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152 Seiten, Note: A
LIST OF TABLES
LIST OF FIGURES
LIST OF ABBREVIATION
CHAPTER ONE: INTRODUCTION
1.1 Background of the study
1.2 Statement of the problem
1.3 Aim and objectives of the study
1.4 Justification of the study
CHAPTER TWO: LITERATURE REVIEW
2.2 Classification of obesity
2.3 Prevalence of obesity
2.4 Causes of obesity
2.5 Effects of obesity on health
2.6 Management of obesity
2.7 Obesity and metabolic syndrome
2.8.1 Synthesis, physiological effects and degradation of insulin
2.8.2 Insulin signal transduction pathway
2.9 Obesity and insulin resistance
2.10 Homeostasis model assessment of insulin resistance
2.11 Adipose tissues cellularity and changes with obesity
2.11.1 White adipose
2.11.2 Brown adipose tissue
2.12 Mechanism of fatty acid mediated insulin resistance
2.13 Obesity induced inflammation and insulin resistance
2.13.1 Obesity induced macrophage accumulation in the adipose tissue
2.14 Role of adipose tissue produced adipokines on insulin resistance
2.14.2 Interleukin-6 (IL-6)
2.15 Tumour necrosis factor-alpha
2.15.1 Structure of TNF-α
2.15.2 TNF-α cell signaling
2.15.3 Role of adipose tissue produced TNF-α in insulin resistance
2.16 Overview of lipid metabolism
2.16.1 Intracellular and extracellular transport pathway
2.16.2 Reverse cholesterol transport pathway
2.17 Obesity and dyslipidemia
2.18 Role of adipose tissue produced adipokines in dyslipidemia
2.19 Cardiovascular diseases
2.19.1 Risk factors for cardiovascular diseases
2.19.2 Symptoms of cardiovascular diseases
2.19.3 Inflammation and cardiovascular diseases
2.20 Obesity inflammation and cardiovascular disease
2.20.1 Risk factors as biomarkers
2.21 Disorders of lipid metabolism
2.21.1 Chylomicron syndrome
2.21.2 Familial hypercholesterolaemia
2.21.3 Familial hypertriglyceridemia
2.21.5 Familial combined hyperlipidaemia
2.21.9 Secondary hyperlipidaemia
CHAPTER THREE: MATERIALS AND METHODS
3.1 Subject selection
3.1.1 Inclusion criteria
3.1.2 Exclusion criteria
3.2 Anthropometric measurements
3.3 Sample collection
3.4 Determination of fasting blood glucose
3.5 Determination of insulin
3.6 Determination of tumour necrosis factor alpha
3.7 Determination of total cholesterol concentration
3.8 Determination of triglyceride
3.9 Determination of high density lipoprotein cholesterol
3.10 Calculation of very low density lipoprotein cholesterol
3.11 Calculation of low density lipoprotein cholesterol
3.12 Calculation of homeostatic model assessment of insulin resistance
3.13 Calculation of atherogenic index of plasma (AIP)
3.14 Statistical analysis
CHAPTER FOUR: RESULTS
CHAPTER FIVE: DISCUSSION AND CONCLUSION
5.3 Contribution to knowledge
I, Agu Chidozie Elochukwu with registration number CPY/M.Sc/13/012 hereby certify that this thesis on “Tumour necrosis factor-alpha and atherogenic index of plasma as predictors of insulin resistance and risks of cardiovascular disease among obese subjects in Calabar, Nigeria” is an original work written by me. It is a record of my research work and has not been presented before in any publication.
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We declare that this thesis entitled “Tumour necrosis factor-alpha and atherogenic index of plasma as predictors of insulin resistance and risks of cardiovascular disease among obese subjects in Calabar, Nigeria” by Agu, Chidozie Elochukwu (Registration Number CPY/M.Sc/13/012) carried out under our supervision has been examined and found to have met the regulations of the University of Calabar. We therefore recommend the work for the award of the Master of Science (M.Sc) Degree in Chemical Pathology.
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With profound gratitude, I thank the Almighty God for giving me life, wisdom, strength and grace that I needed throughout the duration of this research and for making me who I am today.
I wish to express my sincere and immense gratitude to my supervisor, Prof. C.A. Usoro for her priceless contributions, suggestions, corrections and moral support towards the successful completion of this work. My gratitude goes to Dr. Eze-Bassey for her assistance with sample analysis and Mr. Henry Ofobi, Mr. IchaBassey for allowing me use the laboratory.
My gratitude also goes to my Head of department, Prof. A.A.A Alaribe who provided an enabling environment throughout the course of my study and my lecturers Prof. Udoh, Prof. Etukudoh and Dr. Augusta.
I am highly indebted to my loving parents, Chief. And Lolo G.C. Agu who gave me all the financial support, prayers and encouragement I needed to excel in my academies. I am grateful to my beloved brothers, Ndubuisi and Uchechuwku for their encouragement and morale support.
Thanks, Aunty Bukky, Mrs. Faith Effa and Mr. Joseph for technical assistance and support. I can’t stop without saying a big thank you to my typist, Miss Faith who in her capacity contributed to the success of this work. God bless you all.
