ABSTRACT

Low levels of physical activity combined with food intake in excess of daily energy expenditure over extended time periods precede weight gain and promote increases in body fat. Obesity and related insulin resistance are common sequelae of a chronically positive energy balance, potentially resulting in type 2 diabetes (T2D) and nonalcoholic/metabolic dysfunction associated fatty liver disease (NAFLD/MAFLD). The percentage of individuals considered as obese and morbidly obese is continuously rising and developing countries are catching up quickly as compared to industrialized nations. If the observed trend continues, global obesity prevalence will prospectively reach more than 21% in women and 18% in men by 2025. In addition to poor dietary habits, physical activity levels have decreased in recent decades in parallel with an increase in sedentary behavior. Given the technological advances in domestic, community, and working spaces in the last century it is not uncommon for people in industrialized countries to spend one half of their day sitting. As well, for a majority of people, voluntary physical exercise remains of minor importance. Non-Exercise Activity Thermogenesis (NEAT) refers to that portion of daily energy expenditure resulting from spontaneous physical activity that is not specially the result of voluntary exercise. Levels of NEAT ranges widely, with variance of up to 2000 kilocalories per day between two individuals of similar size. These differences are related to complex interactions of environmental and biological factors, including people’s differing occupations, leisure-time activities, individual molecular and genetic factors, and evidence that food intake has independent effects on spontaneous physical activity. Available data support the hypothesis that targeting NEAT could be an essential tool for body weight control. This comprehensive review systematically describes the definition, evaluation methods, and environmental and biological factors involved in the regulation of NEAT. It further emphasizes the association with obesity and related disorders and suggests practical relevant implications, but also potential limitations for the integration of NEAT in daily life.

INTRODUCTION

The prevalence of obesity and resultant adverse health implications continues to rise in both sexes in the United States and worldwide (1). In 2011 Finucane et al. estimated international trends of mean, age-standardized, body mass index (BMI) and suggested that an estimated 1.46 billion adults worldwide had BMI of 25 kg/m² or greater, of these 205 million men and 297 million women were considered obese (2). Indeed, between 1980 and 2008 the prevalence of adult obesity (BMI ≥ 30 kg/m²) has risen in every sub-region of the world except for Central Africa and South Asia (2). The mean BMI increase was reported to be 0.4 kg/m² per decade for men and 0.5 kg/m² for women (2). Comparable data have been presented by the NCD Risk Factor Collaboration Trial (3). With a focus on industrialized countries, the most prominent increases of mean BMI have been observed in the United States, followed by the United Kingdom and Australia. The United States had the highest BMI of all studied high-income countries, recent data from the U.S. National Health and Nutrition Examination Survey indicate that obesity affects approximately 35% of the male and 40% of the female population (1, 4, 5). Similarly, populations of European and Eastern Mediterranean countries show an overweight prevalence of up to 50% (6). Further data indicate that compared to 1975, childhood obesity is much more common in 2016, with one obesogenic key driver in this group representing reduced physical activity (3, 7). This continuous rise in the incidence and prevalence of overweight and obesity in children, adolescents, and adults in the past century has created major burdens for national health care systems worldwide.

Obesity-related diseases are typically of chronic nature and include type 2 diabetes mellitus (T2D), nonalcoholic/metabolic dysfunction associated fatty liver disease (NAFLD/MAFLD), peripheral artery and cardiovascular disease, sleep apnea, hypertension, as well as various cancers. In the United States, seven out of the leading ten causes of premature death and disability are represented by chronic diseases related to obesity (8). In fact, obesity is one of the most important avoidable risk factors for significant morbidity and premature mortality, leads to impaired quality of life, can elevate disability rates, and statistically reduces expected life span by seven years (9, 8, 10, 11).

The etiology of obesity is multifactorial, resulting from genetic and epigenetic, physiological, behavioral, sociocultural, and environmental factors leading to an imbalance between energy intake and expenditure (12). To this end excess body fat consistently results from a period of sustained net-positive energy balance, followed by homeostatically regulated weight maintenance. The roles of diet and caloric intake in these processes have been the principal focus of most mechanistic studies. In countries with high obesity prevalence rates, nutritional quality is mostly poor, but there is a controversy as to whether increased energy intake alone can explain the obesity epidemic. Trends for improved dietary quality and reduced energy intake have been observed in recent decades in the United Kingdom and among parts of the U.S.-population (13–15). For instance, since the 1980s obesity prevalence has substantially risen in Great Britain, while caloric intake appeared to decline on a population level (12). In order to explain such findings it was hypothesized that if physical activity decreases, body weight may increase if energy intake is not altered (16). Therefore, under conditions of unchanged caloric intake, the amount of daily physical activity emerged as major predictor (1, 17). Accordingly, the National Weight Control Registry of the United States has identified strategies for maintaining weight loss, which include engagement in higher levels of physical activity (1). Therefore, greater attention has been paid to addressing the energy expenditure side of the caloric balance equation, in particular with regards to prevention of body weight regain after successful weight loss.

NON-EXERCISE ACTIVITY THERMOGENESIS: A SIGNIFICANT COMPONENT OF DAILY ENERGY EXPENDITURE

Daily total energy expenditure (TEE) is the net amount of energy utilized by animals and humans to maintain core physiological functions and locomotion. Three main components of energy balance determine TEE: Basal metabolic rate (BMR), the thermic effect of food (TEF), also called diet-induced thermogenesis, and the energy expended for physical activity (18). Additional components may exist (e.g. energy costs of emotion), but apparently play a minor role with respect to energy balance (19–22). Multiple factors significantly affect our daily energy needs, including age, body composition, thyroid hormone status, catecholamine levels/sympathoadrenergic activity, ambient and body temperature, disease states, and certain medications (19, 18).

Basal Metabolic Rate

Basal metabolic rate (BMR) represents the minimal amount of energy expended to maintain all the body’s vital processes at rest (the basal state). BMR is essentially a function of lean body mass (LBM), which is estimated to account for 80% of BMR within and across species (23–29) . BMR is sometimes misinterpreted to imply the lowest level of energy expenditure during the day. This is, however, not true, since during sleep or undernutrition metabolic functions may be lower than that observed under basal conditions (27). In free-living individuals, BMR accounts for the main percentage of TEE (Figure 1), in healthy subjects with mainly sedentary occupations it predicts around 60% of the variance (22). Otherwise it is proportionally larger in individuals with minimal physical activity (e.g., in sedated and ventilated patients on an intensive care unit) (30).

Of note, it is not uncommon that the term resting energy expenditure (REE) is synonymously interchanged with BMR. However, BMR is measured in completely rested subjects, both before and during the measurements. Therefore, measurements are classically taken in the morning after 8 hours of sleep. Subjects should be lying supine, fully awake, and be fasted for at least 10-12 hours. The environment in which the measurements are taken should be thermo-neutral (22–26 °C) so that no thermoregulatory effect on heat production biases the results (19); and subjects should be free from emotional stress and familiar with the measurement apparatus (27). By contrast, REE is equivalent to postabsorptive energy expenditure at complete rest at any time of the day and can vary as much as 10% from BMR (21).

Some studies suggest BMR is reduced in those prone to become obese (31–33). It is, however, a matter of controversy as to whether a reduction of energy expenditure due to a decrease in BMR sufficiently explains the positive net energy balance that contributes to excess weight gain in the majority of subjects who become obese (34–36). On the other hand, LBM can be enlarged or even preserved by means of regular physical activity, which in turn can favorably modulate BMR (1, 37–39).

Thermic Effect of Food

Thermic effect of food (TEF) is the increment of energy expenditure above REE following meal ingestion that reflects the energy cost (burned) during food digestion, absorption, and storage. It is a relatively stable component of total energy expenditure (Figure 1). Thermic effect of food typically ranges between 8-15% of TEE and the variance of TEF has been associated with nutrient composition and energy content of consumed foods (40).

Physical Activity: EAT and NEAT

Physical activity is the second most significant contributor to TEE in most people (Figure 1). It is defined as the additional energy expenditure above REE and TEF that is required for performing daily activities and therefore also called physical activity-related or simply activity-related energy expenditure (PEE/AEE). PEE/AEE can be categorized into exercise-related activity thermogenesis (EAT) and non-exercise activity thermogenesis (NEAT). Both vary widely within and between individuals.

For the majority of subjects in industrialized countries, exercise is believed to be negligible (20). According to NHANES data, 36.1% of the studied US population was categorized as sedentary, while a further 47.6% were physically active at low levels (40, 41). Remarkably, only around 16% of subjects in NHANES met recommended guidelines for physical activity or were highly active. Even so, the latter subjects did not necessarily exercise (42, 43, 41). Thus, it is reasonable to conclude that on a population level the percentage of subjects engaging in regular, intense physical exercise is low. In those who habitually participate in purposeful physical training, EAT is believed to maximally account for 15-30% of TEE (18, 44). Other authors suggest that the majority of subjects undergoing regular physical training, defined as “bodily exertion for the sake of developing and maintaining physical fitness,” do not exercise more than two hours a week, accounting for an average energy expenditure of 100 kilocalories (kcal) per day(45).  Such expenditure would contribute to only 1-2% to the variance of TEE. Taken together, for most human subjects EAT seems to not be a major contributor to TEE variance.

Figure 1. Model of human energy expenditure components (adapted from (46)). Exercise-related physical activity is comparable to exercise-related activity thermogenesis (EAT), while spontaneous physical activity is comparable to non-exercise activity thermogenesis (NEAT); for further explanations see text. Note that parts of spontaneous physical activity are beyond voluntary control, also called “fidgeting.” Arrows symbolize the multiple and varying impacts of individual factors, genetic background, and environment.

In contrast, NEAT comprises the largest share of daily activity-related thermogenesis, including for most subjects engaging in regular physical training. It is important to note that both NEAT and spontaneous physical activity are not interchangeable but represent complementary concepts (47): NEAT refers to energy expenditure, while spontaneous physical activity describes the types of bodily activity that are not defined as purposeful movements but still contribute to NEAT. Some authors categorize NEAT into three main subcomponents comprising body posture, ambulation, and all other spontaneous movements including “fidgeting” (48). Accordingly, a certain percentage of spontaneous physical activity (and therefore NEAT) is beyond voluntary control (e.g., “fidgeting”).

NEAT corresponds to all the energy expended with occupation, leisure time activity, sitting, standing, stair climbing, ambulation, toe-tapping, shoveling snow, playing the guitar, dancing, singing, cleaning, and more (20, 22). These activities do not characteristically involve moderate- to vigorous-intensity activities in comparable manner as voluntary exercise, but occur at a low energy workload for minutes and up to hours (12).

The importance of NEAT becomes apparent when considering the following points: The variability in BMR between individuals of similar age, BMI and of equal gender ranges around 7-9% (49), while the contribution of TEF is maximally 15%. Thus, BMR and TEF are relatively fixed in amount and account for approximately three quarters of daily TEE variance. As EAT is believed to be negligible on a population level, NEAT consequently represents the most variable component of TEE within and across subjects. It is responsible for 6-10% of TEE in individuals with a mainly sedentary lifestyle and for 50% or more in highly active subjects (47, 22, 29).

Taken together, with respect to body mass regulation and modification of energy balance, NEAT must be considered as a factor with potentially major impact. This is of particular interest in terms not only of developing therapeutic strategies for the patient with obesity, but also for designing obesity-prevention strategies for populations. The following sections will focus on these issues, including the environmental and biological modification of NEAT.

Measurement of NEAT

For a better understanding of the potential role of NEAT in the context of obesity it is essential to recognize the strengths and limitations of available techniques used for the quantification of NEAT.

NEAT can be principally measured by two approaches: 1) by assessing total NEAT, and 2) by using the factorial approach (for extensive reviews of available techniques see (50, 51)).

TOTAL NEAT

Assuming that EAT is negligible, total NEAT can be calculated by subtracting BMR and TEF from TEE (16):

Equation 1:                 NEAT = TEE – (BMR + TEF)

To complete equation 1, TEE, BMR and TEF need to be measured. Under free-living conditions TEE can be reliably measured by using the doubly-labeled water method (16). This approach requires the application of stable isotopes (deuterium/²H₂ and O¹⁸) containing water (²H₂O¹⁸). The difference in the clearance rate of the two isotopes equals carbon dioxide (CO₂) production generated during energy production and thus represents quantification of TEE (52)(Figure 2).

One necessary precondition of the doubly-labeled water method is that the O₂ of expired CO₂ is in equilibrium with the O₂ in body water. Once ingested, O¹⁸ will be readily distributed in the systemic H₂O, H₂CO₃ and CO₂ pools. ²H₂ will be dispensed in body water and the H₂CO₃ pool (equation 2).

Equation 2:                  CO₂ + H₂O ↔ H₂CO₃

The concentration of labeled O₂ in body water will then decrease over time due to a loss of CO₂ with expiration, perspiration, and in excreted body water (urine).  ²H₂ is mainly lost through excreted body fluids, yet a small percentage can be incorporated into body fat or protein. Since body water becomes tagged with known amounts of tracers at the same time at the beginning of the measurement period, the difference of elimination rates of both tracers equals CO₂ excretion.

Doubly labeled water is usually administered at baseline after samples of blood, urine and saliva have been collected (21). Subsequent sample collections occur within 7-21 days after administration, when the isotopes are completely dispersed within body compartments. Isotope-ratio mass spectroscopy is used to measure ²H₂ and O¹⁸ enrichments in collected samples, which are needed to finally calculate CO₂ production. Thereby, TEE can be ascertained under free-living conditions with an error of about 6-8%. This error can be minimized by repeatedly gathering samples over the measurement period rather than relying on only two time points.

Figure 2. Schematic picture of the doubly labeled water method. CO2, carbon dioxide; 2H2, deuterium; O18, labeled oxygen.

It should be mentioned that the study of TEE classically relied on direct calorimetry (49, 18). Direct calorimetry, or direct measurement of heat loss from a subject, is achieved by using well-controlled environmental chambers. However, this requires participants to be confined to small rooms, which fails to capture the enormous variety of NEAT components in free-living individuals (21, 22).

By contrast, indirect calorimetry is an easily accessible and portable method commonly used to assess BMR, RMR and TEF. Indirect calorimetry is based on the principle that by measuring oxygen consumption (VO₂), CO₂ excretion (VCO₂), or both over a defined time span, the metabolic conversion of fats, carbohydrates, and proteins into energy can be calculated using established formulae (18). The net energy released is typically expressed as kcal or Kilojoule (kJ). The small percentage of physiological protein oxidation can be estimated by nitrogen excretion in urine, or can be neglected without adding a substantial error in subjects who are in stable nitrogen balance (18).

Indirect calorimetry is accordingly one of the most commonly used approaches in clinical investigations, either in controlled metabolic-ward studies or in field settings. For instance, Levine et al. in their study on human NEAT applied indirect calorimetry to measure BMR and TEF (53). To measure TEF they provided participants with a meal containing a third of the subject’s daily weight maintenance energy needs. To estimate TEF over 24 hours, the measured energy expenditure area-under-the-curve obtained when they ate the standardize meal was multiplied by three (16). Alternatively, study protocols may ignore TEF as a non-substantial variable, or simply multiply TEE by 0.1 to yield a crude estimate.

When sensitive techniques are not available, BMR can be alternatively estimated by using validated age-, gender- and population-specific equations. For instance, the Harris-Benedict equation is widely used in clinical settings (18). However, this equation was derived from about 300 healthy, normal-weight Caucasian adults between 1907 and 1917 and its application, therefore, has limitations (18). Substantial error could be introduced by applying it to an inappropriate collective, including subjects who are with obesity, which is also true for other validated equations estimating BMR (18).

The physical activity level (PAL) is frequently used in studies to provide an index of activity when measurement of total NEAT is not possible. It is calculated by expressing TEE relative to BMR (15). Sedentary subjects in industrialized countries typically have a PAL of approximately 1.5 (see Table 1). The PAL rises to values of 2.0-2.4 with strenuous work and under certain conditions it can increase to values up to 3.5-4.5 (22). Using PAL, however, can also introduce significant bias in the assessment of NEAT, as under free-living conditions the cumulative error of PAL measurements can be as high as 7% (21).

THE FACTORIAL APPROACH OF NEAT MEASUREMENT

Measuring total NEAT or using PAL provides no information regarding the individual components contributing to PEE/AEE. Therefore, the factorial approach is widely used to estimate NEAT constituents. Accordingly, all physical activities of a subject of interest are recorded over a defined time span, typically seven days. The energy equivalent of each activity is determined, and these equivalents are then apportioned according to the time spent by the individual with the respective activities. Finally, the data is totaled to get an estimate of the energy expenditure attributable to NEAT (21, 22). The first step when using the factorial approach is the quantification of a subject’s physical activities. Several methods are available to obtain such information, including questionnaires, interviews, and activity diaries. These approaches might be useful for estimates of particular activities, such as those related to occupational duties. Otherwise, they have substantial limitations, including inadequate or incomplete data recording, alteration of habits during assessment periods, and others (21, 22).

Measuring NEAT is a complex task when intensities of transition movements and fidgeting-like activities are the matter of research interest. By applying a combination of direct calorimetry and motion detectors, Ravussins’s group have shown that there is a considerable inter-person variability of daily energy expenditure, even after adjustment for differences in LBM (29). A large percentage of this variability among individuals was due to variability in the degree of e.g., "fidgeting." Thus, estimation of fidgeting-like activities represents a substantial subcomponent under conditions of NEAT measurement. However, subcomponents such as “fidgeting” might not be captured by many methods. Levine et al. proposed an accelerometer method that could predict 86% of activity related energy expenditure for the posture and locomotion component of NEAT compared with a room calorimeter, but predicted only 50% of the variance in fidgeting and did not discriminate various types of fidgeting (50). Therefore, more technically advanced approaches have been developed. Among these are motion detectors, floor-pressure-pad displacement, cine photography, pedometers, accelerometers, and global positioning systems (GPS) (21, 22). Recently, portable intelligent devices for energy expenditure and activity assessment were used to study subtypes of NEAT by the method of mechanical modeling (54).

However, depending on the degree of sophistication, all these approaches have limitations. While data obtained from triaxial accelerometers typically found in wearable activity monitors is often used as an estimate of activity-related energy expenditure, they only measure actual movements (not energy expended), and a combination of methods probably yields the most appropriate estimates (21, 22). For example, energy costs of single NEAT components are typically measured by means of indirect calorimetry, for which highly sophisticated portable systems are now available. Alternatively, tables with listed energy costs of NEAT activities may be used. This latter method is convenient and inexpensive, but for similar reasons as pointed out for PAL, substantial systematic errors can be introduced (21). Similar problems will arise when NEAT is assessed by multiple linear regression approaches at a population level (55).

Furthermore, principal problems ascend with the NEAT measurement per se, as little validated information is available concerning the time necessary to representatively assess spontaneous physical activity. Many studies report extrapolation of measured results of an individual’s energy expenditure obtained during a limited timeframe to longer intervals. In this regard, experienced investigators believe that measurements of approximately seven days will likely provide a representative assessment regarding a 2-4 month time span (21).

Taken together, the difficulty of getting true estimates under free-living conditions is a major reason why available information on human NEAT physiology is limited. Representative studies are needed that are conducted over appropriate time spans (months) and utilize a combination of the factorial approach and total NEAT measurement to overcome major limitations in current estimates.

INDIVIDUAL AND ENVIRONMENTAL DETERMINANTS OF NEAT

There is a close interplay between environmental factors and individual characteristics with respect to NEAT as discussed below.

Effects of Occupation, Gender, and Age

Highly physically-active individuals expend up to three times more energy in 24-hours than subjects with negligible bodily activity (56). As previously discussed, NEAT accounts for a significant proportion of the observed differences at the population level TEE. Thereby, occupation clearly represents the key determinant of NEAT (45). Indeed, NEAT can vary by as much as 2000 Kcal per day when comparing two adults of similar body size, lean body mass, age and gender (Figure 3).

