Obesity
Definitions of Metabolic Syndrome
Metabolism
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Evolution of Obesity
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Diet & Physical Activity
Genetics of Obesity
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Presentations on Obesity
Energy Homeostasis
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Quiz 1 on Obesity
Academic Studies & Resources
Quiz 2 on Obesity
Definition of Key Terms
Metabolic syndrome (MS) is a chronic disorder commonly characterised by comorbid obesity and abnormal metabolism of lipids and carbohydrates (e.g. hypercholesterolemia and insulin resistance). The term MS is believed to have been first acknowledged during Reaven’s 1988 Banting Lecture (Reaven, 1988), and is also identified as syndrome X, Reaven’s syndrome, insulin resistance syndrome, and CHAOS (Coronary artery disease, Hypertension, Atherosclerosis, Obesity, and Stroke). The disorder signifies a major public health issue since it significantly increases the risk of developing type-2 diabetes, cardiovascular disease (CVD) and associated morbidity and mortality (e.g. kidney failure, heart attack and stroke). Previous research suggests that MS presents a three-to-five fold increase risk of type-2 diabetes and a 1.7-1.9 fold increased risk in CVD (Ford, 2005a; Cameron, 2010).
Due to the multifaceted nature of MS, the mechanisms of the interacting pathways involved in this disorder have yet to be fully exposed. The most important factors that are suggested in the development of MS are:
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Weight
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Sedentary lifestyle, i.e., low physical activity and excess caloric intake.
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Genetics and Epigenetics
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Endocrine disorders, such as polycystic ovary syndrome in women of reproductive age
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Aging
Figure 1. Schematic of the metabolic syndrome with suggested mechanisms and subsequent disorders.
Insulin Resistance
Insulin resistance is a physiological disorder where insulin (hormone), becomes less efficient at reducing glucose levels. Depending on nutritional factors, the increase in blood glucose may elevate levels outside the normal range and cause adverse health effects (Reaven, 1993). Particular types of cells such as fat and muscle cells need insulin to absorb glucose. When these cells fail to respond effectively to circulating insulin, blood glucose levels increase. The liver aids in regulating glucose levels by reducing its secretion of glucose in the presence of insulin. This reduction in the liver’s glucose production may not occur in individuals with insulin resistance (Reaven, 1993).
Insulin resistance in muscle and fat cells diminishes glucose uptake, while insulin resistance in the liver cells results in reduced storage and glycogen synthesis and a failure to suppress glucose production and release into the blood. Insulin resistance typically refers to reduced glucose-lowering effects of insulin. However, other functions of insulin can also be disturbed. For example, Reaven, (1993) suggested that insulin resistance in fat cells reduces the effects of insulin on lipids and results in reduced uptake of circulating lipids and increased hydrolysis of stored triglycerides. Increased conscription of stored lipids in these cells raises free fatty acids in the blood plasma. These elevated blood fatty-acid concentrations reduce muscle glucose uptake and increase liver glucose production. These all contribute to elevating blood glucose levels. High plasma levels of insulin and glucose due to insulin resistance are major factors of metabolic syndrome (Grundy et al., 2004). If an individual has insulin resistance, more insulin must be secreted by the pancreas to maintain normal blood glucose levels. If this compensatory increase does not occur, blood glucose concentrations increase and type-two diabetes occurs (Figure 1).
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Dyslipidaemia
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The term dyslipidaemia is used to describe an abnormal amount of lipids in the blood. The most frequent profiled lipids are elevated total cholesterol (TC), elevated low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C). In industrialised nations, the majority of dyslipidaemia are hyperlipidaemias with an elevation of TC and LDL. This is typically due to nutrition and lifestyle issues although various hereditary syndromes are also significant contributors (Smith, 2007). Unfortunately, dyslipidaemia is a significant risk factor in CVD and is one of the leading causes of death worldwide (Laslett et al., (2012). Abnormalities in both lipid and lipoproteins are common in the general populace and are a modifiable risk factor for metabolic disease due to their effect on atherosclerosis (Thomson, 2004). Patents with S and CVD typically have high LDL-C and low HDL-C blood measures which are frequently observed in patients with type-two diabetes (Smith, 2007). Janus and colleagues (2010) reported a prevalence of dyslipidaemia in a randomised rural Australian population to be 48%.
Obesity
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Visceral, obesity has been suggested to be one of the significant factors in the development and progression of metabolic syndrome, as well as several other multifaceted disorders, i.e. CVD and type II diabetes (Grundy, 2004; Shoelson, Herrero & Naaz, 2007). It is common for there to be a development of visceral fat, after which the adipocytes (fat cells) of the visceral fat increase plasma levels of Tumour Necrosis Factor-alpha (TNFα) and alter levels of several other substances (e.g., adiponectin, resistin, Plasminogen activator inhibitor-1 (PAI-1). TNFα has been reported to not only cause the production of inflammatory cytokines but also to trigger cell signalling by interaction with a TNFα receptor that may lead to insulin resistance (Bastard et al., 2006; Nieto-Vazquez et al., 2009; Hajri et al., 2011). Only until recently, adipose tissue was suggested to be only a passive tissue for the storage of excess energy in the form of fat. It is now recognised as an important endocrine organ that may produce several substances with endocrine, paracrine and autocrine activity ((Kershaw & Flier, 2004). There is growing evidence that substances secreted by adipocytes are important determinants of insulin resistance, and ultimately metabolic syndrome, through hormonal effects or local effects on the adipocyte (Tso et al., 2008).
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Obesity - A Heterogeneous Syndrome with Multiple Origins?
The World Health Organisation (WHO) reported that since 1975 obesity has tripled. in 2016, more than 1.9 billion adults were classified as overweight and over 640 million obese. Unfortunately, most of the world's population reside in countries where overweight and obesity kills more people than being underweight except sub-Saharan Africa and Asia. In 2019 the WHO estimated that 38.2 million children age under five years were overweight or obese.
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The predominant precursor for obesity in any individual has been reported to be excess energy input over energy expenditure. The current epidemic of obesity is a mixture of increased sedentary lifestyles combined with available foodstuffs (i.e. high fat, high sugar and energy-dense foodstuffs). Additionally, there is a decrease in physical activity due to the sedentary nature of most forms of employment, coupled with changes in transportation and increased urbanisation. This generates major challenges and barriers for the Department of Health regarding halting the upward drift of obesity in the United Kingdom. That said, not every individual that is exposed to this environment ultimately becomes obese. There is some supporting evidence that obesity can be inherited to an extent, with rare cases of extreme obesity being due to a defective single gene.
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The obesity phenotype, particularly excess intra-abdominal adipose tissue has been strongly correlated with multiple risk factors that may reduce life expectancy. Obesity has negative metabolic adaptations with Tchernof and Despres (2013) stating that it may reduce individuals mortality due to hypertriglyceridemia, hyperglycaemia, pro-inflammatory cytokines, increased very-low-density lipoproteins (VLDL’s) and liver insulin resistance. Methods of preventative care to stop the upsurge in obesity centre around fitness professionals, exercise scientists and health promotion in aiding with changing individuals negative behaviour with a healthier lifestyle.
The World Health Organisation (2020) has defined overweight and or obese via classification of body weight via the individual's body mass index (BMI) (Table 1). BMI is easy to measure and calculate and is frequently used to correlate the risk of health problems with weight at a population level. It was originally developed by Adolphe Quetelet during the 19th century (termed Quetelet index [Eknoyan, 2007 [Link]). During the 1970s and based especially on the data and report from the Seven Countries study (Keys et al. 1984 [Link], researchers noticed that BMI appeared to be a good proxy for adiposity and overweight related problems. To read more about body composition click this link.
BMI formerly known as the Quetelet index is used to measure weight relative to height and is calculated by dividing body weight in kilograms by height in meters squared. For example, an adult who weighs 70 kg and whose height is 1.75 m will have a BMI of 22.9.
Table 1. BMI Categories
It should be noted that BMI is a rudimentary and crude method of measurement and has several limitations due to it being an indirect measurement of body fat. Firstly, BMI does not account for the subjects body structure and the proportion of lean muscle mass. This creates errors within the measurements as some population groups such as athletes will have spuriously high BMI’s due to the increased lean muscle mass. Additionally, the standard BMI measurement according to Freedman and Sherry (2008) is erroneous for certain population groups including different ethnic populations and also children. Importantly, BMI is considered more of a standardised and population-specific measurement tool than for assessing individuals characteristics. There is more of a shift towards the use of BMI in conjunction with waist measurements to evaluate the associated risk of co-morbidities. Likewise, other methods are now employed to measure body composition including sagittal abdominal obesity (SAD) that has been reported to correlate better with morbidities associated with obesity. It should be highlighted, however, that BMI assessment is currently acknowledged as an accurate measurement within the context of body composition evaluation.
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Another method of assessing obesity is assessing waist circumference. This is another measure to assess obesity, as the distribution of visceral adiposity around the the waist is linked to elevated health risks. Tchernof and Despres (2013) stated that intra-abdominal fat is correlated with elevated risk of developing coronary heart disease (CHD), type II diabetes, hyperlipidaemia and also hypertension. Exercise scientists should measure halfway between the super iliac crest and the lower aspect of the ribcage in the mid-axillary line.
Table 2 indicates the suggested cut-off points for various populations. However, it is important to consider that there is significant individual variability in the sum of visceral fat distributed at the waist circumference. Heyward and Gibson (2014) reported that most individuals with an 'at risk' waist circumference are ultimately in an overweight or obese BMI category, therefore most individuals BMI alone is an adequate indicator for treatment.
Table 2. Association of waist circumference with risks of CHD
Hypertension
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High blood pressure or hypertension is a chronic cardiac medical condition in which the systemic arterial pressure is elevated. Hypertension is either classified as primary hypertension or secondary hypertension with 5-to-10% of events (secondary hypertension) produced by other conditions that affect the kidneys, arteries, heart, or endocrine system. Hypertension of >140/90 mmHg is one of the risk factors for myocardial infarction, heart failure, arterial aneurysm, strokes and is a leading factor of chronic kidney failure. It has been suggested that merely having a moderate elevation of arterial blood pressure leads to diminished life expectancy. Modifications to individuals dietary intake and lifestyle modifications are suggested to be effective intervention methods that can improve blood pressure control and reduce the risk of disease. Unfortunately in most individuals cases, drug treatment may be necessary when lifestyle alterations prove to be ineffective or insufficient.
Evolution of Obesity
Obesity is the result of a gene by environment interaction. A genetic legacy from our evolutionary past interacts with our modern environment to make some people obese. Obesity is the outcome of a gene by environment interaction. A genetic legacy from our evolutionary past that interacts with our modern environmental setting to make some individuals obese. The obesity epidemic is regrettably a recent phenomenon and in as little as 50-years there has been a progressive elevation in the prevalence of obesity globally. This drift started in the western world and rapidly shifted to developing countries. This change in fatness over such a short period does not reflect a change in the genetic makeup of the population. Most of these changes, therefore, must be environmental even though large pockets of the populations that are obese remain lean. These inter-individual differences in obesity susceptibility, therefore, reflect genetic factors. The obesity epidemic may be a consequence of a gene by environment interaction (Speakman et al., 2011). In some individuals cases, they may have a genetic predisposition to deposit fat, thus reflecting their evolutionary history resulting in exposure to the modern environment and leading to obesity. However, this interpretation is problematic as the theory of evolution suggests that natural selection will only favour individuals that exhibit phenotypic traits that lead to increases in survival. It is questionable how obesity led to increases in fecundity that offset the survival disadvantage.
Why Do We Have Adipose Tissue?
Living organisms must follow the fundamental physical laws, for example, the first law of thermodynamics states that energy can be neither created nor destroyed but only transformed. The second law states that there is an overall direction in the transformation such that disorder increases. Being low entropy biological systems, we need to continuously fight against the impulse for entropy to increase. Highly complex organic molecules including proteins, lipids, RNA and DNA become impaired and corrupt and must be constantly recycled and remodelled to maintain function. This requires cyclic transformation and constant energy even when an organism is apparently doing nothing, it still uses up sizeable amounts of energy to sustain its low entropy state.
Importantly, all living organisms must propagate, move around to find food, defend themselves against attack by pathogens, processes that require energy. There is a continuous requirement for all living beings for energy and it can only be obtained by feeding which is also discontinuous. Subsequently, energy cannot be stored or destroyed meaning that mammals need to have some mechanism to store energy so that the intermittent supply can be matched to the continuous requirement. The fundamental storage mechanism that allows humans to get from one meal to the next is glucose and specifically glycogen in the liver and skeletal muscles.
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There are numerous situations where mammals struggle to obtain and consume enough food to meet the required demands. In these instances, animals require a superior and long-term storage mechanism than merely glucose and glycogen stores and this is provided by body fat. During periods where animals can consume enough energy in which to deposit into their body fat (saving account) so that it can be available for periods when demand exceeds supply. Adipose tissue, therefore, exists primarily as a buffer that is used to supply energy during periods when food (energy) is insufficient to meet energy demands.