Several studies in different population indicate that inflammation may be the link between obesity, insulin resistance and cardiovascular disease. However, this relationship has not been adequately explored in our population and among Africans with increasing high rate ofobesity and IR. In this study, the association between obesity markers (Body mass index, waist circumference, and waist-hip ratio), inflammatory marker (Tumour necrosis factor-alpha), insulin resistance markers (Homeostasis model assessment of insulin resistance, insulin) and markers of cardiovascular risks (Atherogenic index of plasma, lipid profile, blood pressure) were evaluated in a total of one hundred and sixty subjects (160). One hundred and ten (110) of which were obese subjects (BMI 30kg/m2 and above) and fifty (50) non-obesecontrols. Blood pressure and fasting samples were collected. Anthropometric parameters were measured; body mass index and waist-hip ratio were calculated for all the participants recruited in this study. Obese subjects were further grouped based on their BMI values as; class I (BMI 30-34.9kg/m2), class II (BMI 35-39.9kg/m2) and class III (BMI 40kg/m2 and above). TNF-α and insulin were determined using enzyme linked immunosorbent assay (ELISA). Total cholesterol and triglyceride were determined using the enzymatic colorimetric method; LDL, VLDL, HOMA-1R, AIP were respectively calculated. High density lipoprotein was determined using the precipitation/cholesterol enzymatic method. The mean values of TNF-α, insulin, FPG, HOMA-1R, TC, TG, LDL, VLDL, AIP, SBP and DBP were all significantly higher in obese subjects compared to non-obese controls group (P<0.05). The mean value of HDL was significantly higher in the non-obese controls compared to the obese group (P<0.05). The values of WHR, TG, VLDL, DBP and AIP were significantly higher in male obese subjects compared to obese female (P<0.05). No significant differences were observed in the other parameters in the male and female obese participants. There were no significant variations in the levels of TNF-α, TC, TG, HDL, LDL, VLDL and AIP among the three groups (P>0.05). Insulin and HOMA-1R were significantly higher in class III group compared to the class I group. A significant positive correlation was observed between obesity markers (BMI, WC) and markers of insulin resistance (HOMA-1R, insulin) among the obese subjects (P<0.05). There were also positive correlations among WHR and TG (r = 0.260), TG and HOMA-1R (r = 0.253), WHR and AIP (r = 0.244), SBP and HOMA-1R (r = 0.258) among the obese subjects. In the controls, a positive correlation was observed between AIP and TNF-α (r = 0.380), TG and TNF-α (0.280), while a negative correlation was observed between WC and HDL (r = 0.294), WHR and HDL (r = 0.356). These results suggest that obesity is a strong predictor of insulin resistance which in turn may predict cardiovascular disease risk factors such as high TG, AIP and BP and low HDL levels. Findings from the study also suggest that higher circulating TNF-α level may contribute to adipose tissue related inflammation and associated pathologies among obese subjects.
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TABLE 1: Anthropometric parameters and blood pressure in obese and non-obese control subjects
TABLE 2: Fasting plasma glucose, insulin, Homeostasis model assessment of insulin resistance (HOMA-1R), Tumour necrosis factor-alpha (TNF-α), lipid profile and atherogenic index of plasma (AIP) in obese and non-obese control subjects
TABLE 3: Anthropometric parameters and blood pressure in male and female obese subjects
TABLE 4: Fasting plasma glucose, insulin, Homeostasis model assessment of insulin resistance (HOMA-1R), Tumour necrosis factor-alpha (TNF-α), lipid profile and atherogenic index of plasma (AIP) in male and female obese subjects
TABLE 5: Anthropometric parameters and blood pressure among the three classes of obesity
TABLE 6: Fasting plasma glucose, insulin, Homeostasis model assessment of insulin resistance (HOMA-1R), Tumour necrosis factor-alpha (TNF-α), lipid profile and atherogenic index of plasma (AIP) among the three classes of obesity
FIG 1: Structure of insulin
FIG 2: Schematic view of insulin signaling pathway
FIG 3: Secretion of inflammatory adipokines from adipose tissue in obese state
FIG 4: Mechanisms of dyslipidemia in obesity
FIG 5: Correlation plot of homeostasis model assessment of insulin resistance (HOMA-IR) against body mass index in obese subjects
FIG 6: Correlation plot of insulin against body mass index in obese subjects
FIG 7: Correlation plot of homeostasis model assessment of insulin resistance (HOMA-IR) against waist circumference in obese subjects
FIG 8: Correlation plot of insulin against waist circumference in obese subjects
FIG 9: Correlation plot of triglyceride against waist-hip ratio in obese subjects
FIG 10: Correlation plot of homeostasis model assessment of insulin resistance (HOMA-IR) against triglyceride in obese subjects
FIG 11: Correlation plot of triglycerides against fasting blood glucose in obese subjects
FIG 12: Correlation plot of atherogenic index against waist–hip ratio in obese subjects
FIG 13: Correlation plot of systolic blood pressure against atherogenic index of plasma in obese subjects
FIG 14: Correlation plot of homeostasis model assessment of insulin resistance (HOMA-IR) against systolic blood pressure in obese subjects
FIG 15 Correlation plot of systolic blood pressure (SBP) against body mass index (BMI) in obese subjects
FIG 16: Correlation plot of systolic blood pressure (SBP) against waist circumference in obese subjects
FIG 17: Correlation plot of high density lipoprotein cholesterol (HDL) against waist circumference in control group
FIG 18: Correlation plot of high density lipoprotein cholesterol (HDL) against waist- hip ratio (WHR) in control group
FIG 19: Correlation plot of atherogenic index of plasma (AIP) against tumour necrosis factor- alpha (TNF-α) in control group
FIG 20: Correlation plot of triglyceride against tumour necrosis factor alpha (TNF-α) in control group
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Obesity is an important contributor to ill health and is now so common in our population that it is beginning to relieve undernutrition as a major disease that affects health (Tsigosa et al, 2008). Worldwide, obesity ranks second after smoking as the cause of avoidable deaths and its prevalence is increasing in children and adults.Authorities view it as one of the most serious public health problem of the 21st century (Barness et al, 2007).
“Obesity results from the accumulation of excessive body fat that often presents a risk to health” (WHO, 2014).The state of one being obese is usually defined using body mass index (a ratio of the weight in kg and the square of height in meters of an individual) (Kamazawa et al, 2002). Body mass index of 30kg/m2and above marks obesity, while the range of 25-30kg/m2 is defined as overweight (Barness et al, 2007).
Over 60million fully grown individuals in the U.S. have been identified as been obese with BMI of 30kg/m2and above, also surprizing is the fact that the prevalence of obesity has steadily gone up to30% during the past decade (Flegal et al, 2002). Often thought to be a challenge only in countries with high income, overweight and obesity are now also gradually increasing in developing countries and is affecting the general world population (WHO, 2009). Here in Nigeria, the prevalence of overweight individuals has been reported to range from 20.3% to 35.1%, 8.1% - 22.2% for obesity (Chukwuonye, 2013).
Obesity has an undesirable effect on health. Yearly, obesity-associated co-morbidities cost above “150 billion dollars and result in an estimated 300,000 early deaths in the United States” (Corral et al, 2008). This high-cost results from its related co-morbid conditions that include type 2 diabetes mellitus, coronary artery disease, hypertension, dyslipidemia, respiratory disorders and certain types of cancers such as colon, breast and ovarian cancers (Bray, 2014).
