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Dietary energy density but not glycemic load is associated with gestational weight gain^1,2,3

¹ From the Departments of Nutrition (ALD and AMS-R), Epidemiology (AMS-R), and Biostatistics (AH), University of North Carolina School of Public Health and the Carolina Population Center (AMS-R and AH), Chapel Hill, NC

² Supported by the National Institute of Child Health and Human Development, National Institutes of Health (HD37584 and HD39373), the National Institute of Diabetes and Digestive and Kidney Diseases (DK61981 and DK56350), and the Carolina Population Center.

³ Reprints not available. Address correspondence to AM Siega-Riz, 123 West Franklin Street, Carolina Population Center, Chapel Hill, NC 27514. E-mail: am_siegariz{at}unc.edu.

	ABSTRACT

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 
Background: Most pregnant women gain more weight than the ranges recommended. Excessive weight gain is linked to pregnancy complications and to long-term maternal and child health outcomes.

Objective: The objective was to examine the impact of dietary glycemic load and energy density on total gestational weight gain and the weight gain ratio (observed weight gain/expected weight gain).

Design: Data are from 1231 women with singleton pregnancies who participated in the Pregnancy, Infection, and Nutrition Cohort Study. Dietary information was collected at 26–29 wk of gestation with the use of a semiquantified food-frequency questionnaire. Linear regression models were used to estimate the associations between quartiles of glycemic load and energy density with total gestational weight gain and weight gain ratio.

Results: Dietary patterns of pregnant women significantly differed across many sociodemographic and behavioral characteristics, with the greatest contrasts seen for glycemic load. After adjustment for covariates, compared with women in the first quartile consuming a mean dietary energy density of 0.71 kcal/g (reference), women in the third quartile consuming a mean energy density of 0.98 kcal/g gained an excess of 1.13 kg (95% CI: 0.24, 2.01), and women in the fourth quartile consuming a mean energy density of 1.21 kcal/g gained an excess of 1.08 kg (95% CI: 0.20, 1.97) and had an increase of 0.13 (95% CI: 0.006, 0.24) units in the weight gain ratio. All other comparisons of energy intakes were not statistically significant. Glycemic load was not associated with total gestational weight gain or weight gain ratio.

Conclusion: Dietary energy density is a modifiable factor that may assist pregnant women in managing gestational weight gains.

	INTRODUCTION

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 
Studies have shown that most pregnant women gain weight in excess of the recommended gestational weight gain guidelines (1-5) established by the Institute of Medicine (IOM) in 1990 (6). Although advice from health care providers (4, 7), psychosocial factors (8), and physical activity and smoking (9) have been found to influence gestational weight gain, an inability to properly adjust dietary patterns may also be a determinant of inappropriate weight gain during pregnancy.

There is currently little information on the effect of dietary intake on gestational weight gain and on whether women who gain within the appropriate weight range have different dietary patterns than do women who gain weight outside of the IOM recommendations: 28–40 lb (1 lb = 454 g) for women with a prepregnancy body mass index (BMI; in kg/m²) < 19.8, 25–35 lb for women with a prepregnancy BMI of 19.8–26.0, 15–25 lb for women with a prepregnancy BMI > 26.0–29.0/m², and 15 lb for women with a prepregnancy BMI > 29.0). A recent observational study in Iceland showed that women with excessive gestational weight gains (>18 kg for normal-weight women and >12 kg for overweight or obese women) were more likely to eat more sweets early in pregnancy and drink more milk as well as more food late in pregnancy than were women with suboptimal (<12.1 kg for normal-weight women and <7.1 kg for overweight or obese women) and optimal (12.1–18.0 and 7.1–12 kg for overweight or obese women) gestational weight gains (10). Additionally, results from the Stockholm Pregnancy and Weight Development Study showed that women who expressed an increased interest in sweets during pregnancy experienced 1–2-kg greater weight gains than did the other women in the study (11).

Two dietary characteristics thought to be associated with body weight or fat in the nonpregnancy state are energy density (12, 13) and glycemic load (14). Energy density (kcal/g food consumed) is viewed as a main contributor to overeating in the American population, and increased energy density is linked to less satiety per gram of food consumed (15-17) and is a key regulator of food intake regardless of the amount of calories consumed (18). The glycemic load is a measure of both the dietary glycemic index and the amount of carbohydrate intake, which is used to assess the glycemic response of foods. It is a common component of several popular diets and has been associated with weight loss in some studies (19-21). Currently, there is little research on the effects of energy density or glycemic load on gestational weight gain.