For an average worker spending most of her/his time in a seated position, occupational NEAT is relatively low and associated energy costs range at a maximum of 700 kcal per day (Figure 3). A comparable person working mainly in a standing position can increase their occupational NEAT to up to 1400 kcal per day, while an agricultural occupation would theoretically result in NEAT categories ranging around 2000 kcal per day or more (Figure 3) (45). Thus, occupations relying on intense physical activity can expend 1500 kcal per day more than a sedentary job.

Figure 3. The effect of occupational intensity on NEAT (adapted from (45)). The data assume a BMR of 1600 kcal per day.

This variation in daily NEAT between individuals and populations becomes even more accentuated when considering research from non-industrialized countries (57, 58). For instance, a study by Levine and colleagues included more than 5000 inhabitants of agricultural regions of the Ivory Coast of Africa (Figure 4).

Figure 4. Gender- and age-dependent physical activity levels (time engaged in work activities) of people living in agricultural communities of the Ivory Coast in Africa (adapted from reference (58)).

Each subject was studied over seven days and all of their daily tasks were recorded by a trained evaluator (58). Several lines of available evidence regarding NEAT were established from this study. First, a substantial effect of gender was observed as women apparently worked more than men in these societal constructs. Women were responsible for more than 95% of domestic and for an additional 30% of agricultural tasks. Otherwise, men exclusively worked in agricultural occupations and had more leisure time, resulting in substantially lower NEAT than women (45, 58). In contrast, men in Canada, Australia or the United Kingdom are reported to be up to three times more physically active than females, while data from the United States indicate comparable activity levels of genders (59, 60). Even less data is available on NEAT in children, but in general boys seem to be reproducibly more active than girls (61, 62).

A second important observation from the study of Levine et al. is the fact that decreased occupational NEAT was observed with aging (Figure 4) (58). An age-related decline of NEAT had been previously reported across species (63). The underpinnings for this age-effect on NEAT in free-living humans has been reported by Harris et al. (64), who showed that the substantial decrease in elderly as compared to younger subjects was mainly attributable to the ambulation subcomponent of NEAT. Elderly subjects walked less distance despite having a comparable number of daily walking periods compared to their younger counterparts. Indeed, elderly subjects performed 29% less non-exercise activity, corresponding to three miles less ambulation per day (64).

A further factor that should be considered to potentially impact NEAT could be sleep restriction. It was shown that subjects with 5.5 compared to 8.5 hours nighttime sleep opportunity had 31% fewer daily activity counts, spent 24% less time engaged in moderate plus vigorous physical activity and became more sedentary (65). However, the significance of these findings is currently limited to time spans over few weeks and it remains therefore unclear as to whether the results are also valid in terms of common problems, such as chronic sleep insufficiency or shift working. Although, some short-term experiments support the latter findings, conflicting data have also been reported on that matter (66–69). Future research will need to validate these associations and, if true, better understand mechanisms underlying restricted sleep time and alterations in PEE/AEE. Sleep restriction should be nevertheless regarded as potential biasing factor in future research projects on NEAT.

Industrialization and Societal Status

Industrialization and societal status are distinctive factors affecting NEAT. Urban environment and mechanization are associated with a decrease in daily physical activity. For example, it was shown that sales of labor-saving domestic machines (e.g. washing machines) and obesity rates are closely correlated in the U.S. population, while this association was not present with respect to energy intake (20, 22). Furthermore, it is known that poverty and societal aspects comprising neighborhood and educational status are associated with increased consumption of energy-dense foods, which is thought to contribute to higher obesity rates reported in lower socioeconomic groups (70, 71). Highly educated individuals report more leisure-time physical activity and are three times more likely to be physically active as compared to less educated individuals (20, 22).

Seasonal Effects

Seasonal variations also play a role for NEAT. Time spent in activity is twice as likely during the summer as compared to winter months and contributions to activity from agricultural or construction work plays a greater role in the summer season (20).

Together, available data indicate close relationships between environmental conditions and biological aspects of NEAT. Evidence suggests that the contribution of occupational activity to NEAT can vary by 2000 kcal per day. Since the accumulation of excess body fat is a result of positive energy balance, subtle perturbations of energy balance over sustained time periods are theoretically capable of contributing to unwanted weight gain. But once weight stability is again established at a higher weight, energy expenditure and energy intake need to be precisely matched to achieve a long-term persistency of body mass, as an error of 1% would lead to a gain or loss of 1 kg per year or some 40 kg between the ages of 20-60 years. Therefore, small alterations of energy balance by means of NEAT could theoretically result in significant effects with respect to severity of obesity in an individual and the prevalence of obesity in a population. Consequently, when considering how to influence NEAT as a strategy for the treatment or prevention of obesity, a central question arises: In what fashion does NEAT interact with another environmentally-influenced factor involved in energy balance, that of energy intake?

Alteration of NEAT with Varying Energy Availability from Foods

Alteration of energy balance is followed by multiple adaptations, but relatively few studies have addressed modulation of spontaneous physical activity and NEAT by variations in nutritional energy intake. The latter is part of the concept of adaptive thermogenesis (72).

Adaptive thermogenesis refers to changes in REE, EAT and NEAT, which are independent of changes in LBM and are, instead, modulated by other factors, including increased or decreased caloric intake (72). With caloric restriction, both REE and non-resting energy expenditure substantially contribute to changes in adaptive thermogenesis (72). Interestingly, in most studies, alterations in adaptive thermogenesis in response to overfeeding are in large part explained by raised non-resting energy expenditure (e.g., NEAT). However, the individual ability to adapt NEAT to manipulations of energy balance is highly variable between subjects and situations, particularly with respect to overfeeding (39). But if an individual is able to generate a significant upregulation of NEAT in the face of positive energy balance brought about by an increase in food intake, they may be more able to prevent obesity (34).

 NEAT UNDER CONDITIONS OF OVERFEEDING

As discussed above, by using a combination of direct calorimetry and motion detectors, Ravussin et al. have shown that the inter-person variability of TEE is considerable and remains significant even after adjustment for LBM (29). A substantial percentage of this variability is explained by differences in spontaneous physical activity including “fidgeting” (29). Fidgeting-like activities accounted for a range in energy expenditure of 100-800 kcal per day in the subjects. Interestingly, the researchers observed elevated spontaneous physical activity levels in subjects with obesity as compared to those who were lean, indicating a positive relationship with elevated body mass. However, the latter study was performed under isocaloric conditions and over a short observation period. Therefore, the importance of this report was to establish the significance of spontaneous physical activity for energy balance, but whether acute variability in energy intake affects NEAT was not addressed. In their “weight clamping” study, Leibel et al. (73) overfed adults who were lean and overweight/obese by 10%, or approximately with 5000-8000 kcal per day over 4-10 weeks, followed by a weight maintenance period. Body composition, fecal caloric loss, TEE, REE, TEF and spontaneous physical activity were measured using multiple technical approaches including indirect calorimetry, the doubly labeled water method, and activity trackers in a subgroup (73). They showed that maintaining a 10% elevated body mass induced a significant increase of TEE by 9±7 kcal per kg LBM per day in subjects who had not been previously obese and by 8±4 kcal per kg LBM per day in those who had formerly been obese (73). The majority of this rise in TEE was attributable to non-resting energy expenditure. The magnitude of this effect was comparable in those who had previously not been obese and those who had been overweight or obese, indicating similar adaptations to overfeeding conditions (73). However, a related review pointed out the large inter-individual variability regarding the capability to adjust energy expenditure with overfeeding in the “weight clamping” experiments (46) and emphasized that in other human over-feeding studies there is a wide range of individual weight gain per unit of excessively consumed energy. Therefore, it can be proposed that some individuals show a remarkable capacity to increase energy expenditure in response to overfeeding, while others do not (46). It was further suggested that spontaneous physical activity, and accordingly NEAT, likely play an important role regarding the impact on body weight under such conditions.

Levine’s group was the first to systematically investigate the effect of overfeeding on the individual ability to adapt NEAT in free-living subjects (53). Using sophisticated methods and measuring NEAT over a representative time span, the authors overfed 16 volunteers (12 males, 4 females; age ranging from 25-36 years) by 1000 kcal per day over their weight maintenance requirements (20% of the calories came from protein, 40% from fat, 40% from carbohydrates). The energy surplus was paralleled by a mean TEE increment of 554 kcal per day. In this study 14% of this TEE increase was attributable to a rise in REE, approximating by 79 kcal per day. A further 25% of the TEE increment (136 kcal per day) corresponded to an increase in TEF, probably due to the relatively high percentage of protein intake. The most prominent effect was, however, attributable to enhanced physical activity thermogenesis, corresponding to around 336 kcal per day. As volitional exercise of the study subjects remained at a constant low level and since the authors did not detect changes in exercise efficiency, they concluded that about 60% of the increase in TEE due to overfeeding was attributable to NEAT (53). Again, however, the change in NEAT varied remarkably between subjects, ranging from -98 to +692 kcal per day. The maximal individual increase of NEAT constituted 69% of the excessively consumed 1000 kcal per day in this study (74). Moreover, the change in NEAT was directly predictive of the individual vulnerability or resistance to body fat accumulation (53).

A recent randomized study enrolled young aged female and male participants with variable BMI (19-30 kg/m²) and used gold standard methods to investigate not just the effect of caloric overfeeding, but also of protein intake on PEE/AEE (75). 140% of caloric needs including 5, 15, or 25% of energy from protein was fed for 56 days. Caloric overfeeding resulted in increased physical activity and correspondingly PEE/AEE including NEAT, even after adjustment for changes in body composition. By contrast, changes in physical activity were not related to protein intake. The authors concluded that increased PEE/AEE in response to weight gain might be one mechanism of modulating adaptive thermogenesis (75). But the results indicate also that the observed increase in activity was not likely effective at attenuating weight gain.

Other recent reports have failed to detect compensatory increases in spontaneous physical activity during short-term overfeeding in humans (76–80). While in several cases, methodological differences make them less directly comparable to Levine’s study, (76, 56, 77, 79), a study by Siervo et al. used state-of-the-art methods and applied a highly standardized 17-week protocol with progressive overfeeding from 20-60% energy in excess of maintenance needs (81) in lean, healthy men. Three sequential intervals of stepwise overfeeding were each separated by one week of ad libitum energy intake. In agreement with previous findings, between-subject variability concerning weight change during ad libitum phases was high (63). However, in contrast to Levine’s study (53), Siervo et al. failed to detect a significant systematic change in spontaneous physical activity (81). The authors therefore concluded that systematic elevations in energy intake induce very limited counter-regulatory responses in energy expenditure (63).

In summary, these data underscore the highly individual compensatory response regarding adaptive thermogenesis to overfeeding. From Levine and Apolzan et al., supportive evidence is provided for the susceptibility to gain body mass under standardized overfeeding conditions and identifies an individual’s ability to adapt NEAT as a potential modulator (75, 53, 20, 34). In accordance with findings of other overfeeding studies (reviewed in (74)), adaptations in thermogenesis by changes in NEAT observed between individuals may explain why some individuals are particularly susceptible or resistant to weight gain. The potential impact of age on these adaptational mechanisms remains unclear and should be addressed by future research.

THE IMPACT OF CALORIC DEPRIVATION ON NEAT 

While evidence with respect to systematic NEAT upregulation under overfeeding conditions remains somewhat controversial, TEE and spontaneous physical activity are consistently influenced with energy deprivation in the majority of available studies (72, 80). This is of importance as most subjects who are overweight or have obesity try to lose body mass through various calorie-restricted dietary strategies.

The first study to quantitatively investigate adaptive thermogenesis under conditions of food restriction was the Minnesota Starvation Experiment (reviewed in (46, 49, 72)). This evaluation showed a marked reduction in TEE that was later reproduced by numerous studies, independent of pre-starvation BMI or method used to induce weight loss (reviewed in (72)). For instance, Leibel et al. in a second arm of their “weight clamping” experiment evaluated the effect of a 10% body mass reduction, followed by weight maintenance at this level (73). While overfeeding was accompanied by a 16% increase of adjusted TEE, weight reduction from underfeeding induced a 15% decrement (57). As compared to overfeeding, it is well accepted that both REE and non-resting energy expenditure are affected by energy deprivation in humans and animal models (82, 72, 80).

These findings are supported by results from the so-called Biosphere 2 experiment, where the authors demonstrated a major reduction of TEE and spontaneous physical activity with body mass reduction over a two-year time period, which persisted after six months of body weight regain (46, 83). Demonstrated TEE reductions by adaptive thermogenesis in weight-regainers has been reproduced in other human studies (72). Of note, when subjects recover from starvation they spontaneously overeat and, moreover, if weight loss-induced adaptive thermogenesis persists (e.g. reduced NEAT), there is a significant risk of regaining weight and “overshooting” pre-starvation body mass (72). Thus, changes in non-resting energy expenditure with underfeeding (or overfeeding) appear to occur in a direction tending to return subjects to their initial body mass and to conserve body energy as recently described in humans as so-called “thrifty phenotype” compared to a “spendthrift phenotype” (84).

Regarding this, findings from a recent study in sedentary overweight females are of potential practical relevance (85). In this investigation, 140 pre-menopausal females reduced their body mass by an average of ~11 kg while undergoing a total of 800 kcal per day diet. Subjects were randomly assigned to one of three groups: no exercise, a structured aerobic training schedule, or an exercise program incorporating resistance training. Body composition, strength, TEE, REE and NEAT were measured using various approaches. One main finding was that TEE, REE and NEAT all declined with weight loss in the no-exercise group. Non-exercise activity thermogenesis was reduced by 150 kcal per day, which was equivalent to 27% compared to baseline levels. However, in subjects undergoing the exercise regimens only REE was reduced, while NEAT remained unaffected. Thus, since caloric deprivation appears to consistently decrease spontaneous physical activity, untrained subjects with obesity who plan to lose body mass by means of dietary intervention alone would be advised to engage in a concomitant exercise program so as to avoid negative effects on NEAT and mitigate weight cycling after reaching a lower body weight. Of note, the authors of this study emphasized that, consistent with the concept that “more is not always better,” exercising two days a week was capable of increasing NEAT but, paradoxically, an intense three days weekly schedule was followed by a substantial decrement in NEAT (85).

At this end, one further study of Levine’s group should be considered (78). NEAT as measured by gold standard methods and posture allocation was evaluated in mildly obese subjects and normal weight controls over a period of ten days. This baseline evaluation showed a 164 minute per day longer seated time in obese compared to lean subjects. A subset of eight obese subjects then underwent an intentional weight loss program over a time span of eight weeks and thereby lost significant body weight. Posture allocation was measured for another ten days after intervention. Interestingly, the weight losing obese subjects did not further reduce their NEAT in this experiment (78). The latter study has several limitations, e.g., the small number of enrolled subjects and the relatively short intervention period when compared to some of the above-mentioned trials. Levine and colleagues themselves emphasized that it was a pilot experiment (78). The data otherwise suggest that interindividual differences in posture allocation could at least in part be biologically determined and are therefore not necessarily influenced in every individual by e.g., reduced energy intake over short time periods. Over- and underfeeding studies in identical twins indeed suggest an important role of genetics regarding the variability underlying body mass regulation (86, 87). Specifically regarding NEAT, it is likely that due to the polygenetic nature of obesity, numerous pathways are involved in the regulation of spontaneous physical activity, as the latter has multiple environmental cues and affects a multitude of behaviors (8, 88). Zurlo et al. have shown that spontaneous physical activity levels cluster in families and could prospectively help to explain the propensity for weight gain (89). Moreover, according to a recent review up to 57% of the variability of spontaneous activity has been attributed to inheritance (90).

Otherwise, biological and genetic determinants can only partially explain NEAT variance. Models of human energy budgets related to exercise can provide a conceptual framework to better understand NEAT regulation in humans (reviewed in (39)). For instance, the so-called independent model predicts that changes in basal TEE have no impact on the energy budgeted for behavior, e.g., spontaneous physical activity. This model is analogous to the factorial model of exercise in humans that assumes that exercise has an additive effect on TEE (39). In contrast, the allocation model suggests that the total energy budget is constrained, and therefore, an increase in the energy cost related to maintaining basal functions will reduce the amount of available energy to support other functions, finally altering an individual’s behavior (39). The independent model would for example predict that exercise additively increases TEE, while the allocation model predicts that exercise leads to a reduction in some components, e.g., NEAT. Indeed there is evidence that each of these models is evident in different human populations, although studies suggest that e.g. allocation may be affected by confounders such as age (older more likely to reallocate), sex (males more likely to reallocate), and exercise volume (allocation more likely with higher volumes)  (39). Such aspects have to be considered and could help to integratively explain the contradictory findings of Levine et al. and from other researchers.

In summary, an integrative view of existing human overfeeding studies proposes that the magnitude of adaptations regarding energy expenditure varies largely between individuals. Subjects capable of responding with increased spontaneous physical activity to overfeeding can be categorized as “compensators” and are therefore less susceptible to obesity (“spendthrift phenotype”). Subjects unable to respond to a continued energy surplus with increased NEAT (e.g., “non-compensators” or “thrifty phenotype”) seemingly represent the majority of subjects on a population level, of which a major percentage will attempt dietary weight-loss strategies at some time in their lives. However, REE and non-resting energy expenditure including NEAT are reduced with underfeeding in the majority of subjects. They therefore represent a risk factor for weight regain after dietary intervention. Mild voluntary exercise during and maybe shortly after periods of dietary weight loss could be a promising strategy to avoid decreases in NEAT, although this alone will probably not fully compensate for the obligate decline in REE. Whether the majority of dieting subjects with obesity will continuously adhere to a structured exercise program during extended periods of weight maintenance as suggested by current recommendations is also uncertain (and unlikely) (42, 8, 43, 41). 

Therefore, to advance anti-obesity strategies that systematically increase or even conserve NEAT it is essential to understand the mechanisms contributing to regulation of spontaneous physical activity.

PHYSIOLOGICAL AND MOLECULAR DETERMINANTS OF NEAT

Principles of NEAT Regulation

The ability to adapt thermogenesis, and specifically NEAT, presupposes existing mechanisms in the body that sense, accumulate, and integrate internal and external directionality signals with respect to energy balance (20). Figure 5shows a proposed model of the principal regulation of spontaneous physical activity and correspondingly NEAT. In this model, physiological data regarding caloric intake, energy depots and energy expenditure are compiled centrally by the brain. These signals converge at the data acquisition center, are rectified to a common signal, and directed to a NEAT accumulator, which is constantly summing the net amount of NEAT per unit of time. The latter continuously refers to the energy balance integrator, which, in turn, modulates spontaneous physical activity in response to changes in energy intake. Thereby, energy balance resulting from these three components is in constant flux.

Figure 5. The NEAT hypothesis of energy balance (adapted from (20)).

Continuous arrows represent energy flow through the system, broken arrows stand for putative signaling pathways. BMR, basal metabolic rate; NEAT, non-exercise activity thermogenesis; TEF, thermic effect of food.

For example, under circumstances of positive energy balance during increased food intake, the energy balance integrator may mediate deposition of the caloric surplus in energy stores (adipose tissue) or alternatively dissipate the excess energy in the form of NEAT. More likely, since BMR is essentially fixed, some energy will be spent on food digestion and absorption (increased TEF), some will be deposited as body fat or in other depots, and the rest will be dissipated as NEAT (20). This model also supports the observation that although BMI and energy balance are continuously oscillating around a weight set point (or “set range”) under daily life conditions, body mass is remarkably stable in the long term. This is exemplified by data from the Framingham Study showing a mean body mass increase of only 10% over a 20 year time span in average adults (cited in (91, 92)).