Obesity arises when there is excessive intake of food, especially high-calorie diet and decreased physical activity (sedentary lifestyle), during these events, the mass of adipose tissue increases (Wright et al, 2004). Adipose tissue serves as the main storage site of triglyceride from which energy can be derived during starvation. However, over the past period of ten years, the human adipocytes has been declared an endocrine organ which produces over 50 protein termed adipokines or adipocytokines including; adiponectin, tumour necrosis factor- alpha, interleukin-6, leptin and resistin(Hotamislgil et al, 1993).Expansion of adipose tissue leads to the production and release of these bioactive substances which trigger chronic low-grade inflammation and interact with a range of processes in many different organs (Kahn and Flier, 2000). Although the precise mechanisms are still unclear, deregulated production or secretion of these adipokines caused by excess adipose tissue can contribute to the development of obesity-related metabolic diseases (Greenberg and Obin, 2006).
Insulin resistance is defined as a state in which a given concentration of insulin produces a less than expected biological effect. It can also be defined as the requirement of 200 or more units of insulin per day to attain glycemic control and prevent ketosis (Mari et al, 2015). Insulin resistance is an integral feature of metabolic syndrome and is a major predictor of the development of type 2 diabetes (Eslam et al, 2011). It has long been recognized that obesity is associated with type 2 diabetes, and the major basis for this link is the ability of obesity to induce insulin resistance. Adipose tissue is one of the insulin-responsive tissues, and insulin stimulates storage of triglyceride in adipose tissue by increasing the uptake of glucose and fatty acids derived from circulating lipoproteins and inhibiting lipolysis (Ota et al, 2013).
“Homeostasis model assessment of insulin resistance” is used to determine insulin resistance and can simply be derived from the fasting concentration values of insulin and glucose using the formula; fasting serum insulin (μ/U/ml) x fasting plasma glucose (mmol/L)/22.5 (Eslam et al, 2011).HOMA-IR approaching the value of 1 is considered to be related to insulin sensitivity while the presence of insulin resistance is defined as HOMA >4.65 (Stern et al, 2015).
Tumour necrosis factor-alpha (TNF-α) is a proinflammatory cytokine that has been consistently reported to contribute to the pathogenesis of obesity and insulin resistance (Trayhurn, 2005). Expression of TNF-α is increased in obesity and in insulin resistance in humans and is positively correlated with insulin resistance (Shoelson et al, 2006). Treatment with TNF-α induces insulin resistance in adipose tissue, whereas deletion of TNF-α or its receptors improves insulin sensitivity in obese animals (Suganami, 2005). However, the correlation between plasma TNF-α levels and insulin resistance is relatively weak and chronic neutralization of TNF-α does not improve insulin resistance in healthy overweight subjects with metabolic syndrome and insulin resistance, despite improvements in inflammatory status (Kanda et al, 2006).
The absence of an effect on insulin sensitivity may be due to a compensatory role of other cytokines in the absence of TNF-α, since metabolic deregulation is attributed to numerous proinflammatory cytokines secreted by the adipose tissues (Suganami, 2005).
Cardiovascular diseases (CVD) are a group of diseases that affects the cardiovascular system, especially the heart and blood vessels (Mahabadi et al, 2009). Cardiovascular diseases are the leading causes of death worldwide and mostly results from atherosclerosis and hypertension (Vasan, 2006).Obesity is an established risk factor of CVD and dyslipidemia. Dyslipidemia is defined as the abnormal accumulation of lipids in the blood such as triglycerides and cholesterol (Saleh et al, 1999). Dyslipidemia in obesity is related to increased circulating free fatty acid, high triglyceride concentration and reduced high density lipoprotein cholesterol concentration (Clemente et al, 2011). These events result from the excess release of fatty acid from the adipose tissue in obese state via lipolysiswhich are taken to the liver for synthesis of very low density lipoprotein cholesterol, VLDL directly increases plasma triglyceride concentration (Clemente et al, 2011).
Atherogenic index of plasma (AIP), defined as log (TG/HDL), has recently been proposed as a marker of plasma atherogenicity and reflects the balance between risk and protective lipoprotein forces-Triglyceride refer to atherogenic lipids while HDL refer to the protective lipid (Firdous, 2014).
In many westernized societies, obesity has reached epidemic proportions and consequently attracts commensurate medical attention as well as intervention programmes. However, most developing countries are yet to come to terms with its gradual increase as reported in various epidemiological studies.
This changing pattern in prevalence is attributed to rapid unplanned urbanization, changes from local dietary pattern to western style diets driven by the proliferation of fast food outlets in major cities across the country.
Considering that obesity causes major adverse health outcomes such as type 2 diabetes, cardiovascular disease, dyslipidemia and cancers which are serious problems worldwide, there is every need to investigate the possible mechanisms that modulate the expression of obesity and its related co-morbidities
This study aims to provide information on the association between obesity markers (BMI, WC, WHR), inflammatory marker (TNF-α), insulin resistance markers (HOMA-1R, insulin) and markers of cardiovascular risks (AIP, lipid profile, blood pressure) among obese individuals in our locality.
1) To determine the levels of TNF-α, insulin, FPG, HOMA-IR, lipid profile and atherogenic index in obese subjects and non-obese apparently healthy control subjects.
2) To determine the levels of TNF-α, insulin, FPG, HOMA-IR, lipid profile and atherogenic index in obese subjects based on gender.
3) To determine the levels of TNF-α, insulin, FPG, HOMA-IR , lipid profile and atherogenic index among various classes of obesity using BMI as a classification criterion.
4) To determine if there is any relationship among the levels ofTNF-α, FPG, insulin, HOMA-IR , lipid profile, atherogenic index and obesity related parameters such as BMI, WC and WHR.
With endeavours over the past two decades, outstanding results in the research of obesity induced insulin resistance and cardiovascular disease particularly in terms of the mechanisms involved have been clearly stated. Some of these developments are believed to lead to treatments of the diseases. One of the newly identified and innovative notions is the development of low grade inflammation in obesity.