The present study used data collected from the Pregnancy, Infection, and Nutrition (PIN) Study to examine the effects of energy density and glycemic load on gestational weight gain. It is hypothesized that total gestational weight gain and the weight gain ratio increase with increasing energy density and glycemic load.

	SUBJECTS AND METHODS

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 
Study population
The data for this analysis was taken from the third cohort of the PIN Study, described elsewhere (22). Briefly, the PIN Study is a longitudinal, prospective investigation of adverse birth outcomes being conducted at selected prenatal clinics in central North Carolina. At their second prenatal visit, women with a gestation 20 wk, aged 16 y, carrying a singleton fetus, planning to continue care at the clinic, and with access to a telephone were eligible to participate. Women in this analysis were recruited from 1 January 2001 to 30 June 2005. A total of 2006 women were recruited, 1773 of whom had pregravid BMI and weight gain information available for this analysis. Some women were recruited into the cohort more than once because of additional pregnancies within the recruitment period. For the current analysis, information from only one of the pregnancies from each woman was used (n = 82 excluded pregnancies). In these instances, the pregnancy with the most complete information or the first pregnancy (when information was complete for both pregnancies) was included in the analysis. Pregnancies that did not result in a live birth (n = 13) and women with missing (n = 396) or implausible (n = 51) dietary information were also excluded. Data from the remaining 1231 pregnancies were used in this analysis. In total, data from 542 pregnancies in the initial sample were excluded from this analysis. Compared with women who were included, higher proportions of excluded women were <24 y of age, were obese, were black, were not married, had a low income, had a low education level, and were smokers.

The PIN Study protocols were reviewed and approved by the Institutional Review Board of the School of Medicine at the University of North Carolina at Chapel Hill. Information on pre- and perinatal factors, including sociodemographic characteristics and medical history, were assessed by interviews, self-administered questionnaires, and information from medical records. Medical charts were abstracted for all women in the cohort to collect data on reproductive history, weight gain, pregnancy complications, and labor and delivery events.

Assessment of primary outcomes
Gestational weight gain was defined as the difference between each woman's pregravid weight, which was self-reported at the time of the first prenatal clinic visit, and her weight measured near the time of delivery. Weight measurements taken at the first prenatal clinic visit were compared with the self-reported pregravid weights to identify biologically implausible weight gains. Women with implausible values had their pregravid weight imputed following previously published methods (1, 22). Pregravid body mass index (BMI; in kg/m²) was then calculated by using imputed pregravid weight and measured height.

The weight gain ratio, according to pregravid BMI status, was calculated as a ratio of observed total weight gain over expected total weight gain up until the last prenatal visit using the weight gain recommendations from the 1990 IOM report as previously described (1, 22, 23). To calculate expected weight gain, the following formula was used: expected first-trimester total weight gain + [(gestational age at time of last weight measurement – 13 wk) x rate of weight gain expected for the second and third trimesters]. The expected total first-trimester weight gains were 3.2, 2.2, 1.0, and 0.5 kg, and the rates were 0.5, 0.4, 0.3, and 0.23 kg/wk for underweight, normal-weight, overweight, and obese women, respectively (6). These rates adjusted for the fact that not all women have a weight measurement at the time of delivery. Cutoffs to determine inadequate and excessive weight gains were based on the IOM BMI-specific recommendations. For example, it is recommended that underweight women gain between 12.5 and 18.0 kg, which corresponds to a ratio of 75% to 110% if the pregnancy is carried to term (40 wk). Thus, underweight women who have a ratio >1.10 would be defined as gaining above the IOM recommendation (excessive) and those who have a ratio <0.75 would be defined as gaining below the IOM recommendation (inadequate).

