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The American Journal of Clinical Nutrition logoLink to The American Journal of Clinical Nutrition
. 2021 Jun 2;114(3):1229–1237. doi: 10.1093/ajcn/nqab145

Healthy dietary patterns and common pregnancy complications: a prospective and longitudinal study

Mengying Li 1, Jagteshwar Grewal 2, Stefanie N Hinkle 3, Samrawit F Yisahak 4, William A Grobman 5, Roger B Newman 6, Daniel W Skupski 7, Edward K Chien 8, Deborah A Wing 9,10, Katherine L Grantz 11, Cuilin Zhang 12,✉
PMCID: PMC8408886  PMID: 34075392

ABSTRACT

Background

Adherence to alternate Healthy Eating Index (AHEI), alternate Mediterranean diet (AMED), and Dietary Approaches to Stop Hypertension (DASH) has been linked to lower risks of chronic diseases. However, their associations with common pregnancy complications are unclear.

Objectives

This study investigates the associations of AHEI, AMED, and DASH during periconception and pregnancy with common pregnancy complication risks.

Methods

The study included 1887 pregnant women from the Eunice Kennedy Shriver National Institute of Child Health and Human Development Fetal Growth Studies–Singletons. Women responded to an FFQ at 8–13 gestational weeks, and they performed a 24-h dietary recall at 16–22 and 24–29 wk. Gestational diabetes (GDM), gestational hypertension, preeclampsia, and preterm delivery were ascertained using medical records.

Results

Healthier diet indicated by higher AHEI, AMED, and DASH scores was generally related to lower risks of pregnancy complications. Significant inverse associations were observed between AHEI score reported at 16–22 wk and GDM risk [adjusted RR (95% CI), highest (Q4) vs. lowest quartile (Q1): 0.32 (0.16, 0.66), P-trend = 0.002]; DASH score reported at both 8–13 [adjusted RR (95% CI), Q4 vs. Q1: 0.45 (0.17, 1.17), P-trend = 0.04] and 16–22 wk [adjusted RR (95% CI), Q4 vs. Q1: 0.19 (0.05, 0.65), P-trend = 0.01] and gestational hypertension risk; AHEI score reported at 24–29 wk and preeclampsia risk [adjusted RR (95% CI), Q4 vs. Q1: 0.31 (0.11, 0.87), P-trend = 0.03]; AMED score reported at 8–13 wk [adjusted RR (95% CI), Q4 vs. Q1: 0.50 (0.25, 1.01), P-trend = 0.03] and DASH score reported at 24–29 wk [adjusted RR (95% CI), Q4 vs. Q1: 0.50, (0.26, 0.96), P-trend = 0.03] and preterm delivery risk.

Conclusions

Adherence to AHEI, AMED, or DASH during periconception and pregnancy was related to lower risks of GDM, gestational hypertension, preeclampsia, and preterm delivery.

This study was registered at ClinicalTrials.gov as NCT00912132.

Keywords: dietary patterns, gestational diabetes, gestational hypertension, preeclampsia, preterm delivery, cohort studies

Introduction

Among pregnant women in the USA, 6–9% develop gestational diabetes (GDM) (1) and 9% are affected by gestational hypertension, preeclampsia, or eclampsia (2). In addition, approximately 10% of all births in the USA are preterm (3). These conditions have been associated with higher risks of cardiometabolic disorders for both women and their offspring later in life (4–6). As such, it is a public health priority to identify modifiable risk factors of these common pregnancy complications.

Diet and nutritional status are important for pregnancy health and fetal development (7–9). However, in the USA, specific guidelines on overall dietary patterns for pregnant women remain absent. In the general population, healthy dietary patterns such as alternate Healthy Eating Index (AHEI) (10), alternate Mediterranean diet (AMED) (11), and Dietary Approaches to Stop Hypertension (DASH) (12) have been linked to lower risks of cardiometabolic disorders. It remains unclear whether adhering to these healthy dietary patterns during pregnancy may lower the risk of pregnancy complications.

Intervention studies have examined the role of dietary counseling, but not dietary patterns per se, in preventing pregnancy complications (13, 14), and very few of them were based on these 3 healthy dietary patterns (15, 16). To our knowledge, only 3 prospective cohort studies examined AHEI, AMED, or DASH during pregnancy in relation to common pregnancy complications (17–19), with each study focusing on a single dietary pattern and 1 or 2 pregnancy complications. Further, longitudinal dietary data characterizing diet from periconception through pregnancy remain scarce. The present study aims to comprehensively evaluate longitudinal measures of AHEI, AMED, and DASH from periconception through pregnancy in relation to risks of multiple common pregnancy complications including GDM, gestational hypertension, preeclampsia, and preterm delivery in a prospective, multi-racial US cohort.

Methods

Study population

This is a secondary analysis of the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) Fetal Growth Studies–Singletons (2009–2013), a multi-racial, longitudinal cohort of women from 12 US clinical centers, designed to establish race-specific fetal growth standards (20). The cohort enrolled 2802 non-obese [pre-pregnancy BMI (in kg/m2) < 30, n = 2334] or obese (pre-pregnancy BMI 30.0–45.0, n = 468) women at 8–13 gestational weeks and followed them until delivery. Women were excluded if they had major pre-existing chronic conditions including diabetes and hypertension; non-obese women were also excluded if they had lifestyle risk factors (smoking, alcohol and illicit drugs use) and histories of pregnancy complications including GDM, severe preeclampsia, and early preterm delivery (<34 wk of gestation) (20). All women provided written informed consent. Institutional review board approval was obtained at all participating clinical sites.

Of the 2802 women, 18 were found ineligible to participate after enrollment and excluded. Of the remaining women, 1980 were included in a nutrition sub-study; the characteristics of women in the sub-study are similar to the overall study. At enrollment (8–13 wk), 1615 women completed a semi-quantitative FFQ [a modified version of the Diet History Questionnaire II (21)] on their habitual diet over the past 3 months, reflecting diet during periconception and early pregnancy. At 2 subsequent visits at 16–22 and 24–29 wk, 1801 and 1734 women, respectively, completed the Automated Self-Administered 24-hour (ASA24) Dietary Assessment Tool (versions Beta/2011) (22). Dietary records with an unrealistic total energy intake (<600 or > 6000 kcal/d) were excluded (23). As a result, 1887 women with at least 1 valid dietary assessment were included in the final analytic sample (Supplementary Figure 1).

