Abstract
The rapidly increasing epidemic of obesity has stimulated substantial research focused on fat metabolism. For the alternative model of T2D and related disorders that this book is developing, obesity is not as important and essential as the classical model presumes. By now the new argument is sufficiently clear on its stand on the relationship between obesity and T2D although a part of it is yet to be discussed. In the new emerging picture obesity is not causal to insulin resistance and T2D, but the behavioral factors that cause obesity and those that cause insulin resistance are largely overlapping. Therefore the association between obesity and insulin resistance leading to T2D is not surprising. But it is also not obligate. Physiologically it is possible (and also common in some populations) to be insulin resistant and eventually diabetic without ever being obese. Similarly it is also possible to be obese and still insulin sensitive. The two are independent but largely overlapping etho-physiological states. We will now examine this statement more elaborately starting for the basic functions of fat and why fat metabolism is linked with a diversity of other functions in the body.
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The rapidly increasing epidemic of obesity has stimulated substantial research focused on fat metabolism. For the alternative model of T2D and related disorders that this book is developing, obesity is not as important and essential as the classical model presumes. By now the new argument is sufficiently clear on its stand on the relationship between obesity and T2D although a part of it is yet to be discussed. In the new emerging picture obesity is not causal to insulin resistance and T2D, but the behavioral factors that cause obesity and those that cause insulin resistance are largely overlapping. Therefore the association between obesity and insulin resistance leading to T2D is not surprising. But it is also not obligate. Physiologically it is possible (and also common in some populations) to be insulin resistant and eventually diabetic without ever being obese. Similarly it is also possible to be obese and still insulin sensitive. The two are independent but largely overlapping etho-physiological states. We will now examine this statement more elaborately starting from the basic functions of fat and why fat metabolism is linked with a diversity of other functions in the body.
It is quite well known that fats or lipids are storage molecules, and fat or adipose tissue serves as an energy storage tissue. Fortunately or unfortunately the word fat is used to denote both a kind of macromolecule and a kind of tissue that stores fat. I will make use of this ambiguity below while describing the seven functions of fat in our body. Some of the functions described below are of lipid molecules and some of adipose tissue. This distinction will be obvious as I elaborate on these functions. The important point to be emphasized here is that fat for the body is much more than storage of excess calories, and while talking about the causes and consequences of obesity, it is important to keep all of them in mind with due “weighting.” This is not intended to be an extensive and comprehensive review of the existing literature. Much comprehensive and scholarly work on obesity by several authors is available [1, 2]. I will avoid repeating what has already been said. Instead I will focus on aspects that these authors appear to have missed and those that are directly relevant to the synthesis in this book.
There is a very fundamental function of lipids, phospholipids in particular, in any living organism, and that is they form the outer and inner membranes of cells. Without this structural role of lipids, life would have been just impossible. But this is very basic for all forms of life, and it is taken for granted. I will not include this fundamental structural role of lipids while talking about the seven functions of fat. The seven functions of fat are:
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1.
As an energy reserve
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2.
As the most abundant macromolecular constituent of the brain
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3.
As an insulator against cold temperatures
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4.
As an impact buffer
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5.
As a modulator of aggression, sex, and reproduction
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6.
As an immune modulator
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7.
As a social signal
Some of them are quite well known, others are less well known, and some are recent surprise findings. I will elaborate on each of them soon and show how each one is related in a different way to the four fundamental processes in fat dynamics, i.e., (1) lipogenesis, (2) lipolysis, (3) distribution of fat tissue in the body, and (4) effects of fat on other functions of the body.
1. Fat as an energy reserve: Excess energy left after meeting the current needs of the body is said to be stored, the common energy stores being fat. In this statement there is one fundamental assumption that appears to be uncritically accepted by everyone. That is, there is something like a fixed current requirement of the body above which it will be treated as excess and stored. However, the concept of current requirement of the body can be challenged. The current requirement is not a physiological constant but is a behavioral decision. An individual has a choice of what to do with energy. It is possible to spend an extra quantum of energy to go and chase more females, take another round of the territorial boundary to ensure there are no intruders, go and challenge one’s competitors, or just tease other group-mates. Storing as fat is only one of the many available options of what to do with energy. Which option is the best bet to increase one’s lifetime success is decided by ecological and social circumstances as well as by the personality of the decision maker. In terms of evolutionary optimization the available energy can be best utilized for the present if the situation is favorable to grow and reproduce and/or the future is relatively uncertain. On the other hand if the present circumstances are not so encouraging, there is a chance of the future being better and the probability for surviving till the next possible opportunity is fair; saving rather than spending is a better strategy. For example, if one’s reproductive opportunities are blocked by other more dominant individuals, it is no use spending time and energy in attempting to reproduce; it would rather pay to save energy and wait for a future opportunity. That is why lipogenesis is more adaptive under depressing or subordinate conditions. However, the most important condition necessary for storing for the future is that there is a good chance of surviving sufficiently long to make use of the stored energy. If predator or other environmental risks are large, it is better to spend as much energy as possible at the present and maximize immediate reproductive success. If the prospects for the future are to be counted, one needs to ensure survival before storing fat, and in order to do so, one should avoid all risk-taking behaviors till then. Therefore adiposity and risk-taking behavior have to develop an inverse association. Since physical aggression is necessarily coupled with risk of injury and perhaps death, obesity and aggression also need to be negatively related. Thus the very definition of excess energy on which the concept of energy storage is based is itself decided by ecology, social status, and behavior. Therefore believing in the popular notion that excess energy is stored as fat and ignoring the ecology and ethology of fat storage will not lead us to any insightful understanding of fat metabolism.
Leaving aside this consideration for the time being, let us return to the orthodox view and examine the concept of obesity as the result of net positive energy balance, i.e., calorie intake minus calorie burnt. Calorie burnt has two distinguishable components, the basic or resting metabolic rate and that due to physical activity or exercise. The former and even partly the latter are dependent on the total body mass. The greater the amount of respiring tissue, the greater the total fuel burnt. As a result, the body mass stabilizes at an equilibrium value of M. This is given by a simple baseline equation dM = I − aM − b, where I is energy intake, M is the body mass, a is the basal metabolic rate, and b the expenditure due to physical activity [3]. This equation can explain why at a given food intake and lifestyle, different people can have different body weights.
Unfortunately the reality is not as simple as the equation. What the equation does not incorporate is the influence of M on I. I is taken as an independent variable by this equation which is not true in reality. A large number of different mechanisms of the body regulate food intake and fat storage. There are two parallel lines of thinking and research which have failed to come together effectively. One is the energy balance equation given above which appears to neglect that a large number of mechanisms of intake regulation exist in the body and simply assumes that either a decrease in energy expenditure or increase in intake or both together are responsible for the obesity epidemic. If one or many of the intake regulation mechanisms were effective, neither of them could ever lead to obesity. And not only one but a series of mechanisms of food intake regulation exist which work at different levels (see Table 3.1). If there has to be a positive energy imbalance, not one but all the mechanisms would have to fail or be overridden, and as yet there is no suggestion as to how and why this is happening with increasing frequency only in the last couple of generations in a large and increasing fraction of the population.
We have seen earlier that a variety of at least partially independent mechanisms regulate food intake in the short [4–16] and long run [17, 18]. The body thus has adequate mechanisms to keep the short-term and long-term energy balance. Unless there is something wrong simultaneously with all these mechanisms, we will not eat excessively and will not accumulate fat. The short-term and long-term mechanisms of energy intake control appear to form a multilayered and robust system of regulation. It is difficult to perceive how such a system with a series of backups can fail to regulate food intake. If one system fails, others are there to compensate, and therefore, a single defect is most unlikely to cause an eating disorder. It is more likely that overeating represents an adaptive response. Being an evolved adaptive response, there would be mechanisms to modify the entire series of regulation mechanisms simultaneously. It is possible that the evolved mechanisms of overeating are facing supernormal stimuli today and therefore overexpressing now. In order to understand this we need to know under what conditions overeating would be adaptive in natural settings.
We have seen earlier that food is one of the major causes of aggression. This is certainly true for species having a patchy distribution of available food. For grass eaters, there need not be food-related aggression since the food is abundantly and widely distributed. For large carnivores a successful kill becomes a focus of intense competition and aggression. Frugivores also have a patchily distributed food although competition and aggression may be somewhat less intense than carnivores. On a food patch there is a complex interplay of aggression and food intake, and it is not a coincidence that common signal molecules such as serotonin are involved in regulating both the processes [11, 19]. We can visualize four possible scenarios on a food patch.
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1.
There is competition and the opponents are weaker than you: Here there are clear advantages of being aggressive. You can drive away others and eat. Serotonin is expected to be low in this case. However, as you eat sufficiently, serotonin levels rise and aggression levels come down. At this stage, the competitors will become more active once again, and you retreat since your desperation for food decreases and you want to avoid risk of getting injured unnecessarily. Therefore you suppress aggression by increasing serotonin, and this suppresses hunger as well. This process ends up in moderate eating.