Several studies in different populations indicate that inflammation may be the link between obesity, insulin resistance (IR) and cardiovascular disease. However the relationship has not been adequately explored in our population and among Africans. There is therefore a need to investigate this area in our population in other to assist with the health challenges of obesity, create awareness and also provide a basis for sound management of type II diabetes mellitus and cardiovascular disease.
Obesity has adverse effect on health and has been defined “as a medical condition in which body fat has accumulated excessively that it reduces life expentancy and causes increased health challenges”(Haslam and James, 2005).Body mass index is commonly used to define obesity and it is derived “by dividing a person weight in kilogram by the square of the individuals height measured in meters. A body mass index of greater than or equals to 30kg/m2defines obesity while a BMI ranging between 25-29.9kg/m2 marks overweight”(Haslam and James, 2005). This marker; BMI provides a common standard for classification of obesity, though the risk factors of obesity can increase with lower BMI in some populations. Most East Asian countries use stricter criteria (Adams and Murphy, 2000).
Obesity ranks second after smoking as the cause of avoidable deaths and its prevalence is increasing in adults and children; some medical bodies view it is the most severe factor causing health challenges of this century (Barness et al, 2007). Obese subjects often face stigmatization in recent times, though the condition was seen as an indication of wealth and affluence in older times and is still observed this way in some countriesaround the world (Woodhouse, 2008).The “American Medical Association” defined obesity as a disease in 2013 (Mei et al, 2002).
Obesity can be classified using the following parameters and measurements:
Body mass index: Adolphe Quetelet, a Belgian anthropometrist and statistician brought to light in the 19th century the concept of Body mass index (Corral et al, 2008). BMI is related to body fat percentage and overall body fat.
A graded classification of overweight and obesity using BMI values provides valuable information about increasing body fatness (Flegal et al, 2001). It allows meaningful comparisons of weight status within and between populations and the identification of individuals and groups at risk of obesity morbidity and mortality. It is important to know that owing to differences in body proportions, BMI may not correspond to the same degree of fatness across different populations (sturm, 2007). BMI poorly predicts obesity in body builders and pregnant women.
BMI (kg/m2) classification
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BMI is defined as the subjects weight divided by the square of their height and is calculated as follows; either in metric or US “customary” units.
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Where m and h are the subjects weight and height respectively.
Some modification to the WHO classification listed in the table above has been made by particular bodies. The surgical literature break down “class III” obesity into further categories whose exact values are still disputed (Kamazawa et al, 2002):
- Any BMI ≥35 or 40kg/m2 is severe obesity
- A BMI of ≥ 40 – 44.9kg/m2 is morbid obesity
- A BMI of ≥45 or 50kh/m2 is super obesity
As Asian populations develop negative health consequences at a lower BMI than Caucasians, some nations have redefined obesity, the Japanese have defined obesity as any BMI greater than 25kg/m2, while China uses a BMI of greater than 28kg/m2 (Bei-fanz, 2002).
The BMI-based definition is easy to use and is particularly convenient for statistical purpose, since it only depends on two commonly measured quantities, one’s height and weight. However, it ignores variations between individuals in amounts of lean body mass, particularly muscle mass. Individuals involved in heavy physical labour or sports may have high BMI values despite having little fat (Kamazawa et al, 2002).
Body fat percentage: Body fat percentage (BF%) is the ratio of the total weight of person’s fat to his or her body weight, BMI is viewed merely as a way to approximate BF% (Okorodudu et al, 2010). Levels in excess of 32% for women and 25% for men are generally considered to indicate obesity. However, accurate measurement of body fat percentage is much more difficult than BMI measurement. Several methods of varying accuracy and complexity exist:
i) BF% can be estimated from a person’s BMI using the formula
Body fat percentage = 1.2 x BMI + 0.23 x age – 5.4 – 10.8 x gender
Where gender is 0 if female and 1 if male (Okorodudu et al, 2010).This formula takes into account the fact that body fat percentage tends to be 10 percentage points greater in women than in men for a given BMI (Flynn et al, 2006). It recognizes that a person’s percentage body fact tends to increase as they age, even if their weight and BMI remain constant. Other methods used to determine body fat percentage includes;
- Hydrostatic weighing which involves weight a person under water.
- Skin fold test in which a pinch of skin is precisely measured to determine the thickness of the subcutaneous fat layer (Flegal et al, 2001)
- Bioelectrical impedance analysis which uses electrical resistance but has not been shown to provide an advantage over BMI.
For research purpose, body fat percentage can be measured using techniques such as: computer tomography (CT scan), magnetic resonance imaging(MRI) and dual energy x-ray absorptiometry (DEXA) (Flegal et al, 2001). These techniques provide very accurate measurements, but it can be difficult to obtain in severely obese due to weight limits of most equipment and insufficient diameter of many CT or MRI scanners (Flegal et al, 2001).
Waist circumference and waist-hip-ratio: Waist circumference and waist-hip ratio measures a common form of obesity known as abdominal or central obesity, characterized by excess deposits of fat in the abdominal region and inside peritoneal cavity (Janssen et al, 2002). They have been shown to be comparable to BMI in their ability to predict the role of metabolic abnormalities such as type II diabetes, and possible superior to BMI as predictors of cardiovascular diseases (Janssen et al, 2002).
In the United State, a waist circumference of >102cm (>40inches) in men and >88cm (>34.5 inches) in women or the waist-hip ratio (i.e. the circumference of the waist divided by that of the hips of >0.9 for men and 0.85 for women) are used to define central obesity (Yusuf et al, 2004). A lower cut off point has been set by the European Union, South Asian, Chinese men and Japanese men.
In those with a BMI under 35, intra-abdominal body fat is related to negative health outcomes independent of total body fat. Intra-abdominal or visceral fat has a particularly strong correlation with cardiovascular disease (Yusuf et al, 2004). In a study of 15,000 people, waist circumference correlated better with metabolic syndrome than BMI (Tsigosa et al, 2008).
Obesity is becoming so popular within the world’s population and is gradually replacing infectious diseases and under nutrition as a significant contributor to ill health (Loscalzo et al, 2008). The incidence of obesity has rapidly increased globally, with over 1 billion adults been overweight and atleast 300 million of them been clinically obese (WHO, 2014). Obesity contributes significantly to the global burden of clinical diseases and pathologies (Loscalzo et al, 2008). In developing countries, obesity coexists with under nutrition and is a complex condition which affects all age and socio-economic groups with serious psychological and social dimensions (Moyer, 2012).
