Abstract
Objective:
The goal of this study was to evaluate whether differences in gestational weight gain (GWG) and adverse perinatal outcomes exist for Black and White women who are overweight or have obesity (OW/OB) at entry to prenatal care.
Methods:
We enrolled 183 pregnant women with BMI 25–45 kg/m2 (71% black, 29% white) prior to 14 weeks gestation in a longitudinal study. Data were collected on demographic, medical history, diet and physical activity during pregnancy and at delivery. Relationships between race and maternal outcomes and infant outcomes were assessed using multivariable logistic regression models.
Results:
The average age of pregnant women were 26 years (±4.8), with a mean BMI of 32.1 (±5.1) kg/m2 at the time of enrollment. At delivery, 60 women (33%) had GWG within Institute of Medicine recommendations and 69% had at least one comorbidity. No significant differences by race were found in GWG (in lbs) (11±7.5 vs. 11.4±7.3, p=0.2006) as well as other perinatal outcomes including maternal morbidity, LBW and PTB. Race differences were noted for gestational diabetes, total energy expenditure and average daily calorie intake, but these differences did not result in significant differences in GWG or maternal morbidity.
Conclusion:
The lack of racial differences in GWG and perinatal outcomes demonstrated in this study differs from prior literature and could potentially be attributed to small sample size and. Findings suggest that race differences in GWG and perinatal outcomes may diminish for women with a BMI in the overweight or obese range at conception.
Keywords: Pregnancy, weight gain, maternal, Deep South
BACKGROUND
Over half of all US women of reproductive age are overweight or have obesity (BMI ≥ 25 kg/m2),(Ogden et al., 2016)with higher rates among Black women (Headen, Mujahid, Cohen, Rehkopf, & Abrams, 2015) and residents in the Deep South (Michimi & Wimberly, 2010). Although women are typically encouraged to gain weight during pregnancy to support fetal development, gestational weight gain (GWG) and postpartum weight retention have been increasing over time, resulting in greater risk for numerous adverse health outcomes for mother and offspring (Institute of & National Research Council Committee to Reexamine, 2009).
Metabolic changes during pregnancy are exacerbated by obesity, such that women with obesity have higher and more variable glucose concentrations, reduced insulin sensitivity, and higher triglyceride concentrations (Catalano & Ehrenberg, 2006). Partially as a consequence of these perturbations, pregnant women with obesity have greater risk for gestational diabetes (GDM) (Chu, Callaghan, Bish, & D’Angelo, 2009), hypertension (Cunningham et al., 2018), and obstetric complication (e.g., cesarean delivery, macrosomia, shoulder dystocia, preterm labor) (Hedderson, Gunderson, & Ferrara, 2010; Herring et al., 2009). Infants of women with obesity are also at greater risk for neonatal complications (e.g., congenital anomalies, stillbirth, and birth trauma) (Biggio, Chapman, Neely, Cliver, & Rouse, 2010; Cedergren, 2004; Watkins, Rasmussen, Honein, Botto, & Moore, 2003).
Like many other adverse health outcomes, there are disparities in GWG. However, the relationship between GWG and race is complex. Black women are more likely than White women to have obesity prior to pregnancy (Ogden et al., 2016), but tend to experience less overall GWG than Whites (Chu et al., 2009; Headen et al., 2015). In addition, a retrospective cohort study found obesity was a risk factor for adverse birth outcomes for white women but not Black women(Snowden et al., 2016). Another factor that contributes to disparate health outcomes is geographic location. Residing in the Deep South (i.e., Alabama, Georgia, Louisiana, Mississippi, North Carolina, and South Carolina) is associated with poor health outcomes for adults (Bolen, Rhodes, Powell-Griner, Bland, & Holtzman, 2000) and children (Goldhagen et al., 2005). Geographic disparity is explained, in part, by higher levels of poverty and unemployment, and lower educational attainment (Geronimus, Bound, & Waidmann, 1999). Multiple Deep South states have also failed to adopt and implement Medicaid expansion which may deprive scores of residents from beneficial pre-conception health care. In addition, this region has larger areas in which access to environments conducive to health promoting lifestyles (e.g., supermarkets, recreational facilities) are limited (Liese, Weis, Pluto, Smith, & Lawson, 2007; Robinson et al., 2014; Wilcox, Castro, King, Housemann, & Brownson, 2000). Finally, a large proportion of Black residents in the Deep South disproportionately experience poorer health outcomes; largely due to structural and systemic racism that has led to some of the other social determinants of health (e.g., poverty, access to health care).