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2.
There is competition and opponents are stronger: You need to suppress aggression by increasing serotonin and thereby partially suppressing hunger too. After waiting for variable time, you might get some share of the food, but you are unlikely to overeat.
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3.
No competition: As a rare chance, you have no competitors on the food patch. This is a rare opportunity, and you should take maximum advantage of it. Since there is no competition, there is no need to suppress aggression. As a result serotonin remains low and satiety response is delayed. This is the only situation where overeating is possible and there is nothing wrong in overeating since such a situation is unlikely to come very frequently in a highly competitive environment. Therefore overeating response could be a natural adaptive response when food is abundant and there is no perceived chance of aggression related to food. As a result absence of aggressive competitors could have evolved a link with overeating response in species that normally have food-related aggression. It is unlikely to work the same way in species in which naturally there is no food-related aggression.
Another related factor is the risk of predators. In the wild, one has to forage for food. For species having predators, foraging often increases the risk of predators. Therefore there would be a trade-off between the nutritional benefits of foraging and the increasing risk of predation. The trade-off point is largely decided by the perceived risk of predation. If it is too high, then one should stop foraging after having the minimum intake needed for survival. Predators generally have a large home range and move over a large area. As a result, there is wide day-to-day variation in the risk of predation in any locality. When an animal perceives high risk, it is adaptive to arrest foraging efforts. When a predator is not perceived, it is equally adaptive to compensate by overeating. Thus eating behavior is intimately linked to perceived risk from predators and aggressive competitors. At a proximate level, this is mediated by aggression, anxiety, and fear signals in the brain. The list of regulators inhibiting food intake is considerably longer than that of appetite stimulators. Most of the peptides inhibiting food intake are also the ones mediating fear reactions, whereas the majority of the agents reducing anxiety responses stimulate appetite [20]. Of particular interest are the brain signal molecules, α melanocyte-stimulating hormone (α-MSH) [21–23], histamine [24–28], and cocaine- and amphetamine-regulated transcript (CART) [20, 29, 30], all the three being related to fear, anxiety, and risk avoidance on the one hand and decreased food intake on the other. Inhibition of histaminergic pathway is suspected to be important in hyperphagia and obesity [31]. It is very likely therefore that signaling by CART and histamine in the brain has evolved to fine-tune the trade-off between energy gain by foraging and predator or competitor aggression risk increasing with foraging.
When a higher risk is perceived, higher levels of these signals are generated which will reduce foraging attempts and thereby food intake. Appetite control is a complex interplay between short-term metabolic signals such as insulin, long-term signals such as leptin, and behavioral signals such as CART and α MSH. The action of leptin is at least partially CART dependent, and CART levels are fine-tuned by perceived fear and aggression. It has been shown that the difference between diet-induced obese rats and diet-resistant rats (those that fail to become obese even when kept on a high-fat diet) lies in the CART and α MSH levels in the hypothalamus [32]. If CART and α MSH levels are low, food intake regulation fails and animals become obese. This may be the key to behavior-induced suppression of appetite. On the other hand if an animal is starving, or if leptin signaling is low indicating little stored energy, CART levels are depleted [33]. This impairs the fear response, and the animal is prepared to take greater risk for foraging. It is unlikely to be a coincidence that molecules involved in anxiety are also involved in appetite control. The leptin, CART, and α MSH interactions appear to have evolved in such a way to achieve an optimum trade-off between the metabolic demands of the body and the risk associated with foraging for food. In order to understand the interaction of these molecules in appetite regulation, it is necessary to know what is an optimum trade-off of food intake driven by perceived fear. It is necessary to make it clear at this point that this fear is necessarily physical. We have differentiated between soldier and diplomat anxieties in the last chapter whose physiological effects are expected to be different. Histamine, CART, and α MSH are shown to be driven by physical anxiety. So far, there is little data showing that diplomat anxiety triggers the same response, but this needs further investigation. We will continue with physical fear and anxiety in the discussions below.
The effects of predator fear and aggression anxiety can be incorporated in a formal model that gives good insights into what optimum food intake is from an integrated ecological and metabolic point of view. The model begins with the relationship of net energy intake per unit time such as a day and the evolutionary fitness contributed by this. In calculating net energy intake it is assumed that the energy expenditure in foraging, i.e., obtaining the food, is already subtracted. This relationship is most likely to go in a saturation curve with a maximum fitness that we will assume to be in unity. It is much like the famous Michaelis–Menten curve that every biochemistry student learns in the first year, with two differences. One being that it does not start from the origin. There is some basic energy needed for maintenance above which it starts contributing towards reproductive fitness. The other is that at very high intake the fitness may actually come down. Ignoring the latter for the time being, the curve will look as in Fig. 11.1. An equation for this curve can be written by adding a constant for maintenance energy I 0 to each of the terms in a Michaelis–Menten type of equation:
Since animals have to forage for food and the risk of predation or injuries from aggressive competitors increases with time spent in foraging, the risk will be positively correlated with energy intake. This is shown in the figure by the straight line with a slope c. The optimum food intake would be one at which the difference between the benefit and the risk is maximum. This happens at a point along the fitness curve where the slope becomes equal to c. Since the derivative of a curve is its slope, the optimum food intake I opt is obtained when
This is the optimum food intake per day that maximizes the net evolutionary fitness. It can be seen from Fig. 11.1 that c, the risk of foraging, is an important determinant of optimum foraging and thereby food intake. A small decrease in c can cause a disproportionately large increase in I opt. An assumption behind the model is that evolution will fine-tune the homeostatic mechanisms of the body to achieve this optimum. This is a calculation for a short-term optimum assuming that the animal has no reserve food. If there is a reserve biomass M, a part of M can be made available per day if the animal is undernourished on a given day. As a result the curve will shift to the left by a difference I m where I m is the reserve fat that can be mobilized in a day. This itself would be a saturating function of total body fat with an upper limit j. If the curve shifts to the left, the optimum food intake would also shift to the left. Thus there is a feedback from energy stores to food intake. Food intake I will in turn affect M by the well-known energy balance equation,
where a is the energy consumption per unit body mass for maintenance (basic resting metabolic rate) and b is energy expenditure related to activities other than foraging. The three equations above make an integrated dynamics, and it can be easily seen in this dynamics that a stable M is reached very soon and this steady state M is decided by the parameters of the equations namely, a, b, c, and j. Figure 11.2 shows the relative effects of changes in the four parameters. It is interesting to note that b, which represents physical activity or exercise, has the least effect on body mass. Fat oxidation rate is the most effective fine-tuner, whereas foraging risk and basic metabolic rate per unit body mass have almost the same effect.
The difference between the classical energy balance equation and our model is that we have incorporated the feedback effect of M on I. It is also important to note that in our model c, the risk or the nonenergy cost of obtaining food is one of the most important determinants of food intake. In nature c is not constant and varies according to seasonal cyclic changes such as vegetation, visibility, migration, as well as purely stochastic factors. As a result individuals need to adjust the trade-off point between benefit and risk accordingly. They would eat more when predator risk is less and vice versa. Since long-term feedbacks in terms of effects of M are there in the system, the average c decides the mean M. However, what could we expect if the mean c changes? With decreasing nonenergy costs of food, there would be a dramatic rise in M. This is not a failure of homeostasis; instead, the desired set point of homeostatic regulation is shifted to the right, and the body has programs hardwired to attain the shifted desired level. This is one possible reason for the apparent “failure” of the homeostatic mechanisms in regulating obesity. This model suggests that this can happen when food-related risks disappear. This is the first level of ecological optimization of food intake that we will call risk optimization control. In the case of animals that naturally have high risk associated with foraging, removal of the risk will lead to obesity if food availability is not limiting.
What about species that naturally have a very low mean c? This may happen for animals such as elephant or rhino that are almost free of predators and food-related aggression. Many large ungulate species of the savanna that have predators but do not have a safe predator-free refuge will also have a small c since the difference in predator risk during foraging and non-foraging is not very large. In such animals c is unlikely to be important in the evolution of homeostatic mechanisms. Here we would expect that the deleterious effects of large M would shape the homoeostatic mechanisms to regulate food intake to keep an optimum M directly. This can happen by the short-term and long-term metabolic signals mentioned above. The short-term regulation works through gut and pancreatic signals including insulin, whereas long-term regulation works through leptin and other adipose-generated molecules. Let us call it metabolic regulation. In species where the behavioral regulation is frequently triggered by predation and aggression, metabolic control would not be needed most frequently. As a result metabolic control may not evolve to be strong. Moreover there is a reason why metabolic control will directionally evolve to be weaker. If predator risk is high, the behavioral optimum is always lower than the metabolic optimum. This will eventually lead to loss of weight. However predator risk is variable. Therefore whenever no risk of predator or aggressive competitor is perceived, an animal can make up by eating more. For such a compensatory hyperphagia to work, the metabolic regulation needs to be weaker and overridable. It appears necessary therefore that in species in which behavioral control of food intake prevails, metabolic control would evolve to be weaker and surmountable. The environmental cue for overriding the control mechanisms would be absence of fear and aggression. The mediator of this overriding is likely to be downregulation of CART, which can weaken the action of normal food intake regulators such as leptin.