Obesity ranges below 5% in certain parts of Asia and Africa to upto 75% in urban Samoa (Bessesen, 2008). However in some of the low incident countries such as china, rates of upto 20% have been reported in some cities. Childhood obesity is already highly prevalent in some areas and on the rise in others. Worldwide, roughly 17.6 million children under the age of 5 are estimated to be overweight (Bessesen, 2008). In the United States, the prevalence of overweight children has doubled and that of adolescents tripled since 1980 (Ogden et al, 2006). The prevalence of obesity among youths, aged 12-17 has risen markedly from 5% - 13% in boys and 5% - 9% in girls over the past two decades in USA (Allison et al, 1999). Obesity epidermic is global and is gradually on the increase in developing countries.
In West Africa, the prevalence of obesity is estimated to be 10%, with women been 3 times more likely to become obese than the men. The rates of obesity have however doubled over the last 15years in urban West Africa (Ogden et al, 2006). Obesity rates are rising worldwide and although once thought to be a challenge only to high income countries, it rates are steadily increasing in developing world (Abubakari et al, 2008). The impact of obesity is felt more in urban setting with sub-sahara Africa been the only region in the world where obesity is not common (Abubakari et al, 2008).
Obesity accounts for 2-6% of total health care costs in several developed countries, some estimates put the figure as high as 7% (Sacks et al, 2009). The true costs are undoubtedlygreater as not all obesity-related conditions are included in the calculations.
The rising epidemic reflects the profound changes in society and in behavioral patterns of communities over recent decades. While genes are important in determining a person’s susceptibility to weight gain, energy balance is determine by caloric intake and physical activity (Sacks et al, 2009). Thus societal changes and worldwide nutrition transition are driving the obesity epidemic. Economic, growth, modernization, urbanization and globalization of food markets are just some of the forces thought to underlie the epidemic (Strychar, 2006). As incomes rise and populations become more urban, diets high in complex carbohydrates give way to more varied diets with a higher proportion of fats, saturated fats and sugars. At the same time, large shifts towards less physically demanding work have been observed worldwide (Strychar, 2006). Moves toward less physical activity are also found in the increasing use of automated transport, technology in the home and more passive leisure pursuit (Sacks et al, 2009).
On individual bases, obesity usually results from excessive caloric intake and decreased physical activity (Lau et al, 2007). A few cases results from genetic predisposition, medical cases and psychiatric disorders. At the societal level, obesity cases are seen as a result of easily palatable and accessible foods, increased dependence on vechiles anad automated manufacturing (Drewnowski et al, 2004).
A review carried out in 2006 identified 12 possible contributors to the recent increase in obesity and they include:
- Excess nutritional intake
- Decreased physical activity/sedentary lifestyle
- Inadequate sleep
- Deregulators of endocrine homeostasis including environmental factors that alter lipid metabolism.
- Reduced variability in ambient temperature
- Reduced rate or cessation of smoking, because smoking represses appetite.
- Use of drugs and medications that stimulates weight gain (e.g. some antipsychotic drugs).
- Increases among different age groups and ethinicity that are likely to be heavier.
- Pregnancy at an older age which may predispose the mother and child to obesity.
- Epigenetic risk factors transferred from one generation to another.
- Natural selection for larger body mass index
- Increased susceptibility to obesity rsik factors as a result of assortative mating. This increases the prevalence of obesity by increasing variance in weights among the population (Lau et al, 2007).
Diet: The widespread availability of nutritional guidelines has done little to address the problems of overeating and poor dietary choice. From 1971 to 2000, obesity rates in the United States increased from 14.5% to 30.9% (Marantz et al, 2008). During the same period, an increase occurred in the average amount of food energy consumed. Most of this extra food energy came from an increase in carbohydrate consumption rather than fat consumption (Flegal et al, 2001). The primary sources of these extra carbohydrates are sweetened beverages, potato chips which now account for almost 25% of daily food energy in young adults in America (Marantz et al, 2008). Consumption of sweetened drinks such as soft drinks, fruit drinks, iced tea and energy and drinks is believed to be contributing to the raising rated of obesity and to an increased risk of metabolic syndrome and type 2 diabetes (Wright et al, 2004). As societies become increasingly reliant on energy-dense, big-portions, and fast food meals, the association between fast food consumption and obesity becomes more concerning (Mozaffarian et al, 2001).
Sedentary lifestyle: Sedentary lifestyle or physical inactivity encourages the development of obesity. Huges shifts have been made towards less demanding physical work, about 30% of the general population get inadequate exercise (Ness-Abramof et al, 2006). This is mainly due to increasing use of modernized/automated transport system and an increase in mechanized technologies that save labour at homes. A decline in physical activity in children has also been observed as a result of reduced exercise and physical education (Salmon and Timperio, 2007). Generally an association has been observed between the time spent watching television and the risk of becoming obese as reported in different reviews (Ness-Abramof et al, 2006)
Individuals who are inactive are more prone to becoming obese because their caloric intake from food and drinks is greater than the calorie burnt (Salmon and Timperio, 2007).
Genes and family history: Like many other medical conditions obesity is the result of interaction between genetic and environmental factors (Poirier et al, 2006). Polymorphisms in various genes controlling appetite and metabolism predispose to obesity when sufficient food energy is present. As of 2006, more than 41 of other sites on the human genome have been linked to the development of obesity when a favorable environment is present (Poirier et al, 2006). People with two copies of the FTO gene (fat mass and obesity associated gene) have been found on average to weigh 3-4kg more and have a 1.67-fold greater risk of obesity compared with those without the risk allele (Loos and Bouchard, 2008).Overweight and obesity tends to run in families. The chances of one being overweight are greater if one or both of the parents are overweight or obese (Poirier et al, 2006).
Health conditions and medication: The risk of obesity can be increased by certain mental and physical illnesses in addition to the pharmaceutical substances used to treat them. Medical illness like genetic syndrome, acquired conditions like hypothyroidism, Cushing syndrome, polycystic ovarian syndrome, growth hormone deficiency and some congential conditions can increase the risk of obesity (Rosen et al, 1993).
