12.1. Introduction

Concepts for the control of food intake and the idea of regulation of body weight have been proposed for well over 50 years (Kennedy 1953, Mayer 1953, Mellinkoff et al. 1956). The debate has been based mainly on animal experiments (usually rodents) often using brain interventions. The proposed central mechanisms purported to control food intake have undergone a series of progressive refinements arising from technical advances in neuroscience. The mechanisms discussed in scientific circles today bear little resemblance to the simple notions of “hunger and satiety centers” that were the pinnacle of scientific understanding over 60 years ago.

A major feature of early hypotheses was the influence of the notion of homeostasis and its parallel in cybernetics or control theory. Both of these positions focused on the identity of feedback signals that monitored some deviation from an ideal value and stimulated compensatory adjustments via a central controller. This gave rise to a strong emphasis on the notion of regulation with body weight itself identified as the regulated variable. One view was that the organization of the body’s physiology was similar, in principle, to the control of ambient temperature in a thermostatically controlled room. Indeed, some physiological systems do appear to work in this way, and it was assumed that it applied equally to the relationship of the control of food intake and the “regulation” of body weight. If true regulation does occur, it is not clear if the regulated variable is gross body weight, adipose tissue or fat-free mass (FFM). It is in the light of this background that we have interpreted certain relationships among body composition, energy expenditure and food intake in humans.

12.2. Comment on the Lipostatic Theory of Appetite Control

Early theoretical approaches to the regulation of food intake and body weight were based on the notion that the regulatory mechanisms stemmed from peripheral signals arising from glucose metabolism (e.g., the glucostatic theory; Mayer 1953), amino acids (e.g., the aminostatic theory; Mellinkoff et al. 1956), or adipose tissue (e.g. the lipostatic theory; Kennedy 1957). The discovery of leptin by Zhang et al. (1994) provided a molecular basis and apparent proof of the authenticity of the lipostatic theory. This hypothesis was based on Kennedy’s (1953) classic ventromedial hypothalamus lesions studies in rats, in which he proposed that “lipostasis” was regulated by circulating metabolites that acted on a hypothalamic “calorimetric satiety” center to inhibit feeding (Kennedy 1953, 1966). Interestingly, it is worth noting that Kennedy’s (1953) original lipostatic hypothesis was only concerned with “the prevention of an overall surplus of energy intake over expenditure,” rather than a universal “thermostat” that defended both upper and lower limits of fat mass (FM). Subsequently Hervey (Hervey 1969, Hervey and Hervey 1969) proposed a mechanism for lipostatic regulation based on a fat-soluble hormone, with findings from parabiotic rat studies suggesting that the exchange of a blood-borne factor differentially affected food intake in lean and obese animals (Hervey and Hervey 1969). Similar experiments in the late 1970s using genetically obese rodents (Coleman and Hummel 1969, Coleman 1973) created the conceptual basis for the studies that led to the identification of leptin (Zhang et al. 1994).

12.3. Energy Expenditure and Energy intake

An alternative view of appetite control that did not involve the dominating theme of “regulation” was proposed over 50 years ago based on the relationship between food intake and energy expenditure (Mayer et al. 1954, Edholm et al. 1955a, Mayer, Roy, and Mitra 1956, Edholm 1977). This approach questioned whether food intake is controlled by the dynamics of adipose tissue (as postulated in the lipostatic hypothesis) and proposed instead that food intake might be driven by the body’s demand for energy. However, despite its apparent fundamental importance to our understanding of appetite control, the question of whether energy expenditure influences EI has been rarely investigated and has yet to be fully resolved. Consequently, it remains unclear whether the physiological demand for energy arising from the biological processes and behavioral activities of daily living influence appetite and food intake.

12.4. Role of Energy Expenditure and Body Composition in the Control of Food Intake

Over 50 years ago, Jean Mayer questioned whether an increase in energy expenditure causes an automatic (compensatory) increase in EI (Mayer et al. 1954), stating that “the regulation of food intake functions with such flexibility that an increase in energy output due to exercise is automatically followed by an equivalent increase in caloric intake.” This issue was systematically examined by Edholm and colleagues, who sought to examine whether energy expenditure created a demand for food in a series of studies employing army cadets (Edholm et al. 1955b, 1970, Edholm 1977). While Edholm et al. (1955b) proposed that “the differences in intakes of food must originate in the differences in energy expenditure,” no relationship was found between daily energy expenditure and daily food intake in lean males undergoing infantry training within a single day (n = 12). These findings are consistent with more recent studies demonstrating that acute increases in energy expenditure (via exercise) are not accompanied by immediate (within one day) increases in EI to restore energy balance (King and Blundell 1995, King et al. 1996, 1997, Blundell and King 1998, Hopkins, King, and Blundell 2010).

