4
Factors Affecting Energy Expenditure and Requirements
Total energy expenditure (TEE) is the energy expended during oxidation of energy-yielding macronutrients within a 24-hour period. TEE includes three core components: resting metabolic rate, or resting energy expenditure (REE); the thermic effect of food (TEF), also referred to as diet-induced thermogenesis (DIT); and physical activity. REE, generally the largest contribution to TEE, represents the energy needed to support maintenance of normal body functioning and homeostasis. TEF is the increase in energy expenditure associated with the ingestion of food. Physical activity level (PAL) is the energy expenditure above and beyond the basal state and TEF. These three components and their determinants are shown in Figure 4-1. Table 4-1 further describes these and other terms used to indicate various components of energy expenditure. In this report, some terms are used interchangeably because the committee used the original terminology used in each reviewed paper. Additionally, while alternate terms are identified, not all are used in this report.
Part of the committee’s task was to review the components of energy expenditure. It was not able to identify relevant, high-quality evidence for every component and therefore focused its discussion on topics for which it found sufficient relevant evidence. The committee’s review of the evidence from systematic reviews related to TEE in general and for specific life-stage conditions such as pregnancy and lactation are discussed at the end of this chapter.
NOTE: EER = estimated energy requirement.
SOURCE: Adapted from Lam and Ravussin, 2016.
TABLE 4-1 Definitions for the Components of Total Energy Expenditure and Estimated Energy Requirements
| Component | Alternate Terminology | Definition |
|---|---|---|
| Basal metabolic rate (BMR) | Basal energy expenditure (BEE) | The energy required when the human body is at complete physical, mental, and digestive rest. It is the energy required to maintain the structure and function of cells and, therefore, the minimum amount of energy expenditure compatible with life. It is usually measured after the sleeping state prior to arising from bed with the condition of being 12 or more hours postprandial/postabsorptive. |
| Resting metabolic rate (RMR) | Resting energy expenditure (REE) | The energy required for oxygen uptake when the body is in an awake, resting, postabsorptive, thermoneutral state. It is typically measured laying supine with the condition that there has been no exercise or food/beverage consumption in the prior 4–5 hours. It is the largest component of total energy expenditure, about 10% higher than BMR, and accounts for ~60–70% of total daily energy expenditure. |
| Component | Alternate Terminology | Definition |
|---|---|---|
| Thermic effect of food (TEF) | Diet-induced thermogenesis (DIT) | The increase in metabolic rate after the ingestion of a meal (solid or liquid). It involves the energy expended digesting, absorbing, metabolizing, and storing energy and nutrients. It typically accounts for ~10% of total daily energy expenditure. |
| Physical activity energy expenditure (PAEE) | Physical activity energy expenditure is the most variable component of total daily energy expenditure and involves body movement including exercise and nonexercise activity thermogenesis (NEAT). NEAT is a result of spontaneous activity and represents the energy expended for minor movements like fidgeting and general ambulatory activity. PAEE can be calculated as the difference between total energy expenditure and basal metabolic rate plus diet induced thermogenesis (TEE – [RMR + TEF]). | |
| Total energy expenditure (TEE) | The total daily energy expenditure comprising resting metabolic rate, thermic effect of food, and physical activity energy expenditure. For efficiency, TEE is most often presented in the literature as: (RMR + TEF + PAEE). | |
| Physical activity level (PAL) | An indicator of the level of daily physical activity determined by the ratio of total energy expenditure to basal metabolic rate (TEE/BMR). | |
| Energy deposition | The energy content of newly synthesized tissues estimated from the energy costs of protein and fat deposition during growth. | |
| Energy metabolism | The use of energy from body fat and protein stores to meet energy needs, which may be accelerated in growth, injury, or stress states. |
SOURCES: Butte and Caballero, 2014; Levine, 2002; Poehlman, 1989; Schutz and Jequier, 1998; Westerterp, 2004; Wong et al., 1996.
COMPONENTS OF ENERGY EXPENDITURE
Resting Energy Expenditure
Resting energy expenditure (REE) typically accounts for 60 to 70 percent of total energy expenditure (Lam and Ravussin, 2016; Poehlman, 1989). REE varies both within and between individuals and fluctuates
over the course of the human life span. As shown in Figure 4-1, REE is affected by several factors, including age, sex, body size and composition, and genetics (which may include the influence of race/ethnicity). The most commonly used method to measure REE is indirect calorimetry using metabolic carts that calculate the minute-by-minute exchange of oxygen consumption (VO2) and carbon dioxide production (VCO2) when an individual is at rest in the fasted state (Compher et al., 2006; Lam and Ravussin, 2016). The values of VO2 and VCO2 are then entered into an equation to calculate 24-hour resting metabolic rate (REE).