Known health disparities led to a recommendation from the Institutes of Medicine (IOM) to focus on minority women as targets for intervention to manage pre-pregnancy weight and GWG (Institute of & National Research Council Committee to Reexamine, 2009). However, literature on the impact of GWG on maternal and neonatal outcomes has been mixed. A recent retrospective study (Masho, Bassyouni, & Cha, 2016) found racial variation in risk with white and Hispanic women with obesity having higher risk of hypertensive disorders during pregnancy beginning at 25–35 pounds weight gain whereas Black women with obesity experiencing higher risk beginning at 36 or more pounds gained. Other research has focused on pre-pregnancy BMI without examining GWG (Snowden et al., 2016). Overall, research in this area has been limited due to methodological challenges (e.g., insufficient sample size, inconsistent definitions of GWG, reliance on self-reported BMI, lack of postpartum and infant outcome data) (McKoy, Hartmann, Jerome, Andrews, & Penson, 2010; Viswanathan et al., 2008). The increasing prevalence of obesity and the consistent reports that maternal obesity and excessive GWG have detrimental effects across the lifespan (Poston, 2012) warrant further attention regarding the combined effects of overweight/obesity and excess GWG to inform tailored interventions to promote optimal GWG and pre-pregnancy BMI (Martínez-Hortelano et al., 2020).
The Pregnancy and Early Life in the South (PEARLS) Study examined the relative contribution of socioeconomic factors (e.g., education, income, insurance status), demographics (e.g., age, marital status) and health behaviors (diet, physical activity) on GWG and birth outcomes of Black and White mother-infant pairs receiving obstetric care at a large public academic medical center in the Deep South. This study sought to better understand and chart the course for corrective action to eliminate racial disparities in maternal and infant outcomes and promote health equity in a region with poor maternal and child outcomes using a prospective study design.
In this paper, we present the results of a birth-cohort study to evaluate the relationship between race and perinatal outcomes, among obstetric patients with overweight or obesity. Maternal outcomes included GWG and obstetric complications. Birth outcomes included gestational age, birth weight, neonatal intensive care unit (NICU) admission, as well as fetal insulin and glucose.
METHODS
Study population
Women eligible for this study were recruited from those planning to deliver at the study hospital. An electronic obstetric database search was used to identify new obstetric patients within 24–48 hours of the initial prenatal care visit, who met the eligibility criteria below. Patients were contacted by phone to inform them of the study or approached by a clinical research coordinator when they came to the central clinic for an ultrasound to date the pregnancy. All women identified from the database and presenting to the clinic during the study enrollment period were eligible for enrollment if: they self-identified as Black/African American or White; had a BMI ranging from 25–45 kg/m2 pre-pregnancy (self-reported) and at enrollment (measured in clinic); were pregnant with a singleton gestation; presented prior to 14 weeks gestation; and did not plan to move or change obstetric providers in the next 18 months. Women were excluded if they lived outside of hospital catchment area; had a known fetal anomaly (lethal or major); were previously diagnosed with a serious medical illness (e.g., HIV, sickle cell disease); had a recent major psychiatric illness; had a history of prior preterm birth; had pre-gestational diabetes; reported alcohol or drug use during pregnancy; recently participated in a weight loss program or had experienced weight loss surgery; or were unwilling or unable to complete study-related assessments. The protocol was approved by the University of Alabama at Birmingham’s Institutional Review Board.
Protocol
Pregnant women were followed from 1st trimester through delivery. Data were collected during three prenatal visits (10–14 weeks, 24–26 weeks and 34–36 weeks gestation) and at the time of delivery. Research assistants coordinated study visits so that they coincided with routine clinical care visits when possible. The timeline for data collection is provided in the supplementary table S1.
Measures
Surveys:
A pilot-tested face-to-face interview at the 10–14 week study visit was used to collect baseline information about demographic characteristics (race, age, marital status, home address, education, employment, household income, insurance status), health history (e.g., prior diagnoses, adult pre-pregnancy weight status, parity), and measures of dietary intake and physical activity.
Diet:
Trained research assistants administered 24-hour dietary recalls by interview at each study visit and entered data into the National Cancer Institute (NCI) Automated Self-administered 24-hour Dietary Recall (ASA24).
Physical activity:
The Pregnancy Physical Activity Questionnaire (PPAQ) (Chasan-Taber et al., 2004) was administered via interview at each study visit to measure activity and the duration of physical activity.
Clinical examinations:
Routine clinical techniques were used at each study visit to measure height and weight. A fasting blood sample was collected for glucose, insulin, and a lipid profile. Women also underwent a 2-hour 75-gram glucose tolerance test at each study visit and were defined as having gestational diabetes mellitus if their fasting, 1-hour or 2-hour glucose concentration at the 24–26 week visit met or exceeded 92, 180, or 153 mg/dL, respectively.
