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Laboratory of Biological Modeling, National Institute of Diabetes, Digestive, and Kidney Diseases, National Institutes of Health, Bethesda, Maryland
Submitted 28 October 2005 ; accepted in final form 22 January 2006
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ABSTRACT |
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 TOP ABSTRACT Glossary of Model VariablesMETHODSRESULTSDISCUSSIONAPPENDIXGRANTSREFERENCES |
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body composition; mathematical model; weight loss
My goal was to develop a computational framework to study body composition regulation. I created mathematical models of the individual metabolic processes contributing to daily macronutrient balance that were based on published in vivo human data. Most model parameters were derived from the literature, and the model components were integrated by fitting a few unknown parameter values to match body composition data from a classic long-term feeding study known as the Minnesota human starvation experiment (36). The resulting computer simulation of the Minnesota experiment predicted the underlying adaptations of daily whole body energy expenditure and metabolic fluxes that were not measured.
A particularly important observation from the Minnesota human starvation experiment was that refeeding caused a rapid replenishment and overshoot of body fat mass, clearly an undesirable result with implications for obesity relapse as well as the treatment of malnutrition. Therefore, another goal of this study was to explain the physiological basis of the fat mass overshoot and predict whether or not it was a transient phenomenon.
Finally, to test the validity of the model, I simulated a recently published short-term caloric restriction study by Friedlander et al. (30), who measured changes of body weight, fat mass, resting metabolic rate, and nitrogen balance after energy intake was decreased by 40% and protein intake was maintained. No model parameters were altered for this simulation other than the initial baseline values.
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Glossary of Model Variables |
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TOPABSTRACTGlossary of Model VariablesMETHODSRESULTSDISCUSSIONAPPENDIXGRANTSREFERENCES |
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CO2
O2
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METHODS |
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TOPABSTRACTGlossary of Model VariablesMETHODSRESULTSDISCUSSIONAPPENDIXGRANTSREFERENCES |
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Figure 1 is not intended to represent biochemical pathways and does not imply that macronutrients from the diet must first be converted to the storage pools F, G, or P before being oxidized or flow into gluconeogenic or lipogenic pathways. Rather, Fig. 1 indicates that changes of the body macronutrient pools result from net imbalances of the fluxes entering and exiting the pools. For example, an increase of P requires that PI be greater than the sum of GNGP and ProtOx.
The model of TEE included the resting metabolic rate (RMR), thermic effect of feeding (TEF), and energy expenditure of physical activity (PAE). RMR was determined by a weighted average of the organ basal metabolic rates (22) as well as the energy costs for gluconeogenesis, lipogenesis, and turnover of protein, triacylglycerol, and glycogen (9, 20). TEF was determined by macronutrient composition, and content of the diet (29), and PAE depended on the amount of daily physical activity and was proportional to the body weight (61).
A reduction of energy intake below what is required to maintain body weight causes a reduction of energy expenditure via a process called adaptive thermogenesis (16a, 19). Adaptive thermogenesis is believed to affect both RMR and PAE components of TEE (16a, 16b, 40), and I used the measured adaptation of RMR from the Minnesota experiment to investigate the relative changes of PAE vs. RMR and the contribution of adaptive thermogenesis.
The content of body protein, glycogen, and fat influenced the daily average rates of proteolysis, glycogenolysis, and lipolysis, respectively. These rates, along with the macronutrient intakes, were used to determine the relative macronutrient oxidation rates according to equations presented in the APPENDIX. An important aspect of the macronutrient oxidation model was that, unlike carbohydrate and protein, fat intake did not directly stimulate its own oxidation (26, 55).
Gluconeogenesis was driven by dietary changes as well as endogenous substrate delivery. GNGF varied directly with the dietary fat intake and the endogenous lipolysis rate (7), whereas GNGP depended upon the intake of carbohydrate and protein as well as the proteolysis rate. DNL was a function of the dietary carbohydrate intake and the glycogen content such that DNL became amplified in the case of saturated glycogen and high carbohydrate intake (2).
The body weight was the sum of the body fat mass (F) and the lean mass (L). Lean mass was composed of bone, extracellular water, and the lean tissue (nonadipose) cell mass, including intracellular water, glycogen, and protein. On the basis of typical values reported for the intracellular composition (3), and assuming that water is associated with intracellular protein and glycogen in constant ratios, the lean tissue cell mass was computed from the body protein and glycogen masses.