Why Do Individuals Get Obese?
The previous section discussed why adipose tissue exists now it is important to briefly examine the various types of evolutionary theories as to why in contemporary society we fill up these fats stores to such levels. Table 2 summarises the various evolutionary hypothesis about the origins of obesity.
Table 2. Evolutional Theories On Obesity
Adaptive Explanation of Obesity
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The adaptive viewpoint suggests that obesity was adaptive in the past, but in contemporary society, the positive effects of being obese have been replaced by negative impairments. This evolutionary accumulation of fat issue historically provided fitness rewards and was positively selected by natural selection. This positive selection in the past may be why some individuals have a greater susceptibility to becoming obese today regardless of its negative effects. Humans are not the only species to become obese and numerous mammals deposit large amounts of body fat at levels of human obesity. For example, bears deposit large amounts of fat prior to hibernation and also some birds deposit fat prior to migration. Although humans neither seasonally hibernate nor migrate several authors had drawn direct comparisons between animals and humans (Johnson et al., 2013). This is due to humans surviving with reduced energy supplies during periods of famine. There have been a series of historical events that have been documented about human obesity as a means to function and survive through periods of famine. This according to Neel (1962) would facilitate fat storage comparably to hibernators. Famines may have provided a strong selection on genes that favoured the deposition of fat during periods between famines.
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Individuals with genes that preferred efficient fat disposition would survive following famines while others with genes that were inefficient at fat storage would not. This concept by Neel (1962) termed the ‘thrifty gene hypothesis was published more than 50-years ago and has been restated in numerous forms concerning obesity. This premise proposes that when humans historically were experiencing episodic famines, thrifty genes were advantageous because humans that carried them, therefore, would become fat between famines, and this fat deposition would allow them to survive the next subsequent famine. These thrifty genes would then be passed to the next generation of humans who would then have a distinct survival advantage in any ensuing famine. This is in contrast to individuals not carrying such genes and as such would not prepare for the next famine by depositing as much fat and therefore would die along with their unthrifty genes.
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This premise proposes that when humans historically were experiencing episodic famines, thrifty genes were advantageous because humans that carried them, therefore, would become fat between famines, and this fat deposition would allow them to survive the next subsequent famine. These thrifty genes would then be passed to the next generation of humans who would then have a distinct survival advantage in any ensuing famine. This is in contrast to individuals not carrying such genes and as such would not prepare for the next famine by depositing as much fat and therefore would die along with their unthrifty genes.
Historically, food stocks were believed to be low even between famines with the levels of obesity attained were typically low even with individuals carrying the thrifty genes and so individuals never became fat enough to experience the negative effects of obesity on health. However, from the 1950s there was a significant shift in agricultural production and increases in the food supply chain. This rise in food supply especially in Europe and North America were progressively replicated throughout the rest of the developed world. This led to consequential effects on individuals that carried the thrifty gene as they were more efficient in depositing fat. The once advantageous genes were therefore rendered detrimental by process (Neel 1962).
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Proponents of the thrifty gene notion agree on several essential elements. Specifically that famines are frequent with estimates suggesting that values of one every 10-years (Keys et al., 1950). Additionally, famines produce considerable mortality with reports of between 15-30 per cent mortality rate. However, there are areas of discrepancy including how far back in human history do we consider the exposure to periodic famine. If we consider the point made by Chakravarthy and Booth (2004) who suggested that if the thrifty gene provided a strong selective advantage to survive famines then this would have spread to the entire population. Contemporary society would therefore all have the thrifty gene leading to all humans being obese. There is evidence that even in the most obese societies there remain a population that are lean (Flegal et al., 2010). Speakman (2007) specified that if famine provided a strong selective force for the spread of thrifty genes why are do so many individuals avoid inheriting them.
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Another belief is that famine has not featured throughout our history but is associated with the development of agriculture. The hunter-gather lifestyles have been suggested to be resilient to food shortages because individuals can be mobile and when food is limited can seek food elsewhere or even modify their dietary intake to exploit what is available (Benyshek and Watson, 2006). However, this contrasts with the contemporary society which is agriculturally centred and is dependent on fixed harvests and if these fail the food supply immediately becomes restricted. Since gene mutations that have happened in the last 12,000 years would not have had a chance to extend throughout the human population, the shorter timescale for the process of selection may explain why some individuals in present-day society become obese while others don’t.
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Opponents of this scenario argue that if humans developed agriculture only within the 12,000 years this would equate to approximately 8 famine events with significant mortality rates. Therefore, to be selected a mutation causing a thrifty gene could subsequently have to provide an immense survival advantage to spawn the current prevalence of obesity. Additionally, for a mutation to be selected, all of this mortality would be contingent on the differences in fat content attributable to a solitary genetic mutation. This then assumes that the reason individuals die in famines is that they starve to death and thus people with greater fat reserves would on average be expected to survive longer with lower fat reserves. However, in most famines, the major cause of death is disease-related with some deaths accountable from starvation. This does not contest that body fat is the main factor influencing famine survival. The spread of disease among famine victims is probably contributed to a reduction in their immune systems. An important element in the relationship between energy status and immune status is leptin (Faggioni et al., 2001). Low levels of leptin may reinforce the immunodeficiency of malnutrition. This is because circulating levels of leptin are correlated to adipose tissue stores and it is conceivable that leaner individua’s would have more compromised immune systems and therefore more susceptible to disease during famines.
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The debate surrounding gene selection and obesity were effectively made prior to reliable information about common polymorphisms that cause obesity. Without this evidence, it was plausible to imply that genes may exist and impact fat storage and hence survival during famines. This is now an unattainable position due to the advent of genome-wide association studies (GWAS) which identified the main genes with common polymorphisms associated with increased obesity risk (Day and Loos, 2011). These studies altered the view of the genetics of obesity since the majority of recognised single nucleotide polymorphisms (SNPs) were not involved with established hunger signalling pathways. Currently, there are 50 SNPs genes associated with BMI and it has been suggested that the genetic contract of obesity may involve hundreds or thousands of genes (Hebebrand et al. 2010). The model proposed by Prentice et al., (2008) that selection on these genes has occurred only over the past 12,000 years is therefore redundant. This is because SNPs affecting differences in fat storage of 100-to-1000 could not cause differential survival or fecundity during famines of 10%.
The Neutral Viewpoint
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It has been recognised by evolutionary biologists that natural selection is merely only one of several processes that include phyletic origins, neutral mutations and genetic drift that underpin genetic variations between individuals in a population. It is important not to fully interpret everything biological from the standpoint of adaptation by natural selection. There is now increasing acknowledgement that other non-adaptive evolutionary processes may contribute to our understanding of many forms of human disease. (Puzyrev and Kucher 2011; Valles 2012; Dudley et al. 2012).
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With regards to obesity, there is a nonadaptive explanation for the evolutionary setting termed the ‘drifty gene’ hypothesis (Speakman 2007). This premise is founded on observations made on wild mammals and their ability to accurately regulate and maintain body fatness. There is one theory that suggests that an individuals body weight is restricted by upper and lower limits often termed the dual intervention point model (Levitsky 2002; Speakman 2011). This concept centres on the individual's weight is set between two limits. If the weight fluctuates between these limits then nothing alters, but if the individuals body weight increases above the upper limit it will intervene physiologically to regulate its weight. Therefore, body weight is comparatively stable (between the upper and lower limits) in the face of environmental challenges. These upper and lower limits may be designated depending on the different evolutionary stresses: the lower limit by the risk of malnourishment and the upper limit by the possibility of predation.
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There has been a large body of research that suggests that the “starvation-predation” adjustment has helped the generalised framework for understanding the regulation of adiposity between and within animal species (Lima,1986; Houston et al., 1993; Witter and Cuthill, 1993; Higginson et al., 2012). Current research-based in laboratories are probing the metabolic basis of the effects of stochastic food supply and predation risk on body weight regulation (Zhang et al., 2012; Monarca et al., 2015). The drifty gene premise denotes that early hominins may have had a similar replication system. For example, during the Pliocene era (six-to-12 million years ago) humans were evolving in the presence of large predatory animals. Our descendants were smaller than modern man, placing them at significant risk of being a prey item. During this stage of our evolution, it would be conceivable that upper and lower intervention points evolved to be reasonably close together, and the early hominids possibly had greater control over their body weight.
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Several major events however happened in our evolutionary history around 2.5 million to 2.0 million years ago. There were numerous evolutionary events that formed our history around 2-2.5 million years ago. The first was the evolution of social behaviour, which allowed individuals to group together to enhance their ability to detect predators and protect one another from attack. Secondly, the discovery of fire and weapons which were a powerful means for early humans to safeguard themselves against predation. These two factors helped with predation pressure that maintained the upper intervention point essentially vanished. Speakman (2007) suggested that reduced selective pressure cause this intervention point to alter the genes that defined it was then subject to mutation and random drift. It should be noted that genetic drift is a process that is is favoured by a low populace. The notion that early Homo species had approximately 10,000 of a population would produce a genetic environment where drift effects are common. Mutations and drift for two million years may therefore generate the necessary genetic architecture but is insufficient to produce an obesity epidemic today. If this model was applied with the same genetic architecture would have been present 20,000 years ago.
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Palaeolithic era
Before the Neolithic (the new stone age), the most important factor was probably the level of the food supply. Palaeolithic individuals probably could not increase their body masses sufficiently to reach their drifted upper intervention points because there was insufficient food available. At this stage, each individual or small group would be foraging entirely for their own needs. Things changed in the Neolithic with the advent of agriculture. Power and Schulkin (2009) suggested that the level of food supply and the social distribution of it restricted the potential for humans to achieve their drifted upper intervention points. Before the Neolithic period (the new stone age) the most important factor was feasibly the level of the food supply. In the Paleolithic era, individuals or small groups would be foraging entirely for their own needs. This changed in the Neolithic era with the beginning of agriculture. Individuals harvested food for themselves and small groups but this was subsistence agriculture that was comparable to hunter-gathering. As the agricultural practices improved and yielded more harvest the number of people required to grow and harvest food declined. It is at this stage according to Diamond (1995) that complex human societies started to emerge.
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Civilisations are only possible feasible due to a subsection of individuals to grow and harvest food to maintain a larger number of individuals. This allows other groups of individuals to perform other activities that would have been unfeasible if they had to spend all their time growing and harvesting food. These activities in human society include religion, politics, the arts and war, building projects with stone, pottery making, iron, bronze ware and lastly sport. These activities are only feasible when crop yields are high enough to allow some individuals to stop raising crops and engage in other activities. There is also another crucial element to consider and that is social control of food supply, so that food produced by one section of society can be distributed to those that. However, an essential additional component was the societal regulation of food supply, so that food produced by one sector of society could be distributed to those that do not. This requires the development of a class-based system and fiscal, most of which have their origins in the wake of Neolithic agriculture. This central control of food supply is critical because people can only manage their drift upper intervention points if there is a sufficient supply of food for them to do so.
Neolithic (the new stone age)
During the Paleolithic age, most individuals could not access these resources due to their availability. However, after the Neolithic era, individuals could not access unlimited food supplies due to the central control of food distribution and supply. Most individuals had body weight between their upper and lower intervention points and would not experience the physiological drive motivating them to pursue food, except on rare occasions of famine. This access to food led to the development of a class-related arrangement of variation in body weight. In the lower social classes, where food supply was restricted people did not move to their upper intervention points, whereas in higher social classes, where access to food was essentially unrestricted, attainment of the drifted upper intervention point became conceivable.
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Subsequently at this stage, obesity was limited to the affluent and powerful and became a status symbol. Importantly not all the affluent and powerful individuals became obese only those with the genetic predisposition to do so, but none of the lower classes did. Historically, reports of obesity have dated back at least to the early Greek periods. Hippocrates in the fifth century BC suggested some possible cures for obesity that indicates that obesity must have been prevalent enough to warrant his attention (Procope, 1952). Additionally, it was stated that Hippocrates did not regard obesity as being beneficial but that something that required to be ‘cured’. This evidence helps to provide support against the famine-based “thrifty gene” premise since obesity 2500 years ago should have been viewed as advantageous when famines were supposed to be major selective pressure.
The GWAS offers support [in part] for this premise. SNPs predisposing to obesity have not been under strong positive selection (Southam et al. 2010; Koh et al. 2014), and this absence of strong positive selection is also observed in GWAS targets associated to type 2 diabetes (Ayub et al. 2014). This lack of selection is further supported by the lack of any association between prevalence and effect size among SNPs (Speakman and Westerterp 2013). Fredriksson et al., (2008) observed that the genes identified include a large portion of centrally acting genes that are associated to appetite and food consumption. It is plausible that these identified centrally acting genes may define the upper intervention point. Overall, this premise provides a non-adaptative explanation for why some individuals get obese but others do not.