The fact that body weight can remain relatively stable in some individuals over long periods of time has been taken to suggest that energy expenditure must be coupled (at least loosely) to EI (Bessesen 2011). Indeed, it has been shown that there is a “time lag” in the corrective responses elicited by energy depletion or surfeit, with Bray et al. (2008) reporting that compensatory changes in EI are evident 2–3 days after dietary manipulation of food intake. This would suggest that energy balance is not regulated across a single day, but that attempts are made to restore energy balance over subsequent days. It is interesting to note that while Edholm et al. (1955b) failed to find any relationship between daily energy expenditure and EI within a single day, a strong relationship was found when daily energy expenditure and EI were averaged across 1 week (Edholm 1977) (see Figure 12.1). These findings are consistent with those of Mayer, Roy, and Mitra (1956), who demonstrated a relationship between occupational physical energy expenditure and daily EI. Mayer, Roy, and Mitra (1956) reported that EI was greater in Bengali jute mill workers who performed physically demanding jobs as opposed to those who performed sedentary jobs, consistent with the view that daily energy expenditure does create a demand for food intake.

Figure 12.1

Data of Edholm (1977) demonstrating no relationship between food intake and daily energy expenditure within a single day intake in lean males undergoing infantry training. However, a strong relationship was found when daily energy expenditure and food (more...)

12.5. Fat free mass as an Orexigenic Driver

Recently, a number of studies have examined the specific role that body composition and energy expenditure play in the control of food intake in humans (Blundell et al. 2011, Caudwell et al. 2013, Weise et al. 2013). These studies demonstrate that FFM, but not FM, is a strong determinant of day-to-day food intake (Blundell et al. 2011, Weise et al. 2013). Blundell et al. (2011) reported that self-selected meal size and total daily EI (measured objectively in a laboratory environment) were positively associated with FFM in 92 overweight and obese individuals. However, in contrast, no such associations were found between FM and food intake. These findings have been confirmed by Weise et al. (2013), who reported the FFM index was positively associated with ad libitum EI in 184 lean and obese individuals. A number of earlier observation have also demonstrated associations between FFM and food intake, with Lissner et al. (1989) reporting that lean body mass (but not FM) predicted EI in 63 nonobese and obese women whose EI and body composition were objectively measured in a laboratory environment. The authors argued that “the emphasis of research that focuses on the relationship between EI and obesity is misplaced because energy requirement appears to be a direct function of lean mass rather than of adiposity.” Similarly, Cugini et al. (1998), in a little known and rarely cited body of research, also reported that daily hunger was positively associated with FFM (and negatively related to FM) in lean (Cugini et al. 1998), but not overweight and obese (Cugini et al. 1999), individuals.

It is worth noting that such findings are in keeping with previous research demonstrating that lean tissue acts as an orexigenic feedback signal during periods of weight loss and weight regain (Dulloo, Jacquet, and Girardier 1997, Dulloo, Jacquet, and Montani 2012). Based on findings from semistarvation studies and, in particular, Ancel Key’s Minnesota semistarvation experiment (Keys et al. 1950), Dulloo, Jacquet, and Girardier (1997) have noted that FM and FFM losses during significant weight loss (approximately 25% of initial body weight) independently predicted the poststarvation hyperphagic response. Furthermore, during recovery from weight loss, the restoration of FFM was found to be incomplete at the point at which body mass and FM were fully restored to prestarvation levels. Importantly, hyperphagia was evident despite the full restoration of body mass and FM and persisted until FFM levels were fully restored to prestarvation levels. These data were interpreted as evidence of a “proteinostatic” mechanism in the control of food intake, with feedback signals arising from both FM and FFM that acted to stimulating appetite and food intake in order to restore body weight (Dulloo, Jacquet, and Girardier 1997, Dulloo, Jacquet, and Montani 2012).