Most model parameters were determined from published human data, and the few remaining parameters were chosen to minimize the mean squared difference between the simulation outputs and the data from the Minnesota experiment. In that study, macronutrient intakes were controlled, and body weight and composition changes were measured in 32 healthy young men over a period of 
1 yr (36). The subjects participated in a 24-wk semistarvation period and lost 24% of their body weight. Semistarvation was followed by a 12-wk controlled refeeding period. Twelve subjects went on to participate in an additional 8-wk ad libitum feeding phase. The model was applied to data taken from the 12 subjects that participated in the entire 20-wk refeeding protocol. To investigate how long it would take to return to the original body composition, I continued the simulation for an additional 19 mo with the dietary intake values given by those measured during the prestarvation period of the Minnesota experiment.
To simulate the caloric restriction study of Friedlander et al. (30), I chose the initial values for the body weight, fat mass, RMR, and the balanced diet to match the average subject during the baseline phase of the experiment. Next, the caloric restriction was imposed using the diet composition described by Friedlander et al., where the energy restriction came primarily from decreased fat and carbohydrate intake, whereas protein intake was held approximately constant.
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RESULTS |
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TOPABSTRACTGlossary of Model VariablesMETHODSRESULTSDISCUSSIONAPPENDIXGRANTSREFERENCES |
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Body weight and fat mass. Figure 2 shows the model simulation along with the experimental measurements of body weight and fat mass during the Minnesota experiment. The model simulations matched the experimental data reasonably well during all feeding periods. The initial baseline feeding period resulted in a stable body weight and fat mass. The start of semistarvation induced a rapid decrease of body weight and fat mass that slowed and eventually reached 76 and 34% of their initial values, respectively. During refeeding, body weight and fat mass increased, with both recovering to about 80% of their initial masses by the end of the 12-wk controlled refeeding period. Ad libitum refeeding resulted in further increases of body weight and fat to finally achieve 104 and 145% of their initial values, respectively. Thus the simulation reproduced the overshoot of body fat mass. Continuing the simulation with the original balanced baseline diet and physical activity level eventually restored the original body weight and fat mass, but it took more than a year for the fat mass to decrease to within 5% of its original value (Fig. 3).
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The refeeding periods induced a state of positive energy balance that allowed body weight and fat mass to be regained. The stepwise increases of MEI were met with parallel increases of TEE. The simulated RMR matched the measured values reasonably well, except at the onset of ad libitum feeding, when the measured RMR reached almost 2,200 kcal/day. However, the precision of this measurement was unclear because neither the uncertainty nor the individual subject data were reported.
Figure 5 depicts the measured RMR vs. L, along with the model simulation. The curve traced by RMR vs. L followed the loop shown in Fig. 5, with the sequence of events indicated by the arrows on the curve. Initially, both RMR and lean body mass were constant. Semistarvation caused a rapid decrease of RMR due to adaptive thermogenesis, as indicated by the initial drop of the RMR vs. L curve. As the negative energy balance persisted, lean body mass decreased along with a concomitant decrease of RMR. The state of positive energy balance during refeeding resulted in repletion of lean mass and a parallel increase of RMR. In accordance with the data, the simulated RMR was greater during refeeding vs. semistarvation when compared at the same lean body mass. Specifically, the RMR after 12 wk of semistarvation was significantly lower than the RMR after 12 wk of refeeding despite the fact that both measurements occurred when lean mass was 51.3 ± 1.5 kg (P < 0.0001).
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42% of the TEE. Small weekly variations of the experimental semistarvation diet were introduced, primarily via changes of carbohydrate intake, to obtain the desired rate of weight loss (36). The CarbOx followed carbohydrate intake during the remainder of the semistarvation period. The simulated ProtOx decreased by 12% after the first week of semistarvation and remained suppressed. The simulated FatOx increased by 12% during the initial days of semistarvation. This increase was due to enhanced lipolysis associated with the reduced carbohydrate intake. After the first week of semistarvation, fat oxidation was 46% of the of the TEE. This led to a negative fat balance of more than 1,000 kcal/day that slowly became less negative as the semistarvation progressed and body fat was catabolized. At the end of the semistarvation period, all three macronutrient oxidation rates were roughly equal to their respective dietary intakes.
At the start of the controlled refeeding phase, carbohydrate oxidation increased by 30% and accounted for 64% of the TEE. Protein oxidation increased by 13% and accounted for 12% of the TEE, whereas fat oxidation sluggishly increased and accounted for 22% of the TEE. All macronutrient oxidation rates were less than their respective intake rates during the controlled refeeding period.
