{"id":11724,"date":"2024-02-22T07:36:12","date_gmt":"2024-02-22T07:36:12","guid":{"rendered":"https:\/\/nurs.essaybishops.com\/obesity-history-bmi-classification-determinants-and-effects\/"},"modified":"2024-02-22T07:36:12","modified_gmt":"2024-02-22T07:36:12","slug":"obesity-history-bmi-classification-determinants-and-effects","status":"publish","type":"post","link":"https:\/\/homeworkacetutors.com\/nursing\/obesity-history-bmi-classification-determinants-and-effects\/","title":{"rendered":"Obesity: History, BMI Classification, Determinants and Effects"},"content":{"rendered":"
\n

OBESITY<\/h2>\n

Obesity is fast becoming a serious epidemic in the United States<\/a> due partly to eating habits and physical inactivity amongst Americans. According to the Centre for Disease Control, Seventy-three percent of adults and 43 percent of all children<\/a> in the United States are overweight or obese. Among African-Americans 20 years and over, more than two-thirds are overweight or obese (Gaines, 2010). Generally, the rate of overweight and obesity are higher for African-American and Hispanic women than Caucasian women, higher in the south and Midwest and increases with age (Ogden et al., 2014; Gregg et al., 2009; Sherry et al., 2010). According to the World Health Organization, body mass index (BMI) of an obese person has a value greater than or equal to thirty. Type 2 diabetes and high blood pressure are two diseases that ultimately affect African Americans and this is predominantly caused by an increase in weight as those extra pounds predisposes a person to these diseases (Gaines, 2010). Obesity is one of the primary risk factor for heart diseases, diabetes and a number of cancers and these are major causes of death in American today. The health implication of obesity and the complications associated with it is increasingly becoming more detrimental than cigarette smoking and has therefore become one of the major preventable causes of death worldwide.<\/p>\n

This
\ninvestigation paper focuses on the brief history of obesity; this will take
\nobesity from its discovery over 2000 years to this present day. An understanding
\nof the BMI classification, aetiological determinants, pathophysiology and
\nhealth effects is important if obesity prevalence will be curtailed.
\nFurthermore, the socio economic impact of obesity management on the United
\nStates economy will be looked into. Finally, its treatment options, prevention
\nand trends of the disease will be discussed. <\/p>\n

HISTORY OF OBESITY<\/h2>\n

The
\nAncient Greeks were the first to acknowledge obesity as a health disorder and
\nthis was further recognized by the Ancient Egyptians in a similar way.
\nAccording to Hippocrates, corpulence is not only a disease itself, but the
\nharbinger of other diseases (Haslam & James, 2005). Hippocrates which was
\nthe Ancient Greek Father of Western medicine acknowledged obesity in his work
\nand details of various diseases including diabetes was first given by him.
\nAnother Indian surgeon Sushruta, also discovered the association between
\nobesity, diabetes and heart diseases and he was the first person to find out
\nthe significant signs, symptoms, causes and health implications. In the Ancient
\ndays, man always strived for food due to scarcity or famine and this resulted
\nin obesity being regarded as a sign of wealth and good fortune in the middle
\nage. However, all this changed when the scientific society of the 20th century
\nrevealed the medical implications of obesity (Caballero B., 2007)<\/p>\n

With
\nthe inception of the industrial revolution, body size and strength of soldiers
\nand workers became pertinent as this was attributed to the military and
\neconomic power of Nations (Caballero, 2007). The increase in the average body
\nmass index from underweight to the normal on the BMI charts played an important
\nrole in the development of industrialized societies (Caballero, 2007).\u00a0 Therefore in the 19th century, there was an
\nincrease in weight and height generally. However, during the 20th century, the
\ngenetic potentials for height was reached and this resulted to weight
\nincreasing more than height in this century and thus resulted in the average
\nincrease in BMI (Caballero, 2007). In human evolution, for the first time, the
\nnumber of adults with excess weight exceeded the number of those who were
\nunderweight which further led to obesity (Caballero, 2007). <\/p>\n

The
\nperceptions of the public as regards healthy body weight varied from those
\nregarded as normal in the western society, but this perception was changed in
\nthe beginning of the 20th century. There was a reduction in the weight seen as
\nnormal since 1920s and this was evident by the 2% increase in average height of
\nthe Miss America pageant winners and a 12% decrease in weight between year 1922
\nand 1999 (Rubinstein & Caballero, 2000). Also, the perception of most
\npeople as regards healthy weight has changed, for example in Britain the weight
\nat which people regarded themselves to be overweight was considerably higher in
\n2007 than in 1999 (Johnson & Wardle, 2008). Obesity is still regarded as an
\nindication of wealth and well-being in many parts of Africa and this has become
\nmore widespread since the HIV epidemic began (Haslam & James, 2005).<\/p>\n

BODY MASS INDEX
\n(BMI) CLASSIFICATION<\/h2>\n

According
\nto the World Health Organization, Body Mass Index (BMI) is a simple index of
\nweight-for-height that is commonly used to classify underweight, normal weight,
\noverweight and obesity in adults. It is defined as the weight in kilograms
\ndivided by the square of the height in metres (kg\/m2) (W.H.O. 2004). For
\nexample, an adult who weighs 60kg and whose height is 1.65m will have a BMI of
\n22.0. <\/p>\n

BMI
\n= 60 kg \/ (1.65 m2) = 60 \/ 2.72 = 22.04 <\/p>\n

LEAN BODY MASS<\/h2>\n

Lean
\nBody Mass is a component of body composition, it is calculated by subtracting
\nbody fat weight from total body weight. Total body weight is lean plus fat. <\/p>\n

In equations: LBM
\n= BW \u2212 BF<\/p>\n

Lean Body Mass
\nequals Body Weight minus Body Fat<\/p>\n

LBM + BF = BW<\/p>\n

Lean Body Mass
\nplus Body Fat equals Body Weight<\/p>\n

Lean Body Weight
\n(men) = (1.10 x Weight(kg)) \u2013 128 ( Weight2\/(100 x Height(m))2)<\/p>\n

Lean Body Weight
\n(women) = (1.07 x Weight(kg)) \u2013 148 ( Weight2\/(100 x Height(m))2)<\/p>\n

Ideal Body Weight
\n(men) = 50 + 2.3 ( Height(in) \u2013 60 )<\/p>\n

Ideal Body Weight
\n(women) = 45.5 + 2.3 ( Height(in) \u2013 60 )<\/p>\n

Body Mass Index =
\nWeight(kg) \/ Height(m)2<\/p>\n

The table below
\nfurther explains the classification of BMI in relation to the weight and height
\nof an individual.<\/p>\n

