Fernandez Galgani CurrObRep

Current Obesity Reports (2019) 8:88–97
https://doi.org/10.1007/s13679-019-00339-z
ETIOLOGY OF OBESITY (T GILL, SECTION EDITOR)
Issues in Measuring and Interpreting Energy Balance and Its
Contribution to Obesity
Rodrigo Fernández-Verdejo 1 & Carolina Aguirre 1 & Jose E. Galgani 1,2
Published online: 22 March 2019
# Springer Science+Business Media, LLC, part of Springer Nature 2019
Abstract
Purpose of Review Obesogenic environment challenges individuals’ ability to preserve energy homeostasis, leading to weight
gain. To understand how this energy imbalance proceeds, several methods and analytical procedures to determine energy intake
and expenditure are currently available. However, these methods and procedures are not exempt from issues that may lead to
equivocal conclusions. Our purpose herein is to discuss major issues involved in energy balance assessment.
Recent Findings Measurement of energy intake mostly relies on self-report methods that provide inaccurate data. In contrast,
determination of energy expenditure is more accurate as long as methodological and analytical issues are correctly addressed.
Summary Accurate measurements of energy expenditure can be obtained with the current methods once issues in measuring and
interpreting data are correctly addressed. However, development of new technologies to measure energy intake is imperative to
further understand the small and chronic energy imbalance leading to obesity.
Keywords Energy expenditure . Carbohydrate balance . Body composition . Indirect calorimetry . Metabolic rate
Introduction
A mismatch between energy intake and energy expenditure results in changes in body weight and composition.
Due to the worldwide obesity pandemic, efforts have been
made to determine which factors increase energy intake
relative to expenditure. Special interest exists in identifying inter-individual factors that explain differential susceptibility to weight gain. For this purpose, reliable assessment of energy intake and energy expenditure is essential. Several methods for the assessment of these components are available. Each method carries issues that, if
not considered, may lead to equivocal results and interpretations. Our aim is to discuss the main issues in
This article is part of the Topical Collection on Etiology of Obesity
* Jose E. Galgani
[email protected]
1
Carrera de Nutrición y Dietética, Departamento de Ciencias de la
Salud, Facultad de Medicina, Pontificia Universidad Católica de
Chile, Santiago, Chile
2
Departamento de Nutrición, Diabetes y Metabolismo, Facultad de
Medicina, Pontificia Universidad Católica de Chile, Avda. Libertador
Bernardo O’Higgins 340, Santiago, Chile
measuring and interpreting energy intake and expenditure
in the context of human obesity.
Energy Intake
Recall of Food Eaten
This approach quantitatively assesses recent food intake. The
method usually comprises the collection of foods consumed
during the last day [1]. Although easy to administer, the main
issue is that a single 24-h recall may not reflect habitual intake.
Thus, Ma et al. [2] recommended that three 24-h recalls
should be applied to estimate energy intake. Because reported
intake tends to be higher on weekends, a combination of
weekdays and weekend is advised [2].
Food Records
By weighing the food and drinks consumed over a time frame,
usually 3–7 days, food records are considered the most accurate method of dietary assessment [3]. However, the trouble of
weighing and recording intake over several days makes subjects to alter their dietary pattern as well as under-report their
intake [4, 5].
Curr Obes Rep (2019) 8:88–97
89
Interpreting Energy Intake
Indirect Calorimetry
Translating the amount/type of food consumed into energy intake is a major issue. First, energy intake is calculated by estimating the size of the portions consumed,
which is subjective. Second, energy intake is affected
by whether raw (by complete combustion) or metabolizable (by applying Atwater factors) food energy is considered. Such inconsistency emerges when the proportion
of energy from carbohydrates, lipids, and proteins is
calculated.
Energy intake equals energy expenditure under conditions
of stable body weight. Measurements of total energy expenditure can therefore be used to validate self-reported energy
intake. Doubly labeled water (DLW), the gold standard to
measure total energy expenditure under free-living conditions,
is used for this purpose. Using this approach, self-report
methods have been shown to underestimate energy intake
[5–7]. Obesity, sex, dieting, socioeconomic status, motivation,
social expectation, and the nature of the testing environment
contribute to under-report of energy intake [5]. Considering
these inaccuracies, an expert Energy Balance Measurement
Working Group proposed discontinuing the use of selfreport for estimating energy intake [8].
New technologies have been developed to improve the
limitations of energy intake measurement. A recent review
[9•] described three new methods for estimating energy intake, i.e., devices that monitor intake through sensors,
smartphone-based photographic methods linked to food databases, and a predictive equation based on frequent measurements of body weight over extended periods. A pilot study
using the remote food photography method showed energy
intake values concordant with energy expenditure measured
by DLW [10].
Indirect calorimetry involves the measurement of gas exchange (O 2 consumption [VO 2 ] and CO 2 production
[VCO2]) to estimate substrate oxidation, and thus calculate
energy expended. The conditions to conduct an indirect calorimetry test are worth considering for standard assessment.
Guidelines for the best practices have been published elsewhere [13]. Briefly, individuals need to fast for ≥ 7 h to remove any influence on energy demand from nutrient ingestion, digestion, absorption and utilization. Subjects must rest
quietly for 30 min before testing, and the recommended measuring position is supine, with ambient room temperature at
around 22–25 °C [13]. Additionally, certain hours before testing, individuals must abstain from smoking (> 2.5 h), caffeine/
stimulants (> 4 h), and exercise (depends on workload).
However, evidence is inconclusive regarding the minimal
time to avoid any influence [13]. Finally, subjects should be
encouraged to breathe normally to prevent hyperventilation,
as this will mostly increase VCO2 and thus, the VCO2-to-VO2
ratio will be artificially high, affecting the estimation of fuel
oxidation (but not energy expenditure).
Another issue in the determination of RMR is the inaccuracy of commercial metabolic carts. Schadewaldt et al. infused
two popular metabolic carts with known flows of N2 and CO2
to simulate a given VO2 and VCO2 [14]. They found that
measured VO2 and VCO2 deviated from expected values following an unpredictable pattern, so a fixed correction factor
cannot be applied. We recently confirmed those results [15••].