Commonly used equations to derive the REE include the Weir equation (Brouwer, 1957; Consolazio et al., 1963) and several empirical predictive equations that have been generated to estimate measured REE, particularly in clinical practice. These include the Harris-Benedict equation (developed in 1919), the Owen equation, the Mifflin St-Jeor equation, and the World Health Organization/Food and Agriculture Organization/United Nations University equation. The ability of estimation equations to predict accurately varies, as error rate is influenced by age, sex, ethnicity, and body mass index (BMI) category (Frankenfield et al., 2005). Accuracy in determining REE is highly important, considering its effect on weight status (Marra et al., 2017).
REVIEW OF EVIDENCE ON THE DETERMINANTS OF REE
Age/Sex Group
A recent analysis of Basal Energy Expenditure (BEE) measured by indirect calorimetry in a large sample of males and females over the life course (n = 2,008) from multiple countries (n = 29) found that BEE increased with the amount of fat-free mass (FFM) in a power law manner, after adjusting for body size, age, and sex (Pontzer et al., 2021). Specifically, size-adjusted BEE was found to increase rapidly in infants up to 15 months of age, with BEE values approximately 50 percent higher than adult values. Size-adjusted BEE then declined slowly until around 20 years of age and remained stable from 20 to 60 years before declining in older adults (Pontzer et al., 2021). The decline in BEE for older adults appears to be related to decreases in fat-free mass, and age-related reduction in organ metabolism.
A systematic review by Schwartz and Doucet (2010) of 90 studies that included 2,996 participants did not find a significant difference in sex for the reduction in REE that occurs with reducing body mass through intentional weight loss. Although there is high interindividual variability in REE, when body mass and composition are controlled in the analysis, it appears that sex has little impact on REE.
Body Size
Body size, a function of weight and height, varies among individuals from all races and ethnicities. Systematic reviews of studies that have determined REE from indirect calorimetry show a linear relationship between increasing BMI and REE. In a systematic review comparing constitutionally thin individuals (BMI ≤ 17.5) with no existing medical conditions (including eating disorders) compared to normal weight individuals, constitutionally thin individuals were found to have a lower REE compared to those of normal weight (Bailly et al., 2021). RMR results in 64 percent of the studies showed a lower RMR in constitutionally thin versus normal BMI control subjects, while 36 percent of studies showed no difference.
Whether a linear relationship between body mass and REE holds true in obesity, particularly class III obesity, is a topic of debate and is frequently challenged by studies using dynamic mathematical modeling (Heymsfield et al., 2019). A systematic review of 20 studies by Kee et al. (2012) showed that REE ranged from 1,800 to 2,600 kcal/d among individuals with morbid obesity, and that REE increased with increasing body mass. While body composition was not reported in all studies in the systematic review, Das et al. (2003) demonstrated that fat mass (FM) contributes significantly to REE variability in individuals with BMI ≥ 50, both before and after weight loss.
A number of systematic reviews examining weight loss show an effect of either adaptive thermogenesis or energy compensation such that REE is reduced more than predicted. These studies found that the reduction in REE varied widely, from 12 to 44 percent less than predicted, which equates to about 220 kcal less per day (Dhurandar et al., 2015; Nunes et al., 2022a,b; Schwartz et al., 2012). One systematic review of seven studies with 361 participants showed that a gradual reduction in body mass (about 0.5 kg/week) resulted in less reduction in REE compared to rapid weight change (about 1.1 kg/week) (Ashtary-Larky et al., 2020).
Body Composition
Assessing body composition is a foundational element of energy metabolism research. The human body contains tissues and organs of varying metabolic activity, with the simplest division of total body mass into two compartments: fat mass (FM) also known as stored fat found in adipose tissue, and fat-free mass (FFM), which includes smooth and skeletal muscle, connective tissue, water, and bone. Although adipose tissue is the main storage site for energy, in the form of triglycerides, it has a low metabolic rate at about 5 kcal/kg compared to 20 kcal/kg for FFM (Javed et al., 2010; Wang et al., 2010). In the systematic review
by Bailly et al. (2021), the authors reported that despite very low FFM in constitutionally thin individuals, these individuals have increased metabolic activity when normalized to FFM compared to normal weight individuals, suggesting a highly metabolically active FFM.
