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American Journal of Clinical Nutrition, Vol. 69, No. 4, 697-704, April 1999
© 1999 American Society for Clinical Nutrition

Original Research Communications

Longitudinal assessment of energy balance in well-nourished, pregnant women1,2,3

Lori E Kopp-Hoolihan, Marta D van Loan, William W Wong and Janet C King


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Background: Clinicians often recommend an additional energy intake of 1250 kJ/d to their pregnant patients. Previous studies have shown considerable variation in the metabolic response to pregnancy and thus in the additional energy required to support a pregnancy.

Objective: The purpose of this study was to assess how well-nourished women meet the energy demands of pregnancy and to identify factors that predict an individual's metabolic response.

Design: Resting metabolic rate (RMR), diet-induced thermogenesis (DIT), total energy expenditure (TEE), activity energy expenditure (AEE), energy intake (EI), and body fat mass (FM) were measured longitudinally in 10 women preconception; at 8–10, 24–26, and 34–36 wk of gestation; and 4–6 wk postpartum.

Results: Compared with preconception values, individual RMRs increased from 456 to 3389 kJ/d by late pregnancy. DIT varied from -266 to 110 kJ/meal, TEE from -105 to 3421 kJ/d, AEE from -2301 to 2929 kJ/d, EI from -259 to 2176 kJ/d, and FM from a 0.6-kg loss to a 10.6-kg gain. The only prepregnant factor that predicted FM gain was RMR (r = 0.65, P < 0.05). Women with the largest cumulative increase in RMR deposited the least FM (r = -0.64, P < 0.05).

Conclusions: Well-nourished women use different strategies to meet the energy demands of pregnancy, including reductions in DIT or AEE, increases in EI, and deposition of less FM than anticipated. The combination of strategies used by individual women is not wholly predictable from prepregnant indexes. The use of a single recommendation for increased energy intake in all pregnant women is not justified.

Key Words: Pregnancy • energy expenditure • resting metabolic rate • diet-induced thermogenesis • body composition • women • San Francisco


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
The total energy cost of pregnancy can be divided into 3 parts: the obligatory need for energy deposited in the products of conception, maternal fat storage, and the extra energy needed for basal metabolism to maintain newly synthesized tissues. The estimated energy requirement during a full-term pregnancy, in excess of a woman's nonpregnant needs, is {approx}335 MJ (1). Only {approx}15% of this cost is attributed to the energy deposited in fetal tissues and the products of conception; the rest of the energy is accounted for by the increased rate tof metabolism ({approx}150 MJ) and the energy deposited as fat by the mother ({approx}130 MJ).

See corresponding editorial on page 583.

Different strategies can be used to meet the additional demands for energy during pregnancy. One strategy is to increase food intake. Cross-sectional studies in well-nourished women have failed to detect increases in energy intake during pregnancy (2–4). Longitudinal studies typically show only slight increases in later stages of gestation (5–7), not enough to cover the substantial energy costs of pregnancy. A second strategy is to decrease energy expenditure during pregnancy. This can be done through a reduction in basal metabolic rate (BMR), in diet-induced thermogenesis (DIT), or in the amount of energy used for physical activity—activity energy expenditure (AEE). Studies in chronically undernourished women show that BMR declines during the first half of pregnancy, but increases by 400 kJ/d by the end of pregnancy (8, 9). Studies in well-nourished women indicate that BMR increases gradually throughout pregnancy, reaching 1213–2430 kJ/d higher than prepregnant values by the end of pregnancy (6, 10–12). Although cross-sectional studies have failed to find a reduction in energy for DIT during pregnancy (13–15), one longitudinal study found evidence for an energy-sparing adaptation amounting to a savings of 25–50 MJ over the course of pregnancy (16). A study in undernourished, pregnant women reported no change in energy for DIT (8). Studies of AEE throughout pregnancy have produced conflicting results, with reports of a decrease (17, 18), an increase (19, 20), or no change (6, 16, 21) by late pregnancy.

Finally, the energy demands of pregnancy could be met through a mobilization of fat stores, particularly in well-nourished women who begin pregnancy with sufficient energy reserves. Studies consistently show that rather than mobilizing fat stores to provide energy to the growing conceptus, however, women typically will deposit an additional 2–5 kg fat by the end of pregnancy (6, 11, 12, 22–27). Even in studies of undernourished women, fat deposition of {approx}2 kg occurs (8).

It is apparent that the combination of strategies used to meet the additional need for energy during pregnancy varies with the prepregnant energy status of the woman as well as with environmental factors such as food availability and the demands of physical labor. The purpose of this study was to assess to what degree well-nourished women use these various strategies to balance their energy budget during pregnancy and to assess whether the particular combination of strategies used can be predicted from an individual's prepregnant factors.