In species where behavioral control never works in its ecological setting, metabolic control would be strong and almost infallible. A testable prediction of this would be that animal species that have neither predators nor food-related aggression will not show a tendency to become obese even when food availability is high. A possible example is elephants. So far, I have not been able to put together elephant morphometry data from various sources along with dietary and ecological data for each individual elephant. But such data do exist scattered over various sources, and therefore, the prediction is testable. Having spent a few years in the company of wild and captive Asiatic elephants, I have an impression that morphometric standards in elephants are highly reproducible and the variance around them is relatively small both in the wild as well as in captivity. Although a number of elephants in captivity are given food with high sucrose, other soluble carbohydrates, and oils, they do not drift substantially from the morphometric standards. There is no published data on obesity in elephants, but I have not seen rapid and uncontrolled weight gains in elephants, comparable to what happens to carnivores or primates in captivity. Obesity in carnivores and primates in captivity and occasionally even in the wild or semiwild state is not uncommon. Zookeepers know that the amount of food provided for carnivores needs to be carefully controlled, but excess of food does not matter so much for grass eaters. It does matter for primates that are predominantly fruit eaters. The distinction may not lie in diet since elephants in captivity are given fruits, sugarcane, and coconut which do not appear to lead to obesity.
The ancestral humans had a patchy distribution of food, whether scavenged, hunted, or gathered; therefore, a carnivore-like response is more likely to have evolved. Human ancestors had predators until recently, and the cultural development of death rituals is likely to have discouraged predators according to one theory [34]. Speakman also considers predators to be responsible to arresting obesity for a different reason [35]. Obese individuals are likely to be more susceptible as well as more attractive to predators, and therefore, evolution would strengthen energy regulation mechanisms for species with predators. This reasoning is compatible with our model with some differences. Speakman thinks that after being free of predators, a process of genetic drift increased obesity in humans. The above model says that being free of predation would reduce the risk of foraging c and thereby increase the optimum body weight. In human ancestors food-related aggression was also present to varying degrees almost throughout history. Food could have been one of the causes of wars and fights throughout human history. In hunter-gatherers foraging often involved driving away competing animals. Protection of crops, stored grain, and livestock from wild animals is a major aggressive activity even today in areas of residual wildlife. However, in modern society, an urban consumer is almost never exposed to food-related aggression. Therefore the c of the model in today’s society is almost zero. This makes the risk optimization-related food intake regulation driven by CART and histamine completely ineffective. We have seen above that in species where behavioral regulation is important, metabolic regulation would be partially degenerate. It is possible therefore that the human species is primarily a behavioral regulation species, not a metabolic regulation species. In modern lifestyle behavioral regulation factors have vanished, and metabolic regulation was always weak. Therefore energy intake regulation is failing in modern life.
Another result of the model is of considerable interest to us since it matches very well with reality. The parameter I m which reflects the maximum energy that can be made available for the body from the stored energy per unit time is an important determinant of obesity. The prediction of the model is that the basal metabolic rate or fat oxidation rate has similar effects on M but exercise has a much smaller impact on obesity (Fig. 11.2). For a long time a popular belief has been that obese and non-obese individuals differ in the basal metabolic rate, i.e., a of this model. Although the model supports this, evidence does not appear to give unanimous support to this idea [36, 37]. Exercise is always advocated as an antidote on obesity, but according to the model, exercises have limited efficiency in reducing body weight. Individuals differ dramatically in their tendencies, some rapidly gaining weight in spite of exercises and others remaining lean without any exercises. More robust data relate fat oxidation rate with obesity. Low rate of fat oxidation appears to be the main difference between people of obese versus lean tendencies [38–42]. The traditional models of energy balance equation or homeostasis have not been able to incorporate the rate of fat oxidation in their models which our model does effectively.
The rate of fat oxidation is also aggression dependent by a mechanism that is quite well worked out. One of the main mechanisms that cause rapid lipolysis is sympathetic stimulation [43–45]. Sympathetic stimulation is triggered in a flight-or-fight response. We have seen while discussing stress that flight or fight is typically a soldier response. Moreover the sympathetic activation is received by the adipose tissue by β adrenergic receptors [43], and the formation and distribution of these receptors is testosterone dependent [46]. Therefore testosterone arrests visceral fat accumulation [47–49]. Typical diplomat personalities have lower levels of testosterone [50], and therefore even if they happen to activate a sympathetic response, it does not lead to lipolysis as much as in a high-testosterone individual. Sympathetic nerves also appear to regulate the proliferation of preadipocytes since denervation of fat pad has been shown to increase the number of preadipocytes and weight of fat pad in rats [51–54]. This is another possible mechanism by which absence of aggression could facilitate obesity.
This also partially explains the distribution of fat in the body. The question as to why the distribution of fat is different in different persons remains largely unanswered although the differential effects of fat accumulation in different parts of the body are well known [55–61]. Since the same blood carrying the same concentration of fatty acids and hormones is circulated throughout the body, why does fat deposit in one adipose depot more than another? The answer certainly does not lie in hormones and metabolites since the same blood carrying these molecules circulates to all fat depots. A possible logical answer is nerve supply. Different adipose depots are innervated differently [62]. For example, abdominal visceral fat has an intricate sympathetic supply through the vagus nerve. Vagus does not innervate subcutaneous fat [63]. As a result, a combination of testosterone and sympathetic activation, both triggered by aggression, keeps on mobilizing visceral fat. This is how aggression changes the ratio of visceral to subcutaneous fat. There is no wonder then that sumo wrestlers have little visceral fat in spite of having large amount of total body fat. This is true as long as they practice wrestling. On retirement, their fat may go visceral rapidly, and they might become insulin resistant [64].
This is not the first time that a model is being used to explain obesity. However, the uniqueness of this model is that for the first time it brings together three different concepts, namely, energy balance equation, homeostatic control on energy intake, and foraging theory. Somehow, researchers had so far either not recognized the importance of bringing the three concepts together or had failed to do so, the former being the most likely case. But now we appear to be closer to the reality of how and why the homeostatic mechanisms evolved under ancestral conditions are failing in the modern lifestyle. There are a few more implications of the model. Both predation and aggression are population regulation mechanisms. Removal of predation increases prey population [65, 66], and reduced aggression also enables higher population densities [67]. Therefore if the predatory pressure as well as intraspecific aggression is suddenly removed in a species, there can be anticipation of crowding and thereby decreased food availability. Therefore it would be adaptive to increase thrift on removal of a fear signal. It is possible that because of this reason too, evolution might have built in a program to increase food intake on loss of predator fear and signs of population growth. For wild populations crowding is almost invariably associated with increased food competition, exerting a negative feedback control that would effectively arrest obesity. The problem with the current human condition is that the natural fear signals associated with foraging are absent, eating has lost its connection with foraging associated risks, there is increasing crowding which could be stimulating a thrift response, and there are no signs of reduction in food availability that naturally accompanies these conditions. If vanished risk of predation and food-related aggression anticipating crowding is at the root of these changes, it is logical then that reproductive strategies also change in this situation, and the reduction in fecundity with thrift is likely to be a response evolved for this reason.
Obese persons are often known to be lethargic. In the light of the model described above, it can be easily seen why lethargy is adaptive. Lethargy is not an inevitable result of storing fat. Extra energy makes one lethargic is paradoxical. Lethargy is more likely to have evolved as a feedback mechanism in energy homeostasis and risk minimization. If an individual has sufficient stored energy, it is unprofitable to keep on foraging and being exposed to risks. So lethargy increases the chances of survival in a wilderness environment by reducing foraging-related risks. Simultaneously lethargy decreases foraging and thereby food intake, contributing to effective feedback in energy homeostasis. If fat causes lethargy then the foraging feedback is almost infallible. It is only in the modern human society that feeding is detached from foraging. As a result, in spite of being lethargic, food intake may not be affected. In our hunter-gatherer ancestors the lethargy feedback must have been very effective.
There are other possible means by which evolved responses might be leading to overeating in modern life. There are reward centers in the brain that are important in regulating food intake [17, 68]. In the wilderness, the most common natural rewards activating reward centers are food, sex, and presumably occasional social rewards such as achieving dominance. The reward centers when activated by getting a sumptuous food reward initiate a satiety pathway and stop food intake [17]. The alternative rewards also trigger the same reward centers, and in most of these rewarding situations, temporary suppression of hunger is essential. For example, while having sex, hunger would be an interfering drive, and therefore, reward center activation by sex should also give satiety signals. Such an adaptive loop must have evolved. However, it is also important to put a limit on such nonfood reward suppression of hunger. So if the reward center is suppressed by nonfood rewards too often, there would be a temporary desensitization of the reward center. This would bring back the hunger response and, as a result of the partial desensitization, result into compensatory overeating.