Some drugs that can result in weight gain or variation in body compostion includes; sulfonylaneas and thiozolodinedianes, antipsychotics medications, antidepressants, certain steroids, anticonvulsants (phenytoin and valproate) and some hormonal contraceptive (Haslem and James, 2005).
Environment: Our surroundings support and encourage obesity rather than supporting healthy lifestyle. Some of the reasons include the following;
- Absence of sidewalls and places for recreation around neighbourhoods, lack of parks, trails and affordable gyms make it difficult for individuals to engage in physical activity (Haslem and James, 2005).
- Food adverts: In the United States, people are surrounded with adverts from food industries and they often advertise for high caloric foods and sugary drinks. This influences people to buy them and make them eat more thus encouraging obesity. (Mclaren, 2007).
- Work allocation: Some people claim not to have time for physical activity because of long periods of time they spend working.
Leptin and ghrelin: Leptin was discovered in 1994 by J.M. Friedman’s laboratory (Zhang et al, 1994). These investigators postulated that leptin was a satiety factor. In the ob/ob mouse, mutations in the leptin gene resulted in the obese phenotype, opening up the possibility of leptin therapy for human obesity. However, soon thereafter J.F. Caro’s laboratory could not detect any mutations in the leptin genes in humans with obesity. On the contrary, leptin expression was increased proposing the possibility of leptin resistance in human obesity (Considine et al, 1995). Ghrelin is produced by the stomach, modulating short term appetitive control (i.e. to eat when the stomach is empty and to stop when the stomach is stretched) (Zhang et al, 1994). Leptin is produced by adipose tissue to signal fat storage reserves in the body, and mediates long term appetitive control (i.e. to eat more when fat storages are low and less when fat storages are high). Although administration of leptin may be effective in a small subject of obese individuals who are leptin deficient, most obese individuals are thought to be leptin resistant and have been found to have high levels of leptin (Bessesen, 2008). This resistance is thought to explain in part why administration of leptin has not shown to be effective in suppressing appetite in most obese people (Considine et al, 1995).
The prevalence of obesity is increasing worldwide. Obesity is gradually replacing infectious diseases and under-nutrition as the most notable contributor to ill health (Bray, 2004). In fact, obesity should be regarded as an epidemic that threatens global health and not simply as a cosmetic problem affecting some individuals.
Obesity has a detrimental effect on health. Obesity results in an estimated 300,000 premature deaths and its health implication cost over 150 billion dollars in the U.S each year (Shoelson et al, 2007). The effect of obesity on health and its associated co-morbidites includes the following:
- High blood pressure: Extra adipose tissue that accumulates in the body as a result of obesity needs additional oxygen and nutrient supply in other to survive. For this to be achieved, the blood vessel supplies more blood to the adipose tissue (Haslem and James, 2005). The workload to the heart is increased because it must distribute and pump extra blood through additional blood vessels. Extra pressure is exerted on the arterial walls because of the increased blood flows. This increases the blood pressure and heart rate and reduces the ability of the body to distribute blood through the blood vessels (Darvall et al, 2007).
- Type II diabetes mellitus: Obese individuals are predisposed to the development of insulin resistance. Insulin is a peptide hormone that regulates blood sugar level. In obesity associated insulin resistance, the blood glucose level is elevated and the individual’s risk of developing type II diabetes is increased (Haslem and James, 2005). The chances of developing hypertension and type II diabetes is increased with body fatness, once thought to be a problem in older adults, it is now clear that the disease also affects obese children. About 85% of people with diabetes have type II dibetes, among which 90% are obese or overweight and this is gradually developing into a world problem (Haslem and James, 2005).
- Dyslipidemia: Reduced glucose utilization and increase in hepatic glucose output are not the only causes of increased free fatty acid levels observed in obesity. “Higher levels of free fatty acids affects lipid metabolism by increasing hepatic production/synthesis of very low density lipoprotein cholesterol (VLDL), thus reducing HDL levels and increasing the amount of small dense LDL particles” (Grundy, 2004). These smaller LDL particles can easily penetrate the arterial wall because they are more atherogenic than the larger LDL particle and readily undergoes glycation and oxidation. Even though the LDL cholesterol level does not change remarkably, the presence of small LDL still increase atherogenic risks. These changes in lipid parameters are related to increased cardiovascular risks (Haslem and James, 2005).
- Heart diseases: In obese individuals, the risk of developing atherosclerosis (hardening of the arteries) is 10 times more often compared to non-obese individuals (Haslem and James, 2005). The prevalence of coronary artery disease is also increased because of deposition of fatty streaks in the arteries supplying the heart. Chest pain (angina) and heart attacks may also develop because of narrowed arteries and the reduced flow of blood to the heart. The risk of stroke is also increased because of blood clots that can form in narrowed arteries (Darvall et al, 2007).
- Non-alcoholic fatty liver disease (NAFLD): NAFLD is currently the most common form of chronic liver disease, and it’s incidence has increased in parallel to the rise in the incidence of obesity (Bray, 2004). More than two thirds of patients with NAFLD are obese. NAFLD is charachteristic of triglycerides in the liver (hepatic steatosis), inflammation and subsequent fibrosis (non alcoholic steatohepatitis, NASH)
- Joint problems (osteoarthritis): Obesity has an impact on the knees and hips because of the pressure applied on the joints by extra weight (Haslem and James, 2005).
- Sleep apnea and respiratory problems: An increased deposition of adipose tissue on the chest walls and abdomen affects the diaphragm and chest by increasing mechanical actions and may leads to repiratory excursion during breathing. This may predispose to sleep apnea which causes difficulty in breathing for brief periods during sleep, sleep apnea often results in discomfort, sleeplessiness and heavy snoring (Poulain et al, 2006). In obesity, the extra weight added on the chest wall adds pressure to the lungs and often results in reduced breathing (Haslem and James, 2005).
- Neoplasm: The link between obesity and cancer is still not clearly stated as that of DM and CVD. This is because cancer does not occur independently but rather results from a combination of diseases (Haslem and James, 2005). In 2007, an expert panel made up of the “World Cancer Research” and “American Institute for Cancer Research” reviewed the literature on the link between obesity and cancer and came to the conclusion that from tested hypothesis, there is a link between obesity and cancers that originate from the GIT, breast, kidney and endometrium. Visceral adiposity and gaining of weight during adulthood also showed an association with many cancers (Basen-Engquist et al, 2010).