   SUBJECTS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Subjects
Sixteen healthy, nonsmoking women were recruited from the San Francisco Bay Area to participate in the study. Of these 16 women, 10 became pregnant within 3 mo of their preconception measurement and completed the study. Individual characteristics of the 10 subjects and their gestational outcomes are shown in Table 1Go. All women were classified as normal weight, with a body mass index (BMI; in kg/m2) between 19 and 26, and were having their second or third baby. Mean (±SD) weight gain by 36 wk gestation was 11.6 ± 4.3 kg. All 10 women delivered full-term, healthy singletons with an average birth weight of 3.6 kg. Subject 8 had a cesarean delivery because of prolonged labor; the rest of the group delivered vaginally. All women breast-fed their babies through the 4–6-wk postpartum time point, except subject 4.


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  		TABLE 1. Individual characteristics of the subjects and their infants
 
The study was conducted in the metabolic research unit at the Department of Nutritional Sciences, University of California at Berkeley, and at the US Department of Agriculture, Western Human Nutrition Research Center, San Francisco. The study was approved by the Human Subjects Committees of the University of California and the US Department of Agriculture. Each subject gave written, informed consent before participating.

Study design
Each woman was studied 5 times: before pregnancy (t0); at 8–10, 24–26, and 34–36 wk gestation (t1, t2, and t3, respectively); and 4–6 wk postpartum (tpost). Resting metabolic rate (RMR), DIT, AEE, energy intake, and body composition were assessed on each occasion. Subjects reported to the metabolic ward at the Department of Nutritional Sciences, University of California, Berkeley, on the morning of testing after a 10-h overnight fast. Subjects were instructed to consume their usual diets and to refrain from strenuous physical activity the day before testing.

Resting metabolic rate
Fasting RMR was measured between 0800 and 0830 under standard conditions after a 10-h fast by using a metabolic cart system with a ventilated canopy (Sensormedics, Inc, Yorba Linda, CA). Measurements were made every minute for 30 min while the subjects were awake but at complete rest. Energy expenditure (kJ/min) was calculated from measurements of oxygen consumption and carbon dioxide production by using the classic Weir equation (28).

Diet-induced thermogenesis
DIT was measured after subjects ate a 3135-kJ breakfast meal (75% of energy as carbohydrate, 10% as protein, and 15% as fat). Subjects were allowed 20–25 min to complete the meal, after which metabolic measurements commenced immediately. Data were collected over four 50-min periods, each followed by a 10-min break. Energy expenditure during these breaks was assumed to be the same as that in the previous 5-min interval. Minute-by-minute metabolic data were averaged into twelve 5-min increments for each of the 4 test periods. DIT was calculated from the area under the curve of energy expenditure versus time after subtracting the RMR measured on the same test day. DIT was expressed relative to the test meal size (%DITTM = [DIT/test meal size (kJ) x 100].

Total energy expenditure and activity energy expenditure
TEE was estimated at each time point by using the doubly labeled water method. After the collection of baseline urine samples, subjects were given an oral dose of doubly labeled water (2H218O): 0.15 g H218O and 0.10 g 2H20/kg body wt. Subjects collected midmorning spot urine samples on days 1, 5, 10, and 14 postdose. The urine samples were prepared for hydrogen and oxygen isotope-ratio measurements by gas-isotope-ratio mass spectrometry (29). For hydrogen isotope-ratio measurements, a 10-µL sample was reduced to hydrogen gas with 200 mg Zn reagent at 500°C for 30 min (30). The ratios of 2H to 1H were measured with a Finnigan Delta-E gas-isotope-ratio mass spectrometer (Finnigan MAT, San Jose, CA). For oxygen isotope-ratio measurements, 100 µL sample was allowed to equilibrate with 300 mbar CO2 of known 18O content at 25°C for 10 h with a VG ISOPREP-18 water-carbon dioxide equilibration system (VG Isogas, Limited, Cheshire, United Kingdom) (29). At the end of the equilibration period, the ratios of 18O to 16O in the carbon dioxide were measured with a VG SIRA-12 gas-isotope-ratio mass spectrometer (VG Isogas). The results are expressed in delta ({delta}) per mil (0/00) units, which are defined as follows:


(1)
where Rx and Rs are the ratios of 2H to 1H or 18O to 16O in the sample and standard, respectively. Values of {delta}2H and {delta}18O were normalized against 2 international water standards: Vienna standard mean ocean water and standard light Antarctic precipitation (31).

