person–weeks | 72/23,788 | 200/25,114 | 70/25,114 | |
| Quintile of pound–days | ||||
| Lowest | 1.2 (0.48, 3.0) | 1.8 (1.1, 2.8) | 1.3 (0.39, 4.4) | |
| Second lowest | 1.5 (0.74, 2.9) | 1.4 (0.95, 2.1) | 0.36 (0.1, 1.3) | |
| Middle | 1.00 (Ref.) | 1.00 (Ref.) | 1.00 (Ref.) | |
| Second highest | 1.4 (0.7, 2.7) | 0.72 (0.46,1.1) | 2.0 (0.92, 4.4) | |
| Highest | 0.69 (0.31, 1.6) | 0.81 (0.51, 1.3) | 3.2 (1.5, 6.9) | |
| BMI ≥ 25 | ||||
| Cases/person–weeks | 88/15,444 | 180/31,231 | 132/31,231 | |
| Quintile of pound–days | ||||
| Lowest | 0.77 (0.41, 1.5) | 0.95 (0.61, 1.5) | 0.46 (0.26, 0.83) | |
| Second lowest | 0.93 (0.50, 1.8) | 0.87 (0.54, 1.4) | 0.67 (0.37, 1.2) | |
| Middle | 1.0 (Ref.) | 1.0 (Ref.) | 1.0 (Ref.) | |
| Second highest | 1.0 (0.52, 2.0) | 1.0 (0.63, 1.7) | 1.1 (0.62, 1.9) | |
| Highest | 0.44 (0.19, 1.0) | 0.48 (0.27, 0.87) | 1.00 (0.57, 1.8) | |
| BMI < 25 | ||||
| GWG z-score | ||||
| < −1 | 1.6 (0.82, 3.0) | 1.4 (0.95, 2.1) | 0.4 (0.14, 1.1) | −0.25 (−0.65, 0.15) |
| −1 to 1 | 1.00 (Ref.) | 1.00 (Ref.) | 1.00 (Ref.) | 0.00 (Ref.) |
| > 1 | 0.74 (0.1, 5.5) | 0.57 (0.18, 1.8) | 2.6 (0.91, 7.6) | 0.15 (−0.44, 0.74) |
| BMI ≥ 25 | ||||
| GWG z-score | ||||
| < −1 | 0.79 (0.35, 1.8) | 1.4 (0.84, 2.4) | 0.69 (0.33, 1.5) | −0.23 (−0.59, 0.13) |
| −1 to 1 | 1.00 (Ref.) | 1.00 (Ref.) | 1.00 (Ref.) | 0.00 (Ref.) |
| > 1 | 0.62 (0.08, 4.7) | 0.27 (0.04, 2.0) | 1.4 (0.54, 3.4) | 0.09 (−0.38, 0.57) |
AUC=area under the curve, BMI=body mass index, HR=hazard ratio, GWG=gestational weight gain
Adjusted for age, gravidity, race/ethnicity, education, insurance status, smoking status, initial BMI, maternal height, history of thyroid disease, history of lupus, history of diabetes, gestational diabetes, gestational hypertension, and pre-eclampsia.
Stratification by gravidity (Table 5) showed that the reduced hazard ratio observed between the highest AUC quintile and spontaneous preterm birth is limited to multigravid women (HR 0.44, 95% CI 0.23, 0.84). Among primigravid women, person–time in the lowest quintile had 2.08 times the hazard of SGA (95% CI 1.0, 4.0) compared to those in the middle quintile. This association was not observed among multigravid women.
Table 5.