Thus, it appears that the regulation of energy balance and body mass is realized by a complex network of precisely working auto-regulatory structures controlling effector systems, one of which being spontaneous physical activity. However, while ample available data detail the regulation of food intake, there is lack of information concerning the biological mechanisms driving NEAT (34). Most animal evidence supports the concept that NEAT can help to protect against obesity (93). Therefore, spontaneous physical activity has certainly potential for impacting body mass and energy balance. It is very likely that many of the biological systems involved in the regulation of energy intake are also involved in gathering and integrating information regarding NEAT and determining NEAT adaptation in humans (34).

Tissues, Organs, and Central Mechanisms Involved in NEAT Regulation

Several well-described central neuroendocrine systems are thought to regulate NEAT through interactions with peripheral tissues/organs known to affect energy balance (Figure 6).

Figure 6. Model for the neuroendocrine regulation of NEAT in the service of energy balance (adapted from (34)). Multiple external and internal signals are sensed and integrated, whereby defined brain structures (e.g., arcuate nucleus of the hypothalamus, area postrema and nucleus of the solitary tract of the hindbrain, dopamine pathway of the mesolimbic system) interpret a multitude of sensory cues of energy availability. The involved brain systems have multiple ascending and descending projections affecting the amount of physical activity through arousal and limbic pathways, and descending neural projections and endocrine signals to modulate the energy efficiency of physical activity. Thereby, the central nervous system could adapt NEAT to adjust energy balance under conditions of caloric excess or starvation. Ach, acetylcholine; AgRP, agouti-related peptide; CART, cocaine- and amphetamine-regulated transcript; CCK, cholecystokinin; LC, locus ceruleus; MCH, melanin-concentrating hormone; NPY, neuropeptide Y; POMC, pro-opiomelanocortin; (P)SNS, (para-)sympathetic nervous system; SNS, sympathetic nervous system; TMN, tuberomammilary nucleus; VP, ventral pallidum

These associated neural mechanisms of brain regions involve a multitude of neurotransmitters and neuropeptides (for extensive review see (34) and (94)). While all are important, this section will focus on the biological role of central orexin peptides as promising and potentially targeted regulators of spontaneous physical activity (for extensive review see (47)).

Experimental data indicate that injections of orexin A into defined brain regions reproducibly induce a dose dependent increase in spontaneous physical activity along with significant increments in NEAT. Moreover, current knowledge suggests a role for the orexin system in the control of arousal and sleep, and for reward and stress reactions. This has led to conduction of extensive research showing that the orexins A and B are produced by cleavage from a single pro-peptide (44). The majority of orexin A is synthesized in the lateral hypothalamus and perifornical area. Orexin A binds to two G protein-coupled receptors, namely orexin receptor type 1 (OXR1) and 2 (OXR2). OXR1 has high affinity for orexin A, while OXR2 has equal affinity for the A and B peptide. Receptor binding of orexin A is associated with increased intracellular calcium levels and followed by enhanced neuronal signaling (44).

Physiologically, orexin A is capable of modulating spontaneous physical activity, food intake, and sleep as evidenced by an obesity-resistant rat model with high orexin A activity (34). These animals show more ambulatory and vertical movement, independent of age or food availability (34). Intriguingly, this rat model exhibits lower body weight gain when fed a high-fat diet, despite consuming significantly more kcal per gram body mass. From this study, it appears that orexin A simultaneously enhances feeding behavior and induces physical activity, but the consumed calories are outweighed by those expended with spontaneous physical activity (47, 34). These data were confirmed by a mouse model with postnatal loss of orexin neurons (47). These mice exhibit hypophagia, decreased spontaneous physical activity, and develop spontaneous-onset obesity while consuming a regular chow-diet. Furthermore, in recent years so called DREADDs (Designer Receptors Exclusively Activated by Designer Drugs) have been developed and were applied to manipulate orexin neuron activity in specific animal models. Compared with previous work of targeted infusions of orexin and/or orexin agonists into the brain, the use of DREADDs allows for the sustained activation of the neurons of interest without needing to inject directly into the brain (95).

A high-fat diet model was used to evaluate the effect of DREADDs on NEAT, since spontaneous physical activity can be decreased under such circumstances. It was shown that NEAT was decreased in animals on the high-fat diet, and was increased to the NEAT level of control animals following activation of orexin neurons with DREADDs (95). Thus, these preliminary results clearly support the hypothesis that the orexin signaling system could be a promising target of modulation by drugs.

However, from the perspective of humans, data on orexin are currently scarce. Otherwise it was recently shown in a pilot study that plasma levels of orexin A correlated with physical activity levels including many NEAT features (96). Therefore, plasma orexin A could be considered as a biomarker of NEAT in future studies. Furthermore, the orexin system has been shown to be defective in the sleep disorder narcolepsy. Since obesity is a recognized comorbidity of narcolepsy in both animal models and humans (47), regulation of this signaling system has the potential to impact clinical endpoints, probably including the development of overweight and obesity.

The Role of Peripheral Tissues for NEAT Regulation: Adipose Tissue and Adipokines

Peripheral tissues are also believed to contribute to NEAT regulation (Figure 6). That NEAT can be influenced by over- and underfeeding has been extensively discussed in earlier sections; but it is essential to also reconsider that longitudinal studies of fasting humans show significant decreases in adaptive thermogenesis with starvation, while refeeding may induce a variable elevation (72). These adaptations are likely the result of feedback mechanisms between thermogenesis regulation and energy depots (Figures 5 and 6). It can be further hypothesized that adaptations of thermogenesis in response to fasting or overfeeding result, at least in part, from an adipose tissue-specific impact on thermogenesis (reviewed in (46)). Indeed, studies of white adipose tissue (WAT) have led to the recognition that this energy store represents an important endocrine organ communicating with the brain through secretion of so-called “adipokines.” Accordingly, adipokines could play a role for NEAT regulation (Figure 7) (97).

Figure 7. Essential elements in the regulation of thermogenesis including effects of WAT (adapted from (34, 98)). BAT, brown adipose tissue; cAMP, cyclic 5” adenosine monophosphate; COX, cyclooxygenase; IL, interleukin; LPS, lipopolysaccharide; PAMPs, pathogen associated molecular pattern; PGE, prostaglandin E; PKA, protein kinase A; SNS, sympathetic nervous system; UCP, uncoupling protein; WAT, white adipose tissue.

Leptin is such an adipokine signal that is the product of the rodent obese (ob) gene and human homolog (LEP) first characterized in 1995 (99). Due to its role regarding the sensing of energy stores and regulation of food intake, it was also suggested as an important signal with respect to adaptive thermogenesis (reviewed in (34, 98)). For instance, Dauncey and Brown studied ob⁄ob mice lacking leptin production and their lean littermates at comparable body weights (100). Leptin-deficient mice expended less energy and showed less motor activity as compared to control animals. The energy expended relative to metabolic size was greater in lean littermates, who were also more active than the ob⁄ob mice. It was calculated that activity-related energy expenditure accounted for at least part of the body mass difference observed between ob⁄ob and wild-type mice (100, 101). A central mediator of leptin action on NEAT partly includes the orexin signaling system, since the activity of orexin neurons has been shown to be modulated by leptin among other metabolic indicators (reviewed in (93)).

With respect to humans, Franks et al. have shown that fasting leptin levels were significantly associated with physical activity-related energy expenditure (101). Moreover, it was pointed out in a recent review that replacement of leptin in weight-reduced subjects was capable of reversing neuroendocrine adaptations that accompany caloric reduction as evidenced by an increase in SNS activity, suggesting that this component of the non-resting energy expenditure and adaptive thermogenesis is at least in part under endocrine control (72). In response to caloric restriction, the authors therefore suggest an adaptation model where in an initial fast “first set,” reduced insulin secretion and glycogen stores are accompanied by a reduction of both REE and non-resting energy expenditure within days, while in the long term, with maintenance of reduced body weight, a “second set” mediated by a drop in leptin levels becomes active that keeps energy expenditure low so as to preserve fat depots and maintain reproductive function (72). This endocrine action resulting from the drop in leptin levels with related activation of multiple pathways then enhances the risk of weight regain (72).

Overall, available data suggest that leptin’s central actions include not only reducing food intake but potentially also increasing energy expenditure mediated, in part, through increasing physical activity levels (reviewed in (34)). The potential role of leptin on NEAT in human obesity is of particular interest given the phenomenon of central leptin insensitivity under conditions of overfeeding and related obesity (102, 103). However, it must be also kept in mind that the leptin system represents only one component of peripheral tissue signaling that impacts NEAT. Other endocrine systems, such as thyroid hormone status, also have major influences on energy expenditure. For example, hyperthyroid rats have been shown to have significantly increased NEAT (104).

Energy availability from food and internal energy stores, ambient and body temperature, and systemic inflammation are further determinants of thermogenesis regulation. In WAT, two relevant factors converge: WAT represents the principal energy storage depot and, under conditions of significant obesity, WAT is a site of low-grade inflammatory activity that contributes to systemic inflammation. Recent epidemiological data from humans support this hypothesis (105). It was shown that levels of systemic low-grade inflammation (e.g., circulating interleukin-6 and interleukin-1β) were related to non-exercise physical activity. Although no causal relationship can be drawn from these findings, inflammation is theoretically one signaling system involved in NEAT regulation.

Signals from WAT can induce enhanced sympathetic nervous system (SNS) activity and affect downstream regulators of adaptive thermogenesis (Figure 7). Indeed, experimental data indicate that NEAT is one component affected by WAT signaling (93). Related to the hypothesis of “adaptive thermogenesis”, Dulloo et al. in their review regarding human starvation and overfeeding suggest SNS as key regulatory effector mechanism (46). Of note, activation of brown adipose tissue, which has recently gained a lot of interest with respect to human obesity (106–108), is suggested to be an additional site of adaptive thermogenesis.

Skeletal Muscle as Peripheral NEAT Modulator

Skeletal muscle is a major contributor not only to REE, but also to spontaneous physical activity and has been newly recognized as important NEAT effector. Human subjects maintaining a 10% weight loss in response to caloric restriction have increased skeletal muscle work efficiency (92, 109). This mechanism is believed to be responsible for the phenomenon that, even after adjustment for the reduced body mass, weight-loss maintaining subjects need proportionally fewer calories for performing comparable physical-activity tasks compared to before weight loss (92, 109). Therefore, adaptations of skeletal muscle work efficiency can contribute to reduced NEAT after low-calorie induced weight loss. Several recent animal models are in line with this hypothesis (82, 110). For example, a recent study in rats investigated the contribution of REE and non-resting energy expenditure to reduced TEE observed after three weeks of 50% energy deprivation (82). They reported a 42% reduction in TEE, even after correction for the loss in body weight and LBM (82). Moreover, 48% of the detected TEE reduction was explained by a significant decline in non-resting energy expenditure, e.g. spontaneous physical activity (82). Reduced baseline and activity-related muscle thermogenesis was also found in this study. Reduced skeletal muscle norepinephrine turnover as a surrogate of SNS drive and increased expression of the more energy “economic” myosin heavy chain (MHC) 1 isoform were identified potential mechanisms for the observed changes (82, 92, 109). Thus, according to these findings, restriction of caloric intake apparently not only suppresses spontaneous physical activity, but in addition reduces related caloric demand, which is mediated by reduced SNS activity to skeletal muscle and altered expression of MHC isoforms. These findings are supported by data from another recent animal study investigating rodents with “high intrinsic physical activity” and counterparts with “low intrinsic running capacity” under eucaloric conditions (87). It was found that animals with high intrinsic physical activity were lean, had lower skeletal muscle economy along with increased skeletal muscle heat dissipation during activity. This resulted in higher TEE and NEAT and was related to increased activation of skeletal muscle by SNS (110).

Taken together, skeletal muscle is an intrinsic site for NEAT and not only of great importance quantitatively, but also represents an important effector system for NEAT mediated through modifications on the level of SNS activity and gene expression, particularly under conditions of altered caloric intake. Therefore, evidence indicates that NEAT is modified by a complex network regulation of central and peripheral systems. The latter involve a variety of redundantly organized immediate and delayed mechanisms, including central neuropeptides and signaling systems to and from peripheral tissues. Involvement of such biochemical signaling systems identifies spontaneous physical activity and NEAT as a potential site for pharmaceutical targeting. Pharmaceutical manipulation of such a system could be envisioned to either increase the amounts of activities people undertake, or elevate activity-related energy expenditure.

TREATMENT OPTIONS FOR OBESITY AND RELATED DISORDERS: IMPLICATIONS FOR NEAT-ENHANCED LIVING

As stated in the introduction, obesity is associated with a many adverse health outcomes (1, 111). Energy expenditure in occupational activities has declined by a mean of 140 kcal/d since 1960 in the United States and this reduction is thought to account for a significant portion of the observed increase in mean BMI (reviewed in (1)).The latter hypothesis is supported by observational data showing that reduced energy expenditure of 100 kcal/d below expected values corresponds to a 0.2 kg/year weight gain, of which 0.1 kg/year are fat mass (112). In addition there is ample evidence from epidemiological, cross-sectional, and longitudinal studies showing that individuals with higher physical activity levels have lower mean body weight, gain less weight and fat mass over time, and show reduced weight regain after intended loss in body mass (113–117). Moreover, regular physical activity can moderate or even eliminate weight gain among those carrying a risk at the FTO (fat mass and obesity associated) gene variants ((118), reviewed in (93)). It has further been evidenced that  levels of habitual or spontaneous physical activity are positively related to reduced risk of major cardiovascular disease and premature mortality, while an increase of sitting time correlates with a raise in complications (119–123).

Associations of NEAT with Type 2 Diabetes Mellitus and NAFLD/MAFLD as Common Sequelae of Obesity

In industrialized countries the daily time spent sitting is estimated to be 6-7 hours per day, which is closely correlated not only with obesity rates but also with the incidence of T2D (120, 124). A plethora of data suggest, on the other hand, beneficial effects of lifestyle interventions on insulin resistance, metabolic control, pain and clinical endpoints in these patients (e.g. (125, 126), reviewed in (127)). In subjects with impaired glucose tolerance or prediabetes, lifestyle interventions have proven to decrease the incidence of T2D, of which prominent examples comprise the DaQuing Study, the Finish Diabetes Prevention Study, the Diabetes Prevention Program and others (128–131). This was newly confirmed by the lifestyle intervention and impaired glucose tolerance Maastricht (SLIM) trial, which has shown to impact clinical endpoints even four years after stopping the intervention (132, 133).

The prevalence of NAFLD/MAFLD parallels the pandemic rise in T2D. A recent meta-analysis indicates that on a global perspective more than 55% of T2D patients suffer from bland liver steatosis, while a further 37% show signs of non-alcoholic steatohepatitis (NASH) (reviewed in (127)). “NAFLD” was defined in the 1980s to describe excess hepatocellular lipid accumulation in absence of significant alcohol intake, autoimmune, or viral liver disease, and the course of this pathology was long believed to follow the so-called “two-hit hypothesis” (134). Manifestation of bland steatosis was defined as first hit, while histological signs of liver inflammation, fibrosis and hepatocyte injury were proposed as succeeding second hit (NASH). Insulin resistance has now been recognized as the most valid predictive parameter of NAFLD/MAFLD progression to NASH. Moreover, presence of insulin resistance puts these patients to an elevated risk of morbidity and mortality, while sedentary behavior constitutes a complementary risk factor and correlates with clinical outcomes (reviewed in (135, 127)).

Since physical exercise is capable of improving and maybe reversing insulin resistance, while short term decreases in physical activity reduce multiorgan insulin-sensitivity and in parallel increase liver fat (reviewed in (127)), it is reasonable to assume that physical activity represents a potent treatment modality for NAFLD/MAFLD. There is indeed some evidence from prospective controlled randomized intervention studies that liver fat can be reduced independently from changes in body weight by structured exercise regimens in a limited, yet significant manner, while effects on liver inflammation and fibrosis remain to be investigated (extensively reviewed in (127)). Although no direct research of NEAT effects on NAFLD/MAFLD is currently available, one well-designed study using brisk walking as an intervention has shown reduced liver fat after 6 and 12 months (136). This suggests that low to moderate intensity activities potentially impact hepatic steatosis, which should be further evaluated.

For weight maintenance purposes, current recommendations encourage subjects with obesity to engage in 200-300 minutes per week in activities as e.g. walking (8). Yet, patients with T2D show markedly reduced TEE (< 300 kcal per day), number of steps taken (<1500 per day), physical activity duration (<130 min per day), and activity related energy expenditure (<300 kcal per day) as compared to subjects without diabetes (137). Accordingly, only 28% of patients with T2D in the U.S. achieve the recommended physical activity levels and less than 40% engage in regular voluntary exercise (120, 138, 139). In line with this, recent observational studies have shown that reduced NEAT is related to measures of insulin resistance, glycated hemoglobin A1c, and other features of the metabolic syndrome, consistent with the hypothesis that increasing spontaneous physical activity could be beneficial for those with T2D and NAFLD/MAFLD (140–142). This is supported by data coming from the Nurses’ Health Study showing that regular walking at normal pace (e.g. 3.2-4.8 km h^-1) was associated with a 20-30% relative risk reduction of T2D, while in another study, frequent walking was correlated with an equally remarkable risk reduction of mortality in T2D (120, 143, 144).

On the other hand, it must be acknowledged that data on NEAT, T2D and NAFLD/MAFLD are limited. First, evidence suggests interindividual differences in the response to a standardized exercise schedule at a given dose and there is, moreover, strong indication of genetic components to the variation in human trainability (reviwed in (145)). Second, available evidence almost exclusively comes from observational and cross-sectional studies. Furthermore, the data on lifestyle interventions on sequalae of insulin resistance and manifest T2D also have limitations (146). For instance the 10-year prospective randomized LookAHEAD study in T2D patients was stopped early based on a futility analysis, since the trial was unable to detect substantial effects on cardiovascular events, although the intervention group experienced significant weight loss (147). Otherwise, secondary analyses of this hallmark study were able to show that the magnitude of weight loss may be predictive of outcome measures (148). In more detail, obese and overweight subjects suffering from T2D were examined regarding the association of the magnitude of fitness change (n=4406 and weight loss (n=4834) over a median of 10 years of follow-up for the primary outcome. The primary outcome was a composite of death, cardiovascular disease, myocardial infarction, hospitalization for angina, and stroke. Subjects losing more than 10% of body mass in the first year of the intervention had a 21% lower risk of the primary outcome relative to participants with stable body mass or weight gain. Interestingly, achieving a > 2 metabolic equivalents (MET) fitness change was not significantly associated with the primary, but with the secondary endpoint (composite of coronary–artery bypass grafting, carotid endartectomy, percutaneous coronary intervention, hospitalization for congestive heart failure, peripheral vascular disease, or total mortality (148). Therefore, it appears that life style modifications are principally capable of beneficially modulating clinical endpoints in, while the optimal exercise (and lifestyle intervention) therapy for individuals with T2D, particularly with co-existing complications, remains unknown (120). These findings suggest that priority should be made for subjects who are obese to engage in any type of regular physical activity before T2D and NAFLD/MAFLD become manifest ((144), reviewed in (120, 8)). Accordingly, the “Step It Up! (The Surgeon General’s Call to  Action to Promote Walking and Walkable Communities)” program was recently released, focusing on promoting optimal health before disease occurs (149).

In summary, since EAT is negligible in the vast majority of subjects and NEAT represents the main contributor to daily PEE/AEE and therefore TEE in Western populations NEAT can be recognized as a major potential target for lifestyle modification in subjects suffering from T2D and probably also under conditions of NAFLD/MAFLD.

Personalized Approaches versus Environmental Re-engineering for Promoting NEAT

In free-living individuals, counterbalancing effects of an obesity-promoting environment involves addressing several central questions:

• How can the amount of NEAT be increased or preserved under conditions of caloric restriction?

