Abstract
Background
Birth weight has a U-shaped relation with chronic disease. Diet quality during pregnancy may impact fetal growth and infant birth weight, yet findings are inconclusive.
Objective
We examined the relation between maternal diet quality during pregnancy and infant birth size among women enrolled in a prospective birth cohort.
Methods
Women 18–45 y old with a singleton pregnancy were recruited at 24–28 wk of gestation from prenatal clinics in New Hampshire. Women completed a validated food frequency questionnaire at enrollment. Diet quality was computed as adherence to the Alternative Healthy Eating Index. Infant birth outcomes (sex, head circumference, weight, and length) were extracted from medical records. Weight-for-length z scores, low birth weight, macrosomia, and size for gestational age [small for gestational age (SGA) or large for gestational age (LGA)] were computed. Multivariable regression models fit each outcome on quartiles of diet quality, adjusted for covariates. Models were computed overall and stratified by smoking status.
Results
Analyses included 862 women and infants with complete data. Lower diet quality was associated with lower maternal education, being a smoker, prepregnancy obesity status, and lack of exercise during pregnancy. Overall, 3.4% of infants were born with a low birth weight, 12.1% with macrosomia, 4.6% were SGA, and 8.7% were LGA. In an adjusted model, increased diet quality appeared linearly associated with a reduced likelihood of SGA (P-trend = 0.03), although each quartile comparison did not reach statistical significance. Specifically, ORs for SGA were 0.89 (95% CI: 0.37, 2.15), 0.73 (95% CI: 0.28, 1.89), and 0.35 (95% CI: 0.11, 1.08) for each increasing quartile of diet quality compared to the lowest quartile. Similar trends for SGA were observed among non-smokers (n = 756; P-trend = 0.07). Also among non-smokers, increased diet quality was associated with lower infant birth weight (P-trend = 0.03) and a suggested reduction in macrosomia (P-trend = 0.07).
Conclusions
Increased diet quality during pregnancy was related to a reduced risk of SGA in this cohort of pregnant women from New Hampshire. Additional studies are needed to elucidate the relation between maternal diet quality and macrosomia.
Keywords: fetal origins of disease, childhood obesity, New Hampshire Birth Cohort, pregnancy diet quality, Alternative Healthy Eating Index (AHEI)
Introduction
The prenatal period is a critical time of development that impacts lifelong health (1, 2). Variations in the in utero environment may impact metabolic programming and restrict growth of the fetus and, in turn, predispose an infant to various chronic diseases (1–4). Early research into the effects of fetal programming on lifetime disease risk has used low infant birth weight (<2500 g) as a proxy for restricted intrauterine growth (1, 2). Low birth weight often leads to rapid weight gain in early childhood (i.e., catch-up growth), increasing the risk of obesity (5) and metabolic disorders (6) as an adult. Low birth weight is also associated with an increased risk of several chronic diseases in adulthood, including hypertension (1), metabolic syndrome (7), type 2 diabetes (1), coronary heart disease (1, 8–11), and stroke (9).
Size for gestational age is another proxy measure of intrauterine growth which reflects birth weight relative to gestational age. Infants born small for gestational age (SGA) are those with a birth weight below the 10th percentile given their gestational age at birth. Thus, SGA is sensitive to infants born underweight for their age, even when their birth weight is >2500 g. Like infants born with a low birth weight, infants born SGA may be predisposed to obesity and metabolic disorders as marked by achieving a greater percentage of body fat by age 4 y (12), greater central adiposity and insulin resistance during childhood (13), and increased blood pressure in adolescence (14) as compared to their peers of adequate weight for their gestational age.
There are several risk factors for impaired fetal growth (15), and maternal diet during pregnancy is one modifiable factor. The evidence linking maternal diet to fetal growth has principally focused on undernutrition and nutrient deficiencies among mothers. For example, reduced caloric intake or malnutrition during gestation limits the availability of micro- and macronutrients needed for healthy fetal development (2, 16, 17), and fetal growth may be impaired or restricted as a result (18, 19). While a Western dietary pattern, characterized in part by added sugars, refined carbohydrates, and solid fats, may provide an overabundance of energy, such a dietary pattern is also often nutritionally poor. In a large cohort of 44,612 Danish women, a Western dietary pattern as compared to a more healthful dietary pattern during pregnancy was associated with an increased likelihood of an infant born SGA (20), and in the Generation R birth cohort (21) a poorer diet quality was related to a lower birth weight, although findings from other populations have been mixed (22–24). Additionally, smoking during pregnancy, particularly late in pregnancy (25), relates to restricted fetal growth (26), and it is possible that the effects of diet quality on infant birth size differ by maternal smoking status (23). A higher maternal diet quality was related to increased birth size among women who smoked during pregnancy in 2 Mediterranean cohorts, while that association was inverse or null among nonsmokers; findings were null in one other Mediterranean cohort (23). Studies to assess if diet may modify the effects of smoking on infant birth size among women in the United States are scant.
While research has focused on the effects of dietary quality and low birth weight or SGA, less is known regarding the influence of dietary quality on macrosomia (i.e., birth weight >4000 g) and large for gestational age (LGA, or a birth weight above the 90th percentile given gestational age at birth). Both macrosomia (27, 28) and LGA (29, 30) are associated with adiposity later in life. A Western dietary pattern is closely correlated with other modifiable risk factors for macrosomia and LGA, including prepregnancy weight status, excessive gestational weight gain, and gestational diabetes (31, 32). Surprisingly, little data exist on the associations between maternal dietary quality and the risk of an infant born with macrosomia or LGA, independent of other risk factors (24).