It is very likely that this is happening in today’s monetary economics. In modern life money is perceived as a reward and is perhaps the most common nonfood reward of modern life. Money keeps on stimulating the reward center repeatedly. Neurobiological studies have shown that the brain areas activated by getting prize money and those activated by a sweet taste are the same [69–73]. Experimental psychologists have also demonstrated that there is a neuronal cross talk between food and money such that hunger affects food-related decisions and thinking about money affects food intake [74]. It is very likely that repeated activation of the reward centers by money over a long time results into chronic desensitization of the reward center resulting into habitual overeating. A downregulation of dopamine signaling is indeed demonstrated in obesity [75, 76]. There is a link with aggression here too. Aggression is linked to the dopamine reward pathway, and aggression increases dopamine activity [77]. This is perhaps one more mechanism by which aggression arrests obesity.
In an attempt to test the hypothesis that nonfood rewards affect obesity, we carried out a survey of people working as full-time cashiers in different types of organizations. Some of the cashiers were owners or partners in the endeavor, and others were only salaried cashiers. Since they were all doing physically very similar jobs, the levels of on-job physical activities could be assumed to be similar. There was an obvious difference between the reward values of the money being handled. For the owner cashiers the money had a strong reward value, whereas for the salaried cashiers, the reward value of the money they handled would have been at the most weak. The survey showed that owners had a greater BMI, waist circumference, and waist-to-hip ratio than salaried cashiers. Also, two other variables, namely, the amount of money handled per day and the duration of doing a cashier’s job, were also positively related with obesity, but the duration of doing a sedentary job and self-reported exercise or absence of it was not. The most important part of the study, in spite of its small sample size, was that this single behavioral factor explained about 20% of the population variance [16]. This is interesting on the background that genome-wide fishing for obesity-related genes has so far explained not more than 2% of population variance in obesity [78–83]. Yet researchers as well as laymen have a strong belief that obesity is genetic and are reluctant to believe that behavior could be a more important determinant of it.
The purpose of the above discussion was not to claim to have identified all factors and mechanisms responsible for overeating but to highlight that neurobehavioral processes are perhaps the most important contributors to it. Unless the neurobehavioral mechanisms that have evolved to face social and ecological conditions are clearly identified and addressed, all efforts to mitigate obesity are unlikely to work.
One more important point to be recognized before ending a discussion on fat as energy storage is that energy is not only stored for future starvation. Energy storage is needed for a variety of other functions including migration, breeding, maternal care, and immunity. This aspect is insightfully discussed by Pond [1], and I will avoid repeating it.
In a nutshell, all components of the energy balance, namely, energy intake, lipogenesis, lipolysis, and fat distribution, are affected by personality and behavior, particularly physically aggressive behavior and exposure to physical risks. We will see below that most other functions of fat also have significant evolutionary and behavioral components.
2. Fat as insulation: People in extreme cold weather need a layer of subcutaneous fat that acts as an effective insulator and prevents heat loss from the body. As a result ethnic groups evolved in colder climates for sufficient number of generations should have a more peripheral distribution of fat. Tropical people, on the other hand, want to dissipate heat as rapidly as possible and therefore have little subcutaneous fat. When these people accumulate fat, it tends to be abdominal. Not surprisingly colder climate people such as Tibetans do not show much tendency towards central obesity [84]. Although this looks more of a common sense, analytical inputs to examine this question are rare [1].
3. Fat as an impact buffer: The important function of fat as a subcutaneous impact buffer and its relevance to aggression and to metabolic syndrome has almost never attracted the attention of obesity researchers. A subcutaneous layer of fat takes a major part of the impact in a blunt trauma [85, 86] and prevents or reduces injury to inner parts. Therefore subcutaneous fat is adaptive for an aggressive individual. This cushion effect is purely physical and may not involve any hormonal or biochemical processes. Therefore if subcutaneous fat is mainly evolved for this purpose, it need not be metabolically or endocrinologically active. Nevertheless, what drives fat subcutaneously is an important question, and neuroendocrine mechanisms are likely to have evolved to drive fat subcutaneously in anticipation of injurious impacts. One possibility is that the dissolution of visceral fat by a combination of testosterone and sympathetic activation is sufficient to deposit the same fat subcutaneously. But perhaps more complex signaling involving impact sensing by the subcutaneous nerves may be at work.
Both wrestlers and body builders need strong muscle. However, there is a major difference in the requirement and function of fat in the two. For wrestlers, subcutaneous fat is helpful to tolerate blows, punches, kicks, and other impacts that they receive frequently. Body builders, on the other hand, should make all-out efforts to avoid subcutaneous fat, because they want to make every muscle curve conspicuous and a layer of fat over it will mask them. As a result body builders will not be able to make good wrestlers, and wrestlers will not show every minute muscle although they need to be muscular. They signal strength by overall body size and muscle mass but not by showing every curve. It would be adaptive for a fighter to have his fat distribution more subcutaneous and less visceral, but the mechanisms that do so are only partially known.
4. Fat in neuronal development: As opposed to muscle tissue which is chemically predominantly proteins, the brain and nervous tissue is predominantly fat. The brain is about 70% fat by dry weight. Lipids are therefore important in the development as well as maintenance of brain and peripheral nervous system. Lipid metabolism and certain classes of lipids, such as omega three fatty acids, are particularly known to help brain development, maintenance, and function [87–92]. It would be logical to expect therefore that a muscle-dependent lifestyle will be associated with a protein anabolic metabolism and a brain-dependent lifestyle with a lipid anabolic bias in the metabolism. T2D is known to be associated with a slow decay of muscle strength and build up of fat, but a quantitative model of this shift in bias or fine-tuning of metabolism to suit the requirement has not been attempted so far.
In an interesting study, rats malnourished for several generations showed a highly insulin-resistant phenotype. In these rats the relative brain weight was substantially higher, and the absolute brain weight was marginally higher than controls. Accompanying the brain-biased development, they had a higher fat content, although total body weight was reduced [93]. Greater percentage of fat in malnourished individuals is a contradiction for the energy-centric view but not for the wider view that understands the greater relative importance of brain during muscle-wasting malnourishment and role of fat in brain.
Brain development specifically needs certain classes of fatty acids which are not synthesized in the body and have to be supplied by dietary intake. In a diplomat lifestyle this requirement might be higher. Now, if diet is deficient in these fatty acids, brain will encourage fat intake until the demand is fulfilled. This will be less intense in a soldier life if the requirement is relatively less. Deficiency of omega 3 fatty acids may thus interact with diplomat strategy to result in obesity.
5. Fat as a modulator of immunity: The adipose tissue secretes a number of proinflammatory and anti-inflammatory signals, which modulate the dynamics of the innate immune cells. The role of adipose tissue in initiating a state of low-grade chronic systemic inflammation is well known. We have seen this in sufficient details in Chap. 8. The prevalent paradigm only blames the adipose tissue for the state of this systemic inflammation which underlies many of the pathological consequences of obesity and T2D. What is less appreciated is that the same adipose tissue also gives anti-inflammatory signals too. The adipose-derived molecules adiponectin and sFRP5 have significant anti-inflammatory effects [94, 95], and the role of visfatin is debated [96–99]. This means that the normal role of adipose tissue in the fine-tuning of innate immunity must be more subtle and balancing. In obesity-related disorders there is a suppression of the anti-inflammatory signals and overexpression of the proinflammatory ones [100]. Why this happens is not clearly known. A possible player in this shift of balance is likely to be testosterone, which is known to suppress the systemic inflammatory response. It is likely therefore that in the absence of physical aggression, the balance between pro- and anti-inflammatory signals is shifted. This is a likely parsimonious measure in anticipation of a less injury-prone lifestyle as discussed in Chap. 8. It is also likely that adiponectin, which is a marker of r reproductive strategy, is suppressed by crowding, and this leads to the shift in immune balance as well as greater accumulation of fat. Both are speculative at present but provide a logically sound explanation to the shift of balance. In contrast there does not appear to be any alternative explanation coming from the classical school that works at both ultimate and proximate level.
Adipose tissue has another interesting connection with lymphoid tissue. Most lymph nodes are physically intimately associated with small masses of adipose tissue. These are likely to be important energy sources for activated lymph nodes [101–105]. Lymphocytes appear to prefer fatty acids as fuel sources [103]. It is possible therefore that the dispersed adipose masses associated with lymph nodes have a critical role in acquired immunity. However, in an obese person, the fraction of the total adipose tissue in association with lymph nodes is quite small as compared to total body fat. Therefore it is unlikely that proneness to obesity may have evolved primarily to serve an immune function, although this is interesting hypothesis and needs to be investigated [106].