Maternal and neonatal outcomes:
Information pertaining to medical diagnoses and complications during pregnancy, labor and delivery outcomes, and infant birth weight, length, head and abdominal circumferences were retrieved via hospital record abstraction. A sample of umbilical cord blood was obtained at delivery following established protocols (Andrews et al., 2006; Neta et al., 2010), processed for serum and stored at −80C until assay for C-peptide and glucose concentrations.
Data Analysis
Primary outcomes were defined as: (1) proportion of women with GWG within 2009 IOM Recommendations (Pre-pregnancy BMI 25–29.9: 15–25 lbs; BMI ≥30: 11–20 lbs) (Institute of & National Research Council Committee to Reexamine, 2009), and (2) maternal composite morbidity including at least one of the obstetric risk factors including GDM, cesarean delivery, shoulder dystocia, gestational hypertension, pre-eclampsia/eclampsia, or elevated insulin resistance. In addition to being a binary variable, GWG was also treated as a categorical variable with three levels: (1) GWG below IOM recommendations; (2) GWG above IOM recommendations, and (3) GWG within IOM recommendation.
Secondary outcomes including gestational diabetes (yes/no), cesarean delivery (yes/no), shoulder dystocia (yes/no), gestational hypertension (yes/no), pre-eclampsia/eclampsia (yes/no), insulin resistance (yes/no), low birth weight (<2500 grams vs ≥2500 grams), pre-term births (<37 weeks vs. ≥37 weeks) were treated as binary variables. Cord blood C-peptide and glucose concentrations were treated as continuous variables.
Descriptive statistics were computed including mean, standard deviation and median for continuous variables; and frequencies and percentages for categorical variables. Participant demographic and baseline characteristics were compared across racial groups (White vs. Black or African American) using the student’s t-test, Analysis of Variance (ANOVA), Wilcoxon’s and Kruskal Wallis for parametric and non-parametric continuous variables; and the chi-square (χ2) test or Fisher’s Exact test for categorical variables as appropriate. Primary outcomes including proportion of women with GWG within 2009 IOM recommendations and maternal composite morbidity were coded as binary variables (had the outcome = 1; no outcome = 0), respectively. Maternal composite morbidity was defined as having at least one of the following conditions: gestational diabetes, cesarean delivery, shoulder dystocia, gestational hypertension, pre-eclampsia/eclampsia, or elevated insulin resistance. Secondary outcomes were modeled as categorical variables. Bivariate analyses compared the covariates across racial groups to identify potential risk factors.
Logistic regression for dichotomous outcomes and cumulative logit models for polytomous outcomes were used to compute unadjusted odds ratio and their 95% confidence intervals to evaluate the relationship between racial groups and primary and secondary outcomes. Multivariable models were then used to compute adjusted measures of association between racial groups and outcomes, adjusted for potential confounders. Variables were included in model if they were significantly differed at p-value <0.05, by race in the bivariate comparisons. The covariates included in the multivariable model were maternal age, pre-pregnancy BMI, maternal education (high school or more vs. less than high school), government insurance (yes vs. no), income (below $20,000 vs. above), marital status (married vs. not married), total average weekly energy expenditure (Metabolic Equivalent of Task (MET), average calorie intake daily, and maternal morbidity (present or absent). Results were determined to be statistically significant when the accompanying statistical test yielded a two-tailed probability of 0.05 or less. All analyses were conducted using SAS 9.4.
RESULTS
A total of 208 pregnant mothers with singleton gestations were enrolled in this study at an average gestational age of 10.3 weeks (range 6.0–14.0). Twenty-five women were subsequently excluded from final analyses due to miscarriage (n=2), stillbirth (n=1), failure to follow-up (n=10), voluntary withdrawal from the study (n=5), removal from the study due to relocation outside of hospital catchment area (n=6), and participation in a clinical trial (n=1). Thus, 183 mothers completed follow up to the child’s birth and were included in the present analyses.
As shown in Table 1, the n=183 women for final analyses included 53 women who self-identified as White and 130 Black. Overall, the average age of mothers was 26 years (±4.8), with a mean BMI of 32 (±5.1) kg/m2 at the time of enrollment. Forty-six percent of women were educated beyond high school, 72% were employed, 51% were never married and 55% had income below $20,000. Comparing across the two racial groups, Black pregnant women were less likely to be educated beyond high school, more likely to have never married, more likely to earn less than $20,000 and more likely to receive government insurance, as compared to White pregnant women. The two groups were similar in their age, gestational age at enrollment, baseline BMI, and employment status.