The final 8 wk of ad libitum feeding allowed the subjects to consume very large quantities of food, and they disproportionately increased their fat intake. Rapid adaptations of carbohydrate and protein oxidation accompanied their increased dietary intake. Fat oxidation continued its sluggish, almost linear increase during the entire refeeding phase, resulting in substantial differences between fat intake and oxidation, reaching almost 1,200 kcal/day during ad libitum feeding. Thus the difference between ad libitum fat intake and oxidation was the primary cause of the poststarvation body fat overshoot.
Gluconeogenesis. Figure 7 shows that GNGF and GNGP were initially constant at their baseline values, but the onset of semistarvation caused GNGF to decrease by 30%. This was caused by the reduced fat intake and the corresponding 78% decrease of exogenous glycerol, whereas endogenous glycerol gluconeogenesis increased by 23% due to increased adipose lipolysis (not shown). Despite the reduced protein intake at the onset of semistarvation, GNGP remained approximately constant in the initial days of semistarvation due to the decrease of carbohydrate intake. Both GNGF and GNGP decreased slowly over the course of semistarvation as body fat and protein, respectively, were catabolized. Refeeding caused suppression of GNGP as carbohydrate intake increased. The inhibition of lipolysis upon refeeding and concomitant reduction of endogenous glycerol entering the gluconeogenic pathway was counterbalanced by the increase of exogenous glycerol from dietary fat. Therefore, GNGF was only slightly reduced upon refeeding and gradually increased as body fat, and thereby lipolysis, recovered.
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100 g after the first week of semistarvation as expected. However, as the semistarvation progressed, the glycogen content surprisingly increased and eventually exceeded baseline levels by about 40%. This was caused by the progressive reduction of physical activity because glycogen remained low in simulations where the physical activity level was maintained throughout the semistarvation phase (not shown). The rate of glycogen increase during prolonged semistarvation was equivalent to an average positive carbohydrate balance of about 10 kcal/day. Given that the carbohydrate intake during the semistarvation period averaged 1,100 kcal/day, this implies that carbohydrate was remarkably well balanced to within 1% error. Refeeding initially induced further increases of glycogen content, but there was a trend toward normalization of glycogen, because carbohydrate oxidation was stimulated.
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Daily respiratory gas exchange.
Figure 9 shows the simulated daily respiratory exchange of oxygen and carbon dioxide that was based upon the stoichiometry for carbohydrate, fat, and protein oxidation along with corrections introduced by gluconeogenic and lipogenic fluxes (21, 23). As expected from the above descriptions of energy expenditure and macronutrient oxidation, semistarvation caused a decrease of both oxygen consumption (O2) and carbon dioxide production (CO2), with a more rapid decrease of CO2. Thus the resulting decrease of both the respiratory quotient (RQ) and the nonprotein respiratory quotient (NPRQ) reflected the increased reliance on fat oxidation. As semistarvation progressed, RQ and NPRQ gradually increased, whereas both O2 and CO2 continued to decrease. This indicated that the contribution from fat oxidation was decreasing, because body fat was catabolized and energy expenditure continued to decrease. Refeeding caused O2 and CO2 to increase, with CO2 increasing more rapidly as DNL was stimulated and the RQs transiently exceeded 1.
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Figure 10C shows the simulated daily glycogen turnover and illustrates that, except during transient changes of energy intake, glycogen synthesis and glycogenolysis rates were closely matched over the entire course of the study.
Nutrient balances. Figure 11 depicts the dynamic changes of the energy balance and individual macronutrient balances. Figure 11A shows that the fat balance closely tracked the energy balance, indicating that most of the energy deficit and surplus was accounted for by body fat changes. Figure 11B shows that long-term carbohydrate balance was more tightly controlled than protein balance because protein imbalances were sustained, whereas transient carbohydrate imbalances were quickly suppressed.
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Body weight and fat mass. Figure 12 shows the simulated changes of body weight and fat mass during 3 wk of caloric restriction by 40% of baseline energy requirements. Despite using model parameters derived from the Minnesota experiment, the model predictions matched the experimental measurements of Friedlander et al. (30), thereby providing support for the validity of the model. The body weight and fat mass had an almost linear decline during the 2 wk of caloric restriction, and the subjects lost 4 kg of body weight with slightly less than one-half coming from loss of body fat.