- Metabolic syndrome: The national cholesterol Education programme has identified metabolic syndrome as a complex risk factor for cardiovascular disease (Haslem and James, 2005). Metabolic syndrome consists of six major components, abdominal obesity, elevated blood cholesterol, elevated blood pressure, and insulin resistance with or without glucose intolerance, elevation of certain blood components that indicate inflammation and elevation of certain clotting factors in the blood (Basen-Engquist et al, 2010). In the US, approximately one-third of overweight or obese persons exhibit metabolic syndrome.
- Psychosocial effects and social stigmatization: obese and overweight individuals often suffer a disadvantage in a culture where physical attractiveness is attributed to overly thin people (Haslem and James, 2005). Overweight and obese persons are seen to be lazy and weak-willed and are often thought to be the cause of their condition. These individuals often earn lower incomes at work and find it hard to be in sexual relationships (Poulain et al, 2006). This results in discrimination, low self esteem and even torment in these individuals.
Effective management of obesity consists of a series of long-term strategies. Some of which includes; weight maintenance, prevention, management of associated co-morbid conditions and weight loss (Tate et al, 2007). They should be obliged to engage in coordinated, multi-sectoral and population-based approach, some of which consists of environmental aid for consistent physical exercise and healthy diet.
Key features include:
- Establishing helpful population-based surroundings via policies that encourage steady provision of a range of low fat, high fiber foods, and opportunities that encourage physical activity (Tate et al, 2007).
- Encouraging healthy behaviors that promote, motivate and aid obese individuals to shed weight by;
- Consuming fruits and vegetables together with nuts and whole grains.
- Promoting moderate physical activity for at least 30 minutes daily.
- Reducing the intake of high caloric and sugary diets.
- Eating more of unsaturated fatty based vegetable oil foods rather than saturated animal based fatty foods.
- Ensuring effective support for obese individuals thus reducing the burden of obesity and its associated co-morbidities by establishing clinical programmes and aiding staff trainings thus encouraging weight loss and preventing further weight gain (Shick et al, 1998).
- Exercise: Adequate physical activity using body muscles helps breakdown energy derived from glycogen and fat. Because of the huge size of the leg muscles, cycling, running and walking are the most efficient means of exercising to decrease body fat (Tate et al, 2007). The “American Heart Association” advices individuals to engage in atleast 30minutes of moderate exercise at a minimum of 5days a week to maintain adequate health (Sahlin et al, 2008). Arrows and signs that promote the use of stairs, together with community campaign programmes have shown to aid improved exercise in the population at large. Cities such as Bogota and Colombia on holidays and Sundays block off about 113kilometers (70miles) to get citizens to exercise more. These pedestrian walk ways are part of the initiative to reduce the increasing prevalence of obesity (Sahlin et al, 2008).
- Medication: Three medications orlistat (xenical), lovcaserin (Belviq) and combinations of phentermine and toparamate (Qsymin) are currently available and have evidence for long term use (Astrup, 2010). Weight loss with orlistat is modest and average of 2.9kg (6.416) at 1-4years (Bays, 2011). Its use is associated with high rates of gastrointestinal side effects and concerns have been raised about negative effects on the kidneys (Bays, 2011). The other drugs mentioned effect weight loss but also have significant side effects.
- Surgery: The use of surgical interventions for the treatment and management of obesity is reffered to as weight loss surgery (bariatric sugery). Bariatric sugery is however recommended for mordid obese individuals (BMI > 40) in whom failure to lose weight by dietary modifications and medical treatment has been achieved because all surgical operations have consequences (Encinosa et al, 2006). Reducing the size of the stomach (gastric banding and vertical banded gastroplasty) which makes satiety from food come early and reduction in the length of bowel that comes in contact with food which reduces absorption (bypass gastric surgery) are the two most common approaches used in weight loss surgery (Sjostrom et al, 2007). Scentists and researchers are searching for less invasive treatment techniques because of the cost and risks of bariatric surgery, some of which include inserting devices that can fill the space of the stomach (Encinosa et al, 2006).
Obesity is gradually increasing in epidemic proportions in the United States and many other developed communities. Its prevalence has surpassed 30% of the population in the US and presents the highest worldwide prevalence of obesity (Bessesen, 2008). The prevalence of obesity in adults has doubled in the US since 1980. However, the greater burden is that childhood obesity has tripled in this same period (Bessesen, 2008).
About 20 years ago, the concept of insulin resistance syndrome was brought to light by Dr. Gerald M. Reaven and he outlined it as the primary cause of elevated LDL, glucose intolerance, hypertension, along with reduced HDL (Reaven, 2004). This clinical notion developed into the more popular term metabolic syndrome. The term metabolic syndrome (syndrome X), is defined as a disorder that possess both metabolic and cardiovascular risk factors (Kaur, 2014). Some of these risk factors include; hyperinsulinaemia, insulin resistance, central/visceral adiposity (accumulation of adipose tissue around the abdominal and waist region), glucose intolerance, dyslipidemia, hypertension, increase production of proinflammatory substances and microalbuminemia (Kaur, 2014). Other clinical conditions have recently be related to metabolic syndrome, some of which includes; oxidative stress, atherosclerosis, non-alcoholic fatty liver disease and polycystic ovarian syndrome (Reaven, 2004).
Given the complexities of the factors contributing to the metabolic syndrome numerous health groups in various countries have defined the disorder with slightly different criteria (Ford et al, 2002). Some health organization believes that insulin resistance is the single most important predictor for future development of type 2 diabetes and cardiovascular disease that the disorder is defined as the insulin resistance syndrome (Ford et al, 2002). The following table lists the criteria set forth by the American Heart Association and the National Heart, Lung and Blood Institute in defining the metabolic syndrome.