Importantly, we observed that inaccuracy of the metabolic cart
led on average to 14% overestimation in RMR, i.e., 97 to
279 kcal/day depending on the magnitude of RMR [15••].
To overcome this issue, accuracy can be determined by comparing simulated vs. measured gas exchange before or after
each test. Then, gas exchange can be corrected accordingly.
We found that applying this procedure increased the predictive
power of classical determinants of RMR such as body weight,
sex, and age [16].
Once VO2 and VCO2 are obtained, an equation allows
calculating metabolic rate. The most commonly used is
Weir’s equation [17], although the equations by Consolazio
et al. and by Brouwer are also used [18]. These equations
include urinary N2 excretion, but urinary N2 is seldom measured in studies. Protein oxidation is therefore dismissed or
assumed a fixed value (75 g/day or 0.83 g/kg × day) [19].
The conditions under which the indirect calorimetry is conducted, the gas exchange accuracy, the equation used, and the
protein oxidation assumptions are issues worth considering
when measuring and reporting RMR. Table 1 shows how
these issues may determine an intra-subject difference of up
to 167 kcal/day (e.g., measured exchange, Weir equation, and
no protein oxidation vs. measured and corrected exchange,
Consolazio equation, and 75 g/day protein oxidation).
Energy Expenditure
The factors that determine daily energy expenditure are resting
metabolic rate (RMR), physical activity, and the thermic effect
of food (TEF). These factors account for about 60%, 30%, and
10% of daily energy expenditure, respectively [11]. Different
methods are used for their measurement, entailing issues
worth considering.
Resting Metabolic Rate
Resting metabolic rate represents the energy to maintain bodily functions at rest and accounts for most daily energy expenditure [11, 12]. Common methods to approach RMR are indirect calorimetry and predictive equations.
90
Curr Obes Rep (2019) 8:88–97
Table 1 Resting metabolic rate (RMR) calculated using indirect
calorimetry with different parameters, and using predictive equations
Indirect calorimetry
RMR (kcal/day)
Measured gas exchange
294.3
VO2 (L/day)
220.2
VCO2 (L/day)
Weir equation
No protein
75 g/day protein
1403
1377
0.83 g/kg × day protein 1378
Consolazio equation
No protein
1368
75 g/day protein
1332
0.83 g/kg × day protein 1333
Brouwer equation
Measured and corrected gas exchange
272.9
VO2 (L/day)
207.5
VCO2 (L/day)
No protein
75 g/day protein
1402
1385
0.83 g/kg × day protein
Weir equation
No protein
75 g/day protein
0.83 g/kg × day protein
Consolazio equation
1385
1305
1279
1280
No protein
1272
75 g/day protein
1236
0.83 g/kg × day protein 1238
Brouwer equation
No protein
1304
1287
1288
Predictive equations
WHO equation
Lazzer equation
75 g/day protein
1 g/kg × day protein
RMR (kcal/day)
1548
1638
de la Cruz equation
ten Haaf equation
1637
1621
Oxford equation (not including height)
1537
Calculations for an actual 49-year-old woman of 86.5 kg and 1.63 m,
assuming 6.25 g of urinary nitrogen excretion per gram of protein
oxidized
Importantly, inaccuracy in gas exchange explains most of the
difference in estimates. This analysis highlights the need for
careful consideration of these factors, particularly for comparison among individuals and studies. Since daily energy imbalance leading to weight gain is small for most individuals,
precise and accurate measurements of RMR are required to
identify metabolic traits that confer differential propensity to
weight gain.
Predictive Equations
RMR relates to characteristics of the subjects such as body
weight, age, sex, and height [20, 21]. These relationships have
been exploited to generate predictive equations. Predictive
equations allow estimating RMR easily and inexpensively,
but the results are less accurate than indirect calorimetry at
individual level. Although equations are easy to apply, the
selection of the appropriate equation is a main issue.
Equations have been generated from populations with specific characteristics. Therefore, equations are presumably not
applicable for populations differing in those characteristics.
For instance, Lazzer’s equation originated from severely
obese Italian women [22], de la Cruz’s equation from
Spanish population [23], and ten Haaf’s equation from recreational athletes from North Europe [24]. The conventionally
accepted equations for all populations are the WHO equations
[25], which were later updated by excluding individuals with
relatively high RMR and including individuals from Central
America (Oxford equations) [20]. Table 1 shows how these
equations can produce an intra-individual difference of up to
101 kcal/day (Lazzer vs. Oxford). Notably, it is unclear how
critical the phenotypic differences or similarities between populations are for explaining concordance between predicted
and measured RMR. Moreover, the extent to which equations
concord with a reliable measurement of RMR is not necessarily predicted by the phenotypic characteristics of populations
[15••]. Clearly reporting the equation used is nevertheless essential to compare results between studies.
However, the question arises as to which equation works
better for populations other than those used to generate the
equations. Comparing predictive equations against a reliable
RMR determination may serve to identify valid equations for
specific populations. We recently analyzed 13 predictive
equations by comparing their estimations against the RMR
measured by indirect calorimetry with post-calorimetric correction [15••]. In our admixed Chilean population, the Oxford
equations showed the higher accuracy. Future studies are required to identify valid predictive equations in other populations. The generation of population-specific equations, based
on indirect calorimetry with calorimetric correction, is anticipated to improve RMR estimation. Such information can be
critical for identifying individuals with high or low RMR.
Interpreting RMR
A low RMR may predispose to weight gain, because
RMR accounts for a high proportion of daily energy expenditure. However, people with obesity have higher
RMR (in kcal/day) than subjects with normal weight
[11, 26]. This situation seems counterintuitive, but it is
explained by the direct association between body weight
and RMR [20]. Specifically, fat-free mass accounts for ≈
60% of the variance in RMR, and fat mass for ≈ 7% [12,
21]. RMR should thus be adjusted for these covariates for
identification of individuals with low RMR. This correction is also required to study the metabolic adaptation
induced by weight loss or gain [27•].