Our goal in this analysis was thus to understand how usual variations in maternal diet quality related to infant birth size among a sample of US women enrolled in the New Hampshire Birth Cohort Study (NHBCS). Diet quality was defined as adherence to the Alternative Healthy Eating Index-2010 (AHEI-2010), which is an evidenced-based diet quality score that is strongly predictive of chronic disease risk in Western populations (33). We hypothesized that increased diet quality would be associated with a reduced risk of low birth weight and SGA, and we examined differences by smoking status. We also examined the effect of dietary quality on macrosomia and LGA, independent of gestational weight gain.
Methods
Data were from the NHBCS, a prospective cohort study to examine the effects of environmental exposures during pregnancy on maternal and child health (34). Briefly, pregnant women aged 18–45 y were recruited from prenatal clinics in New Hampshire beginning in January 2009. Women were enrolled between 24 and 28 wk of gestation, and inclusion criteria included English literacy, a singleton pregnancy, and not planning to move. The NHBCS was initially designed to examine the effects of environmental toxicant exposure on child growth and development, and thus women were also required to use a private, unregulated water system (e.g., private well) at home. Women completed a baseline questionnaire and a validated FFQ at enrollment and a questionnaire mailed 2 wk postpartum. Infant birth outcomes were extracted from medical records. At the time of this analysis, data were available on 1140 women with a live singleton birth. For this analysis we excluded mother-child dyads with unrealistic or missing maternal dietary intake data (n = 64) or missing data on key covariates from the self-reported questionnaires (n = 89); women who were underweight prepregnancy (BMI <18.5) (n = 20); incomplete or unrealistic infant birth outcome data (n = 62); or missing urinary arsenic concentrations (a covariate) (n = 43). Analyses were thus completed on a final sample of 862 mother-infant dyads. Participants provided informed consent, and the study was approved of by the Committee for the Protection of Human Subjects at Dartmouth College.
Diet quality
Women completed a validated FFQ (35) at enrollment (24–28 wk of gestation). Women were asked to complete the questionnaire with respect to their usual dietary intake during pregnancy. Diet quality was assessed as adherence to the AHEI-2010 (33). The AHEI-2010 is appropriate for US samples because it includes food components that are characteristic of a Western dietary pattern (e.g., sugary drinks, red and processed meats, trans fatty acids). Variations of the Healthy Eating Index have been used to assess diet quality during pregnancy among US samples (22, 36, 37). The AHEI-2010 includes 11 dietary components: 7 healthful components to encourage [fruits, vegetables, whole grains, nuts and legumes, long-chain n–3 FAs from foods and supplements, polyunsaturated fats, and moderate alcohol consumption] and 4 components to reduce (sugary beverages—sugar-sweetened beverages and fruit juice, red and processed meats, trans fatty acids, and sodium). All food components are scored from 0 to 10 such that a higher score indicates a healthier intake; specifically, a greater intake of components to increase or a decreased intake of components to reduce. Because women in the NHBCS completed the FFQ during pregnancy, moderate alcohol use was not included in the final score as a healthful food component. Thus, total AHEI-2010 scores in this study were based on 10 components for a final score ranging from 0 to 100.
Infant birth size outcomes
Infant sex and gestational age (weeks), head circumference (centimeters), weight (grams), and length (centimeters) were extracted from medical records. Low birth weight was defined as <2500 g and macrosomia was defined as ≥4000 g. We computed infant weight-for-length z scores, an adiposity metric using the WHO child growth standards (38), and infant size-for–gestational age using age- and sex-adjusted weight percentiles (39). SGA was defined as <10th percentile and LGA was defined as >90th percentile.
Maternal weight status and gestational weight gain
Prepregnancy BMI (in kg/m2) was computed using height abstracted from prenatal medical records and usual weight when not pregnant as self-reported at the baseline visit. Prepregnancy weight status was classified as healthy weight (18.5–24.9), overweight (25–29.9), or obese (≥30). Weights at each prenatal visit were abstracted from prenatal medical records, and gestational weight gain was computed using prepregnancy weight and the last recorded prenatal weight. Gestational weight gain was categorized based on the 2009 Institutes of Medicine recommendations for total weight gain during gestation given prepregnancy BMI (40); the ranges for adequate weight gain are 12.7–18.1 kg (28–40 pounds) for those with a prepregnancy BMI <18.5, 11.3–15.9 kg (25–35 pounds) for those with a prepregnancy BMI 18.5–24.9, 6.8–11.3 kg (15–25 pounds) for those with a prepregnancy BMI 25.0–29.9, and 5.0–9.1 kg (11–20 pounds) for those with a prepregnancy BMI ≥ 30.0. Weight gain below those levels was classified as insufficient, and weight gain above those levels was classified as excessive.
Maternal smoking
Analyses were completed for the sample overall and stratified by maternal smoking status. Women reported their usual smoking status (“How often do you smoke when you are not pregnant? Never, sometimes, or everyday”) on the baseline questionnaire. On the postpartum questionnaire, women also reported if they smoked during their pregnancy; questions were specific to each trimester. Women who never smoked before pregnancy and who did not smoke during pregnancy were classified as nonsmokers, women who usually smoked but did not smoke during pregnancy were classified as former smokers, and women who smoked during pregnancy were classified as current smokers. Self-reported smoking during pregnancy has been demonstrated as valid (41); however, we did not combine women who typically smoked prepregnancy yet did not smoke during the current pregnancy as nonsmokers to account for the possibility of unreported smoking during pregnancy.
Other covariates
Women self-reported their demographics and medical histories at enrollment; medical history included any history of type 1, type 2, or gestational diabetes; any history of hypertension; and number of previous pregnancies. Trained study staff also conducted a medical record review to document preeclampsia status and the results of gestational blood glucose testing conducted as part of routine prenatal care; gestational diabetes was defined based on the results from oral glucose challenge testing using the American Diabetes Association (ADA) criteria (42). Women provided spot urine samples collected at 24–28 wk of gestation that were analyzed for toxicant exposures (34). We have previously reported that low levels of arsenic exposure may impact fetal growth in this population (43), and thus we included arsenic exposure as a covariate. Women self-reported their exercise habits during pregnancy (“Did you exercise during your recent pregnancy? Yes versus no”) on the postpartum questionnaire.