\"\"
Table 1: The International Classification of adult underweight, overweight and obesity according to BMI <\/figcaption><\/img><\/figure>\n

Source: Adapted from WHO,
\n1995, WHO, 2000 and WHO 2004.<\/em><\/p>\n

BMI values are age
\ndependent and are the same for both males and females (WHO, 2000). The health
\nrisks associated with increasing BMI are many and the interpretation of BMI
\nvalues in relation to risk may vary for different populations in different geographical
\nlocations (WHO, 2004).<\/p>\n

AETIOLOGY
\nDETERMINANTS OF OBESITY<\/h2>\n

Obesity is a heterogeneous group of conditions with numerous causes, it is not merely a single disorder and it is predominantly expressed phenotypically (Susan A.J, 1997). Obesity is hereditary, but the genetic component does not follow simple Mendelian principles and the effect of the genotype on the aetiology of obesity may be decreased or increased by factors that are non-genetic (Susan A.J, 1997). Several factors determine the body weight, and these are interactions of genetic, environmental and psychosocial factors which are in relation to the amount of energy consumed and the amount of energy expended and the resulting acting through the physiological mediators
Table 1: The International Classification of adult underweight, overweight and obesity according to BMI of energy intake and energy expenditure and the resulting equilibrium between both (Susan A.J, 1997).<\/p>\n

ENDOCRINE AND
\nHYPOTHALAMIC DISORDERS<\/h3>\n

Certain
\nendocrinological disorders may lead to obesity, but this applies to a very
\nsmall percentage of the total number of cases (Susan A.J, 1997). The
\nendocrinological determinants of obesity have been reviewed recently (Bouchard
\nC., Perusse L., Leblanc C., Tremblay A, & Theriault, 1988). The single
\ndisorder that causes obesity in this group is hypothyroidism in which increased
\nweight occurs largely as a result of reduced energy expenditure (Susan A.J, 1997).
\nOther endocrinological factors contributing to obesity include Cushing\u2019s
\nsyndrome and disorders of corticosteroid metabolism, where weight gain is
\ntypically accompanied by a distinctive prototype of fat deposition in the
\ntrunk, sex hormone disorders including hypogonadism in men and ovariectomy in
\nwomen, insulinoma and growth hormone deficiency (Susan A.J, 1997). The key
\ncauses of weight gain in these cases are the amount of energy intake. Certain
\nhypothalamic tumors or damage to the hypothalamic part of the brain as a result
\nof excessive exposure to radiation, infectious agents or head trauma can also
\nlead to obesity with defect in appetite control and hyperphagia (Susan A.J,
\n1997). A hypothalamic disorder is also believed to be the foundation of a
\nnumber of congenital abnormalities which could also result in obesity, e.g.
\nPrader-Willi syndrome, which is an abnormality that could be a primary cause of
\nobesity (Susan A.J, 1997).<\/p>\n

GENETIC INFLUENCE<\/h3>\n

At
\na population level, the genetic influence of obesity is expressed in terms of
\nheritability (Susan A.J, 1997). This refers to the percentage of the total
\ndifference in a character which is attributable to genetic factors (Susan A.J,
\n1997). The heritability of obesity may be considered either in terms of the
\ntotal fatness of an individual or the distribution of body fat in an individual
\n(Susan A.J, 1997). Several discoveries have been made over the years regarding
\nthe influence of genetics on chronic diseases like cardiovascular disease and
\nobesity (R. C. Whitaker, J.A. Wright, M.S. Pepe, K.D. Seidel, &W.H. Dietz.,
\n1997). Recent reports indicate that at least 32 genes contribute to common
\nforms of obesity. Many of these genes are thought to be related to the
\ndevelopment of obesity through the deregulation metabolic hormones in the body
\n(Susan A J, 1997).<\/p>\n

\u00a0The obesity related variant in the fat mass
\nand obesity-associated protein also known as alpha-ketoglutarate-dependent
\ndioxygenase FTO, has aroused interest in pediatrics due to its relationship
\nwith increased weight and ponderal index at 2 weeks of age (A. Lopez-Bermejo,
\nC.J. Petry, M. Diaz, et al., 2008). FTO is located on the long arm of the
\nchromosome 16 and is expressed in the brain, specifically the hypothalamic
\nnuclei (Khung E. Rhee et al. 2012). Those who are homogenous for the at-risk
\nallele have been found to be 3kg heavier than those who do not have the allele
\n(T.M. Frayling, N. J. Timpson, M. N. Weedon et al. 2007). This weight gain is
\nlikely due to the gene\u2019s involvement in the regulation of energy intake (Khung
\nE. Rhee et al. 2012). According to recent studies, individuals carrying the
\nat-risk allele prefer dense energy foods (J.E Cecil, R. Tavendale, P. Watt, M.
\nM. Hetherington, & C.N.A Palmer, 2008), have reduced feeling of satiety (J.
\nWardle, S. Carnell, C.M.A. Haworth, I.S. Farooqi, S. O\u2019Rahilly, & R.
\nPlomin, 2008), display loss of control over eating (M. Tanofsky-Kraff, J.C.
\nHan, K. Anandalingam et al. 2009), consume more fat and calories (even after
\nadjusting for BMI) (N. J. Timpson, P.M. Emmett, T.M. Frayling, et al. 2008) and
\ndisplay a greater tendency towards consuming palatable foods after eating a
\nmeal (J. Wardle, C.Llewellyn, S. Sanderson, & R. Plomin, 2009). Therefore,
\nFTO isn\u2019t associated with energy expenditure, but it increases the susceptibility
\nof individuals to higher calorie consumption and decreased satisfaction. A
\nmeta- analysis of 45 studies found that adults who were physically active
\nattenuate the odds of obesity associated with FTO by almost 30% (T.O.
\nKilpelainen, L. Qi, S. Brage, et al. 2011). Thus carrying a gene for obesity
\ndoes not necessarily predestine one to be obese (D. Meyre, K. Proulx, H.
\nKawagoe-Takaki et al. 2010), but rather increases the risk in the face of an
\nobesogenic environment (Khung E. Rhee et al. 2012).<\/p>\n