The Maladaptive State
The maladaptive perspective is that obesity has never been advantageous. Historically, it may have never even existed, except in some rare individuals with unusual genetic abnormalities – perhaps represented in Paleolithic sculptures such as the “Venus of Willendorf.” However, the idea is that genes that ultimately predispose us to obesity become selected as a by-product of selection on some other trait that was advantageous. The best example of a “maladaptive” interpretation of the evolution of obesity is the suggestion that it is caused by individual variability in the capacity of brown adipose tissue to burn off excess caloric intake (Sellayah et al. 2014).
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The maladaptive standpoint is that obesity has never been beneficial. Historically, it may have only existed in some rare individuals with unusual genetic abnormalities. The notion that genes that ultimately predispose humans to obesity become selected as a by-product of selection on some other attribute that was beneficial. For example, the ‘maladaptive’ explanation of the evolution of obesity suggests that it is produced by individual variability in the capacity of brown adipose tissue to burn off excess intake (Sellayah et al., 2014). Brown adipose tissue (BAT) is found exclusively in mammals. Brown adipocytes typically contain large multilocular lipid droplets and abundant mitochondria, in contrast to white fat which contains a single large droplet. Contained within the BATs mitochondria is a protein called uncoupling protein-1 (UCP-1) which resides on the inner membrane (Figure 2). Uncoupling protein-1 acts as a stoma through which protons in the intermembrane space can return to the mitochondrial matrix.
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Conversely, unlike protons that migrate from the intermembrane space to the matrix through ATP synthase, the protons travelling via UCP-1 are not coupled to the formation of ATP. The chemiosmotic potential energy moved by the protons travelling by UCP-1 is released directly as heat. The main purpose of BAT is to generate heat for thermoregulation, therefore, it is unsurprising that it is located in the neonates of humans (including large mammals) and small mammals, which have a poor surface-to-volume ratio for heat loss. During the winter period, the volume of BAT and UCP-1 increases, however, during summer, BAT and UCP-1 are lower (Speakman 1996).
Figure 2. Mechanism of energy expenditure in brown adipocyte cell by uncoupling protein 1 (UCP1).
Research in the late 1970s (Rothwell and Stock 1979; Himms-Hagen 1979) suggested that BAT may have an additional function in which to ‘burn off’ excess calorie intake. This notion was rejected due to the belief that adult humans do not have significant deposits of BAT. However, the work by Nedergaard et al., (2007) observed that active BAT was indeed found in adult humans. Since that time the premise that variability in BAT function may result in the variable exposure to obesity has re-emerged (Sellayah et al. 2014). Further observations (Cypress et al., 2009; van Marken- Lichtenbelt et al., 2009) have supported the hypothesis that the amount and activity of BAT are inversely correlated to obesity and that there is an age-related reduction in BAT activity, associated with the age-related increase in body fatness (Cypress et al. 2009; Yoneshiro et al. 2011). Furthermore, the cyclical changes in seasons and reactions to cold exposure in animals are also witnessed in humans (Saito et al. 2009), suggesting essential functional activity. Laboratory studies with rodents have established that transplanting extra BAT tissue into an individual can protect both against diet-induced (Stanford et al. 2013; Liu et al. 2013) and genetic obesity (Liu et al. 2015).
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The “maladaptive” setting for the evolution of obesity is as follows. Humans are believed to differ in their BAT thermogenesis due to their variation in evolutionary exposure to cold (Sellayah et al. 2014), which required the use of BAT for thermogenesis. Some humans might have increased levels of active BAT, while others have lower levels, either due to their reduced exposure to cold or because they eluded cold exposure by other mechanisms such as clothing and the use of fire. Therefore, elevated levels of BAT would be one of a number of alternative adaptive approaches for thermoregulation. Due to the variety of approaches, a genetic predisposition to develop high and active levels of BAT would only be present in some humans and population groups. This would lead to individual and population disparity in the capacity to recruit BAT for its secondary function which is burning off excess energy intake.
An essential consideration is why individuals might have a disproportionate intake of energy in the first instance. This idea contradicts the fundamental assumption that underlies the ‘thrifty‘ gene premise that energy supply is almost always limited. One explanation for this effect is that individuals may not necessary eat only for energy but also for some essential nutrient, with any excessive nutrient intake excreted. Nevertheless, another scenario is that the quality of the food might change and the ratio of energy to the critical nutrient might increase. Again, if individuals continued to eat to meet their energy requirements, then intake of the nutrient would become deficient. In both of these scenarios to avoid nutrient deficiency, individuals might consume more food to meet their demands for the nutrient. The result would be that their consumption of energy would then exceed their demands.
If individuals continued to consume food to address their energy demands then they would reduce their intake, but this could impact on the essential nutrient consumption creating a nutrient deficit. However, evidence from direct measurements of energy demand in humans from the 1980’s do not support this contention that activity energy demands have declined (Westerterp and Speakman 2008; Swinburn et al. 2009). Another suggested notion is that the quality of food may have altered and the ratio of energy to the essential nutrient might have increased. Therefore, if individuals continued to consume food to meet their energy requirements then intake of the nutrient would become deficient. In both these instances to avoid deficiency, individuals might eat more food to meet their demands of nutrients. The end result would be that their consumption of energy would exceed their demands.
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Simpson and Raubenheimer (2005) have suggested that the nutrient that may drive overconsumption of energy is protein. This premise is termed the ‘protein leverage hypothesis’. This hypothesis suggests that the main driver for food intake is always the demand for protein and that individuals and animals primarily consume food to satisfy their protein requirements and energy balance comes secondary. The notion is worth at least considering as across societies the intake of protein is generally constant regardless of the diverse diets. This is supported by evidence that a high ratio of protein to energy diets are effective for weight loss. Gosby et al., (2014) reviewed 34 studies of dietary intake, reporting that dietary protein was negatively correlated with energy intake. Experimental research on rodents and diet choice (Sorensen et al. 2008; Huang et al. 2013) has also suggested that protein content is the factor regulating energy intake and hence body weight. Consequently, the protein leverage theory may provide necessary insight into the BAT premise. However, Bender and Dufour (2015) have suggested that there is limited evidence in support of the protein leverage theory in food intake records over time in the USA, but Dhurandhar et al., (2015) notes that this may be reflective of the poverty of the food intake reports rather than the hypothesis.
If individuals do over-consume energy due to the necessity for protein, then the capability to burn off the excess energy might be dependent on the level of BAT. Humans with large BAT deposits my burn off the excess and remain lean while others with lower BAT may be unable to burn off the excess intake and become obese. The environmental trigger is the change in the energy to the nutrient ratio in modern food that stimulates overconsumption of energy. There is no need by this viewpoint to infer that obesity has ever provided an advantage or even that we have in our history ever been fat. If we infer that obesity is a maladaptive consequence of variation in adaptive selection on BAT capacity. The environmental trigger is the change in the energy to nutrient ration in contemporary food that fuels overconsumption of energy.
If BAT is the main element that influences the predisposition to become fat then it could be anticipated that by knocking out (KO) the UCP-1 gene in rodents would lead to obesity. However, Enerbäck et al. (1997) knocked out UCP-1 in mice but they reported that the mice did not become any more obese than wild-type mice when exposed to a high-fat diet. A potential issue with this experiment was that the genetic background of these mice was a mixture of two strains (one susceptible and one not) to weight gain on a high-fat diet. This experiment was repeated by Liu et al., (2003) but with mice with a pure C57BL/6 background (susceptible to high-fat diet-induced weight gain). Liu and colleagues reported that the mice lacking UCP-1 were more resistance to high-fat diet obesity than the wild-type mice, but the protective effect was eliminated when the mice were raised at 27 degrees. To further compound the results when the same mice were studied at 30 degrees at which the KO UCP-1 became fat even on a chow diet and this was further multiplied with high-fat feeding. This leads to confusion because at 30 degrees it would be anticipated that UCP-1 would not be active in the mice that had it, thus no difference should occur from the KO mice. This raises questions about the role BAT has in the development of of obesity in humans.
It is unclear then why humans could not also burn off excess intake by other methods – for example, physical activity or shivering. Another issue with the BAT premise is that obesity genes identified so far from the GWAS studies are not associated with BAT function but instead connected to the development in the brain and individual variation in food intake. Finally, other mechanisms may explain why there might be an association between BAT depot size and obesity. Adipose tissue acts as an insulator and the thermoregulatory demands on obese individuals are reduced due to the shift downwards in the thermoneutral zone (Kingma et al. 2012). Severily obese individuals may be under considerable heat stress because of their reduced capacity to dissipate heat at ambient temperatures whereas lean individuals are in the thermoneutral zone. In these settings, the requirement for thermoregulatory heat production would be reduced, thus it is theoretically the case that the link between BAT activity and adiposity comes about because obesity reduces the need for BAT and not because variation in BAT causes variation in the capacity to burn off excess intake.
Genetics of Obesity
Numerous clinical definitions exist on metabolic syndrome which universally agrees that excess body weight is an essential factor. The so-called obesogenic environment has been attributed to the surge in obesity in the general population. However, there are individual differences in susceptibility to this environment , which attributes to the genetic differences between individuals. This section focuses on the genetic aetiology of obesity and discusses finding on genes which influence body mass index (BMI)
Forms of Obesity with Different Aetiologies
A distinction is often made between three types of obesity: monogenic, syndromic, and polygenic forms of obesity. Differences have often been made between three types of obesity [1] monogenic; [2] syndromic; and [3] polygenic forms of obesity. In monogenic obesity differences in a single gene lead to severe and extensive obesity in the absence of cognitive alterations or non-food linked behavioural changes. Syndromic obesity also leads to severe obesity but obesity develops as part of a multifaceted disorder which comprises severe intellectual disability, dysmorphic features and morphological changes in organ development. Lastly, polygenic obesity (often termed common obesity) has a complex aetiology involving many genes of small effect which act in addition to and interact with environmental contributors to obesity.
Monogenic Forms of Obesity
One of the earliest models came through the development of the ob/ob mouse strain at the Jackson Laboratory in 1950 (Ingalls et al., 1950). Ob/ob mice lack the OB protein and have body weights approximately three times higher than wild type mice. Additionally, ob/ob mice have display reduced physical activity, hyperphagia and have diminished glucose tolerance and hyperglycaemia to the point of diabetes. Injections of the OB protein into ob/ob mice increase physical activity levels and basal metabolic rate while having a reduction in food intake. Pelleymounter et al., (1995) suggest that OB protein regulates body weight by both metabolic and behavioural means.
The early work of Ingalls et al., (1950) observed through the development of the ob/ob mouse strain are the absence of the OB protein and have bodyweight approximately three times higher than wild mice. Zhang et al., (1994) was able to localise the position of the OB gene and the OB protein and gene named leptin (LEP) after the Greek ‘leptos’ meaning ‘thin’. Deficits in the LEP gene (similar to the ob/ob mice mutations) are rare in humans Individuals care characterised by severe early-onset obesity with hyperphagia together with other endocrine abnormalities (Ozata et al. 1999). Ozata et al., (1999) observed that as in ob/ob mice hyperinsulinemia with diabetes. Obesity resulting from leptin deficiency is one of a few forms of obesity with effective therapeutic options. Injections daily of leptin result in a reduction in fat mass attributed mostly to treating the hyperphagic aspect of leptin deficiency (Farooqi et al. 2002). It has been reported that ob/ob mice lack the leptin protein while db/db mice lack the leptin receptor. The db/db mice have a similar phenotype to ob/ob mice but are not responsive to leptin therapy, unlike ob/ob mice.
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Other monogenic forms of human obesity result in less severe obesity, for example, melanocortin-4 receptor (MC4R) mutations are the most common, seen in over 0.05 % of the population (Govaerts et al., 2005). MC4R is less characterised than leptin deficiency it is present in greater numbers and is the main cause of obesity in one-to-six obese individuals. MC4R mutations have different phenotypes across the lifespan. For example, during prepuberty, the extent of functional impairment in MC4R signalling relates positively with adiposity and hyperphagic behaviours but not after post-puberty. Other genes associated with monogenic obesity involve proopiomelanocortin (POMC) and prohormone convertase-1 (PCSK1), which are involved in the leptin-melanocortin signalling pathway. The main mechanism of action is not metabolic but rather behavioural as leptin acts to restrain food intake leading to obesity from excess food caloric intake (Zhou and Rui 2013).
Syndromic Obesity
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It is believed that at least 20 syndromes categorised by reduced intellectual ability and obesity as well as marked alterations in behaviour are caused by distinct genetic mutations or chromosomal irregularities. The most frequent of these syndromes is Prader-Willi syndrome that affects one in 25,000 births. Prader-Willi syndrome is an autosomal dominant disorder that is denoted by obesity developing from hyperphagia. Prader–Willi syndrome occurs from a deletion at 15q11.2–q12, often inherited from the paternal gene. Obesity in Prader–Willi develops from a continued increase in food intake, which may be from reduced satiety (feeling of fullness) rather than increased hunger (Lindgren et al. 2000). Food behaviour phenotypes in Prader–Willi is linked with several endocrine irregularities developing from hypothalamic impairments.