Curr Obes Rep (2019) 8:88–97
A common correction is to express RMR per kilogram of
body weight (or fat-free mass). Since the relationship between
RMR and body weight does not have a zero intercept, using
ratios is wrong. The alternative consists in computing the difference between measured and predicted RMR (i.e., RMR
residual) [27•]. Individuals with negative residuals have lower
RMR than predicted. Those individuals have elevated risk of
weight gain [28]. Considering that ≈ 27% of the variance in
RMR remains unexplained [21], whether a “low RMR” reflects an intrinsic metabolic defect or results from another
unmeasured anthropometric variable (e.g., organ size [29])
remains unclear.
PAEE
Physical activity energy expenditure (PAEE) represents the
energy required for body movement, which can be volitional
exercise or spontaneous activity [11]. It is the most variable
component of daily energy expenditure. PAEE accounts for ≈
30% of daily energy expenditure but ranges from none in
people with no activity to > 50% in very active people
[30••]. The methods to assess PAEE involve measuring total
energy expenditure, and then PAEE is derived by subtracting
the energy expended at rest (and sometimes the TEF). The
most common methods are DLW, self-report, and
accelerometers.
91
measuring RMR may also affect PAEE. To accurately estimate PAEE, DLW needs to be accompanied with accurate
determinations of RMR. Using predictive equations for
RMR, and assuming a fixed value for the TEF (usually 10%
of total energy expenditure—see “Thermic effect of food”
section below), reduces the accuracy of PAEE calculation.
After considering RMR, physical activity is responsible for
most of the remaining energy expenditure [12]. The total energy expenditure-to-RMR ratio is therefore an index of physical activity. This index is known as physical activity level
(PAL) and represents the energy requirements of physical activity as multiples of RMR [25]. DLW is used to identify the
PAL of different lifestyles. Thus, the PAL for a sedentary or
light activity lifestyle is 1.40–1.69, for an active or moderately
active lifestyle is 1.70–1.99, and for a vigorous or vigorously
active lifestyle is 2.00–2.40 [25]. In the clinic, determination
of PAL by the self-report method (see next sub-section) allows
to estimate daily energy requirements, as PAL × RMR [25].
Note, however, that PAL has been considered physiologically
and mathematically wrong for estimating total energy requirements [33] and for comparing subjects who differ in body
weight [34]. This is because the relationship between total
energy expenditure and RMR does not have a zero intercept.
Moreover, the estimation of total energy requirements
worsens if PAL is determined by self-report.
Self-Report
Doubly Labeled Water
DLW determines CO2 production based on the turnover rates
of H and O2. Subjects ingest water labeled with 2H and 18O
(i.e., doubly labeled water) to enrich their body water compartment. 2H is eliminated in water, while 18O is eliminated in
water and CO2. Elimination rate for 18O is thus faster than for
2
H, and the difference between both elimination rates represents VCO2 (the total volume of carbon dioxide that you
breathe out). Consequently, this technique allows determining
VCO2 rate over the course of 1–2 weeks [30••]. VO2 over that
period is calculated from CO2 production once a given dietary
carbohydrate, lipid, and protein composition (i.e., food quotient [31, 32]) is assumed. Such way of estimating VO2, and
then energy expenditure, is acceptable if null energy balance is
assumed.
DLW is the reference method to measure total energy expenditure under free-living conditions [25]. PAEE can be subsequently derived from total energy expenditure. The main
methodological issues of DLW have been recently discussed,
i.e., the observational interval, isotope dose, sample collection, and sample analysis [30••]. Herein, we focus on issues
that arise when using DLW to estimate PAEE.
PAEE is estimated by subtracting RMR and the TEF from
the DLW-derived total energy expenditure. This implies that
besides the methodological issues of DLW [30••], issues in
PAEE can be calculated using self-reports of the physical activity performed in a typical day. Extensive data exist with
estimations of the energy cost of several activities, including
home, occupation, self-care, and sports [25, 35]. Thus, total
energy expenditure can be calculated, and PAEE derived by
subtracting RMR. Although easy to apply, this method has
major issues. The main issue is that people tend to overreport PAEE [36]. This may lead to erroneous conclusions
[8]. Another issue is that the TEF is often dismissed or assumed as 10% of total energy expenditure. This also reduces
the accuracy of PAEE.
In the self-report, the energy cost (intensity) of activities is
expressed as metabolic equivalents (MET). The MET is the
ratio between the rate of energy expended during an activity to
the rate of energy expended at rest [25, 37]. For instance, an
activity of 1.5 MET requires a rate of energy expenditure 50%
higher than the rate of energy expenditure at rest. The product
of the activity MET times the minutes spent in the activity is
an index of energy expended [25, 35]. Table 2 shows an example for the activities of a woman on a typical day.
The approach used to calculate total energy expenditure
and PAEE represents another issue. Table 2 displays two approaches (A and B). (A) This approach calculates PAL by
summing up the index of energy expended for each activity,
and dividing that value by 1440 min (minutes in 1 day). The
92
Curr Obes Rep (2019) 8:88–97
Table 2 Calculations of physical activity energy expenditure (PAEE) from self-reported data according to (A) FAO/WHO/UNU Expert Consultation
(2005), and (B) Ainsworth et al. (2011)
Activities
Intensity
(MET)a
× Time
(min)
= Index of energy expended
(MET × min)
(A) Considering PAL
(B) Considering 1
MET = 1 kcal/kg × h
Energy
expenditure (kcal)
PAEE
(kcal)
Energy
expenditure (kcal)
PAEE
(kcal)
Sleeping
1.0
480
480
–
–
692
–
Self-care in general
Eating sitting
2.3
1.5
60
80
138
120
–
–
–
–
199
173
–
–
Watching TV
Sitting working in the
computer
Car driving
Child care in general
1.3
1.5
180
420
234
630
–
–
–
–
337
908
–
–
2.5
2.5
60
100
150
250
–
–
–
–
216
360
–
–
Running at 6.4 km/h
Household tasks
6.0
2.8
30
30
180
84
–
–
–
–
260
121
–
–
1440
2266
2413
876
Total for the whole day
3266
1190
Theoretical data for a woman (49 years old, 86.5 kg, 1.63 m) with an office work, who runs 30 min in her leisure time. Her physical activity level (PAL) is
1.57 [2266 MET × min/1440 min]. Total energy expenditure and PAEE were calculated with two approaches: (A) According to the PAL, where [total
energy expenditure = PAL × RMR], [PAEE = total energy expenditure − RMR], and [RMR = 1537 kcal/day by Oxford equation]. (B) Considering that
one MET represents 1 kcal/kg × h, where total energy expenditure is the sum of the energy expended in each activity computed as [activity MET × time
in h × 86.5 kg × 1 kcal/kg × h], and [PAEE = total energy expenditure − 1 kcal/kg × h × 86.5 kg × 24 h]
a
From Ainsworth et al. (2011)
PAL in the example is 1.57 (2266 MET × min/1440 min).