Multivariable associations between time-varying quintile of AUC, total weight gain z-score, and birth outcomes, stratified by gravidity (primigravid (n=564) vs. multigravid (n=1,802)
| Outcome | Spontaneous preterm birth HR (95% CI) | Small for gestational age HR (95% CI) | Large for gestational age HR (95% CI) | Weight for gestational age z-score β (95% CI) |
|---|---|---|---|---|
| Primigravid | ||||
| Cases/person–weeks | 40/12,880 | 91/14,071 | 41/14,121 | |
| Quintile of pound–days | ||||
| Lowest | 3.7 (0.79, 17.0) | 2.1 (1.1, 4.0) | 1.3 (0.34, 4.7) | |
| Second lowest | 3.8 (1.0, 14.1) | 1.8 (1.0, 3.3) | 0.56 (0.17, 1.8) | |
| Middle | 1.0 (Ref.) | 1.0 (Ref.) | 1.0 (Ref.) | |
| Second highest | 6.5 (1.9, 22.5) | 0.84 (0.42, 1.7) | 1.3 (0.5, 3.7) | |
| Highest | 1.8 (0.42, 8.0) | 0.58 (0.27, 1.3) | 1.4 (0.5, 3.8) | |
| Multigravid | ||||
| Cases/person–weeks | 127/41,175 | 296/44,607 | 177/44,607 | |
| Quintile of pound–days | ||||
| Lowest | 0.72 (0.41, 1.3) | 1.16 (0.81, 1.66) | 0.53 (0.3, 0.94) | |
| Second lowest | 0.89 (0.54, 1.5) | 1.0 (0.72, 1.5) | 0.64 (0.35, 1.2) | |
| Middle | 1.0 (Ref.) | 1.0 (Ref.) | 1.00 (Ref.) | |
| Second highest | 0.69 (0.39, 1.2) | 0.84 (0.57, 1.2) | 1.3 (0.81, 2.2) | |
| Highest | 0.44 (0.23, 0.84) | 0.66 (0.44, 0.99) | 1.6 (0.98, 2.6) | |
| Primigravid | ||||
| GWG z-score | ||||
| < −1 | 1.7 (0., 5.0) | 1.6 (0.80, 3.0) | 0.96 (0.26, 3.5) | −0.11 (−0.86, 0.64) |
| −1 to 1 | 1.00 (Ref.) | 1.00 (Ref.) | 1.00 (Ref.) | 0.00 (Ref.) |
| > 1 | 3.3 (0.64, 17.3) | 0.7 (0.15, 2.9) | 0.99 (0.20, 4.9) | −0.02 (−0.81, 0.78) |
| Multigravid | ||||
| GWG z-score | ||||
| Adjusted GWG z-score | ||||
| < −1 | 1.1 (0.6, 1.9) | 1.3 (0.92, 1.9) | 0.47 (0.23, 0.94) | −0.21 (−0.50, 0.08) |
| −1 to 1 | 1.00 (Ref.) | 1.00 (Ref.) | 1.00 (Ref.) | 1.00 (Ref.) |
| > 12 | - | 0.30 (0.07, 1.3) | 1.8 (0.82, 3.8) | 0.06 (−0.35, 0.47) |
AUC=area under the curve, CI=confidence interval, HR=hazard ratio, GWG=gestational weight gain
A post-hoc sensitivity analysis that excluded participants who lost weight showed similar results to those reported above (data not shown). EAppendix 4 displays the results of a sensitivity analysis that compared hazard ratio estimates for the association between the AUC between 18 and 26 weeks and each outcome between those with measured weights available at these time points and all participants. The point estimates for the association between quintiles of the AUC between 18 and 26 weeks and spontaneous preterm birth were similar between groups, though the confidence interval was substantially wider for the hazard ratio pertaining to participants with measured weights at these time points. The associations seen with SGA and LGA among all participants were not present, however, among participants for whom the AUC between 18 and 26 weeks was derived from measured values only. Sensitivity analyses excluding participants with gestational diabetes, pre-eclampsia, and gestational hypertension (eAppendix 5) and excluding participants with the median number of weight measurements or below (eAppendix 6) did not substantially alter the point estimates seen in Table 3, though the precision of these estimates was reduced.
DISCUSSION
In this study, we estimated that BMI, maternal age, and pregnancy morbidities including gestational diabetes, preeclampsia, and gestational hypertension influenced both the gestational weight gain AUC and total weight gain z-score. A larger weight gain AUC, suggesting more rapid weight gain earlier in pregnancy, was associated with a reduced hazard of preterm birth among multigravid women and a reduced hazard of SGA among all women. On the other hand, higher AUC values were associated with an increased hazard of LGA among normal weight and underweight women. Lower weight gain z-scores were associated with an increased risk of SGA.