• What are realistic goals for daily spontaneous physical activity and what are appropriate activities for increasing NEAT?

When aiming to prevent or treat obesity, it can be argued that in an obesogenic environment each person needs to increase physical activity levels (45). Alternatively, it could be argued that the epidemic of obesity needs to be addressed at the population level, since obesity has emerged as a result of environmental pressures that have led to decreased population-wide activity levels (e.g., more sedentary jobs, more time at work and less in leisure) (45). In reality, these contributors are not mutually exclusive. However, in order to substantially increase NEAT it is useful to categorize these perspectives as the “individualized approach” versus the “environmental re-engineering approach”(45). Accordingly, Levine and Kotz have developed the “egocentric” and the “geocentric” models, which provide a theoretical framework to understand important environmental determinants of NEAT from these two perspectives (20).The egocentric model focuses on a single person. Accordingly, environmental factors that impact a particular person’s spontaneous physical activity levels are considered, such as “my occupation”, “my transportation to work”, or “my leisure time activities” (Figure 8) (20). By contrast, the geocentric model is focused on how the environment impacts NEAT of multiple subjects, such as city planning to ensure walk-friendly or bike-accessible environments (Figure 8) (20). These models may help to elucidate how NEAT can be effectively modulated. 

Figure 8. Environmental determinants of NEAT – the egocentric versus the geocentric model (adapted from (20)).

In the geocentric model a variety of environmental factors impact spontaneous physical activity. The most prominent negative influences on NEAT are represented by urbanization and mechanization, which are largely a phenomenon of high- and middle-income countries. Examples include televisions, drive-through restaurants, clothes washing machines, motorized walkways, and others (20).

Accordingly, when comparing daily energetic costs of mechanized tasks with the same tasks performed manually a century ago, the difference in daily energy costs approximates 111 kcal, or more than 40,000 kcal per year (150). Evidence such as this has been used in support of the proposal that urbanization and mechanization have likely had a dramatic impact on energy balance (20). Hence, proposals to increase NEAT on a population level have included environmental “re-engineering,” including provisions for adequate walkways, to build schools within walking distance, and to adapt employment laws for promoting office fitness. The problem with this argument is that it includes imposing high economic costs on business and localities without agreed upon standards for success (45).

It is self-evident that to intentionally increase NEAT over a protracted time period presupposes a significant amount of self-discipline. To facilitate behavior modification Levine has published approaches for promoting NEAT that focus on behavioral economic theory (for extensive review see (45)). Behavioral economic theory is a framework for conceptualizing how people make behavioral choices based upon their perceived relative value. When applied to NEAT, behavioral economic theory is concerned with how people choose between various activity/inactivity options. Consequently, Levine proposes four key elements (45): 1) It is critical to provide individuals with free-choice. By contrast, forcing a subject to choose a specific NEAT-promoting activity is likely to have the opposite to the intended effect. Otherwise, if an activity is self-selected, it is likely to be more reinforcing and consequently self-selected more often; 2) The delay between performing a NEAT-promoting behavior and the outcome needs to be minimized. While sedentary behaviors that people enjoy have immediately reinforcing consequences, health benefits of standing or ambulation may take longer to manifest. Therefore, it is important to choose NEAT-promoting physical activities; that are pleasing. For example, walking while listening to music or walk-and-talk with a friend. 3) Behavioral “costs” determine which sorts of activity/inactivity become selected. If a person has to work strenuously to participate in a given activity, they will be less likely to do it. For example, it is unlikely that many people will drive 40 minutes to the gym long-term just to engage in fitness training. People are more likely to choose physical activities that are easily accessible, such as home- or even office-based activities that do not require changing location or clothes; and 4) For an individual to choose a NEAT-promoting activity, it has to be more attractive than available alternatives. For example, behavior will change when providing a competing behavior that is more valued: a given person may prefer to surf the internet while seated rather than visiting the gym for a fitness workout. If, however, walk-and-talk with a friend was an option, the individual could choose that instead of internet-surfing. These behavioral components have been synthesized into a simplified approach termed, STRIPE (45): STRIPE is an acronym that represents S = Select a NEAT-activity that is enjoyed and start it; T = targeted, specific individual goals must be defined; R = rewards need to be identified for reaching the defined goals; I = identify barriers and remove them; P = plan NEAT-activity sessions; E = evaluate adherence and efficacy.

Overall, to increase NEAT in the prevention and treatment of obesity, egocentric and geocentric approaches should be considered. Occupation and leisure time are the two principal time frames that have to be targeted for promoting individual NEAT. To increase NEAT by means of changing behavior, the STRIPE approach is considered as safe and well-grounded in conceptual evidence. Unfortunately, there is no evidence as to whether an individualized or the population-based approach is more effective in terms of increasing physical activity levels and/or to affect body mass. Despite this, however, it is our opinion that both approaches should be still considered when counseling patients regarding ways they can improve their daily life conditions (45).

Increasing NEAT: Realistic Goals, Pitfalls and Appropriate Activities

Obesity is not a problem in only one component of energy balance and food restriction alone is surely not the complete answer on a long-term perspective. Hill et al. hypothesized that a person who is physically highly active could maintain energy balance and a healthy body weight by eating and expending at, say, an estimated 3000 kcal per day. This person, if adopting a sedentary lifestyle, could alternatively maintain energy balance and consequently body weight by eating and expending 2000 kcal per day. If, however, this sedentary person fails to strictly keep energy intake at 2000 kcal per day to match reduced energy expenditure over time, experiencing weight gain would be the unavoidable consequence (16). In other words, for a majority of subjects in a given population, maintaining energy balance at substantial higher levels by increasing NEAT could be in the long term much more realistic under free-living conditions (e.g. in an environment with high access to energy dense foods), than maintaining caloric intake at lower levels (16). Therefore, when attempting to increase NEAT, it is important to establish what goals have to be addressed for prevention or treatment of obesity. According to a review, the amount of physical activity necessary for weight loss approximates 2000-2500 kcal per week or about 2 ½ hours of additional daily ambulation, which is considered realistic for a majority of subjects with obesity (45). This is in line with current recommendations, suggesting ≥ 150 minutes per week of aerobic exercise in combination with a hypocaloric diet (8).

However, evidence is lacking from randomized, controlled studies as to whether strategies to promote NEAT in absence of dietary manipulation are effective for obesity prevention or treatment. Moreover, some lines of evidence suggest a compensatory upregulation of energy intake with rising physical activity energy expenditure, especially at higher workloads and energy expenditure levels (reviewed in (90, 112, 151, 80). Therefore, it remains unclear whether substantially increasing NEAT for body mass control under free-living conditions will be capable of inducing weight loss without caloric restriction. Probably this shows large inter-individual variability, since it was recently shown that subjects losing weight over a two year time span increased their daily activity by about 35 minutes, while subjects gaining weight showed a reciprocal decrease (113). Energy intake was comparable in both of these groups. Contrasting with that, subjects either losing body weight or weight maintainers did not show alterations in daily activity as compared to weight gainers in another study (152). Thus, even under real life conditions there might be “responders” and “non-responders” in terms of daily activity/NEAT and body weight control. These groups remain, however, to be better defined.

Recent work could help to better categorize “responders” and “non-responders” by applying the above mentioned constrained energy expenditure model (153). The authors hypothesized that total energy expenditure would increase with physical activity at low levels, while plateauing at higher activity levels. It was found that after adjusting for body size and composition, TEE is indeed positively associated with PEE/AEE, but the relationship was markedly stronger over the lower range of physical activity. For subjects in the upper range TEE plateaued (Figure 9). According to the author’s hypothesis, body fat and activity intensity appeared to modulate the metabolic response to physical activity (153). In other words, subjects who maintain high levels of physical activity in their daily lives could show a smaller response to attempts of further increasing NEAT as it contributes to TEE increment, while subjects with habitually low activity levels could increase NEAT contribution and can therefore possibly be categorized as “responders”.

Figure 9. Effects of physical activity on PEE/AEE and its components (adapted from (153)).

Since hypocaloric diets can significantly reduce NEAT (see previous sections), a major goal has to include prevention of this reduction in NEAT. There is strong evidence that physical activity is essential for the prevention of weight regain after weight loss, and for weight maintenance (16, 80). For instance, it was shown that low physical activity levels are related to significant weight regain at follow up after hypocaloric diets (154). Furthermore, when subjects gaining weight under free-living conditions are compared to weight stable counterparts, the “weight gainers” demonstrated markedly lower physical activity related energy expenditure and less muscle strength (155). Of note, after a one year period, lower physical activity energy expenditure explained approximately 77% of body mass increase in the “weight gainers” as compared to “weight maintainers” (155). A recent meta-analysis on weight loss maintenance shows that physical activity has a significant treatment effect of approximately 1.6 kg in the short term (156). However, long term this effect was lost, probably due to reduced compliance since effects of voluntary physical exercise were examined (156).

As discussed above, for prevention of a decrease in NEAT resulting from hypocaloric dieting, an effective strategy could therefore be to recommend moderate voluntary exercising during weight reduction, and then to engage in NEAT-enhancing activities at the initiation of weight maintenance. This strategy could mitigate poor long-term compliance and substantially contribute to lower body mass preservation. However, when purposeful training is initiated in combination with a hypocaloric diet, the exercise schedule must be moderate as too intense of an exercise regimen during caloric restriction can disproportionally reduce NEAT rather than stabilize it (157, 85). Otherwise, even purposeful exercise training cannot completely overcome hypocaloric diet-induced reductions of TEE, as the REE-component remains largely unaffected by this kind of intervention even when LBM is preserved (85, 72, 36, 80).

From a practical point of view this is a central issue, as after weight loss reduced TEE will persist over years. This was recently shown by a follow up study on participants of the “The Biggest Loser” televised weight loss competition in the U.S.(158). Fourteen subjects who were severely obese lost approximately 59 kg during a 30 week period of strenous physical exercise in combination with dieting. Fat mass was primarily lost, while LBM was largely preserved (158). Compared to baseline, by the end of the 30-week competition TEE decreased significantly by 800 kcal per day, which was mainly explained by the decline in RMR, approximating 600 kcal per day (158).  Unfortunately, NEAT was not directly measured. However, keeping in mind that subjects underwent an intense physical exercise schedule it can be hypothesized that the observed difference of TEE and RMR (e.g. 200 kcal per day) was contributed to by a decline in spontaneous physical activity, which is comparable to earlier studies (157, 39, 72). After six years, the participants had regained 41 kg, TEE was reduced by roughly 400 kcal day-, and in spite of the significant weight regain, RMR remained reduced by 600 kcal per day (158). Therefore, after six years, “The Biggest Loser” televised weight loss competition resulted in a total weight loss of approximately 12 kg in subjects who were previously severely obese. The data indicate that these individuals will have to live with remarkably reduced dietary energy needs, even after adjustment for body weight and age (158) that contributes to their ongoing weight regain. Remarkably, the fact that after six years the reductions in RMR were lower than TEE indicates that some form of compensation is occuring, but it is unclear which component of daily energy needs is accounting for it (Figure 1). From the previously reported findings it can surmised that decreased NEAT was one of the factors contributing to the large weight regain (158, 39). Indeed, it was shown that participants of the “The Biggest Loser” television show with high levels of physical activity over years had significantly lower body mass regain compared to subjects with low physical activity levels. Weight loss maintainers had physical activity thermogenesis of roughly 12 kcal/kg/d as compared to weight regainers with approximately 8 kcal/kg/d, which was reflectetd by a significant negative correlation of body mass regain and physical activity levels (114).

Overall, moderate voluntary exercise results in significantly preserved weight loss during hypocaloric dieting (159, 160), which can be a helpful tool for preventing significant decreases in NEAT that will later help to stabilize body weight loss. With initiation of weight maintenance, subjects should be encouraged to engange in actvities increasing NEAT, to compensate at least in part for the obligatory weight loss-induced reduction in RMR. As stated previously, additional voluntary exercise is a useful tool for weight maintenance, but engagement in NEAT-related activities can be more easiliy integrated in daily life and will therefore likely result in higher adherence and compliance rates.

In terms of appropriate activities, it can be relatively easy to increase NEAT (Figure 10). Standing instead of sitting burns three times more kcal per hour, gum chewing increases energy expenditure four times, and stair climbing more than 40 times above resting levels (45). Ambulation (e.g., walking) in particular can raise NEAT and is easily performed at almost any place and at any time. But how can a goal of 2.5 hours of additional daily ambulation/standing time be realistically be integrated into daily routine? A key problem is that people’s occupations and personal lives regularly contribute to prohibit this degree of adaptation (45). As one’s occupation is the principal determinant of NEAT in adulthood and can represent an efficient means of promoting physical activity (45), how NEAT can be primarily integrated at the site of occupation needs examining. One important issue in this context addresses transportation to work. Similar to the NHANES data for the United States (42, 43, 41), recent results from the English and Welsh 2011 Census show that among the 23.7 million adult commuters, approximately 67% used private motorized transport as their usual main commute mode, while about 18% used public transport (161). In contrast, only 10.9% walked and 3.1% cycled (161). Thus, the majority of subjects rely on motorized transport. Promoting cycling or walking as a daily routine, not only with regard to transportation to work but also with respect to other daily needs (such as going to the grocery store), could represent a promising and realistic way for a majority of subjects to increase individual NEAT levels.

Figure 10. Effects of physical activity on PEE/AEE and its components (adapted from (153)).

Together, 2.5 hours of additional ambulation and standing time per day are proposed as goals for those considering weight loss. However, potential beneficial effects on weight loss could be at least partially compensated for by increasing energy intake from food. Increasing NEAT as an adjunct for weight maintenance could overcome potential compensatory effects to the low-calorie state and hypothetically represent an alternative strategy for body mass control.

Limitations to Increasing NEAT

A number of factors potentially affect time spent in NEAT, of which some (sociological and environmental factors) can be influenced, while others cannot (genetic and endocrine factors) (Figure 11). Two points are important to make. First, as was shown previously in this chapter, skeletal muscle can increase in fuel economy and work efficiency not only in response to the loss of body mass, but also so that benefits of NEAT may be diminished. Moreover, it has also been reported that under weight-loss conditions, subjects with obesity oxidize proportionally more carbohydrates and less fat as compared to lean counterparts, and that differences in skeletal muscle metabolism and SNS activity underlie some of the observed differences between subjects who are and are not obese (reviewed in (21)). And second, even though regular physical activity can increase the capacity of skeletal muscle to oxidize lipids and to store glycogen (85), regular physical activity can also increase work efficiency and thereby possibly reduce the energetic costs of NEAT (85). Even so, given the known contribution of physical activity to body weight maintenance, the recommendation to patients who are overweight and with obesity to increase NEAT-related activities such as ambulation or bicycling is logical, even before definitive evidence is reported.

Figure 11. Factors interfering with spontaneous physical activity and NEAT (adapted from (120)).

CONCLUSION

The epidemic of worldwide obesity in past decades is contributing to serious health concerns. Apart from poor diet, reduced physical activity and increased sedentary behavior contribute to the pathogenesis of obesity. Non-exercise activity thermogenesis is a highly variable component of daily total energy expenditure and essentially a function of environmental and individual factors. Whether caloric overfeeding systematically affects non-exercise activity thermogenesis is a matter of debate, while a negative energy balance due to voluntary caloric restriction and/or exercise can decrease it. As physical activity contributes to weight maintenance and prevention of body mass regain after hypocaloric dietary interventions, overcoming current obesogenic environmental pressures and increasing non-exercise activity thermogenesis could holds tremendous promise as a tool for body weight control.

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ABSTRACT

Knowledge of the factors influencing food intake is crucial to form an understanding of energy balance and obesity. Classical physiological feedback models propose that eating behavior is stimulated and inhibited by internal signaling systems (for the drive and suppression of eating, respectively) to maintain stability of the internal environment (usually energy or nutrient stores). However day-to-day food intake involves complex interactions of both internal and external inputs coordinated through homeostatic, hedonic, and cognitive processes in the brain. Twenty-five years ago, the term ‘obesogenic environment’ entered into scientific discourse and implies that prompts, cues, and triggers from the external environment are largely responsible for the increases in food intake that underlie the epidemic of obesity. This approach revitalized interest in the sensory and external stimulation of food intake and has drawn attention to the hedonic dimension of appetite. There is now a very strong current of thought that energy balance regulation is asymmetric and excess food intake is due to poor homoeostatic defense against positive energy balances in an environment rich in available, accessible, and readily assimilated food energy. This does not mean that regulatory signals concerned with energy balance regulation are unimportant. Indeed, they appear to be of incremental importance in prolonged negative energy balances and help explain control and loss of control of food intake as a dynamic continuum.

INTRODUCTION

Traditionally food intake has been researched within the homeostatic approach to physiological systems pioneered by Claude Bernard (1), Walter Cannon (2) and others, and because eating is a form of behavior, it forms part of what Curt Richter referred to as the behavioral regulation of body weight (or behavioral homeostasis) (3). This approach views food intake as the vehicle for energy supply whose expression is modulated by the metabolic requirement to replenish energy stores. The idea was that eating behavior is stimulated and inhibited by internal signaling systems (for the drive and suppression of eating, respectively) in order to maintain stability in the internal environment (energy stores, tissue needs). Since this time, it has become clear that energy intake is not as tightly regulated under modern environmental conditions as symmetrical negative feedback models initially suggested. Compensatory changes in physiology and behavior are more pronounced in response to negative than positive energy balances. Furthermore, energy intake is determined by eating behavior, which itself, may be determined by a number of complex physiological, environmental, social, and cultural factors. One should not perhaps expect to see strict regulation of food or energy intake on a day-to-day basis in modern environments.

IS EATING BEHAVIOUR REGULATED?

The control of appetite is often viewed to operate within an energy balance model of body weight regulation, but this should not lead to the view that appetite is controlled simply as an outcome of energy balance. Eating (food, energy, and nutrient intake) is a form of behavior, and like many aspects of volitional behavior, the factors leading to changes in that behavior are complex and are the outcome of factors which can be described by behavior change models. Using a COM-B model of behavior (Capability, Opportunity, Motivation, Behavior) (4), these factors include capability (psychological or physical ability to enact the behavior), motivation (reflective and automatic mechanisms activate or inhibit behavior), opportunity (physical and social environments that enables the behavior). Given the interacting and complex nature of these influences it is to be expected that unless homeostatic or other physiological feedback signals are particularly powerful, their effects on eating behavior would be difficult to detect. It is often implied (but not explicitly stated) that eating behavior is part of a feedback loop that is subsumed to the regulation of energy balance, but under the conditions of modern environments, there is little rationale for why eating behavior per se should be a regulated phenomenon as described by classic homeostatic models. Eating behavior may show repeated patterns which are stable over time, but these patterns may be quite different from the concept of regulation described in typical homeostatic models of blood glucose regulation or thermoregulation for example.

While eating behavior is influenced by a number of factors, it is reasonable to argue that some of those factors may become particularly salient under specific physiological or environmental circumstances. This is important as energy balance regulation appears asymmetric, with compensatory changes in physiology and behavior more pronounced in response to negative than positive energy balances. The exact mechanisms that oppose energy deficits are complex, inter-related, and individually subtle (5, 6). While energy expenditure and its components change in response to energy deficits in a quantitatively important manner, it is likely that changes in energy intake (EI) have a greater capacity to produce relatively large alterations in energy balance and body composition (7). The physiological and psychological impacts of weight loss likely occur on a continuum, with the point on this continuum influenced by (i) the degree of energy deficit, (ii) its duration, (iii) body composition at the onset of the energy deficit, and (iv) the psychosocial environment in which it occurs (8). In contrast, as weight is progressively gained there is very little evidence of physiological or behavioral systems exerting negative feedback to actively limit further weight gain. However, there is considerable heterogeneity in the rate of weight gain during overfeeding (9), which may in part, reflect inter-individual variations in physical activity and/or partitioning the excess energy between fat mass (FM) and fat-free mass (FFM) (10, 11).