Total energy expenditure is the product of PAL times RMR
(1.57 × 1537 kcal/day [Oxford equation] = 2413 kcal/day)
[25]. (B) This approach assumes that 1 MET equals 1 kcal/
kg × h, an assumption that simplifies calculations but increases inaccuracy [35]. Energy expenditure of each activity
can be computed as activity MET × time (h) × weight (kg) ×
1 kcal/kg × h [35], simplified as activity MET × time (h) ×
weight (kg). Total energy expenditure is obtained by summing
up the energy expenditure of all activities on a day. With the
data in Table 2, total energy expenditure reaches 3266 kcal/
day, and PAEE reaches 1190 kcal/day (3266 kcal/day
− [1 kcal/kg × h × 86.5 kg × 24 h]). Thus, total energy expenditure and PAEE can differ by 853 kcal/day and 314 kcal/day,
respectively, depending on the method used.
Given these drawbacks, the Energy Balance Measurement
Working Group recommends discontinuing the use of selfreported PAEE [8]. The group states that inaccuracy is so high
that self-reported PAEE does not have place in scientific research. However, more conservative standpoints have also
been raised [38], along with recommendations to improve
self-reporting [39].
Accelerometers
Accelerometers are small devices that measure accelerations
in up to three axes. By attaching them to a subject’s body,
accelerometers register bodily movements. Results are often
provided as “counts” in a time interval. These devices represent a valuable technological advance for the assessment of
PAEE. However, there are issues worth considering when
acquiring and interpreting their data output.
The most relevant issue is the accuracy of accelerometers to
measure PAEE. Since PAEE is derived from total energy expenditure, accelerometers are often validated against DLW.
Jeran et al. recently reviewed the literature to determine the
capacity of different accelerometers to predict PAEE [40•].
Most studies computed PAEE as total energy expenditure
(by DLW) − RMR (by indirect calorimetry) − TEF (assumed
10% of total energy expenditure). They observed that accelerometers explained a median of 26% of the variance of DLWderived PAEE. Results ranged between 4 and 80%, and increased to 13–86% when other predictors (weight, fat-free
mass, etc.) were considered [40•].
The inability of accelerometers to detect some activities may
underlie the unexplained variance. For instance, an accelerometer placed on the hip cannot detect activities such as fidgeting,
which may represent an important proportion of total energy
expenditure [12, 41]. Finally, to what extent discordances in
PAEE by accelerometers vs. DLW result from issues related
to accelerometers or to inaccuracies in DLW is unknown.
Another issue relates to practical considerations for acquiring data. Migueles et al. reviewed this issue for one of the most
used accelerometers [42]. The authors provided recommendations by age group regarding placement, sampling frequency,
recording period, and valid day and week. However, further
Curr Obes Rep (2019) 8:88–97
studies are required to better determine the influence of these
parameters on accelerometer-derived PAEE [42]. In any case,
recording period, placement, and wear time do not significantly influence the variance of PAEE explained by the accelerometers [40•].
93
purposes. PAL serves to estimate daily energy requirements,
independently of the intensity of physical activity. The index
based on moderate-to-vigorous physical activity serves to
identify subjects at lower risk for non-communicable diseases.
TEF
Interpreting PAEE
An elevated PAEE is expected to help preventing and treating
obesity. However, PAEE (in kcal/day) may be similar or even
larger for obese compared to normal-weight counterparts [11,
43]. This is because the cost of physical activities is proportional to body weight. Therefore, for the same physical activity, subjects with obesity expend more energy compared to
subjects with normal weight [12].
Correction for body size is thus necessary to compare
PAEE between individuals. Thus, PAEE can be computed as
the difference in total energy expenditure vs. RMR plus the
TEF. Then, the residuals of the relationship between PAEE
and body weight represent PAEE adjusted for body size [26].
Another aspect worth discussing in relation to PAEE is the
categorization of subjects as “physically active”. As described
in the DLW sub-section, the PAL is used to identify lifestyles
with different levels of activity. A physically active lifestyle is
promoted to prevent the development of non-communicable
diseases [44]. However, the PAL of the population has not
changed from 1998 to 2006 [45], a period in which obesity
increased. PAL is neither different between individuals with
normal weight, overweight and obesity [43]. The benefits of a
physically active lifestyle seem, thus, not supported.
Nevertheless, the guidelines of physical activity for health
focus specifically on activities of moderate-to-vigorous intensity, recommending to achieve ≥ 500 MET × min/week [37,
46]. Subjects meeting those recommendations are considered
as physically active. The PAL calculated from DLW is not
useful for this categorization, because the intensity of activities cannot be measured. But, accelerometers provide an alternative, as activity counts relate directly to the intensity of
physical activity [47]. Thus, counts per minute cutoffs for
moderate and vigorous intensities have been established [47,
48], and used to identify physically active subjects [49].
The bottom line is that someone may be classified as “sedentary” according to PAL, but as physically active according
to the moderate-to-vigorous physical activity criterion. Table 2
shows an example for a woman with a PAL of 1.57, thus
classified as sedentary. Note that running (6.0 MET) at
6.4 km/h for 30 min is the only activity of moderate-tovigorous intensity she performs [37]. By repeating this activity pattern four times per week, she would accumulate 720
MET × min/week (180 MET × min × 4 days) of moderate-tovigorous physical activity, classifying her as physically active.
The difference between these two indexes of physical activity
lays in the fact that they were designed to accomplish different
TEF represents the energy required for ingestion and digestion
of food, along with absorption, transport, and storage of nutrients. It is the smallest component of total energy expenditure, accounting for ≈ 10% over the entire day [11, 12]. TEF
represents the increase in energy expenditure above RMR
induced by meal ingestion. It is commonly expressed as percentage, by dividing the increase in energy expenditure by the
caloric content of the meal [50]. Indirect calorimetry is used to
measure TEF. Besides issues related to indirect calorimetry,
others affecting specifically the assessment of TEF have been
previously reviewed [51]. These include caloric content and
composition of the test meal, and duration of the measurement
period.