Analyses
Bivariate analyses compared the distribution of maternal demographics, lifestyle characteristics, medical history, and pregnancy characteristics across quartiles of dietary quality; chi-square tests and Fisher's exact tests (when a sample size for a category was ≤5) were used for categorical measures, ANOVAs were used to compare means for normally distributed measures, and Kruskal-Wallis tests were used to compare medians for nonnormally distributed measures. Continuous infant birth size outcomes were compared by maternal smoking status using ANOVA; categorical infant birth size outcomes were compared by maternal smoking status using Fisher's exact tests. Multivariable regression models were used to fit birth size outcomes on quartiles of diet quality for the sample overall and stratified by maternal smoking status. Specifically, linear regression models were used for the continuous outcomes of gestational age, head circumference, weight, length, and weight-for-length z score, and logistic regression models were used for the categorical outcomes of low birth weight, macrosomia, SGA, and LGA. Models were adjusted for covariates that differed by diet quality or were associated with infant birth size and included infant gestational age and sex, and maternal age (continuous), education (categorical), nulliparous status (yes or no), prepregnancy BMI, gestational weight gain (categorical), regular exercise during pregnancy (yes or no), maternal urinary arsenic (continuous, log-transformed), and total daily caloric intake (kilocalories per day). The models for gestational age, low birth weight, macrosomia, SGA, and LGA did not include gestational age as a covariate, and the model for head circumference was also adjusted for delivery mode. We also ran additional logistic regression models for macrosomia and LGA further adjusting for gestational diabetes (yes or no). Because of the low frequency of maternal smoking, we were unable to run adjusted logistic regression models for the categorical outcomes of low birth weight, macrosomia, SGA, and LGA among smokers. Therefore, we conducted a sensitivity analysis for those outcomes by restricting the models to nonsmokers. For main effects, we considered P < 0.05 statistically significant. We also checked if diet quality modified the effect of gestational weight gain on the outcomes of macrosomia and LGA by including an interaction term between diet quality and gestational weight gain; we used a likelihood ratio test to check for statistical significance of that interaction term and considered P < 0.10 statistically significant. In another series of sensitivity analyses, we 1) adjusted all models for moderate alcohol use and 2) restricted all analyses to those without preeclampsia, and findings did not materially change. We also checked for effect modification by infant sex in all models, and we did not find any evidence of differential effects. Thus, models are presented for male and female infants combined. All analyses were conducted with the R statistical programming language (version 3.1) (44).
Results
In this sample of 862 women, median diet quality score was 52.4 (IQR: 43.7, 60.0). Younger maternal age, lower education, smoking during pregnancy, weight status prepregnancy, and lack of exercise during pregnancy were each associated with lower diet quality (Table 1). Infant sex was unevenly distributed by diet quality scores. A history of hypertension was unbalanced by diet quality score, yet the number of women with such a history was small (n = 16). Rates of preclampsia did not differ by diet quality (P = 0.21).
TABLE 1.
Maternal and pregnancy characteristics by quartiles of maternal diet quality among 862 women enrolled in the New Hampshire Birth Cohort Study1
| AHEI-2010 diet quality score | |||||
|---|---|---|---|---|---|
| Q1, | Q2, | Q3, | Q4, | ||
| n = 215 | n = 215 | n = 216 | n = 216 | P value2 | |
| Median AHEI-2010 score | 38.7 | 48.4 | 55.7 | 65.7 | |
| Demographics | |||||
| Age, y | 28.9 ± 5.0 | 30.8 ± 4.5 | 32.0 ± 4.3 | 33.2 ± 4.4 | <0.001 |
| White, non-Hispanic | 204 (94.9) | 208 (96.7) | 210 (97.2) | 212 (98.2) | 0.31 |
| Education | <0.001 | ||||
| High school graduate or less | 90 (41.9) | 65 (30.2) | 43 (19.9) | 33 (15.3) | |
| Some college | 50 (23.3) | 54 (25.1) | 54 (25.0) | 50 (23.2) | |
| College graduate or more | 75 (34.9) | 96 (44.7) | 119 (55.1) | 133 (61.6) | |
| Smoking status3 | <0.0014 | ||||
| Nonsmoker | 171 (79.5) | 193 (89.8) | 190 (88.0) | 202 (93.5) | |
| Former smoker | 19 (8.8) | 10 (4.7) | 20 (9.3) | 10 (4.6) | |
| Smoker | 25 (11.6) | 12 (5.6) | 6 (2.8) | 4 (1.9) | |
| Prepregnancy weight status | 0.05 | ||||
| Healthy weight (BMI: 18.5–24.9) | 100 (46.5) | 120 (55.8) | 123 (56.9) | 126 (58.3) | |
| Overweight (BMI: 25.0–29.9) | 59 (27.4) | 53 (24.7) | 55 (25.5) | 60 (27.8) | |
| Obese (BMI: ≥30.0) | 56 (26.1) | 42 (19.5) | 38 (17.6) | 30 (13.9) | |
| Medical history | |||||
| Any past diabetes | 15 (7.0) | 9 (4.2) | 18 (8.3) | 9 (4.2) | 0.17 |
| Hypertension | 7 (3.3) | 2 (0.9) | 7 (3.2) | 0 (0) | <0.0014 |
| Nulliparous | 80 (37.2) | 92 (42.8) | 90 (41.7) | 91 (42.1) | 0.63 |
| Pregnancy characteristics | |||||
| Gestational weight gain5 | 0.78 | ||||
| Insufficient | 53 (24.7) | 58 (27.0) | 53 (24.5) | 60 (27.8) | |
| Adequate | 27 (12.6) | 22 (10.2) | 18 (8.3) | 20 (9.3) | |
| Excessive | 135 (62.8) | 135 (62.8) | 145 (67.1) | 136 (63.0) | |
| Regular exercise during pregnancy | 92 (42.8) | 126 (58.6) | 136 (63.0) | 168 (77.8) | <0.001 |
| Gestational diabetes | 12 (5.6) | 15 (7.1) | 18 (8.6) | 16 (7.5) | 0.714 |
| Preeclampsia | 4 (1.9) | 3 (1.4) | 10 (4.6) | 6 (2.8) | 0.214 |
| Urinary arsenic (μg/L) | 0.9 (0.1, 4) | 0.5 (0.1, 5.9) | 0.5 (0.1, 1.9) | 0.8 (0.1, 3.4) | 0.35 |
| Infant sex: male | 122 (56.7) | 120 (55.8) | 98 (45.4) | 101 (46.8) | 0.03 |
| Intake,6 kcal/d | 2032 ± 696 | 2038 ± 667 | 2133 ± 744 | 2142 ± 568 | 0.17 |
1Values are n (%), means ± SDs, or medians (IQRs). Percentages for categorical variables sum down the columns, and percentages for binary variables are the percent of participants within each diet quality quartile. AHEI-2010, Alternative Healthy Eating Index, 2010 version; Q, quartile.