Summarily it appears that fat has a negative role in innate immunity, in the context of injuries, but a positive role in acquired immunity which is more important in infectious diseases. This is compatible with diplomat life which is less prone to physical injuries but presumably more to certain types of infections such as respiratory infections. But our understanding of the interaction between adipose and immune cells is still in infancy. More efforts are needed to understand the normal role of adipose tissue in immune modulation before we understand the pathological role of excess adiposity. This is another major area of unanswered questions.
6. Fat as a modulator of sexual, reproductive, and social behavior: The interaction between obesity, particularly abdominal obesity, and sexual and reproductive performance is well documented, but the mechanisms linking the two are not yet very clearly known. Abdominal obesity is associated with reduced sexual desire [107], loss of libido [108], substantially compromised sexual attractiveness [57], as well as reduced fertility [109]. On the other hand fat is not always bad for sex, fertility, and reproduction. In fact an optimum level of fat is essential for fertility [110].
As fat affects sexual and reproductive function, we can expect that a change in sexual and reproductive strategy, capacity, or function would in turn affect fat deposition. There is some evidence in this direction. Castration has been shown to induce obesity in rats [111]. Also, adiponectin, whose primary function is to facilitate r reproductive strategy, is differentially associated with body fat distribution [112, 113] and is likely to be a mediator rather than an effect of central fat distribution. We have seen time and again that testosterone, a major player in both sex and aggression, has important effects on lipolysis. Sex and reproductive life therefore is evidently a major player in the dynamics of adiposity.
7. Fat as a social signal: Until recently, the scientific community had almost entirely ignored this important social function of the fat tissues and particularly fat distribution in the body. It is a relatively recent suggestion that fat serves as a social signal [1] signaling recent nutritional history [114] but more important than that, signaling biological as well as personality characteristics [115]. The female body form, particularly waist-to-hip ratio, is believed to reflect fertility [1]. In males the role of central obesity is of particular interest. It is well known that central obesity is a better predictor of obesity-related disorders and that visceral fat is metabolically more active than subcutaneous fat. If the metabolic and behavioral role of adipose tissue and fat distribution as a social signal coevolved, it makes sense that metabolically active fat should be deposited abdominally. Subcutaneous fat is difficult to differentiate from muscle mass and therefore can be of little signal value. Abdominal fat, on the other hand, changes the body proportions substantially and therefore stands out quickly. For a person approaching from a distance, body proportions can be perceived much before facial expressions. Further the theory of honest signaling or the handicap principle states that only costly signals can be evolutionary-stable honest signals [116–118]. Fat has a high energy cost, and therefore, signaling by fat can evolve to be honest.
People are known to make personality judgments very quickly based on facial characters. The judgments are made instantaneously, and thinking for a longer time appears to make little difference [119, 120]. Furthermore subjects may not be able to state how they made these judgments suggesting that the judgments are not always made at a conscious level. Most of the earlier studies are restricted to facial features, whereas we showed in an earlier study that when faces are not shown, people make use of body proportions to read personality characteristics. Faceless drawings of three male body forms, namely, lean, muscular, and feminine, each with and without abdominal obesity (Fig. 11.3), were shown in a randomized order to a group of 222 respondents. A list of 30 different adjectives or short descriptions of personality traits was given to each respondent, and they were asked to allocate the most appropriate figure to each of them independently. The traits included those directly related to physique, those related to nature, attitude, and moral character, and also those related to social status. For 29 out of the 30 adjectives, people consistently attributed specific body forms. Based on common choices, the 30 traits could be clustered into distinct “personalities” which were strongly associated with particular body forms (Fig. 11.4). A centrally obese figure was positively associated with the adjectives lethargic, greedy, political, money-minded, selfish, and rich. It was also negatively associated with physical aggression and swiftness.
The respondents were asked whether they could reason out their choices of figures for each trait. For physical traits, the proportion of people choosing with reason was significantly higher as expected. For traits related to nature, attitude, moral character, and social status, there was a high proportion of “just felt like” responses. However, the high level of concordance showed that these responses were highly nonrandom. This indicates that most of these choices could have been made at a subconscious level, and although respondents largely converged on their choices, they were not able to give explicit reasons. In a further and yet unpublished study, the same questionnaire was used on a group of university students in Germany and in the Caribbean islands. Although the precise meaning of the set of adjectives associated with the figures could be somewhat different in different cultures, there was some convergence in the responses. The negative association between central obesity and physical aggression was consistent across cultures (Fig. 11.5). There appears to be a cross-cultural consistency in many if not all associations of body form with personality characteristics (Fig. 11.6). An interesting inference of this study is that people already seem to know what I am trying to argue in several chapters of this book. Most researchers may not have yet suspected that there is a consistent negative association between obesity and aggression. But cross-culturally people seem to know this relation subconsciously.
If fat indeed evolved to work as a social signal, in addition to its metabolic and other functions, it explains why there is a difference in male and female abdominal fat distribution. We have seen earlier that food is one of the main natural causes of aggression. A hungry individual having greater desperation for food is more aggressive, and one with a full stomach avoids aggression. The link is mainly through serotonin. It is possible therefore that instinctively a full stomach is taken as a signal of nonaggression. When a large-sized and dominant animal is at a kill, others are most likely to keep distance. But when the dominant animal has a full stomach, the waiting animals would start advancing since there is less risk of aggression from the dominant individual. The dominant animal would generally retract from the kill at this stage. A distended stomach is the most likely signal that brings about this change. This is frequently observed during the fights between carnivores and scavengers over a kill. Abdominal fat mimics the same signal and gives the same message. This is true for both genders, but for females there is an additional context for controlling aggression. That is pregnancy. A pregnant female needs to be risk averse and therefore nonaggressive. Perception of this signal also appears to have evolved. Female abdominal obesity therefore mimics pregnancy. This is likely to be the reason why male (android) and female (gynoid) abdominal obesity differs in form (Fig. 11.7) although biochemically it is accumulation of fat. Male obesity is more in the upper abdomen which resembles a full stomach, whereas female abdominal obesity covers the lower abdomen and resembles the appearance of pregnancy. Apart from aggression, fat distribution is also likely to signal other social and behavioral strategies and status. They may include fertility [121–124], parity status, or cognitive abilities [61, 125].
Appreciating the multiple functions of fat in the body itself resolves many unresolved paradoxes and questions. So far, a large volume of research is centered on mainly a single function of fat, that of energy storage. All others have been looked at as pathophysiological by-products of excess fat. We need to come out of this thinking trap and appreciate that normally lipids and the adipose tissue have a wide variety of functions in the body [1, 2] and no single function can be studied in isolation. One of the common threads linking the different functions appears to be aggression or the absence of it. However, this is not intended to deny other links. The multifunctionality makes it likely that a change in any one or more of them can have an effect on fat accumulation and distribution and that will inevitably alter other functions too. For example, castration in rats and rabbits is shown to increase obesity [111] and insulin resistance [126]. A change in one’s social role may induce changes in fat distribution with associated metabolic effects. Owing to the heavy energy balance bias in obesity research, so far we have left so many loose ends and information voids. Unless obesity research adequately covers the virgin areas and information voids, our understanding of obesity and its effects on health can never be sound and useful.
What is the exact nature of association of obesity and type 2 diabetes? Historically there have been arguments in both directions. Obesity causes insulin resistance, and insulin resistance or a diabetic tendency in some form causes obesity. We are now leading to a novel viewpoint that neither obesity nor insulin resistance is the causal agent of the other. It is behavioral syndrome that precedes both, and the neuroendocrine processes driven by specific behavioral strategies initiate pathways leading to obesity on the one hand and T2D and associated pathophysiology on the other. We have already seen a number of different pathways by which behavior affects hyperinsulinemia, insulin resistance, systemic inflammation, and angiogenesis dysfunction. Behavior could be related to the origins of obesity by a number of possible links.
An interesting observation that provides additional support to the neurobehavioral hypothesis is that it is relative rather than absolute obesity that appears to be related to insulin resistance. There are four reasons why I say relative rather than absolute obesity is more closely related to insulin resistance parameters:
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1.
The obesity–insulin resistance relationship between high-obesity countries versus low-obesity countries: The prevalence and intensity of the obesity epidemic in the USA is substantially greater than that in India. A comparison of data from the two countries shows that in the USA where obesity is more prevalent, the slope of the regression line between obesity parameters and insulin resistance parameters is substantially lower than that in India. In other words where obesity is less, people appear to become diabetic at lower BMI. Where obesity is more they need greater BMI to become diabetic.
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2.
A similar pattern exists between rural and urban populations within a country. Obesity is generally more prevalent in urban areas where the slope of the line is less than that in rural areas where fewer obese individuals are found (Fig. 11.8). This pattern has been noted across many countries [127, 128].
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3.
The mortality rate is a U-shaped function of body weight. There is an optimum BMI that ensures maximum health. In the USA over 100 years, the mean BMI increased substantially, and along with it the optimum also shifted substantially towards the right (Fig. 11.9).