The isotope-dilution spaces for 2H (NH) and 18O (NO) were calculated as follows (32):


(2)
where d is the dose of 2H2O or H218O (in g), A is the amount of laboratory water (in g) used in the dose dilution, a is the amount of 2H2O or H218O (in g) added to the laboratory water in the dose dilution, Ea is the rise in 2H or 18O abundance in the laboratory water after the addition of the isotopic water, and Ed is obtained from the zero-time intercepts of the decay curves for 2H and 18O in the urine samples.

Carbon dioxide expiration rates (rCO2) were calculated from the fractional turnover rates of 2H (kH) and 18O (kO) and the isotope-dilution spaces as follows:


(3)
In this equation, the in vivo isotope fractional factors 0.945 [f1, 2H2O(liquid) {leftrightarrow} 2H2O(gas)], 0.990 [f2, H218O(liquid) {leftrightarrow} H218O(gas)], and 1.039 [f3, H218O(liquid) + C16O2(gas) {leftrightarrow} H216O(liquid) + C18O2(gas)] measured at 37°C were used (33–36). rCO2 was converted to TEE by using the Weir equation (28) as follows:


(4)
where rO2 was calculated from the food quotient (FQ) (37) based on the 3-d weighed food intakes by using the relation rO2 = CO2/FQ, and UN is the 24-h urinary nitrogen excretion (in g). UN values were measured by using the micro-Kjeldahl method from 24-h urine samples collected within 1 wk of each time point. Limits of error for the doubly labeled water method, including those incurred due to the anabolic state of pregnancy, were discussed elsewhere (38–40). AEE was estimated as the difference between TEE and RMR at each time point.

Energy intake
Subjects kept 3-d weighed food intake records at each time point. Records were analyzed by using NUTRITIONIST III software (version 7.2; N-Squared Computing, Salem, OR) and energy intake and macronutrient content were estimated at each time point from the 3-d average value.

Body composition
Body density was measured by densitometry after subjects voided, removed all jewelry, and changed into bathing suits. Body volume was corrected for residual lung volume, measured by oxygen dilution at the time of the densitometry measurement, with the method of Wilmore et al (41). Total body water (TBW) was measured by deuterium dilution as part of the doubly labeled water technique. TBW was estimated as deuterium space/1.04 to account for deuterium exchange with acidic body proteins. Bone mineral content was measured at t0 and tpost with a dual-energy X-ray absorptiometer (Lunar DPX software version 3.6; Lunar Corp, Madison, WI).

The 4-compartment model was used to determine body composition (42). The density of fat-free mass (DFFM) was calculated for each subject at each time point from the proportions of bone mineral, protein, and water comprising FFM and the component densities of each [Dwater = 0.993 kg/L, Dprotein = 1.34 kg/L, and Dmineral = 3.0 kg/L (43)]. Fat mass (FM) was then calculated as follows:


(5)
where WB is body weight, DB is density of the body, and DFM was assumed to be 0.9007 (43, 44). Body protein was estimated by subtracting TBW and bone mineral content from FFM.

Statistical analysis
Longitudinal data were analyzed by univariate repeated-measures analysis of variance. If significant effects were observed, Tukey's Studentized range test at a procedure-wise error rate of 5% was used to determine which stage of pregnancy significantly affected the variables measured. Multivariate regression analyses were done to determine the individual contribution of each predictor variable to the outcome variables (FM gain and change in RMR). SAS software (version 6; SAS Institute Inc, Cary, NC) was used for all analyses.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
Resting metabolic rate
The average increase in RMR by t3 was 1578 ± 876 kJ/d, or 29% above the average t0 value (Table 2Go). The considerable variation in individual patterns of change in RMR throughout pregnancy and in the absolute change by t3, which varied from 456 kJ/d (subject 2) to 3389 kJ/d (subject 3), is shown in Figure 1Go. By tpost, RMRs were not significantly different from prepregnant values.


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  		TABLE 2. Absolute values for RMR, DIT, AEE, EI, and FM throughout pregnancy1
 


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  		FIGURE 1. Individual changes from baseline (t0, prepregnancy) in resting metabolic rate (RMR) throughout pregnancy: t1, 8–10 wk gestation; t2, 24–26 wk gestation; t3, 34–36 wk gestation; tpost, 4–6 wk postpartum.

 
Diet-induced thermogenesis
The average DIT response to the breakfast meal was 7.2% of the energy content of the meal at t0; this decreased to 5.7% by t3 (Table 2Go). There was considerable interindividual variation in this response. The DIT response decreased from 10.5% to 2.3% of the energy content of the meal by t3 in subject 4, whereas the DIT response of subject 1 increased from 6.4% to 9.9% of the energy content of the meal (Figure 2Go). By tpost, each woman's DIT response was similar to her t0 value.