Table 1.
Baseline characteristics of cohort of pregnant mothers at screening
| Total | White | Black | P-value* | ||||
|---|---|---|---|---|---|---|---|
| N | 183 | 53 | 130 | ||||
| Age | 0.3768 | ||||||
| Mean (SD) | 26 (4.8) | 26 (4.5) | 26 (4.9) | ||||
| Gestational age at baseline (wks) | 0.0584 | ||||||
| Mean (SD) | 10 (2) | 11 (1.7) | 10 (2.1) | ||||
| BMI (Kg/m2) | 0.8044 | ||||||
| Mean (SD) | 32.1 (5.1) | 32.2 (5.1) | 32 (5.1) | ||||
| Baseline Weight (In Kgs) | 0.1060 | ||||||
| Mean (SD) | 85 (16) | 88 (15) | 84 (17) | ||||
| Education (n,%) | 0.0002 | ||||||
| ≤ High School | 101 (54) | 18 (34) | 83 (64) | ||||
| ˃ High School | 82 (46) | 35 (66) | 47 (36) | ||||
| Government insurance (n,%) | <0.001 | ||||||
| Yes | 138 (75) | 27 (51) | 111 (85) | ||||
| No | 45 (25) | 26 (49) | 19 (15) | ||||
| Employment (n,%) | 0.7341 | ||||||
| Employed | 132 (72) | 37 (70) | 94 (72) | ||||
| Othera | 52 (28) | 16 (30) | 36 (28) | ||||
| Marital Status (n,%) | <.0001 | ||||||
| Married | 35 (19) | 26 (49) | 9 (6.9) | ||||
| Never Married | 94 (51) | 12 (23) | 82 (63) | ||||
| Living with a partner | 44 (24) | 10 (19) | 34 (26) | ||||
| Other b | 10 (5.5) | 5 (9.4) | 5 (3.9) | ||||
| Income (n,%) | <.0001 | ||||||
| < $10k | 64 (35) | 9 (17) | 55 (42) | ||||
| $10k to $19,999 | 36 (20) | 6 (11) | 30 (23) | ||||
| $20k to $29,999 | 21 (11) | 5 (9) | 16 (12) | ||||
| > $30k | 39 (21) | 26 (48) | 13 (10) | ||||
| Don’t know/refused answer | 23 (13) | 7 (13) | 16 (12) |
Other category for employment includes self-employed, out of work, homemaker, and student.
Other category for marital status includes widowed, divorced, and separated.
P-value associated with Student’s t-test for continuous variables, and Pearson chi-square for categorical variables. Bold typeface indicates statistical significance at α=0.05.
Physical activity and Diet
The median values of PPAQ were significantly different between White and Black women for estimated total physical activity per week (in hours) (p= 0.0348) but not by total average weekly expenditure (METs−1wk) (Table 2). Overall, Black women reported significantly more median total average energy expenditure (MET) hours doing household/caregiving activities compared to White mothers (median total physical activity per week in hours: 49 vs 32; p=0.0096). The METs among Black pregnant women were slightly higher (Median, 350; IQR, 225–445) compared to White pregnant women (median, 330; IQR, 236–386) but did not differ significantly (p=0.2028). Further, no significant differences were noted by activity-specific MET expenditures. The average daily calorie intake calculated from ASA-24 recall was significantly higher among Black pregnant women compared to White pregnant women during their pregnancy (2384 ±929 vs. 1991±641; p=0.0013).
Table 2.