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For example, ghrelin (dubbed the hunger hormone) stimulates hunger, and fasting levels of ghrelin are increased in both adults and children with Prader–Willi (Cummings et al. 2002). Postprandial secretion of the pancreatic polypeptide from the gastrointestinal tract, which reduces food intake, is reduced in patients with Prader–Willi and infusions of pancreatic polypeptide reduce food intake, although this outcome may be specific to only females (Berntson et al. 1993). The full cause of hyperphagia in Prader–Willi syndrome remains indefinable. However, contributing to obesity in Prader–Willi is the substantial reduction in physical activity, the hormonal mechanisms behind which are not adequately understood (Davies and Joughin 1993).
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Mutations through 15 Bardet–Biedl genes have been linked with various forms of Bardet–Biedl syndrome (BBS). While there is a strong association between BBS and obesity, one study determined that just over 50 per cent of patients were obese, and the functional role of mutations in the BBS complex of genes is inadequately categorised. The single-minded homologue-1 (SIM1) gene is another gene that is associated with syndromic obesity. Experimental research on mice that are homozygous for a null allele of SIM1 display brain irregularities which instigate perinatal death, whereas those with only one null SIM1 allele display milder structural variances in the hypothalamus resulting in hyperphagia and early-onset obesity (Michaud et al. 2001). In humans, deletion of the SIM1 region results in comparable excessive food intake and early-onset obesity. While therapeutic management for Prader–Willi are accessible, persistent hyperphagia in syndromic obesity is problematic to exclude. Behavioural therapy for syndromic obesity is marginally successful due to the associated intellectual disabilities, and more success may be found with restrictive diets. It would be beneficial if the underlying genetics of syndromic obesity were netter characterised especially for understanding individuals patterns of eating behaviours that underlie excess adiposity in these disorders.
Common (Polygenic) Obesity
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Common ( also termed polygenic) obesity is believed to account for the increase in obesity prevalence over the last decade (Ogden et al. 2006). Polygenic obesity tracks in families, but is not hereditary in the expected pattern displayed in monogenic obesity. Common obesity is not accompanied by persistent changes in non-food behaviours or marked cognitive variations including intellectual disability. The multifaceted separation pattern of common obesity indicates that it is polygenic and predisposed by many genes of small effect, none are appropriate nor significant enough to convey obese status on their own. This etiological model of traits is acknowledged as the “quantitative trait locus (QTL) approach,” and a consequence of this model is that syndromes or diseases are seen as the extreme end of the normal spectrum. While monogenic forms of obesity are reported as having discrete foundations and consequences which are not present in those without these forms of obesity, common obesity is seen as occurring from an excess of the risk factors which function across the whole spectrum of BMI. Therefore, research into the causes of common obesity often uses BMI as the outcome, rather than weight class.
Genes with Known Metabolic Functions Which Relate to Obesity
The Genetic Investigations of Anthropometric Traits (GIANT) consortium is a recent example of large meta-analysed studies. The GIANT consortium replicated the 32 loci associated with BMI detected in their previous analysis and added 65 new loci (Locke et al. 2015). This study analyzed data from up to 339,224 subjects across 125 studies. In addition to confirming existing and identifying new genotype-BMI associations in genes with an identified function in obesity metabolism (Table 3).
Table 3. Genes with known functions in metabolism linked to BMI
Insulin Signalling and Glycaemic Control
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Insulin resistance is the ‘trademark’ of metabolic syndrome with the diminished capacity of the muscle cells to react to the release of insulin and the subsequent hyperinsulinemia leading to an array of metabolic irregularities, one of which includes increased adiposity. Insulin has several adiposity stimulating functions including fostering the differentiation of preadipocytes to adipocytes and inhibiting lipolysis. Adipose tissues are insulin resistant, and the degree to which insulin resistance is a cause and not a consequence of obesity is uncertain. The functionality of many BMI-associated genes in the insulin signalling pathway illustrates the mechanistic associations between the two traits.
FTO is one of the genes (Table 3) that is consistently linked to BMI across several GWAS and candidate gene studies (Dina et al. 2007; Hinney et al. 2007; Scuteri et al. 2007; Hunt et al. 2008; Speliotes et al. 2010). The association between FTO and BMI is not isolated to the oft-studied “adults of European ancestry” group. For example, FTO has been linked with BMI or obesity status in Chinese (Chang et al. 2008), Korean (Lee et al. 2010), and pediatric (Dina et al. 2007; Frayling et al. 2007; Haworth et al. 2008; Sovio et al. 2011) populations. A recent review specified that each FTO risk allele increases the BMI equivalent of 0.40-to-0.66 BMI points. Loos and Bouchard (2008) reported that those carrying two FTO risk alleles weigh 3–4 kg more than those with no risk allele. While the role of FTO in obesity appears to be mostly behavioural, FTO seems to have pleiotropic effects as the risk alleles may reduce insulin response in the brain (Tschritter et al. 2007).
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KLF7 (Table 3) encodes a protein that inhibits insulin expression and secretion in pancreatic beta cells. In addition to the relationship between KLF7 and obesity, KLF7 is considered a risk gene for type 2 diabetes (Kanazawa et al. 2005; Zobel et al. 2009). Muurling et al., (2004) narrated that APOC1 is expressed mainly in the liver, where overexpression of APOC1 in ob/ob mice leads to hepatic steatosis and severe hepatic insulin resistance.
APOE may also apply its influence on BMI through altered insulin sensitivity. Elosua et al., (2003) reported that obese men with the APOE4 genotype displayed higher levels of insulin and glucose than obese men in the other genotype groups. In addition to associations between SH2B1 and whole-body fat mass in females (Jamshidi et al. 2007; Hotta et al. 2011), the distribution of body fat and the amount of visceral adipose tissue (Hotta et al. 2011) and the amount of visceral fat area (Haupt et al. 2010). SH2B1 variants have also been related to type 2 diabetes independently of BMI (Sandholt et al. 2011). Circulating GDF-15 concentrations are increased with type 2 diabetes (Dostálová et al. 2009; Vila et al. 2011), and GDF-15 predicts upcoming insulin resistance glucose control (Kempf et al. 2012). IRS1 encodes a protein that is phosphorylated by insulin receptor tyrosine kinase. Mutations in the IRS1 gene are related to type 2 diabetes and predisposition to insulin resistance (Rung et al., 2009). PRKD1 regulates insulin secretion obstructing PKD in vitro cells inhibiting insulin secretion, but not insulin production (Sumara et al., 2009). TCF7L2 is expressed in most human tissues, including mature pancreatic β-cells and adipose tissue, except the muscle (Cauchi et al., 2006). Variants in this gene are associated with type 2 diabetes (Herder et al. 2008) and reduce the insulin response to glucose in nondiabetic individuals (Saxena et al., 2006). Toll-like receptor 4 (TLR4) activation was related to insulin resistance in adipocytes (Song et al., 2006), which implied that activation of TLR4 in adipocytes might be involved in the onset of insulin resistance in obesity and type 2 diabetes. TUB mutations in mouse models are the cause of maturity-onset obesity and insulin resistance and are not directly supported by human functional studies but mirror a C. elegans model (Mukhopadhyay et al. 2005).
Fatty Acid and Lipid Metabolism
Research using GIPR null mice established the significance of GIPR signalling in regulating lipid metabolism (Kim et al., 2011). HMGCR is the rate-limiting enzyme for cholesterol biosynthesis (Dietschy et al., 1993). HMGCR enzyme is suppressed by cholesterol derived from low-density lipoprotein (LDL) catabolism through the LDL receptor. APOE binds to the LDL receptor and aids catabolism (Mahley, 1988). SCARB2 mediates selective uptake of cholesteryl esters from HDL particles (Eckhardt et al. 2004). In C. elegans, loss of tub-1, the worm orthologue of TUB, increases in the storage of triglycerides (Mukhopadhyay et al., 2005). FOXO3 gene expressions can reduce LDL-cholesterol levels through the regulation of the PCSK9 gene. FTO mediated the downregulation of some genes involved in fatty acid catabolism (Fawcett and Barroso, 2010).
Endocrine System Functions
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Obesity leads to changes in hormones metabolism, with increased serum oestrogen levels being linked with obesity. Obesity increases serum concentrations of 17-β-estradiol, estrone, and also estrone sulfate, which are all substrates of SULT1A2 (Emaus et al. 2008). Harris et al., ( 2000) narrated that SULT1A2 may be associated with body weight and that it is mediated by the regulation of sex hormones. The absence of NCOA1, a coactivator for steroid and nuclear hormone receptors, causes obesity in knockout mice (Picard et al. 2002; Maquoi et al. 2005). The role of leptin in obesity through food intake alterations is known via monogenic studies. ERK1 protected leptin-deficient mice from insulin resistance which suggests that deregulation of the ERK1 pathway could be an important component in insulin-associated obesity, although the ensuing changes to BMI have yet to be demonstrated (Jager et al. 2011). SH2B1 is associated with serum leptin levels in females.
Principles of Energy Homeostasis
Energy metabolism is regulated by genetic and environmental factors which affect energy intake and energy expenditure. Energy balance is achieved when energy intake is equal to energy expenditure. When energy intake exceeds energy expenditure, a state of positive energy balance occurs, and this leads to obesity, a condition characterised by increased body weight, especially fat, in adipose tissue and other organs. A negative energy balance ensues when an individuals energy intake is significantly reduced concerning energy expenditure. There is significant individual variability in the time to reach energy balance and the patterns of weight gain and weight loss. It is difficult to lose weight and maintain weight loss over long periods due to metabolic, behavioural, neuroendocrine, and autonomic responses that foster weight regains and maintain energy stores in adipose tissue (Ahima, 2011). Multiple neuronal and hormonal signals oppose the state of weight reduction and influence positive energy storage. For example, the reduction in leptin offsets weight loss by stimulating appetite and decreasing energy expenditure (Ahima, 2011). Hyperinsulinemia stimulates energy storage in the forms of glycogen, fat, and protein (Leibel et al., 1995; Redman et al., 2009).
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Energy Intake
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The energy required for physiological and metabolic is derived from the chemical energy bound in macronutrient components of food, namely carbohydrates, fats and proteins. The digestion of food is enabled by mastication, mixing with saliva, gastric movements and key enzymes that blend the food into chyme. Within the upper intestines, the chyme is digested to yield glucose, fatty acids and amino acids which are absorbed. The chemical energy in nutrients is released and converted into heat, mechanical and other forms of energy. Fats and carbohydrates are the main sources of dietary requirements with protein an essential source of energy when total dietary energy intake is restricted.
Units of Energy
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The International System of Units defines energy as a joule (J), which is the energy expended when 1kg is moved 1-metre by a force of 1 newton. The conversation for joules to calories is 1kJ = 0.24 Kcal and 1 Kcal = 4.18kJ. The ingested energy is the maximum amount of energy measured after complete combustion to carbon dioxide and water in a bomb calorimeter. Food energy remaining after accounting for losses due to digestion is known as metabolisable energy (ME), most of which is available for the production of ATP. Some of the ME is used during metabolic processes associated with digestion, absorption and intermediary metabolism of food and be measured as heat production often referred to as diet-induced thermogenesis or thermic effect of food. The net metabolisable energy (NME) is attained by subtracting the energy lost to microbial fermentation and diet-induced thermogenesis from ME.
ME is defined as the amount of energy accessible for the whole body (total) heat production in a state of nitrogen and energy balance (Joint 2007). The NME is defined on the basis of ATP-producing capability of food instead of the total heat-producing capacity. NME refers to the food energy available for body functions needing ATP. The measurement of the food energy content is by chemical analysis or estimated from food composition tables. The Atwater general factor system of food energy is based on the heat of combustion of protein, fat, and carbohydrate, corrected for energy losses through digestion, absorption, and urinary excretion. The ME values are 17 kJ/g (4.0 kcal/g) for carbohydrates, 17 kJ/g (4.0 kcal/g) for protein, 37 kJ/g (9.0 kcal/g) for fat.
Recommendations for dietary intake must address the energy requirements in addition to providing all the needed nutrients required for the maintenance of optimum health and physical functioning. Humans daily energy requirements are estimated from the measurement of energy expenditure in addition to the extra energy required for growth.
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To attain an equal state of energy balance the dietary energy intake must be equal to total energy expenditure. For example, if an individual is considered to be in a steady-state the energy intake and expenditure are equal. Recommended daily intake represents an average of energy needs over a number of days and does not indicate exactly how much energy should be consumed daily. These energy requirements are only estimated from data based on groups of individuals of similar sex, age, BMI and physical activity levels (PAL). It is important to consider that there may be individual variations due to lifestyle factors that alter the energy requirements within populations.