Given that FFM is a strong predictor of REE, accounting for 60 to 80 percent of interindividual variance in REE, measurement of REE is often adjusted for FFM by sex as a means of adjusting REE for differences in body size, since body weight alone can explain only about 50 percent of the variance in REE (Gallagher et al., 1996).
More recent investigations consider differences in organ energy expenditure as a component of FFM, which may account for interindividual variability in REE associated with age, sex, and race/ethnicity. Older individuals appear to have a lower REE, however, even after controlling for organ and tissue mass. Thus, age-related changes in body composition, including loss of body water, bone mineral content, FFM, and an increase in the distribution of FM, influence REE.
Periods of underfeeding are typically accompanied by compensatory metabolic responses and losses of FFM during episodes of energy deficit, which generally result in reduced energy expenditure. Taken together, metabolic responses to decreased energy intake and weight loss are part of a complex and dynamic energy balance system in which changes to individual components can lead to interrelated compensatory responses (Casanova et al., 2019).
Genetic Traits: Race and Ethnicity
Self-reported race is the only legal basis for racial categorization (Cooper, 1994), and nutrition research almost exclusively uses self-reported race and ethnicity to describe participants and population groups engaged in research. In the public health context, planners use conventional racial or ethnic population characteristics as a proxy for planning programs, facilitating program accessibility, and targeting public health messages. The understanding and use of the concepts of race and ethnicity have evolved over the years.
Currently, the social and political construct that is race/ethnicity is thought to reflect differential distribution of resources, including the availability of high-quality foods, housing, education, transportation, and access to health care, leading to significant inequities among certain population groups (Cooper, 2013; White et al., 2020). These upstream factors influencing health equity are commonly referred to as social determinants of health (WHO, 2022).
In this case, race and ethnicity are not modifiable factors but rather act as proxies for other determinants that can be changed to improve health.
About 10 percent of the U.S. population identified as multiracial in the 2020 census, up almost 300 percent from 2010 (Jones et al., 2021). In addition, more than 15 percent identified as “some other race either alone or in combination,” a description that is exclusive of the five categories listed in the census survey: White, Black/African American, American Indian/Alaskan Native, Asian, or Native Hawaiian/Other Pacific Islander. The evidence quantifying the effect of race and/or ethnicity on energy expenditure remains inconclusive despite a relatively robust examination in the scientific literature. The vast majority of studies over the past 20 years have focused on the comparison of REE between Black and White individuals, most with an aim of elucidating documented differences in overweight and obesity between these racial groups.
A preponderance of studies, as shown in Appendix J, Table J-5, reported a significantly lower REE among Black compared to White adults, even after adjustment for body composition, meaning FFM and FM (Adzika Nsatimba et al., 2016; Most et al., 2018; Olivier et al., 2016; Reneau et al., 2019; Spaeth et al., 2015). The same pattern was observed among studies of prepubescent children and adolescents (Bandini et al., 2002; McDuffie et al., 2004; Pretorius et al., 2021; Sun et al., 2001; Tershakovec et al., 2002). Of the 19 studies reporting lower adjusted REE for Black adults, the range of mean differences was 50 to 250 kcal/d with the median of mean differences about 120 kcal/d; for children, the range of mean differences was 36 to 120 kcal/d and the median was 77 kcal/d. The observed differences in REE tended to be attenuated, however, for studies in which REE was adjusted for truncal lean mass, meaning highly metabolically active organ mass, and/or appendicular lean body mass (the sum of the lean muscle mass of the upper and lower extremities adjusted for height) (Byrne et al., 2003; Gallagher et al., 1997, 2006; Hunter et al., 2000; Javed et al., 2010; Jones et al., 2004).
