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 . Although easy to administer, the main issue is that a single 24-h recall may not reflect habitual intake. Thus, Ma et al.  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 . 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 . 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 . Considering these inaccuracies, an expert Energy Balance Measurement Working Group proposed discontinuing the use of selfreport for estimating energy intake . 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 . 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 . 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 . 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 . 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 . 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 . Once VO2 and VCO2 are obtained, an equation allows calculating metabolic rate. The most commonly used is Weir’s equation , although the equations by Consolazio et al. and by Brouwer are also used . 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) . 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 . 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 , de la Cruz’s equation from Spanish population , and ten Haaf’s equation from recreational athletes from North Europe . The conventionally accepted equations for all populations are the WHO equations , which were later updated by excluding individuals with relatively high RMR and including individuals from Central America (Oxford equations) . 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 . 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 . Considering that ≈ 27% of the variance in RMR remains unexplained , whether a “low RMR” reflects an intrinsic metabolic defect or results from another unmeasured anthropometric variable (e.g., organ size ) remains unclear. PAEE Physical activity energy expenditure (PAEE) represents the energy required for body movement, which can be volitional exercise or spontaneous activity . 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 . 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 . 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 . In the clinic, determination of PAL by the self-report method (see next sub-section) allows to estimate daily energy requirements, as PAL × RMR . Note, however, that PAL has been considered physiologically and mathematically wrong for estimating total energy requirements  and for comparing subjects who differ in body weight . 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 . 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 . This may lead to erroneous conclusions . 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) . (B) This approach assumes that 1 MET equals 1 kcal/ kg × h, an assumption that simplifies calculations but increases inaccuracy . Energy expenditure of each activity can be computed as activity MET × time (h) × weight (kg) × 1 kcal/kg × h , 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 . 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 , along with recommendations to improve self-reporting . 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 . 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 . 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 . 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 . 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 . However, the PAL of the population has not changed from 1998 to 2006 , a period in which obesity increased. PAL is neither different between individuals with normal weight, overweight and obesity . 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 . Thus, counts per minute cutoffs for moderate and vigorous intensities have been established [47, 48], and used to identify physically active subjects . 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 . 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 . Indirect calorimetry is used to measure TEF. Besides issues related to indirect calorimetry, others affecting specifically the assessment of TEF have been previously reviewed . 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 . 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 . A careful control of these variables is thus required for an accurate determination of TEF . 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 . 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 . 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 . Family membership was found to be a determinant of total energy expenditure, independently of body composition, age, and sex . 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 . 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 . Notably, subjects with low RMR had the highest incidence of gaining weight . Genetic factors are also important determinants of the individual levels of physical activity . 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 . These results suggested that low PAEE due to low spontaneous physical activity could contribute to obesity . 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 . 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 . 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 . Interventions to override genetic predisposition to obesity should thus include modifications of the environmental causes of obesity . 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. 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