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4.
In an unpublished rat experiment in Pune, a colony of chronically malnourished rats showed high levels of insulin resistance. These rats, which we will call Hardikar rats after the experimenter, were smaller in size but had larger brains. In comparison with normal rats, the Hardikar rats had lower body weights and higher insulin resistance, leading to a negative association between body weights and insulin resistance between groups. But within both the groups, there was a positive correlation between body weights and insulin resistance [93]. The malnourished group that had lower mean body weight appears to become insulin resistant at much lower body weight.
All the four observations support my speculation that it is relative obesity that correlated with insulin resistance than absolute obesity. So even in a group that has lower mean obesity, the relatively more obese are more susceptible to insulin resistance. If obesity had a direct connection with insulin resistance metabolically, then only one’s own obesity should have mattered. What appears to matter is one’s obesity in comparison with others in the group. This can happen only if perception of relative obesity is involved somewhere. An individual’s perception of where he stands among the obesity ranking of the society could be affecting the metabolic actions of obesity. This is consistent with the observation above that people perceive obesity as a social signal. This is an indication that obesity has a perceptional and neuronal connection rather than a direct metabolic connection with insulin resistance.
There is one more piece of evidence that relates perception of calories to metabolism, this time from Drosophila. Caloric restriction is shown to increase life span in widely different species, and Drosophila is not an exception. In an interesting experiment the smell of food was shown to partially reverse the effects of caloric restriction [133]. There is a similar finding in Caenorhabditis elegans too [134]. In both the experiments actual intake of calories was not necessary to bring about the metabolic change that marked the reversal of the effects of caloric restriction. Perception of calories was sufficient. These experiments also indicate the involvement of neuronal mechanisms in mediating the metabolic effects of calories.
If relative obesity matters, relative muscle mass and perhaps more importantly muscle strength should matter even more. There are no studies in this direction. But this could explain certain perplexing known patterns. South Asians who migrated to Western (Caucasoid-dominated) countries show a very high prevalence of T2D which is not matched by the urban populations of their native places. Even if we assume that there is a genetic difference across ethnic groups, why should the ones who migrated to Western societies be more susceptible? A plausible answer worth investigating is that South Asians have a lower proportionate muscle mass as compared to Caucasoids. In their own country they are not inferior in muscle mass, but when they migrate to another society that is dominated by an ethnic group having larger muscle mass on an average, their perceived ranking in muscle strength is low, and that may matter in shaping behavioral and metabolic strategies.
Beginning from Chap. 5 till now, we have seen a large number of factors and processes linking obesity to insulin resistance. In this light obesity has a strong association with insulin resistance without being an inevitable biochemical cause of insulin resistance. We can strengthen this statement using an alternative method. It can be seen that increasing food intake in the network model (Appendix II) does not result into all components of metabolic syndrome if behavioral changes are blocked. On the other hand, if behavioral changes are primary, then through altered adiponectin, leptin, and insulin-related mechanisms, the system becomes sensitive to diet-induced obesity.
I will briefly recapitulate now the origins of obesity and its links with insulin resistance syndrome according to the new line of thinking:
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(a)
Overeating and obesity are not possible as long as the food intake regulation mechanisms of the body are functional. Therefore the origin of obesity does not lie in greater availability of food or macronutrient composition of food. It lies in disruption of the regulatory mechanisms.
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(b)
In species that have predators or food-related aggression, behavioral regulation of food intake works more frequently than metabolic regulation; also in these species, metabolic regulation evolves to be overridable by behavioral cues. Human species belongs to this category; therefore, the key to obesity lies in behavior rather than metabolism. Absence of food-related aggression and freedom from predation may deplete the levels of anxiety-related peptides which are crucial in food intake regulation.
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(c)
A transition from soldier to diplomat behavior is accompanied by hyperinsulinemia, and sustained hyperinsulinemia needs to be accompanied by weakening of food intake regulation since insulin itself is anorectic. A hyperinsulinemic individual will die of undernutrition unless the food intake regulation mechanisms are partially disrupted. Sustained hyperinsulinemia must also be accompanied by insulin resistance, without which hypoglycemia will threaten life. Therefore insulin resistance and propensity to be obese have a common origin.
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(d)
Other concurrent changes accompanying soldier to diplomat transition are testosterone deficiency, lipid anabolic bias, and early activation of central fatigue mechanisms. These change body composition and pattern of fat deposition.
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(e)
Obesity reinforces diplomat behavior by making some components of soldier behavior such as agility more difficult and also by enhancing cognitive function through the agency of leptin and cholesterol.
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(f)
Energy storage and risk-taking behavior have conflicting life history consequences, because of which obesity has evolved to be associated with decreases in risk-taking behavior, weakening the soldier component of behavior further.
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(g)
Adiponectin downregulation as an intrinsic part of K like reproduction in response to high population density or social subordination can also trigger a chain of events similar to the above.
It is no surprise therefore that obesity and insulin resistance are so tightly linked, but I would still maintain that obesity is neither necessary nor sufficient for the development of insulin resistance. This relationship of obesity with insulin resistance is different from what is traditionally perceived. Brain and behavior appear to be involved in almost every step in some way or the other. This is only a small part of the central point that I want to emphasize on a much broader scale throughout the book. It is impossible to understand metabolism without understanding behavioral ecology.
References
Pond CM (1998) The fats of life. Cambridge University Press, Cambridge
Wells JCK (2009) The evolutionary biology of human body fatness. Cambridge University Press, Cambridge
Summary of SIAM talk « Scientific Clearing House. http://sciencehouse.wordpress.com/2010/07/23/summary-of-siam-talk/
Gibbs J, Young RC, Smith GP (1973) Cholecystokinin decreases food intake in rats. J Comp Physiol Psychol 84:488–495
Pappas TN, Melendez RL, Debas HT (1989) Gastric distension is a physiologic satiety signal in the dog. Dig Dis Sci 34:1489–1493
Myers R, McCaleb M (1980) Feeding: satiety signal from intestine triggers brain’s noradrenergic mechanism. Science 209:1035–1037
Gibbs J, Smith GP (1986) Satiety: the roles of peptides from the stomach and the intestine. Fed Proc 45:1391–1395
Levin BE, Dunn-Meynell AA, Routh VH (1999) Brain glucose sensing and body energy homeostasis: role in obesity and diabetes. Am J Physiol 276:R1223
Debons A, Krimsky I, From A (1970) A direct action of insulin on the hypothalamic satiety center. Am J Physiol 219:938–943
Rodin J, Wack J, Ferrannini E, DeFronzo RA (1985) Effect of insulin and glucose on feeding behavior. Metabolism 34:826–831
Leibowitz SF, Alexander JT (1998) Hypothalamic serotonin in control of eating behavior, meal size, and body weight. Biol Psychiatry 44:851–864
Benoit SC et al (2002) The catabolic action of insulin in the brain is mediated by melanocortins. J Neurosci 22:9048–9052
Schwartz MW et al (1992) Inhibition of hypothalamic neuropeptide Y gene expression by insulin. Endocrinology 130:3608–3616