We are aware of only two previous studies that have used the AUC to characterize gestational weight gain,25,26 so comparability of these findings with those of other studies is limited. Nonetheless, our finding that higher maternal BMI may be associated with slower weight gain earlier in pregnancy seems largely consistent with prior reports of lower weight gain among overweight and obese women during the first19 and second trimester19, 37 followed by greater weight gain among these women during third trimester.19, 43 The lower AUC values and total weight gain z-scores observed among older women compared to younger women are likewise supported by reports of lower total gestational weight gain.21, 44, 45 and second and third trimester weight gain19 in this group.
Only one previous study to our knowledge has evaluated the relation between the gestational weight gain AUC and pre-term birth.26 Contrary to our results, this study found no association between the first and second trimester AUC and either spontaneous or indicated preterm birth among 12,526 singleton pregnancies in Oregon and Washington State but did not examine the association within strata of gravidity. Our observation that a higher AUC was associated with a reduced hazard of spontaneous preterm birth only among multigravid women is novel. We can speculate that earlier, more rapid weight gain among multigravid women may counteract the depletion of maternal nutritional reserves through previous pregnancies and breastfeeding,46 but additional evidence is needed to confirm this finding.
We did not observe an association between gestational age-adjusted total weight gain z-score and spontaneous preterm birth in either primigravid or multigravid women, which may reflect the importance of incorporating the timing of weight gain into measures of gestational weight gain. It may also reflect the need for caution in interpreting the results discussed above, especially since the sample size was greatly reduced in the primigravid stratum. Studies evaluating the impact of total weight gain or average weight gain on the risk of spontaneous preterm birth47–52 have found both high and low weight gain to be associated with this outcome. Not all of these studies have accounted for the correlation between gestational weight gain and gestational duration, however.
Associations between inadequate gestational weight gain and decreased birthweight and fetal growth are well established,53 but no consensus exists regarding the impact of weight gain timing on this association. Some studies suggest that low weight gain in the second trimester is particularly detrimental to infant birth weight.54–58 These findings are somewhat aligned with our results, which imply that those who gain weight more weight earlier in pregnancy have a reduced risk for SGA infants. This association is logical since body fat and protein accretion of the fetus begin at approximately 20 weeks of gestation and accelerate thereafter until term.59 The ways in which the different components of weight gain (fat mass vs. fat free mass) during timepoints through gestation may influence fetal growth nevertheless remains poorly understood60 and should be prioritized in future investigations.
Although we found that greater weight gain during the first part of pregnancy was associated with reduced risk of SGA, normal and underweight women who accumulated the most pound–days had an increased risk of LGA in our study. The lack of an association between high weight gain and LGA among overweight and obese women was unexpected, since an overall link between high weight gain and LGA has been reported among obese women previously61 and could potentially be explained by insufficient power to after stratification of the data. Still, our results lend support to a study that showed an increased risk of LGA associated with excess weight gain in the second but not third trimester.24
Strengths of this study include its prospective design, repeated measures of maternal weight starting in early pregnancy, ultrasound confirmation of gestational age, and use of a survival framework to account for the correlation between birth outcomes and gestational duration. A limitation of this study was the reliance on interpolated data. The survival modeling approach we used required weight values for each week of gestation. Since weekly weight measurements during pregnancy are rarely available, interpolation is necessary.17 The results of our sensitivity analyses suggested that the use of interpolated weight values may have resulted in overestimates of the associations between accumulated pound–days and SGA. In addition, we cannot rule out residual and unmeasured confounding as a source of bias in this observational study. Last, this study is limited by the omission of first trimester weight gain. Weight measurements were less consistently available during this period; we are unsure that a linear interpolation model for missing weights would be applicable to the first trimester, since previous studies have suggested that maternal weight gain in the first trimester is low, on average 0.5–2.0 kg.40
In conclusion, our study lends additional support to the importance of maternal weight gain to ensuring adequate fetal growth. It further suggests that a high gestational weight gain AUC, indicating weight gain earlier in pregnancy, may be associated with SGA and that a high AUC may also be associated with spontaneous preterm birth among multigravid women. On the other hand, it may increase the risk of LGA among normal and underweight women. Further research into optimizing the gestational weight AUC through the development of recommended AUC ranges that are specific to trimester, BMI, and gravidity may be warranted.