Numerous
\nstudies in different ethnic groups suggest that the familial correlation in the
\ntotal body fatness, expressed as body mass index, (BMI; kg\/m2) from parent to
\noffspring is about 0.2 and for sibling-sibling relationships about 0.25
\n(Bouchard C, Perusse L, Leblanc C, Tremblay A, Theriault G. 1988). As would be
\nexpected, studies of twins show a much higher concentration, particularly in
\nmonozygotic pairs (Susan A.J, 1997). However, these findings do not segregate
\nthe independent effects of genetic transmission and a shared environment (Susan
\nA.J, 1997). Further studies of twins reared apart attribute 50-70% of the
\ndifference in BMI in later life to genetic factors (Stunkard A, Harris J,
\nPedersen N, McClearn G. 1990). Adoption studies, where an individual is compared
\nboth to their biological parent and their adopted parents, have also
\ndemonstrated the importance of genetic influences (Susan A.J, 1997). There is a
\nstrong relationship between the BMI of the adoptee and their biological parents
\nacross the entire range of fatness, but no relationship between the adoptee and
\ntheir adoptive parents (Stunkard A, Sorensen T, Hanis C. et al. 1986).<\/p>\n

Studies
\nof fat distribution have considered both the ratio of subcutaneous to total fat
\nmass and the distribution of subcutaneous fat in the trunk relative to the
\nlimbs (Susan A.J, 1997). Data from the Quebec Family Study, suggest that the
\nsize of the internal fat stores are more strongly influenced by genetic factors
\nthan subcutaneous depots (Bouchard C., Perusse L., Leblanc C., Tremblay A,
\nTheriault, 1988). Familial clustering suggests that genetic factors may account
\nfor 37% of the variance in the trunk to extremity skin fold thickness ratio
\n(Rice T, Bouchard C, Perusse L, Rao D. 1995). These combined evidence from
\nthese genetic analysis suggests that obesity is a polygenic disorder and that a
\nconsiderable proportion of the variance is non-additive (Susan A.J, 1997). This
\nwould explain the higher correlations between siblings than those between
\nparent and offspring, and the 2-fold greater correlation between monozygotic
\nthan dizygotic twins (Susan A.J, 1997). These genetic influences seem to
\noperate through susceptible genes; the occurrence of the gene increases the
\nrisk of developing a characteristic but not essential for its expression nor is
\nit, in itself, sufficient to explain the development of the disease (Susan A.J,
\n1997). Unlike animal models, where a number of single genes can lead to
\nobesity, no human obesity gene has yet been characterized, but the
\nheterogeneous nature of human obesity does not preclude the identification of
\nsmall number of individuals with a single defect which leads to obesity (Susan
\nA.J, 1997). In man, a number of genetically determined conditions result in
\nexcess body weight or fatness (e.g Prader-Willi syndrome or Bardet-Biedl
\nsyndrome), but these account for only a very small proportion of the obese
\npopulation (Susan A.J, 1997).<\/p>\n

PHYSIOLOGICAL
\nMEDIATORS<\/h2>\n

Energy expenditure<\/h3>\n

Studies
\nin animals have postulated that at the time of overfeeding, a remarkable increase
\nin metabolic rate may deplete the excess energy thus reducing the rate of
\nweight gain below theoretical values (Rothwell N., Stock M., 1983). Genetically
\nobese animals tend to gain more weight than their lean controls even when they
\nare pair-fed, thus implying a greater metabolic rate (Thurby P., Trayhurn P.,
\n1979). One possible explanation for this effect is the decrease in diet-induced
\nthermogenesis which is lessened in animal models of obesity due to a decrease
\nin the sympathetic activation of brown adipose tissue (Rothwell N., Stock M.,
\n1983). These unequivocal effects on energy expenditure in obese animals
\ncontrast with the paucity of evidence in humans (Susan A Jebb, 1997). Susan A.J
\n(1997) stated that in obese humans, there have been constant reports of
\nabnormally low energy intake which indirectly imply that there must be a defect
\nin energy expenditure. There are three basic elements to energy expenditure
\nwhich have each been the focus of extensive research.<\/p>\n

Basal Metabolic
\nRate<\/h3>\n

In
\n1997, Susan A Jebb defined basal or resting metabolic rate as the energy
\nexpended by an individual at rest, following an overnight fast and at a
\ncomfortable environmental temperature in the thermo neutral range. Several
\nstudies of basal metabolic rate have concluded that obese subjects have a
\nhigher BMR compared to their lean counterparts. Researchers like Swinburn B.
\n& Ravussin E, reported that approximately 80% of the inter- individual
\nvariance in BMR can be accounted for by age, fat-free mass, fat mass and
\ngender. Nevertheless, this still gives room for some likelihood that
\ninter-individual difference in BMR which may influence individuals with a
\nrelatively low BMR to become obese (Susan A. Jebb, 1997). <\/p>\n

Diet induced
\nthermogenesis <\/h3>\n

A
\nnumber of studies have suggested that the post-prandial increase in energy
\nexpenditure is attenuated in obese subjects, perhaps due to decreased
\nSympathetic Nervous System activity (Astrup A. 1996). Similar effects have also
\nbeen demonstrated in the post-obese. However this is not a consistent finding,
\neven among studies from the same laboratory. A recent review by Ravussin E.
\n& Swinburn B. (1993) identified 28 studies in favour of a defect in
\nthermogenesis in humans and 17 against. However, since thermogenesis accounts
\nfor only a fraction of total energy expenditure (approximately 10%), the
\npotential for a significant effect on total energy expenditure is insufficient
\n(Susan A. Jebb 1997).<\/p>\n

Physical activity <\/h3>\n

The
\nmost significant component of energy expenditure is physical activity which may
\nrepresent 20-50% of total energy expenditure. Studies of fidgeting movements in
\nPima Indians within a whole-body calorimeter have shown significant
\ninter-individual variations in the daily energy cost of these actions from
\n400-3000 kJ\/day, with low levels predictive of subsequent weight gain at least
\nin males but not females (Zurlo F., Ferraro R., Fontvielle A. et. al. 1988).
\nHowever, in free-living conditions, the freedom to undertake conscious physical
\nactivity or exercise increases the inter-individual variability even further
\n(Susan A Jebb). Research in this area has been hampered by imprecision in the
\nmethods to measure physical activities which have included various actometers,
\nheart rate monitoring, activity diaries and direct observation (Susan A. Jebb,
\n1997).<\/p>\n