Few studies have examined the effect of race/ethnicity on TEE in either adults or children. In studies among adults, seven reported a significantly lower TEE in Blacks (median of mean differences about 138 kcal/d) (Blanc et al., 2004; DeLany et al., 2014; Dugas et al., 2009; Lam et al., 2014; Most et al., 2018; Walsh et al., 2004; Weinsier et al., 2000), and four reported no statistical differences after adjustment for body composition (Hunter et al., 2000; Katzmaryk et al., 2018; Kushner et al., 1995; Lovejoy et al., 2001). Studies of children reported similar results; two studies reported lower TEE among Blacks (mean difference of 86 kcal/day) (Bandini et al., 2002; DeLany et al., 2002), and two reported no statistically significant difference (Goran et al., 1998; Sun et al., 1998). Attempts to understand the mechanisms responsible for the lower observed REE (and to a lesser extent, TEE) among Blacks compared to Whites in the United States suggest regional body composition
differences, i.e., high metabolically active truncal organ mass or low metabolically active appendicular skeletal muscle mass, as one potential explanation for the lack of significant differences (Gallagher et al., 1997).
The relatively few studies that have compared REE or TEE in race/ethnic groups other than Blacks and Whites generally reported no statistically significant differences between groups. Groups examined include adult Hispanics (Deemer et al., 2010), Pima Indians (Christin et al., 1993; Fontveille et al., 1994; Saad et al., 1991), Maori and Pacific Islanders (Rush et al., 1997), Asians (Song et al., 2016; Wouters-Adriaens and Westerterp, 2008), and South Asian Indians (Soares et al., 1998; Song et al., 2016). A few studies also examined energy expenditure among children: Pima Indians (Fontveille et al., 1992), Hispanics (Dugas et al., 2008), Mohawks (Goran et al., 1995, 1998), and Maori and Pacific Islanders (Rush et al., 2003). See Appendix J, Table J-5 for additional details.
Attempts to understand the mechanisms responsible for the lower observed REE (and to a lesser extent, TEE) among Blacks compared to Whites in the United States point to regional body composition differences—meaning highly metabolically active truncal organ mass or low metabolically active appendicular skeletal muscle mass—as one potential explanation (Gallagher et al., 1997). Differences in mitochondrial function (Toledo et al., 2018) and mitochondrial DNA haplotypes (Tranah et al., 2011) may also contribute to differences in energy expenditure between population groups.
Using ancestry informative markers among the participants of a substudy of the U.S.-based Health, Aging and Body Composition Study, investigators reported a significant association between proportion of European genetic admixture among Black participants and REE adjusted for body composition (Manini et al., 2011). Each percent of European admixture was associated with a 1.6 kcal/day higher adjusted REE in these older adults. If confirmed in additional studies, this finding may help explain the variability across studies reporting differences in energy expenditure between Black and White individuals. For context, multiple studies have reported wide variability in the degree of West African and European admixture among self-identified Blacks or African Americans in the United States. The mean European admixture among self-identified Blacks in any given study ranges from about 15 to 25 percent (Klimentidis et al., 2016; Parra et al., 1998; Worsham et al., 2011); however, the range of European admixture can be as wide as 0 to 70 percent (Al-Alem et al., 2014; Manini et al., 2011).
Thermic Effect of Food
Factors that influence TEF include age, physical activity, and a meal’s energy content, composition (i.e., quantity and type of carbohydrate,
protein, and fat content of a meal), and size (Calcagno et al., 2019). The TEF, which has been shown to comprise approximately 10 percent of daily energy expenditure, includes obligatory thermogenesis. Obligatory thermogenesis is accounted for by the energy cost of absorption and transport of nutrients, and synthesis of carbohydrate, protein, and fat in tissues (Saito et al., 2020).
Review of Evidence on the Determinants of TEF
Physical Activity
A review by Calcagno and colleagues (2019) identified one study that examined the effect of physical activity on TEF. The study showed that in both younger and older men, those who were active had an approximately 45 percent higher TEF than those who were inactive. Further evidence from a study of active females suggests that consumption of a meal in combination with a short period of moderate to vigorous physical activity (MVPA) results in a greater total energy expenditure than similar activity performed in a fasted state (Binns et al., 2015).
Meal Energy Content, Composition, and Size
The main determinant of TEF is energy and macronutrient composition of the meal, of which proteins have the highest thermogenic response. DIT values are approximately 0 to 3 percent for fat, 5 to 10 percent for carbohydrate, 20 to 30 percent for protein, and 10 to 30 percent for alcohol (Westerterp, 2004).
A systematic review that examined differences in the effects on DIT of meals consumed after fasting conducted mixed model meta-regression analyses that included only energy intake and DIT. It showed that for every 24 kcal increase in energy intake, DIT increased by 0.26 kcal/day (Quatela et al., 2016).