The implication of asymmetric energy balance regulation would be that appetite is under stronger physiological control in relation to negative energy balances, whereas in a state of energy balance or positive energy balances weak linkages (negative feedback) exist between physiological functioning, food intake, and the motivation to eat. If energy balance regulation is asymmetric and modern environments are spatially and temporally rich in energy dense foods, it is logical to assume that many of the factors that shape eating behavior and energy intake are due to environmental influences such as sensory and environmental cues for food intake (which are presumably mediated through hedonic and other affective mechanisms). There is now a strong current of thought that a major cause of an increase in food intake associated with the rise of obesity resides in the hedonic rather than the homeostatic system. Some authors argue that in the resource limiting environments in which we evolved, hedonic and homeostatic systems functioned in a synchronized manner to facilitate over consumption during relatively brief periods of food abundance. In an environment where food resources are unpredictable and finite, overconsumption would have been an adaptive behavior limited (capability, opportunity) by environmental uncertainty. Natural selection would favor such behaviors. There would have been little need to evolve systems that protect against weight gain as it would be an improbable outcome in resource limited environments. This does not mean that the so-called ‘energy homeostasis system’ is no longer important in modern environments. Modern day environments have changed very rapidly and radically relative to the environment shaping energy balance regulation. Therefore, to understand how homeostasis and hedonics may influence food intake in modern environments requires an appreciation of the asymmetry of energy balance regulation, the time course over which such regulation may operate, and the very rapid time-course over which the modern food environment has changed.

FOOD INTAKE AND APPETITE CONTROL

The Motivation to Eat

Within the COM-B model of behavior, motivation is an important factor influencing behavior if capability and opportunity do not constrain behavior (as is the case in today’s so called ‘obesogenic’ environment). In the field of ingestive behavior, motivation to eat usually refers to reflective processes that are subjectively experienced or expressed. They are often believed to relate to underlying physiological or external environmental influences, but have the status of a self-reported, subjectively expressed psychological construct. Appetite has been defined as the subjective expression of willingness or motivation associated with qualitative selection and quantitative consumption of specific foods during an ingestive event (12). Appetite is not necessarily solely related to situations of nutritional depletion and can be influenced by a number of physiological and non-physiological factors. Appetites are specific to certain foods, often learned and frequently sensory specific (13). Unfortunately, the term appetite control is often used without specifying what is being controlled. Does eating behavior change to maintain some constancy and motivation to eat? Does appetite change in order to control food intake around some central tendency? Does appetite change with the aim of changing food intake to regulate energy balance? Often it is the latter view that is implied but not clearly articulated.

Because a great deal of human behavior is both reactive and learned, it is possible that the environment can produce prompts, cues, and stimuli that influence learned patterns of motivation to eat. Because eating behavior is a significant determinant of energy balance it is often argued that manipulation or control of motivation to eat (commonly termed appetite control) can be used as a means to prevent excess energy intake and obesity, usually via putative mechanisms of satiety. This is a logical proposition particularly if appetite is not actually very tightly controlled with reference to overconsumption and the development of obesity. Some characteristics of the expression of appetite do appear to render individuals vulnerable to over-consumption of food - these characteristics can be regarded as risk factors that vary between individuals (16). Other significant and salient environmental, acquired, and inherited influences on eating behavior aside, there has been considerable work dedicated to trying to conceptualize and understand putative mechanisms that may link motivation to eat to food and energy intake.

A CONCEPTUAL THEORETICAL MODEL LINKING PHYSIOLOGY, MOTIVATION, AND BEHAVIOR TO FOOD INTAKE

One of the most commonly accepted theoretical models for the control of appetite is the satiety cascade, a putative network of interactions between physiological, psychological, and behavioral factors which form a psychobiological system. The term psychobiological assumes that physiological functions provide internal cues that may impact motivation to eat, and can be conceptualized on three levels (Figure 1). These are the levels of psychological events (hunger perception, cravings, and hedonic sensations) and behavioral operations (meals, snacks, energy, and macronutrient intakes); the level of peripheral physiology and metabolic events; and the level of neurotransmitter and metabolic interactions in the brain (17) (see for Andermann & Lowell (18) for a review of the central control of food intake). Appetite reflects the synchronous operation of events and processes in the three levels. Implicit in this theoretical model is the notion that the physiological signaling systems influence motivation to eat and that motivation to eat shapes eating behavior. The model suggests that neural events trigger and guide behavior, but each act of behavior involves a response in the peripheral physiological system. In turn, these physiological events are translated into brain neurochemical activity that is related to the strength of motivation to eat and the willingness to refrain from eating. In this model it is assumed that motivations to eat change with the aim of changing food intake to regulate energy balance. The lower part of the psychobiological system (Figure 1) illustrates the satiety cascade links motivation and behavior to peripheral and central signals related to eating. It also includes those behavioral actions which actually form the structure of eating, and those processes which follow the termination of eating and which are referred to as post-ingestive or post-prandial events.

Figure 1. The satiety cascade, as originally presented (17), showing the expression of appetite as the relationship between three levels of operations: the behavioral pattern, peripheral physiology and metabolism, and brain activity. See for Andermann & Lowell (18) for a more recent review of the central control of food intake. PVN, paraventricular nucleus; NST, nucleus of the tractus solitarius; CCK, cholecystokinin; FFA, free fatty acids; T: LNAA, tryptophan: large neutral amino acids.

EPISODIC AND TONIC SIGNALS OF APPETITE CONTROL

Traditionally a distinction has been drawn between episodic and tonic signals in the control of appetite (19). Episodic signals are mainly inhibitory (but can be excitatory) and are usually generated by episodes of eating. These signals oscillate in accordance with the pattern of eating, and most are closely associated with the signaling of satiety. Tonic signals arise from tissue energy stores such as adipose tissue and metabolically active tissues to exert some degree of feedback on the expression of appetite to match day-to-day food intake with longer-term energy needs. These two sets of signals, one set responding sharply to nutrient flux and the other providing a slow modulation of appetite and food intake, are integrated within complex brain networks that control the overall expression of appetite. Examination of these putative mechanisms tend to be more common in acute or short-term studies of ingestive behavior in which the primary change is motivation to eat or eating behavior rather than longer term studies. Short-term experiments are valuable for mechanistic understanding, but such studies often make the assumption that changes in the motivation to eat or eating behavior will translate in the long term into aspect of energy balance regulation. However, concentrating only on short-term effects without considering the longer-term time scale may fail to reveal the way that food intake is affected or energy balance is regulated as the experimental time window is much narrower than is relevant for such regulation to occur (e.g., weeks and months rather than minutes, hours or days). Put simply, many investigators may be looking for evidence of regulation over a period where no such regulation is likely to occur. It is therefore important to distinguish between longer term (tonic) mechanisms of putative energy balance regulation and shorter term (acute, episodic) mechanisms that may affect motivation to eat or EI.

Episodic Appetite Signals

Episodic signals are those physiological events that are triggered as responses to the ingestion of food. These form the inhibitory processes which first of all stop eating and then prevent its re-occurrence and are therefore termed satiety signals. The types of signals involved in terminating a meal (satiation) and preventing further consumption (post meal satiety) can be represented by the satiety cascade. Initially the brain is informed about the amount of food ingested and its nutrient content via sensory input. The gastrointestinal tract is equipped with specialized chemo- and mechano-receptors that monitor physiological activity and pass information to the brain mainly via the vagus nerve (20). This afferent information constitutes one class of ‘satiety signals and forms part of the pre-absorptive control of appetite. It is usual to identify a postabsorptive phase that arises when nutrients have undergone digestion and have crossed the intestinal wall to enter the circulation. These products, constitute the flux of energy and nutrients into the circulation, may be metabolized in the peripheral tissues or organs, or may enter the brain directly via the circulation. In either case, these products constitute a further class of metabolic satiety signals. Additionally, products of digestion and agents responsible for their metabolism may reach the brain and bind to specific chemoreceptors, influence neurotransmitter synthesis or alter some aspect of neuronal metabolism. In each case the brain is informed about some aspects of the metabolic state resulting from food consumption. It seems likely that chemicals released by gastric stimuli or by food processing in the gastro-intestinal tract are involved in the control of appetite (21). Many of these chemicals are peptide neurotransmitters, and many peripherally administered peptides cause changes in food consumption (22). (Please refer to ENDOTEXT chapter ‘Endocrinology of The Gut and the Regulation of Body Weight and Metabolism’ by Andrea Pucci and Rachel L. Batterham for additional information on gut hormones physiology).

Cholecystokinin

Cholecystokinin (CCK) is a hormone released in the proximal small intestine mediating meal termination (satiation) and possibly early phase satiety. CCK reduces meal size and also suppresses hunger before the meal; these effects do not depend on the nausea that sometimes accompanies an IV infusion (23). Food consumption (mainly protein and fat) stimulates the release of CCK (from duodenal mucosal cells), which in turn activates CCK-A type receptors in the pyloric region of the stomach. Fat in the form of free fatty acids (FFA) of carbon chain lengths C12 and above produce pronounced CCK releases (24, 25). This signal is transmitted via afferent fibers of the vagus nerve to the nucleus tractus solitarius (NTS) in the brain stem. From here the signal is relayed to the hypothalamic region where integration with other signals occurs.

Animal data suggest that endogenous CCK release mediates the pre-absorptive satiating effect of intestinal fat infusions, and may in turn be critical in regulating the intake of fat (26). As in rats, intestinal infusions of fat produce a reduction in food intake and promote satiety in humans (27). In humans the satiety effect of fat infused directly into the duodenum can be blocked by the CCKA receptor antagonist loxiglumide (28). High-fat breakfasts have been shown to produce both greater feelings of satiety (signified by reduced levels of hunger, desire to eat and prospective consumption) and elevated endogenous plasma CCK levels. Collectively, these studies support the theory that CCK plasma levels are a potent fat (or fatty acid) -stimulated endogenous satiety factor, whose effects on food intake and eating behavior are mediated by CCKA receptors.

It has also been shown that synthetic CCK-A type agonists suppress food intake in humans. A drug, known by the number ARL1718, caused a significant reduction in meal size and had a longer duration of action than observed after infusions of CCK itself. A number of other CCK analogues / CCK 1 receptor agonist treatments have been developed including most recently GW181771 (GlaxoSmithKline) and SR146131 (Sanofi-Aventis). Studies with such drugs, together with those on the peptide hormone itself, do suggest that CCK has the properties of a true satiation signal which contributes, under normal circumstances, to the termination of a meal. However, CCK is not uniquely involved in the expression of satiety and is also involved in a spectrum of physiological responses generated following nutrient consumption. The action of CCK certainly acts in concert with other meal-related events, such as gastric distention for example.

Glucagon-Like-Peptide-1

Glucagon-like peptide (GLP)-1 is an incretin hormone, released from the gut into the blood stream in response to intestinal nutrients. Endogenous GLP-1 levels increase following food intake, particular of carbohydrate (29, 30). These studies suggest a role for GLP-1 in mediating the effects of carbohydrate (specifically glucose) on appetite. In healthy men of normal weight, infusions of synthetic human GLP-1 (7-36) during the consumption of a fixed breakfast test meal, enhanced ratings of fullness and satiety when compared to the placebo infusion (31). During a later ad libitum lunch, food intake is also significantly reduced by the earlier GLP-1 infusion. Intravenous GLP-1 also dose-dependently reduces spontaneous food intake and adjusts appetite in healthy weight male volunteers. This marked reduction in food intake and enhancement in satiety is also observed in male patients living with overweight or obesity and type 2 diabetes. In men living with obesity, intravenous GLP-1 potently reduces food intake either during or post-infusion (32) and, at lower sub-anorectic doses, slows gastric emptying. Reductions in intake and slowed gastric emptying are accompanied by decreased feelings of hunger, desire to eat and prospective consumption, and a prolonged period of post-meal satiety. These data demonstrate that exogenous GLP-1 reduces food intake and enhances in satiety in humans, both those healthy weight and living with obesity. However, it should be kept in mind that the doses of GLP-1 often administered are usually higher than the normal values seen in blood after a meal. Consequently, although GLP-1 receptors could be a possible target for anti-obesity drugs, the physiological role of GLP-1 itself in the normal mediation of satiety is still not confirmed. Nonetheless, GLP-1 through its action as an incretin which prompts the release of insulin, will certainly have some indirect role on the pattern of eating behavior. Interestingly, two of the most promising drug options for people living with obesity are liraglutide and semaglutide; both GLP-1 agonists. These drugs have been shown to decrease hunger (33) and/or increase satiety (34). The action of the pharmacological agents may mean that people living with obesity gain some control over their eating behaviors, however longer-term studies are required to investigate their true potential. In addition, it should be noted that the GLP-1 receptors responsible for the anti-obesity action of semaglutide and liraglutide are located in the brain rather than the periphery.

Peptide YY _3-36

Peptide YY _3-36 (PYY _3-36) is one of the two main endogenous forms of PYY. It is produced from the cleavage of PYY _1-36 (the other major form of PYY) by dipeptidyl peptidase IV (DPP IV). PYY is a 36 amino acid ‘hind gut’ peptide released from endocrine cells in the distal small intestine and large intestine. This hormone is similar in structure to the orexigenic neuropeptide NPY (70% amino acid sequence identity), and in the past, PYY has been regarded, like NPY, as a potent stimulator of food intake. However, in a series of studies in rats, mice and in one human study (all included in one paper), Batterham et al. (35) have demonstrated that peripheral PYY _3-36 administration reduces food intake and inhibits weight gain in rodents. These effects on intake and body weight are not observed in transgenic animals lacking NPY Y2 receptors (the NPY Y2 receptor knock-out), thereby implicating these receptors in mediating the anorectic effects of PYY. PYY release in the distal intestine is triggered by a variety of nutrients, including fats (particularly FFA), some forms of fiber and bile acid (24, 25). In humans, endogenous PYY is released predominantly after, rather than during a meal (35, 36) and causes a decrease in gastric emptying (the so-called ‘ileal brake’). Thus, it is more associated with post-meal satiety. PYY (including PYY _3-36) can cross the blood brain barrier via a non-saturable mechanism. Moreover, some of the effects of peripheral PYY _3-36 on food intake are either independent of or dependent on vagal afferents running from the periphery to the brain (37, 38).

With regard to the effect of PYY on human appetite, Batterham et al. (35) demonstrated that in healthy humans a 90-minute PYY _3-36 infusion reduced hunger and subsequent food intake two hours later. In a further report, PYY infusions in people who were either a healthy weight or living with obesity caused a 30% reduction in lunch intake post infusion and decreased the 24 h energy intake by 23% in those with a healthy weight and by 16% in those living with obesity (36). The natural plasma levels of PYY were lower in those with living with obesity than in the healthy weight participants, and were inversely correlated with the body mass index. The lower levels of PYY in those with living with obesity could mean a weaker satiety signaling through this hormone and therefore a greater possibility of over-consumption. However, as the authors noted these effects required doses greater than the normal physiological range of endogenous PYY and marked nausea was observed in one experiment (39-41).

Amylin

Research has also focused on amylin, a pancreatic rather than a gastrointestinal hormone, which also has a potent effect on both food intake and body weight (42). Peripheral administration of amylin reduces food intake in mice and rats, and meal size in rats. Chronic or peripheral administration of amylin over a period of 5 to 10 days produces significant reductions in cumulative food intake and body mass of rats (43). Thus, amylin appears to be a component part of the appetite regulation system. The effects of amylin on human food intake, food choice or appetite expression has yet to be fully assessed. However, pramlintide (a human amylin analogue), given to replace deficits in endogenous amylin in people with diabetes, has been shown to alter body weight in people living with obesity and diabetes treated with insulin (44-46) and people with obesity without diabetes (47). In healthy weight participants pramlintide induces reductions in meal intake and duration, and reduces pre meal appetite (48). Similar effects of pramlintide on intake and eating behavior are reported in people living with obesity (with and without type 2 diabetes) (49, 50).

Ghrelin

In contrast to the peptides mentioned above, ghrelin is the only known excitatory peptide released in the gastrointestinal system. Ghrelin is a 28-amino acid peptide that stimulates the release of growth hormone from the pituitary (51). Secreted primarily from the stomach, it is also found in a number of other tissues (52, 53). Ghrelin was the first excitatory peptide discovered and acts upon the hypothalamic arcuate nucleus (51, 54, 55). The composition and action of ghrelin is uniquely modified by the addition of an octanoyl group to the serine residue at position three. Some studies suggest this acylation is crucial for ghrelin to bind to the growth hormone-secretagogue receptor (GHS-R) and cross the blood-brain barrier (56). Ghrelin’s effects on food intake are mediated by neuropeptide Y (NPY) and agouti-related protein in the central nervous system (57).

The majority (80-90%) of circulating ghrelin is in the deacylated form (51). Two theories have been proposed, firstly that deacylated ghrelin could result from incomplete acylation of the peptide, with both forms utilising differently regulated pathways, or secondly, that DG could result from the deacylation of ghrelin (58). More recently, deacylated ghrelin has been termed the inactive form. Ghrelin is thought to be involved in meal initiation as it is high during periods of fasting and decreases in response to food intake, thus suggesting a physiological role for ghrelin in meal initiation (59). Intravenous infusion or subcutaneous injection of ghrelin in humans increases both feelings of hunger and food intake (56, 60) and to promote increased food intake, weight gain and adiposity in rodents (60).

Satiety Cascade Peptides

In the overall control of the eating pattern, the sequential release and then de-activation of the peptides described above, can account for the evolving biological profile of influence over the sense of hunger and the feeling of fullness (61). The actions of these hormones therefore contribute to the termination of an eating episode (thereby controlling meal size) and subsequently influence the strength and duration of the suppression of eating after a meal. Evidence of this is shown by Gibbons et al. (62) whereby the post meal period was separated into early and late phases of satiety. It should be noted that whilst the profiles of peptide response shown in papers often show the expected increase and decrease (in the case of satiety peptides) and decrease and increase (in the case of ghrelin), there is a wide degree of individual variability in peptide responses. This is not often commented on, or shown. Furthermore, the majority of papers do not measure a range of peptides but rather focus on one or two. Individual variability in the release and maintenance of the levels of hormones (or the sensitivity of receptors) may determine whether some individuals are prone to snacking between meals or to other forms of opportunistic eating. The overall strength or weakness of the action of these peptides will help to determine whether individuals are resistant or susceptible to weight gain. Individual variability in the response of gut peptides to different food types has been shown more recently (63) and can be seen in Figure 2. At present, it does not appear that a poor response in one peptide means a poor response in all peptides, and it is likely that the cumulative response of the peptides (of which there are many) is key for the modulation of appetite and EI. Since different foods may produce the same effect on hunger and fullness but display quite distinctive profiles of post-prandial peptides, this suggests that the satiety signaling system is complex and there is no single unique pattern of peptides that defines satiety. This questions to what extent short term satiety can be accounted for by individual changes in putative satiety peptides, and what other factors may contribute to subjective satiety or cessation of eating behavior in humans.

Figure 2. Panel A shows the average ghrelin suppression after high fat and low-fat meals and Panel B and C shows the individual profiles of ghrelin for each participant after both high and low-fat meals. Adapted from (63).