Schwartz MW, Figlewicz DP, Woods SC, Porte D Jr, Baskin DG (1993) Insulin, neuropeptide Y, and food intake. Ann N Y Acad Sci 692:60–71
Loftus TM, Maggs DG, Lane MD (1997) The adipose tissue/central nervous system axis. Diabetologia 40(Suppl 3):B16–B20
Karve S et al (2011) Money handling and obesity: a test of the exaptation hypothesis. Curr Sci 100:1695–1700
Schwartz MW, Woods SC, Porte D Jr, Seeley RJ, Baskin DG (2000) Central nervous system control of food intake. Nature 404:661–671
Munzberg H, Myers MG (2005) Molecular and anatomical determinants of central leptin resistance. Nat Neurosci 8:566–570
Duman EA, Canli T (2010) Social behavior and serotonin. In: Handbook of the behavioral neurobiology of serotonin, vol. 21, pp. 449–456
Kask A, Schiöth HB, Mutulis F, Wikberg JES, Rägo L (2000) Anorexigenic cocaine- and amphetamine-regulated transcript peptide intensifies fear reactions in rats. Brain Res 857:283–285
File SE (1981) Contrasting effects of org 2766 and α MSH on social and exploratory behavior in the rat. Peptides 2:255–260
Kokare DM, Dandekar MP, Chopde CT, Subhedar N (2005) Interaction between neuropeptide Y and α melanocyte stimulating hormone in amygdala regulates anxiety in rats. Brain Res 1043:107–114
Rao TL et al (2003) GABAergic agents prevent α-melanocyte stimulating hormone induced anxiety and anorexia in rats. Pharmacol Biochem Behav 76:417–423
Hasenöhrl RU, Weth K, Huston JP (1999) Intraventricular infusion of the histamine H 1 receptor antagonist chlorpheniramine improves maze performance and has anxiolytic-like effects in aged hybrid Fischer 344 × Brown Norway rats. Exp Brain Res 128:435–440
Privou C, Knoche A, Hasenöhrl RU, Huston JP (1998) The H1- and H2-histamine blockers chlorpheniramine and ranitidine applied to the nucleus basalis magnocellularis region modulate anxiety and reinforcement related processes. Neuropharmacology 37:1019–1032
Ookuma K et al (1993) Neuronal histamine in the hypothalamus suppresses food intake in rats. Brain Res 628:235–242
Ookuma K, Yoshimatsu H, Sakata T, Fujimoto K, Fukagawa K (1989) Hypothalamic sites of neuronal histamine action on food intake by rats. Brain Res 490:268–275
Mercer LP, Kelley DS, Humphries LL, Dunn JD (1994) Manipulation of central nervous system histamine or histaminergic receptors (H1) affects food intake in rats. J Nutr 124:1029–1036
Stanek LM (2006) Cocaine- and amphetamine related transcript (CART) and anxiety. Peptides 27:2005–2011
Asakawa A et al (2001) Cocaine-amphetamine-regulated transcript influences energy metabolism, anxiety and gastric emptying in mice. Horm Metab Res 33:554–558
Teff KL, Kim SF (2011) Atypical antipsychotics and the neural regulation of food intake and peripheral metabolism. Physiol Behav 104:590–598
Tian D-R et al (2004) Changes of hypothalamic α MSH and CART peptide expression in diet induced obese rats. Peptides 25:2147–2153
Kristensen P et al (1998) Hypothalamic CART is a new anorectic peptide regulated by leptin. Nature 393:72–76
Watve M (1993) Why man has no predator. Curr Sci 65:120–122
Speakman JR (2008) Thrifty genes for obesity, an attractive but flawed idea, and an alternative perspective: the ‘drifty gene’ hypothesis. Int J Obes 32:1611–1617
Bandini LG, Schoeller DA, Dietz WH (1990) Energy expenditure in obese and nonobese adolescents. Pediatr Res 27:198–203
Dietz WH, Bandini LG, Schoeller DA (1991) Estimates of metabolic rate in obese and nonobese adolescents. J Pediatr 118:146–149
Frisancho AR (2003) Reduced rate of fat oxidation: a metabolic pathway to obesity in the developing nations. Am J Hum Biol 15:522–532
Zunquin G, Theunynck D, Sesboüé B, Arhan P, Bouglé D (2009) Comparison of fat oxidation during exercise in lean and obese pubertal boys: clinical implications. Br J Sports Med 43:869–870
Zurlo F et al (1990) Low ratio of fat to carbohydrate oxidation as predictor of weight gain: study of 24-h RQ. Am J Physiol Endocrinol Metab 259:650–657
Rogge MM (2009) The role of impaired mitochondrial lipid oxidation in obesity. Biol Res Nurs 10:356–373
Sawaya AL, Verreschi I, Tucker KL, Roberts SB, Hoffman DJ (2000) Why are nutritionally stunted children at increased risk of obesity? Studies of metabolic rate and fat oxidation in shantytown children from São Paulo, Brazil. Am J Clin Nutr 72:702–707
Weiss B, Maickel RP (1968) Sympathetic nervous control of adipose tissue lipolysis. Int J Neuropharmacol 7:395–403
Yoshimatsu H et al (2002) Histidine induces lipolysis through sympathetic nerve in white adipose tissue. Eur J Clin Invest 32:236–241
Bartness TJ et al (2010) Sensory and sympathetic nervous system control of white adipose tissue lipolysis. Mol Cell Endocrinol 318:34–43
De Pergola G (2000) The adipose tissue metabolism: role of testosterone and dehydroepiandrosterone. Int J Obes Relat Metab Disord 24:S59–S63
Mrin P, Arver S (1998) Androgens and abdominal obesity. Baillières Clin Endocrinol Metab 12:441–451
Mårin P, Odén B, Björntorp P (1995) Assimilation and mobilization of triglycerides in subcutaneous abdominal and femoral adipose tissue in vivo in men: effects of androgens. J Clin Endocrinol Metab 80:239–243
Mårin P et al (1993) Androgen treatment of abdominally obese men. Obes Res 1:245–251
Sellers JG, Mehl MR, Josephs RA (2007) Hormones and personality: testosterone as a marker of individual differences. J Res Pers 41:126–138
Cousin B et al (1993) Local sympathetic denervation of white adipose tissue in rats induces preadipocyte proliferation without noticeable changes in metabolism. Endocrinology 133:2255–2262
Penicaud L, Cousin B, Leloup C, Lorsignol A, Casteilla L (2000) The autonomic nervous system, adipose tissue plasticity, and energy balance. Nutrition 16:903–908
Shi H, Song CK, Giordano A, Cinti S, Bartness TJ (2005) Sensory or sympathetic white adipose tissue denervation differentially affects depot growth and cellularity. Am J Physiol Regul Integr Comp Physiol 288:R1028–R1037
Youngstrom TG, Bartness TJ (1998) White adipose tissue sympathetic nervous system denervation increases fat pad mass and fat cell number. Am J Physiol Regul Integr Comp Physiol 275: R1488–R1493
Larsson B et al (1984) Abdominal adipose tissue distribution, obesity, and risk of cardiovascular disease and death: 13 year follow up of participants in the study of men born in 1913. Br Med J (Clin Res Ed) 288:1401–1404
Risica PM, Ebbesson SO, Schraer CD, Nobmann ED, Caballero BH (2000) Body fat distribution in Alaskan Eskimos of the Bering Straits region: the Alaskan Siberia Project. Int J Obes Relat Metab Disord 24:171–179
Singh D (1993) Body shape and women’s attractiveness. Hum Nat 4:297–321
Landin K, Krotkiewski M, Smith U (1989) Importance of obesity for the metabolic abnormalities associated with an abdominal fat distribution. Metab Clin Exp 38:572–576
Berman DM et al (2001) Racial disparities in metabolism, central obesity, and sex hormone-binding globulin in postmenopausal women. J Clin Endocrinol Metab 86:97–103
Kerwin DR et al (2010) The cross-sectional relationship between body mass index, waist-hip ratio, and cognitive performance in postmenopausal women enrolled in the Women’s Health Initiative. J Am Geriatr Soc 58:1427–1432
Lassek WD, Gaulin SJC (2008) Waist-hip ratio and cognitive ability: is gluteofemoral fat a privileged store of neurodevelopmental resources? Evol Hum Behav 29:26–34
Bartness TJ, Song CK (2007) Thematic review series: adipocyte biology. Sympathetic and sensory innervation of white adipose tissue. J Lipid Res 48:1655–1672
Kreier F et al (2002) Selective parasympathetic innervation of subcutaneous and intra-abdominal fat–functional implications. J Clin Invest 110: 1243–1250
Nesto RW (2005) Obesity. Tex Heart Inst J 32: 387–389
Pech RP, Sinclair ARE, Newsome AE, Catling PC (1992) Limits to predator regulation of rabbits in Australia: evidence from predator-removal experiments. Oecologia 89:102–112
Tapper SC, Potts GR, Brockless MH (1996) The effect of an experimental reduction in predation pressure on the breeding success and population density of grey partridges Perdix perdix. J Appl Ecol 33:965–978
Auger P, Pontier D (1998) Fast game theory coupled to slow population dynamics: the case of domestic cat populations. Math Biosci 148:65–82
Morton GJ, Cummings DE, Baskin DG, Barsh GS, Schwartz MW (2006) Central nervous system control of food intake and body weight. Nature 443:289–295
Elliott R, Newman JL, Longe OA, Deakin JFW (2003) Differential response patterns in the striatum and orbitofrontal cortex to financial reward in humans: a parametric functional magnetic resonance imaging study. J Neurosci 23:303–307
Johnson PM, Kenny PJ (2010) Dopamine D2 receptors in addiction-like reward dysfunction and compulsive eating in obese rats. Nat Neurosci 13: 635–641
Wang G-J et al (2004) Exposure to appetitive food stimuli markedly activates the human brain. NeuroImage 21:1790–1797