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Other components of the metabolic syndrome are; high blood pressure, abnormal lipid concentrations, chronic inflammation, impaired fibrinolysis, procoagulation and insulin resistance which is the hallmark of the syndrome (Poulsen et al, 2001). Many researchers and medical practioners state that insulin resistance is the basis of cardiovascular pathologies associated with metabolic syndrome (Pollex and Hegele, 2006), one of the main reasons for this is the obvious role insulin plays in fat homeostasis. Insulin stimulates the storage of energy in the form of triglycerides in the adipose tissue or as glycogen from carbohydrate food sources in the skeletal muscles and liver (Pollex and Hegele, 2006). At the level of fat homeostasis, insulin resistance increases the circulating level of triglycerides. As a result of insulin resistance, the level of peripheral fatty acids delivered to the liver is increased; this in return increases hepatic triglyceride synthesis. TG’s producedare packaged and formsVLDL which are returned to the circulation (Ford et al, 2002).
“Insulin is a peptide hormone produced by the beta cells in the pancreas”. It regulates the metabolism of carbohydrate and fats by promoting the absorption of glucose from the blood to skeletal muscles and fat tissue and by causing fat to be stored rather than used for energy (Sonksen and Sonksen, 2000). Insulin also inhibits the production of glucose by the liver.
The human insulin protein is composed of 5I amino acid, and has a molecular weight of 5808 Da (Sonksen and Sonksen, 2000). It is a dimer made up of A and B-chains which are linked together by disulfide bonds. The primary structure of bovine insulin was first determined by Frederick Sanger in 1951 (Dunn, 2005). After that, this polypeptide was synthesized by several groups. The 3-dimensional structure of insulin was determined by x-ray crystallography in Dorothy Hodgkins laboratory in 1969 (Sonksen and Sonksen, 2000)
Insulin is synthesized in the pancreas within the β-cells of the islets of Langerhans. Insulin consists of two polypeptide chains, the A- and B-chains, linked together by disulfide bonds. It however is first synthesized as a single polypeptide called preproinsulin in pancreatic β-cells (Rhoades et al, 2009). Preproinsulin contains a 24-residue signal peptide which directs the nascent polypeptide chain to the rough endoplasmic reticulum (RER).
The signal peptide is cleaved as the polypeptide is translocatedto the rough endoplasmic reticulum, forming proinsulin (Ronald, 2005). About 5 – 10minutes after its assembly in the endoplasmic reticulum, proinsulin is transported to the trans-Golgi network (TGN) where immature granules are formed, this transport process takes about 30minutes (Rhoades et al, 2009).
Through the actions of prohormone convertases (PC1 and PC2) an endopeptidase and carboxypeptidase E an exopeptidase, proinsulin is converted to active insulin (Steiner and Oyer, 1967). The prohormone convertases cleaves at two sites, producing a fragment called C-peptide and mature insulin molecule (Thomas, 1993). “The mature insulin produced is packaged inside mature granules waiting for metabolic signals (such as leucine, arginine, glucose and mannose) and vagal nerve stimulation to be exocytosed from the cell into the circulation” (Najjar, 2001).
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FIG 1: Structure of Proinsulin
Primary structures of porcine insulin and porcine proinsulin. The primary sequence of porcine insulin and proinsulin as determined by Sanger and co-workers (Brown H, Sanger F, Kitai R. 1955 Biochem J; 60:556-565).
- Physiological effects of insulin: The actions of insulin on the global human metabolism level include:
- Control of cellular uptake of glucose in muscle and adipose tissues
- Increased glucogen synthesis-insulin forces storage of glucose in liverand musclecells in the form of glycogen; lowered levels of insulin cause liver cells to convent glycogen to glucose and excrete it into the blood.
- Increased lipid synthesis-insulin forces fat cells to take up blood lipids, which are converted to triglycerides; lack of insulin causes the reverse (Sonksen and Sonksen, 2000).
- Decreased proteolysis-decreasing the breakdown of protein
- Decreased lipolysis-forces reduction in conversion of fat cell lipid stores into blood fatty acid, lack of insulin causes the reverse.
- Decreased gluconeogenesis – decreased production of glucose from non-sugar substrates, primarily in the liver.Lack of insulin causes the reverse (Dunn, 2005)
- Increased amino acid and potassium uptake-forces cells to absorb circulating amino acid; lack of insulin inhibits absorption.
The signal transduction pathway of insulin begins at the cellular level and it is a biochemical pathway that affects glucose homeostasis (Dunn, 2005). The pancreas on sensing digested and absorbed carbohydrate and the immediate rise in blood glucose level stimulates the release of insulin which promotes the absorption of glucose from the blood. Insulin binds to its receptors on the cell membrane and leads to a cascade of events that stimulates the usage and or storage of glucose within the cell (Gustavsson et al, 1999). Based on the tissues involved, the actions of insulin vary, its most significant action is to stimulate the absorption of glucose by adipose tissue and muscle (Dunn, 2005).
Insulin transduction signaling mediates the metabolic effects of insulin. Insulin binds to alpha subunit of its receptors on the cell surface and initiates its signaling pathway (Gustavsson et al, 1999). “The insulin molecule acts as a ligand where it binds to the alpha subunit of the insulin receptor forming a ligand-receptor complex. As a result of this binding, a conformational change is induced which brings about an autophosphorylation of some residues in the beta subunit. These β-subunit residues are also known as insulin receptor substrate (IRS) protein. This phosphorylation activates two main signaling pathways; the “phosphatidylinositol 3-kinase (P13k) – AKT/protein kinase B (PKB) pathway and the Ras-mitogen-activated protein kinase (MAPK) pathway” (Hubbard et al, 1994). The metabolic effects of insulin are mediated by the P13K – AKT/PKB pathway (Hubbard et al, 1994). The insulin receptor phosphorylates IRS-1 which activates P13K via binding to its SH2 unit. “A lipid second messenger (phosphatidylinositol- 3, 4, 5 triphosphate)” is generated from PI3K which activates other phosphorylation reactions including the AKT/PKB pathway (Hubbard, 1997). In the plasma membrane, these events eventually lead to the transfer of glucose transporter four into the membrane which stimulates the absorption of glucose. The growth and mitogenic effect of insulin is stimulated by the MAPK pathway and are not involved in controlling of the metabolic events of insulin (Gustavsson et al, 1999). In the adipose tissue, insulin also has an anti-lipolytic effect whereby the activation of P13K stimulates phosphodiesterase-3 so that more adenosive 3’,5’-cyclic monophosphate is hydrolyzed in adipocytes, which in turn limits the release of fatty acids from adipocytes (Hubbard, 1997).
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