2 P values are from ANOVAs for means, Kruskal-Wallis tests for medians, and chi-square or Fisher's exact tests for frequencies.
3Smoking status defined as nonsmoker when not pregnant (nonsmoker), usually a smoker when not pregnant but did not smoke during current pregnancy (former smoker), and smoked anytime during current pregnancy (smoker).
4Fisher's exact test P value.
5Gestational weight gain based on 2009 Institute of Medicine recommendations for total gestational weight gain based on prepregnancy BMI (in kg/m2) (40); the ranges for adequate weight gain are 12.7–18.1 kg (28–40 pounds) for those with a pre-pregnancy BMI <18.5, 11.3–15.9 kg (25–35 pounds) for those with a prepregnancy BMI 18.5–24.9, 6.8–11.3 kg (15–25 pounds) for those with a prepregnancy BMI 25.0–29.9, and 5.0–9.1 kg (11–20 pounds) for those with a prepregnancy BMI ≥30.0. Weight gain below those levels was classified as insufficient, and weight gain above those levels was classified as excessive.
6Measured at 24–28 wk of gestation.
On average (mean ± SD), women were consuming 2086 ± 672 total kilocalories, 3.1 ± 1.9 servings of vegetables, 1.7 ±1.1 servings of fruit, and 1.1 ± 0.8 servings of whole grains/d. About half (44.7%) of women averaged >2300 mg Na/d, and saturated fats made up >10% of the total daily caloric intake for 71.9% of women. Characteristics of dietary intake stratified by quartiles of diet quality are presented in Table 2. Dietary characteristics differed in the expected directions for 9 of the 10 dietary components of the AHEI-2010 score; only sodium intake (milligrams per day) did not differ across quartiles of diet quality score (2350 ± 819 mg/d; P = 0.86). When stratified by maternal smoking status (Supplemental Table 1), smokers had the lowest-quality diet compared to nonsmokers or former smokers as assessed on several indicators of the AHEI-2010, including the lowest intake of fruits (P = 0.002) and whole grains (P = 0.10), and the greatest intake of red and processed meats (P < 0.001), sodium (P = 0.01), and sweet beverages (P < 0.001).
TABLE 2.
Characteristics of dietary intake by quartiles of maternal diet quality among 862 women enrolled in the New Hampshire Birth Cohort study1
| AHEI-2010 diet quality score | |||||
|---|---|---|---|---|---|
| Q1, | Q2, | Q3, | Q4, | ||
| n = 215 | n = 215 | n = 216 | n = 216 | P value2 | |
| Median AHEI-2010 score | 38.7 | 48.4 | 55.7 | 65.7 | |
| AHEI dietary components | |||||
| Vegetables, servings/d | 2.0 ± 1.2 | 2.8 ± 1.5 | 3.4 ± 1.8 | 4.3 ± 2.2 | <0.001 |
| Fruits, servings/d | 1.0 ± 0.7 | 1.4 ± 1 | 1.8 ± 1 | 2.3 ± 1.1 | <0.001 |
| Whole grains, servings/d | 0.7 ± 0.5 | 1.0 ± 0.8 | 1.2 ± 0.8 | 1.6 ± 1 | <0.001 |
| Nuts, legumes, and soy, servings/d | 0.4 ± 0.4 | 0.7 ± 0.5 | 1.0 ± 0.7 | 1.6 ± 1 | <0.001 |
| Long-chain n–3 fats (EPA + DHA), mg/d | 70.4 ± 94.8 | 128.7 ± 151.9 | 207.0 ± 222.3 | 342.6 ± 309.6 | <0.001 |
| PUFA, % energy | 5.5 ± 1 | 5.8 ± 1 | 6.2 ± 1 | 6.8 ± 1 | <0.001 |
| trans Fatty acids, % energy | 1.2 ± 0 | 1.1 ± 0 | 1.0 ± 0 | 0.9 ± 0 | <0.001 |
| Red and processed meat, servings/d | 1.1 ± 0.7 | 0.9 ± 0.5 | 0.8 ± 0.5 | 0.5 ± 0.4 | <0.001 |
| Sodium, mg/d | 2328 ± 848.5 | 2325 ± 765.3 | 2382 ± 914.4 | 2364 ± 742.7 | 0.86 |
| Sweet beverages,3 servings/d4 | 1.8 ± 1.5 | 1.4 ± 1.4 | 1.2 ± 1.1 | 0.6 ± 0.6 | <0.001 |
| Other dietary characteristics | |||||
| Total daily caloric intake, kcal/d | 2032 ± 695 | 2038 ± 667 | 2133 ± 744 | 2142 ± 568 | 0.17 |
| % Total energy/d | |||||