The caloric content of the meal directly influences TEF
[52]. Proteins and lipids show the highest and lowest thermogenic effect, respectively [50, 53]. The duration of the measurement in energy expenditure is also important to consider.
This time period is highly variable among studies, which may
explain some discordant results [51]. A careful control of
these variables is thus required for an accurate determination
of TEF [51].
Besides the activation of energy-consuming processes, the
extent of TEF also depends on the adaptive inhibition of certain energy-consuming processes. We recently measured the
TEF after two successive (3-h apart) 75-g glucose loads in
healthy humans. Compared with the first glucose load, the
TEF after the second load was attenuated [54]. We speculate
that the attenuated TEF may partly result from stronger suppression of the energy-consuming endogenous glucose production, as demonstrated in another study with a similar design [55].
Energy Balance and Obesity
Obesity results from a long-term positive energy imbalance
and has a large genetic component. Evolutionary theories have
been proposed to explain the obesity pandemic [56••]. Herein,
we discuss how genetic predisposition impacts the components of the energy balance, augmenting the chances to gain
weight when living in an obesogenic environment. To what
extent each of these components explains the obesity pandemic is unknown.
Of note, we describe the effects of genetic predisposition
on each component separately. These components are, however, interrelated [11, 56••]. A positive energy balance, due to
94
Curr Obes Rep (2019) 8:88–97
Fig. 1 Main issues in measuring and interpreting energy balance. RMR resting metabolic rate, PAEE physical activity energy expenditure, TEF thermic
effect of food, DLW doubly labeled water
high energy intake and/or low energy expenditure, progressively increases body weight. This results in concomitant increases in energy expenditure until reaching energy balance at
a higher body weight. Different models for fat storage predict
such response [11, 56••]. The dual-intervention point model
proposed by Speakman [56••] also predicts an upper limit for
body weight. In subjects with a genetic predisposition to obesity, the upper limit would have drifted towards a higher level.
Prospective and cross-sectional studies in the Pima Indians
have provided valuable insight into the heritability of energy
expenditure and its influence on weight gain. Classic studies
analyzing the heritability and consequences of alterations in
energy expenditure were conducted through indirect calorimetry in respiratory chambers [12]. Family membership was
found to be a determinant of total energy expenditure, independently of body composition, age, and sex [28]. Families
differed by up to ≈ 400 kcal/day in their level of total energy
expenditure. Interestingly, total energy expenditure correlated
negatively with weight change measured (at least) 3 months
after assessment. A proportional-hazards model showed that
total energy expenditure was a significant predictor of weight
gain [28]. These findings suggest that total energy expenditure
is a familial trait, and low levels predispose to weight gain.
The contribution of each component of total energy expenditure has also been studied. In Pima Indians, family membership explained 11% of the variance in RMR independent of
body composition, age, and sex [57]. Notably, subjects with
low RMR had the highest incidence of gaining weight [28].
Genetic factors are also important determinants of the individual levels of physical activity [58]. Subjects with a genetic
predisposition for low physical activity may thus be susceptible to weight gain. In male Pima Indians, the level of spontaneous physical activity correlated negatively with the rate of
weight change [41]. These results suggested that low PAEE
due to low spontaneous physical activity could contribute to
obesity [59]. This idea is also supported by comparisons between different Pima Indian communities. Pima Indians living
in Arizona have higher obesity prevalence and lower PAEE
(adjusted for body weight) than age- and sex-matched Pima
Indians living in Northern Mexico [26]. Reductions in PAEE
may thus be partly responsible for the obesity pandemic.
TEF has also been studied to determine whether low values
associate with obesity. However, contradictory results are observed, in part explained by methodological issues [51].
Importantly, although genetic factors increase the susceptibility to develop obesity, environment factors could override
this predisposition. Lessons learned from the comparison of
American vs. Mexican Pima Indians demonstrate such effect
[60]. Interventions to override genetic predisposition to obesity should thus include modifications of the environmental
causes of obesity [61].
Conclusion
Identifying the contribution of the components of energy balance to the development of obesity might help to prevent and
treat obesity. Thermodynamic laws apply to energy balance
achieved in individuals. Why some individuals remain lean
while many others gain weight despite living in the same
environment is still poorly understood. Development of accurate and precise technologies will allow identifying specific
metabolic phenotypes that are resistant or prone to obesity.
Taking into account the issues of each method (Fig. 1) and
correctly interpreting data are essential to accomplish this
complex task.
Funding This work has been partially funded by Comisión Nacional de
Investigación Científica y Tecnológica-Chile (Fondecyt 1130217 and
1170117 [JEG]).
Compliance with Ethical Standards
Conflict of Interest Rodrigo Fernández-Verdejo, Carolina Aguirre, and
Jose E. Galgani declare they have no conflict of interest.
Curr Obes Rep (2019) 8:88–97
Human and Animal Rights and Informed Consent This article does not
contain any studies with human or animal subjects performed by any of
the authors.
References
Papers of particular interest, published recently, have been
highlighted as:
• Of importance
•• Of major importance
1.
2.
3.
4.
5.
6.
7.
8.
9.•
10.
11.
Morgan RW, Jain M, Miller AB, Choi NW, Matthews V, Munan L,
et al. A comparison of dietary methods in epidemiologic studies.
Am J Epidemiol [Internet]. 1978;107:488–98 Available from:
http://www.ncbi.nlm.nih.gov/pubmed/665663.
Ma Y, Olendzki BC, Pagoto SL, Hurley TG, Magner RP, Ockene
IS, et al. Number of 24-hour diet recalls needed to estimate energy
intake. Ann Epidemiol [Internet]. 2009;19:553–9 Available from:
http://www.ncbi.nlm.nih.gov/pubmed/19576535.
Barrett-Connor E. Nutrition epidemiology: how do we know what
they ate? Am J Clin Nutr [Internet]. 1991;54:182S–7S Available
from: http://www.ncbi.nlm.nih.gov/pubmed/2053559.