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DISCUSSION |
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TOPABSTRACTGlossary of Model VariablesMETHODSRESULTSDISCUSSIONAPPENDIXGRANTSREFERENCES |
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Several investigators have used mathematical modeling to study the regulation of body weight (6, 37) and composition (4, 5, 11, 13, 14, 28, 38, 50, 64, 68). Most previous models of body weight and composition regulation assumed that the macronutrient composition of the diet had no effect on the partitioning of energy between lean and fat tissue, an assumption that runs counter to the nutrient balance concept (27, 31). Rather, most models defined a parameter or a simple function of initial body composition that determined that the fraction of energy imbalance partitioned toward deposition or mobilization of body protein vs. fat (11, 13, 14, 50). The physiological basis for this partitioning is unclear and begs the question of how body composition is regulated. A more recent model (28) incorporated carbohydrate and fat balances but ignored protein. A few previous models have also been applied to the data from the Minnesota experiment (4, 5, 37, 50), but the present study is the first to validate a human model by comparing model predictions with body composition and metabolic data from an independent human feeding study.
Previous mathematical models have represented RMR as a linear function of lean body mass (4, 5, 13, 14, 50, 64, 68), with coefficients occasionally significantly greater than those determined from cross-sectional analysis (16, 46). Such models fail to capture the loop traced by the RMR vs. L curve throughout semistarvation followed by refeeding. In a pair of elegant studies reanalyzing the Minnesota experiment, Dulloo et al. (17, 18) argued for the existence of an adaptive thermogenic mechanism to explain the measured RMR data. In agreement with these authors, the mathematical model presented here suggested that adaptive thermogenesis at the onset of semistarvation caused a rapid drop of RMR, which then decreased slowly as lean tissue was catabolized and protein turnover decreased. During refeeding, the level of adaptive thermogenesis and the energy costs of DNL and protein turnover were increased, resulting in a higher RMR at the same lean mass during refeeding vs. semistarvation.
The physiological mechanisms underlying adaptive thermogenesis are unknown. Several investigators (47, 50) have suggested that underfeeding causes metabolically active organs such as the liver, intestines, or kidneys to rapidly decrease their mass. Alternatively, the concentrations of circulating catecholamines and thyroid hormones have been observed to rapidly decrease with underfeeding (53, 65) and may reflect a reduction of sympathetic outflow and concomitant decrease of RMR. The present model was empirical and did not distinguish between these mechanisms.
Although changes of RMR contributed significantly to energy balance, the decrease of PAE during semistarvation was responsible for the majority of the slow decline of TEE. The physiological mechanisms underlying the changes of PAE are unclear. Because PAE for most common activities is proportional to body weight, the loss of body weight itself contributed to some decrease of PAE, but this was insufficient to account for the required decrease.
Adaptation of PAE has been hypothesized to involve altered energy efficiency of muscular work (16b, 40, 54). However, no change of physical activity energy efficiency was observed during treadmill tests at various stages of the Minnesota experiment (36, 58). Nevertheless, it is possible that changes of muscular work efficiency during typical daily activities may not have been reflected by the more physically demanding treadmill tests. Keys (36) noted that the subjects became apathetic and avoided voluntary physical activity toward the end of the semistarvation phase, but no systematic monitoring of physical activity was performed. Questionnaires completed by the subjects showed a progressive decrease of their physical "activity drive" over the course of semistarvation, which slowly improved with refeeding (36). Therefore, it is possible that PAE changes with semistarvation were the result of decreased voluntary as well as altered spontaneous physical activity expenditures such as fidgeting, posture control, and muscle tone (41).
The remarkable regulation of long-term carbohydrate balance was due to the limited whole body glycogen storage capacity. Therefore, relatively little energy could be accumulated or lost in the form of glycogen compared with protein or fat. However, large short-term changes of glycogen were permitted and led to potent modulation of both carbohydrate oxidation (24) and DNL (2). Thus glycogen feedback ensured that carbohydrate imbalances were only transient. In comparison, the relatively less significant short-term change of the body protein pool had little effect on protein oxidation so that protein imbalances were more sustained and led to long-term changes of the lean body mass. Unlike carbohydrate and protein, fat intake did not directly stimulate its own oxidation, and the relative change of the body fat pool only weakly affected fat oxidation (26, 55). Thus large fat imbalances were observed during semistarvation and refeeding, and these imbalances were sustained, resulting in significant changes of body fat mass.
The model predicted that the fat mass overshoot was not permanent provided that the original prestarvation diet and physical activity level were returned. However, recovery of the original body composition was predicted to take more than 1 yr. The predicted mechanism of the fat mass overshoot was an enhanced rate of de novo lipogenesis in the early refeeding period, followed by a dramatic increase of fat intake during ad libitum feeding. In contrast, Dulloo et al. (17, 18) postulated that the improved energy economy resulting from adaptive thermogenesis was somehow specifically channeled to accelerate fat mass gain during refeeding on the basis of a "fat stores memory." The present study demonstrates that such a novel mechanism was not necessary to explain the data.