The
\nenergy requirements of an individual encompass the summation of basal
\nexpenditure, thermogenesis and physical activity. A whole-body calorimeter can
\nbe used to measure the total energy expenditure of an individual. The analysis
\nof total energy expenditure in 319 obese subjects clearly demonstrates a
\nsignificant increase in energy expenditure with increasing body weight such
\nthat individuals with a BMI in excess of 35 kg\/m2 have energy expenditure
\napproximately 30% higher than those with BMI less than 25 kg\/m2 (Susan A Jebb,
\n1997). The outstanding difficulty with these studies , as stated by Susan A.
\nJebb in 1997 is that the increase in energy expenditure seen in obese subjects
\nas a result of their increased body size may mask pre-existing metabolic
\ndefects in the pre-obese state which exposes the individual to excessive weight
\ngain. However, in experimental overfeeding researches, there is no remarkable
\ndifference in the degree of weight gain between lean and obese subjects when
\nmatched for their excess energy intake (Diaz E. Prentice A. M et. al.
\n1992).\u00a0 Studies of total energy
\nexpenditure in post-obese subjects have not arrived at a definite conclusion;
\nsome studies show no difference in energy expenditure in the post-obese relative
\nto never-obese controls (Goldberg G.R., et. al. 1991), whilst others show a
\nmodest suppression of energy expenditure (Geissler C. Miller D., Shah M. 1987).
\nIn general, there is little evidence to support the hypothesis that human
\nobesity may be due to a specific defect in energy expenditure in predisposed
\nindividuals (Susan A Jebb, 1997). Susan A Jebb further stated that advocates of
\na metabolic basis to obesity, argue that only very small differences in energy
\nexpenditure are neccessary to produce significant weight gain over many years,
\nand this difference may be lower than the limits of precision of even the most
\nadvanced methodology.<\/p>\n

Energy Intake<\/h3>\n

The failure to identify a defect in the metabolic control of energy expenditure and the contrary observation of high levels of energy expenditure, and the contrary observation of high levels of energy expenditure in obese subjects has led to a focus on food intake to explain the aetiology of obesity (Susan A Jebb, 1997). The increase in energy expenditure associated with the development of obesity should automatically help to prevent continued weight gain; hence the failure of this auto-regulatory system suggests that there must be a considerable error in the regulation of food intake (Susan A Jebb, 1997). Furthermore, habitually lean individuals are able to regulate intake to match energy requirements over a wide range of energy requirements yet those who become obese seem unable to achieve this balance (Susan A Jebb, 1997).<\/p>\n

\u00a0Breakthrough in discerning the role of energy
\nintake in the aetiology of obesity has been critically disconcerted by
\nunder-reporting which is now largely recognized as a feature of obesity (Susan
\nA Jebb, 1997). Comparisons of energy intake and energy expenditure indicate
\nconsistent shortfalls in self-reported intake, averaging approximately 30% of
\nenergy requirements in obese subjects (Prentice A.M., Black A.E., Coward W.A.,
\n1986; Lichtman S., Pisarska K., Berman E., et al., 1993). This phenomenon also
\nextends to post-obese subjects and to others who may be very weight conscious
\n(Susan A Jebb, 1997).<\/p>\n

Under-reporting
\nmay be cause by several factors and it is natural for individuals to change
\ntheir eating pattern when they are to record their food intake.\u00a0 This is usually associated with a reduction
\nin intake as subjects consciously or sub-consciously adopt a self-imposed
\n\u2018diet\u2019. (Susan A Jebb, 1997). Therefore they might give accurate results about
\ntheir intake for that duration, but it may not be a true representation of
\ntheir habitual pattern. Forgetfulness, underestimation of meal size and lack of
\nbasic knowledge of food consumption can also lead to under-reporting. Although,
\nit is possible to have falsification and fabrication of dietary records, there
\nare also instances of self-deception or deliberate manipulation of dietary
\nrecords.<\/p>\n

Recent
\nresearch into the appetite control system by Blundell J.; Bouchard C., Bray G.
\n(1996), has identified a network of synchronous interactions which govern
\neating behavior. These effects are mediated through the central nervous system
\nparticularly the hypothalamus, where a number of neuropeptides appear to
\nregulate feeding behavior via effects on hunger and satiety (Susan A Jebb,
\n1997). Laboratory studies of feeding behavior by Spiegel T., et al., in 1989,
\nproposed that, following a convert energy preload, obese subject may be less
\nable to accurately compensate for the energy content of the preload at a
\nsubsequent meal than lean subjects. However, these studies are usually of short
\nduration in laboratory settings and may not accurately reflect eating behavior
\nin a naturalistic setting, where knowledge of foods consumed and conditioned
\nlearning may invoke other regulatory processes (Susan A Jebb, 1997). <\/p>\n

There
\nis also significant evidence that the individual macronutrients (protein, fat,
\ncarbohydrate and alcohol) have different influences on eating behavior, majorly
\ndue to their effects on satiety (Stubbs R., 1995). Experimental studies of
\nmanipulated foods and retrospective analyses of dietary records suggest that
\nprotein is the most satiating (DeCastro J., 1987; Hill A., Blundell J., 1990).
\nCarbohydrate is also an efficient inhibitor of later food consumption, at least
\nin the short terms, meal-to-meal context (Rolls B., et al. 1994). Fat seems to
\nhave a satiating capacity (Lawton C., Burley V., 1993). Fat hyperphagia occurs
\nduring a single meal due to subjects overeating high fat foods and is also
\nknown as passive over consumption. In 1994, Poppitt S., stated that fat has two
\ntimes the energy per gram of carbohydrate or protein which may be due to the
\nlevel of energy density and not necessarily a characteristic of dietary fat.
\nAppetite is said to be stimulated by alcohol and according to DeCastro J &
\nOrozco (1990), in free living circumstances, alcohol consumption with meals is
\nassociated with higher energy intakes, but this may also reflect that alcohol
\nis more likely to be consumed on special occasions which in themselves are
\nassociated with increased food intake.<\/p>\n

Basically,
\ntaste preference can have an effect on the amount of food consumed and the kind
\nof food.\u00a0 The individual preference for
\ncertain meals would make them more likely to consume more of that meal.
\nTherefore, sensory preferences plays a role on energy balance since is it
\nassociated with energy intake. According to Witherley S, Pangborn R & Stern
\nJ (1980), several reports of sensory preferences for particular food groups in
\nassociation with obesity, but inter-subject variability is so great as to
\nobscure any underlying obese-lean differences. The relationship between sensory
\npreference for fat versus sugar and BMI was pinpointed by Drewnowski in 1992.
\nObese women had preference for foods with high fat to sugar ratio while women
\nwith low BMI had preference for high sugar to fat ratio, therefore increase in
\nweight is closely related to increase for fatty foods.<\/p>\n