Healthy dietary patterns

Food groups and nutrients were calculated based on the FFQ and ASA24. The AHEI (10) consists of 11 components, each scored on a scale of 0 (least healthy) to 10 (most healthy) based on reference values [for details, see (10)]. The AMED (24) consists of 9 components scored based on their distributions in the study sample, with healthy components scored 1 for above the median intake and 0 for below, and unhealthy components scored reversely. The DASH consists of 8 components also scored based on their distributions in the study sample, with components scored on a scale of 1 (least healthy) to 5 (most healthy) based on quintiles of intake (25) (Supplementary Table 1). Of note, several components, including vegetables, (whole) fruit, whole grain, nuts and legumes, and red/processed meat, are common across the 3 patterns. We removed the trans-fat component from AHEI measured at 16–22 and 24–29 wk as it was not estimated from ASA24. We also removed the alcohol component from AHEI and AMED as it is controversial to regard alcohol as part of a healthy diet for pregnant women. Higher scores represent a healthier diet.

Pregnancy complications

Pregnancy complications were the primary outcomes of this study. They were identified using information abstracted from medical records. Gestational diabetes was defined by women's oral glucose challenge test results using the Carpenter-Coustan criteria (at least 2 values met or exceeded: fasting—95 mg/dL, 1 h—180 mg/dL, 2 h—155 mg/dL, 3 h—140 mg/dL) (26), and/or by receipt of GDM medications. The comparison group was women without GDM. Hypertensive disorders of pregnancy were abstracted from the postpartum discharge diagnosis and classified into gestational hypertension (including gestational hypertension and unspecified hypertension) and preeclampsia [including mild, severe preeclampsia, HELLP (hemolysis, elevated liver enzymes, and low platelet count) syndrome, and eclampsia] (27). At the time of study data collection, clinicians at participating centers typically followed the 2002 American College of Obstetrician and Gynecologists (ACOG) criteria (28) for the diagnosis of hypertensive disorders of pregnancy; a previous study demonstrated that ACOG 2002 criteria and the more recent ACOG 2013 criteria differed little in identified preeclampsia cases (29). The 2002 ACOG criteria defined gestational hypertension as new onset of elevated blood pressure (a systolic blood pressure ≥ 140 mm Hg or a diastolic blood pressure ≥ 90 mm Hg) after 20 wk of gestation without proteinuria; preeclampsia as new onset of elevated blood pressure after 20 wk with proteinuria (≥0.3 g of protein in a 24-h urine specimen). The comparison group for both gestational hypertension and preeclampsia was women without either diagnosis (27). Lastly, preterm delivery was defined as delivery before 37 completed weeks of gestation, where gestational age of delivery was calculated from ultrasound verified last menstrual period date and delivery date was abstracted from medical records. The comparison group was women who delivered after 37 wk of gestation. Women with any of the aforementioned conditions were considered to have a composite outcome of “any pregnancy complications,” which is the secondary outcome of this study. The comparison group was women with none of the conditions.

Covariates

Demographic variables including age, race-ethnicity, marital status, parity, and education were self-reported. Pre-pregnancy BMI was calculated using self-reported weight before pregnancy and height measured at enrollment. At enrollment and follow-up visits, total energy intake (kcal) was estimated from the dietary assessments, light to vigorous physical activities were estimated from women's response to the Pregnancy Physical Activity Questionnaire (30), and average sleep duration (hours) per day was self-reported.

Statistical analysis

Distributions of participants’ characteristics at enrollment were compared across quartiles of dietary patterns using the chi-square test for categorical variables and analysis of variance test for continuous variables.

Relative risks of pregnancy complications were estimated by quartiles of dietary pattern scores using log-binomial regression models. Linear trend of the associations was assessed using the median value for each quartile of dietary pattern scores as a continuous variable. Significant association was defined as p-trend < 0.05. Models were first adjusted for maternal age (y) (Model 1), then additionally adjusted for race (non-Hispanic white, non-Hispanic black, Hispanic, Asian/Pacific Islander), education (less than high school, high school, some college, Bachelor, Graduate), marriage/cohabiting (yes, no), nulliparity (yes, no), pre-pregnancy BMI, family history of diabetes (yes, no), light to vigorous physical activities [metabolic equivalent (MET)-h/wk], sleep duration (5–6, 7, 8–9, 10 + h/d), and total energy intake (kcal/d) (Model 2). To ensure prospective associations, only dietary data collected at study visits before the diagnosis of complications was utilized. Thus, for GDM (usually diagnosed between 24–28 wk), dietary data collected at 8–13 and 16–22 wk were utilized and those collected after a GDM diagnosis were excluded. For gestational hypertension, preeclampsia, and preterm delivery, as the diagnosis was uncommon before 30 wk (31–33), dietary data collected at 8–13, 16–22, and 24–29 wk were utilized.

As preterm delivery has heterogeneous etiologies, we examined the 2 most common forms of preterm delivery subtypes, spontaneous and medically indicated preterm delivery, separately. Moreover, we stratified the analysis by other major risk factors including parity (nulliparous vs. parous) and pre-pregnancy BMI (<25, 25–29, ≥30) for potential effect modification. All analyses were performed using women with complete data using SAS 9.4 (SAS Institute).

Results

In the study analytical sample, 85 women developed GDM (5.0% of the 1718 women assessed), 61 developed gestational hypertension (3.5% of the 1752 women assessed), 61 developed preeclampsia (3.5% of the 1752 women assessed), and 112 had preterm delivery (6.2% of 1794 women assessed). Collectively, 284 women had any of the pregnancy complications (16.4% of 1728 women assessed). At enrollment, compared with women in the lowest quartile of AHEI score (least healthy), those in the highest quartile of AHEI score (most healthy) were more likely to be older, white, or Asian/Pacific Islanders, and married, and to have higher levels of education, a normal weight, and a healthier profile of dietary intake. Distributions of women's characteristics across quartiles of AMED and DASH scores were largely similar to those described above (Table 1). Women who completed the FFQ at baseline (n = 1615) were slightly older, more educated, more likely to be non-Hispanic white or Hispanic, and to be married compared with those who did not (n = 365), but they were still largely representative of all women selected for dietary assessment (n = 1980; Supplementary Table 2). Distributions of the dietary pattern scores by quartiles at each study visit are shown in Supplementary Table 3.