Trabulsi J, Schoeller DA. Evaluation of dietary assessment instruments against doubly labeled water, a biomarker of habitual energy
intake. Am J Physiol Endocrinol Metab [Internet]. 2001;281:E891–
9 Available from: http://www.ncbi.nlm.nih.gov/pubmed/11595643.
Hill RJ, Davies PS. The validity of self-reported energy intake as
determined using the doubly labelled water technique. Br J Nutr
[Internet]. 2001;85:415–30 Available from: http://www.journals.
cambridge.org/abstract_S0007114501000654.
Livingstone MB, Prentice AM, Coward WA, Strain JJ, Black AE,
Davies PS, et al. Validation of estimates of energy intake by
weighed dietary record and diet history in children and adolescents.
Am J Clin Nutr [Internet]. 1992;56:29–35 Available from: http://
www.ncbi.nlm.nih.gov/pubmed/1609757.
Archer E, Hand GA, Blair SN. Validity of U.S. nutritional surveillance: National Health and nutrition examination survey caloric
energy intake data, 1971-2010. PLoS One [Internet]. 2013;8:
e76632 Available from: http://www.ncbi.nlm.nih.gov/pubmed/
24130784.
Dhurandhar NV, Schoeller D, Brown AW, Heymsfield SB, Thomas
D, Sørensen TIA, et al. Energy balance measurement: when something is not better than nothing. Int J Obes (Lond) [Internet].
2015;39:1109–13 Available from: http://www.ncbi.nlm.nih.gov/
pubmed/25394308.
Archundia Herrera MC, Chan CB. Narrative review of new
methods for assessing food and energy intake. Nutrients
[Internet]. 2018;10:1064 Available from: http://www.mdpi.com/
2072-6643/10/8/1064. This narrative review discusses
innovative tools for dietary assessment that are under
development to improve the accuracy of self-report energy
intake.
Martin CK, Correa JB, Han H, Allen HR, Rood JC, Champagne
CM, et al. Validity of the remote food photography method (RFPM)
for estimating energy and nutrient intake in near real-time. Obesity
(silver spring) [Internet]. 2012;20:891–9 Available from: http://
www.ncbi.nlm.nih.gov/pubmed/22134199.
Hall KD, Guo J. Obesity Energetics: Body Weight Regulation and
the Effects of Diet Composition. Gastroenterology [Internet].
Elsevier, Inc; 2017;152:1718–1727.e3. Available from: https://doi.
org/10.1053/j.gastro.2017.01.052.
95
12.
Ravussin E, Lillioja S, Anderson TE, Christin L, Bogardus C.
Determinants of 24-hour energy expenditure in man. Methods
and results using a respiratory chamber. J Clin Invest [Internet].
1986;78:1568–78 Available from: http://www.ncbi.nlm.nih.gov/
pmc/articles/PMC423919/.
13. Fullmer S, Benson-Davies S, Earthman CP, Frankenfield DC,
Gradwell E, Lee PSP, et al. Evidence analysis library review of best
practices for performing indirect calorimetry in healthy and noncritically ill individuals. J Acad Nutr Diet [Internet]. 2015;115:
1417–1446.e2 Available from: http://www.ncbi.nlm.nih.gov/
pubmed/26038298.
14. Schadewaldt P, Nowotny B, Strassburger K, Kotzka J, Roden M.
Indirect calorimetry in humans: a postcalorimetric evaluation procedure for correction of metabolic monitor variability. Am J Clin
Nutr [Internet]. 2013;97:763–73 Available from: http://www.ncbi.
nlm.nih.gov/pubmed/23446893.
15.•• Galgani JE, Castro-Sepulveda M, Pérez-Luco C, FernándezVerdejo R. Validity of predictive equations for resting metabolic
rate in healthy humans. Clin Sci (Lond) [Internet]. 2018;132:1741–
51 Available from: http://www.ncbi.nlm.nih.gov/pubmed/
29967004. In this report, a post-calorimetric method was used
to correct the measured gas exchange during indirect calorimetry. The measured and corrected resting metabolic rate was
then used to validate 13 equations used to predict resting metabolic rate.
16. Galgani JE, Castro-Sepulveda MA. Influence of a gas exchange
correction procedure on resting metabolic rate and respiratory quotient in humans. Obesity (Silver Spring) [Internet]. 2017;25:1941–7
Available from: http://www.ncbi.nlm.nih.gov/pubmed/28924987.
17. WEIR JBDB. New methods for calculating metabolic rate with
special reference to protein metabolism. J Physiol [Internet].
1949;109:1–9 Available from: http://www.ncbi.nlm.nih.gov/
pubmed/15394301.
18. Jéquier E, Acheson K, Schutz Y. Assessment of energy expenditure
and fuel utilization in man. Annu Rev Nutr [Internet]. 1987;7:187–
208 Available from: http://www.ncbi.nlm.nih.gov/pubmed/
3300732.
19. Li M, Sun F, Piao JH, Yang XG. Protein requirements in healthy
adults: a meta-analysis of nitrogen balance studies. Biomed Environ
Sci [Internet]. 2014;27:606–13 Available from: http://www.ncbi.
nlm.nih.gov/pubmed/25189607.
20. Henry CJK. Basal metabolic rate studies in humans: measurement
and development of new equations. Public Health Nutr [Internet].
2005;8:1133–52 Available from: http://www.journals.cambridge.
org/abstract_S1368980005001394.
21. Johnstone AM, Murison SD, Duncan JS, Rance KA, Speakman JR.
Factors influencing variation in basal metabolic rate include fat-free
mass, fat mass, age, and circulating thyroxine but not sex, circulating leptin, or triiodothyronine. Am J Clin Nutr [Internet]. 2005;82:
941–8 Available from: http://www.ncbi.nlm.nih.gov/pubmed/
16280423.
22. Lazzer S, Agosti F, Silvestri P, Derumeaux-Burel H, Sartorio A.
Prediction of resting energy expenditure in severely obese Italian
women. J Endocrinol invest [Internet]. 2007;30:20–7 Available
from: http://www.ncbi.nlm.nih.gov/pubmed/17318018.