While the demands imposed by semistarvation on body weight regulation clearly exceed those experienced under normal free-living conditions, the idea of a physiological drive for food intake stemming from FFM is consistent with the “protein-stat” (Millward 1995) and “amino-static” (Mellinkoff et al. 1956) theories of lean tissue and appetite regulation, respectively. Millward’s protein-stat theory suggests that lean mass, and skeletal muscle in particular, is under tight regulation and food intake is directed to meet the needs of lean tissue growth and maintenance (Millward 1995). The basis of this theory is the existence of an “aminostatic” appetite control mechanism, in which food intake is adjusted in response to amino acid availability to meet the protein demands of lean tissue growth and maintenance. However, it should be noted that evidence of such regulation, or the existence of a “protein-stat,” is not extensive, mainly because the concept has not been a target for investigation.

It has also been suggested that day-to-day food intake is regulated via nutrient-specific appetite control mechanisms rather than an energy-based regulatory system reliant on the energy content of the diet. In particular, the protein-leverage hypothesis (Simpson and Raubenheimer 2005 ) proposes that dietary protein intake is (i) tightly regulated, (ii) independent of the regulation of dietary fat and carbohydrate intake, and (iii) prioritized over the energy content of the diet. It has been proposed that such regulatory control can lead to excess EI when a diet is low in protein as fat and carbohydrate are consumed in excess to “compensate” for the perceived protein deficit by “leveraging” dietary fat and carbohydrate intake (Sørensen et al. 2008). Evidence for “nutrient-specific appetites” exists in a range of animal models (see Morrison and Laeger 2015 for a review) Furthermore, experimental and cross-sectional survey data suggest that humans also compensate for reductions in dietary protein via increased food intake and, in so doing, consume excessive amounts of dietary fats and carbohydrates (Gosby et al. 2014). While such data fit with the notion of a protein-stat or amino-stat, such leverage by appetites for specific nutrients suggests that EI is not linked to “energy sensing” mechanisms (that reflect energy need). However, the underlying mechanisms behind such nutrient-specific appetite control mechanisms in humans remain poorly defined (Morrison and Laeger 2015). Furthermore, it is quite feasible that a nutrient-specific protein-sensing mechanism could function alongside a regulatory mechanism that reflects energy need.

12.6. Resting Metabolic Rate and Total Daily Energy Expenditure as Drivers of Food Intake

The reported associations between FFM and food intake (Lissner et al. 1989, Cugini et al. 1998, Blundell et al. 2011, Weise et al. 2013) may have important implications for day-to-day appetite control. It is well established that FFM is the primary determinant of resting metabolic rate (RMR) (Ravussin et al. 1986a), which in turn is the largest component of daily total energy expenditure (TEE) (Ravussin et al. 1986b). The findings of Edholm et al. (1955b) that energy expenditure and EI were not coupled within a single day (Edholm 1977) would initially suggest that daily energy expenditure is not a determinant of food intake. However, it is almost inevitable that the high degree of day-to-day variability in sedentary and active behaviors contributes to the high variability in daily energy expenditure (Ravussin et al. 1982) and could mask such a relationship. By contrast, RMR is relatively stable between days and typically accounts for 60%–75% of total daily energy expenditure (Ravussin et al. 1986a). Its enduring stability is in part due to the fact that FFM is the main determinant of RMR, accounting for 50%–60% of the between-subject variance (Johnstone et al. 2005). Therefore, given the more constant energetic demand arising from RMR (or FFM as its main determinant), it is possible that RMR may provide a more stable signal of energy demand.

In line with this proposal, Caudwell et al. (2013) demonstrated that RMR (but again, not FM) was a strong determinant of daily hunger, self-selected meal size, and daily EI in overweight and obese individuals. RMR and daily EI under conditions of high and low energy density were objectively measured in 41 overweight and obese individuals during a 12-week exercise intervention. A higher RMR was associated with greater daily hunger, self-selected meal size, and daily EI (independent of sex and energy density). Furthermore, Westerterp-Plantenga et al. (2003) have also demonstrated a positive association between RMR and self-reported meal frequency (derived from food diaries) in 12 older men (62 ± 4.0 years), with RMR explaining 40% of the between-subject variance in meal frequency. Interestingly, a negative association between RMR and meal frequency was noted in 19 young men (23.1 ± 3.9 years), with RMR explaining 85% of the variance in meal frequency. While it was suggested that meal frequency might represent a method of “tuning” EI to energy expenditure, it is unclear why the direction of association differed between young and older participants. However, it is certainly possible that RMR could influence either meal size or meal frequency according to the constraints imposed by the experimental design.