Eating
\nfrequency has effect on weight gain, because people who eat several small meals
\nat intervals have less weight than those that eat fewer meals in larger
\nquantity and therefore large quantity of food consumed at a time may be a risk
\nfactor for obesity, however, studies as regards this, showed no remarkable
\nrelationship (Bellisle F, McDevitt R, Prentice A.M. 1997). Research in this
\narea is contradicted by under-reporting of food consumption in obese subjects
\nand by post-hoc variations in eating patterns as a result of obesity and
\nefforts to control weight (Susan A Jebb, 1997). Eating frequency in obese
\nsubjects is however an unreliable blueprint to the eating patterns involved in
\nthe aetiology of obesity (Susan A Jebb, 1997).<\/p>\n

ENVIRONMENTAL
\nINFLUENCE<\/h2>\n

Obesogenic
\nenvironment which was first coined in the 1990s, in a bid to explain the
\npresent obesity epidermic. According to King D (2007), obesogenic environment
\nis the sum of the influences that the surroundings, opportunities or conditions
\nof life have on promoting obesity in individuals and populations. This
\nencompasses the cultural, social and infrastructural conditions that affect the
\nability of a person to embrace a healthy lifestyle. Individuals in a population
\nrespond to unhealthy environment and the more urbanized the environment, the
\nmore individuals are pressurized to adopt unhealthy habits. The pressure from
\nthe surrounding makes it difficult for individuals to change their lifestyle and
\npractice healthy habits when the environment itself is unhealthy. Environmental
\nfactors may have a critical effect in the development of obesity by unmasking
\ngenetic or metabolic susceptibilities (Susan A.J, 1997). Environmental
\ninfluences on diet involve a wide range of factors including accessibility to
\nfood and high calorie drinks. Eating habits are commonly influenced by the
\navailability and accessibility of unhealthy food, which is an important
\nconsideration in the effect on obesity. Studies in the United States recommend
\nthat the availability of high quality, affordable \u2018healthy\u2019 food is limited for
\npeople who reside in low-income communities and such scarcity is associated
\nwith unhealthy diet and obesity (White 2007) .However despite several epidemiological
\nstudies that shows environmental influences play an important role in the
\naetiology of obesity, it is a fact that some people within the same \u2018unhealthy
\nenvironment\u2019 still managed to maintain a healthy weight (Susan A.J, 1997).<\/p>\n

PSYCHO-SOCIAL INFLUENCES<\/h2>\n

Food
\nis sometimes used as a coping mechanism by individuals with weight issues,
\nespecially when they are unhappy, nervous, stressed, bored and depressed. In
\nmany obese individuals there seems to be a perpetual cycle of mood disturbance,
\novereating, and weight gain (Jennifer C. Collins & Jon E. Bentz 2009). When
\nthey feel frustrated, they rely on food for comfort, even though this coping
\nmechanism may pacify their mood, the resultant weight gain that results may
\ncause a dysphoric mood due to their inability to control their stress (Jennifer
\nC. Collins & Jon E. Bentz, 2009). Eventually a guilty feeling may restart
\nthe cycle and might steer a habitual pattern of eating food to get comfort.
\nThis habitual pattern is specifically significant if there is a genetic risk
\nfactor for obesity or an \u2018obesogenic\u2019 environment where foods high in calorie
\n& density are readily accessible and sedentary lifestyle is present.
\nRegrettably, these situations are popular in America. <\/p>\n

In
\naddition to depression and anxiety, other risk factors include problematic
\neating behaviors such as \u201cmindless eating,\u201d frequent snacking on high calories
\nfoods, overeating, and night eating (Glinski J., Wetzler S., Goodman E.2001).
\nAmerican Psychiatric Association has currently included Binge eating disorder
\n(BED) in an appendix of the Diagnostic and Statistical Manual of Mental
\nDisorders (DSM-IV-TR) and is characterized by: recurrent episodes of eating
\nduring a discrete period of time (at least 2 days a week over a 6 month
\nperiod); eating large quantity of food than majority of the people would eat at
\nthe same time; a feeling of loss of control during the episodes; and guilt or
\ndistress following the episodes (Jennifer C. Collins & Jon E. Bentz, 2009).
\nAccording to Wadden T.A., Sarwer D. B., Fabricatore A. N., Jones L., Stack R.,
\n& Williams N.S (2007), BED is estimated to occur in approximately 2% of the
\ngeneral population and between 10% and 25% of the bariatric population. An
\nimportant differentiation pointed out by the American Psychiatric Association,
\nbetween BED and bulimia\/anorexia is that BED is not associated with any regular
\ncompensatory behaviors, such as purging, fasting, or excessive exercise. It can
\ntherefore be implied that the majority of individuals with BED are overweight.<\/p>\n

Night
\neating, which was first identified in 1955 as another disorder that can lead to
\nremarkable weight gain, though night eating syndrome (NES) is not currently
\nrecognized by the American Psychiatric Association as a distinct diagnosis in
\nthe DSM-IV-TR. Night eating syndrome is characterized by excessive late night
\nconsumption (> 35% of daily calories after the evening meal), unhealthy
\neating patterns, \u201cmorning anorexia,\u201d insomnia, and distress (Stunkard A. J.,
\nGrace W. J. & Wolff H. G. 1955). NES occurs in approximately 1% of the
\ngeneral population and an estimated 5-20% of the bariatric population (Wadden
\nT.A., Sarwer D. B., Fabricatore A. N., Jones L., Stack R., & Williams N.S.
\n2007). More recently, NES has been seen as a disorder of circadian rhythm that
\nincludes a delay of appetite in the mornings and the continuation of appetite
\nand over consumption of food during the night (Jennifer C. Collins & Jon E.
\nBentz, 2009).<\/p>\n