TABLE 1.

Characteristics of study participants at enrollment (gestational weeks 8–13, n = 1615) according to quartiles of the AHEI, AMED, and DASH scores, NICHD Fetal Growth Studies–Singletons

AHEI AMED DASH
Q11 (n = 403) Q41 (n = 404) P value2 Q11 (n = 356) Q41 (n = 339) P value2 Q11 (n = 358) Q41 (n = 363) P value2
Age, y 25.9 ± 5.57 30.5 ± 4.84 <0.001 26.4 ± 5.70 29.9 ± 5.29 <0.001 25.5 ± 5.43 30.4 ± 4.84 <0.001
Race-ethnicity <0.001 <0.001 <0.001
 Non-Hispanic white 72 (17.9) 111 (27.5) 73 (20.5) 88 (26.0) 36 (10.1) 136 (37.5)
 Non-Hispanic black 198 (49.1) 60 (14.9) 142 (39.9) 76 (22.4) 198 (55.3) 51 (14.0)
 Hispanic 97 (24.1) 106 (26.2) 102 (28.7) 86 (25.4) 77 (21.5) 106 (29.2)
 Asian/Pacific Islanders 36 (8.9) 127 (31.4) 39 (11.0) 89 (26.3) 47 (13.1) 70 (19.3)
Married 234 (58.2) 355 (87.9) <0.001 237 (66.8) 272 (80.2) <0.001 196 (54.9) 311 (85.7) <0.001
Education <0.001 <0.001 <0.001
 < High school 49 (12.2) 38 (9.4) 52 (14.6) 28 (8.3) 53 (14.8) 31 (8.5)
 High school/equivalent 111 (27.5) 50 (12.4) 99 (27.8) 42 (12.4) 111 (31.0) 38 (10.5)
 Associate degree/some college 148 (36.7) 89 (22.0) 124 (34.8) 90 (26.5) 130 (36.3) 87 (24.0)
 Bachelor's degree 67 (16.6) 111 (27.5) 49 (13.8) 91 (26.8) 44 (12.3) 96 (26.4)
 Graduate degree 28 (6.9) 116 (28.7) 32 (9.0) 88 (26.0) 20 (5.6) 111 (30.6)
Nulliparity 199 (49.4) 188 (46.5) 0.07 151 (42.4) 165 (48.7) 0.21 164 (45.8) 180 (49.6) 0.44
Pre-pregnancy BMI, kg/m2 <0.001 <0.001 0.01
 <25 201 (49.9) 272 (67.3) 173 (48.6) 230 (67.8) 184 (51.4) 230 (63.4)
 25–29 120 (29.8) 86 (21.3) 107 (30.1) 76 (22.4) 106 (29.6) 91 (25.1)
 ≥ 30 82 (20.3) 46 (11.4) 76 (21.3) 33 (9.7) 68 (19.0) 42 (11.6)
Light to vigorous physical activities, MET-h/wk 298 ± 160 289 ± 142 0.57 291 ± 156 309 ± 161 0.10 291 ± 165 306 ± 150 0.28
Sleep duration, h/d 0.22 0.77 0.01
 5–6 72 (18.0) 53 (13.1) 58 (16.3) 45 (13.3) 69 (19.3) 45 (12.4)
 7 53 (13.2) 56 (13.9) 58 (16.3) 46 (13.6) 41 (11.5) 56 (15.4)
 8–9 170 (42.4) 203 (50.2) 157 (44.1) 159 (46.9) 138 (38.7) 178 (49.0)
 10 + 106 (26.4) 92 (22.8) 83 (23.3) 89 (26.3) 109 (30.5) 84 (23.1)
Total energy intake, kcal/d 2052 ± 1085 2088 ± 813 0.003 1663 ± 821 2545 ± 986 <0.001 2242 ± 1150 2242 ± 882 0.03
Vegetables, serving/d 1.89 ± 1.54 5.06 ± 3.31 <0.001 1.91 ± 1.74 5.65 ± 3.26 <0.001 2.30 ± 2.06 5.08 ± 2.93 <0.001
Whole fruit, serving/d 1.69 ± 2.07 4.92 ± 3.29 <0.001 2.01 ± 2.87 5.34 ± 3.89 <0.001 2.21 ± 3.47 5.45 ± 3.72 <0.001
Whole grain, grams/d 17.7 ± 15.2 32.8 ± 23.2 <0.001 14.0 ± 10.9 38.4 ± 21.8 <0.001 15.8 ± 15.4 38.2 ± 21.7 <0.001
Legumes, serving/d 0.17 ± 0.34 0.36 ± 0.49 <0.001 0.12 ± 0.25 0.50 ± 0.63 <0.001 0.16 ± 0.29 0.46 ± 0.64 <0.001
Nuts, serving/d 0.18 ± 0.34 0.45 ± 0.52 <0.001 0.09 ± 0.12 0.63 ± 0.63 <0.001 0.16 ± 0.32 0.60 ± 0.68 <0.001
Red and processed meat, serving/d 0.51 ± 0.39 0.44 ± 0.38 <0.001 0.49 ± 0.35 0.53 ± 0.52 0.28 0.72 ± 0.54 0.34 ± 0.29 <0.001
Fish, serving/d 0.14 ± 0.20 0.48 ± 0.39 <0.001 0.14 ± 0.17 0.51 ± 0.43 <0.001 0.26 ± 0.28 0.33 ± 0.34 0.01
Dairy, serving/d 1.66 ± 1.55 1.72 ± 1.31 0.05 1.42 ± 1.16 1.97 ± 1.39 <0.001 1.33 ± 1.21 2.37 ± 1.70 <0.001
Low-fat dairy, serving/d 0.77 ± 1.43 1.17 ± 1.40 <0.001 0.64 ± 1.04 1.31 ± 1.40 <0.001 0.27 ± 0.70 1.93 ± 1.76 <0.001
Sugar sweetened beverages, serving/d 3.71 ± 4.82 0.79 ± 1.31 <0.001 2.16 ± 3.33 1.71 ± 2.37 0.07 3.47 ± 4.68 1.37 ± 1.83 <0.001
n3 fatty acid (DHA + EPA), mg/d 115 ± 148 363 ± 288 <0.001 108 ± 124 385 ± 314 <0.001 198 ± 203 255 ± 252 0.01
MUFA:SFA ratio 1.10 ± 0.20 1.39 ± 0.34 <0.001 1.08 ± 0.20 1.39 ± 0.29 <0.001 1.16 ± 0.23 1.33 ± 0.34 <0.001
PUFA, % of total energy 6.23 ± 2.05 7.32 ± 1.79 <0.001 6.81 ± 2.41 7.08 ± 1.54 0.03 7.08 ± 2.40 6.66 ± 1.64 0.02
trans-fat, % of total energy 2.10 ± 0.66 1.73 ± 0.49 <0.001 2.07 ± 0.63 1.81 ± 0.53 <0.001 2.20 ± 0.66 1.63 ± 0.51 <0.001
Sodium, mg 2922 ± 1422 3480 ± 1485 0.03 2550 ± 1200 4137 ± 1651 <0.001 3368 ± 1662 3518 ± 1422 0.03
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1