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It is also important to understand the energy density of a food. This is defined as the amount of energy contained in 1-gram of food. Very low-density foods contain less than 0.6 calories per gram ( i.e. broccoli, lettuce). Low-density foods contain 0.6-1.5 calories per gram and include oatmeal, cooked whole grain rice and whole milk. Medium-density foods contain 15.4 calories per gram with examples including roast chicken, cheddar cheese and white bread. High energy-dense foods contain more than 4-calories per gram and include cookies, crisps, bacon and butter. Low-density foods have high water content, complex carbohydrate and fibre content, whilst high-density foods have a low content of water and fibre but high levels of fat and sugar.
Basal Metabolism
Basal metabolism refers to the energy required to maintain essential functions to support life. This includes the maintenance of the cellular structure, metabolic pathways, temperature regulation, cardiopulmonary and brain functioning. Basal metabolic rate (BMR) is the rate of energy expenditure measured under normal conditions such as being awake and in a supine position after 10-to-12 hours of overnight fasting and a thermoneutral temperature setting. BMR is the largest component of energy expenditure and signifies 45-to-70% of the total daily energy expenditure (TEE). BMR has been reported as being hereditary and is correlated with body composition, body size, sex age (Rising et al., 1992). The Fat-Free Mass (FFM) accounts for appropriately 67% of the BMR difference between individuals (Bosy-Westphal et al., 2003; Keys et al. 1973). It has been reported that males have a higher BMR compared to females, and ageing is linked with a decline in BMR, each of these differences are attributable to FFM.
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In contrast to the BMR, the Resting Metabolic Rate (RMR) measures the amount of energy used in a calm state and requires the individual to be in a thermoneutral environment (Weststrate, 1993). The less rigorous criteria make the RMR more practical than BMR for clinical and research studies.
Energy Expenditure and Consumption
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Consumption of food requires energy for ingestion and digestion of food and absorption, transport, interconversion, oxidation, and storage of nutrients. These processes increase oxygen consumption and heat production and are sometimes known as the thermic effect of food (TEF). The TEF is conditional on the dietary composition and may account for approximately 10% of the TEE in an individual eating a varied diet.
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Adaptive Thermogenesis
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Adaptive thermogenesis refers to heat production and ambient temperatures. Exposure to cold therefore induces non-shivering thermogenesis in brown adipose tissue and shivering thermogenesis in the skeletal muscles. Non-shivering thermogenesis is a key thermoregulatory mechanism against cold exposure in rodents, and it is intermediated through activation of systematic nervous system activity and production of heat by uncoupling protein (UCP)-1 (Golozoubova et al., 2001).
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Physical Activity
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This is the most inconstant element of TEE as physical activity may be divided into mandatory and discretionary undertakings. Essential activities include work, daily activities and other home activities and other demands imposed by the economic, social, and cultural environment. Discretionary activities include exercise for fitness and health and other voluntary but desirable activities for social interaction. The physical activity level (PAL) can be estimated from the 24-h TEE and BMR ratio (PAL = TEE/BMR) (Joint 2007). A sedentary or light activity person has a PAL of 1.40–1.69, a moderately active or active person has a PAL of 1.70–1.99, and a very active person has a PAL of 2.0–2.4.
Energy Expenditure Measurements
The notion that energy expenditure (EE) is associated with chemical combustion was suggested by Lavoisier (Green and Zande 1981). Energy expenditure can be measured by direct calorimetry. The subject is contained in a testing chamber, and the non-evaporative heat loss is measured from the temperature gradient across the walls of an insulated chamber, and evaporative heat loss is measured in the water vapor in the test chamber. The total heat loss is measured as the sum of evaporative and non-evaporative loss. Direct calorimetry is accurate, but it requires a specialised testing facility (Figure 3).
Figure 3. Direct Calorimetry
Indirect calorimetry is founded on the principle that the combustion of food to generate energy requires oxygen consumption. Indirect calorimetry, therefore, estimates the EE from the rates of respiratory gas exchange and nitrogen elimination (Livesey and Elia, 1988). The heat generated by the consumption of oxygen changes according to the contents of carbohydrates, fat, and protein. Indirect calorimetry is regularly used to measure BMR or RMR for hours using a ventilation hood (Figure 4) or for days in a respiratory chamber. A continuous supply of air is provided to the subject, and the respiratory gas exchange is measured by analysing the air inflow and outflow and the flow rate. Oxygen consumption, carbon dioxide production, and urinary nitrogen excretion are measured.
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EE = MR (kcal/day) = 3.941 VO2 (L/day) + 1.106 VCO2 (L/day) – 2.17 N Urine (g/day)
where MR = metabolic rate, VO2 = oxygen consumption, VCO2 = carbon dioxide production, and N Urine = nitrogen excreted in urine.
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The urinary nitrogen resulting from incomplete combustion of protein is small, and it is projected to be 12 g/day (0.5g/h). The ratio of VCO2 and VO2 is known as the respiratory quotient (RQ) or respiratory exchange ratio (RER) and ranges from 0.7 to1.0. The RQ is 1 when carbohydrate is the only fuel being oxidised and 0.7 when fat is the only fuel being oxidised. The proportion of energy used from carbohydrate or fat can be estimated using standard equations (Ravussin et al., 1986).
Figure 4. Indirect Calorimetry with Canopy Hood
The doubly labelled water technique permits the TEE to be measured under free-living conditions (Schoeller 1999). An oral dose of water enriched in deuterium (2H) and 18oxygen (18O) is given orally to label the body water. After equilibrium is reached in 3–6 h, 18O is lost as CO18O and H2 18O, and deuterium is lost in water. 18O and 2H enrichment is measured by isotope ratio mass spectrometry in the urine or saliva. Carbon dioxide production rate is centred on the difference in turnover rates between the oxygen and hydrogen labels. The difference between the slopes for the log-transformed disappearance rates of 2H and 18O is proportional to the amount of carbon dioxide produced. Based on a 24-hour RQ value of 0.85, the oxygen consumption and TEE values are calculated (Schoeller 1999; Ravussin et al. 1991).
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Assessment of body movement using accelerometery is a common method for measuring physical activity. Accelerometers can measure body movement and provide data about physical activity levels over extended periods. Advancements in sensor technologies have enabled the development of accelerometers with different capabilities. For example, the Piezoresistive sensors distinguish between physical activity intensity and postures. In order to assess physical activity patterns, the monitoring needs to be performed over several days and even weeks. Exposure of specific types of activities may even require a multiple sensor system. It is important to note that accelerometers do not measure energy expenditure; therefore, the data may need to be compared to energy expenditure measured by the doubly labeled water method.
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The TEE can be assessed by factorial calculations based on the time and energy cost of habitual activities. Factorial calculations combine the energy spent while sleeping, resting, working, and doing social or household and leisure activities, and the EE estimate is based on the time allocated to each activity and the corresponding energy cost.
Energy Segregation
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Most of the energy in the body is stored as fat in adipose tissue. Many organs are involved in the production, storage, and deployment of energy. For example, the liver is the main distributor of energy to other organs and regulates blood glucose levels within a limited range in response to irregular food intake. The liver also produces urea and other nitrogenous waste products. After a carbohydrate-rich meal, glucose enters hepatocytes via GLUT2, and the glucose is phosphorylated by glucokinase to form glucose 6-phosphate (G6-P).
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Amino acids entering hepatocytes are used as precursors for protein synthesis or exported to other organs. Amino acids are transaminated, deaminated, and degraded in hepatocytes to produce pyruvate and other intermediates in the citric acid cycle. Ammonia released from amino acid catabolism is excreted as urea. Pyruvate generated from amino acids is converted to glucose and glycogen via gluconeogenesis, or to acetyl-CoA to be oxidised via the citric acid cycle and oxidative phosphorylation to produce ATP or converted to lipids. During the phase between meals, a limited amount of muscle protein is degraded to amino acids, which contribute their amino groups via transamination to pyruvate, producing alanine, which is transported to the liver and deaminated into pyruvate, which is then converted to glucose via gluconeogenesis.
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The liver plays an important role in lipid metabolism. Triglycerides are synthesized from the esterification of glycerol and fatty acyl-CoA. Fatty acids are the main fuel for oxidative metabolism in hepatocytes, producing acetyl-CoA and NADH. Acetyl-CoA is oxidised via the citric acid cycle to produce ATP, and the excess acetyl-CoA is converted to acetoacetate and beta-hydroxybutyrate. These ketones are important fuels for the heart and brain during continued fasting. Acetyl-CoA derived from fatty acids is used for the synthesis of cholesterol, needed for membrane assembly, and for the synthesis of steroid hormones and bile salts. Fatty acids are also converted to phospholipids and triglycerides, which are exported via lipoproteins to adipose tissue for storage. Non-esterified fatty acids are bound to serum albumin and transported to the heart and skeletal muscles to be used as fuel.
White Adipose Tissue
White adipose tissue (WAT) is the key energy storage organ and comprises 15–20 % of the mass of an average adult. Adipocytes network with the brain, liver, skeletal muscles, heart, and other organs. During stages of high carbohydrate intake, adipocytes are proficient in converting glucose into fatty acids and fatty acids to triglycerides. Importantly most of the triglycerides amassed in human adipocytes are derived from the VLDL distributed from liver and chylomicrons from the intestinal tract. When energy demand increases, adipocyte lipases release free fatty acids from triglycerides which are transferred to skeletal muscles and heart. Epinephrine stimulates adipocyte lipolysis through hormone-sensitive lipase (HSL), while insulin inhibits lipolysis.
Most of the fatty acids produced by triacylglycerol lipase in adipocytes are re-esterified to triglycerides by glycerol kinase which uses glycerol phosphate derived largely from pyruvate via gluconeogenesis. Skeletal muscle performs mechanical work essential for maintaining body posture, breathing, and movement. Type I myofibers has low tension, is highly resistant to fatigue, and produces ATP via oxidative phosphorylation. This muscle fibre is rich in mitochondria and blood supply. Type II myofiber has fewer mitochondria than type I, is less vascular, generates greater tension and faster contraction, however it is quicker to fatigue.
Resting muscle uses free fatty acids from adipose tissue and ketone bodies from the liver. These fuels are oxidised and degraded into acetyl-CoA, which enters the citric acid cycle for oxidative phosphorylation and ATP production. Moderately active muscle uses glucose in addition to fatty acids and ketones. The glucose is converted to pyruvate via glycolysis and then to acetyl-CoA and oxidized via the citric acid cycle. Maximum contraction of fast-twitch muscle rapidly depletes ATP, and this cannot be replenished by aerobic respiration. Glycogen stored in muscle is broken down to produce lactate, but the amount of glycogen in skeletal muscle is limited and cannot endure glycolysis during prolonged exercise. Therefore, skeletal muscle also produces phosphocreatine to provide energy. After exercise, the lactate is transported from muscle to liver, and glucose is produced via gluconeogenesis and transported back to muscle to replenish the glycogen stores.
The brain has a very active oxidative metabolism, accounting for about 20 % of oxygen consumption. The brain uses glucose as its main fuel, but it can also use fatty acids and ketones during starvation. The liver is the main source of glucose for the brain. Neurons metabolize glucose via glycolysis and the citric acid cycle, and this provides most of the ATP needed to establish and maintain the membrane electrical potential and also generate action potentials during neurotransmission.
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Brown Adipose Tissue
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Oxidative metabolism ensues within mitochondria via the citric acid cycle and electron transport chain. Oxygen is consumed, water and carbon dioxide are produced, and the ATP produced is used for numerous cellular functions. The uncoupling of oxidative phosphorylation is noticeable in brown adipose tissue (BAT) and is mediated by UCP1, a 32 kDa protein situated in the inner mitochondrial transmembrane of brown adipocytes, which permits protons to re-enter the mitochondrial matrix from the inner membrane space. BAT uses glucose and fatty acids as fuel, and the heat is released by H+ going down its electrochemical gradient. BAT activity is increased in response to cold exposure, β3-adrenergic agonist, and ephedrine and results in weight loss (Shekelle et al., 2003; Babaet al., 2007). Other factors associated with the browning of adipose tissue include thyroid hormone, bile acids, leptin, melanocortin-4-receptor agonists, and FGF-21 (Harms and Seale, 2013).
Energy Dysregulation in Obesity
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There has been a year on year increase globally in obesity and metabolic syndrome due to overconsumption of food, particularly energy-dense foods rich in sugar and fat. This surplus energy is deposited mainly as fat in adipose tissue and also in the liver, pancreatic beta-cells and other tissues. The ectopic fat deposition has been suggested to increase insulin resistance, inflammation, and other changes that predispose to type 2 diabetes and elevate cardiovascular risk. Nutritional control and restriction is the primary strategy for obesity treatment, but this unaided is often ineffective due to an adaptive reduction in energy expenditure, increased appetite, and other physiological and behavioural responses that resist weight loss (Weyer et al., 2000; Sims et al., 1973). Breakthroughs in molecular genetics, in addition to a large body of evidence from population and laboratory studies, have enhanced our knowledge of mechanisms underlying obesity and associated diseases.