TONIC SIGNALS OF APPETITE CONTROL

Ghrelin and the Hunger Drive

As noted above, ghrelin is an episodic peptide increasing during periods of fasting and decreasing in response to food intake. However, ghrelin is also unique since it has been proposed that in addition to being linked to the initiation of eating, ghrelin also acts as a compensatory hormone. Circulating ghrelin decreases in response to overfeeding and increases in response to chronic negative energy balance such as occurs with exercise or anorexia nervosa (64). This means that in people living with obesity and in animals experimentally made fat, circulating ghrelin levels would be reduced in an apparent attempt to restore a normal body weight status. Therefore, ghrelin illustrates the characteristics of both an episodic and tonic signal in appetite control. From meal to meal the oscillations in the ghrelin profile act to initiate and to suppress hunger; over longer periods of time, some factor associated with fat mass applies a general modulation over the profile of ghrelin and therefore, in principle, over the experienced intensity of hunger. At some point, it seems likely that people living with obesity are insensitive to lower ghrelin levels and/or other factors outweigh the relative importance of circulating ghrelin. When weight is lost, for example following a period of food restriction and weight loss, ghrelin levels would rise (or normalize), and therefore promote the feeling of hunger. This is likely to be one of the signals that makes the loss of body weight difficult to maintain. Ghrelin blockade therefore may prove a useful anti-obesity treatment. Whilst people living with obesity have lower fasting ghrelin levels, they have been shown to show a similar response to infused ghrelin as normal-weight participants, that is, increased food intake (65). Ghrelin levels in people living with obesity do fall after food, but not to the same degree as healthy weight participants in whom different calorie loads were shown to decrease ghrelin levels in a dose-response manner, but in people living with obesity this clarity was not shown as clearly (66). This points towards a potential mechanism for weight gain to be a consequence of a down regulation of gut peptide signaling and that the sensitivity to ghrelin is being overridden by other factors, for example, hedonic control of appetite. 

The Role of Leptin

One of the classical theories of appetite control has involved the notion of a long-term signal, leptin, which informs the brain about the state of energy stored in adipose tissue (67). In 1994 a mouse gene that controls the expression of a protein by adipose tissue which could be measured in the peripheral circulation (leptin) was discovery. It is now well accepted that there is a good correlation between the plasma levels of leptin and adipose tissue (68), and leptin interacts with NPY, one of the brain’s most potent neurochemicals involved in appetite, and with the melanocortin system, in the central control of appetite. Alongside other neuromodulators involved in a peripheral-central circuit, leptin links adipose tissue with central appetite mechanisms and metabolic activity. A number of mutations in the genes controlling molecules in the leptin-insulin pathways are associated with the loss of appetite control and obesity. For example, the MC4-R mutation (melanocortin concentrating hormone receptor 4) leads to an excessive appetite and massive obesity in children, similar to leptin deficiency. Studies have shown for individuals with a genetic form of leptin deficiency that leptin treatment produces dramatic weight loss, an effect associated with marked decreases in hunger (69-71). Further, the administration of exogenous leptin to humans, with either an insufficiency in or a specific deficit of endogenous leptin appears to strengthen within meal satiation and post meal satiety (71, 72). However, leptin-based obesity treatments for most individuals with obesity seem inappropriate given they appear leptin insensitive rather than leptin deficient. Indeed, despite extensive literature on leptin and other putative feedback signals arising from adipose tissue (73, 74), there appears to be limited evidence in humans of the extent to which changes in adipose tissue exert strong negative feedback on motivation to eat or EI in individuals in approximate daily energy balance or modest positive imbalances (as characterize the majority of individuals in the modern environment). As a number of questions still exist regarding the applicability of a 'lipostatic' control system to the regulation of appetite in humans free from congenital leptin deficiency (75). Consequently, recent models of human appetite have attempted to integrate the role of both FM and FFM into the control of appetite and energy balance to better account for the peripheral signals of appetite.

Fat-Free Mass and Resting Metabolic Rate and Associations with Appetite

A conceptual model of human appetite that incorporates the energetic demands of metabolically active tissues has been proposed, with a tonic drive to eat arising from components of energy expenditure (e.g., resting metabolic rate; RMR) and its main determinants (e.g., FFM). This model is based on a series of studies demonstating that FFM, but not FM, is associated with hunger and EI under conditons of energy balance (76-81) (see Figure 3). For example, Blundell et al. (78) reported that FFM was associated with self-selected (ad libitum) meal size and total daily EI in 93 individuals living with overweight or obesity. In contrast, no associations were found between FM and EI. Resting metabolic rate, of which FFM is the main determinant (82), has also been found to be associated within-day hunger sensations and EI (80, 81, 83). These findings have been replicated in studies employing a wide range of participants and under a variety of experimental conditions, with the associations between FFM and EI observed under laboratory (79-81, 84) and free-living settings (85, 86), in new born babies (87), adolescents (88, 89), normal weight women (90),people living with moderate (91) or extreme obesity (92), and people of varying ethnic origin (93, 94). These studies provide evidence that the relationship between FFM and EI exists across the entire age spectrum from birth (87), through childhood and adolescence (88) and into adulthood (78, 81, 84, 93, 95) and older age (96). There is also limited evidence that losses of both FFM and FM may be associated with changes in appetite (97) and weight regain (98) following weight loss, suggesting that integrated models of weight loss that account for FFM and FM losses may better explain changes in appetite during prolonged energy deficit (see below sections). However, there are few prospective longitudinal studies relating changes in functional body composition to appetite or EI, and where there is evidence, it often from studies with extreme weight loss induced via semi-starvation or military training.

Figure 3. Association between fat-free mass, resting metabolic rate and daily energy intake (top panels), and a path diagram illustrating a mediation model for the direct effects of FM and FFM on RMR and RMR on EI, the indirect effect of FM and FFM on EI mediated by RMR, and the squared multiple correlations (R2) for RMR and EI. Data originally reported in Hopkins et al. (80, 85).

While often not explicitly discussed in relation to models of human appetite, the concept that energy needs exert influence on food intake is not new. In 1962, Kenneth Blaxter (99) noted that the basal metabolism of mature animals of different species increases to a fractional power of weight, with small animals have a higher basal metabolism per kilogram of weight than the large ones. This implies that to maintain body weight, small animals must obtain each day from food a larger number of calories per unit of the weight the large ones. If the total habitual energy expenditures of different species are all about the same multiples of their basal energy expenditure, then both the calorie intake and the basal metabolism are likely to be proportional to the same fractional power of bodyweight. Furthermore, the energetic demands of individual tissue-organs such as the liver (100) or the growth and maintenance of lean tissues (101, 102) have previously been suggested as sources of appetitive feedback. For example, Millward’s protein-stat theory suggests that lean mass, and in particular skeletal muscle mass, is tightly regulated such that food intake (specifically, dietary protein) is directed to meet the needs of lean tissue growth and maintenance (101). This theory is based on the existence of an ‘aminostatic’ feedback mechanism in which food intake is adjusted in response to amino acid availability to meet the protein demands of lean tissue growth and maintenance.

Studies Examining the Associations between Fat-free Mass, Resting Metabolic Rate and Energy Intake

Using statistical mediation models, a number of cross-sectional studies conducted under conditions of energy balance have demonstrated that the effect of FFM on EI is mediated by RMR (80, 103) and total daily energy expenditure (TDEE) (104), suggesting that energy expenditure per se may exert influence over EI. For example, Hopkins et al. (80) reported that the effect of FFM on EI was fully mediated by RMR i.e. FFM had no ‘direct’ effect on EI but rather ‘indirectly‘ influenced EI via its effect on RMR. In agreement with these findings, Piaggi et al. (104) reported that TDEE accounting for 80% of the observed effect that FFM exerted on EI in 107 healthy individuals. Such findings suggest that the associations between FFM and RMR with EI may reflect a ‘mass-dependent’ effect arising from the energetic demands of FFM and its constituent tissue-organs rather than a specific endocrine signal secreted by these tissue-organs. However, it should be acknowledged that such findings represent statistical rather than biological pathways. Furthermore, skeletal muscle, a major component of FFM by weight, secretes a large number of myokines (105)which provide a molecular signal for bi-directional communication with other organs (106). While myokines such as interleukin 6 (107) and irisin (108) have been linked to food intake and energy expenditure, the specific role that these (and other myokines) play in the control of appetite is unclear.

An important consideration in the proposed relationship between FFM and EI is that FFM is a heterogeneous tissue compartment that is comprised of numerous individual tissue-organs with wide ranging metabolic functions and mass-specific metabolic rates (109-111). Tissue-organ structure and function are tightly coupled and determine their tissue-specific metabolic rate (112). In turn, the tissue-specific metabolic rates of individual organs summate to determine whole-body metabolic rate (e.g., RMR). The maintenance of tissue-organ structural integrity and function is therefore a metabolic priority (112), but to date there has been little attempt to integrate individual tissue-organs and their mass-specific energy expenditures into homeostatic models of human appetite. Recently however, Casanova et al., (113)used whole-body magnetic resonance imaging to examine whether the masses of high-metabolic rate organs (brain, liver, heart and kidneys) were associated with fasting hunger in 21 healthy males (age= 25 ± 3 years; BMI = 23.4 ± 2.1 kg/m²) (114). As expected, fasting hunger was associated with FFM (r = 0.39; p = 0.09) but not FM (r = -0.01; p = 0.99). Interestingley, the association between the combined masses of the high-metabolic rate organs and fasting hunger (r = 0.58; p = 0.01) was stronger than with FFM as a single uniform body compartment. In particular, liver (r = 0.51; p = 0.02) and skeletal muscle mass (r_s = 0.57; p = 0.04) were strongly associated with fasting hunger. As the masses of the liver and skeletal muscle explained ~17% and ~21% of the variance in RMR, respectively, these findings again suggest that energy expenditure per se may exert influence over food intake.

Another important consideration is how behavioral components of total daily energy expenditure (e.g., physical activity or activity energy expenditure) influence energy intake. The effects of physical activity and/or exercise on appetite is discussed below. As noted, physical activity may influence the control of appetite via a number of physiological and psychological pathways (e.g., alterations in gastric emptying (115), appetite-related hormones (116), food reward (117), and eating behavior traits (118)). In addition, physical activity or exercise may also exert influence, albeit modestly, on appetite and EI via its contribution to TDEE. Hopkins et al. reported in 242 individuals in which physical activity and EI were measured under free-living conditions that activity energy expenditure was independently associated with daily EI alongside FFM and RMR (86). As activity energy expenditure only explained 3% of the variance in total daily EI, its effect on daily EI was much more modest than that seen for FFM or RMR. This is perhaps not surprising given the smaller and more variable contribution of physical activity energy expenditure to TDEE as compared to FFM and RMR (119). It could be argued that while FFM and RMR are well placed to exert stable influence over day-to-day food intake, the contribution of physical activity energy expenditure to daily EI is likely to be weaker and more variable (and therefore, also harder to quantify).

Factors Affecting the Strength of Association Between Fat-Free Mass and Energy Expenditure with Energy Intake

It has been suggested that excess FM may disrupt the coupling between FFM and EI, with associations between FFM and EI weaker in those living with obesity than in healthy weight individuals (90, 95, 104, 120). Early work by Cugini et al. reported that a positive association existed between FFM and hunger, while FM and hunger were negatively associated, in healthy weight individuals (121). However, no such associations were seen between FFM or FM and hunger in those living with obesity (120). Based on these data, the authors suggested that FM accumulation may disrupt the feedback mechanisms linking these tissues to hunger. More recently, Grannell et al. reported a positive association between FFM and EI during an ad libitum test meal in 43 individuals living with severe obesity, but the strength of this association was weaker in individuals with a higher BMI (92). To further explore the moderating effect of FM, Casanova et al., (90) examined the linear and non-linear associations between body composition (FFM and FM), energy expenditure (RMR and TDEE) and EI (ad libitum test meal intake and free-living 24-hour EI) in 45 healthy weight and 48 individuals living with obesity. Percentage body fat moderated the associations between RMR (β=-1.88; p=0.02) and TDEE (β=-1.91; p=0.03) with free-living 24-hour EI. Furthermore, FM was negatively associated with test meal EI only in the leaner group (r=-0.43; p=0.004), with a weak non-linear association observed between FM and EI in the whole sample (r²=0.092; p=0.04).

Such findings point to a non-linear relationships between FM and EI, and this may help account for why negative associations between FM and EI have been observed in healthy weight individuals (121, 122), but studies in those living with overweight or obesity often report no association between FM and EI (79, 88, 91, 123). A weaker negative association between FM and EI at higher body fatness is in line with the notion of leptin and insulin resistance (124, 125), which may alter central and peripheral sensitivity to appetite-related feedback signals (126-128). Furthermore, while the contribution of FM to RMR is smaller than FFM (129), its contribution to RMR becomes proportionally larger as FM increases with excessive weight gain. Therefore, differences in the strength and direction of association between FM and EI at higher body fatness may reflect the increased contribution of FM to body weight and RMR alongside a blunting of its inhibitory influence on EI. It should also be acknowledged that the associations between FM and EI likely reflects both biological and psychological factors. Indeed, Hopkins et al., (85) have demonstrated that psychological factors such as cognitive restraint are robust predictors of EI when considered alongside physiological determinants of EI (e.g. FFM and RMR), and have the potential to play a mediating role in the overall expression of EI (85). A recent paper also suggests that the associations between FFM and TDEE with EI may become weaker with age (96). Based on a secondary analysis of the Interactive Diet and Activity Tracking in AARP Study, a biomarker validation study of self-reported diet and PA measures in older adults, Hopkins et al., reported that FFM and TDEE (derived from doubly labelled water) predicted self-reported EI in 590 older adults (mean age 63.1 ± 5.9 years). Interestingly, while the associations between FFM or TDEE and EI existed across age quintiles, age moderated the associations between FFM and TDEE with EI such that these associations weakened with increasing age (96). Please refer to ENDOTEXT chapter ‘Control of Energy Expenditure in Humans’ by Klaas R Westerterp for additional information).

Associations Between Body Composition and Energy Intake During Prolonged Negative or Positive Energy Balances

Another important point to note is that the aforementioned associations between body composition, energy expenditure, and EI are from cross-sectional analysis performed in weight stable individuals at or close to energy balance. However, the effect of FFM on appetite appears to be dependent on energy balance status, with evidence suggesting that losses of FFM may also act as an orexigenic signal during energy deficit. During the Minnesota semi-starvation study (130), 32 healthy men undertook 24 weeks of semi-starvation (25% of weight loss), 12 weeks of controlled refeeding and 8 weeks of ad libitum refeeding. During the last phase (n = 12), hyperphagia remained until baseline levels of FFM were restored. This led to FM accumulation that surpassed baseline levels (i.e., “fat-overshoot”), a phenomenon that has been reported elsewhere following underfeeding or military training (131, 132). As hyperphagia persisted until FFM had been restored to pre-weight loss levels, it was suggested that independent appetitive feedback signals from both adipose tissue and FFM (e.g., a ‘proteinostatic’ mechanism) contributed to the changes in  hunger and food intake seen and restoration of body weight (133, 134).

While the demands imposed by semi-starvation or military training on energy balance clearly exceed those experienced during common diet and/or weight loss interventions, evidence also exists to suggest that FFM loss during clinically relevant weight loss may also act as an orexigenic signal. Following weight loss, it is commonly suggested that subjective hunger and orexigenic hormone concentrations increase (135, 136), but studies have also reported no change or reduced hunger following weight loss (137-139). The composition of the weight lost, which is a function of initial body fat, the rate and extent of weight loss, diet composition and exercise (140, 141), may also influence any accompanying changes in appetite. It has been suggested that greater FFM loss during weight loss is associated with increased hunger (97) and weight regain (98) following weight loss. To assess whether changes in body composition occurring during weight loss were associated with subsequent energy balance behaviors under conditions of therapeutic weight loss, Turicchi et al. conducted a systematic review and meta-regression examining weight loss studies in weight clinical weight loss was achieved (mean = 10.9%) and weight regain occurred in the follow-up period (mean = 5.4%) (98). They found that while both greater rate and amount of WL predicted weight regain, the composition of weight loss i.e. the (amount of FM and FFM) explained greater variance in weight loss alone (40% vs 29%) (98). Furthermore, Turicchi et al., reported that greater FFM loss following a 12% reduction in body weight via a low-calorie diet was associated with greater increases in hunger in men (r = 0.69, p = 0.002) but not women (r = 0.25, p = 0.24) (97). In line with these findings, after 5 weeks of very-low calorie diet (500kcal/d) or 12 weeks of low-calorie diet (1250kcal/d) Vink (142) reported that greater FFM loss during energy restriction was associated with greater weight regain during a subsequent 9-month follow-up period. Data examining FFM loss during extensive periods of energy deficit are therefore suggestive that FFM loss may be part of an integrated response driving post-weight loss increases in EI and weight regain, potentially as a means to restore the structural integrity of FFM compartments (although the influence of FFM loss appears more modest than FM loss). These data also emphasize the importance of developing integrative models of energy balance that consider the dynamic relationships between body structure, physiological function, and the way these mechanistic interactions influence key psychological and behavioral determinants of energy balance such as appetite. However, there are few prospective longitudinal studies relating changes in functional body composition to appetite or EI, and further research using advanced imaging methods for tissue-organ composition and multi-compartmental body composition models across a range of initial body compositions and weight losses would provide additional mechanistic insight (97).

Taken together, cross-sectional research in weight stable individuals indicates that greater FFM is associated with increased EI, but research also indicate that FFM loss is associated with increased appetite and EI. If greater FFM is associated with increased appetite, how is it that FFM loss during weight loss is also associated with increased appetite? One explanation is that FFM exerts ‘passive’ and ‘active’ effects on appetite under situations of differing energy balance (6, 143). At or near to energy balance, Dulloo et al. (143) has suggested that the energy demand of FFM and its constituent components create a ‘passive’ background pull on EI that ensures the energetic demands of metabolically active tissues are met through day-to-day food intake. In contrast, during weight loss, FFM loss may act as an ‘active’ orexigenic signal that stimulates increased hunger and EI in an attempt to ensure the preservation of FFM and the functional integrity of its constituent tissue-organs (143). However, it should be noted that long-term studies with longitudinal tracking of appetite, body composition and energy expenditure are rare, particularly under-conditions in which body composition is systematically manipulated.

Body weight gain leads to an expansion of FFM which increases RMR, but how such changes causally influence appetite or food intake has not been examined. As weight is gained, both FM and FFM expand but at different rates. While such changes may not drive weight gain per se, a higher FFM and associated RMR may increase the background tonic drive to eat, favoring maintenance of a higher body weight. Expansion of FM over the long term induces insulin and leptin resistance, expansion of FFM and, in extremis, some slight elevation of RMR, could account for the apparent diminishing negative feedback from FM as adipose tissue expands (90, 95, 104, 120). Therefore, it may be argued that any putative effect of FFM or RMR on the drive to eat may decrease with increasing BMI, since FFM and RMR increase at a decelerating rate with increase in weight, while the energy content of the body expands disproportionately as FM expands. While factors associated with energostatic models of appetite may be unlikely to drive body weight up in the first place, it is not inconsistent with maintenance of a higher body weight once this is achieved by other means. Thus, it may be that RMR is associated with EI at or close to energy balance, but that RMR (and its primary determinant FFM) become dissociated from the process of overconsumption during significant weight gain as the signal(s) becomes ‘overwhelmed’ by other stimuli important in driving weight gain e.g., food availability, sensory variety, dietary energy density and food reward.

HOMEOSTATIC AND HEDONIC PROCESSES OF APPETITE CONTROL

Food intake is clearly influenced by homeostatic processes of hunger and satiety, modulated according to short term and long-term signals of nutritional and energy status and moderated by lifestyle factors such as physical activity. However, human eating behavior is a complex phenomenon and people eat for many other reasons too: for friendship and celebration, in response to sadness or stress. All these cognitive and emotional motives converge on the fact that eating is a potent source of reward. It provides the eater with an instant but temporary hit of gratification. The greater the reward, the harder it is to resist the behaviors that produce it even when the consequences are risky or harmful and especially when the risk is removed in time. Therefore, a key issue in the study of appetite control is the relationship between hedonic and homeostatic drives (144). Historically, hedonic processes have been viewed as a function of nutritional need-state. In a state of depletion, the hedonic response (experienced palatability or pleasure) to energy providing foods is enhanced and when replete, the hedonic effect of these foods is reduced (145). This view is compatible with the association between energy density and palatability (146) and also that the consumption of fats and sugars “energy-dense nutrients“ may be under neuro-regulatory control (147). However, the idea of reward as merely serving the fulfilment of nutritional need is not sufficient to explain non-homeostatic food intake and it is perhaps more useful to try and distinguish the substrates of homeostatic and hedonic systems and to assign them separate identities (148).