Mean ± SD for continuous variables and frequency (%) for categorical variables.

2

Estimated by chi-square test for categorical variables and analysis of variance test for continuous variables.

AHEI, alternate Healthy Eating Index; AMED, alternate Mediterranean diet; DASH, Dietary Approaches to Stop Hypertension; DHA, docosahexaenoic acid; EPA, eicosapentaenoic acid; NICHD, Eunice Kennedy Shriver National Institute of Child Health and Human Development.

Healthier diet characterized by higher AHEI, AMED, or DASH scores was independently associated with lower risks of pregnancy complications in general, although the magnitude and statistical significance of the associations varied by the timing of dietary assessment and complications. Specifically, for GDM, a higher AHEI score reported at 16–22 wk was significantly related to lower risk [adjusted RR (95% CI), highest (Q4) vs. lowest quartile (Q1): 0.32 (0.16, 0.66), P-trend = 0.002; Table 2]. For gestational hypertension, a higher DASH score reported at both 8–13 [adjusted RR (95% CI), Q4 vs. Q1: 0.45 (0.17, 1.17), P-trend = 0.04] and 16–22 [adjusted RR (95% CI), Q4 vs. Q1: 0.19 (0.05, 0.65), P-trend = 0.01] wk was significantly related to lower risk (Table 3). For preeclampsia, a higher AHEI score reported at 24–29 wk was significantly related to lower preeclampsia risk [adjusted RR (95% CI), Q4 vs. Q1: 0.31 (0.11, 0.87), P-trend = 0.03; Table 3]. For preterm delivery, a higher AMED score reported at 8–13 wk [adjusted RR (95% CI), Q4 vs. Q1: 0.50 (0.25, 1.01), P-trend = 0.03] and DASH score reported at 24–29 wk [adjusted RR (95% CI), Q4 vs. Q1: 0.50, (0.26, 0.96), P-trend = 0.03] were significantly related to lower risk (Table 4). When the 2 subtypes of preterm delivery were examined, all 3 healthy dietary patterns at 8–13 wk were associated with lower risk of spontaneous preterm delivery (P-trend was 0.007 to 0.06; Supplementary Table 4). When the complications were considered together, all 3 healthy dietary patterns reported at 8–13 and 16–22 wk were associated with a lower risk of having any pregnancy complications (P-trend was 0.001 to 0.07; Figure 1). In stratified analyses, results did not differ appreciably by parity (nulliparous, parous) or pre-pregnancy BMI (<25, 25–29, ≥30).

TABLE 2.

Relative risk (95% CI) of GDM1 by quartiles of AHEI, AMED, and DASH, NICHD Fetal Growth Studies–Singletons

Dietary patterns GW Models Total Q1 Q2 Q3 Q4 P-trend
AHEI 8–132 Case/Obs. 72/1483 20/362 16/375 18/369 18/377
Model 14 1.00 0.71 (0.37, 1.34) 0.68 (0.37, 1.27) 0.58 (0.31, 1.09) 0.10
Model 25 1.00 0.49 (0.26, 0.92) 0.51 (0.28, 0.95) 0.60 (0.31, 1.16) 0.17
16–223 Case/Obs. 77/1648 24/416 21/404 21/408 11/420
Model 14 1.00 0.78 (0.44, 1.37) 0.71 (0.40, 1.25) 0.32 (0.16, 0.64) 0.001
Model 25 1.00 0.69 (0.40, 1.20) 0.68 (0.38, 1.21) 0.32 (0.16, 0.66) 0.002
AMED 8–132 Case/Obs. 72/1483 13/324 34/573 15/269 10/317
Model 14 1.00 1.33 (0.72, 2.48) 1.13 (0.55, 2.32) 0.58 (0.26, 1.30) 0.23
Model 25 1.00 1.14 (0.60, 2.14) 1.00 (0.46, 2.15) 0.61 (0.25, 1.48) 0.33
16–223 Case/Obs. 77/1648 29/442 17/403 15/375 16/428
Model 14 1.00 0.61 (0.34, 1.09) 0.54 (0.29, 0.99) 0.46 (0.25, 0.83) 0.008
Model 25 1.00 0.58 (0.32, 1.06) 0.46 (0.23, 0.90) 0.61 (0.33, 1.15) 0.15
DASH 8–132 Case/Obs. 72/1483 13/330 26/467 21/357 12/329
Model 14 1.00 1.13 (0.59, 2.18) 1.13 (0.57, 2.22) 0.61 (0.28, 1.33) 0.21
Model 25 1.00 1.04 (0.55, 1.97) 1.01 (0.52, 1.96) 0.71 (0.32, 1.56) 0.42
16–223 Case/Obs. 77/1648 20/375 23/489 15/386 19/398
Model 14 1.00 0.76 (0.42, 1.36) 0.56 (0.29, 1.08) 0.63 (0.34, 1.17) 0.11
Model 25 1.00 0.58 (0.32, 1.06) 0.46 (0.23, 0.90) 0.61 (0.33, 1.15) 0.15
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1

Models included all women without GDM and women with GDM who were diagnosed after the dietary assessment at respective visits.