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Pathway studies provide strong support for genetic loci linked to CNS circuits and molecules related to energy metabolism and glucose homeostasis (Loos and Bouchard 2008; Loos et al. 2008; Locke et al. 2015). Several studies have demonstrated associations of RMR, TEF, RQ, or SNS activity with weight loss or weight gain (Smith et al. 2000; Hill et al. 1991; Ravussin et al. 1988; Astrup et al. 1999; Tataranni et al. 1997). An influential study by Bouchard et al., (1990) showed that monozygotic twins who were overfed exhibited similar gains in body weight and fat between each twin pair, suggesting a strong genetic influence on energy metabolism. It is, therefore, conceivable that genetic factors predispose toward obesity by affecting multiple factors including food digestion, absorption, availability of metabolizable energy (ME), TEF, and mitochondrial energy metabolism.
Restricted physical activity [PA} is commonly cited as the main reason for obesity, but the evidence is disputable. Several studies have intimated that increasing obesity drift parallels the sedentary lifestyle in several populations (Caleyachetty et al., 2015). Non-exercise PA may avert the development of obesity (Villablanca et al. 2015). Reduced PA may predispose to sarcopenia, insulin resistance, and metabolic syndrome, particularly among the older populations (Kim and Choi 2015). However, other investigators have found no significant changes in PA to describe the increasing trend of obesity (Westerterp and Speakman 2008). Hill et al., (2012) have suggested that energy balance may be easier to achieve at a higher level of EE. Above a threshold PA level, the energy intake and energy expenditure are very sensitive to changes in each other within the “regulated zone” of energy balance. In contrast, the energy intake and expenditure are inadequately sensitive to changes in each other in the “unregulated zone” below the PA threshold, and this promotes overeating and weight regain following caloric restriction (Hill et al., 2012).
Overview of Carbohydrate Metabolism
Carbohydrates (CHO) is the main source of dietary energy for humans, and glucose is the main energy substrate for cells. Red blood cells lack mitochondria and consequently depend entirely on glucose for energy. Likewise, the brain and renal medulla also depend mainly on glucose as their energy source. The brain, due to its high metabolic rate needs approximately 100 g per day of glucose in which to function. Dietary CHO and glucose intake oscillate over 24-hours, being zero when we sleep and intermittent over our awaking period. Conversely, the cells have continuous glucose needs. This metabolic challenge is compensated by a multifaceted neuroendocrine regulatory system that delivers a constant glucose supply while preventing hyperglycemia after meals and hypoglycaemia over the fasting periods (Mizgier et al., 2014).
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This efficient glucose uptake buffers the massive increase in blood glucose levels that otherwise dietary glucose will impose. After a typical intake of glucose in healthy adults, approximately 70 per cent of this glucose is taken up by the skeletal muscles and 20 per cent by the liver. Importantly, insulin-dependent and insulin-independent glucose uptake work in coordination with increased glucose oxidation and glycogen synthesis inhibiting postprandial hyperglycemia. Therefore, high blood glucose concentration is the key driver of hepatic glucose uptake, while hyperinsulinemia is the main promotor of glucose uptake and utilisation in the skeletal muscle. Postprandial suppression of hepatic glucose production is also an important mechanism to prevent hyperglycemia and maintaining glycemia within a range (Bonuccelli et al. 2009).
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However, under certain conditions such as minimal or zero exogenous [external of the body] glucose supply, the concentration gradient of glucose between the extracellular and intracellular partitions is maintained by the liver's ability to release glucose into the circulation. This process is achieved via hydrolysis of hepatic glycogen and the conversion of specific metabolites to glucose (Brosnan 1999). As this is occurring other tissues such as the skeletal muscles spares glucose by adapting its energy demand to different energy substrates. Lastly, energy at the organ, cellular and whole-body system levels is achieved after adapting fuel oxidation to fuel availability (known as metabolic flexibility). In this setting, insulin plays an essential role in determining fuel partitioning, so dietary macronutrient accessibility matches the oxidation rate.
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Glucose Uptake
Glucose uptake happens through an assisted passage in a process involving 14 glucose transporter (GLUT) isoforms. GLUT1 is expressed universally and is located in the plasma membrane. GLUT2 is present in the pancreatic beta (β) cells, hepatocytes, and basolateral membrane of intestinal and kidney epithelial cells. Its elevated capacity for glucose passage permits translocation of glucose between the extra- and intracellular compartments depending on the glucose concentration gradient. GLUT2 also facilitates the efflux of glucose from the liver into the circulation under conditions of restricted exogenous glucose supply. GLUT3 is expressed in the brain and has an elevated attraction for glucose. This allows it to deliver a continuous supply of glucose to the neurons, even with limited extracellular glucose concentration. GLUT4 is located in striated myocytes and in adipocytes and is generally responsible for insulin-stimulated glucose uptake in those cells. Furthermore, GLUT4 translocation from the cytosol to the plasma membrane is also stimulated by muscle contraction, and this appears to be determined by a reduction in cellular oxygen concentration (Egan and Zierath 2013).
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To inhibit the depletion of newly unified glucose, the sugar is quickly phosphorylated. This is an ATP-dependent reaction catalysed by hexokinases. The type 4 hexokinase, glucokinase isoform which is located in the liver and has a somewhat low attraction for glucose. This dynamic feature permits hepatocytes to phosphorylate glucose that comes from the intestine after meals. Once glucose is converted to glucose-6-phosphate (G6P), it has two major metabolic outcomes: (1) glycolytic oxidation to pyruvate and further conversion to lactate (anaerobic condition) or oxidation to acetyl-CoA (aerobic condition) and (2) conversion to glucose-1-phosphate (G1P), the precursor of glycogen synthesis. Under non-insulin-stimulated settings such as overnight fasting, circulating glucose is generally taken up by non-skeletal muscle tissues, with about 20 % being cleared up by the skeletal muscle. Intriguingly, both lean and obese individuals have comparable glucose clearance rates (Kelley et al. 1999a), which is consistent with the observations made that most of the glucose uptake in fasting conditions is dependent on insulin-independent mechanisms. Furthermore, the transport of a non-metabolizable glucose analogue (3-Omethylglucose) and the content of G6P (Allenberg et al. 1988) was comparable in muscle biopsies from lean and obese.
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Under insulin-stimulated settings, Bonadonna et al., (1990) compared glucose uptake at various insulin doses in lean and obese subjects by the glucose clamp procedure. The authors observed that the insulin dose-response curve was right-shifted in obese when compared with lean subjects suggesting that there was an impaired insulin action. Laakso et al., (1990) stated that the most important contributor to the decreased whole-body glucose uptake in obese patients was the diminished insulin-stimulated glucose clearance by skeletal muscle. At the molecular level, insulin-stimulated GLUT4 translocation is lower in insulin-resistant obese individuals compared to normal individuals, which is consistent with the impaired insulin-stimulated glucose transport identified in muscle biopsies of obese versus lean subjects (Goodyear et al. 1995).
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Storage of Glucose
Conversion of G6P to G1P is mediated through phosphoglucomutase. G1P is then converted to uridine diphosphate (UDP) glucose and lastly bounded to a growing glycogen polymer. Insulin stimulates glycogen synthesis by releasing the inhibition that glycogen synthase kinase 3 places on glycogen synthase. Additionally, insulin-mediated GLUT4 translocation to the plasma membrane results in higher glucose flux and, therefore, higher availability of the substrates for glycogen synthase action(Yki-Jarvinen et al., 1987). Glycogen is synthesized by both the liver and skeletal muscle. The liver has a higher content per gram of wet tissue, whereas the skeletal muscles have a greater contribution to total body glycogen due to its greater contribution to an individuals body mass. Hepatic glycogen has a systemic role due to its contribution to tolerate hepatic glucose production and normoglycemia during periods of fasting.
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In contrast, skeletal muscle glycogen predominantly maintains ATP production during contractions. Under conditions of limited exogenous glucose supply, glycogen is hydrolysed so to increase G6P supply, which is then converted to glucose by the action of glucose-6-phosphatase in the liver. G6P cannot be converted to glucose in skeletal muscle because it lacks glucose-6-phosphatase, so G6P is metabolized to pyruvate. It has been suggested that obese individuals have decreased glycogen synthase activity under fasting conditions, although muscle glycogen content remained unaltered (Allenberg et al., 1988)
Overview of Fat Metabolism
Fats are essential components of all cellular systems and fulfil energetic, structural, and regulatory functions. Fatty acids and cholesterol are abundant dietary lipids. Dietary fatty acids are generally found as triacylglycerols (TAG) in white adipose tissue (WAT), which is able to store a vast amount of energy (~7,000 kcal/kg). Due to this ability to buffer short and long term fluctuations in calorie intake, WAT is a significant evolutionary adaptation against starvation in humans. Additionally, WAT secretes adipokines that integrate whole-body energy balance, feeding behaviours, BMR, insulin sensitivity and vascular function. Fatty acid oxidation fulfils 25–45 % of our daily energy needs, which is an average healthy adult that represents about 60–110 g of fat per day. Unlike fatty acids, cholesterol cannot be oxidised for energy production and can only be converted to derivative sterols, steroids, and biliary acids for disposal. Abnormal lipid accretion in non-adipose tissues, such as blood, muscle, and the liver, is a recurrent abnormality in obesity, and it may be related to the insulin resistance existing in these individuals (McGarry, 2002).
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Dietary TAG is hydrolysed in the lumen of the small intestine by pancreatic lipase, and the released fatty acids are absorbed and re-esterified by enterocytes into TAG. Small intestine combines this TAG and other lipid nutrients and vitamins in chylomicrons, which finally reach the systemic circulation. Upon extracellular hydrolysis mediated by lipoprotein lipase (LPL), chylomicrons transport their lipids to the WAT, skeletal muscle, and heart. Lastly, chylomicron fragments are cleared by the liver and the hepatocytes integrate TAG into secreted VLDL, which can then be hydrolysed by LPL and the released FFA took up by WAT and muscle.
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LPL is situated on the endothelial surface of the WAT capillaries and is regulated by insulin. Also circulating are a number of binding proteins that are present in the plasma membrane of adipocytes, including the scavenger receptor FAT/CD36 and members of fatty acid transport protein (FATP) family (Hajri and Abumrad, 2002). FAT/CD36 is abundant in adipose tissue and skeletal muscle but is expressed at very low levels in the adult liver. Inside the cell, fatty acids are promptly esterified with coenzyme A (CoA) by the action of acyl-CoA synthetase. Acyl-CoAs are then esterified to glycerol-3-phosphate forming a framework for glycerolipid and glycerophospholipid synthesis in a series of reactions catalysed by acyltransferases and phosphatases. In the muscle and liver, acyl-CoAs are destined to mitochondrial beta (β)-oxidation for ATP synthesis. In the brown adipose tissue, acyl-CoAs are burnt out for heat generation upon cold or adrenergic stimulation (Ravussin and Galgani, 2011).
Fatty acids can also be esterified to sphingosine to form ceramide. Some of these lipids (e.g., diacylglycerol, DAG, and ceramide) are considered second messengers in signalling pathways and have been constantly associated in the pathogenesis of insulin resistance (Coen and Goodpaster, 2012). In fasted subjects, circulating FFA is the primary source for the synthesis of hepatic TAG, and they comprise the majority of fatty acids incorporated in TAG of secreted VLDL particles (Parks et al. 1999). Furthermore, low insulin-to-counter regulatory hormone ratio activates intracellular lipolysis of TAG in adipocyte lipid droplets. FFA are released to the extracellular space and circulate bound to plasma proteins, mostly albumin. Then, FFA is taken up in non-adipose tissues for re-esterification, oxidation, or hepatic conversion to ketone bodies.
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Oxidation of Fatty Acids
Fatty acids are the primary metabolic fuel for oxidation in the conversion from a nourished to fasted condition. Fatty acid oxidation is controlled at three enzyme-mediated steps: [1] fatty acid activation to acyl- CoA in the cytosol; [2] acyl-carnitine translocation to the mitochondrial matrix (catalysed by carnitine palmitoyltransferase [CPT] 1); and [3] mitochondrial beta (β)-oxidation through four consecutive enzymatic reactions. Compromised fatty acid oxidation is credited to mitochondrial abnormalities and has been suggested as a major driver of muscle and hepatic fat increase that leads to insulin resistance (Shulman 2014). In line with this premise, Kim et al., (2000) found reduced palmitate (CPT1-dependent) and palmitoyl-carnitine (CPT1-independent) oxidation and lower CPT1 activity in muscle biopsies from obese vs lean subjects. In vivo, human studies have only partly substantiated this finding (Galgani et al., 2008). However, in vivo rodent models, fatty acid oxidation through the administration of etomoxir led to higher glucose-to-fat oxidation ratio, which was accompanied by higher sarcolemmal GLUT4 content and lower circulating glucose, suggestive of higher insulin sensitivity. In turn, etomoxir treated mice had increased muscle TAG and DAG content with improved insulin-stimulated GLUT4 translocation (Timmers et al., 2012).