The present version of the computational model does not explicitly include the effects of hormones, but several hormonally-mediated effects are implicitly included. For example, insulin's effect is implicit in the function of dietary carbohydrate to inhibit lipolysis as well as stimulate carbohydrate oxidation, glycogen synthesis, and DNL. Future work will explicitly account for the effects of important hormones and will extend the model to study overfeeding and body composition regulation in altered metabolic states like obesity, anorexia nervosa, and cachexia.
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APPENDIX |
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TOPABSTRACTGlossary of Model VariablesMETHODSRESULTSDISCUSSIONAPPENDIXGRANTSREFERENCES |
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The individual components of the mathematical model were based on a variety of published in vivo human data, as described below. Each model component was relatively simple, and only the most important physiological effectors have been incorporated. Because continued development of the model is part of an ongoing research program, additional relevant physiological data will be incorporated into the existing computational framework to improve the realism and predictive capabilities of the model.
The nutrient balance model depicted in Fig. 1 is an expression of the conservation of energy such that changes of the body's energy stores were given by the sum of fluxes entering the pools minus the fluxes exiting the pools. Thus the mathematical representation of the nutrient balance model was given by the following differential equations:
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C = 4.2 kcal/g, F = 9.4 kcal/g, and P = 4.7 kcal/g were the energy densities of carbohydrate, fat, and protein, respectively (43). MTG = 860 g/mol and MFFA = 273 g/mol were the molecular masses of triacylglycerides and free fatty acids, respectively. The oxidation rates CarbOx, FatOx, and ProtOx summed to the total energy expenditure (TEE). Because body composition changes take place on the time scale of weeks, months, and years, the model was targeted to represent daily changes of energy metabolism and not fluctuations of metabolism that occur within 1 day. The nutrient balance equations were integrated using the 4th order Runge-Kutta algorithm with a timestep size of 0.1 days (52). Body Composition
The body weight (BW) was the sum of the lean body mass (L) and the fat mass (F). L was computed using the following equation:
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 | (2) |
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W was a constant amount of intracellular water computed to attain the appropriate initial intracellular composition, assuming that G = 500 g. I assumed that BM was 4% of the initial BW, as estimated by Keys (36). ECW varied slightly throughout the Minnesota experiment, increasing significantly at the end of the semistarvation phase (corresponding to clinical edema) and returning to baseline by the end of the refeeding period (36). I used the measured changes of ECW as a model input.
Whole Body Total Energy Expenditure
TEE was modeled by the following equation:
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Thermal Effect of Feeding
Feeding induces a rise of metabolic rate associated with the digestion, absorption, and short-term storage of macronutrients and was modeled by the following equation:
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F = 0.025, P = 0.25, and C = 0.075 defined the short-term thermic effect of fat, protein, and carbohydrate feeding (29). Adaptive Thermogenesis
Energy imbalance causes an adaptation of metabolic rate that opposes weight change (16a, 16b, 19, 40). Whether or not the adaptation of energy expenditure is greater than expected based on body composition changes alone has been a matter of some debate (16a, 16b, 19, 40, 65, 66). The so-called adaptive thermogenesis is believed to affect both resting and nonresting energy expenditure (16a, 16b, 40) and has maximum amplitude during the dynamic phase of weight change (16a, 16b, 40, 65). Adaptive thermogenesis may also persist during weight maintenance at an altered BW, but the persistent effect has been debated (66). The non-RMR component of adaptive thermogenesis may reflect either altered efficiency or amount of muscular work (16a, 16b, 40, 41, 54).
The onset of adaptive thermogenesis is rapid and may correspond to altered levels of circulating thyroid hormones or catecholamines (53, 65). I defined a dimensionless adaptive thermogenesis parameter (T) that was generated by a first-order process in proportion to the departure from the baseline metabolizable energy intake (MEIb) = CIb + FIb + PIb:
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MEI was the change from the baseline metabolizable energy intake, 
= 7 days was the estimated time constant for the onset of adaptive thermogenesis, and 
was a parameter to be determined from the best fit to the Minnesota experiment data. The adaptive thermogenesis parameter, T, acted on both the RMR and PAE components of energy expenditure, as defined below. This simple model assumed that adaptive thermogenesis reacted to perturbations of MEI and persisted as long as MEI was different from baseline. Importantly, the model allowed for the possibility that = 0, meaning that no adaptive thermogenic mechanism was required to fit the data from the Minnesota experiment. The amount that the best fit value for differs from zero provides an indication of the extent of adaptive thermogenesis that occurred during the Minnesota experiment. Physical Activity Expenditure
The energy expended for typical physical activities, such as walking or running, is proportional to the BW of the individual (61). Thus the following equation was used for the physical activity expenditure:
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was the physical activity coefficient (in kcal·kg–1·day–1) that defined the daily physical activity level, and BW = L + F was the BW. The proportion of T that was allocated to the modification of PAE was determined by the parameter 
that was computed from the measured RMR data from the Minnesota experiment. The adaptation of PAE with T did not distinguish between altered efficiency vs. the amount of muscular work.