| Carbohydrates | 52.6 ± 6.0 | 52.5 ± 6.0 | 51.7 ± 6.0 | 52.1 ± 7.0 | 0.40 |
| Protein | 16.2 ± 3 | 16.7 ± 3 | 16.8 ± 3 | 16.9 ± 3 | 0.04 |
| Total fat | 32.3 ± 5 | 32 ± 5 | 32.9 ± 5 | 33 ± 6 | 0.09 |
| Saturated fat | 12.2 ± 2 | 11.6 ± 2 | 11.4 ± 2 | 10.5 ± 2 | <0.001 |
| Fiber, g/d | 17 ± 5.8 | 21.1 ± 6.6 | 24.3 ± 7.8 | 30.5 ± 9.8 | <0.001 |
| Fish and seafood, servings/d | 0.1 ± 0.1 | 0.2 ± 0.1 | 0.2 ± 0.2 | 0.2 ± 0.2 | <0.001 |
| All dairy, servings/d | 3.7 ± 2.3 | 3.7 ± 2 | 3.8 ± 2 | 3.6 ± 1.7 | 0.81 |
| Low-fat dairy, servings/d | 1.6 ± 1.7 | 1.7 ± 1.4 | 1.8 ± 1.3 | 1.6 ± 1.2 | 0.62 |
| Sugar-sweetened beverages excluding | 1 ± 1.3 | 0.6 ± 0.9 | 0.3 ± 0.5 | 0.1 ± 0.3 | <0.001 |
| 100% fruit juice, servings/d4 | |||||
| 100% fruit juice, servings/d4 | 0.8 ± 0.8 | 0.8 ± 0.9 | 0.8 ± 0.9 | 0.4 ± 0.5 | <0.001 |
| Average alcohol intake4 | 0.22 | ||||
| 0 servings/d | 55 (25.6%) | 69 (32.1%) | 73 (33.8%) | 72 (33.3%) | |
| >0 servings/d | 160 (74.4%) | 146 (67.9%) | 143 (66.2%) | 144 (66.7%) | |
1Values are means ± SDs or n (%) except for median AHEI-2010 scores. AHEI-2010, Alternative Healthy Eating Index, 2010 version; Q, quartile.
2 P values are based on ANOVAs for means or chi-square tests for proportions.
3Sweet beverages include sugar-sweetened beverages and 100% fruit juice.
41 serving of sugar-sweetened beverages was defined as 1 glass, bottle or can; 1 serving of fruit juice was defined as 1 small glass; 1 serving of alcohol was equivalent to 14 g alcohol.
Most women (87.0%) gave birth at or later than 38 wk of gestation. Mean birth weight and length were 3449 ± 507 g and 50.8 ± 2.7 cm, respectively. A small percentage (3.4%, n = 29) of infants were born with a low birth weight, 12.1% (n = 104) with macrosomia, 4.6% (n = 40) of infants were SGA and 8.7% (n = 75) were LGA. Among infants born SGA, 27.5% (n = 11) had a low birth weight. Among infants born LGA, 78.7% (n = 59) had macrosomia. There were no statistically significant differences in any birth size outcome by maternal smoking status in unadjusted analyses (all P values >0.14).
Table 3 presents linear regression models fitting each continuous birth size outcome to diet quality, adjusted for total caloric intake and child and maternal characteristics. In the full sample, there were no significant associations between quartiles of diet quality and gestational age (P-trend = 0.15), head circumference (P-trend = 0.85), birth weight (P-trend = 0.57), birth length (P-trend = 0.80) or weight-for-length z score (P-trend = 0.76). However, when stratified by maternal smoking status, there was a linear trend of decreased infant birth weight over increasing quartiles of diet quality among nonsmokers (P-trend = 0.03), although none of the individual quartile comparisons were significant at the P < 0.05 level. For example, infants born to nonsmoking women in the highest quartile of diet quality weighed an average of 34.0 ± 47.2 g less than those born to nonsmoking women in the lowest quartile of diet quality (P = 0.47). Conversely, while there were no statistically significant relations between diet quality and birth weight in former or current smokers, it appeared that birth weight tended to increase with increasing quartiles of diet quality in these subgroups. Findings were similar when combining former and current smokers together (data not shown, P-trend = 0.13). There were no other significant associations between diet quality and the continuous birth size outcomes overall or by maternal smoking status (all P-trend > 0.15).
TABLE 3.