Assessment of primary exposures
Information on diet during the second trimester was collected at 26–29 wk of gestation via a self-administered 110-item Block-98 food-frequency questionnaire (FFQ), which was modified to include local foods and focus on a 3-mo time frame as well as solicit information concerning portion sizes using a serving-size visual. This FFQ was validated in several populations (24, 25), including the PIN Study. The validity of the FFQ was assessed among 99 women in PIN 1 and in 82 women in PIN 2 by comparing the nutrient results from the FFQ with three 24-h dietary recalls collected at random or on nonconsecutive days. The deattenuated Pearson correlation coefficients between the FFQ and the 24-h dietary recalls for total energy and carbohydrates were 0.32 and 0.44, respectively, for PIN 1 and 0.33 and 0.61, respectively, for PIN 2. The FFQs were analyzed by using DIETSYS+PLUS, version 5.6 (26). The food-composition table for DIETSYS was updated with nutrient values based on data from the third National Health and Nutrition Examination Survey (NHANES III) and from the US Department of Agriculture's 1998 nutrient database (27).

Daily energy intakes in kilocalories and grams were calculated. The number of grams consumed included grams derived from all foods and beverages (grams contributed by water and other noncaloric beverages were excluded from the analyses). Daily energy density (the amount of energy per gram of food consumed) was calculated by dividing daily kilocalories by daily grams. Daily energy densities were calculated for food alone, calorie-containing beverages alone, and food and calorie-containing beverages combined.

Glycemic index values were applied to the FFQ data by the Department of Nutrition's Clinical Research Unit Epidemiology Core using published values (28). Approximately 25% of the questions on the FFQ contained a single food that has a direct match to published values. However, as is often the case with FFQs, there were a number of mixed foods as well as those combined in a single line. One glycemic index value was derived in those situations through calculations that were proportional to the number of foods embedded in each question. From this, the average glycemic index (the average of the glycemic indexes for the foods consumed regularly) and glycemic load (the product of the glycemic index and the carbohydrate content of the foods contributing to it) were calculated.

Physical activity assessment
Data on physical activity patterns were collected by interviewer-administered questionnaires at 17–22 and 27–30 wk and were designed to capture moderate and vigorous activities in the past week. The questionnaire assessed the frequency and duration of all moderate and vigorous physical activities, including activity related to work, recreation, transportation, childcare, and adult care and both indoor and outdoor household activities. The intensity of activity was assessed by using a modified Borg scale (29) to capture the participant's perception of intensity (metabolic equivalents; METs).

Statistical methods
The final analyses were conducted using STATA version 9.2 (30) with information from 1231 women. Variables were assessed as both effect modifiers and confounders. None of the variables were found to be significant effect modifiers. To be included in the analysis as a confounder, variables must have met the following criteria: associated with the exposure (P 0.15), associated with the outcome (P 0.15), and at least ±10% change in the β coefficient when included individually in the regression models. The influence of individual and collective measurements of physical activity during the second and third trimesters (in MET h/wk and h/wk) on the association of gestational weight gain with glycemic load and energy density was examined. Only second-trimester recreational physical activity was identified as a confounder of the association of weight gain ratio with both energy density and glycemic load and was included in multivariate regression models as a continuous variable (MET h/wk). Daily energy intake was regressed on glycemic load to create a residual of energy intake (the part of energy intake not explained by glycemic load). All models that included glycemic load values were adjusted for residual energy intakes. Similarly, models that included energy density were adjusted for energy intake using a residual of energy intake that was created by regressing energy intake on energy density (the part of energy intake not explained by energy density). This method was chosen so that the glycemic load and energy density variables could be directly interpreted.

Glycemic load was evaluated in models as a continuous variable, as a dichotomous variable (using a cutoff of >165 to designate "high intake" as previously used by Salmeron et al; 31), and in quartiles. Energy density was evaluated as both a continuous variable and in quartiles; t tests of means, analyses of variance, and tests of linear trend were used to examine glycemic load and energy density across sociodemographic strata. Tests of linear trend used variables in their continuous forms. The association of glycemic load and energy density with the 2 main outcome variables—total gestational weight gain and weight gain ratio—were modeled by linear regression. The residuals for all linear regression models were assessed for normality by using both a Q-Q plot of the residuals and an RXP plot that compared residuals with predicted values.