The activity coefficient, , in the Minnesota experiment was chosen to linearly decrease at the onset of semistarvation from its basal value, b, to reach a minimum value, s, by the end of the semistarvation period corresponding to the observed decrease of voluntary physical activity and the "activity drive" (36, 58). I used the measured RMR values during the baseline period and at the end of the semistarvation period to estimate the activity coefficients as follows:
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linearly returned to its basal value at the end of the refeeding period. Resting Metabolic Rate
RMR includes the energy required to maintain irreversible metabolic fluxes such as de novo lipogenesis and gluconeogenesis as well as the turnover costs for protein, fat, and glycogen. The following equation included these components:
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d = 0.8 was the efficiency of de novo lipogenesis (25), g = 0.8 was the efficiency of gluconeogenesis (9), and the constant Ec was a parameter chosen to ensure that the model achieved energy balance during the balanced baseline diet (see Nutrient Balance Parameter Constraints below).
The specific metabolic rate of adipose tissue was 
F = 4.5 kcal·kg–1·day–1. The brain metabolic rate was B = 240 kcal·kg–1·day–1, and its mass was MB = 1.4 kg, which does not change with weight gain or loss (22). The basal specific metabolic rate of the lean tissue cell mass, 
BCM = 24 kcal·kg–1·day–1, was determined by the average organ masses and their specific metabolic rates according to the following equation:
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i and Mi are the average specific metabolic rate and mass, respectively, of the organ indexed by i. The organs included skeletal muscle (SM = 13 kcal·kg–1·day–1, MSM = 28 kg), liver (L = 200 kcal·kg–1·day–1, ML = 1.8 kg), kidney (K = 440 kcal·kg–1·day–1, MK = 0.31 kg), heart (H = 440 kcal·kg–1·day–1, MH = 0.33 kg), and residual lean tissue mass (R = 12 kcal·kg–1·day–1, MR = 23.2 kg), as provided by Elia (22). T affected the baseline specific metabolic rate for lean tissue cell mass according to the following equation:
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PSynthP to synthesize P and that the energy required for degradation was PDP. Because dP/dt = SynthP – DP, the energy cost for protein turnover was given by (P+P)DP + PdP/dt. Similar arguments led to the other terms of Eq. 8 representing the energy costs for fat and glycogen turnover, where the energy cost for degradation was negligible. The values for the parameters were: F = 0.18, G = 0.21, P = 0.17, and P = 0.86 kcal/g. These values were determined from the adenosine 5'-triphosphate (ATP) costs for the respective biochemical pathways (i.e., 8 ATP/TG synthesized, 2 ATP/glycosyl units of glycogen synthesized, 4 ATP/peptide bond synthesized + 1 ATP for amino acid transport, and 1 ATP/peptide bond hydrolyzed) (9, 20). I assumed that 19 kcal of macronutrient oxidation was required to synthesize 1 mol ATP (22). Daily Average Lipolysis
The daily average lipolysis rate (DF) was modeled as
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F = 140 g/day was the baseline daily average TG turnover rate given by 2/3 of the fed lipolysis rate plus 1/3 of the overnight fasted lipolysis rate (34). The first (F/Fb)2/3 factor accounted for the dependence of the basal lipolysis rate on the total fat mass, and the 2/3 power reflected the hypothesis that basal lipolysis scales with adipocyte surface area (63).