Differences in group means for adjusted measures of infant birth size by quartiles of maternal diet quality among 862 women enrolled in the New Hampshire Birth Cohort study, overall and stratified by maternal smoking status1
| AHEI-2010 diet quality score | |||||
|---|---|---|---|---|---|
| Q1 | Q2 | Q3 | Q4 | P-trend | |
| Median AHEI-2010 score | 38.7 | 48.4 | 55.7 | 65.7 | |
| Overall (n = 862) | |||||
| Gestational age,2 wk | Referent | 0.00 ± 0.16 | 0.19 ± 0.16 | 0.18 ± 0.17 | 0.15 |
| Head circumference,3 cm | Referent | 0.06 ± 0.20 | –0.03 ± 0.20 | 0.04 ± 0.21 | 0.85 |
| Birth weight, g | Referent | –7.9 ± 40.9 | 3.0 ± 42.3 | –12.1 ± 42.3 | 0.48 |
| Birth length, cm | Referent | –0.16 ± 0.24 | 0.20 ± 0.25 | –0.21 ± 0.26 | 0.76 |
| Weight-for-length z score4 | Referent | 0.11 ± 0.14 | –0.09 ± 0.15 | 0.13 ± 0.15 | 0.71 |
| Nonsmokers (n = 756)5 | |||||
| Gestational age,2 wk | Referent | –0.09 ± 0.18 | 0.08 ± 0.18 | 0.18 ± 0.19 | 0.22 |
| Head circumference,3 cm | Referent | 0.07 ± 0.22 | –0.11 ± 0.23 | 0.00 ± 0.23 | 0.75 |
| Birth weight, g | Referent | –5.3 ± 44.4 | –21.5 ± 45.8 | –34.0 ± 47.2 | 0.03 |
| Birth length, cm | Referent | –0.17 ± 0.26 | 0.06 ± 0.27 | –0.35 ± 0.28 | 0.38 |
| Weight-for-length z score4 | Referent | 0.15 ± 0.16 | –0.06 ± 0.16 | 0.16 ± 0.17 | 0.63 |
| Former smokers (n = 59)5 | |||||
| Gestational age,2 wk | Referent | –0.09 ± 0.6 | 0.58 ± 0.5 | –0.61 ± 0.63 | 0.65 |
| Head circumference,3 cm | Referent | 1.26 ± 0.57 | 1.68 ± 0.48** | 0.94 ± 0.61 | 0.43 |
| Birth weight, g | Referent | 18.9 ± 190.7 | 356.5 ± 162.3** | 304.3 ± 204.1 | 0.17 |
| Birth length, cm | Referent | –0.24 ± 1 | 1.87 ± 0.85** | 1.38 ± 1.07 | 0.26 |
| Weight-for-length z score4 | Referent | 0.23 ± 0.62 | –0.17 ± 0.53 | –0.02 ± 0.67 | 0.70 |
| Smokers (n = 47)5 | |||||
| Gestational age,2 wk | Referent | 0.45 ± 0.5 | 1.59 ± 0.73** | –0.93 ± 0.86 | 0.76 |
| Head circumference,3 cm | Referent | –0.14 ± 0.61 | –0.86 ± 0.94 | –0.15 ± 1.06 | 0.66 |
| Birth weight, g | Referent | 1.3 ± 159.5 | 19.0 ± 244.3 | 250.8 ± 275.0 | 0.17 |
| Birth length, cm | Referent | –0.18 ± 0.87 | –0.81 ± 1.33 | 1.62 ± 1.49 | 0.44 |
| Weight-for-length z score4 | Referent | 0.10 ± 0.56 | 0.57 ± 0.85 | –0.19 ± 0.96 | 0.92 |
1Values represent the adjusted beta coefficient from the linear regression model ± SE. Models adjusted for infant gestational age and sex, and maternal age (continuous), education (categorical), nulliparous status, prepregnancy BMI, gestational weight gain (categorical), physical activity during pregnancy (any vs. none), smoking status during pregnancy (nonsmoker, former, or current smoker), maternal urinary arsenic (log-transformed), and total daily caloric intake (kilocalories per day). *P < 0.10; **P < 0.05. AHEI-2010: Alternative Healthy Eating Index, 2010 version; Q, quartile.
2Model not adjusted for gestational age.
3Model also adjusted for delivery mode (cesarean vs. vaginal).
4Weight-for-length z score computed using WHO infant and child growth standards.
5Smoking status defined as nonsmoker when not pregnant (nonsmoker), usually a smoker when not pregnant but did not smoke during current pregnancy (former smoker), and smoked anytime during current pregnancy (smoker).
Table 4 presents the adjusted logistic regression models for low birth weight, macrosomia, SGA, and LGA. Among all participants, there were no statistically significant associations between diet quality and low birth weight (P-trend = 0.95), macrosomia (P-trend = 0.21), or LGA (P-trend = 0.28). There was a statistically significant trend for a decreased likelihood of SGA with increasing dietary quality (P-trend = 0.03), although none of the quartile comparisons were statistically significant at the P < 0.05 level. For example, when compared to women in the lowest quartile of diet quality, women in the highest quartile had 65% decreased odds of having an infant born SGA (P = 0.07). When analyses were limited to nonsmokers, diet quality remained unrelated to low birth weight (P-trend = 0.66) and LGA (P-trend = 0.25). The possible linear trend between increasing quartiles of diet quality and the decreased likelihood of SGA remained (P-trend = 0.04), although, again, none of the individual quartile comparisons were statistically significant. Finally, there was a suggestive linear trend among nonsmokers between increasing quartiles of diet quality and a decreased likelihood of macrosomia (P-trend = 0.07), yet none of the individual quartile comparisons were statistically significant at the P < 0.05 level. Because of the low number of infants born with low birth weight, macrosomia, SGA, or LGA among former smokers and smokers, adjusted logistic regression models were not possible among these subsets.
TABLE 4.