	RESULTS

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 
The mean (± SD) total gestational weight gains in this population were 15.4 ± 4.4, 16.6 ± 5.3, 15.5 ± 6.2, and 12.0 ± 7.1 kg for underweight, normal-weight, overweight, and obese women, respectively. The mean weight gain ratio (observed/expected gestational weight gain) was 1.51 ± 0.79, with mean (± SD) values of 0.99 ± 0.27, 1.38 ± 0.43, 1.85 ± 0.73, and 2.01 ± 1.25 for underweight, normal-weight, overweight, and obese women, respectively. The mean (± SD) energy intake for the study population was 2162.0 ± 713.1 kcal, glycemic load was 149.40 ± 54.07, and energy density was 0.94 ± 0.21. Energy density was not significantly correlated with total gestational weight gain or weight gain ratio (r = 0.04 and 0.05, respectively). Similarly, glycemic load was not correlated with total gestational weight gain (r = –0.003), but was weakly correlated with the weight gain ratio (r = 0.06, P = 0.04).

The distribution of the study population by selected sociodemographic characteristics and the mean energy density and glycemic load values for these characteristics are shown in Table 1. Dietary energy density was calculated in 3 ways: foods alone, caloric beverages alone, and foods and caloric beverages combined. Only the energy densities derived from foods and caloric beverages combined were significantly associated with gestational weight gain and are presented here. Dietary energy densities did not differ between women for all of the selected characteristics, except for an inverse relation between energy density and second-trimester recreational activity; more active women than less active women consumed diets lower in energy density (P for trend = 0.02).

The crude and adjusted associations of total gestational weight gain and weight gain ratio with quartiles of energy density are shown in Table 2. The crude values differed slightly from their respective adjusted values; however, the statistical significance of the values remained the same after adjustment (with the exception of the last quartile for total gestational weight gain, which was significant after adjustment). Compared with women in the first quartile who consumed a mean dietary energy density of 0.71 kcal/g (reference), women in the second quartile who consumed a mean energy density of 0.86 kcal/g gained an excess of 0.49 kg (95% CI: –0.40, 1.37), women in the third quartile who consumed a mean energy density of 0.98 kcal/g gained an excess of 1.13 kg (95% CI: 0.24, 2.01), and women in the fourth quartile who consumed a mean energy density of 1.21 kcal/g gained an excess of 1.08 kg (95% CI: 0.20, 1.97). Women in the third and fourth quartiles gained significantly (P = 0.01 and 0.02, respectively) more weight than did women in the first quartile. On average, women in the first, second, third, and fourth quartiles gained 14.46 ± 6.04, 15.31 ± 5.64, 16.10 ± 5.83, and 15.38 ± 6.40 kg, respectively. Regardless of energy density intake, women gained in excess of the IOM guidelines; the weight gain ratios of women in the first, second, third, and fourth quartiles were 1.46 ± 0.73, 1.49 ± 0.74, 1.50 ± 0.74, and 1.61 ± 0.94, respectively. Women in the last quartile of energy density intakes had weight gain ratios that were 0.13 (95% CI: 0.006, 0.24) units greater than those for women in the first quartile (P = 0.04). Energy density was also modeled as a continuous variable, but was not associated with either of the gestational weight gain outcomes (data not shown).

Table 3

	DISCUSSION

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Cross-sectional studies have shown a positive association between energy density and body weight; individuals who reported consuming lower energy-dense diets had lower body weights than did those who reported consuming higher energy-dense diets (32, 33). In the laboratory setting, low-energy-dense meals have been shown to decrease energy intakes and increase satiety compared with high-energy-dense meals (18, 34, 35). The results from the current analysis are consistent with these findings; energy intakes increased across quartiles of energy density, with mean energy intakes of 1927, 2120, 2198, and 2411 kcal for women in the first, second, third, and fourth quartiles, respectively. Women who reported consuming higher dietary energy densities during the second trimester of pregnancy had greater gestational weight gains and weight gain ratios than did women who reported consuming the lowest dietary energy density, independent of energy intake.