In a systematic review that included 15 studies, 9 showed a significant effect of the type of fatty acids on DIT. Three studies described a DIT increment with the use of polyunsaturated fatty acid, two reported a greater DIT as a result of the use of medium chain fatty acids, and four reported differences with the use of specific foods or oils. Specifically, postprandial fat oxidation and postprandial energy expenditure were greater with the use of alpha linolenic acid–enriched diacylglycerol compared to triacylglycerol. However, no conclusion could be drawn when only the fatty acid composition of the diet was evaluated for DIT (Cisneros et al., 2019).
Park et al., examined dietary factors affecting DIT in studies that included individuals with obesity. In this systematic review of studies
published from 2009 to 2019, only two studies of very small sample sizes showed no differences in DIT between obese and lean individuals with varying carbohydrate and protein composition of isocaloric meals (Park et al., 2020). This finding is in contrast to an older review by de Jonge and Bray (1997), which reported that in 29 studies of age-matched individuals, 22 reported a reduction in DIT for individuals with obesity compared to lean individuals. Thus, the issue of the obese state due to insulin resistance being associated with lower DIT remains undecided. The variability in how DIT is measured and the complex interaction of human behaviors including physical activity makes it difficult to estimate DIT accurately and compare results across studies.
Physical Activity Level
Physical activity is the most variable energy component. Energy expenditure from activity is the energy required for the body to move (i.e., perform muscular work) during non-exercise activity thermogeneis (e.g., fidgeting, maintaining posture, and activities of daily living) and voluntary (e.g., exercise, sports) activity. It varies greatly as a proportion of TEE and has been shown to range from a low of 15 percent for sedentary individuals up to 50 percent of TEE for physically active individuals (Livingstone et al., 1991; Ravussin et al., 1986).
Determinants of PAEE include age, sex, body size and composition, movement economy, exercise training, and genetic traits, all of which interact and can result in energy adaptations. Resources such as the Compendium of Physical Activities can provide estimates of an individual’s energy expenditure for specific activities (Ainsworth et al., 2011; Butte et al., 2018).
Review of Evidence on the Determinants of PAL
Age/Sex
PAL varies across the life span. Researchers can obtain precise measures of intraindividual or interindividual differences in PAL using doubly labeled water (DLW), indirect calorimetry, and room calorimetry. Because DLW is used only for measuring free-living TEE and may be cost-prohibitive, researchers often use estimates of physical activity from questionnaires or device-based measures. Questionnaires tend to have a high degree of error because they rely on individual recall and quantification of activity level (see Chapter 6 for further discussion of methodologies). Device-based measures (e.g., ActiGraph; GeneActive, Apple Watch, and Fitbit) use sensors such as accelerometers to capture an
individual’s movement and are considered to provide a better estimate of typical activity patterns than questionnaires, but they lack details about the type of activity performed. Furthermore, a lack of consensus on intensity criteria along with variation in device wear location make it challenging to quantify time in intensity categories and comparing estimates across studies (Watson et al., 2014).
Craigie et al. (2011) conducted a systematic review of literature on tracking physical activity and dietary intake from childhood to adulthood. Three studies in this review, which included over 2,000 participants, found that tracking of physical activity from adolescence into adulthood was stronger among males than females. Between 44 and 59 percent of males maintained physical activity during the 5- to 8-year follow-up.
Tanaka et al. (2014) examined longitudinal changes in overall sedentary behavior and how those changes were associated with adiposity in children and adolescents. This systematic review included 7,238 children and adolescents and found that during a 1- to 10-year follow-up among 3- to 13-year-olds, sedentary behavior increased with age, by approximately 30 minutes of additional daily sedentary behavior per year. Little evidence was available to demonstrate any influence of changes in sedentary behavior on changes in adiposity.
Body Size and Composition
A systematic review by Carneiro et al. (2016) examined differences in activity-based energy expenditure in individuals with and without obesity. All four studies included in the analysis reported that individuals with obesity had higher absolute activity energy expenditure than those without obesity. After adjustment for FFM or body weight, two studies showed no difference between the two population groups. The conclusion of the review was that activity energy expenditure was not different in individuals with obesity; rather, they have altered activity patterns and greater amounts of sedentary time, resulting in overall lower activity energy expenditure values. However, higher REE in those with obesity that was reported in most studies could be caused by not adjusting for body composition.