Supplementary Material
Sources of funding
This study was supported by T32 HD052458 (Boston University Reproductive, Perinatal and Pediatric Epidemiology training program) from the National Institutes of Health.
Footnotes
Conflicts of interest
None declared
References
- 1.Branum AM, Sharma AJ, Deputy NP. QuickStats: Gestational Weight Gain Among Women with Full-Term, Singleton Births, Compared with Recommendations — 48 States and the District of Columbia, 2015. Morbidity and Mortality Weekly Report (MMWR), 2016; 65:1121. [DOI] [PubMed] [Google Scholar]
- 2.Stotland NE, Cheng YW, Hopkins LM, Caughey AB. Gestational weight gain and adverse neonatal outcome among term infants. Obstet Gynecol. 2006;108:635–643. [DOI] [PubMed] [Google Scholar]
- 3.Margerison Zilko CE, Rehkopf D, Abrams B. Association of maternal gestational weight gain with short- and long-term maternal and child health outcomes. Am J Obstet Gynecol. 2010;202:574. [DOI] [PubMed] [Google Scholar]
- 4.Chung JG, Taylor RS, Thompson JM, Anderson NH, Dekker GA, Kenny LC, McCowan LM. Gestational weight gain and adverse pregnancy outcomes in a nulliparous cohort. European Journal of Obstetrics & Gynecology and Reproductive Biology. 2013;167:149–153. [DOI] [PubMed] [Google Scholar]
- 5.Durie DE, Thornburg LL, Glantz JC. Effect of second-trimester and third-trimester rate of gestational weight gain on maternal and neonatal outcomes. Obstet Gynecol. 2011;118:569–575. [DOI] [PubMed] [Google Scholar]
- 6.Dietz PM, Callaghan WM, Smith R, Sharma AJ. Low pregnancy weight gain and small for gestational age: a comparison of the association using 3 different measures of small for gestational age. Am J Obstet Gynecol. 2009;53.e1–7 [DOI] [PubMed] [Google Scholar]
- 7.El Rafei R, Abbas HA, Charafeddine L, Nakad P, Al Bizri A, Hamod D, Yunis KA. Association of Pre‐Pregnancy Body Mass Index and Gestational Weight Gain with Preterm Births and Fetal Size: an Observational Study from Lebanon. Paediatric and Perinatal Epidemiology. 2016; 38–45. [DOI] [PubMed] [Google Scholar]
- 8.Moore Simas TA, Waring ME, Liao X, Garrison A, Sullivan GM, Howard AE, Hardy JR. Prepregnancy weight, gestational weight gain, and risk of growth affected neonates. Journal of Women’s Health. 2012;410–417. [DOI] [PubMed] [Google Scholar]
- 9.Han Z, Mulla S, Beyene J, Liao G, McDonald SD; Knowledge Synthesis Group. Maternal underweight and the risk of preterm birth and low birth weight: a systematic review and meta-analyses. Int J Epidemiol. 2011;40:65–101. [DOI] [PubMed] [Google Scholar]
- 10.Dietz PM, Callaghan WM, Cogswell ME, Morrow B, Ferre C, Schieve LA. Combined effects of prepregnancy body mass index and weight gain during pregnancy on the risk of preterm delivery. Epidemiology. 2006;17:170–177. [DOI] [PubMed] [Google Scholar]
- 11.Carmichael SL, Abrams B. A critical review of the relationship between gestational weight gain and preterm delivery. Obstet Gynecol. 1997;89:865–873. [DOI] [PubMed] [Google Scholar]
- 12.Carnero AM, Mejía CR, García PJ. Rate of gestational weight gain, pre-pregnancy body mass index and preterm birth subtypes: a retrospective cohort study from Peru. BJOG. 2012;119:924–935. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Cedergren M Effects of gestational weight gain and body mass index on obstetric outcome in Sweden. Int J Gynaecol Obstet. 2006;93:269–274. [DOI] [PubMed] [Google Scholar]