Based on the findings that FFM and RMR (but not FM) are associated with day-to-day food intake, Blundell et al. (2011) have proposed that energy expenditure arising from FFM (as the main determinant of RMR) represents a physiological source of hunger that drives food intake at a level proportional to basal energy requirements. This long-term (tonic) signal of energy demand would help “tune” EI to energy expenditure and help ensure the maintenance and execution of key biological and behavioral processes. This signal would appear to be robust, with FFM (Blundell et al. 2011) and RMR (Caudwell et al. 2013) remaining strong determinants of hunger, meal size, and daily EI even when total daily energy expenditure was strongly perturbed during 12 weeks of aerobic exercise (Blundell et al. 2011).

This proposal has recently been supported by results from Hopkins et al. (2016) in a study that modeled the associations between body composition, energy expenditure, and EI in the context of total energy balance in order to determine whether it was body composition per se or energy expenditure that drives food intake. Measures of RMR (indirect calorimetry), total daily energy expenditure (doubly labeled water), body composition (deuterium dilution), and daily EI (laboratory weighed intakes) were taken in 59 individuals during a 14-day stay in a residential feeding behavior suite. During days 1 and 2, participants consumed a fixed diet designed to maintain energy balance. On days 3–14, food intake was covertly measured in subjects who had ad libitum access to a wide variety of foods typical of their normal diets. Consequently, although the study was scientifically controlled, the subjects followed a near-normal pattern of daily activities. After controlling for age and sex, both FFM and RMR (but not FM) were found to predict daily EI. However, a mediation model using path analysis indicated that the effect of FFM (and FM) on EI was fully mediated by RMR (Figure 12.2); i.e., FFM had no direct effect on food intake but rather indirectly influenced food intake via its effect on RMR. However, it should be noted that this model cannot distinguish between the effects of FFM-associated energy expenditure and the effects of any molecular signaling arising from lean tissue that may also covary with RMR.

Figure 12.2

Path diagram illustrating the direct and indirect effects of FM, FFM, and RMR on EI in 59 individuals during a 14-day stay in a residential feeding behavior suite. As can be seen, the effect of FFM (and FM) on EI was fully mediated by RMR. (From Hopkins, (more...)

A similar approach was adopted by Piaggi et al. (2015), who examined whether 24-hour energy expenditure or substrate oxidation predicted the overconsumption of food (independent of FFM) in 107 individuals. Twenty-four-hour energy expenditure was measured in a whole room calorimeter, with ad libitum food intake measured during the subsequent 3 days using a computerized vending machine procedure. Twenty-four-hour energy expenditure and respiratory quotient were found to independently predict food intake, with a 100 kcal surplus in 24-hour energy expenditure associated with a 175 kcal increase in EI, while a 1% change in 24-hour respiratory quotient was associated with a 204 kcal change in EI. In line with Hopkins et al. (2015), mediation analysis indicated that FFM did not have any direct effect on EI, with 24-hour energy expenditure accounting for 80% of the observed effect FFM exerted on EI. However, it should be noted that participants overconsumed by approximately 60% during the 3-day ad libitum measurement period of this study, with EI equal to 159% ± 40% of weight maintenance needs. As such, this paper provides insight into overconsumption rather than the mechanisms that control more normal day-to-day food intake under conditions close to energy balance.

The studies of Piaggi et al. (2015) and Hopkins et al. (2016) raise the question of whether the relationship between RMR and food intake is a function of, or independent of, total daily energy expenditure. While Piaggi et al. (2015) reported that 24-hour energy expenditure independently predicted EI, RMR was not measured in this study. Interestingly, sleeping metabolic rate, which equates to approximately 95% of RMR (Goldberg et al. 1988), failed to predict EI (although sleeping metabolic rate was adjusted for age, sex ethnicity, FM, and FFM). However, 24-hour energy expenditure was restricted in the respiratory chamber and equivalent to only 1.4 times sleeping metabolic rate. Therefore, it still remains to be seen whether RMR and total daily energy expenditure independently predict food intake in conditions when total daily energy expenditure can vary more freely. However, such an effect of TEE would be unlikely to be mediated by FFM, as individuals can exhibit a wide range of TEE (depending on the amount of volitional active or sedentary behaviors carried out) for a given level of body composition or RMR.