2

At this GW interval, AHEI, AMED, and DASH scores were estimated using self-reported diet in the past 3 months assessed by FFQ.

3

At this GW interval, AHEI, AMED, and DASH scores were estimated based on Automated Self-Administered 24-h dietary recall.

4

Log-binomial regression models adjusted for maternal age (y).

5

Log-binomial regression models adjusted for maternal age (y), race (non-Hispanic white, non-Hispanic black, Hispanic, Asian), education (<high school, high school, some college, Bachelor, Graduate), marriage/cohabiting (yes, no), nulliparity (yes, no), pre-pregnancy BMI (kg/m2), family history of diabetes (yes, no), light to vigorous physical activities (MET-h/wk), sleep durations (5–6, 7, 8–9, 10+ h/d), and total energy intake (kcal/d).

AHEI, alternate Healthy Eating Index; AMED, alternate Mediterranean diet; DASH, Dietary Approaches to Stop Hypertension; GDM, gestational diabetes; GW, gestational week; NICHD, Eunice Kennedy Shriver National Institute of Child Health and Human Development.

TABLE 3.

Relative risk (95% CI) of hypertensive disorders of pregnancy by quartiles of AHEI, AMED, and DASH, NICHD Fetal Growth Studies–Singletons

Dietary patterns GW Models Total Q1 Q2 Q3 Q4 P-trend
Gestational hypertension1
AHEI 8–132 Case/Obs. 55/1505 25/374 13/375 12/374 5/382
Model 14 1.00 0.54 (0.28, 1.04) 0.53 (0.27, 1.06) 0.23 (0.09, 0.61) 0.002
Model 25 1.00 0.59 (0.30, 1.15) 0.71 (0.35, 1.46) 0.38 (0.14, 1.04) 0.06
A Case/Obs. 57/1682 18/416 15/419 14/413 10/434
Model 14 1.00 0.87 (0.44, 1.70) 0.85 (0.42, 1.70) 0.61 (0.28, 1.34) 0.23
Model 25 1.00 0.91 (0.47, 1.79) 0.91 (0.46, 1.82) 0.77 (0.34, 1.72) 0.54
24–293 Case/Obs. 58/1652 20/411 18/413 9/407 11/421
Model 14 1.00 0.95 (0.51, 1.77) 0.51 (0.23, 1.12) 0.65 (0.30, 1.38) 0.13
Model 25 1.00 0.92 (0.49, 1.71) 0.48 (0.21, 1.06) 0.80 (0.36, 1.77) 0.27
AMED 8–132 Case/Obs. 55/1505 18/333 26/580 5/273 6/319
Model 14 1.00 0.88 (0.49, 1.59) 0.38 (0.14, 1.02) 0.41 (0.16, 1.04) 0.02
Model 25 1.00 0.86 (0.47, 1.59) 0.40 (0.14, 1.12) 0.47 (0.18, 1.28) 0.08
16–223 Case/Obs. 57/1682 18/447 19/419 11/380 9/436
Model 14 1.00 1.15 (0.61, 2.17) 0.76 (0.36, 1.59) 0.56 (0.25, 1.26) 0.11
Model 25 1.00 1.13 (0.60, 2.10) 0.81 (0.39, 1.70) 0.71 (0.31, 1.61) 0.32
24–293 Case/Obs. 58/1652 17/429 15/407 14/399 12/417
Model 14 1.00 0.98 (0.49, 1.93) 1.00 (0.49, 2.01) 0.86 (0.41, 1.80) 0.73
Model 25 1.00 1.03 (0.52, 2.03) 1.12 (0.55, 2.27) 1.09 (0.51, 2.35) 0.77
DASH 8–132 Case/Obs. 55/1505 22/333 22/478 5/358 6/336
Model 14 1.00 0.75 (0.42, 1.35) 0.23 (0.09, 0.62) 0.32 (0.13, 0.80) 0.001
Model 25 1.00 0.91 (0.51, 1.60) 0.33 (0.12, 0.88) 0.45 (0.17, 1.17) 0.04
16–223 Case/Obs. 57/1682 21/378 18/501 15/399 3/404
Model 14 1.00 0.67 (0.36, 1.24) 0.72 (0.37, 1.39) 0.15 (0.04, 0.49) 0.002
Model 25 1.00 0.82 (0.43, 1.56) 0.79 (0.40, 1.58) 0.19 (0.05, 0.65) 0.01
24–293 Case/Obs. 58/1652 17/368 19/486 13/399 9/399
Model 14 1.00 0.94 (0.49, 1.79) 0.83 (0.40, 1.73) 0.60 (0.26, 1.37) 0.23
Model 25 1.00 0.97 (0.50, 1.86) 1.06 (0.49, 2.29) 0.82 (0.34, 1.96) 0.75
Preeclampsia6
AHEI 8–132 Case/Obs. 52/1502 16/365 16/378 13/375 7/384
Model 14 1.00 1.01 (0.51, 2.00) 0.89 (0.43, 1.85) 0.50 (0.20, 1.25) 0.16
Model 25 1.00 1.24 (0.61, 2.51) 1.14 (0.53, 2.43) 0.77 (0.30, 2.00) 0.71
16–223 Case/Obs. 60/1685 20/418 15/419 21/420 4/428
Model 14 1.00 0.79 (0.41, 1.53) 1.16 (0.63, 2.12) 0.23 (0.08, 0.69) 0.03
Model 25 1.00 0.83 (0.43, 1.60) 1.22 (0.66, 2.24) 0.26 (0.09, 0.78) 0.07
24–293 Case/Obs. 55/1649 19/410 16/411 14/412 6/416
Model 14 1.00 0.88 (0.46, 1.70) 0.82 (0.41, 1.63) 0.37 (0.14, 0.95) 0.05
Model 25 1.00 0.85 (0.44, 1.63) 0.69 (0.34, 1.41) 0.31 (0.11, 0.87) 0.03
AMED 8–132 Case/Obs. 52/1502 13/328 23/577 9/277 7/320
Model 14 1.00 1.07 (0.55, 2.09) 0.92 (0.40, 2.14) 0.65 (0.26, 1.65) 0.44
Model 25 1.00 1.09 (0.54, 2.21) 0.93 (0.38, 2.30) 0.68 (0.25, 1.85) 0.53
16–223 Case/Obs. 60/1685 25/454 7/407 15/384 13/440
Model 14 1.00 0.33 (0.14, 0.75) 0.77 (0.41, 1.45) 0.63 (0.32, 1.22) 0.31
Model 25 1.00 0.30 (0.13, 0.69) 0.77 (0.41, 1.45) 0.67 (0.34, 1.32) 0.43
24–293 Case/Obs. 55/1649 19/431 15/407 14/399 7/412
Model 14 1.00 0.87 (0.45, 1.69) 0.88 (0.45, 1.75) 0.45 (0.19, 1.07) 0.10
Model 25 1.00 0.91 (0.47, 1.75) 1.02 (0.51, 2.04) 0.47 (0.18, 1.21) 0.22
DASH 8–132 Case/Obs. 52/1502 15/326 16/472 14/367 7/337
Model 14 1.00 0.81 (0.40, 1.63) 0.95 (0.46, 1.96) 0.56 (0.22, 1.40) 0.31
Model 25 1.00 0.99 (0.49, 2.02) 1.27 (0.59, 2.74) 0.72 (0.28, 1.89) 0.75
16–223 Case/Obs. 60/1685 21/378 18/501 6/390 15/416
Model 14 1.00 0.70 (0.38, 1.30) 0.32 (0.13, 0.79) 0.79 (0.40, 1.56) 0.26
Model 25 1.00 0.77 (0.41, 1.45) 0.33 (0.13, 0.83) 0.94 (0.45, 1.96) 0.50
24–293 Case/Obs. 55/1649 17/368 16/483 14/400 8/398
Model 14 1.00 0.80 (0.40, 1.57) 0.90 (0.44, 1.84) 0.54 (0.23, 1.27) 0.21
Model 25 1.00 0.70 (0.35, 1.41) 0.82 (0.39, 1.73) 0.56 (0.23, 1.38) 0.25
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1