Elliott R, Newman JL, Longe OA, William Deakin JF (2004) Instrumental responding for rewards is associated with enhanced neuronal response in subcortical reward systems. NeuroImage 21:984–990
Stice E, Yokum S, Burger KS, Epstein LH, Small DM (2011) Youth at risk for obesity show greater activation of striatal and somatosensory regions to food. J Neurosci 31:4360–4366
Briers B, Pandelaere M, Dewitte S, Warlop L (2006) Hungry for money: the desire for caloric resources increases the desire for financial resources and vice versa. Psychol Sci 17:939–943
Wang G-J, Volkow ND, Fowler JS (2002) The role of dopamine in motivation for food in humans: implications for obesity. Expert Opin Ther Targets 6:601–609
Wang G-J et al (2001) Brain dopamine and obesity. Lancet 357:354–357
Couppis MH, Kennedy CH (2008) The rewarding effect of aggression is reduced by nucleus accumbens dopamine receptor antagonism in mice. Psychopharmacology 197:449–456
Sladek R et al (2007) A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature 445:881–885
Scott LJ et al (2007) A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science 316:1341–1345
Li S et al (2010) Cumulative effects and predictive value of common obesity-susceptibility variants identified by genome-wide association studies. Am J Clin Nutr 91:184–190
McCarthy MI, Zeggini E (2009) Genome-wide association studies in type 2 diabetes. Curr Diab Rep 9:164–171
Thorleifsson G et al (2009) Genome-wide association yields new sequence variants at seven loci that associate with measures of obesity. Nat Genet 41:18–24
Rankinen T et al (2006) The human obesity gene map: the 2005 update. Obesity (Silver Spring) 14:529–644
Vaz M et al (1999) Body fat topography in Indian and Tibetan males of low and normal body mass index. Indian J Physiol Pharmacol 43:179–185
Arbabi S et al (2003) The cushion effect. J Trauma 54:1090–1093
Wang SC et al (2003) Increased depth of subcutaneous fat is protective against abdominal injuries in motor vehicle collisions. Annu Proc Assoc Adv Automot Med 47:545–559
Haag M (2003) Essential fatty acids and the brain. Can J Psychiatry 48:195–203
Willatts P, Forsyth J, DiModugno M, Varma S, Colvin M (1998) Effect of long-chain polyunsaturated fatty acids in infant formula on problem solving at 10 months of age. Lancet 352:688–691
Stevens LJ, Zentall SS, Abate ML, Kuczek T, Burgess JR (1996) Omega-3 fatty acids in boys with behavior, learning, and health problems. Physiol Behav 59:915–920
Hibbeln JR et al (1998) A replication study of violent and nonviolent subjects: cerebrospinal fluid metabolites of serotonin and dopamine are predicted by plasma essential fatty acids. Biol Psychiatry 44:243–249
Reisbick S, Neuringer M, Hasnain R, Connor WE (1994) Home cage behavior of rhesus monkeys with long-term deficiency of omega-3 fatty acids. Physiol Behav 55:231–239
Kitaysky AS, Kitaiskaia EV, Piatt JF, Wingfield JC (2006) A mechanistic link between chick diet and decline in seabirds? Proc Biol Sci 273:445–450
Hardikar A (1999) Role of environmental factors in induction, prevention and reversal of diabetes mellitus. PhD thesis, University of Pune
Ouchi N, Walsh K (2007) Adiponectin as an anti-inflammatory factor. Clin Chim Acta 380:24–30
Ouchi N et al (2010) Sfrp5 is an anti-inflammatory adipokine that modulates metabolic dysfunction in obesity. Science 329:454–457
Sonoli SS et al (2011) Visfatin–a review. Eur Rev Med Pharmacol Sci 15:9–14
Tilg H, Moschen AR (2008) Role of adiponectin and PBEF/visfatin as regulators of inflammation: involvement in obesity-associated diseases. Clin Sci 114:275
Sethi JK, Vidal-Puig A (2005) Visfatin: the missing link between intra-abdominal obesity and diabetes? Trends Mol Med 11:344–347
Stephens JM, Vidal-Puig AJ (2006) An update on visfatin/pre-B cell colony-enhancing factor, an ubiquitously expressed, illusive cytokine that is regulated in obesity. Curr Opin Lipidol 17:128–131
Bastard J-P et al (2006) Recent advances in the relationship between obesity, inflammation, and insulin resistance. Eur Cytokine Netw 17:4–12
Pond CM, Mattacks CA (1995) Interactions between adipose tissue around lymph nodes and lymphoid cells in vitro. J Lipid Res 36:2219–2231
Pond CM (2003) Paracrine interactions of mammalian adipose tissue. J Exp Zool A Comp Exp Biol 295A:99–110
Wells AS, Read NW, Laugharne JD, Ahluwalia NS (1998) Alterations in mood after changing to a low-fat diet. Br J Nutr 79:23–30
Pond CM, Mattacks CA (1998) In vivo evidence for the involvement of the adipose tissue surrounding lymph nodes in immune responses. Immunol Lett 63:159–167
Caroline MP (2005) Adipose tissue and the immune system. Prostaglandins Leukot Essent Fatty Acids 73:17–30
Wells JCK (2009) Ethnic variability in adiposity and cardiovascular risk: the variable disease selection hypothesis. Int J Epidemiol 38:63–71
Kolotkin RL et al (2006) Obesity and sexual quality of life. Obesity (Silver Spring) 14:472–479
Hammoud A et al (2009) Effect of Roux-en-Y gastric bypass surgery on the sex steroids and quality of life in obese men. J Clin Endocrinol Metab 94:1329–1332
Gesink Law DC, Maclehose RF, Longnecker MP (2007) Obesity and time to pregnancy. Hum Reprod 22:414–420
Mitchell M, Armstrong DT, Robker RL, Norman RJ (2005) Adipokines: implications for female fertility and obesity. Reproduction 130:583–597
Hausberger FX, Hausberger BC (1966) Castration-induced obesity in mice. Acta Endocrinol 53:571–583
Staiger H et al (2003) Relationship of serum adiponectin and leptin concentrations with body fat distribution in humans. Obesity 11:368–376
Park K-G et al (2004) Relationship between serum adiponectin and leptin concentrations and body fat distribution. Diabetes Res Clin Pract 63:135–142
Pond CM (2001) Long-term changes in adipose tissue in human disease. Proc Nutr Soc 60:365–374
Mankar M, Joshi RS, Belsare PV, Jog MM, Watve MG (2008) Obesity as a perceived social signal. PLoS One 3:e3187
Zahavi A, Zahavi A, Balaban A, Ely MP (1999) The handicap principle: a missing piece of Darwin’s puzzle. Oxford University Press, New York, NY
Lachmann M, Szamado S, Bergstrom CT (2001) Cost and conflict in animal signals and human language. Proc Natl Acad Sci USA 98:13189–13194
Gintis H, Smith EA, Bowles S (2001) Costly signaling and cooperation. J Theor Biol 213:103–119
Ambady N, Rule NO (2008) Brief exposures: male sexual orientation is accurately perceived at 50 ms. J Exp Soc Psychol 44:1100–1105
Elfenbein HA, Ambady N (2002) On the universality and cultural specificity of emotion recognition: a meta-analysis. Psychol Bull 128:203–235
Rilling JK, Kaufman TL, Smith EO, Patel R, Worthman CM (2009) Abdominal depth and waist circumference as influential determinants of human female attractiveness. Evol Hum Behav 30:21–31
Singh D, Renn P, Singh A (2007) Did the perils of abdominal obesity affect depiction of feminine beauty in the sixteenth to eighteenth century British literature? Exploring the health and beauty link. Proc Biol Sci 274:891–894
Renato P (2006) Obesity, fat distribution and infertility. Maturitas 54:363–371
Diamanti-Kandarakis E, Bergiele A (2001) The influence of obesity on hyperandrogenism and infertility in the female. Obes Rev 2:231–238
Lassek WD, Gaulin SJC (2006) Changes in body fat distribution in relation to parity in American women: a covert form of maternal depletion. Am J Phys Anthropol 131:295–302
Georgiev IP et al (2009) Evaluation of insulin resistance in obese castrated New Zealand white rabbits. Rev Méd Vét 160:335–340
Al-Nuaim AR (1997) Prevalence of glucose intolerance in urban and rural communities in Saudi Arabia. Diabet Med 14:595–602
Snehalatha C, Ramachandran A, Vijay V, Viswanathan M (1994) Differences in plasma insulin responses in urban and rural Indians: a study in Southern-Indians. Diabet Med 11:445–448
Yajnik CS et al (2008) Adiposity, inflammation and hyperglycaemia in rural and urban Indian men: Coronary Risk of Insulin Sensitivity in Indian Subjects (CRISIS) Study. Diabetologia 51:39–46
Zimmet P, Dowse G, Finch C, Serjeantson S, King H (1990) The epidemiology and natural history of NIDDM–lessons from the South Pacific. Diabetes Metab Rev 6:91–124
Linares C, Su D (2005) Body mass index and health among Union Army veterans: 1891–1905. Econ Hum Biol 3:367–387
Su D (2005) Body mass index and old-age survival: a comparative study between the Union Army Records and the NHANES-I Epidemiological Follow-Up Sample. Am J Hum Biol 17:341–354
Libert S et al (2007) Regulation of drosophila life span by olfaction and food-derived odors. Science 315:1133–1137
Smith ED et al (2008) Age- and calorie-independent life span extension from dietary restriction by bacterial deprivation in Caenorhabditis elegans. BMC Dev Biol 8:49
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Watve, M. (2012). Fat: Beyond Energy Storage. In: Doves, Diplomats, and Diabetes. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-4409-1_11
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