Homeostasis and Hedonics: Cross-Talk and Interaction

Advances in our understanding of the molecular and neural mechanisms behind food intake regulation and appetite control are revealing how the reward system can interact with homeostatic mechanisms. For example, cannabinoid receptors and their endogenous ligands (e.g., anandamide) are implicated in the reward system. Peripheral and central administration of anandamide increased appetite in rodents, and this seemed to be related to alterations in incentive value (wanting) for palatable foods (149). However, the cannabinoid system has been shown to interact with homeostatic processes in a number of ways: Leptin signaling becomes defective when hypothalamic endocannabinoid levels are high (150); activation of CB1 receptors prevent the melanocortin system from altering food intake (151); furthermore, CB1 receptors can be found on adipocytes where they may directly increase lipogenesis (152). Opioid neurotransmission also forms part of the biological substrate mediating reward processes of consumption. For example, endogenous opioids are associated with the reinforcing effect of food (especially when palatable) (153, 154). However, there is evidence to show that in a fasted state, the reinforcing effect of food can be reinstated in enkephalin and β-endorphin knock-out mice (155). Therefore, homeostatic processes may interact with hedonic signaling to override selective reward deficit.

Hence although homeostatic and hedonic systems can be given separate identities (148), they are also - to an extent - inseparable, with neural cross-talk permitting functional interactions which may influence the organization of eating behavior. From this standpoint, the interaction of homeostatic and non-homeostatic pathways in the neuro-regulatory control of eating may be more important than the two systems studied in isolation. From behavioral and anatomical observations (156), it has been suggested that projections from the hypothalamus to the nucleus accumbens may modulate the motivation to eat via metabolic signals. Furthermore, direct and indirect projections from the accumbens to the hypothalamus may explain the ability for mesolimbic processes “activated by relevant environmental cues and incentives” to essentially hijack the homeostatic regulatory circuits and drive-up energy intake. Further research is necessary to identify the pathways that mediate such interactions; however progress has been made (157).

Liking vs. Wanting Food

The hedonic perspective on appetite control accounts for eating behavior motivated by the expectation or experience of pleasure from consuming specific foods and involves dissociable processes of “wanting” and “liking”. The “liking” component refers to the subjective experience of pleasure elicited by the sensory perception of food and is associated with the release of endogenous opioids acting on localized clusters of neurons termed “hedonic hotspots” (158) The “wanting” component of reward refers to the process by which food is assigned motivational significance or “incentive salience attribution” and is associated with the release of the neurotransmitter dopamine in the mesocorticolimbic pathway. This latter component can be activated by thoughts or cues signaling food and often precedes the actual receipt of food (159).

In human neuroimaging studies, regional differences in the neural activation to food stimuli during either anticipatory or consummatory phases of reward processing are broadly supportive of the distinction between liking versus wanting. Response to passive viewing of high- versus low-calorie foods or cues signaling the imminent receipt of a tasty food are more reliably observed in the amygdala and ventral striatum, whereas the response to the actual taste and consumption of a palatable food is associated with activation in the primary taste cortex in the insular and opercular cortices (160). Some researchers have proposed that differences between individuals who are healthy weight and those with obesity in neural activation to palatable food can be understood as a dissociation in both the direction and region of responding during the anticipatory and consummatory phases of food intake- with greater striatal activation in individuals living with obesity compared to healthy weight controls when a food is wanted but lower activation in liking-related regions when a food is actually tasted (161). However, several inconsistencies in the brain imaging literature have been noted (162), and further research is needed to substantiate this hypothesis.

Liking and wanting for food are often viewed in relation to subjective states or explicit feelings that refer to the everyday understanding of these terms in the context of food choice and food intake (163). Wanting might describe subjective states of desire, craving or perceived deprivation of pleasure, whereas liking is characteristically understood as the perceived hedonic effect of a food, the appreciation of its sensory properties or some evaluative judgment of its potential to give pleasure. As the subjective sensations of liking and wanting often overlap and are subject to interference or misinterpretation, their relationship with behavior is often difficult to discern (163). However, liking and wanting responses to food are not necessarily consciously monitored or even always accessible to the individual. Although people tend to be very good at estimating and reporting their liking for food, they are often unable to accurately gauge their implicit wanting for food (i.e., why they are unconsciously drawn to one food over another).

The hedonic aspect of eating is important in a well-functioning homeostatic system for the directing and motivating an adequate supply of nutrients and energy. Increasingly, evidence for the interplay between liking and wanting with hunger and satiety is helping to clarify the role of hedonics in the control and loss of control over food intake (164). This extension to the conventional homeostatic model recognizes that hedonic processes are affected by acute nutritional need states and might modulate food intake through their interaction with other physiological processes involved in satiation and satiety. Likewise, cognitive and sensory inputs implicated in food liking and wanting can modulate the metabolic processes associated with homeostatic control over food intake (165). In addition to the effect of liking and wanting on episodic appetite responses, more recent evidence is emerging to suggest that tonic signals of nutritional status might affect liking and wanting for food to influence food preference and the composition of the diet (164).

MODULATION OF APPETITE THROUGH PHYSICAL ACTIVITY

Physical Activity and Control of Food Intake

Some individuals believe that the energy expended during exercise will automatically drive-up hunger and food intake to compensate for the energy deficit incurred. However, evidence shows that interventions of acute exercise generate little or no immediate effect on levels of hunger or daily EI (see (166-170) for reviews). One reason that studies do not demonstrate an increase in EI following acute exercise could be that they fail to track EI for sufficiently long periods after the bout of exercise, or that the exercised-induced energy expenditure is not large enough to stimulate appetite. However, even with a high dose of exercise (gross exercise-induced increase in energy expenditure = 4.6 MJ) in a single day and following tracking of EI for the following two days, there is no automatic compensatory rise in hunger and EI (171).

Exercise training interventions have shown similar findings with regards to EI (169, 172). In a recent systematic review and meta-analysis of exercise training interventions in people living with overweight or obesity, Beaulieu et al. (172)found that among 25 exercise groups, exercise training (ranging 2-72 weeks) did not lead to any significant post-intervention differences in EI compared to non-exercise control groups. Meta-regression showed this was not affected by intervention duration. However, due to the high number of poor-quality studies (i.e., using self-reported measured of dietary intakes), further analyses were conducted in only fair/good quality studies (reduced to 5 exercise groups), and found a 102-kcal post-exercise difference between exercisers and controls. It is important to note that this is an effect observed on the average and does not illustrate inter-individual variability (discussed below). Therefore, over time, on average there may be small compensatory increases in EI in response to the increased energy demands from greater physical activity levels. Indeed, the review also found a small increase in fasting hunger after exercise training in 19 exercise groups (172). Nevertheless, depending on the daily energy expenditure accrued with exercise training, this should still lead to some degree of weight loss and favorable changes in body composition, as reviewed by Bellicha et al.(173). Small positive changes were also observed in relation to eating behavior traits, with a reduction in disinhibition (13 exercise groups) and an increase in restraint (12 exercise groups) assessed by the Three-Factor Eating Questionnaire (172). A small number of studies in the review also suggested that exercise training may reduce reward or preference for high-fat foods (172). Overall, most of the evidence indicates that exercise training in people living with overweight or obesity appears to have a relatively small but positive influence on eating behavior. This effect may be heightened with longer-term physical activity by altering the sensitivity of appetite regulation. It should be kept in mind that most exercise training studies are of modest duration (e.g. up to 72 weeks the meta-analysis of Beaulieu et al. (172)), and the long-term changes in appetite and eating behavior, and the time-course over which these occur, are yet to be fully understood. Fewer studies have examined the relationship between changes in energy expenditure and eating behavior in non-obese participants who do not have a preconceived goal associated with weight reduction.

Does Habitual Physical Activity Level Affect Appetite Sensitivity?

Appetite sensitivity refers to the capacity to detect over- or under-consumption with the potential to subsequently adjust intake accordingly. This can be achieved by compensating for a particular EI by adjusting the size of the next meal. There is some evidence to suggest that regular exercisers, or habitually physically active individuals, have a better capacity to control food intake and energy balance due to increased appetite sensitivity. Long et al. (174)demonstrated that habitual exercisers have an increased accuracy of short-term regulation of EI in comparison to non-exercisers. In this study, participants were given either a low or high energy preload for lunch, and were then asked to eat ad libitum from a test meal buffet. Energy intake did not significantly differ following the two preloads in the non-exercise group, indicating a weak compensation. However, the habitual exercisers demonstrated nearly full compensation (~90%) by reducing their EI following the high energy preload compared to the low energy preload. This study has been replicated in groups varying in objectively-assessed habitual physical activity levels (175). Similarly, Martins et al. (176) reported that the sensitivity of short-term appetite control increased in previously sedentary individuals following 6 weeks of aerobic exercise training, with participants again better able to adjust subsequent EI following high and low energy pre-loads following the exercise intervention. Furthermore, King et al. (177) examined the effects of 12 weeks of supervised aerobic exercise on hunger and satiety in 58 individuals living with overweight or obesity. Two separate processes were revealed that acted concurrently to influence the impact of exercise on appetite regulation. Post-intervention, a significant increase in fasting hunger was seen, but this increased orexigenic drive was offset by a parallel increase in post-prandial satiety (as measured in response to a fixed energy meal). Therefore, this ‘dual process’ may reflect the balance between the strength of tonic and episodic signaling following chronic exercise, and may be an important factor which determines whether individuals successfully lose weight or not. Interestingly, these improvements in appetite control appear to be related to satiety and perhaps not to satiation (178), but more research on the interplay between diet composition, satiation/satiety and physical activity level is required, as well as underlying mechanism.

Findings of improved appetite sensitivity with increased physical activity are in line with Jean Mayer’s work over 60 years ago. Mayer et al. (179) demonstrated a non-linear relationship between energy expenditure and EI in Bengali jute mill workers. Daily occupational physical activity and EI were closely matched in those performing physically demanding jobs. However, in those performing light or sedentary occupational roles, this coupling was lost such that daily EI exceeded expenditure. Such work has led Blundell et al. (180) to suggest a J relationship between physical activity and appetite regulation, with ‘regulated’ and ‘non-regulated’ zones of appetite regulation seen across the physical activity spectrum. Sedentary or low levels of physical activity coincide with an ‘unregulated zone’ of appetite in which EI and energy expenditure are disassociated (thereby promoting overconsumption of food at low levels of physical activity). At higher levels of physical activity however, stronger regulation of appetite and food intake exists such that EI better matches energy expenditure. This would promote the better maintenance of energy balance, albeit at higher levels of absolute intake and expenditure, i.e. higher ‘energy turnover/flux’ (180). This non-linear relationship has since been replicated in a systematic review (181) and in a study of over 400 participants (182). The model proposed by Blundell was recently updated by Beaulieu et al. (183), as shown in Figure 4. Furthermore, emerging evidence from a highly-controlled metabolic chamber study suggests that appetite control is indeed improved at higher levels of energy turnover (184). Longer-term studies are required to understand the impact of high energy turnover on appetite control, but it is believed that high levels of EI relative to expenditure may improve weight management via a favorable impact on physiological adaptations (185).

Figure 4. Regulated and non-regulated zones of appetite with varying levels physical activity from Beaulieu et al. (183). Model based on Jean Mayer’s study in Bengali jute mill workers (179) and previously published in Blundell (180).

While the mechanisms behind an improvement in appetite control with regular physical activity remains unclear, insulin sensitivity has been proposed as one mechanism by which activity-induced improvements in appetite regulation may occur. Exercise is known to increase insulin sensitivity (186-188), and insulin sensitivity is known to be involved in satiety induced by particular foods (189) and in the compensatory response to high energy loads (190). A further mechanism by which exercise could affect appetite is through altering gut peptide action. For example, CCK is implicated in the short-term regulation of appetite, and levels of CCK have been shown to rise after exercise (191). Interestingly, Martins et al. (192) measured fasting and post-prandial levels of orexigenic (total and acylated ghrelin) and anorexigenic (PYY, GLP-1) peptides in 15 individuals living with overweight or obesity during 12 weeks of supervised aerobic exercise. A significant increase in fasting hunger was again seen following the intervention, but this was offset by greater satiety in response to a fixed energy meal following the intervention. Interestingly, there was also a significant increase in the suppression of acylated ghrelin following the fixed energy meal, and a tendency toward an increase in the post-prandial release of GLP-1 following the exercise intervention. These hormonal responses would have acted to augment satiety during the post-prandial period. Further work by Gibbons et al. (193)revealed that exercise training had no overall impact on pre and postprandial appetite peptide release; however, differences emerged when participants were classified as ‘responders’ and ‘non-responders’. Compared to non-responders, responders had overall greater suppression of acylated ghrelin and greater increase in GLP-1 and total PYY. This effect was independent of the exercise intervention as differences were observed from baseline. Therefore, the specific role that appetite-related peptides play in activity-induced improvements in appetite regulation remains to be fully understood.

In addition to gastrointestinal mechanisms, it is important to understand how physical activity timing and patterns impact energy balance and appetite control. Physical activity can be prescribed by the FITT principle: frequency, intensity, time (duration) and type, and more recently, timing as another parameter has been proposed, as emerging evidence suggests that when exercise is performed relative to a meal or the time-of-day may influence obesity and cardiometabolic health in both adults and children (194). Indeed, physical activity is also another cue that influences circadian rhythms (195). In a systematic review and meta-analysis of exercise training interventions in individuals living with overweight or obesity (117), only 3 studies (1 RCT) on diurnal exercise timing were identified (196-198).These studies, in addition to observational studies (reviewed in (199)), suggest that early relative to late day exercise timing may lead to greater weight/fat loss. However, a recent RCT suggests no effect of exercise timing on weight loss, but this may have been due to the relatively low dose of aerobic exercise (200). The underlying mechanisms the greater weight loss in response to early exercise timing likely involve an impact on both behavioral and physiological processes regulating energy balance and appetite control (201). Performing morning exercise may lead to greater weight loss by enhancing the satiety response to food throughout the day, reducing the desire for and intake of high-energy-dense foods, and shifting food intake timing to earlier in the day, thus reducing daily energy intake (199).Evidence for this hypothesis is scarce, with an acute study showing that morning exercise led to greater post-exercise satiety; however, this was not examined in response to actual food intake (196). More rigorous research in this area is needed to clarify these proposed effects.

CONCLUSION

The control of food intake in humans is a biopsychological phenomenon. Motivation to eat is an important factor influencing food intake if the environment does not constrain behavior. Food intake is not necessarily solely related to situations of nutritional depletion and can be influenced by a number of homeostatic and non-homeostatic factors. Appetites are often learned and frequently sensory specific. The implication of asymmetric energy balance regulation is that food intake is under stronger physiological control in relation to negative energy balances, whereas in a state of energy balance or positive energy balances the linkages between physiological signaling and subjective motivation to eat are weaker. In obesogenic environments where palatable, energy dense food is ubiquitous, intake can easily exceed energy requirements due to motivation underpinned by food reward. Another consideration is how physical activity or exercise influence energy intake. Physical activity may influence the control of appetite via a number of pathways (e.g., alterations in gastric emptying, appetite-related hormones, food reward, circadian synchrony).

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ABSTRACT

Obesity is a chronic, complex, and challenging disease to address by clinicians. Thus, it is crucial for practitioners to obtain a thorough weight history from patients to identify potential triggers that influence weight gain trajectories and their relationships to development of disease co-morbidity and mortality. Obtaining a weight history from a patient can be approached systematically, similar to key elements of a history of present illness, as we will discuss.  Furthermore, patient-drawn life-events graph or readily available electronic health records graphs can elucidate in visual context pertinent contributing factors to the etiology of obesity. Often, biological, social, behavioral, and psychological causes of weight gain can be elicited through the use of weight histories. In this chapter, we will also explore life-events graph more in detail as they can provide remarkable value to the overall assessment and plan of care (whether lifestyle intervention alone or in combination with pharmacological, surgical, or combination thereof) in the patient with obesity.

INTRODUCTION

Obesity adversely affects all organ systems in the human body and causes and/or exacerbates numerous medical disorders such as cardiovascular disease, diabetes, kidney disease, and some forms of cancer. Today, the average adult weight has increased (1), disproportionately skewing rightward (2) in the body mass index (BMI) distribution curve (Figure 1) with a higher percentage of the population meeting criteria for Class 1 obesity or greater (>30 kg/m²) and more disease severity (Class 2 obesity or higher;  BMI >35 kg/m²). In addition, the average waist circumference has increased across US adults since 1999 (3). Increases in abdominal girth (>35 inches for women;  >40 inches for men), commonly called central or abdominal obesity, is a surrogate for visceral adiposity, which increases risk for the metabolic syndrome, type 2 diabetes, inflammation, and cardiovascular disease (4).

Figure 1.  Changing shape of BMI distribution curve over time (2).

Obesity is defined as a pathologically elevated and defended body fat mass due to dysregulation of the pathways that determine energy balance. The complexity in these pathways, including biological, genetic, developmental, epigenetic, environmental, or behavioral factors, lead to substantial variability in the pathophysiological expression of both amount of unwanted weight gain experienced by an individual as well as the number and severity of co-morbid conditions (diabetes, hypertension, etc.) (5-9). 

In addition to the variability in phenotypic presentation of weight gain and fat distribution in individuals with obesity (10), weight loss responses to lifestyle, pharmacological, and surgical treatment are also heterogeneous. Although most patients elicit an average response to a distinct type of treatment, some patients will have an above average response to the intervention, while responses in others may be sub-optimal, or they may not respond at all. Thus, a thorough weight history in combination with a clinically applied and integrated understanding of this disease, its root causes, and etiology can help guide successful treatment.

THE OVERWEIGHT AND OBESITY-FOCUSED ENCOUNTER

History, Physical, and Laboratory Testing of the Patient who is Overweight or Has Obesity

The evaluation and diagnosis of a patient with obesity includes the taking of a standard medical history with special attention to identification of potentially weight-promoting medications and obesity-related complications (11), a medical examination that characterizes the amount and distribution of weight as well as possible signs of secondary causes of unwanted weight gain, and review of relevant clinical laboratory tests. In addition, the history of present illness includes generation of a chronological weight graph (ideally using the electronic health record (EHR)) that incorporates a review of life events, lifestyle patterns and preferences, and previous successful and unsuccessful interventions. 

The physical exam should note the distribution of weight (especially truncal and abdominal) and areas of conspicuous absence of fat characteristic of lipodystrophies (12), both of which herald increased cardiometabolic risk, as well as areas of disproportionate accumulation of excess fat, such as lipomas and lipedema (13); documentation of cardiac status looking specifically for evidence of heart failure; abdominal palpation for hepatomegaly; identification of inflammatory or degenerative joint issues that may limit activity; and skin/neurological examinations to look for evidence of hypercortisolism (wide striae, proximal muscle weakness), hypothyroidism, hirsutism/acne that might indicate polycystic ovarian syndrome, and acanthosis nigricans over extensor surfaces/neck/axilla. Laboratory evaluation at the initial visit should include a comprehensive metabolic panel, complete blood count, assessment of thyroid status, and cardiometabolic risk assessment including a lipid panel and A1c (Table 1). 