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Fatty Acid Turnover
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Fatty acids are deposited as TAG in lipid droplets, which are active structures that regulate intracellular fatty acid trafficking. Therefore, the signalling cascade mediating lipolysis joins in the elevation of intracellular cAMP and activation of protein kinase A. The balance between glycerolipid synthesis and intracellular lipolysis eventually determines tissue lipid balance and the synthesis of lipid intermediates. Thus, the increase in lipolytic rates led to higher fatty acid availability and new ceramide synthesis in a muscle cell line overexpressing adipose TAG lipase (ATGL) (Liu et al. 2007). Conversely, incomplete TAG hydrolysis might also favour DAG accumulation (Badin et al. 2011).
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The significance of fatty acid turnover is emphasised by studies reporting that whole-body adiposity correlates directly with intramyocellular lipid content, but not with muscle content of DAG or ceramide (Moro et al. 2009). Therefore, muscle-specific lipid metabolism is a factor of muscle fatty acid turnover that is independent of total body adiposity level. In this regard, the DAG-to-TAG hydrolase activity ratio appears to be lower in obese individuals, which is accompanied by increased muscle ceramide and DAG content as well as impaired insulin sensitivity (Moro et al. 2009).
Effects of Changed Tissue Lipid Equilibrium
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Obesity is categorised by increased WAT mass at the subcutaneous and intra-abdominal level. Obese individuals usually have increased tissue lipid accumulation in the liver, skeletal muscle, and heart. It seems that raised fat content in ectopic versus eutopic (i.e., WAT) location is more harmful to whole-body and tissue metabolic homeostasis.
Moreover, clinical and experimental evidence indicates that disproportionate fat accumulation in non-adipose cells is causative of insulin resistance in obese individuals (Virtue and Vidal-Puig, 2008; Moro et al., 2009). Upon this premise, chronic caloric overload may result in a chain of pathologic alterations in the WAT, including amplified hypertrophy of adipocytes, activated immune cells infiltration, abnormal vascular supply, and fibrotic extracellular matrix (Rutkowski et al., 2015). This clinically remodelled adipose tissue cannot fully develop and therefore leaks fatty acids toward cells and tissues that are not adapted to store massive amounts of these molecules (Rutkowski et al. 2015).
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In support of this premise, type 2 diabetic patients have augmented intramyocellular TAG content and postprandial hepatic and skeletal muscle fat storage (Ravikumar et al., 2005). These findings are similar to the observation that intrahepatic fat relates to impaired glucose tolerance, systemic insulin resistance, and increased circulating levels of enlarged VLDL particles (Fabbrini et al., 2009). Interventions that reduce ectopic tissue lipid load are typically related with improved insulin sensitivity, further providing evidence that supports the role of disproportionate lipid levels in insulin resistance pathogenesis. Significantly, the role of physical activity and exercise, a well-established insulin-sensitising means, on intramuscular TAG remains contentious. Some studies have reported that physical training decreases intramyocellular TAG (Bergman et al., 1999), whereas others stated conflicting results (Hoppeler et al., 1985). However, what appears to be consistent is that the increased intramyocellular lipid content generally observed in endurance-trained athletes does not associate with impaired muscle insulin sensitivity (a phenomenon referred to as the athlete’s paradox) (Goodpaster et al., 2001).
Overview of Protein Metabolism
Proteins are heterogeneous macromolecules with a range of molecular masses, structures, and functions. All the biological properties of proteins are determined by their sequence of amino acids. Amino acids are organic structures comprising a minimum of one atom of nitrogen. Essential amino acids (i.e., those that cannot be synthesised in human cells), and nitrogen must be gained from our diet to equal amino acid requirements for protein synthesis.
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Dietary amino acids and those resulting from endogenous protein hydrolysis are substantial energy substrates for humans, typically consistent with 10-to-20 % (70–100 g per day) of our total energy needs. As a by-product of amino acid oxidation, nitrogen is lost in the urine, mostly in the form of urea, suggesting that amino acids undergoing oxidation must be replaced by dietary amino acids. Therefore, the balance between protein degradation, synthesis, and intake is critical for conserving whole-body lean mass. Dietary amino acids reach the liver via the portal vein, and a large quantity is held by hepatic tissue. Intriguingly, branched-chain amino acids (BCAA) are inadequately metabolised by hepatocytes and are better channelled to skeletal muscle for energy production and conversion into alanine and glutamine. These two amino acids are then released from the muscle and taken up by the liver and other tissues for further use.
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Amino acid turnover is conditional on the level of energy adequacy that regulates the degree to which amino acids are spared as energy sources including the balance between protein synthesis and degradation. Insulin is a key mechanism of this balance, although its effectiveness depends on circulating insulin concentration. Therefore, low circulating insulin concentration in the presence of elevated amino acid supply stimulates muscle protein synthesis without disturbing skeletal muscle protein breakdown (Greenhaff et al., 2008). Though, increasing blood insulin concentration does not promote protein synthesis, while it suppresses protein degradation. At the molecular level, insulin increases protein kinase B (PKB) activity, which then reduces the inhibition over the mammalian target of rapamycin (mTOR). As a result, the activity of eukaryotic initiation factor-binding protein1 (4E-BP1) and ribosomal protein S6 kinase (p70S6K) increases leading to greater protein synthesis. Insulin also decreases protein degradation by inhibiting proteasome activity.
Protein Turnover in Obesity
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Hypothetically, obesity-related hyperinsulinemia would foster protein accumulation unless defective insulin action also extends to amino acid use. However, studies have demonstrated this suggestion is unfounded. Several studies have been performed that compare whole-body and tissue-specific amino acid metabolism between lean and obese subjects. Several of these studies found that, in fasting conditions, obese subjects have increased protein degradation in comparison with lean individuals (Nair et al. 1983; Bruce et al. 1990; Welle et al. 1992; Chevalier et al. 2005). That said, several other studies did not corroborate these findings (Luzi et al. 1996; Solini et al. 1997; Guillet et al. 2009). Upon insulin stimulation, obese subjects have either impaired or unchanged suppression of protein degradation. Considering protein synthesis, comparable results also exist (Luzi et al. 1996; Solini et al. 1997; Chevalier et al. 2005, 2006; Guillet et al. 2009). Hence, it is unclear what role amino acids plays in insulin resistance and protein metabolism. Alterations in study design and in subject characteristics including body fat distribution may partly explain the lack of uniformity across studies. Alternatively, insulin regulation of glucose and amino acid metabolism may not lie on the same molecular pathways or insulin dose-response kinetic.
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Branched-Chain Amino Acids (BCAA) and Obesity
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It has been known for over 50 years, that circulating BCAA concentration is elevated in human obesity (Newgard 2012). Furthermore, there is evidence suggesting that increased blood BCAA is an independent risk factor for insulin resistance (McCormack et al., 2013) and type 2 diabetes (Wang et al., 2011). Notably, Everman et al., (2015) disputed the concept that BCAA may be a causal factor of insulin resistance. Everman et al., (2015) found that a short-term infusion of BCAA in healthy subjects did not change insulin sensitivity.
There are still several questions as to why blood BCAA is increased in obesity and what is its pathophysiological relevance remains unclear. One feasible explanation is that most of dietary BCAA reach peripheral circulation, which prompts the notion that increased protein intake in obese individuals may lead to higher circulating BCAA. Studies have suggested that there is a direct correlation between BCAA intake and blood BCAA concentration (Meguid et al. 1986a, b). Additionally, impaired tissue clearance of circulating BCAA may also play a role as decreased content of enzymes involved in the oxidation of BCAA in skeletal muscle biopsies of obese donors (Lefort et al., 2010; Lackey et al., 2013). Furthermore, the content of mitochondrial BCAA aminotransferase and branched-chain keto acid dehydrogenase subunit E1 was increased after gastric bypass induced weight loss in the WAT of obese subjects, and this was paralleled by a reduction in circulating BCAA concentration (She et al., 2007). Although the likely contribution of WAT to whole-body BCAA metabolism seems negligible (Lackey et al. 2013), studies indicate that high blood BCAA concentration in obesity may not just be a result of higher food/ protein intake.
Diet and Physical Exercise
People who are overweight and obese have numerous reasons that they may be motivated to lose weight and sustain that weight loss. These reasons include medical influences, such as improvement of cardiovascular health, improved management of type 2 diabetes and sleep apnoea, and prevention of several cancers. However, even with severe health fears, individuals may not be driven to take appropriate action. There are numerous psychosocial factors, such as quality of life, self-esteem, and body image that can serve as motivating factors for weight management. For most individuals, there are combined factors that result in readiness for behavioural change that is necessary for weight loss treatment.
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Usually, behavioural weight management interventions consist of three key factors; [1] the individual's diet, the combination of macronutrients, how many calories, and other dietary features such glycaemic index, particular types of fats or carbohydrates, and the timing of meals; [2] physical activity, which is recommended for weight loss and weight maintenance; and [3] behavioural modification strategies that are applied to aid in following the diet and engaging in physical activity. These strategies normally include attention to goal-setting, self-monitoring, and stimulus control.
Figure 5. Components of lifestyle modification for weight management and improvements in metabolic functioning
Dietary Consumption and Approaches
Low Fat, Low-Calorie Approach
Diet fads over the decades, have come and gone and in some instances come back again. The government’s method in the United Kingdom (Table 1) has consistently been based on a low-fat dietary approach. The current recommendations, found at https://www.gov.uk/government/publications/the-eatwell-guide. This low-fat, low-calorie approach, with macronutrient goals of 20–35 % of intake from fats, 45–60 % from carbohydrates, and 15–20 % from proteins, has been used by many successful weight loss interventions, including the Diabetes Prevention Program, Look AHEAD, and POWERTrials (DPP Research Group 2002; Look AHEAD Research Group 2010; Wadden et al. 2011; Appel et al. 2011).
Table 1. Government recommendations for energy, macronutrients, salt and dietary fibre for males and females aged 19+ years
Other organisations including the American Heart Association and the American Cancer Society use this method, even though each is presented through different means. The concept behind a low-fat, low-calorie diet is based on the notion that a serving of fat is more calorically dense (9 kcals per gram) than that of protein or carbohydrate (each 4 kcals per gram). Therefore, when eating a low-fat diet, an individual can consume a higher volume of food than on a higher-fat diet. This may translate into feeling like you can eat more food and for a longer period, while also filling the stomach with a higher volume of food than one could consume with a higher fat intake. These approaches typically propose an intake goal of 1,200–1,500 kcals for individuals weighing less than 115 kg and 1,500–1,800 kcals for those weighing more than 115kg. Individuals at higher weight categories will need more kcals per day given their higher metabolic needs. A low-fat approach specifically concentrating on reducing saturated and trans fats may be beneficial for people with diabetes mellitus and/or cardiovascular disease, as these fats are known to increase the risk for cardiovascular episodes and impair insulin sensitivity (Vetter et al. 2014).
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Low-fat methods have been extensively examined and usually yield a weight reduction of 5–10 % of the individual's initial body weight, which can provide clinically meaningful improvements in metabolic factors. For example, the Look AHEAD studies included 5145 overweight and obese older subjects with type 2 diabetes mellitus and randomised them to an Intensive Lifestyle Intervention (ILI; including a low-fat diet and ongoing group and individual weight management counselling) or a Diabetes Support and Education group (DSE; yearly support meetings [Ryan et al. 2003]). Both groups continued treatment with their doctor for management of their diabetes and other health matters over the 12 years of the study. It should be noted that the occurrence of cardiovascular events was the study’s primary outcome. The ILI group displayed greater weight loss during the study compared to the DSE group (8.6% vs 0.7 % at 1 year; 6.0 % vs 3.5 % at post-assessment). However, this weight loss did not lower the rate of cardiovascular events (Look AHEAD Research Group 2013). There were other significant improvements in the ILI group, including greater reductions in HbA1c levels, sleep apnea, urinary incontinence, and depression, and improvements in quality of life, physical functioning, and mobility.
High Protein
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While the low-fat/low-calorie method adopts the notion that “a calorie is a calorie” no matter what its dietary quality, other dietary recommendations have concentrated on aspects such as satiety promoting foods, glycaemic control, or reduction of food cravings for refined flour and sugar. Possibly the most known and examined approach after the low-fat diet is the high-protein diet, popularised in the Atkins diet (Atkins 1972) and the South Beach Diet (Agatstan 2003).
High protein consumption is considered between 1.2 and 1.6 g protein per kilogram of bodyweight, which is about 25–30 % of our daily intake. This is approximately 25–30 g of protein. Protein is known to have a higher thermic effect than that of carbohydrate and fat and has been suggested to promote satiety more than the other macronutrients (Astrup et al., 2015, Leidy et al., 2015). Protein may help preserve lean muscle mass which could help prevent the body from becoming more efficient in using its energy stores and, consequently, requiring fewer calories for continued weight loss. For these reasons, this approach is attractive to many individuals and anecdotally is often particularly appealing to males.