Adjusted likelihood of low birth weight, macrosomia, SGA, and LGA by quartiles of maternal diet quality among 862 women enrolled in the New Hampshire Birth Cohort study, overall and for nonsmokers1
| AHEI-2010 diet quality score | |||||
|---|---|---|---|---|---|
| Q1 | Q2 | Q3 | Q4 | P-trend | |
| Median AHEI-2010 score | 38.7 | 48.4 | 55.7 | 65.7 | |
| Overall (n = 862) | |||||
| Low birth weight (<2500 g) | Referent | 1.15 (0.38, 3.49) | 0.54 (0.14, 2.11) | 1.20 (0.34, 4.24) | 0.95 |
| Macrosomia (>4000 g) | Referent | 0.79 (0.43, 1.46) | 0.88 (0.48, 1.63) | 0.76 (0.39, 1.46) | 0.21 |
| SGA | Referent | 0.89 (0.37, 2.15) | 0.73 (0.28, 1.89) | 0.35 (0.11, 1.08)* | 0.03 |
| LGA | Referent | 1.20 (0.62, 2.33) | 0.86 (0.42, 1.79) | 0.71 (0.32, 1.57) | 0.28 |
| Nonsmokers (n = 756) | |||||
| Low birth weight (<2500 g) | Referent | 1.14 (0.35, 3.73) | 0.79 (0.2, 3.22) | 1.27 (0.32, 4.97) | 0.66 |
| Macrosomia (>4000 g) | Referent | 0.78 (0.41, 1.48) | 0.67 (0.34, 1.30) | 0.65 (0.32, 1.29) | 0.07 |
| SGA | Referent | 0.78 (0.28, 2.14) | 0.78 (0.27, 2.27) | 0.44 (0.13, 1.47) | 0.04 |
| LGA | Referent | 1.24 (0.62, 2.49) | 0.79 (0.36, 1.70) | 0.60 (0.26, 1.38) | 0.25 |
1Values represent the adjusted OR from the logistic regression model (95% CI). Models adjusted for infant sex and maternal age (continuous), education (categorical), nulliparous status, prepregnancy BMI, gestational weight gain (categorical), physical activity during pregnancy (any vs. none), smoking status during pregnancy (nonsmoker, former or current smoker—for the sample overall), maternal urinary arsenic (log-transformed), and total daily caloric intake (kilocalories per day). *P = 0.07. AHEI-2010, Alternative Healthy Eating Index, 2010 version; LGA, large for gestational age; Q, quartile; SGA, small for gestational age.
In the adjusted logistic regression models (Table 4), there were no statistically significant main effects of total daily caloric intake on any outcome (data not shown, all P > 0.16) except for a positive association with macrosomia among nonsmokers (OR per 100-kcal increase/d: 1.03; 95% CI: 1.00, 1.07; P = 0.05). Prepregnancy weight was also not associated with low birth weight, macrosomia, SGA, or LGA (data not shown), in models where prepregnancy weight was modeled as continuous (BMI) or as categoric weight status. In the series of sensitivity analyses that were further adjusted for gestational diabetes status, there were no main effects between gestational diabetes status and macrosomia or LGA (data not shown; all P > 0.64). However, in the adjusted logistic regression models for macrosomia and LGA (Table 4), excessive compared with adequate gestational weight gain was associated with an increased likelihood of macrosomia among all participants (OR: 2.40; 95% CI: 1.35, 4.27; P = 0.003) and nonsmokers (OR: 2.20; 95% CI: 1.22, 3.96; P = 0.009), as well as an increased likelihood of LGA among all participants (OR: 2.21; 95% CI: 1.13, 4.30; P = 0.02) and nonsmokers (OR: 2.07; 95% CI: 1.04, 4.08; P = 0.04). Among all women and nonsmokers, there was no evidence for diet quality modifying the effect of gestational weight gain on macrosomia (P-interactions = 0.95, 0.95, respectively) or LGA (P-interactions = 0.24, 0.21, respectively).
Discussion
In this sample of US women from New Hampshire who were primarily white and non-Hispanic, we observed a trend between better diet quality and a decreased likelihood of an infant born SGA, independent of total caloric intake and other maternal characteristics. However, we did not detect an association with low birth weight itself as an outcome. SGA reflects restricted growth given gestational age at birth, and thus SGA is likely more sensitive to intrauterine growth restriction than is low birth weight alone. Taken together, our findings support the idea that diet quality during pregnancy may influence intrauterine growth.
There are few data on the associations between dietary patterns during pregnancy and infant birth size, yet our findings align with those from other cohorts. In a Danish cohort, an empirically defined “health conscious” dietary pattern was linearly associated with a decreased likelihood of SGA (20). Also, in a Mediterranean cohort, increased adherence to a Mediterranean dietary pattern was related to decreased likelihood of fetal growth restriction (using a customized measure adjusted for gestational age) (23); it should be noted that in other cohorts there were no associations between diet quality and fetal growth restriction (23) or SGA (24). SGA has been related to increased adiposity among infants (21) and children (12, 13), and diet quality during pregnancy has been associated with adiposity in childhood. For example, in a Colorado cohort, increased diet quality—defined as greater adherence to the Healthy Eating Index—was related to a decreased fat mass percentage among infants yet not related to birth weight (22), and greater adherence to the Mediterranean diet pattern during pregnancy was associated with decreased child adiposity at age 4 y (45). Taken together, the accumulating data support the idea that a quality diet during pregnancy may reduce a child's risk of SGA and excess adiposity early in life.
We measured diet quality using the AHEI-2010, and diet quality as measured with this index has been related to a reduced risk of several chronic diseases prospectively (33), including cardiovascular disease and type 2 diabetes. Higher scores reflect a greater dietary intake of nutrient-rich foods high in antioxidants, fiber, and unsaturated fats, and a lower intake of foods high in added sugars, saturated fats, trans fatty acids, and sodium. Such a dietary pattern is related to improved blood glucose control (46) and reduced systemic inflammation in adults (46–48) and among women during gestation (21). Oxidative stress experienced during pregnancy is associated with reduced birth weight (49), and a higher-quality diet that includes high amounts of antioxidants may offset that oxidative stress (50). A diet marked by lower intakes of trans fats—also a component of the AHEI-2010—may also impact fetal growth, as an increased intake of trans fats during gestation has also been related to a lower birth weight (51). Furthermore, a Western dietary pattern has been associated with biomarkers of inflammation (52), and such a dietary pattern may compound the oxidative stress experienced during pregnancy.
Among nonsmokers, diet quality also appeared linearly related to decreased birth weight and possibly a reduced likelihood of an infant born with macrosomia, suggesting that a good-quality diet during pregnancy may reduce the risk both of SGA and macrosomia. In addition to countering the effects of oxidative stress, a higher-quality diet may improve insulin resistance (53). For example, there is strong evidence that a higher-quality diet during pregnancy may reduce the risk of gestational diabetes (54). Given that gestational diabetes is marked by poor blood glucose control and insulin resistance and is a risk factor for macrosomia (32), it is possible that a dietary pattern that improves insulin resistance during pregnancy also reduces the risk for excess birth weight and macrosomia. Given the small number of women who developed gestational diabetes in our study, we were not able to elucidate if diet influences birth weight by affecting risk of gestational diabetes. Additional studies are needed to address that question.