Identification of obesity-related co-morbidities during the patient encounter and lab testing may necessitate referral for further evaluation, such as non-invasive imaging or liver biopsy to establish non-alcoholic steatohepatitis, a sleep study to diagnose obstructive sleep apnea, or X-rays to assess osteoarthritis in weight-bearing joints. Patients reporting low-level chest discomfort, dyspnea on exertion, or orthopnea should be considered for referral to a cardiologist for the possibility of cardiac ischemia or heart failure with preserved ejection fraction (HFpEF), an increasingly recognized disorder of severe obesity. Findings of weight-related medical conditions during the initial visit may prompt increased motivational drive toward behavioral and lifestyle changes as well as acceptance of recommendations to use anti-obesity medications or for referral for bariatric surgery. 

CLINICAL IMPORTANCE OF A WEIGHT TRAJECTORY

During the weight-focused portion of the history of present illness, it is important to document changes in the health that led the patient to seek medical attention over time and establish a clear and chronological description of the sequential events, including weight gain or loss, leading up to the current visit (14-16) (Table 2).

Multiple studies have demonstrated that an early childhood upward weight trajectory can be predictive of future development of obesity, obesity-related comorbid conditions, disability, and mortality (17-21). A lifetime maximum BMI (compared to a single baseline BMI measurement) in the overweight or obesity categories, coupled with 16 or more years of weight history, is associated with an increased all-cause and cause-specific mortality including cardiovascular disease and coronary heart disease (22). 

As will be discussed below, temporal patterns of weight gain that raise concerns in a weight history might include: (a) an early adiposity rebound during infancy or early childhood years (23,24), (b) adolescent weight gain that correlates with progression to severe adult obesity and related medical conditions (25), and (c) excessive weight gain during pregnancy or menopause. Other temporal associations with weight gain often not appreciated by patients and providers include those that accompany smoking cessation (26), resolution of  hyperthyroidism (27), initiation of medications for depression, anxiety, and pain management (e.g., beta-blockers, amitriptyline, gabapentin, others) (11), and normal age-associated sarcopenia where  skeletal muscle mass gradually declines and visceral fat preferentially increases (28).

Early Growth, Childhood, and Puberty

The timing of excessive body weight gain during one’s life is a predictor of future disease severity. Early and rapid weight gain during youth is predictive of co-morbidities later in life and these patients often experience a more steeply inclined weight trajectory into later stages of adulthood (19). During the ages of 2-6 years, children normally have lower adiposity and are usually at their nadir weight before “rebounding” back up during normal growth (Figure 2, blue line). When adiposity rebound occurs at less than one year of age as visualized on the BMI for-age and gender appropriate growth charts, typically expressed in percentile for age and sex, syndromic, congenital, and monogenic causes of obesity should be considered. Such a very early clinical weight gain history should trigger consideration for genetic testing of genes in the canonical melanocortin pathways (LEPR, PCSK1, POMC, MC4R), which range in incidence from very rare to being implicated in up to 1-6% of early-onset severe obesity (23,29,30). Genetic screening for obesity genes can also be triggered by rapid weight gain from early infancy, development of severe obesity (> 97^thBMI percentile) at early ages (especially before the age of 10), persistent food-seeking behaviors indicative of poor appetite control, parental consanguinity, and tall stature/increased growth velocity (31). A weight trajectory following an early adiposity rebound (> 1 year age, ≤ 6 years of age, Figure 2, red dotted line) is a strong risk factor for obesity in adolescence and beyond (24,32) and should be can be an indication for early family-oriented lifestyle interventions.

Figure 2. BMI Percentile Growth Chart.

Weight gain leading to obesity during the adolescent development period correlates highly with progression to severe adult obesity. Development of adolescent obesity can affect timing of puberty (early vs. delayed) when body composition and fat distribution are rapidly changing (33). During normal pubertal development and growth, males acquire greater fat-free and skeletal muscle mass, whereas females attain higher fat mass (34).    

Figure 3. Schematic depiction of abnormal BMI percentile growth curves for adolescents with severe obesity.  Greater than or equal to the 95^th BMI percentile corresponds to cut-offs for pediatric obesity. Other important considerations that determine therapeutic criteria are if the adolescent’s trajectory falls at or above 120^th% and 140^th% the 95^th BMI percentile for age/gender (see Table 3).

Most patients who experience unwanted, excess weight gain in childhood and adolescence develop obesity-related complications that, when severe, often require pharmacologic intervention and/or bariatric surgery. Early identification and treatment are recommended as for prevention (35).  Currently there are six FDA-approved anti-obesity medications available for chronic weight management (orlistat, phentermine/topiramate, liraglutide 3.0 mg, semaglutide 2.4 mg, and setmelanotide). Phentermine/topiramate and orlistat are approved for >12 years respectively, phentermine alone is approved for age >16 years, and setmelanotide can be used as early as 6 years of age  (35,36). Use of anti-obesity pharmacotherapy in adolescents with severe obesity (>95^th BMI percentile plus the presence of obesity-related comorbidity or >120^th of 95^th BMI percentile) is now considered standard of care for pediatric obesity treatment (35).  Furthermore, vertical sleeve gastrectomy and Roux-en-Y metabolic and bariatric surgery procedures are available options for adolescents with severe obesity (>120^th of 95^th BMI percentile plus obesity-related comorbidity or BMI >140^th of 95^th BMI percentile) (37). 

Pregnancy, Breast Feeding, and Menopausal Transition

Pregnancy and menopause can be a time when women’s weights and body composition may become permanently altered under the influence of dramatic shifts in sex steroid levels.  Excessive weight gain during pregnancy can result in epigenetic changes in the developing fetus leading to adult-onset chronic disease such as diabetes, cardiovascular disease, and obesity (39-45) .  Furthermore, maternal obesity and excessive gestational weight gain have been linked to maternal-fetal complications such as increased risk of C-sections, preeclampsia, shoulder dystocia, and macrosomia in the infant (46-48). Data from large population-based epidemiological studies have shown that roughly 50% to 60% of women after pregnancy will return to their pre-pregnancy weight, but the other 50% will retain extra weight, with a third of all pregnant women shifting a BMI category (normal to overweight or obesity) (49).

The post-partum period following delivery of the newborn infant is also a vulnerable time for weight retention. Moreover, the relationship between breastfeeding practices and postpartum weight changes is largely unclear due to the difficulties examining breastfeeding and weight management in observational research and confounding variables (50,51). Breastfeeding overall has other notable health benefits to the infant, including reducing atop and improving , cognitive development, bone health, and maternal-infant attachment (52). 

Weight gain during midlife is common, and about two-thirds of women ages 40 to 59 and nearly three-quarters of women older than 60 are overweight or have obesity (BMI greater than 25 kg/m²). On average, midlife women gain 1.5 pounds (0.7 kg) per year (53). Thus, it is not surprising that menopause is often depicted as a weight-gain trigger on a patient’s life-event graph, especially in older women who gain weight after a period of weight maintenance.  Towards midlife, women undergo redistribution of body composition with increase in total body fat and enhanced inclination toward central abdominal visceral adiposity (54).  Excess body weight during menopause is associated with elevated cardiovascular (55) and metabolic risk, including insulin resistance and Type 2 diabetes mellitus (56,57). Early or late-onset menopause (with final menstrual cycle age < 45 years or age > 55 years respectively) compared to age 46-55 years is associated with increased risk of Type 2 diabetes mellitus [HR 1.04, 95% CI 0.99, 1.09 and HR 1.08, 95% CI 1.01, 1.14, respectively] (58). Undergoing a hysterectomy or an oophorectomy increases diabetes risk further (RR 1.17, 95% CI 1.07-1.29) compared to peri-/post-menopausal women (59).

CASE LESSONS IN PATIENT-GENERATED WEIGHT GRAPHS

While in the clinic, having a patient generate their own drawing of weight graph accomplishes two-fold goals. First, it provides a template on which weight inflections in the patient’s life can be potentially identified with causative or contributory life events, medical conditions, and medications; and secondly, it provides a platform to guide the clinical discussion regarding appropriate goal setting and best approaches to help them achieve as close to a healthy weight range as possible. 

Impact of Medications

The patient in Figure 4 experienced steroid-induced weight gain, a very common iatrogenic cause of obesity.  Exploring reasons for why this patient was initiated on steroids and communication with other specialists regarding opportunities to switch to another non-steroid dependent medication, if available, might mitigate the weight gain and prove to be a successful weight management strategy. Similar discussions of alternative approaches may also be undertaken with other commonly prescribed medications that promote weight gain, including some birth control methods (e.g., Depo-Provera), histamines, beta-blockers, amitriptyline, gabapentin, pregabalin, sulfonylureas, thiazolidinediones, and insulin (60).

Figure 4. Life graph showing effects of repeated exposures to steroids on weight for chronic inflammatory arthritis.

Effects of Situational Life Changes That Impact Weight

In Figure 5, the patient’s weight was at its nadir during college years until graduation.  Subsequently, marriage and job change were identified as life factors timed to weight gain.  In addition, pregnancy and menopause were identified biological associations with upward weight trajectory over time. Psychological stressors appear to have further augmented weight gain over time. Resilience to major life stressors (marriage, divorce, loss of spouse, unemployment, death of a loved one, major illness or injury, moving/relocation) is, unfortunately, not as common as is thought and can precipitate psychosocial disorders such as anxiety, depression, and alcoholism  (61,62).

Figure 5.  Life graph showing effects of several situational life changes that impact weight.

The patient In Figure 6 had a stable weight prior to a physically traumatic incident that led to immobility and sedentary behaviors. Identification of this specific event contributing to upward weight trajectory in the patient helped tailor the treatment strategy toward physical therapy, rehabilitation, and a customized exercise prescription to mitigate the weight gain.

Figure 6.  Life graph showing effect of a traumatic incident (a motor vehicle accident in this case) on weight.

Identification of Response to Weight Loss Interventions

The patient in Figure 7 gained over 40 pounds during exposure to various antipsychotic medications for the treatment of bipolar disorder. The downward shift in the weight graph occurred after initiation of metformin 500 mg once daily (to mitigate antipsychotic medication induced weight gain (63)) and phentermine. If certain medications are critical and cannot be substituted with an alternative, weight neutral medication, as is often the case in patients requiring anti-psychotic medications, anti-obesity medications (64), or bariatric surgery can often reverse the weight gain.

Figure 7. Life graph showing the effects of antipsychotic medications on weight and effective therapeutic intervention with metformin and anti-obesity medications.

Obesity has a multifactorial etiology leading to wide variability in its presentation.  Understanding causation and association of weight gain promoting factors in a patient’s life can help elucidate appropriate treatment strategies. Furthermore, initial non-response to anti-obesity medication does not indicate that the medication is ineffective. Rather it may not be targeting the pathophysiological pathways involved in metabolic and body weight dysregulation in the individual patient and an alternative anti-obesity medication or combination should be trialed for synergistic or additive weight loss effects. In Figure 8, the first blue arrow shows the time of initial visit when the patient started to gain weight due to multifactorial etiology (strong family history of obesity, maladaptive stress related to work, unhealthy nutritional habits), followed by initiation of anti-obesity medicine therapy and intensive lifestyle intervention (phentermine; 2^nd blue arrow). Subsequently, the patient developed side effects and phentermine monotherapy was discontinued (3^rd blue arrow). Several other anti-obesity pharmacological options were trialed (4^th blue arrow); however, the patient did not respond. Ultimately, the patient underwent bariatric surgery (Roux-en-Y gastric bypass (RYGB)) to achieve successful weight loss response. 

Figure 8. The life events graph depicts the response to treatment and strategies for further intervention.

Identification of Weight Regain after Successful Weight Loss:  Importance of Prompt Intervention and Identification of Lifetime Maximum Weight

Figure 9.  Life graph showing effect of weight loss program intervention over time with weight regain.

The life-events graph of a 55-year-old patient with a history of hypertension, obstructive sleep apnea, and severe obesity (BMI 41.8 kg/m²) in Figure 9 shows a 95 pounds over a 1.5-year period through self-monitoring using an electronic smart phone tracking application, 1800 kcal/day intake, and an increase in physical activity as tolerated. However, after 1.5 years of successful weight loss, despite continued intensive lifestyle changes, the patient experienced weight regain over the next 3 years. Weight regain after a period of caloric restriction is physiologic as long-term persistence of metabolic adaptation occurs and hunger and satiety signals resist weight loss (65). It important to initiate anti-obesity pharmacotherapy at this critical stage to help sustain the weight loss and prevent weight regain. In this patient, starting an anti-obesity medication after 1.5 years when weight gain had started to occur would be recommended. It is also important to identify the patient’s lifetime max as the starting point when gauging effectiveness of anti-obesity medications in this scenario. Simply using the pre-medication weight might lead to inappropriate stopping of this treatment if weight stabilization is achieved, since the absence of subsequent weight loss might be misinterpreted as medication “failure.” By determining the lifetime maximum weight, however, weight stabilization is a very successful ~20% to 30% total weight loss (50 to 100 lbs. weight loss compared to the lifetime maximum weight of 293 lbs., depending on when it was started).

Figure 10.  Life graph showing a patient status post gastric-bypass surgery with weight regain following situational life transitions (marriage, birth of children).

Figure 11.  Life graph showing weight loss in a patient after bariatric surgery followed by weight regain after high job-related stress and retirement. 

Figures 10 and 11 depict life-event graphs of two patients who underwent metabolic and bariatric surgery. Following successful weight loss, they both experienced weight regain.  Weight regain in the postoperative bariatric patient is often difficult to treat and is usually of multifactorial etiology (as examples, recurrence of obesity-related comorbidities, non-compliance and adherence to treatment recommendations and routine bariatric care, physiologic return of hunger signals, introduction of life stressors). Early recognition, evaluation and medical management, provided anatomical causes have been ruled out, with multidisciplinary team support are crucial to removing obstacles to weight maintenance and patient’s continued commitment to lifestyle improvements. Further, it presents an opportunity to intervene early and initiate anti-obesity pharmacotherapy, ideally at the expected weight loss nadir, usually 1-3 years after surgery, to help optimize weight loss response and prevent weight regain (66).    

Communicating with Patients About the Disease of Obesity

To effectively communicate obesity treatment recommendations to patients, medical professionals first need to translate obesity pathophysiology in language understood by patients, without bias or stigma. General questions, concerns, or sentiments often echoed by patients during their weight journey include:

1. “Doc, are you calling me ‘fat?’”
2. “I have tried every diet in the past. I lose weight only to regain it back.”
3. “I know it’s my fault I am this weight.”
4. “I have always been 300 pounds my entire life, no matter what.”
5. “I don’t think I need surgery. I’ll just have to diet and exercise anyways.”
6. “I don’t need medications to lose weight.”

Clinically, there are three important concepts to convey to patients about the disease state: 

1. Obesity is a disease, like hypertension or diabetes.
2. Because it is a disease, we can treat it as such, either starting with lifestyle and then moving on to medical and surgical treatments for obesity, or initiating lifestyle plus these treatments in patients with severe obesity.
3. It is not the patient’s fault that they developed obesity.There are strong biological and genetic forces at play that determine body mass in addition to medical, behavioral, lifestyle, psychosocial, and environmental influences.

To clearly communicate with patients about the disease of obesity, a thorough understanding of energy metabolism is often necessary. Energy metabolism is complexly regulated in the arcuate nucleus of the hypothalamus to set a defended body weight (10,36,67). This body weight “set point” is oftentimes biologically and genetically determined with influences from the modern macro- and micro-environments. Chronic exposure to an obesogenic environment can increase body fat mass set point in susceptible individuals causing the body to defend a higher weight regardless of well-intended volitional efforts to lose weight. Thus, current treatment strategies include coupling intensive lifestyle therapy with anti-obesity medications and/or bariatric surgery, to allow significant and sustained reductions in the fat mass set point. Discontinuation of treatments that successfully lower the body mass set point will result in regain of body weight to the original, pathologically higher body mass set point. Therefore, patients need to understand that treatments for obesity are long-term, as is true for any medication prescribed for a chronic medical condition. Regain of weight after discontinuation of therapy is not evidence of failed treatment or lack of willpower; rather it is physiologically expected. 

Medical professionals can incorporate this scientific understanding in a manner that evades bias and stigma and transforms the patient’s queries into a constructive dialogue. In response to the questions or concerns posed by the patients above, medical professionals can reply in the following manner: 

1. “Doc, are you calling me ‘fat’?” Response: “We only use the word ‘fat’ to describe mice and rodents, not humans. We are people. There are various parameters we check to make sure your body is healthy, not only from the outside, but also from the inside, similar to us checking your blood pressure and cholesterol. Your weight indicates that your body-mass-index is elevated. It means that it places you at high risk for, or already contributing to, other weight-related medical conditions. A high BMI is a result of a disease process inside your body, like diabetes. You have obesity, or adiposity-based chronic disease. The good news is that you have treatment options beyond just saying ‘eat less and exercise more’.” 
2. “I have tried every diet in the past. I lose weight only to regain it back.”  Response: “Contrary to popular belief, we don’t consciously control our weight. It is regulated by a fat mass set point in our brains.  If your fat mass set point is abnormally high (again, not your fault), your body will continue to defend that weight. This abnormal physiology is why weight regain is common. There are treatments that more effectively lower that fat mass set point, so you do not have to struggle. That way, when you are making healthy food choices and are active, your body starts to respond. If you’d like, we can discuss those treatment options and how they may benefit you.” 
3. “I know it’s my fault I am this weight.” Response: “First of all, I want you to understand that it’s not your fault you developed obesity. You did not choose to ‘have’ obesity. If you were here today to discuss your diabetes diagnosis, it would be a completely different conversation, correct? Just like you did not have a choice in selecting your parents, you did not have a choice in the development of your obesity.  Again, contrary to popular belief, majority of our weight is not under our control.  The good news is that we can treat it.” 
4. “I have always been 300 pounds my entire life, no matter what.” Response: “Have you wondered why that has been the reason?  We now know that there is a weight set point in our brain that determines what our brains think of as “normal.”. It is biologically and genetically determined with influences from the outside environment, medications, and other factors. In patients with obesity, this weight set point is abnormally high—your brain thinks that weight is now “normal.”  Thus, when you try to lose weight, you regain it back to this set point and, sometime, more. If improving your lifestyle isn’t working, we have treatments to lower this fat mass set point when it is high.  Anti-obesity medications and bariatric surgery are great tools to allow your brain to be happy with a lower weight set point so that you do not have to fight against your body.”
5. “I don’t think I need surgery. I’ll just have to diet and exercise anyways.”  Response: “Let’s explore this further.  Can you help me understand more your reasons as to why you think you do not need surgery? What are your concerns and fears? What is your understanding about bariatric surgery? Let’s see if we can address some of those today.”
6. “I don’t need medications to lose weight.” Response: “Can you help me understand your reasons for not considering medications? What is your understanding of obesity medications and how they work? If we try to manage your weight without medications, I would like to reassess your progress in 12 weeks.  If you are still struggling despite all your best efforts, please know that it is not your fault, and that your body is telling you that it needs something more intensive to treat the weight gain. You do have options still!”

CONCLUSIONS

An obesity-focused medical history is important for the care of the patient who is overweight or has obesity. In addition to identifying obesity-related complications by means of a thorough history, physical, and appropriate laboratory work up, using electronic health record weight graphs and identifying significant medical and life-events that influence body weight provide context and relevance for weight trajectories in the management of a patient with obesity.  For example, these events can help guide the clinical discussion in regard to potentially modifiable influences on a patient’s weight, including identifying causative medical conditions or culprit medications that promote weight gain, associating weight gain with known life-transitions that increase a patient’s risk for becoming overweight or obese (e.g., puberty, pregnancy, menopause), help patients to re-commit to improved lifestyle choices, assess an individual’s responsiveness to recommended therapies, and help with timing for when initiating anti-obesity, therapies such as weight loss medications, weight loss surgery, or both, when indicated.       

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