Models included all normotensive women (i.e., without preeclampsia or gestational hypertension) and women with gestational hypertension.

2

At this GW interval, AHEI, AMED, and DASH scores were estimated using self-reported diet in the past 3 months assessed by FFQ.

3

At this GW interval, AHEI, AMED, and DASH scores were estimated based on Automated Self-Administered 24-h dietary recall.

4

Log-binomial regression models adjusted for maternal age (y).

5

Log-binomial regression models adjusted for maternal age (y), race (non-Hispanic white, non-Hispanic black, Hispanic, Asian), education (<high school, high school, some college, Bachelor, Graduate), marriage/cohabiting (yes, no), nulliparity (yes, no), pre-pregnancy BMI (kg/m2), family history of diabetes (yes, no), light to vigorous physical activities (MET-h/wk), sleep durations (5–6, 7, 8–9, 10+ h/d), and total energy intake (kcal/d).

6

Models included all normotensive women (i.e., without preeclampsia or gestational hypertension) and women with preeclampsia.

AHEI, alternate Healthy Eating Index; AMED, alternate Mediterranean diet; DASH, Dietary Approaches to Stop Hypertension; GW, gestational week; NICHD, Eunice Kennedy Shriver National Institute of Child Health and Human Development.

TABLE 4.

Relative risk (95% CI) of preterm delivery1 by quartiles of AHEI, AMED, and DASH, NICHD Fetal Growth Studies–Singletons

Dietary patterns GW Models Total Q1 Q2 Q3 Q4 P-trend
AHEI 8–132 Case/Obs. 90/1542 21/386 31/388 23/384 15/384
Model 14 1.00 1.43 (0.84, 2.45) 1.03 (0.57, 1.84) 0.64 (0.33, 1.25) 0.14
Model 25 1.00 1.56 (0.89, 2.73) 1.18 (0.64, 2.18) 0.78 (0.39, 1.57) 0.40
16–223 Case/Obs. 107/1724 30/429 30/431 27/431 20/433
Model 14 1.00 1.00 (0.61, 1.63) 0.90 (0.54, 1.50) 0.67 (0.38, 1.18) 0.15
Model 25 1.00 1.08 (0.66, 1.76) 1.01 (0.60, 1.69) 0.76 (0.42, 1.36) 0.34
24–293 Case/Obs. 93/1691 29/425 19/424 28/419 17/423
Model 14 1.00 0.65 (0.37, 1.15) 0.96 (0.58, 1.61) 0.57 (0.31, 1.05) 0.17
Model 25 1.00 0.63 (0.36, 1.12) 0.88 (0.52, 1.50) 0.55 (0.29, 1.04) 0.14
AMED 8–132 Case/Obs. 90/1542 25/343 39/599 12/278 14/322
Model 14 1.00 0.87 (0.53, 1.41) 0.56 (0.28, 1.10) 0.55 (0.29, 1.05) 0.04
Model 25 1.00 0.78 (0.47, 1.30) 0.49 (0.24, 1.01) 0.50 (0.25, 1.01) 0.03
16–223 Case/Obs. 107/1724 25/468 31/423 26/389 25/444
Model 14 1.00 1.38 (0.83, 2.31) 1.27 (0.74, 2.17) 1.08 (0.63, 1.87) 0.86
Model 25 1.00 1.29 (0.78, 2.14) 1.19 (0.70, 2.04) 1.06 (0.61, 1.84) 0.93
24–293 Case/Obs. 93/1691 28/444 16/415 25/410 24/422
Model 14 1.00 0.61 (0.34, 1.12) 0.97 (0.57, 1.64) 0.91 (0.53, 1.56) 0.95
Model 25 1.00 0.65 (0.36, 1.19) 1.01 (0.59, 1.72) 0.92 (0.52, 1.61) 0.93
DASH 8–132 Case/Obs. 90/1542 25/345 25/488 26/369 14/340
Model 14 1.00 0.67 (0.39, 1.15) 0.91 (0.53, 1.55) 0.51 (0.26, 0.98) 0.10
Model 25 1.00 0.70 (0.40, 1.21) 1.00 (0.57, 1.77) 0.56 (0.28, 1.13) 0.21
16–223 Case/Obs. 107/1724 32/393 36/517 15/402 24/412
Model 14 1.00 0.86 (0.54, 1.36) 0.46 (0.25, 0.84) 0.72 (0.42, 1.22) 0.08
Model 25 1.00 0.92 (0.57, 1.48) 0.49 (0.26, 0.93) 0.79 (0.44, 1.40) 0.21
24–293 Case/Obs. 93/1691 29/380 27/498 20/410 17/403
Model 14 1.00 0.69 (0.41, 1.15) 0.61 (0.34, 1.08) 0.52 (0.28, 0.96) 0.03
Model 25 1.00 0.69 (0.40, 1.17) 0.58 (0.32, 1.07) 0.50 (0.26, 0.96) 0.03
Open in a new tab
1

Models included all women who have not given birth at the respective visit.