Evidence from laboratory studies shows positive effects of high protein meals, such as lowered ghrelin levels and increased satiety through higher peptide YY (PYY) and glucagon-like peptide 1 (GLP-1) levels. Longer-term studies have demonstrated varied effects on weight loss and cardiovascular improvements. For example, a meta-analysis by Wycherley et al., (2013) examined 24 controlled trials, including 1,063 overweight or obese adults, comparing higher-protein with lower-protein energy restriction diets for an average duration of 12 weeks. While energy deficits were comparable between the diets, higher-protein diets led to greater weight and body fat loss, lower fasting triglycerides, and preservation of more lean body mass compared with the lower-protein diets. However, no differences were reported between the diets for fasting glucose, fasting insulin, blood pressure, and total, LDL, and HDL cholesterol. In general, those individuals who can maintain a high-protein diet more consistently are the ones who lose the most weight, maintain that weight loss, and show the cardiovascular benefits. Schwingshackl and Hoffmann (2013) reported that at 12 months, the benefits shown above for 12 weeks were no longer significant between higher-protein and lower-protein diets.
Low Glycaemic Index Diet
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The glycaemic index (GI) denotes the degree to which foods containing carbohydrates affect the release of blood glucose after the food is consumed. Foods have been classified on a 0–100 scale, with glucose and white bread being the standard for comparison at a GI of 100. Low GI foods fall between 0 and 55, intermediate is between 55 and 69, and high range from 70-to-100. The GI of foods is affected by many reasons, including processing, cooking, and storage (Makris and Foster 2011). Examples of some low GI fruits include cherries, plums, grapefruit, and peaches, while low GI vegetables include broccoli, cabbage, tomatoes, and green beans. Low GI snack foods include nuts and hummus, compared to high GI items such as rice, cakes, sweets and scones. However, the goal of reducing peaks in blood glucose and insulin response may be critical to those with metabolic syndrome, polycystic ovarian syndrome (PCOS), and type 2 diabetes. Energy intake levels are not reduced merely by eating a low GI diet. Therefore, calorie control must be combined with the low GI approach for weight loss.
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Generally, weight loss and glucose control outcomes from studies of low GI diets have been varied. For example, Thomas and Elliott (2007) examined six RCTs with a total of 202. Intervention length ranged from 5 weeks to 6 months. Decreases in body mass, total fat mass, and BMI were significantly greater among subjects receiving low GI diets compared to other diets. Reductions in total cholesterol and LDL cholesterol were also significantly greater with low GI compared to other diets. Additionally, a study by Giacco et al., (2000) reported significantly reduced episodes of hypoglycaemia with the low GI compared to high GI diet, while Gilbertson et al., (2001) showed a smaller proportion of subjects reporting 15 or more hyperglycaemic episodes per month (35 % vs. 66 %). Even though these reviews reported significant benefits of the low GI diet on body mass and glycaemic control, the clinical significance of these variations for a given individual is uncertain.
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Combining High Protein and Low Glycemic Index Approaches
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A study by Astrup et al., (2015) examined the potential additive effects of the low GI and the high protein dietary methods. The Diet, Obesity and Genes (Diogenes) trial included families with at least one overweight or obese parent and at least one child in the household across eight European cities. All of the parents (n = 932 adults) received a very low-calorie diet (800 kcal per day) for 8 weeks, aiming for a minimum weight reduction of 8 %. The mean weight loss over this period was 11 kg. Families of those who reached this level (n = 773 adults from 634 families) were randomised to 1-of-4 six-month dietary interventions to maximise and maintain weight loss and a control group. The interventions included (1) a high-protein, high GI diet; (2) a high-protein, low GI diet; (3) a normal protein, a high GI diet; and (4) a normal protein, a low GI diet. There was greater adherence in the high protein and low GI groups. However, only the low-protein, high GI group gained significant weight as compared to the control group. For the other groups, Astrup and colleagues reported that subjects in the high-protein groups regained 2.8 kg less weight and 1.6 kg less fat mass than the normal protein groups over 6 months, but no coherent effect of GI level on weight was established. The low GI groups showed significant decreases in high-sensitivity C-reactive protein blood levels than the high GI diet groups. The low GI, the high-protein group showed lower insulin response, and both low GI groups produced substantial decreases in fructosamine levels during the 6-month intervention. The authors suggest that there is a considerable genetic component that may suggest who might benefit the most from these approaches (Astrup et al., 2015).
Moderate Fat Diets (Mediterranean Diet)
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Moderate fat diets have been promoted as the Mediterranean Diet and contain 35–45 % fat. These diets promote the use of healthy fats such as olive oil, nuts, legumes, fish, and poultry. The Mediterranean Diet aims to target both weight loss and improvements and cardiovascular risk factors. Rees et al., (2013) performed a meta-analysis that investigated the effect of this diet on subjects at high CVD risk. Included in the review were studies of 52,044 subjects from 11 randomised controlled trials that used at least two of the following parameters to define a Mediterranean Diet: (1) high monounsaturated/ saturated fat ratio, (2) low to moderate red wine consumption, (3) high consumption of legumes, (4) high consumption of grains and cereals, (5) high consumption of fruits and vegetables, (6) low consumption of meat and meat products and increased consumption of fish, and/or (7) moderate ingestion of milk and dairy products. There was a large inconsistency in the diets, so the results were modest, with no effect on mortality, but slight reductions in LDL cholesterol. Studies with more of the components listed above had stronger CVD-related outcomes, including lower total cholesterol.
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Furthermore, a study by Martinez-Gonzalez et al., (2015) titled the PREDIMED (Prevención con Dieta Mediterránea) trial included 7447 older (mean age 67 years) Spanish adults at high risk for CVD events, randomising them to one of three groups: (1) Mediterranean diet (MeDiet) supplemented with extra virgin olive oil (EVOO), (2) MeDiet supplemented with nuts, and (3) control diet (advice on a low-fat diet). There was no specific caloric reduction or advice for physical activity included. Over a mean follow-up period of 4.8 years, subjects in both of the MeDiet groups significantly reduced their risk of a CVD event by about 30 % and peripheral artery disease by 64 % and 46 %, respectively, as compared to controls. Moreover, non-diabetic subjects in the MeDiet with EVOO reduced their risk of developing diabetes compared to control subjects by 40 %. These significant differences were recognised despite no significant weight loss at 3 years in any group on average (Razquin et al. 2009). However, risk reductions were associated with degree of weight reduction.
Other Dietary Approaches
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New trends are continually finding their way into the public domain. Some of the methods gaining popularity have been the Paleo diet and an intermittent fasting diet. The usual western diet can be characterised by high levels of processed meat and other foods, high-fat dairy products, and refined grains, all of which are connected with the increased occurrence of type 2 diabetes, hypertension, and dyslipidaemia (Masharani et al. 2015). Diets metabolically matched to primitive human diets of hunter and gatherers, the Paleolithic-type diet, consisted of meats, fish, fruits, vegetables, and nuts (Frassetto et al. 2009). Paleo diets normally are lower in sodium and very much higher in potassium, antioxidants, micronutrients, and fibre and with a much lower diet acid content, and some consider that it could reduce the risk of our modern-day diseases (Cordain 2002). However, only small, short-term studies of Paleo diets have been reported (e.g., Masharani et al. 2015), so the evidence is still limited on the standard weight loss outcomes and impact on cardiovascular and metabolic disease risk.
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Likewise, large trials for “intermittent fasting” or “modified fast” diets have yet to be performed. This method usually consists of fasting for 2 days, followed by 5 days of ad libitum eating. It is simple in its approach but possibly too difficult to continue longer term, as hunger is increased during the fasting period (Johnstone 2015). Small studies have reported that the calorie deficit created during the fast is not entirely compensated for during the days of ad-lib eating, thus producing weight loss. A small meal may be added to the fasting period to make this approach more acceptable. Generally, this approach may be used as a short-term weight loss approach, a few individuals would likely be able to maintain such a pattern long term.
Physical Activity and Exercise
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As discussed previously, exercise is often included in weight management methods and is one part of a multifaceted approach to generate an energy deficit big enough to produce weight loss. The American College of Sports Medicine (ACSM; Donnelly et al. 2009) recommends that adults participate in moderate to vigorous physical activity (MVPA) greater than or equal to 150 min per week of (equivalent to 1,200 kcals burned) to inhibit weight gain and possibly promote weight loss.
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Activity levels are regularly measured in metabolic equivalents (METs) which are the amount of oxygen required and the number of calories you burn at rest. The moderate activity would be a session reaching three to six METs (e.g., walking at 4 miles per hour), while vigorous activity would be working at greater than six METs (e.g., jogging at 6 miles per hour). These are very high levels of physical activity for most individuals who are obese and sedentary. The majority of evidence suggests that most obese and sedentary individuals cannot reach these physical activity levels. Therefore, exercise alone, without caloric restriction and modification of the diet, is inadequate to produce significant weight loss. Exercise is publicised as a reliable predictor of weight loss maintenance. Several studies have demonstrated that individuals who have lost weight and who exercise most days of the week are more likely than those who do not exercise to maintain their weight loss.
Founded on these studies, the ACSM’s guidelines recommended greater than or equal to 250 minutes of exercise per week (equivalent to about 2,000 kcals burned) for weight maintenance (Donnelly et al., 2009). The exercise described in the ACSM guidelines refers to cardiovascular exercise where your heart rate is raised for at least 10 min at a time. This can include a variety of aerobic activities, including brisk walking, swimming or water aerobics, aerobic fitness classes, or cardiovascular workout machines, such as the elliptical or recumbent cycle. Strength training in isolation does not produce significant weight loss or reductions in fat mass, although it increases lean muscle mass (Janssen and Ross 1999). Regular MVPA has constantly been recognised as one of the main predictors of weight loss maintenance.
Exercise Versus Lifestyle Activity
Well organised exercise and physical activity produces positive effects on weight management as described earlier. However, increasing lifestyle activity has also been reported to have benefits. The basis for lifestyle activity lies in NEAT (non-exercise activity thermogenesis). This involves energy expended during everyday activities, such as walking, climbing steps, housework. This type of activity is normally measured in steps through a pedometer or smart activity trackers found in phones and smart wrist or arm bands. For every increase in 2,100 steps per day, BMI is reduced by 0.4 kg/m2 (holding energy intake equal) (Donnelly 2009). As a reference point, 2,000 steps is approximately a mile. The overall goal for steps per day would be 10,000 steps.
Andersen and colleagues (1999) compared the effects of a dietary intervention plus a organised gym based aerobic activity treatment versus an intervention that encouraged increasing lifestyle activity, both recommended for the same number of minutes per week. The authors found that subjects in the increased lifestyle activity condition showed less weight regain at 1 year and performed equally to those in the aerobic group on metabolic parameters, such as reductions in LDL cholesterol and triglycerides.
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Effects of Exercise on Metabolic Parameters
Although the effect of physical activity on weight reduction is not as large as many people seeking treatment would wish, exercise can increase cardiovascular and metabolic parameters and reduce mortality risk independent of weight loss (Ross et al., 2000). Therefore, the adage “fit versus fat” has become a hotly debated topic amongst researchers, practitioners and the general public. However, body fat still poses a risk for CVD outcomes and mortality independently of exercise, so physical activity does not halt that effect.
Arem et al., (2015) provided evidence from a pooled analysis of over 660,000 subjects across studies of the National Cancer Institute Cohort Consortium examining the 2008 physical activity guidelines and their effect on mortality risk. They reported that engagement in any moderate or vigorous activity reduced risk by 20 % as compared to those engaging in no activity. Those who engaged in 1-to-2 times the recommended amount of activity per week – 7.5 metabolic equivalent hours per week – decreased their mortality risk by 31 %. Activity at higher levels was related with marginally higher reductions in mortality risk and was not considered harmful. This protective effect was found across BMI categories, while others have found that subjects in moderate to vigorous lifestyle activity was valuable in reducing mortality risk in those with a BMI < 30 kg/m2, but not in those with obesity (Willey et al., 2015).
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Continuous Versus Interval Training
Conventional lifestyle modification programs stress the importance of the number of minutes consumed engaging in MVPA each week. Interval training has become increasingly popular as a mode of increasing cardiovascular fitness and muscular strength. A contemporary systematic review by Ramos et al., (2015) investigated the effects of high-intensity interval training (HIIT) and moderate-intensity continuous training (MICT) on vascular functioning and other metabolic outcomes among subjects with impaired vascular function
The authors examined seven randomised trials with 182 subjects. The most frequent prescription of HIIT included four intervals of four minutes at a maximum heart rate of 85-to-90%, with three minutes of active recovery targeting 60-to-70 % of maximum heart rate. This mode of treatment was studied three times per week for 12–16 weeks. This was compared to traditional continuous moderate activity (the MICT group). Overall findings indicated that HIIT was more effective in improving vascular function, cardiorespiratory fitness, and insulin sensitivity and reducing oxidative stress and inflammation as compared to MICT (Ramos et al. 2015). Therefore, varying individuals intensity of physical activity may provide greater benefits to improvements in cardiovascular and metabolic risk elements compared to traditional constant moderate to vigorous physical activity.