Maternal physical activity and diet during pregnancy
| Total (n=183) | White (n=53) | Black (n=130) | P-value* | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Percentiles | 25th | 50th | 75th | 25th | 50th | 75th | 25th | 50th | 75th | |||||
| Total Physical Activity per Week (hrs) | 119 | 148 | 189 | 117 | 139 | 169 | 124 | 157 | 206 | 0.0348 | ||||
| By intensity | ||||||||||||||
| Sedentary | 42 | 61 | 82 | 39 | 54 | 68 | 47 | 65 | 86 | 0.0092 | ||||
| Light | 37 | 52 | 68 | 36 | 47 | 64 | 38 | 54 | 68 | 0.2254 | ||||
| Moderate | 16 | 34 | 58 | 18 | 33 | 50 | 15 | 35 | 62 | 0.6880 | ||||
| Vigorous | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |||||
| By type | ||||||||||||||
| Household/Caregiving | 25 | 43 | 70 | 18 | 32 | 55 | 28 | 49 | 75 | 0.0096 | ||||
| Occupational | 0 | 40 | 65 | 12 | 53 | 63 | 0 | 35 | 65 | 0.3208 | ||||
| Sports/Exercise | 0.5 | 1.5 | 3.8 | 1 | 2.3 | 3.5 | 0.5 | 1.3 | 3.8 | 0.2523 | ||||
| Total Avg. Weekly Expenditure (METs) † | 231 | 333 | 431 | 236 | 330 | 386 | 225 | 350 | 445 | 0.2028 | ||||
| Sedentary | 56 | 76 | 102 | 48 | 67 | 94.5 | 58.1 | 78.8 | 105 | 0.1059 | ||||
| Light | 89 | 124 | 161 | 84 | 114 | 151 | 91.2 | 131 | 161 | 0.2094 | ||||
| Moderate | 54 | 119 | 197 | 66 | 118 | 177 | 50 | 122 | 210 | 0.7028 | ||||
| Vigorous | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |||||
| By type | ||||||||||||||
| Household/Caregiving | 60 | 114 | 192 | 43 | 87 | 154 | 70 | 132 | 205 | 0.0152 | ||||
| Occupational | 0 | 89 | 169 | 23 | 113 | 160 | 0 | 81 | 176 | 0.5821 | ||||
| Sports/Exercise | 2 | 7 | 16 | 4 | 9 | 16 | 2 | 5 | 16 | 0.2554 | ||||
| Avg. 24-hour intake (kcals/day) | ||||||||||||||
| Mean (SD) | 2270 (873) | 1991 (641) | 2384 (929) | 0.0013 | ||||||||||
P-value associated with non-parametric Wilcoxon’s-signed rank test comparing median and Student’s t-test comparing means, for continuous variables. Bold typeface indicates statistical significance at α=0.05.
Total average MET hours per week for activity; MET, Metabolic Equivalent of Task.
GWG and Maternal Outcomes
As shown in Table 3, 33% of the women met IOM recommendations for GWG (n=60); while 59% had GWG below IOM recommendation (n=107) and 8.7% had GWG above IOM recommendations (n=16). No significant differences in maternal GWG were noted by race (White: 12.5 kg vs. Black 11 kg; p=0.20), even after accounting for baseline BMI. Prevalence of maternal composite comorbidity was similar across the two race groups (White, 62% vs. Black, 72%; p=0.1811). The prevalence of gestational diabetes was significantly higher among Black pregnant women compared to White pregnant women (39% vs. 30%, p=0.0341). No other differences between Black and White pregnant women were found for all other secondary maternal and infant outcomes (Tables 3 and 4).
Table 3.
Maternal outcomes at the time of Delivery*
| Total (n=183) | White (n=53) | Black (n=130) | P-value‡ | ||||
|---|---|---|---|---|---|---|---|
| Primary outcomes | |||||||
| Admission Weight (kg) | 0.05 | ||||||
| Mean (SD) | 97 (17) | 100(15.3) | 95(17.1) | ||||
| Gestational weight gain (in lbs) | 0.2006 | ||||||
| Mean (SD) | 11.4 (7.3) | 12.5 (6.9) | 11 (7.5) | ||||
| N | % | N | % | N | % | ||
| GWG within IOM Recommendations (frequency) | 0.1085 | ||||||
| Within IOM recommendation | 60 | 33 | 22 | 42 | 38 | 29 | |
| Outside IOM recommendations | 123 | 67 | 31 | 58 | 92 | 71 | |
| Gestational weight gain | 0.2361 | ||||||
| Within IOM recommendation | 60 | 33 | 22 | 42 | 38 | 29 | |
| Below IOM recommendation | 107 | 59 | 26 | 49 | 81 | 62 | |
| Above IOM recommendation | 16 | 8.7 | 5 | 9.4 | 11 | 8.5 | |
| GWG within IOM Recommendations (frequency) | 0.1706 | ||||||
| Overweight at baseline and within IOM recommendation | 24 | 13 | 7 | 29 | 17 | 13 | |
| Obese at baseline and within IOM recommendation | 36 | 20 | 15 | 28 | 21 | 16 | |
| Outside IOM recommendations | 123 | 67 | 31 | 59 | 92 | 71 | |