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  		FIGURE 2. Individual changes from baseline (t0, prepregnancy) in diet-induced thermogenesis (DIT) throughout pregnancy: t1, 8–10 wk gestation; t2, 24–26 wk gestation; t3, 34–36 wk gestation; tpost, 4–6 wk postpartum.

 
Total energy expenditure
The average increase in TEE by t3 was 2187 kJ/d, or 24% higher than the average value at t0 (Table 2Go). Individual responses in TEE throughout pregnancy, which varied from a decrease of 105 kJ/d (subject 8) to an increase of 3421 kJ/d (subject 10), are shown in Figure 3Go. The average tpost value was not significantly different from the mean t0 value.



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  		FIGURE 3. Individual changes from baseline (t0, prepregnancy) in total energy expenditure (TEE) throughout pregnancy: t1, 8–10 wk gestation; t2, 24–26 wk gestation; t3, 34–36 wk gestation; tpost, 4–6 wk postpartum.

 
Energy for activity
AEE increased on average by 610 kJ/d by t3, or 23% higher than the mean t0 value (Table 2Go). By t3, individual values varied from a reduction in activity of 2301 kJ/d (subject 3) to an increase of 2929 kJ/d (subject 2). Individual patterns of change in AEE throughout pregnancy are shown in Figure 4Go.



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  		FIGURE 4. Individual changes from baseline (t0, prepregnancy) in activity energy expenditure (AEE) throughout pregnancy: t1, 8–10 wk gestation; t2, 24–26 wk gestation; t3, 34–36 wk gestation; tpost, 4–6 wk postpartum.

 
Energy intakes
The 10 women increased their energy intake on average by 9%, or 775 kJ/d above t0 values by t3 (Table 2Go). All except 2 of the subjects showed increases in energy intake (Figure 5Go). The largest recorded increment in energy intake was in subject 3, who consumed 2176 kJ/d more than her t0 value. The average values for energy intake at t1, t2, and tpost were all within 2% of the average t0 value, but interindividual variation was large.



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  		FIGURE 5. Individual changes from baseline (t0, prepregnancy) in energy intake (EI) throughout pregnancy: t1, 8–10 wk gestation; t2, 24–26 wk gestation; t3, 34–36 wk gestation; tpost, 4–6 wk postpartum.

 
Fat mass
Individual changes in FM are shown in Figure 6Go. The mean fat deposition by t3 was 4.5 kg, with a range from a loss of 0.6 kg (subject 3) to a gain of 10.6 kg (subject 10). Most of the FM was deposited during the second trimester, with little change taking place in the first and third trimesters (Table 2Go). By postpartum, the subjects still retained an average of 2.2 kg FM over the mean t0 value.



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  		FIGURE 6. Individual changes from baseline (t0, prepregnancy) in fat mass (FM) throughout pregnancy: t1, 8–10 wk gestation; t2, 24–26 wk gestation; t3, 34–36 wk gestation; tpost, 4–6 wk postpartum.

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
Previous studies have shown an immense amount of variability in the metabolic changes taking place during pregnancy (6, 11, 45), particularly cross-sectional studies. It was our hope that by conducting a longitudinal study, using each subject as her own control and making measurements before conception, we could eliminate some of this variability in the results. This was not the case. However, the longitudinal study design gave us the ability to examine the pattern of those changes taking place over the course of pregnancy and to look for relations between the changes in various components of energy expenditure and body composition in individual women. Such relations are important in examining the underlying causes for individual variability in metabolic changes. The longitudinal design, which included preconception baseline measurements, also provided us with data necessary to estimate cumulative changes in indicators of energy expenditure over the course of pregnancy. For example, a woman's RMR might decrease in the first and second trimesters of pregnancy, then increase by late gestation. This could result in a cumulative decrease in energy for RMR over the course of pregnancy—a phenomenon that occurs in undernourished women (8), which might not be detected in a cross-sectional study.

One of the objectives of this study was to determine whether any prepregnant factors could predict the changes that would take place in body composition and metabolism during pregnancy. Previous studies reported that the increase in RMR was positively related to prepregnant energy stores (8, 9, 45, 46) and to a higher energy intake during pregnancy (9). We found no relation between the increase in RMR by t3 and any measured prepregnant factors, including body weight, BMI, FM, FFM, RMR, or energy intake. The increase was also not correlated with maternal energy intake during pregnancy. It is unclear why we found no predictors of the change in RMR during pregnancy, but it is possible that the influence of prepregnancy factors and of energy intake on RMR varies among different cultural groups and among populations in whom food availability differs. The relatively small variance in prepregnant energy status (as assessed by BMI) in our group of well-nourished subjects may have also prevented us from identifying predictors of the change in RMR during pregnancy.