PATHOPHYSIOLOGY OF
\nOBESITY<\/h2>\n

There
\nare several possible pathophysiological mechanisms involved in the advancement
\nand prolongation of obesity. This field of research had been almost
\nunapproached until the leptin gene was discovered in 1994 by J. M. Friedman\u2019s
\nlaboratory (Zhang, Y., Proenca, R., Maffei, M., Barone, M., Leopold, L.,
\nFriedman, J.M., 1994). These researchers proposed that leptin was a satiety
\nelement. However, soon after J. F. Caro\u2019s laboratory could not ascertain any
\nmutations in the leptin gene in humans with obesity. In 1995, Considine, RV;
\nConsidine, EL; Williams, CJ; Nyce, MR; Magosin, SA; Bauer, TL; Rosato, EL;
\nColberg, J., & Caro, J.F. proposed a contrary view that Leptin expression
\nwas increased, postulating the possibility of Leptin-resistance in human
\nobesity. Since the discovery of leptin, insulin, ghrelin, orexin, cholecystokinin,
\nadipokines, peptide tyrosine tyrosine, as well as many other mediators have
\nbeen researched. The adipokines are intermediators produced by adipose tissue;
\ntheir action is thought to revise many obesity-related diseases. Leptin and
\nghrelin are considered to be interrelated in their effect on appetite, with
\nghrelin produced by the stomach regulating short-term appetitive control (i.e.
\nhunger pangs when the stomach is empty and satiety when the stomach is
\nstretched). Leptin is created by adipose tissue to signal fat storage
\nreservoirs in the body, and mediates long-term appetitive controls (i.e. to eat
\nmore when fat storages are low and less when fat storages are high). Although
\nadministration of leptin may be effective in a small subset of obese humans who
\nhave deficiency in leptin, most obese humans are considered to be leptin
\nresistant and have been found to have high levels of leptin (Hamann A., &
\nMatthaei S. 1996). This resistance is thought to explain in part why
\nadministration of leptin has not been shown to be effective in suppressing
\nappetite in most obese people (Flier J.S. 2004).<\/p>\n

Leptin
\nand ghrelin act on the hypothalamus and are produced peripherally. They control
\nappetite through their actions on the central nervous system. They act on the
\nhypothalamus, a region of the brain central to the coordination of food
\nconsumption and energy expenditure. There are several circuits within the
\nhypothalamus that contribute to its performance in integrating appetite, the
\nmelanocortin pathway being the most well understood (Flier J.S. 2004). The
\ncircuit starts with an region of the hypothalamus, the arcuate nucleus, that
\nhas outputs to the lateral hypothalamus (LH) and ventromedial hypothalamus
\n(VMH), the brain\u2019s feeding and satiety centers, respectively (Boulpaep, Emile
\nL., Boron, & Walter F. 2003).<\/p>\n

According
\nto Flier J.S. (2004), the arcuate nucleus contains two distinct groups of
\nneurons; the first group co expresses neuropeptide Y (NPY) and agouti-related
\npeptide (AgRP) and has stimulatory inputs to the LH and inhibitory inputs to
\nthe VMH and the second group coexpresses pro-opiomelanocortin (POMC) and
\ncocaine- and amphetamine-regulated transcript (CART) and has stimulatory inputs
\nto the VMH and inhibitory inputs to the LH (Flier J.S. 2004). Consequently,
\nNPY\/AgRP neurons stimulate feeding and inhibit satiety, while POMC\/CART neurons
\nstimulate satiety and inhibit feeding (Flier J.S. 2004). Both groups of arcuate
\nnucleus neurons are regulated in part by leptin. Leptin inhibits the NPY\/AgRP
\ngroup while stimulating the POMC\/CART group (Flier J.S. 2004).\u00a0 Researches done by Flier J.S., 2004, thus
\nconcluded that a deficiency in leptin signaling, either via leptin deficiency
\nor leptin resistance, leads to overfeeding and may account for some genetic and
\nacquired forms of obesity. <\/p>\n

EFFECT ON HEALTH<\/h2>\n

Obesity
\nis a severe medical condition and a chronic health issue worldwide. The
\nassociation between body weight and mortality is a subject of concern,
\nespecially in regards to the optimal weight for longevity (JoAnn E. Manson,
\nM.D., Walter C. Willett, M.D., et al, 1995). The significance of understanding
\nthe true relationship between weight and mortality is underlined by the
\nincreasing prevalence of obesity in the United States (Kuczmarski RJ, et al,
\n1994) especially women (Harlen WR, et al, 1988). Obesity is a major risk factor
\nfor cardiovascular diseases (e.g., heart disease, stroke and high blood
\npressure), diabetes (e.g. type 2 diabetes), musculoskeletal disorders (e.g.,
\nosteoarthritis), some cancers (e.g., endometrial, breast, and colon cancer),
\nhigh total cholesterol or high levels of triglycerides, liver and gallbladder
\ndiseases, sleep apnea and respiratory problems, reproductive health
\ncomplications such as infertility and mental health conditions (WHO, 2012).<\/p>\n

Obesity and Cancer<\/h3>\n

Obese
\npeople are more vulnerable to cancer and their prognosis is extremely worse when
\ndiagnosed. Men that are obese are 33% more likely to die from cancer and obese
\nwomen also have a 50% higher likelihood of dying from breast cancer (Weight
\nManagement Centre, 2010). Additional to obesity, cancer has recently been
\nlinked to diet and physical activity status (Bray 2004, Barnard 2004, Wiseman
\n2008). The cancers most significantly associated with obesity in women are
\ncervical, uterine, kidney, breast and endometrial cancer and in men are colon,
\npancreatic and liver cancer (Calle, Rodriguez, Walker-Thurmond & Thun
\n2003). One study, using National Cancer Institute Surveillance, Epidemiology,
\nand End Results data, estimated that in 2007 in the United States, about 34,000
\nnew cases of cancer in men (4 percent) and 50,500 in women (7 percent) were due
\nto obesity. The percentage of cases attributed to obesity varied widely for
\ndifferent cancer types but was as high as 40 percent for some cancers,
\nparticularly endometrial cancer and esophageal adenocarcinoma (National Cancer
\nInstitute, 2012).<\/p>\n

Obesity and
\ncardiovascular disorders<\/h3>\n

Cardiovascular
\ndisease (CVD) is one of the major cause of death in U.S. Obese people are more
\nliable to die from CVD largely due to accelerated atherosclerosis,
\nhyperlipidaemia, loss of glyceamic control and hypertension. Until recently the
\nrelationship between obesity and coronary heart disease was viewed as indirect,
\ni.e., through covariates related to both obesity and coronary heart disease
\nrisk (Lew E.A., Garfinkel L., 1979) including hypertension; dyslipidemia,
\nparticularly reductions in HDL cholesterol; and impaired glucose tolerance or
\nnon\u2013insulin-dependent diabetes mellitus. Insulin resistance and accompanying
\nhyperinsulinemia are typically associated with these comorbidities (Reaven G.M.,
\n1988). Although most of the comorbidities linking obesity to coronary artery
\ndisease increase as BMI increases, they also relate to the total distribution
\nof body fat. Long-term longitudinal studies, however, indicate that obesity as
\nsuch not only relates to but independently predicts coronary atherosclerosis
\n(Manson J.E., et al., 1995; Garrison R. J., et al. 1985; Rabkin S.W., 1977).
\nMesserli F. H. (1982) stated that left ventricular hypertrophy is mostly seen
\nin patients with obesity and is related to systemic hypertension and may be related
\nto the severity of obesity. Hypertension is approximately three times more
\ncommonly found in obese individuals than normal-weight persons (Van Itallie
\nT.B., 1985). This relationship may be directly related such that when weight
\nincreases, there is an increase in blood pressure (Kannel W.B., Brand N., et
\nal., 1967) and when weight decreases, blood pressure also decreases (Reisin E.,
\nFrohlich E.D., et al., 1983). <\/p>\n