| Overweight (n=74) | 0.8931 | ||||||
| Overweight - GWG Below recommendations | 46 | 62 | 11 | 58 | 35 | 64 | |
| Overweight - GWG Above recommendations | 4 | 5.4 | 1 | 5.2 | 3 | 5.5 | |
| Overweight- GWG within recommendation | 24 | 32 | 7 | 37 | 17 | 31 | |
| Obese (n=109) | 0.2118 | ||||||
| Obese - GWG Below Recommendations | 61 | 56 | 15 | 44 | 46 | 61 | |
| Obese - GWG Above Recommendations | 12 | 11 | 4 | 12 | 8 | 11 | |
| Obese – GWG within recommendation | 36 | 33 | 15 | 44 | 21 | 28 | |
| Maternal composite comorbidity | 0.1811 | ||||||
| No | 56 | 31 | 20 | 38 | 36 | 28 | |
| Yes | 127 | 69 | 33 | 62 | 94 | 72 | |
| C-section delivery | 0.3774 | ||||||
| No | 126 | 69 | 39 | 74 | 87 | 67 | |
| Yes | 57 | 31 | 14 | 26 | 43 | 33 | |
| Pre-eclampsia | 0.8677 | ||||||
| No | 160 | 87 | 46 | 87 | 114 | 88 | |
| Yes | 23 | 13 | 7 | 13 | 16 | 12 | |
| Gestational hypertension | 0.8379 | ||||||
| No | 168 | 92 | 49 | 93 | 119 | 92 | |
| Yes | 15 | 8 | 4 | 8 | 11 | 9 | |
| Gestational diabetes | 0.0341 | ||||||
| No | 104 | 59 | 33 | 62 | 77 | 59 | |
| Yes | 73 | 41 | 16 | 30 | 51 | 39 | |
183 participants who delivered.
GWG, Gestational weight gain: Screening weight from enrollment vs. measured weight at Delivery.
P-value associated with Student’s t-test for continuous variables, and Pearson chi-square for categorical variables. Bold typeface indicates statistical significance at α=0.05.
Table 4.
Birth outcomes at the time of Delivery*
| Total (n=183) | White (n=53) | Black (n=130) | P-value† | ||||
|---|---|---|---|---|---|---|---|
| Birth weight | 0.1865 | ||||||
| Mean (SD) | 3243 (548) | 3330 (577) | 3207 (534) | ||||
| Gestational age at Delivery (wks) | 0.8861 | ||||||
| Mean (SD) | 38.9 (1.8) | 38.9 (1.8) | 38.9 (1.9) | ||||
| Cord blood glucose (n=161) | 0.6249 | ||||||
| Mean (SD) | 83 (21) | 85 (16) | |||||
| Cord blood C-peptide (n=161) | 0.2639 | ||||||
| Mean (SD) | 1.1 (0.9) | 1.0 (1.2) | |||||
| N | % | N | % | N | % | ||
| Low birth weight (<2500 gms) | 1.000 | ||||||
| No | 171 | 93 | 50 | 94 | 121 | 93 | |
| Yes | 12 | 6.6 | 3 | 5.7 | 9 | 6.9 | |
| Preterm births (gestational age <37 weeks) | 0.1821 | ||||||
| No | 164 | 90 | 45 | 85 | 119 | 92 | |
| Yes | 19 | 10 | 8 | 15 | 11 | 8.5 | |
| Neonatal intensive care unit | 0.6462 | ||||||
| No | 159 | 87 | 47 | 89 | 112 | 86 | |
| Yes | 24 | 13 | 6 | 11 | 18 | 14 | |
| Shoulder dystocia | 0.0594 | ||||||
| No | 177 | 97 | 49 | 93 | 128 | 98 | |
| Yes | 6 | 3.3 | 4 | 7.6 | 2 | 1.5 | |
183 participants who delivered
P-value associated with Student’s t-test for continuous variables, and Pearson chi-square for categorical variables. Bold typeface indicates statistical significance, comparing column percentiles
Association between Race and Gestational Weight Gain
Results of the multivariable logistic regression models (Table 5) found that compared to White pregnant women, Black pregnant women had 40% lower odds of GWG outside IOM recommendations (Odds ratio (OR) =0.58, 95%CI: 0.25–1.31, p=0.1850), adjusted for covariates; however, the results statistically non-significant Results of the cumulative logit model found that the odds of GWG being below the IOM recommended cutoffs were 96% higher among Black pregnant women as compared to White pregnant women (OR=1.96; 95%CI=0.83–4.65; p=0.1266); while the odds of GWG being above the IOM recommendations were 19% lower among Black pregnant women compared to White pregnant women (OR=0.81; 95% CI=0.18–3.70; p=0.7901). However, there was insufficient evidence to reject the null hypothesis of a race difference in GWG. The odds of maternal composite morbidity and gestational diabetes were 1.6 times and 2.0 times, respectively, higher among Black pregnant women compared to White pregnant women, but the results were not statistically significant. No differences were noted in birth outcomes by race in the adjusted models (Table 6).