In the United States, ∼50% of pregnant women are overweight or obese prepregnancy (55), and 48% of women exceed the recommended level of weight gain during pregnancy (56)—2 important risk factors for having an infant born with macrosomia or LGA (57, 58). In our study, 46% of women were overweight or obese prepregnancy and 64% exceeded the recommended levels of weight gain during their pregnancy. There was no evidence that prepregnancy weight affected macrosomia or LGA occurrence in our cohort, although there were strong positive effects between excessive gestational weight gain and an increased odds of macrosomia and LGA. Although diet quality did not appear to modify the relation between gestational weight gain and macrosomia or LGA in our sample, we found that a higher diet quality was related to reduced birth weight and possibly a reduced risk of macrosomia among nonsmokers, independent of gestational weight gain. Given the borderline statistical significance of our findings related to macrosomia, more studies are needed to better understand the role of diet quality on this outcome.
Unfortunately, the average diet quality among US women of childbearing age is poor. In a US cohort of 7511 previously nulliparous pregnant women, 34% of women's total caloric intakes early in pregnancy were from added sugars and solid fats (59). Overall, the dietary quality of women in this study was below national recommendations (60). In our analysis, we did not identify any particular dietary component driving AHEI-2010 diet quality scores; 9 of the 10 dietary components differed in the expected direction across quartiles of diet quality. Thus, continued efforts are needed to improve women's diet quality during pregnancy and focusing on dietary patterns rather than individual food components that may be most beneficial.
We found that diet quality may have had differential effects on infant birth size depending on maternal smoking status. While an inverse association with birth weight was evident among nonsmokers, there was a suggestion that a higher diet quality may be positively associated with birth weight among former and current smokers. A differential effect of diet quality on birth weight by smoking status is consistent with findings from 2 European cohorts (23) where increased diet quality, measured as a greater adherence to a Mediterranean dietary pattern, was associated with increased infant head circumference, birth weight, and length only among women who smoked during their pregnancy. Future studies that include biomarkers of smoking and exposure to cigarette smoke may provide a more precise understanding of how diet quality and smoking might interact in relation to infant birth size.
In our study, we did not assess infant fat mass or fat free mass. Additionally, the link between maternal undernutrition and a child's lifetime risk of cardiovascular disease may not be completely dependent on restricted fetal growth (4). Thus, it is critical to prospectively track children to determine how maternal dietary intake during gestation impacts risk factors for metabolic and cardiovascular disease independent of birth weight. For example, greater maternal adherence to a Mediterranean dietary pattern during gestation was related to lower child blood pressure at age 7 y, independent of child birth weight or child BMI at that age (45).
There are several strengths to this study. Our cohort included relatively healthy women with uncomplicated pregnancies, and thus our findings are relevant to a wide population of women in Westernized societies. Diet quality was assessed with a valid FFQ, and our assessment of diet quality used an evidence-based score with documented associations with chronic disease risk. Outcome data were collected via medical records. Nevertheless there are some study limitations to note. Foremost, while we report statistically significant linear trends between better diet quality and decreased birth weight among nonsmokers and SGA among all women and the subset of nonsmokers, none of the quartile comparisons in those analyses reached statistical significance at the P < 0.05 level. Additionally, given the small number of SGA events in our sample (n = 40), we were underpowered to detect significant effects between diet quality and SGA. However, our findings are consistent with the few other studies examining maternal diet quality during gestation and infant birth size. There are other limitations to note. Our sample was primarily white non-Hispanic. Thus, other studies are needed to confirm that the associations we report are evident in other, more diverse samples. However, various aspects of our findings align with the findings from other birth cohorts, which supports that the effects observed are replicable and generalizable across populations. As another limitation, we only assessed diet quality at 24–28 wk of gestation and we cannot confirm diet patterns remained stable during gestation. However, data on dietary patterns throughout pregnancy among a cohort of 12,572 women in the United Kingdom (61) support that dietary patterns are stable throughout pregnancy.
In summary, in this study of US women enrolled in the NHBCS, there was a linear trend between better maternal diet quality during pregnancy and a decreased risk of SGA. Additional studies are needed to confirm these findings and to specifically better understand the associations between diet quality during pregnancy and the risk of macrosomia. Findings highlight the importance of supporting women in their efforts to follow a quality dietary pattern during pregnancy as one way to reduce children's lifetime risk of chronic disease.
Supplementary Material
Supplemental Table 1 is available from the “Supplementary 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/jn/.
Acknowledgments
We thank Jenna Schiffelbein, RD, for her insightful review. The authors’ responsibilities were as follows—JAE, DG-D, and MRK: designed the research; ERB and MRK: conducted the research; MRK: provided essential materials; JAE: analyzed the data and performed statistical analysis; JAE and DG-D: wrote the paper; JAE, DG-D, and MRK: had primary responsibility for final content; and all authors: read and approved the final manuscript.
Funding for this research was provided by National Institutes of Health (NIH) National Institute of Environmental Health Sciences P42ES007373 and P01ES022832, NIH National Institute of General Medical Sciences grants P20GM104416, Environmental Protection Agency RD83544201. None of the funding bodies had any role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Author disclosures: JAE, MRK, ERB, and DG-D, no conflicts of interest.
Abbreviations used
- AHEI-2010
Alternative Healthy Eating Index, 2010 version
- LGA
large for gestational age
- SGA
small for gestational age
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Supplementary Materials
Supplemental Table 1 is available from the “Supplementary 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/jn/.