- 14.Chen A, Feresu SA, Fernandez C, Rogan WJ. Maternal obesity and the risk of infant death in the United States. Epidemiology. 2009;20:74. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Hutcheon JA, Bodnar LM. Good Practices for Observational Studies of Maternal Weight and Weight Gain in Pregnancy. Paediatric and perinatal epidemiology. 2018. March;32(2):152–60. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Hinkle SN, Mitchell EM, Grantz KL, Ye A, Schisterman EF. Maternal Weight Gain During Pregnancy: Comparing Methods to Address Bias Due to Length of Gestation in Epidemiological Studies. Paediatric and Perinatal Epidemiology. 2016;30:294–304. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Hediger ML, Scholl TO, Belsky DH, Ances IG, Salmon RW. (1989) Patterns of weight gain in adolescent pregnancy: Effects on birth weight and preterm delivery. Obstet. Gynecol 1989;74:6–12. [PubMed] [Google Scholar]
- 18.Brown JE, Murtaugh MA, Jacobs DR Jr, Margellos HC. Variation in newborn size according to pregnancy weight change by trimester. Am J Clin Nutr 2002;76:205–209. [DOI] [PubMed] [Google Scholar]
- 19.Abrams B, Selvin S. Maternal weight gain pattern and birth weight. Obstet Gynecol. 1995;86:163–169. [DOI] [PubMed] [Google Scholar]
- 20.Widen EM, Factor-Litvak PR, Gallagher D, Paxton A, Pierson RN Jr, Heymsfield SB, Lederman SA. The pattern of gestational weight gain is associated with changes in maternal body composition and neonatal size. Matern Child Health J. 2015;19:2286–2294. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Strauss RS, Dietz WH. Low maternal weight gain in the second or third trimester increases the risk for intrauterine growth retardation. J Nutr. 1999;129:988–993. [DOI] [PubMed] [Google Scholar]
- 22.Hawley NL, Johnson W, Hart CN, Triche EW, Ching JA, Muasau-Howard B, McGarvey ST. Gestational weight gain among American Samoan women and its impact on delivery and infant outcomes. BMC pregnancy and childbirth. 2015. February 3;15(1):1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Hickey CA, Cliver SP, McNeal SF, Hoffman HJ, Goldenberg RL. Prenatal weight gain patterns and birth weight among nonobese black and white women. Obstet Gynecol. 1996;88:490–496. [DOI] [PubMed] [Google Scholar]
- 24.Drehmer M, Duncan BB, Kac G, Schmidt MI. Association of second and third trimester weight gain in pregnancy with maternal and fetal outcomes. PLoS One. 2013;8:e54704. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Kleinman KP, Oken E, Radesky JS, Rich-Edwards JW, Peterson KE, Gillman MW. How should gestational weight gain be assessed? A comparison of existing methods and a novel method, area under the weight gain curve. International Journal of Epidemiology. 2007;36:1275–1282. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Sharma AJ, Vesco KK, Bulkley J, Callaghan WM, Bruce FC, Staab J, Hornbrook MC, Berg CJ. Associations of gestational weight gain with preterm birth among underweight and normal weight women. Matern Child Health J. 2015;19:2066–2073. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Bodnar LM, Hutcheon JA, Parisi SM, Pugh SJ, Abrams B. Comparison of Gestational Weight Gain z‐Scores and Traditional Weight Gain Measures in Relation to Perinatal Outcomes. Paediatric and Perinatal Epidemiology. 2015;29:11–21. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Restall A, Taylor RS, Thompson J, Flower D, Dekker GA, Kenny LC, Poston L, McCowan LM. Risk factors for excessive gestational weight gain in a healthy, nulliparous cohort. Journal of Obesity. 2014;2014. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Herring SJ, Nelson DB, Davey A, Klotz AA, Oken E, Foster GD. Determinants of excessive gestational weight gain in urban, low-income women. Women’s Health Issues. 2012;22:e439–446. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Samura T, Steer J, Michelis LD, Carroll L, Holland E, Perkins R. Factors Associated with Excessive Gestational Weight Gain: Review of Current Literature. Global Advances in Health and Medicine. 