Table 5.
Relationship between race and primary maternal outcomes*
| Unadjusted OR | 95% CI | P-value | Adjusted OR | 95%CI | P-value | |
|---|---|---|---|---|---|---|
| Race and GWG within IOM Recommendations (Yes vs.no) † | 0.58 | 0.30–1.13 | 0.1103 | 0.57† | 0.25–1.31 | 0.1850 |
| Race and Gestational weight gain† | ||||||
| GWG (Below Recommendations vs. within recommendation) | 1.80 | 0.91–3.58 | 0.0920 | 1.96‡ | 0.83–4.65 | 0.1266 |
| GWG (Above Recommendations vs. within recommendation) | 1.27 | 0.39–4.15 | 0.6880 | 0.81 | 0.18–3.70 | 0.7901 |
| Overweight (n=74) † | ||||||
| Overweight - GWG Below Recommendations | 2.19 | 0.91–5.29 | 0.0815 | 3.13 | 0.93–10.52 | 0.0655 |
| Overweight - GWG Above Recommendations | 1.43 | 0.36–5.63 | 0.6104 | 1.88 | 0.28–12.43 | 0.5110 |
| Obese† | ||||||
| Obese - GWG Below Recommendations | 1.31 | 0.43–3.98 | 0.6336 | 0.28 | 0.01–9.78 | 0.5443 |
| Obese - GWG Above Recommendations | 1.24 | 0.11–14.0 | 0.8646 | 0.60 | 0.12–3.11 | 0.4792 |
| Race and Maternal Composite Morbidity | 1.58 | 0.80 – 3.11 | 0.1828 | 1.57⁋ | 0.67–3.69 | 0.3039 |
| Race and Gestational diabetes | 2.14 | 1.05–4.35 | 0.0362 | 2.02⁋ | 0.85–4.81 | 0.1103 |
Results of logistic regression comparing race and primary maternal outcomes, unadjusted and adjusted odds ratio (OR) and 95% confidence intervals (CI).
GWG, gestational weight gain.
Reference category is GWG within IOM recommendations; Reference category for Race is ‘White women’ Model adjusted for maternal age, baseline BMI, marital status (married vs. no married), income (<$20,000 vs. ≥$20,000), Government insurance (yes vs. no), employed (ever vs. never), Total Average Weekly Energy Expenditure (METs), and average daily calorie intake (kcals), and maternal composite morbidity.
Generalized logit model; Reference category is GWG within IOM recommendations; Model adjusted for maternal age, baseline BMI, marital status (married vs. no married), income (<$20,000 vs. ≥$20,000), employed (ever vs. never), Total Average Weekly Energy Expenditure (METs), and average daily calorie intake (kcals), and maternal composite morbidity.
Model adjusted for maternal age, baseline BMI, marital status (married vs. no married), income (<$20,000 vs. ≥$20,000), Government insurance (yes vs. no), employed (ever vs. never), Total Average Weekly Energy Expenditure (METs), and average daily calorie intake (kcals).
Maternal composite morbidity- presence of at least one comorbid condition including c-section delivery, pre-eclampsia, gestational hypertension, gestational diabetes, shoulder dystocia, pregnancy insulin resistance.
Table 6.
Relationship between race and birth outcomes*
| Unadjusted OR | 95% CI | P-value | Adjusted OR | 95%CI | P-value | |
|---|---|---|---|---|---|---|
| Race and LBW | 1.24 | 0.32–4.77 | 1.000 | 1.12† | 0.21–7.61 | 0.9179 |
| Race and Preterm births | 0.52 | 0.20–1.38 | 0.1878 | 0.87⁋ | 0.67–3.69 | 0.3039 |
Results of unadjusted and adjusted logistic regression, odds ratio (OR) and 95% confidence intervals (CI). LBW, low birth weight.
Reference category is normal birth weight (≥2500 grams). Model adjusted for maternal age, baseline BMI, marital status (married vs. no married), income (<$20,000 vs. ≥$20,000), employed (ever vs. never), Total Average Weekly Energy Expenditure (METs), and average daily calorie intake (kcals), and maternal composite morbidity, and preterm birth.
Reference category is normal births (≥ 37 weeks). Model adjusted for maternal age, baseline BMI, marital status (married vs. no married), income (<$20,000 vs. ≥$20,000), employed (ever vs. never), Total Average Weekly Energy Expenditure (METs), and average daily calorie intake (kcals), and maternal composite morbidity.