2

At this GW interval, AHEI, AMED, and DASH scores were estimated using self-reported diet in the past 3 months assessed by FFQ.

3

At this GW interval, AHEI, AMED, and DASH scores were estimated based on Automated Self-Administered 24-h dietary recall.

4

Log-binomial regression models adjusted for maternal age (y).

5

Log-binomial regression models adjusted for maternal age (y), race (non-Hispanic white, non-Hispanic black, Hispanic, Asian), education (<high school, high school, some college, Bachelor, Graduate), marriage/cohabiting (yes, no), nulliparity (yes, no), pre-pregnancy BMI (kg/m2), family history of diabetes (yes, no), light to vigorous physical activities (MET-h/wk), sleep durations (5–6, 7, 8–9, 10+ h/d), and total energy intake (kcal/d).

AHEI, alternate Healthy Eating Index; AMED, alternate Mediterranean diet; DASH, Dietary Approaches to Stop Hypertension; GW, gestational week; NICHD, Eunice Kennedy Shriver National Institute of Child Health and Human Development.

FIGURE 1.

FIGURE 1

Open in a new tab

Relative risk (95% CI)1 of having any pregnancy complications2 by quartiles of AHEI, AMED, and DASH scores, NICHD Fetal Growth Studies–Singletons. The models for weeks 8–13 included 1493 women, of whom 241 had any pregnancy complications; the models for weeks 16–22 included 1636 women, of whom 246 had any pregnancy complications. At each visit, sample sizes are the same across models for AHEI, AMED, and DASH. 1Log-binomial regression models adjusted for maternal age (y), race (non-Hispanic white, non-Hispanic black, Hispanic, Asian), education (<high school, high school, some college, Bachelor, Graduate), marriage/cohabiting (yes, no), nulliparity (yes, no), pre-pregnancy BMI (kg/m2), family history of diabetes (yes, no), light to vigorous physical activities (MET-h/wk), sleep durations (5–6, 7, 8–9, 10+ h/d), and total energy intake (kcal/d). 2Any pregnancy complications include gestational diabetes, gestational hypertension, preeclampsia, and preterm delivery.*P < 0.05. AHEI, alternate Healthy Eating Index; AMED, alternate Mediterranean diet; DASH, Dietary Approaches to Stop Hypertension; GDM, gestational diabetes; NICHD, Eunice Kennedy Shriver National Institute of Child Health and Human Development.

Discussion

In this multi-racial US cohort of pregnant women, healthier dietary patterns during periconception and pregnancy characterized by higher AHEI, AMED, or DASH scores were related to lower risks of common pregnancy complications including GDM, gestational hypertension, preeclampsia, and preterm delivery.

Two intervention studies have examined the effects of dietary counseling based on a Mediterranean-style diet, starting at 12 wk and 18 wk, respectively, on the risks of developing GDM, and both found a significant protective effect (15, 16). However, these studies did not estimate the effect of dietary patterns per se. Most observational studies examining the associations of dietary patterns with GDM risk used an ad hoc, data-driven approach (e.g., principal component analyses) to derive dietary patterns (34). Only 2 observational studies examined the dietary patterns of interest in pregnancy in relation to GDM risk, with inconsistent findings. In 1 study of 1777 US women (17), a modified AHEI measured during the first trimester and late second trimester was not significantly related to GDM risk. However, the study modified the AHEI for pregnancy (i.e., removal of nuts and soy protein components and the addition of folate, iron, and calcium components from food), making it not directly comparable to our study. In the other study with 1076 women in 10 Mediterranean countries (18), the Mediterranean diet before GDM screening was significantly related to a lower GDM risk. The present study uniquely evaluated multiple healthy dietary patterns. Our findings suggest an inverse association between all 3 dietary patterns across visits and GDM risk, given the substantial and consistent RR estimates (this observation also applies to results on other pregnancy complications discussed below), although the estimate was only significant for AHEI at 16–22 wk. Notably, the 3 dietary patterns shared several common components and were highly correlated (r = 0.59–0.72, all Ps < 0.001), suggesting common protective mechanisms for GDM.

Our study presents the first evidence that the 3 healthy dietary pattern scores were generally inversely related to gestational hypertension and preeclampsia risks across visits, with significant associations for DASH during periconception through mid-pregnancy, and AHEI at 24–29 wk, respectively. In the previous study of 1777 US women (17), the modified AHEI in the late second trimester, but not the first trimester, was also significantly related to lower preeclampsia risk.

We were aware of 1 study examining the 3 healthy dietary patterns in pregnancy in relation to preterm delivery risk. In the study of 3143 US women (19), DASH at 26–29 wk was related to a lower risk of preterm delivery. With unique longitudinal data, we found that higher scores of all 3 healthy dietary patterns were generally associated with a lower risk of preterm delivery across visits, with significant associations for AMED score during periconception and early pregnancy and DASH at 24–29 wk.

Recently, systematic reviews have found evidence that dietary patterns before and/or during pregnancy characterized by higher intakes of vegetables, fruits, whole grains, nuts, and legumes, and lower intakes of red and processed meat are associated with lower risks of GDM (35), hypertensive disorders of pregnancy (35), and preterm delivery (36), but not necessarily birthweight outcomes where findings were more inconsistent. The current study, providing original data in a prospective cohort, extends these reviews by specifically focusing on AHEI, AMED, and DASH.