2016;5:87–93. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Cohen AK, Kazi C, Headen I, Rehkopf DH, Hendrick CE, Patil D, Abrams B. Educational Attainment and Gestational Weight Gain among US Mothers. Women’s Health Issues. 2016;26:460–467. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Huynh M, Borrell LN, Chambers EC. Maternal education and excessive gestational weight gain in New York city, 1999–2001: the effect of race/ethnicity and neighborhood socioeconomic status. Matern Child Health J. 2014;18:138–145 [DOI] [PubMed] [Google Scholar]
- 33.Mitchell EM, Hinkle SN, Schisterman EF. It’s about time: a survival approach to gestational weight gain and preterm delivery. Epidemiology. 2016;27:182–187. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.McElrath TF, Lim KH, Pare E, Rich-Edwards J, Pucci D, Troisi R, Parry S. Longitudinal evaluation of predictive value for preeclampsia of circulating angiogenic factors through pregnancy. Am J Obstet Gynecol. 2012;207:407.e1–7. [DOI] [PubMed] [Google Scholar]
- 35.Darling AM, Werler MM, Cantonwine DE, Fawzi WW, McElrath TF. Validating an interpolation method for gestational weight gain. Manuscript in preparation. [Google Scholar]
- 36.Hutcheon JA, Platt RW, Abrams B, Himes KP, Simhan HN, Bodnar LM. A weight-gain-for-gestational-age z score chart for the assessment of maternal weight gain in pregnancy–. The American of Clinical Nutrition. 2013;97(5):1062–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Hutcheon JA, Platt RW, Abrams B, Himes KP, Simhan HN, Bodnar LM. Pregnancy weight gain charts for obese and overweight women. Obesity. 2015;23(3):532–5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Ferguson KK, McElrath TF, Meeker JD. Environmental phthalate exposure and preterm birth. JAMA pediatrics. 2014;168:61–68. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Oken E, Kleinman KP, Rich-Edwards J, Gillman MW. A nearly continuous measure of birth weight for gestational age using a United States national reference. BMC pediatrics. 2003;3:6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Institute of Medicine. Weight Gain During Pregnancy: Reexamining the Guidelines. Washington, DC: National Academies Press, 2009. [PubMed] [Google Scholar]
- 41.Nohr EA, Vaeth M, Baker JL, Sørensen TI, Olsen J, Rasmussen KM. Pregnancy outcomes related to gestational weight gain in women defined by their body mass index, parity, height, and smoking status. Am J Clin Nutr 2009;90:1288–94. [DOI] [PubMed] [Google Scholar]
- 42.Lan-Pidhainy X, Nohr EA, Rasmussen KM. Comparison of gestational weight gain-related pregnancy outcomes in American primiparous and multiparous women. Am J Clin Nutr. 2013;97(5):1100–6. [DOI] [PubMed] [Google Scholar]
- 43.Carmichael S, Abrams B, Selvin S. The pattern of maternal weight gain in women with good pregnancy outcomes. Am J Public Health. 1997;87:1984–1988. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Ancri G, Morse EH. Clarke RP. Comparison of nutritional status of pregnant adolescents with adult pregnant women. III. Maternal protein and calorie intake and weight gain in relation to size of infant at birth. Am. J. Clin. Nutr 1977;30:568–572. [DOI] [PubMed] [Google Scholar]
- 45.Muscati SK, Mackey MA, Newsom B. The influence of smoking and stress on prenatal weight gain and infant birth weight of teenage mothers. J. Nutr. Educ 1988;20:299–302. [Google Scholar]
- 46.King JC. The risk of maternal nutritional depletion and poor outcomes increases in early or closely spaced pregnancies. The Journal of nutrition. 2003;133(5):1732S–6S. [DOI] [PubMed] [Google Scholar]