23. de la Cruz MS, de Mateo SB, Camina Martín MA, Carreño Enciso
L, Miján de la Torre A, Galgani Fuentes JE, et al. Proposal for a new
formula for estimating resting energy expenditure for healthy spanish population. Nutr Hosp [Internet]. 2015;32:2346–52 Available
from: http://www.ncbi.nlm.nih.gov/pubmed/26545697.
24. ten Haaf T, Weijs PJM. Resting energy expenditure prediction in
recreational athletes of 18-35 years: confirmation of Cunningham
equation and an improved weight-based alternative. PLoS One
[Internet]. 2014;9:e108460 Available from: http://www.ncbi.nlm.
nih.gov/pubmed/25275434.
96
Fao/WHO/UNU Expert Consultation. Human energy requirements: report of a joint FAO/ WHO/UNU Expert Consultation.
Food Nutr Bull [Internet]. 2005;26:166. Available from: http://
www.ncbi.nlm.nih.gov/pubmed/15810802
26. Esparza J, Fox C, Harper IT, Bennett PH, Schulz LO, Valencia ME,
et al. Daily energy expenditure in Mexican and USA Pima indians:
low physical activity as a possible cause of obesity. Int J Obes Relat
Metab Disord [Internet]. 2000;24:55–9 Available from: http://www.
ncbi.nlm.nih.gov/pubmed/10702751.
27.• Galgani JE, Santos JL. Insights about weight loss-induced metabolic adaptation. Obesity (Silver Spring) [Internet]. 2016;24:277–8
Available from: http://www.ncbi.nlm.nih.gov/pubmed/26813520.
This report describes the correct analytical procedure to
study resting metabolic rate in the context of metabolic
adaptation.
28. Ravussin E, Lillioja S, Knowler WC, Christin L, Freymond D,
Abbott WG, et al. Reduced rate of energy expenditure as a risk
factor for body-weight gain. N Engl J Med [Internet]. 1988;318:
467–72 Available from: http://www.ncbi.nlm.nih.gov/pubmed/
3340128.
29. Bosy-Westphal A, Reinecke U, Schlörke T, Illner K, Kutzner D,
Heller M, et al. Effect of organ and tissue masses on resting energy
expenditure in underweight, normal weight and obese adults. Int J
Obes Relat Metab Disord [Internet]. 2004;28:72–9 Available from:
http://www.ncbi.nlm.nih.gov/pubmed/14647174.
30.•• Westerterp KR. Doubly labelled water assessment of energy expenditure: principle, practice, and promise. Eur J Appl Physiol
[Internet]. Springer Berlin Heidelberg. 2017;117:1277–85
Available from: http://www.ncbi.nlm.nih.gov/pubmed/28508113.
This report comprehensively reviewes the doubly labeled
water method, including historic and methodological aspects.
31. Hill JO, Peters JC, Reed GW, Schlundt DG, Sharp T, Greene HL.
Nutrient balance in humans: effects of diet composition. Am J Clin
Nutr [Internet]. 1991;54:10–7 Available from: http://www.ncbi.
nlm.nih.gov/pubmed/2058571.
32. Livesey G, Elia M. Estimation of energy expenditure, net carbohydrate utilization, and net fat oxidation and synthesis by indirect
calorimetry: evaluation of errors with special reference to the detailed composition of fuels. Am J Clin Nutr [Internet]. 1991;54:10–
7 Available from: http://www.ncbi.nlm.nih.gov/pubmed/3281434.
33. Goran MI. Estimating energy requirements: regression based prediction equations or multiples of resting metabolic rate. Public
Health Nutr [Internet]. 2005;8:1184–6 Available from: http://
www.journals.cambridge.org/abstract_S1368980005001412.
34. Carpenter WH, Poehlman ET, O’Connell M, Goran MI. Influence
of body composition and resting metabolic rate on variation in total
energy expenditure: a meta-analysis. Am J Clin Nutr [Internet].
1995;61:4–10 Available from: http://www.ncbi.nlm.nih.gov/
pubmed/7825536.
35. Ainsworth BE, Haskell WL, Herrmann SD, Meckes N, Bassett DR,
Tudor-Locke C, et al. 2011 compendium of physical activities: a
second update of codes and MET values. Med Sci Sports Exerc.
2011;43:1575–81.
36. Walsh MC, Hunter GR, Sirikul B, Gower BA. Comparison of selfreported with objectively assessed energy expenditure in black and
white women before and after weight loss. Am J Clin Nutr
[Internet]. 2004;79:1013–9 Available from: http://search.
ebscoh ost.com/lo gin.aspx?direct=true&db=rzh&AN=
106672022&site=ehost-live.
37. Garber CE, Blissmer B, Deschenes MR, Franklin BA, Lamonte
MJ, Lee IM, et al. Quantity and quality of exercise for developing
and maintaining cardiorespiratory, musculoskeletal, and
neuromotor fitness in apparently healthy adults: guidance for prescribing exercise. Med Sci Sports Exerc. 2011;43:1334–59.
38. Satija A, Yu E, Willett WC, Hu FB. Objective measures are complementary to, rather than a replacement for, self-reported methods.
Curr Obes Rep (2019) 8:88–97
25.
39.
40.•
41.
42.
43.
44.
45.
46.
47.
48.
49.
50.
51.
52.
53.
Int J Obes (Lond) [Internet]. 2015;39:1179 Available from: http://
www.ncbi.nlm.nih.gov/pubmed/25924712.
Booth DA, Laguna-Camacho A. Physical versus psychosocial measures of influences on human obesity. Comment on Dhurandhar
et al. Int J Obes (Lond) [Internet]. Nat Publ Group; 2015;39:
1177–1178. Available from: https://doi.org/10.1038/ijo.2015.62.
Jeran S, Steinbrecher A, Pischon T. Prediction of activity-related
energy expenditure using accelerometer-derived physical activity
under free-living conditions: a systematic review. Int J Obes
[Internet]. Nature Publishing Group; 2016;40:1187–1197.
Available from: https://doi.org/10.1038/ijo.2016.14. This
systematic review gathered information regarding the
capacity of accelerometers to predict physical activity energy
expenditure.