FM gain during pregnancy in these 10 women was not predicted from prepregnant energy intake, body weight, BMI, FM, or FFM. Goldberg et al (6) similarly found no correlation between FM gain and prepregnant weight or BMI. We found that prepregnant RMR expressed per kg FFM was positively correlated with FM gain (r = 0.65, P < 0.05), indicating that the higher a woman's RMR before pregnancy was, the more fat she would ultimately deposit. This correlation explained {approx}43% of the variance in the FM deposited. The mechanism for this relation is unknown, but one could speculate that a high prepregnant RMR may be linked to a specific hormone, which might favor fat deposition during the anabolic state of pregnancy. This relation between prepregnant RMR and gestational FM deposition had not been reported previously and merits further investigation.

Cumulative changes in RMR and energy intake over the course of pregnancy were estimated from the area under each subject's curve above their preconception value. These results are summarized for each subject in Table 3Go. The mean incremental energy needed for RMR of 151 MJ was identical to that of Hytten and Leitch's (1) theoretical estimate of 150 MJ and fell within the range of mean values reported in other studies: 200 MJ in Sweden (20), 144 MJ in the Netherlands (10), 126 MJ in Scotland (48), and 112 MJ in England (6). Differences in mean values between these studies are likely due to the different assumptions used to extrapolate the data to 40 wk gestation as well as to the length of the intervals between RMR measurements used to calculate incremental changes in RMR.


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  		TABLE 3. Individual estimates of the energy cost of pregnancy (MJ)
 
The range of individual values in incremental RMR costs is interesting (Table 3Go). Although every subject's RMR had increased by t3, RMR in subject 2 dropped by 389 kJ/d in the second trimester, resulting in a negative cumulative maintenance cost. Subject 8 also experienced an early reduction in RMR, offsetting the rise in late gestation and resulting in a lower-than-average cumulative cost. These responses are similar to those metabolic responses commonly seen in undernourished women (10, 11). Interestingly, energy intake in subject 2 dropped during pregnancy and her cumulative energy intake was the most negative of any subject, indicating a possible relation between energy intake and RMR during pregnancy. Such was not the case for subject 8, however, who had a small but positive cumulative energy intake.

In the multiple regression analysis used to examine interrelations between cumulative changes in RMR and other metabolic and body-composition changes taking place during pregnancy, we found a negative correlation between the cumulative increase in RMR and FM gain (r = -0.64, P < 0.05), indicating that the more a subject's RMR increased during pregnancy, the less fat she deposited. This correlation indicates that from early on in pregnancy, energy is directed primarily toward either an increase in metabolism or fat deposition. There may be physiologic advantages for one woman to increase her fat stores and for another to increase her metabolic rate. On the other hand, those women who naturally have large increases in metabolism may have less energy remaining for fat deposition. It is unknown at this point whether fat deposition drives metabolism or vice versa, or what other factors are involved in how energy expenditure is directed during pregnancy. Goldberg et al (6) found no significant association between FM gain and the cumulative changes in BMR in a group of 12 well-nourished, pregnant British women. We also observed a borderline significant correlation between the cumulative increase in RMR and FFM deposition (r = 0.58, P < 0.08), similar to the strong relation between RMR and FFM seen in nonpregnant subjects.

Finally, we looked at DIT and AEE throughout pregnancy to examine other metabolic and behavioral adjustments that might be offsetting a woman's increased energy needs during pregnancy. The reductions in DIT and AEE observed could potentially account for significant energy savings if extrapolated throughout pregnancy. If summed over the last half of pregnancy, the blunted DIT effect observed in these 10 women could spare up to 29.3 MJ, similar to that seen in British women (16). The average amount of energy spared by reducing AEE in these 10 women was probably minimal, but on an individual level may have contributed to an energy savings. For example, subject 3, who had the highest prepregnant AEE of 5335 kJ/d, decreased her activity steadily throughout pregnancy, reaching a low of 3033 kJ/d by t3. Summed over pregnancy, this reduction amounted to a savings of 294 MJ, a significant proportion of the total estimated cost of pregnancy. Subjects 4, 5, and 8 also reduced their AEE, thereby sparing {approx}135 MJ over the course of pregnancy. Extremely active women or those with heavy physical workloads have the greatest potential for saving energy through a reduction in activity. Indeed, studies in Gambian women also showed reductions in AEE during pregnancy (17, 18).

The cumulative change in energy intake averaged 19 MJ, accounting for only 5% of our subjects' estimated total energy cost of pregnancy (Table 3Go). Individual values varied greatly. Subjects 2 and 3 had drastic reductions in their energy intake during pregnancy compared with their prepregnant values, which resulted in cumulative negative values for energy intake by term. Because each subject's prepregnant value was used as her baseline, erroneously high prepregnant measurements could account for these results. The subjects in general were compliant, motivated, and trustworthy. However, because the method was new to them at the first time point and because we used prepregnant data as baseline values, it would have been prudent to ask the subjects to repeat their prepregnant energy intake measurements or to verify the accuracy of their records by using another method.