- 47.Savitz DA, Stein CR, Siega-Riz AM, Herring AH. Gestational weight gain and birth outcome in relation to prepregnancy body mass index and ethnicity. Annals of Epidemiology. 2011;21:78–85. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Siega-Riz AM, Adair LS, Hobel CJ. Maternal underweight status and inadequate rate of weight gain during the third trimester of pregnancy increases the risk of preterm delivery. J Nutr. 1996;126:146–153. [DOI] [PubMed] [Google Scholar]
- 49.Spinillo A, Capuzzo E, Piazzi G, Ferrari A, Morales V, Di Mario M. Risk for spontaneous preterm delivery by combined body mass index and gestational weight gain patterns. Acta Obstet Gynecol Scand.1998;77:32–36. [PubMed] [Google Scholar]
- 50.Schieve LA, Cogswell ME, Scanlon KS. Maternal weight gain and preterm delivery: Differential effects by body mass index. Epidemiology. 1999;10:141–147. [DOI] [PubMed] [Google Scholar]
- 51.Wise LA, Palmer JR, Heffner LJ, Rosenberg L. Prepregnancy body size, gestational weight gain, and risk of preterm birth in African-American women. Epidemiology. 2010;21:243–252 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 52.Carnero AM, Mejía CR, García PJ. Rate of gestational weight gain, pre‐pregnancy body mass index and preterm birth subtypes: a retrospective cohort study from Peru. BJOG. 2012;119:924–935. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 53.Hickey CA, Cliver SP, McNeal SF, Hoffman HJ, Goldenberg RL. Prenatal weight gain patterns and spontaneous preterm birth among nonobese black and white women. Obstet Gynecol. 1995;85:909–914. [DOI] [PubMed] [Google Scholar]
- 54.Siega-Riz AM, Viswanathan M, Moos MK, Deierlein A, Mumford S, Knaack J, Thieda P, Lux LJ, Lohr KN. A systematic review of outcomes of maternal weight gain according to the Institute of Medicine recommendations: birthweight, fetal growth, and postpartum weight retention. Am J Obstet Gynecol. 2009;201:339–e1. [DOI] [PubMed] [Google Scholar]
- 55.Hickey CA, Cliver SP, McNEAL SF, Hoffman HJ, Goldenberg RL. Prenatal weight gain patterns and birth weight among nonobese black and white women. Obstet Gynecol. 1996;88:490–496. [DOI] [PubMed] [Google Scholar]
- 56.Sridhar SB, Xu F, Hedderson MM. Trimester-Specific Gestational Weight Gain and Infant Size for Gestational Age. PloS One. 2016;11:e0159500. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 57.Hinkle SN, Johns AM, Albert PS, Kim S, Grantz KL. Longitudinal changes in gestational weight gain and the association with intrauterine fetal growth. European Journal of Obstetrics & Gynecology and Reproductive Biology. 2015;190:41–47. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 58.Brown JE, Murtaugh MA, Jacobs DR, Margellos HC. Variation in newborn size according to pregnancy weight change by trimester. Am J Clin Nutr. 2002;76:205–209. [DOI] [PubMed] [Google Scholar]
- 59.Toro-Ramos T, Paley C, Pi-Sunyer FX, Gallagher D. Body composition during fetal development and infancy through the age of 5 years. European journal of clinical nutrition. 2015;69(12):1279. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 60.Widen EM, Gallagher D. Body composition changes in pregnancy: measurement, predictors and outcomes. European journal of clinical nutrition. 2014. June;68(6):643. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 61.Kominiarek MA, Seligman NS, Dolin C, Gao W, Berghella V, Hoffman M, Hibbard JU. Gestational weight gain and obesity: is 20 pounds too much? Am J Obstet Gynecol. 2013;209:214–e1. [DOI] [PMC free article] [PubMed] [Google Scholar]
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