Obesity and mental
\nhealth<\/h3>\n

Individuals
\ndiagnosed with obesity tend to be less favorable on all levels of the
\npsychological assessment and may exhibit several symptoms ranging from mere
\nsadness to chronic depression. Evident are more episodes of mood swings,
\nanxiety, personality and eating disorders, basically related to or associated
\nwith obesity experienced by individuals with obesity (Pickering, Grant, Chou,
\nCompton 2007). Obesity may be an inception of psychiatric manifestations and
\nvice versa and is related to psychosocial deterioration and bias based on
\nweight. This comprises of loss of self-worth, and reduced self-esteem
\nassociated with stigmatization. Stigmatization can further lead to desolation
\nand withdrawal and thus many obese individuals seek solace in binge eating,
\nthereby gaining more weight. Based on reports from Roberts, Deleger,
\nStrawbridge & Kaplan 2003; Herva, Laitinen, Miettunen, Veijola, Karvonen
\n& Lasky 2006; Kasen, Cohen, Chen &Must 2008, concern, shame and guilt
\nassociated with low self-worth, which is finally related to excessive food
\nconsumption completes the obesity-mental disorder circle.<\/p>\n

There
\nis bias and discrimination associated with obesity. They generally report
\nreduced quality of life and functional wellbeing, collectively called
\nHealth-related quality of life (HRQOL) (Puhl & Brownell 2001; Wadden &
\nPhelan 2002). This relationships is majorly expressed by women (Fontaine 2001)
\nand for people with severe obesity (Hudson, Hiripi, Pope & Kessler 2007;
\nScott, Bruffaerts, Siomn, Alonso, Angermeyer, de Girolamo et al. 2008).<\/p>\n

Obesity and
\ndiabetes<\/h3>\n

Diabetes
\nis usually a terminal illness. i.e. it is a lifelong chronic disease
\ncharacterized by high levels of sugar in the blood. One of the major risk
\nfactors for diabetes is obesity. Obesity is directly associated with Diabetes
\n2. The association between obesity and type 2 diabetes are firmly established
\nand without the intervention of a healthy diet and proper exercise, obesity can
\nlead to type 2 diabetes over a very short period of time.\u00a0 In fact, obesity is believed to account for
\n80-85% of the risk of developing type 2 diabetes, while recent research
\nsuggests that obese people are up to 80 times more likely to develop type 2
\ndiabetes than those with a BMI of less than 22 (National Health Service, 2014).
\nIt is a known fact that obesity carries a greater risk of developing type 2
\ndiabetes, especially if you have excess weight around your abdomen. Studies
\npostulates that abdominal fat causes fat cells to releases \u2018pro-inflammatory\u2019
\nchemicals, which can reduce the body\u2019s sensitivity to the insulin, this can
\nalso disrupt the function of insulin responsive cells and their ability to
\nreact to insulin. This is known as insulin resistance\u00a0 which is a primary activator for type 2
\ndiabetes. Excess abdominal fat is a major high-risk form of obesity. <\/p>\n

SOCIO-ECOMOMIC\/
\nFINANCIAL COST OF OBESITY<\/h2>\n

In
\n1999-2000, nearly 65 percent of U.S. adults were either obese or overweight.
\nObesity accounts for $117 billion a year in direct and indirect economic costs.
\nObesity is associated with 300,000 deaths per year, and is fast becoming the
\nleading cause of preventable deaths\u201d (Mancino, Lin, and Ballenger, 2004).
\nCertainly, obesity has become a large problem in America. Recent increase in
\nmeal portions and reduction in availability of natural food production may
\npropose why people find it challenging to maintain a healthy diet. Although,
\ncertain People have been successful at maintaining a healthy nutritional status
\nand avoiding this unhealthy situation. Gary Becker\u2019s human capital theory is a
\ngroundwork that helps to clarify the effect of weight status on the economy in
\nterms of the labor market outcomes for the individual. Human capital is the
\neducational qualification, job experience\/training, and the health condition
\nthat workers devote their time in to boost their capacity and skills to be
\n\u201crented out\u201d to employers (Ehrenberg and Smith, 2005). Healthy weight status in
\nrelation to labour is a type of human capital investment. According to Robert
\nPindyck and Daniel Rubinfeld (2004), \u201cWhen an investment decision is made, the
\ninvestor commits to a current outlay of expenses in return for a\u00a0 stream of expected future benefits.\u201d These
\nstated costs for a healthy weight may include buying of food with high
\nnutritional values and creating time for physical activities. As an investment,
\nthe individual sacrifices money, time and other resources to attain a healthy
\nweight to become more productive in the future and, hence, earn higher income.
\nObese workers miss more days of work and inflict more cost on employers
\nespecially in medical and disability claims and also workers compensation
\nclaims. As a result, firms end up with extra costs associated with obesity,
\nthis is one of the economic effects of obesity. <\/p>\n

Obesity
\nplaces significant burden on the society through health care expenditures and
\ndisability payments combined through group health insurance and public
\nprograms. The estimated annual medical cost of obesity in the U.S. was $147
\nbillion in 2008 U.S. dollars; the medical costs for people who are obese were
\n$1,429 higher than those of normal weight (CDC, 2011). Obesity there has direct
\nand indirect effect on the Nation\u2019s resources, as more money is spent on the
\nobese due to the high risk of comorbidity with other life threatening diseases
\nlike type 2 diabetes, osteoarthritis and cardiovascular diseases.<\/p>\n

TREATMENT<\/h2>\n

There
\nare several weight-loss schemes available but many are ineffectual and
\nshort-term, especially for those who are morbidly obese. The strategies for
\nweight loss with non-surgical programs usually involve a combination of diet
\nmodification, behavior modification therapy and appropriate exercise. <\/p>\n