Carneiro et al. (2016) also examined differences in daily energy expenditure between those with and those without obesity. In the three studies included in the systematic review, absolute daily energy expenditure was higher in the group with obesity (approximately 2,690 kcal/d) than in those without obesity (approximately 2,380 kcal/d). Similar to the findings on activity energy expenditure, the difference between the two groups disappeared after adjusting for FFM and body weight.
Movement Economy and Exercise Training
Movement economy is the oxygen cost to perform a given submaximal task. The more trained an individual is, the better their economy (i.e., the oxygen cost or energy expenditure will be lower) (Barnes and Kilding, 2015). This principle also relates to motor coordination, which is a measure of the ability to coordinate muscle activation in multiple body parts to perform a given task. Motor coordination is still developing in children and youth, thus their movement economy is typically poorer (i.e., the energy cost of an activity such as walking is higher) than an adult’s. Children’s motor coordination improves along with movement economy as skill development proceeds. In adults, training improves movement economy.
OTHER CONSIDERATIONS
Carbohydrate Restriction
There has been great interest in understanding the effect of a restricted carbohydrate diet on TEE to explain the heterogeneity found in weight loss clinical trials. The rationale for examining this relationship is the hypothesis that with moderate restriction of carbohydrate over a longer period of time, a shift in the metabolic pathway can occur from carbohydrate oxidation to fat oxidation without bringing on a ketosis condition, thereby subsequently reducing TEE through several mechanisms including a reduction in voluntary physical activity energy expenditure. Ludwig et al. (2021) conducted an updated systematic review with a meta-analysis of previous work by Hall and Guo (2017) and added trials conducted since 2016 up through March 2020. Carbohydrate restriction was allowed to vary in the trials, but study duration was dichotomized at greater than or less than 2 weeks. In studies with short-term carbohydrate restriction (<2.5 weeks), the systematic review found that a lower carbohydrate diet did result in reduced TEE. However, when a restricted carbohydrate diet was maintained for more than 2.5 weeks, TEE increased by approximately 50 kcal/day for every 10 percent decrease in carbohydrate as a percentage of energy intake. The stratification by study duration accounted for the most variability in TEE (R2 = 57.2 percent). The method used to measure TEE, whether whole-room calorimetry or DLW, did not significantly add to the heterogeneity. A conclusion of this work is that shorter versus longer duration of carbohydrate restriction studies are not examining the same physiological states, which may explain the pattern of weight loss seen in clinical trials and thus, not indicative of the success of these short-term trials to treat obesity.
Pregnancy and Lactation
Many metabolic and physiological changes that influence energy requirements occur during the life stages of pregnancy and lactation. Previous derivations of requirements for pregnancy were based on theoretical energy costs associated with the products of conception (e.g., the fetus, placenta, maternal breast and uterine tissue, and maternal fat). For lactation, requirements have been based on the energy costs associated with producing a specific volume of breast milk for the infant, accounting for the mobilization of maternal fat stores from pregnancy to provide additional energy resources during the postpartum period. Butte and King (2005) comprehensively examined these energy costs and how their estimates have changed over time.
Previous estimates of the energy costs of pregnancy (which considered FM and FFM accretion associated with the products of conception) may have led to overestimation of energy requirements during this life stage. A recent systematic review and meta-analysis provides evidence of wide variability in TEE and in REE and other energy expenditure components during pregnancy (Savard et al., 2021). The data support the notion that REE and TEE increase over the course of pregnancy, with greater increases observed when baseline measurement included a preconception time point. Median increases in TEE were 6.2 percent (144 kcal), 7.1 percent (170 kcal), and 12.0 percent (290 kcal) between early and mid-, mid- and late, and early and late pregnancy, respectively. Most of the included studies enrolled normal weight, Caucasian women, however, and had small sample sizes. The two studies that stratified results by prepregnancy BMI showed smaller increases in TEE for women with overweight and obesity. Most studies did not stratify by adequacy of gestational weight gain. Therefore, the constant physiological adaptation during pregnancy (such as gradual reductions in physical activity expenditure and in DIT) imply that the energy cost of pregnancy should be lower than the costs published by the Institute of Medicine (IOM, 2002/2005).