12.7. Implications for the Control of Appetite

Studies demonstrating that FFM or RMR (rather than FM) are the main predictors of day-to-day food intake (Lissner et al. 1989, Cugini et al. 1998, Blundell et al. 2011, Caudwell et al. 2013, Weise et al. 2013) are not consistent with the traditional “adipocentric” view of appetite control. However, this should not be taken to imply that FM does not play an important role in appetite control. Indeed, consistent with an inhibitory action, a negative association between the FM index and daily EI was reported by some of these studies (Cugini et al. 1998). Furthermore, the mediation analyses performed by Hopkins et al. (2016) and Piaggi et al. (2015) indicated that FM indirectly influenced EI via its effect on RMR and daily energy expenditure, respectively (albeit to a lesser extent than FFM). It is also worth noting that studies demonstrating a relationship between FFM or RMR and food intake have been carried out under conditions close to energy balance (Weise et al. 2013, Hopkins et al. 2015, 2016, Piaggi et al. 2015). As such, they may not provide insight into the mechanisms controlling EI during dynamic periods of substantial energy or weight change. It is possible that FM and other regulatory signals (such as leptin) may influence appetite control more strongly during sustained weight loss (Rosenbaum et al. 2010). This highlights the need to examine the roles of FM and FFM (and associated putative signals) under varying conditions of energy balance. Indeed, the majority of studies examining the relationship between FFM/RMR and EI are cross-sectional in nature (Lissner et al. 1989, Cugini et al. 1998, 1999, Westerterp-Plantenga et al. 2003, Weise et al. 2013, Hopkins et al. 2015, 2016, Piaggi et al. 2013), and therefore, inferences cannot be made regarding how systematic changes in body composition or RMR influence EI.

It is important to note that if energy expenditure and EI are linked as part of a biologically regulated system, a mechanism must exist that tunes EI to the rate of energy expenditure (Hall et al. 2012). However, how the demand for energy is translated into motivated behavior (i.e., food intake) is unclear, but the first step in seeking signals that “translate” a physiological state into a behavioral action is to demonstrate the reliability and robustness of the underlying relationships. It has previously been suggested that the energy demand of tissues such as the liver might be translated into tonic signals of hunger, creating a constant drive to eat (Halford and Blundell 2000). This notion fits with a proposed energostatic control of food intake (Friedman 1995), in which changes in hepatic energy status have been suggested to influence EI through the stimulation of vagal afferent nerve activity (Leonhardt and Langhans 2004). It is also becoming clear that skeletal muscle is a major endocrine organ, capable of producing and secreting a large number of myokines (Pedersen and Febbraio 2012). These myokines provide a molecular basis through which skeletal muscle can communicate bidirectionally with organs such as the liver, the brain, and adipose tissue (Trayhurn, Drevon, and Eckel 2011). A number of myokines such as interleukin 6 (Ropelle et al. 2010) and irisin (Swick, Orena, and O’Connor 2013) have been linked to food intake and energy expenditure, but the specific role that these myokines (and others) play in appetite control is unclear.

It may be worth recognizing that a signal linking energy demand with brain activity (and ultimately behavior) may not be a single circulating molecule. The signal may reflect the degree of intracellular metabolism; however, there is no reason why this should not be sensed via biochemical pathways. Interestingly, a recent study used positron emission tomography (PET) technology to investigate how energy needs (arising from FFM) could be sensed by the brain and translated into homeostatically relevant behavior (Weise et al. 2015). The study demonstrated significant associations between FFM and several brain regions, but no associations with FM. Moreover the study indicated a link between FFM, hunger, and brain activity (cerebral blood flow) in the periacqueductal gray. As the authors point out, this area is a key station on the ascending homeostatic pathways, and neural activity here can plausibly be envisaged as part of a system that transforms FFM-induced energy demand into motivated feeding behavior.