Other studies in Western populations have similarly found little or no increase in energy intake during pregnancy (3, 48, 49). The possibility that women become more efficient and extract more energy from their food during pregnancy was refuted by de Groot et al (50). Indian women reportedly increase their intake enough to cover 96% of their estimated cost of pregnancy (15). The disparity in reported energy intakes between Western populations and Indian women could be due in part to cultural differences, given that Indian women may not feel the pressures that Western populations do to maintain their thin profile by controlling their food intake. For these reasons and because food records—even in motivated, compliant subjects—are known to underestimate true intakes (51–53), we placed more confidence in the metabolic and body-composition data than in the energy intake data.

The average total energy cost of pregnancy, estimated from the sum of the energy deposited as fat and the cumulative increase in RMR, was 384 MJ—similar to the theoretical value of 335 MJ (1) and the FAO/WHO/UNU estimate of 335 MJ (54). Individual values ranged from 204 to 632 MJ (Table 3Go), in agreement with ranges reported in other well-nourished women (6). The average proportion of the total energy cost contributed by FM gain and the increase in RMR, 53% and 39% respectively, were also similar to the theoretical values of 40% and 46%.

In summary, we found in a group of well-nourished women that the metabolic response to pregnancy varies widely. Women have the capacity to compensate for large increases in metabolism during pregnancy by minimizing fat deposition and possibly by reducing the energy needed for DIT and activity. Energy-sparing adaptations may play a bigger role in balancing the energy budget in populations in whom food intake is restricted, in whom the demands of physical labor are high, or in whom both conditions exist. The variability displayed in a woman's response to the energy requirements of pregnancy should be seen as a means by which the potential for a healthy gestational outcome, for both mother and infant, is optimized.


   FOOTNOTES
 
1 From the Department of Nutritional Sciences, University of California, Berkeley; the US Department of Agriculture, Western Human Nutrition Research Center, San Francisco; and the US Department of Agriculture, Agricultural Research Service, Children's Nutrition Research Center, Houston.

2 Supported by the Tobacco-Related Disease Research Program (project no. RT327).

3 Address reprint requests to LE Kopp-Hoolihan, Dairy Council of California, 80 Swan Way, Suite 210, Oakland, CA 94621. E-mail address: hoolihan @dairycouncilofca.org.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