In this analysis there was a positive association between pregravid BMI and glycemic load, with obese women consuming diets with the highest glycemic load values, although there was no association between glycemic load and gestational weight gain once we adjusted for confounding. Studies examining the impact of both glycemic index and load on body weight have yielded mixed results, ie, positive associations for both glycemic index and load (36), glycemic index alone (37), as well as no association for either measure (38). Thirty percent calorie-restricted diets of high and low glycemic loads have shown comparable effects on weight loss (39), which suggests that energy intake may completely explain the relation between glycemic index and load and body weight and fatness. However, a recent systematic review of 6 randomized controlled trials found that overweight or obese individuals lost more body mass and total fat mass with low-glycemic-index or low-glycemic-load diets than with control diets, regardless of energy restriction (40). Many of the studies that have examined the association between body weight and glycemic index or glycemic load have differed in type, length, size, diet composition, manipulation of glycemic index or load, and source population characteristics (eg, age and BMI status), which makes it difficult to understand the true association and what, if any, effect measure modifiers exist.

Currently, there is little information in the literature regarding the impact of glycemic load on gestational weight gain. Data from the Camden Study (Camden, NJ), which used a cohort of young, racially diverse, low-income, nondiabetic pregnant women found that high maternal serum insulin concentrations were associated with greater gestational weight gain and postpartum weight retention (41). However, analyses from this cohort found no association between dietary glycemic index (the average of three 24-h dietary recalls) and rate of weight gain (kg/wk) or adequacy of weight gain (42).

Despite the lack of evidence for an association between dietary glycemic load and gestational weight gain, glycemic load has been shown to affect fetal growth (43) and the development of gestational diabetes (43) as well as other nonpregnancy-related health conditions. The current analysis revealed several sociodemographic and behavioral characteristics that are associated with high-glycemic-load diets within a population of pregnant women, specifically, maternal age, race, education, income, marital status, parity, smoking status, and recreational physical activity. This information may prove useful when designing dietary interventions targeted to pregnant women.

One limitation of this study was the use of an FFQ to measure dietary glycemic load and energy density because FFQs are not specifically designed to capture these aspects of the diet. Glycemic load may be difficult to measure with an FFQ because of the inability to accurately assess combinations and portions of foods, both in recipes and during meals, which can affect the overall glycemic effect and carbohydrate amounts of the foods. Measurement of glycemic load via FFQs has been validated in previous studies, which have shown that nutrient intakes assessed by using standardized FFQs are reasonably correlated with those from more detailed methods and provide a valid representation of usual intake for ranking subjects (44-46). Similarly, the ascertainment of energy density from FFQs has been validated against multiple 24-h dietary recalls and was found to be an acceptable measure of energy density (47). Additionally, the original Block questionnaire was validated in a variety of populations, and the modified version used in the PIN study was validated in previous PIN cohorts; therefore, we are confident that both glycemic load and energy density were at least reasonably measured by our FFQ.

Another limitation is that pregravid weights were self-reported. Although self-reported weights have been shown to be reliable, there is a tendency for overweight and obese women to underestimate their weights (48-50). Considering that approximately one-third of the study population reported being overweight or obese before pregnancy, underestimation of pregravid weight may have resulted in an overestimation of gestational weight gains. This bias was likely to have been attenuated by the imputation of pregravid weights when implausible weight gains were found between the self-reported weights and clinically measured weights recorded during the first prenatal visits; however, any bias that remained may have exaggerated the association between energy density and gestational weight gain.

The results from this analysis provide practical information regarding the influence of glycemic load and energy density on gestational weight gain. Glycemic load was not associated with gestational weight gain in this population, but was associated with several sociodemographic and behavioral characteristics. Dietary energy density was associated with energy intakes, total gestational weight gain, and weight gain ratio. Both energy density and glycemic load may prove to be useful modifiable dietary factors in guiding women to choose nutritious foods, such as fruit, vegetables, and whole grains and to promote overall health throughout pregnancy; however, neither factor appears to be sufficient for helping women achieve appropriate weight gains. Future research may seek to investigate dietary patterns and specific food groups as well as other behavioral characteristics that are associated with gestational weight gains within the IOM recommendations.

	ACKNOWLEDGMENTS

 
We thank all of the PIN Postpartum investigators (Kelly Evenson, Nancy Dole, David Savitz, June Stevens, and John Thorp) for obtaining funding and designing the study and Kathryn Carrier for managing the PIN study.

The authors' responsibilities were as follows—AMS-R (Principal Investigator): guided the statistical analysis and writing of the paper; AH (coinvestigator): guided the statistical analysis; and ALD: responsible for the analysis and writing of the paper. None of the authors had any conflicts of interest.

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