A further issue concerns the role of FFM and FM in EI in people under varying conditions of FM. Would it be expected that the relationship between body composition variables and EI would remain uniform during the progressive increase in FM during the development of obesity? Interestingly, Cugini et al. (1998, 1999) reported that the relationship between body composition and hunger varied between lean and obese individuals, with FM being negatively associated with hunger in lean individuals, but not obese individuals. Consistent with this, it has been reported that in young, lean active men and women FM is inversely associated with EI (Blundell et al. 2015). This evidence fits with the interpretation that the influence of FM on appetite varies according to the amount of fat (and therefore its biological function) in the body. Considering these data, it can be envisaged that a threshold exists at which the level of FM changes from being inhibitory to becoming disinhibitory as an individual passes from leanness to fatness. However, such a threshold would necessarily be an individual parameter, and it is not conceivable that “average” FM would possess such functional potential.

Figure 12.3 displays a conceptual model that highlights the tonic drive to eat arising from body composition and resting energy expenditure, the tonic inhibition arising from signals of energy storage, and the signals involved in the short-term (episodic) regulation of food intake. This model provides a theoretical approach to the biology of appetite control in which the influence of FFM and RMR is incorporated alongside signals stemming from adipose tissue and gastrointestinal (GI) peptides. FFM, as the main determinant of RMR, provides a tonic drive to eat that reflects basal energy requirements. This excitatory drive is under tonic inhibition from adiposity signals such as leptin, whose action reflects the size of stored energy reserves in the body. However, as the amount of adipose tissue increases, leptin and insulin insensitivity develop, and this tonic inhibition is reduced. This attenuation in tonic inhibition can contribute to overconsumption in obese individuals (despite the abundance of stored energy), as the tonic drive to eat stemming from FFM (which is elevated in the obese) remains unabated.

Figure 12.3

Conceptual model illustrating the major tonic and episodic processes that influence appetite control using an energy balance framework. Tonic signals of energy need arising from body composition (primarily FFM) are mediated by RMR. FM also indirectly (more...)

12.8. Summary

Modern theories of appetite control embody the view that episodic and tonic inhibitory signals arising from adipose tissue and GI peptides modulate a constant excitatory drive to eat (Blundell and Gillett 2001). However, the source of this excitatory drive has been poorly defined, with current models of appetite control better able to account for the inhibition rather than initiation of feeding (Halford and Blundell 2000). Recent data indicate that FFM and FM play important (but distinct) roles in the control of appetite and food intake (Blundell et al. 2011, Caudwell et al. 2013, Weise et al. 2013, Hopkins et al. 2016). While FFM (as the main determinant of RMR) represents a potential physiological source of hunger that drives day-to-day food intake at a level proportional to basal energy requirements, FM (and associated adipokines such as leptin) appears not to strongly influence day-to-day food intake under conditions of energy balance. Indeed, despite the commonly held view that FM (and leptin) plays a key role in the control appetite, evidence indicating that peripheral leptin concentrations influence appetite and food intake is not as strong as commonly assumed (and confined to conditions of negative energy balance).

Recent findings suggesting that FFM and RMR play important roles in day-to-day food intake suggest that the classic adipocentric model of appetite control could easily be revised to reflect the influence of RMR and energy demands. Acting conjointly, the influence of RMR (and other components of EE) and signals stemming from adipose tissue and GI peptides appear to better account for the role of whole-body peripheral signals involved in human appetite control. These data may help to further our understanding of the relationship between an excitatory drive to eat and the intermittent suppression of eating (i.e., episodic satiety signaling and tonic inhibition). However, there is a need to examine how FM and FFM (and the associated putative signals) operate conjointly under varying conditions of energy balance. Indeed, while the energy expenditure associated with FFM and RMR appear to be stronger determinants of food intake under conditions of energy balance, it is possible that FM and other regulatory signals (such as leptin) may influence appetite control more strongly during sustained weight loss. It is likely that future studies will indicate how different components of the energy balance budget influence eating patterns in different groups of individuals under different physiological and environmental conditions. Such studies will inevitably provide a more comprehensive account of the relationship between energy demands and EI.

Conflict of Interest

The authors declare no conflict of interest.

Funding

Research relating to this study was funded by the Biotechnology and Biological Sciences Research Council, grant numbers BBS/B/05079 and BB/G005524/1 (DRINC).

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