Although the exact molecular mechanisms remain to be elucidated, findings from the present study are biologically plausible. Both the Mediterranean diet and DASH have been linked to a favorable glucose and lipid metabolism profile, lower body weight, and lower systolic and diastolic blood pressure (37). The Mediterranean diet has also been linked to lower levels of inflammation (38) and better endothelial function (39). Similarly, AHEI has been related to lower insulin resistance and triglyceride (40). Insulin resistance existing before pregnancy and induced by pregnancy coupled with relatively inadequate insulin secretion is the main characteristic of GDM (41). Insulin resistance beyond the level characteristic of normal pregnancy (42), inflammation (43), and endothelial dysfunction (44) have been implicated in the etiology of preeclampsia. Inflammation is also one of the major pathways for preterm delivery. Besides mechanisms for the overall dietary patterns, mechanisms may also exist for specific aspects of the diet. For example, lower glycemic load related to AMED (45) may be particularly relevant for a lower GDM risk (46).

Our study has several unique strengths. First, it examined 3 commonly recommended a priori-defined healthy dietary patterns in the USA (47) in relation to risk of multiple common pregnancy complications that collectively affected 16% of pregnancies in our study. Consequently, the results may inform a general dietary strategy to improve pregnancy health. Second, the risk of reverse causation where women modified their diet owing to health concerns was minimized, given that women with major chronic conditions were excluded from the overall cohort, and that in general, dietary data collected before each pregnancy complication were used. Lastly, inclusion of a multi-racial population in the present study makes the findings more generalizable to US pregnant women in general.

Our study also has some potential limitations. First, it has a modest sample size, which may have limited our statistical power to detect significant associations in some instances; larger sample size is particularly needed to better evaluate potential effect modifications by other major risk factors of pregnancy complications. Second, the dietary assessments are likely subject to measurement errors. Among non-pregnant individuals, dietary pattern scores are often derived to reflect habitual diet. Different from non-pregnant individuals, as metabolism constantly changes during pregnancy, diet in pregnancy may be subject to more variations (48, 49). In the present study, an FFQ was administered at 8–13 gestational weeks to characterize habitual diet in the past 3 months covering periconception and the first trimester, which does not capture potential dietary changes due to pregnancy. At subsequent study visits, a 24-h recall was administered to characterize trimester-specific diet, but a single recall is subject to daily dietary variations. However, given the prospective study design, measurement errors for both types of dietary assessments should be largely non-differential and usually result in an unknown degree of attenuation of the diet–disease relations toward the null. Third, the 3 dietary pattern scores are not directly comparable, particularly as AHEI assessed absolute adherence, whereas AMED and DASH assessed adherence relative to this study sample (these scores were based on median or quintile intake values of the study sample). However, instead of absolute pattern scores, the study intended to examine variability in the score and how they relate to the risk of complications. Lastly, it is possible that a very small number of gestational hypertension and preeclampsia cases may have been diagnosed before dietary assessment at 24–29 wk (31–33); however, as women might have adopted a healthier diet as a result of the diagnosis, this cannot explain the observed inverse association of dietary quality with gestational hypertension and preeclampsia risks.

Conclusions

In this multi-racial US cohort of pregnant women, greater adherence to any of the 3 healthy dietary patterns—AHEI, AMED, or DASH—during periconception through the second trimester was associated with lower risks of GDM, gestational hypertension, preeclampsia, and preterm delivery. Future intervention studies initiated in early or before pregnancy are warranted to investigate the effects of healthy dietary patterns on the prevention of common pregnancy complications.

Supplementary Material

nqab145_Supplemental_File
Click here for additional data file. (69KB, docx)

Acknowledgments

The authors’ responsibilities were as follows—CCZ: conceptualized, designed, and oversaw the study; ML: analyzed the data and drafted the manuscript; WAG, RBN, EKC, DAW, JG, and CZ: obtained funding; KLG, JG, WAG, RBN, DWS, EKC, DAW, and CZ: contributed to data acquisition; ML, JG, SNH, SFY, WAG, RBN, DWS, EKC, DAW, KLG, and CZ: contributed to data interpretation and critical revision of the manuscript for important intellectual content; and all authors: read and approved the final version of the manuscript.

The authors report no conflicts of interest.

Notes

This research was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development intramural funding as well as the American Recovery and Reinvestment Act funding (contract numbers HHSN275200800013C, HHSN275200800002I, HHSN27500006, HHSN275200800003IC, HHSN275200800014C, HHSN275200800012C, HHSN275200800028C, HHSN275201000009C, and HHSN275201000001Z).

Supplemental Tables 1–4 and Supplemental Figure 1 are available from the “Supplemental data” link in the online posting of the article and from the same link in the online table of contents at https://academic.oup.com/ajcn/.

Abbreviations used: ACOG, American College of Obstetrician and Gynecologists; AHEI, alternate Healthy Eating Index; AMED, alternate Mediterranean diet; ASA24, Automated Self-Administered 24-hour; DASH, Dietary Approaches to Stop Hypertension; GDM, gestational diabetes; GW, gestational week; HELLP, hemolysis, elevated liver enzymes, and low platelet count; MET, metabolic equivalent; NICHD, Eunice Kennedy Shriver National Institute of Child Health and Human Development; Q1-Q4, 1st-4th quartile.

Contributor Information

Mengying Li, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA.

Jagteshwar Grewal, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA.

Stefanie N Hinkle, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA.

Samrawit F Yisahak, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA.

William A Grobman, Department of Obstetrics and Gynecology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.

Roger B Newman, Department of Obstetrics and Gynecology, Medical University of South Carolina, Charleston, SC, USA.

Daniel W Skupski, Department of Obstetrics and Gynecology, New York-Presbyterian Hospital/Queens, Queens, NY, USA.

Edward K Chien, Department of Obstetrics and Gynecology, Brown University Alpert Medical School, Providence, RI, USA.

Deborah A Wing, Division of Maternal-Fetal Medicine, Department of Obstetrics-Gynecology, University of California School of Medicine, Irvine, CA, USA; Fountain Valley Regional Hospital and Medical Center, Fountain Valley, CA, USA.

Katherine L Grantz, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA.

Cuilin Zhang, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA.

Data Availability

The data, along with a set of guidelines for researchers applying for use of the data, will be posted to a data-sharing site, NICHD Data and Specimen Hub (DASH) at https://dash.nichd.nih.gov/.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

nqab145_Supplemental_File
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Data Availability Statement

The data, along with a set of guidelines for researchers applying for use of the data, will be posted to a data-sharing site, NICHD Data and Specimen Hub (DASH) at https://dash.nichd.nih.gov/.

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