DISCUSSION
The PEARLS study is unique in its examination of individual, social and behavioral determinants of GWG among Black and White pregnant women with overweight or obesity living in the Deep South. The study sought to explain previously observed differences in GWG between Black and White women, by focusing on a group (women with overweight and obesity) particularly vulnerable to poor health outcomes for both mother and infant. Leading public health officials recognize that there is a tremendous need to intervene among women with pre-pregnancy obesity or at risk for excess GWG but noted the need for additional research to elucidate the influence of race/ethnicity (IOM, 2009).
In the current paper, we investigated whether GWG during pregnancy differed by race among 183 Black and White pregnant women with overweight or obesity. Overall, only 33% of women in our study met the IOM criterion for optimal GWG. We found moderate differences in GWG at the time of delivery between Black and White pregnant women, which persisted after adjusting for social determinants such as age, income, marital status, employment, insurance and clinical assessments such as presence of comorbidity such as GDM, pre-eclampsia, gestational hypertension or insulin resistance, average energy expenditure and average daily calorie intake. However, all results were statistically non-significant, and the effect estimates had wide confidence limits indicating small sample size and reduced power. We also evaluated relationships between race and maternal comorbidity during pregnancy and other perinatal outcomes but did not find a meaningful association. These results are consistent with other studies that suggest that while racial/ethnic disparities exist for maternal and child outcomes, the disparities may reduce with increasing BMI of mothers effectively leveling off racial/ethnic disparities at high BMIs (Marshall, Guild, Cheng, Caughey, & Halloran, 2014; Snowden et al., 2016).
This study has some limitations, which we have diligently tried to minimize in our research design. First, while the study aimed to recruit equal numbers of Black and White participants, the final sample included only 29% of White pregnant women, which is likely due in part to the differences in demographics of patients served by the two prenatal care clinics used for recruitment in this study. Although all women delivered at the same hospital, most of the Black women enrolled in the study received care from a clinic where the majority of patients identify as Black (unpublished observation), whereas the other clinic’s patient population consisted of mostly persons who identify as White (unpublished observation). In addition, the recruitment of women who were employed full time (most of whom had private insurance) proved more of a challenge as many cited concerns about taking time off work for study visits (unpublished observation). While the two racial groups in this study differed in terms of education, insurance status, and marital status, they were similar in age, gestational age and BMI at the time of enrollment. Further, Black women reported higher energy intake than white women, but this was offset by greater physical activity, potentially contributing to the lack of difference in GWG. Baseline demographic factors were adjusted in the multivariable model to account for possible confounding. Our study found a moderate protective effect among Black pregnant women who had GWG within IOM recommendation; however, the lack of statistical significance limit our ability to draw any firm conclusions. This could potentially be attributed to disparate numbers between the Black and White women and the small sample size for White women in the study resulting in reduced statistical power. In addition, we are cognizant that our study participants may not be representative of pregnant women with obesity in non-urban communities and other Deep South states. It may also limit the generalizability of the results, as the results may only be applicable to women who initiate early prenatal care, and not to women who may have difficulty accessing prenatal care in a timely manner
Still, the study adds to the body of literature to examine the relationship between GWG and sociodemographic and behavioral factors associated with maternal and infant outcomes in women with overweight and obesity. Given rises in the rates of obesity, associated racial disparities and the established negative lifetime trajectory of offspring of women with excess weight gain and/or pre-pregnancy obesity, it is imperative to identify potential areas of intervention. While our study findings suggest diminished racial/ethnic disparities in maternal and child outcomes among individuals with higher BMIs, helping women to achieve healthy pre-pregnancy weights and achieve IOM-recommended GWG are still recommended strategies to help lower risk for adverse maternal and child outcomes for all women. Additional research is needed to explore the role of other potential individual- (e.g., maternal stress, health literacy) and institutional-level (e.g., systemic racism, provider bias) contributors to racial/ethnic disparities in maternal and child outcomes.
Supplementary Material
SIGNIFICANCE.
Literature regarding race disparities in gestational weight gain (GWG) and the subsequent impact on maternal and neonatal outcomes has been mixed, in part due to race differences in the prevalence of overweight and obesity prior to conception. Our study is among the first to examine the relationships among race, GWG and maternal and infant outcomes in women with pre-pregnancy overweight or obesity. Results suggest that race differences in GWG and perinatal outcomes may diminish for women with a BMI in the overweight or obese range at conception.
Financial Disclosure:
No financial disclosures were reported by the authors of this paper.
Footnotes
Conflict of Interest Statement: The authors’ work was supported by a grant from the National Institute of Minority Health and Health Disparities (U54MD008176).
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