Zurlo F, Ferraro RT, Fontvielle AM, Rising R, Bogardus C,
Ravussin E. Spontaneous physical activity and obesity: crosssectional and longitudinal studies in Pima Indians. Am J Physiol
[Internet]. 1992;263:E296–300 Available from: http://www.ncbi.
nlm.nih.gov/pubmed/1514610.
Migueles JH, Cadenas-Sanchez C, Ekelund U, Delisle Nyström C,
Mora-Gonzalez J, Löf M, et al. Accelerometer data collection and
processing criteria to assess physical activity and other outcomes: a
systematic review and practical considerations. Sports Med
[Internet]. 2017;47:1821–45 Available from: http://www.ncbi.nlm.
nih.gov/pubmed/28303543.
Westerterp KR. Physical activity and physical activity induced energy expenditure in humans: measurement, determinants, and effects. Front Physiol [Internet]. 2013;4:90 Available from: http://
www.ncbi.nlm.nih.gov/pubmed/23637685.
Handschin C, Spiegelman BM. The role of exercise and
PGC1alpha in inflammation and chronic disease. Nature
[Internet]. 2008;454:463–9. Available from: http://www.ncbi.nlm.
nih.gov/pubmed/18650917.
Westerterp KR, Speakman JR. Physical activity energy expenditure
has not declined since the 1980s and matches energy expenditures
of wild mammals. Int J Obes (Lond) [Internet]. 2008;32:1256–63
Available from: http://www.ncbi.nlm.nih.gov/pubmed/18504442.
World Health Organization. Global Physical Activity Questionnaire
(GPAQ). Analysis Guide [Internet]. Available from: http://www.
who.int/chp/steps/resources/GPAQ_Analysis_Guide.pdf.
Sasaki JE, John D, Freedson PS. Validation and comparison of
ActiGraph activity monitors. J Sci Med Sport [Internet]. Sports
Medicine Australia; 2011;14:411–6. Available from: https://doi.
org/10.1016/j.jsams.2011.04.003.
Freedson PS, Melanson E, Sirard J. Calibration of the computer
science and applications, Inc. accelerometer. Med Sci Sports
Exerc [Internet]. 1998;30:777–81 Available from: http://www.
ncbi.nlm.nih.gov/pubmed/9588623.
Troiano RP, Berrigan D, Dodd KW, Mâsse LC, Tilert T, McDowell
M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc [Internet]. 2008;40:181–8 Available
from: http://www.ncbi.nlm.nih.gov/pubmed/18091006.
Westerterp KR. Diet induced thermogenesis. Nutr Metab (Lond)
[Internet]. 2004;1:5 Available from: http://www.ncbi.nlm.nih.gov/
pubmed/15507147.
Granata GP, Brandon LJ. The thermic effect of food and obesity:
discrepant results and methodological variations. Nutr Rev
[Internet]. 2002;60:223–33 Available from: http://www.ncbi.nlm.
nih.gov/pubmed/12199298.
D’Alessio DA, Kavle EC, Mozzoli MA, Smalley KJ, Polansky M,
Kendrick ZV, et al. Thermic effect of food in lean and obese men. J
Clin Invest [Internet]. 1988;81:1781–9 Available from: http://www.
ncbi.nlm.nih.gov/pubmed/3384951.
Karst H, Steiniger J, Noack R, Steglich HD. Diet-induced thermogenesis in man: thermic effects of single proteins, carbohydrates
and fats depending on their energy amount. Ann Nutr Metab
Curr Obes Rep (2019) 8:88–97
[Internet]. 1984;28:245–52 Available from: http://www.ncbi.nlm.
nih.gov/pubmed/6476790.
54. Galgani JE, Gómez C, Mizgier ML, Gutierrez J, Santos JL, Olmos
P, et al. Assessment of the role of metabolic determinants on the
relationship between insulin sensitivity and secretion. PLoS One
[Internet]. 2016;11:e0168352 Available from: http://www.ncbi.
nlm.nih.gov/pubmed/28002466.
55. Bonuccelli S, Muscelli E, Gastaldelli A, Barsotti E,
Astiarraga BD, Holst JJ, et al. Improved tolerance to sequential glucose loading (Staub-Traugott effect): size and mechanisms. Am J Physiol Endocrinol Metab [Internet]. 2009;297:
E532–E537. Available from: https://doi.org/10.1152/ajpendo.
00127.2009.
56.•• Speakman JR. The evolution of body fatness: trading off disease
and predation risk. J Exp Biol [Internet]. 2018;221:jeb167254.
Available from: https://doi.org/10.1242/jeb.167254. This report
discusses the theories explaining the obesity pandemic of
modern societies. An update of one of the prevailing theories
is also presented.
57. Bogardus C, Lillioja S, Ravussin E, Abbott W, Zawadzki JK,
Young A, et al. Familial dependence of the resting metabolic rate.
97
N Engl J Med [Internet]. 1986;315:96–100 Available from: http://
www.ncbi.nlm.nih.gov/pubmed/3724804.
58. Lightfoot JT, DE Geus EJC, Booth FW, Bray MS, DEN Hoed M,
Kaprio J, et al. Biological/genetic regulation of physical activity
level: consensus from GenBioPAC. Med Sci sports Exerc
[Internet]. 2018;50:863–73 Available from: http://www.ncbi.nlm.
nih.gov/pubmed/29166322.
59. Kotz CM, Perez-Leighton CE, Teske JA, Billington CJ.
Spontaneous physical activity defends against obesity. Curr Obes
Rep [Internet]. Current Obesity Reports. 2017;6:362–70 Available
from: http://www.ncbi.nlm.nih.gov/pubmed/29101738.
60. Schulz LO, Chaudhari LS. High-Risk Populations: The Pimas of
Arizona and Mexico. Curr Obes Rep [Internet]. 2015;4:92–8.
Available from: http://www.ncbi.nlm.nih.gov/pubmed/25954599.
61. Allender S, Owen B, Kuhlberg J, Lowe J, Nagorcka-Smith P,
Whelan J, et al. A Community Based Systems Diagram of
Obesity Causes. PLoS One [Internet]. 2015;10:e0129683.
Available from: https://doi.org/10.1371/journal.pone.0129683.
Publisher’s Note Springer Nature remains neutral with regard to
jurisdictional claims in published maps and institutional affiliations.