The term in the square brackets accounted for the modulation of lipolysis by the carbohydrate intake. For example, complete starvation (CI = 0) stimulated average daily lipolysis by a factor of AL = 3.1, as computed by dividing the glycerol rate of appearance (Ra) after a 60-h fast (12) by the daily average glycerol Ra (34). Halving the carbohydrate intake increased the average lipolysis rate by factor of 1.4, as estimated by the increased area under the circulating free fatty acid curve after an isocaloric meal consisting of 33 vs. 66% carbohydrate (69). Given the above value for AL, the effect of halving the carbohydrate content was modeled by choosing BL = 0.9. The following choice for kL ensured that the lipolysis rate was normalized for the baseline diet:
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Daily Average Proteolysis
The daily average protein degradation rate (DP) was given by
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P = 300 g/day was the baseline daily protein turnover rate (62), and I assumed that the protein degradation rate was proportional to the normalized protein content of the body. Daily Average Glycogenolysis
The daily average glycogen degradation rate (DG) was given by the following equation:
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G = 180 g/day, was determined by assuming that 70% was from hepatic glycogenolysis and 30% from skeletal muscle, with the hepatic contribution computed as 2/3 of the fed plus 1/3 of the overnight-fasted hepatic glycogenolysis rate (44). Daily Average Fat, Protein, and Glycogen Synthesis Rates
Mass conservation required that the daily average synthesis rates of fat, protein, and glycogen (SynthF, SynthP, and SynthG, respectively) were given by
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 | (15) |
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Glycerol 3-Phosphate Production
Because adipose tissue lacks glycerol kinase, the glycerol 3-phosphate backbone of adipose TG is derived primarily from glucose. Thus the TG synthesis rate (SynthF) determined the rate of G3P according to
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Glycerol Gluconeogenesis
Lipolysis of both endogenous and exogenous TG results in the release of glycerol that can be converted to glucose via gluconeogenesis (7). Recently, Trimmer et al. (60) demonstrated that glycerol disappearance could be fully accounted for by glucose production. Therefore, I assumed that all exogenous and endogenous glycerol entered the GNG pathway according to
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Net Gluconeogenesis From Amino Acids
The GNGP rate in the model referred to the net rate of gluconeogenesis from amino acid-derived carbon. Although all amino acids except for leucine and lysine can be used as gluconeogenic substrates, the primary gluconeogenic amino acids are alanine and glutamine. Much of alanine gluconeogenesis does not contribute to the net amino acid gluconeogenic rate because the carbon skeleton of alanine is largely derived from carbohydrate precursors via skeletal muscle glycolysis (51). Nurjhan et al. (48) used a multiple isotopic tracer methodology to determine that the net glutamine and alanine gluconeogenic rate derived from amino acid carbon was 
66 kcal/day in normal humans. Because the tracer techniques are known to underestimate gluconeogenesis by as much as 40% due to carbon exchange in the Krebs cycle (35), I estimated that the net basal gluconeogenic rate from amino acids (G
GP) was 100 kcal/day.
Several factors may regulate GNGP, but for simplicity I have assumed that GNGP was proportional to the normalized proteolysis rate and was influenced by the diet as follows:
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C = 0.5 and P = 0.3 were determined by solving Eq. 18 using two sets of data. The first measured a 56% increase of gluconeogenesis when protein intake was increased by a factor of 2.5-fold and carbohydrate intake was decreased by 20% (42). The second study measured a 42% decrease of alanine gluconeogenesis when both carbohydrate and protein were increased by 2.1-fold (15). De Novo Lipogenesis
De novo lipogenesis (DNL) occurs in both the liver and adipose tissue. Under free-living conditions, adipose DNL has recently been measured to contribute about 20% of new TG with a measured TG turnover rate of 50 g/day (57). Thus adipose DNL is about 94 kcal/day. Measurements of daily hepatic DNL in circulating very-low-density lipoproteins (VLDLs) have found that about 7% of VLDL TG occurs via DNL when consuming a basal diet of 30% fat, 50% carbohydrate, and 15% protein (33). Given that the daily VLDL TG secretion rate is about 33 g/day (56), this corresponds to a hepatic DNL rate of about 22 kcal/day. For an isocaloric diet of 10% fat, 75% carbohydrate, and 15% protein, hepatic DNL increases to 113 kcal/day (33).