  1. Hytten FE, Leitch I. The physiology of human pregnancy. Oxford: Blackwell Scientific Publications, 1971.
  2. Van den Rijst MPJ. On the role of nutrition in normal human pregnancy. In: van den Berg H, Bruinse HW, eds. Utrecht, Netherlands: University of Utrecht Press, 1983.
  3. English RM, Hitchcock NE. Nutrient intakes during pregnancy, lactation and after the cessation of lactation in a group of Australian women. Br J Nutr 1968;22:615–24.[Medline]
  4. Blackburn MW, Calloway DH. Energy expenditure and consumption of mature, pregnant and lactating women. J Am Diet Assoc 1976;69:29–36.[Medline]
  5. Durnin JVGA. Energy requirements of pregnancy. Acta Paediatr Scand Suppl 1991;373:33–42.[Medline]
  6. Goldberg GR, Prentice AM, Coward WA, et al. Longitudinal assessment of energy expenditure in pregnancy by the doubly labeled water method. Am J Clin Nutr 1993;57:494–505.[Abstract/Free Full Text]
  7. Durnin JVGA, Grant S, McKillop FM, Fitzgerald G. Is nutritional status endangered by virtually no extra intake during pregnancy? Lancet 1985;2:823–5.[Medline]
  8. Poppitt SD, Prentice AM, Jequier E, Schutz Y, Whitehead RG. Evidence of energy sparing in Gambian women during pregnancy: a longitudinal study using whole-body calorimetry. Am J Clin Nutr 1993;57:353–64.[Abstract/Free Full Text]
  9. Lawrence M, Lamb WH, Lawrence F, Whitehead RG. Maintenance energy cost of pregnancy in rural Gambian women and influence of dietary status. Lancet 1984;2:363–5.[Medline]
  10. van Raaij JMA, Schonk CM, Vermaat-Miedema SH, Peek MEM, Hautvast JGAJ. Body fat mass and basal metabolic rate in Dutch women before, during, and after pregnancy: a reappraisal of energy cost of pregnancy. Am J Clin Nutr 1989;49:765–72.[Abstract/Free Full Text]
  11. Forsum E, Sadurskis A, Wager J. Resting metabolic rate and body composition of healthy Swedish women during pregnancy. Am J Clin Nutr 1988;47:942–7.[Abstract/Free Full Text]
  12. Lindquist S. A longitudinal study of body composition and resting energy expenditure in pregnant women. Master's thesis. University of California, Berkeley, 1992.
  13. Nagy LE, King JC. Postprandial energy expenditure and respiratory quotient during early and late pregnancy. Am J Clin Nutr 1984;40:1258–63.[Abstract/Free Full Text]
  14. Bronstein MN, Mak RP, King JC. The thermic effect of food in normal-weight and overweight pregnant women. Br J Nutr 1995;74:261–75.[Medline]
  15. Piers LS, Diggavi SN, Thangam S, van Raaij JMA, Shetty PS, Hautvast JGAJ. Changes in energy expenditure, anthropometry, and energy intake during the course of pregnancy and lactation in well-nourished Indian women. Am J Clin Nutr 1995;61:501–13.[Abstract/Free Full Text]
  16. Illingworth PJ, Jung RT, Howie PW, Isles TE. Reduction in postprandial energy expenditure during pregnancy. Br Med J 1987;294:1573–6.
  17. Lawrence M, Whitehead RG. Physical activity and total energy expenditure of child-bearing Gambian village women. Eur J Clin Nutr 1987;42:145–60.
  18. Roberts SB, Paul AA, Cole TJ, Whitehead RG. Seasonal changes in activity, birthweight and lactational performance in rural Gambian women. Trans R Soc Med Hyg 1982;76:668–78.
  19. Singh J, Prentice AM, Diaz E, et al. Energy expenditure of Gambian women during peak agricultural activity measured by the doubly-labelled water method. Br J Nutr 1989;62:315–29.[Medline]
  20. Forsum E, Kabir N, Sadurskis A, Westerterp K. Total energy expenditure of healthy Swedish women during pregnancy and lactation. Am J Clin Nutr 1992;56:334–42.[Abstract/Free Full Text]
  21. Acheson KJ, Campbell IT, Edholm OG, Miller DS, Stock MJ. The measurement of daily energy expenditure—an evaluation of some techniques. Am J Clin Nutr 1980;33:1155–64.[Abstract/Free Full Text]
  22. Taggart NR, Holliday RM, Billewicz WZ, Hytten FE, Thomson AM. Changes in skinfolds during pregnancy. Br J Nutr 1967;21:439–51.[Medline]
  23. Pipe NGJ, Smith T, Halliday C, Edmonds CJ, Williams C, Coltart TM. Changes in fat, fat-free mass and body water in human normal pregnancy. Br J Obstet Gynaecol 1979;86:929–40.[Medline]
  24. Hytten FE, Thomson AM, Taggart N. Total body water in normal pregnancy. J Obstet Gynaecol Br Commonw 1966;73:553–61.[Medline]
  25. Forsum E, Sadurskis A, Wager J. Estimation of body fat in healthy Swedish women during pregnancy and lactation. Am J Clin Nutr 1989;50:465–73.[Abstract/Free Full Text]
  26. van Raaij JMA, Peek MEM, Vermaat-Miedema SH, Schonk CM, Hautvast JGAJ. New equations for estimating body fat mass in pregnancy from body density or total body water. Am J Clin Nutr 1988;48:24–9.[Abstract/Free Full Text]
  27. Seitchik J, Alper C. The body compartments of normal pregnant, edematous pregnant and pre-eclamptic women. Am J Obstet Gynecol 1954;68:1540–5.
  28. Weir JBV. New methods for calculating metabolic rate with special reference for protein metabolism. J Physiol 1949;109:1–9.