Dietary
\nModification<\/h3>\n

Dietary
\nmodifications for obesity are designed to create a negative energy
\nintake-energy expenditure balance (i.e., calories consumed < calories
\nexpended) by reducing daily energy intake below the required level. The
\nrequired energy varies by weight, sex and level of physical exercise such
\nindividuals with higher weights, more activity have greater energy needs,
\nincluding men (Melanson K. & Dwyer J. 2002). Uniformly, higher energy
\ndeficits results in higher weight losses. Low calorie diet is recommended for
\nobese individuals and they are advised to check calorie content of meals before
\nconsumption. Very low calorie diet is recommended for morbidly obese
\nindividuals with little or no success in low diet consumption.<\/p>\n

Behaviour Therapy<\/h3>\n

The
\noldest report of the use of behavioral therapy in the management of obesity
\noccurred in 1967. Since then, it has been widely used in the management of
\nobesity (Gupta R. & Misra A. 2007). Behavior therapy involves setting out
\ngoals and principles to patients to aid their adherence to the diet
\nmodification and activity goals for weight loss. Conventional tactics include
\nself-monitoring of food intake and exercise, reduced portion of meals and
\nnumber of times of food intake, intellective restructuring, problem solving,
\nand prevention of regression. The primary aim of behavior modification therapy
\nis to change eating pattern and exercise practices to promote weight loss (CDC,
\n2011).<\/p>\n

Components of
\nbehavioral therapy<\/p>\n

    \n
  1. Self-monitoring: <\/strong>This is one of the main elements of behavior therapy in obesity. Self-monitoring includes maintaining food dairies and activity logs (Guare J.C., et. Al., 1989).<\/li>\n
  2. Stimulus Control:<\/strong> This is the second key element in behavior therapy. In this element, focus is placed on altering the environment that initiates eating and modifying it to help prevent overeating. Stimulus control includes proper purchase of food items, excluding energy-dense processed food and introducing more fruits and vegetables (Wing R.R., 2004) <\/li>\n
  3. Slower eating:<\/strong> Reducing the speed of eating so as to allow signals for fullness come into play.<\/li>\n
  4. Goal setting: Setting realistic goals for one\u2019s self or setting goals for patients as appropriate (Bandura A. & Simon K.M., 1977). <\/li>\n
  5. Behavioral contracting:<\/strong> Reinforcing of successful outcomes or rewarding good behaviors plays a key role (Volpp K. G., et. al., 2008).<\/li>\n
  6. Education:<\/strong> Nutritional education is a necessary component of a successful behavior therapy for obesity. A structured meal plan in conjunction with consultation with a dietician will be helpful (Pedersen S. D., et. al., 2007).<\/li>\n
  7. Social support:<\/strong> Behavioral modification is more sustainable in the long term when there is social support. Enhancing social support is essential for behavioral therapy (Avenell A. et. al., 2004).<\/li>\n<\/ol>\n

    Physical activity<\/h3>\n

    Physical
    \nactivity is the third component of non-surgical weight loss interventions and
    \nlifestyle modification. The advantages of physical activities include promoting
    \nnegative energy balance by maximizing calorie expenditure, preserving fat-free
    \npart during weight loss, and improving cardiovascular fitness. Physical
    \nactivity, however, is ineffective in weight loss in the absence of diet
    \nmodification. The greatest benefit of physical activity is in facilitating the
    \nmaintenance of weight loss (Pronk N.P & Wing R.R. 1992). Case studies have
    \nshown that people who exercise regularly are more successful in maintaining
    \nweight losses than are those who do not exercise. Kayman S., Bruvold W., Stern
    \nJ.S. 1990; Klem M.L., Wing R.R., McGuire M.T., Seagle H.M., Hill J.O.1997).
    \nAdditional evidence comes from randomized trials. Participants who receive diet
    \nplus exercise maintain greater weight losses 1 year after treatment than do
    \nthose who receive diet alone, although the differences are not always
    \nstatistically significant (Wing, R.R. 1999).<\/p>\n

    PREVENTION<\/h2>\n

    Obesity
    \nis a long-lasting medical condition, which is linked with several debilitating
    \nand life-threatening conditions. The increasing rate of obesity globally is a public
    \nhealth concern (Srinivas N., et. al., 2004). Hence an effective way to control
    \nobesity requires strategies that would tackle the major issues relating to
    \nprevention (Srinivas N., et. al., 2004). The treatment and prevention of
    \nobesity are interrelated. The prevention of obesity involves several levels i)
    \nPrimary ii) Secondary iii) Tertiary (Timothy P.G., 1997). <\/p>\n

      \n
    1. Primary prevention: <\/strong>The goal of primary prevention is to reduce the number of new cases. Diet modification\/ healthy diet habits is a primary way of preventing obesity. Sedentary life style which is one of the causes of obesity can be prevented by appropriate exercises and activities that help burn out excess calories in the body and also prevent accumulation of fat. Simple habits ranging from 30 minutes walk in a day to weekly work out at the gymnasium can go a long way in maintaining a healthy weight. Health education is also very important in this aspect because some individuals in the community are unaware of the health implications of their habits. Appropriate health education programs should be organized to increase awareness. Accessibility to healthy food is also an important factor in the prevention of obesity. Formulations of policies that would facilitate healthy eating habit should be adopted by the Government; this would go a long way in reducing the economic effects of obesity and the burden on the Nation\u2019s resources. Policy and environmental approaches that make healthy choices available, affordable and easy can be used to extend the propagation of strategies designed to raise awareness and support people who would like to make healthy lifestyle changes (CDC, 2011).<\/li>\n
    2. Secondary prevention:<\/strong> Secondary prevention is to lower the rate of established cases in the community (Srinivas N., et. al., 2004). Secondary prevention includes strategies to diagnose and treat an existing medical condition in its early stage to avoid complications. (Jeffery G.K., 2014). <\/li>\n
    3. Tertiary prevention:<\/strong> Tertiary prevention is to stabilize or reduce the amount of disability related to obesity ((Srinivas N., et. al., 2004). For those who are already obese and showing signs and symptoms of complications, there are clinical preventive maintenance and treatment regimes (Srinivas N., et. al., 2004). These treatment includes medications and increase in fruit and vegetable consumption. Some extreme cases may include surgery and this is used usually when BMI exceeds 30kg\/m2 or 40 kg\/m2 and when other treatment options have failed. Examples of surgical procedures to treat obesity and its complications includes gastric partitioning and gastric by-pass (Srinivas N., et. al., 2004).<\/li>\n<\/ol>\n

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      \nadolescence: Afield guide for the uninitiated. Nutritional Journal<\/em> ,
      \n6<\/em> (1), 32.<\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

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