When carbohydrate intake is excessively large and glycogen is saturated, DNL can be greatly amplified (2). Therefore, I modeled DNL as a Hill function of the normalized glycogen content with a maximum DNL rate given by the carbohydrate intake rate
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Macronutrient Oxidation Rates
The whole body energy expenditure was equal to the sum of the carbohydrate, fat, and protein oxidation rates. I assumed that the minimum carbohydrate oxidation rate was equal to the sum of the gluconeogenic rates. Thus the remaining energy expenditure was apportioned between carbohydrate, fat, and protein oxidation according to the fractions fC, fF, and fP, respectively. Therefore, the substrate oxidation rates were given by
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 | (20) |
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On the basis of these physiological considerations, the substrate oxidation fractions were computed according to the following expressions:
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 | (21) |
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CI and PI were changes from CIb and PIb, respectively. The small parameter, Gmin = 1 g, was chosen such that carbohydrate oxidation was restrained when glycogen was depleted. To normalize for the baseline physical activity, the constant kA was chosen such that kA = ln(SA). Z was a normalization factor equal to the sum of the numerators. Respiratory Gas Exchange
Oxidation of carbohydrate, fat, and protein was associated with consumption of oxygen (O2) and production of carbon dioxide (CO2) according to the stoichiometry of the net biochemical reactions (23):
 | (22) |
 | (23) |
The simulated O2 consumption (O2) and CO2 production (CO2, in L/day) were computed according to
 | (24) |
CO2 by O2 (23). To compute the nonprotein respiratory quotient (NPRQ), the total nitrogen excretion was calculated as
 | (25) |
Nutrient Balance Parameter Constraints
The initial feeding period of the Minnesota experiment provided a controlled diet for several weeks to maintain the baseline BW. I assumed that this basal diet achieved a state of nutrient and energy balance such that TEE = MEIb, where b refers to the balanced state. Therefore, at energy and nutrient balance
 | (26) |
Nutrient balance implies that the left-hand sides of Eq. 1 are zero. Thus rearrangement of the nutrient balance equations gave
 |
 | (27) |
 |
 | (28) |
 | (29) |
 | (30) |
Carbohydrate Perturbation Constraint
The parameters wC and SC determined how the model adapted to changes of carbohydrate intake. I specified that an additional dietary carbohydrate intake (CI) above baseline (CIb) resulted in an initial positive carbohydrate imbalance of 
CCI, where 0 < C < 1 specified the proportion of CI directed towards glycogen storage. Thus the glycogen increment was G = C CI/C. The goal was to solve for the parameter SC such that the correct amount of carbohydrate was oxidized and deposited as glycogen during short-term carbohydrate overfeeding. On the basis of the carbohydrate overfeeding study of Horton et al. (32), I chose C = 0.5 when CI = 1,500 kcal/day.
The change of total energy expenditure was given by
 | (31) |
 | (32) |
 | (33) |
 | (34) |
 | (35) |
DNL was computed at the midpoint of the glycogen increment according to
 | (36) |
GNG, was given by
 | (37) |
 | (38) |
 | (39) |
 | (40) |
 | (41) |
was defined as:
 | (42) |
 | (43) |
 | (44) |
Protein Perturbation Constraint
The parameters wP and SP determined how the model adapted short-term substrate oxidation rates to changes of protein intake. In a meticulous study of whole body protein balance, Oddoye and Margen (49) measured nitrogen balance in subjects consuming isocaloric diets with moderate or high protein content. These studies found that almost all of the additional dietary nitrogen on the high-protein diet was rapidly excreted such that P = 0.07 when PI = 640 kcal/day, CI = –310 kcal/day, and FI = –330 kcal/day.
To compute the value for SP to match the data of Oddoye and Margen (49), I began with the protein balance equation
 | (45) |
 | (46) |
 | (47) |
 | (48) |
 | (49) |
 | (50) |
CI was small and its effect counterbalanced by changes of GNG due to the large increase of protein intake. Therefore, I assumed that
 | (51) |
 | (52) |
was defined as
 | (53) |
 | (54) |
Model Parameter Values
The model parameter values listed above were obtained from the cited published literature and are listed in Table 1. The parameters SA, wP, wC, , and were determined using a downhill simplex algorithm (52) to minimize the sum of squares of weighted residuals between the simulation outputs and the data from the Minnesota human starvation experiment (36). I used the following measurement error estimates to define the weights for the parameter optimization algorithm: BW = 0.2 kg, FM = 1 kg, and RMR = 50 kcal/day. The best fit parameter values are listed in Table 2, and the constrained parameters are listed in Table 3.
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GRANTS |
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TOPABSTRACTGlossary of Model VariablesMETHODSRESULTSDISCUSSIONAPPENDIXGRANTSREFERENCES |
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
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REFERENCES |
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TOPABSTRACTGlossary of Model VariablesMETHODSRESULTSDISCUSSIONAPPENDIXGRANTSREFERENCES |
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and 
) of body weight (A) and fat mass (B) during baseline (B), semistarvation (SS), controlled refeeding (CR), and ad libitum refeeding (ALR) phases of the Minnesota human starvation experiment.