  29. Wong WW, Lee LS, Klein PD. Deuterium and oxygen-18 measurements on microliter samples of urine, plasma, saliva, and human milk. Am J Clin Nutr 1987;45:905–13.[Abstract/Free Full Text]
  30. Wong WW, Clarke LL, Llaurador M, Klein PD. A new zinc product for the reduction of water in physiological fluids to hydrogen gas for 2H/1H isotope ratio measurements. Eur J Clin Nutr 1992;46:69–71.[Medline]
  31. Gonfiantini R. Report on advisory group meeting on stable isotope reference samples for geochemical and hydrological investigations. Vienna: International Atomic Energy Agency, 1984.
  32. Coward A. Calculation of pool sizes and flux rates. In: Prentice AM, ed. The doubly-labelled water method for measuring energy expenditure. Technical recommendations for use in humans. Vienna: IDECG/IAEA, NAHRES-4, 1990:48–68.
  33. Halliday D, Miller AG. Precise measurement of total body water using trace quantities of deuterium oxide. Biomed Mass Spectrom 1997;4:82–7.
  34. Pflug KP, Schuster KD, Pichotka JP, Forstel H. Fractionation effects of oxygen isotopes in mammals. In: Klein ER, Klein PD, eds. Stable isotopes: Proceedings of the Third International Conference. New York: Academic Press, 1979:553–61.
  35. Schoeller DA, Leitch CA, Brown C. Doubly labeled water method: in vivo oxygen and hydrogen isotope fractionation. Am J Physiol 1986;1:R1137–43.
  36. Wong WW, Cochran WJ, Klish WJ, Smith EO, Lee LS, Klein PD. In vivo isotope-fractionation factors and the measurement of deuterium- and oxygen-18-dilution spaces from plasma, urine, saliva, respiratory water vapor, and carbon dioxide. Am J Clin Nutr 1988;47:1–6.[Abstract/Free Full Text]
  37. Black AE, Prentice AM, Coward WA. Use of food quotients to predict respiratory quotients for the doubly-labelled water method of measuring energy expenditure. Hum Nutr Clin Nutr 1986;40C:381–91.[Medline]
  38. Cole TJ, Franklin M, Coward WA. Estimates of error. In: Prentice AM, ed. The doubly-labelled water method for measuring energy expenditure. Technical recommendations for use in humans. Vienna: IDECG/IAEA, NAHRES-4, 1990:69–89.
  39. Coward WA. The 2H218O method—principles and practice. Proc Nutr Soc 1988;47:209–18.[Medline]
  40. Prentice AM. Applications of the doubly labelled water method (2H218O) in free-living adults. Proc Nutr Soc 1988;47:259–68.[Medline]
  41. Wilmore JH, Vodak PA, Parr RB, Girandola RN, Billing JE. Further simplification of a method for determination of residual lung volume. Med Sci Sports Exerc 1980;12:216–8.[Medline]
  42. Lohman TG. Applicability of body composition tehniques and constants. Exerc Sport Sci Rev 1986;14:325–57.[Medline]
  43. Garrow JS. Indices of adiposity. Nutr Abstr Rev Clin Nutr Ser 1983;53:697–708.
  44. Brozek J, Grande F, Anderson JT, Keys A. Densitometric analysis of body composition: revision of some quantitative assumptions. Ann N Y Acad Sci 1963;110:113–40.
  45. Prentice AM, Goldberg GR, Davies HL, Murgatroyd PR, Scott W. Energy-sparing adaptations in human pregnancy assessed by whole-body calorimetry. Br J Nutr 1989;62:5–22.[Medline]
  46. Bronstein MN, Mak RP, King JC. Unexpected relationship between fat mass and basal metabolic rate in pregnant women. Br J Nutr 1996;75:659–68.[Medline]
  47. Roberts SB, Young VR. Energy costs of fat and protein deposition in the human infant. Am J Clin Nutr 1988;48:951–5.[Abstract/Free Full Text]
  48. Durnin JVGA, Grant S, McKillop FM, Fitzgerald G. Energy requirements of pregnancy in Scotland. Lancet 1987;2:897–900.[Medline]
  49. van Raaij JMA, Vermaat-Miedeme SH, Schonk CM, Peek MEM, Hautvast JGAJ. Energy requirements of pregnancy in the Netherlands. Lancet 1987;2:953–5.[Medline]
  50. de Groot LCPGM, Boekholt HA, Spaaij CJK, et al. Energy balances of healthy Dutch women before and during pregnancy: limited scope for metabolic adaptations in pregnancy. Am J Clin Nutr 1994;59:827–32.[Abstract/Free Full Text]
  51. Black AE, Goldberg GR, Jebb SA, Livingstone MBE, Cole TJ, Prentice AM. Critical evaluation of energy intake data using fundamental principles of energy physiology: 2. Evaluating the results of published surveys. Eur J Clin Nutr 1991;45:583–99.[Medline]
  52. Schoeller DA, Bandini LG, Dietz WH. Inaccuracies in self-reported intake identified by comparison with the doubly-labelled water method. Can J Physiol Pharmacol 1990;68:941–9.[Medline]
  53. Lafay L, Basdevant A, Charles MA, Vray M. Determinants and nature of dietary underreporting in a free-living population: the Fleurbaix Laventie Ville Sante (FLVS) Study. Int J Obes 1997;21:567–73.[Medline]
  54. FAO/WHO/UNU. Energy and protein requirements. Report of a joint FAO/WHO/UNU expert consultation. World Health Organ Tech Rep Ser 1985;724:1–206.[Medline]
Received for publication April 16, 1998. Accepted for publication November 3, 1998.




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