For lactation, a systematic review that examined volumes and the energy content of breast milk showed a weighted mean milk transfer of 779 g/day at 3 to 4 months, 826 g/day at 5 to 6 months, and 894 g/day at 6 months. Among nine studies, no marked increase in milk transfers were reported during the 2- to 5-month period. The weighted mean metabolizable energy content of milk from 25 studies of 777 mother–infant dyads was 2.6 kJ/g (equivalent to 0.62 kcal/g) (Reilly et al., 2005).
Four individual studies on the energy costs of lactation have been conducted since the systematic review mentioned above (see Appendix J for details). Thakkar et al. (2013) measured the energy content of human milk at 65.92 kcal/100 ml starting at 1 month of age and 70.24 kcal/100 ml
at 3 months of age. The energy content of human milk produced for male infants was 24 percent higher at 3 months of age than that produced for females. Two additional studies of the same group of mother–infant dyads used DLW to estimate mean milk intake at 923 g/day at 15 weeks and 999 g/day at 25 weeks among exclusively breastfed infants (Nielsen et al., 2011, 2013). Milk energy content was the same for males and females, 2.72 kJ/g at 15 weeks and 2.62 kJ/g at 25 weeks. Significant differences in total energy intakes by sex were observed at 25 weeks: males consumed 2,582 kJ/d and females 2,403 kJ/d at 15 weeks, and males consumed 2,748 kJ/d and females 2,449 kJ/d at 25 weeks.
Pereira et al. (2019) used whole-body calorimetry to measure REE at 3 and 9 months and TEE at 9 months in a sample of approximately 50 mother–infant dyads. Average breast milk volume was 771 g/d at 3 months, equating to a breast milk energy output of 678 kcal/d. Average breast milk volume was 530 g/day at 9 months (in the presence of complementary feeding), equating to 465 kcal/day. REE increased by 3.2 percent from 3 to 9 months. No difference in TEE was observed between lactating and nonlactating women at 9 months.
FINDINGS AND CONCLUSIONS
Determinants of Resting Energy Expenditure
Findings
The committee’s review of the current evidence confirms that REE is the largest contributor to TEE, varies both within and between individuals, and fluctuates over the course of the human life span. The committee found evidence for a linear relationship between increasing body size and REE. The evidence shows that REE adjusted for body size increases rapidly in infants up to 15 months of age and then begins to decline slowly up to age 20, when REE becomes stable to about age 60 years. Evidence reviewed confirmed that the potential impact of sex on REE is related to differences in body mass and composition. The committee found systematic review evidence was lacking on the influence of Class III or morbid obesity on REE. Also lacking was systematic review evidence on the influence of the gut microbiome and organ tissue energy expenditure to explain the variability in REE among individuals.
The committee finds that data stratified by prepregnancy BMI are lacking, especially for women with overweight and obesity. Further, most of the studies examined did not stratify by adequacy of gestational
weight gain. Among lactating women, evidence reviewed by the committee showed that REE increased by 3.2 percent from 3 to 9 months postpartum, although no significant differences were observed in TEE between lactating and nonlactating women at 9 months.
The committee finds that the current evidence confirms that physical activity is the most variable energy component, ranging from 15 to 50 percent of TEE. Additionally, physical activity decreases with age and is influenced by previous activity levels. Activity energy expenditure and total daily energy expenditure were shown to differ between individuals with and without obesity in terms of absolute levels, but differences disappeared after adjusting for FFM and body weight. Systematic review evidence on the influence of movement economy and motor coordination, particularly in persons with obesity, remains lacking.
Conclusions
The committee concludes that overall, the evidence to support an interaction between BMI and REE is limited, especially to examine the influence of BMI on REE by age/sex or life stage. Further, the total energy requirements for pregnancy have not been aligned with current recommendations for rates of weight gain. The IOM (2002/2005) energy requirements may have overestimated requirements during pregnancy among women with overweight or obesity.
Race and Ethnicity
Findings
The committee finds that race and ethnicity are not modifiable factors but rather social constructs that act as proxies for other determinants. While studies reported a significant lower REE among Black compared to White adults, regional body composition differences, and differences in mitochondrial function and mitochondrial DNA haplotypes provide potential explanations for these data. Furthermore, using ancestry informative markers may help explain the variability across studies reporting differences in energy expenditure between Black and White individuals.
Conclusions
The committee concludes that a better understanding of whether race/ethnicity reliably and consistently affects energy expenditure or is a social and political construct that serves as a proxy for other determinants affecting
energy expenditure such as cultural, environmental, physical activity, and/or behavioral differences, is crucial to both research and public health efforts.
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