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CHAPTER ONE

1.0                                                                                                      INTRODUCTION

1.1     OBESITY

Obesity can be defined as an excess of Body Fat (BF). There is no consensus on a cutoff point for excess fatness of overweight or obesity in children and adolescents. Obesity is now so common within the world’s population that it is beginning to replace undernutrition and infectious diseases as the most significant contributor to ill health. In particular, obesity is associated with diabetes mellitus, coronary heart disease, certain forms of cancer, and sleep-breathing disorders (Caterson and Gill, 2002).

Obesity is defined by a body-mass index (weight divided by square of the height) of 30 kg m–2 or greater, but this does not take into account the morbidity and mortality associated with more modest degrees of overweight, nor the detrimental effect of intra-abdominal fat (Chan, 1994). The global epidemic of obesity results from a combination of genetic susceptibility, increased availability of high-energy foods and decreased requirement for physical activity in modern society. Obesity should no longer be regarded simply as a cosmetic problem affecting certain individuals, but an epidemic that threatens global well-being  (Caterson and Gill, 2002).

Obesity causes or exacerbates many health problems, both independently and in association with other diseases. In particular, it is associated with the development of type 2 diabetes mellitus, coronary heart disease (CHD), an increased incidence of certain forms of cancer, respiratory complications (obstructive sleep apnoea) and osteoarthritis of large and small joints (Styne et al., 2005).

There are also several methods to measure the percentage of body fat. In research, techniques include underwater weighing (densitometry), multi-frequency bioelectrical impedance analysis (BIA) and magnetic resonance imaging (MRI). In the clinical environment, techniques such as body mass index (BMI), waist circumference, and skin fold thickness have been used extensively. Although, thesemethods are less accurate than research methods, they are satisfactory to identify risk (Caterson and Gill, 2002; Styne et al., 2005).

Table 1 - Cut-off points proposed by a WHO expert committee for the

classification of overweight

BMI* (kg m–2)

WHO classification

Popular description

<18.5

Underweight

Thin

18.5–24.9

_____

 ‘Healthy’, ‘normal’, ‘acceptable’

25.0–29.9

Grade 1 overweight

Overweight

30.0–39.9

Grade 2 overweight

Obesity

≥40.0

Grade 3 overweight

Morbid obesity

*BMI is the weight in kilograms divided by the square of the height in metres.

The data presented in Tables 1 and 2 reflect knowledge acquired largely from epidemiological studies in developed countries. Preliminary information from developing nations indicates that lower cut-off levels for both BMI and waist circumference (see Table 2) are necessary for certain populations who are at particular risk from comparatively modest degrees of overweight.

Source: Peter, 2000

Table 2 – Waist circumference predicts risk of metabolic complications

Increased risk

Substantially increased risk

Men

≥94 cm

≥102 cm

Women

≥88 cm

≥88 cm

Gender-specific waist circumferences are presented that denote ‘increased risk’ (level 1) and‘substantially increased risk’ (level 2) of metabolic complications associated with obesity in Caucasians. Level 1 is intended to alert clinicians to potential risk for CHD whereas level 2 should initiate therapeutic action

Source: Peter, 2000

While BMI seems appropriate for differentiating adults, it may not be as useful in children because of their changing body shape as they progress through normal growth. In addition, BMI fails to distinguish between fat and fat-free mass (muscle and bone) and may exaggerate obesity in large muscular children (Eckel and Krauss, 1998).While health consequences of obesity are related to excess fatness, the ideal method of classification should be based on direct measurement of fatness. Although methods such as densitometry can be used in research practice, they are not feasible for clinical settings (Chan, 1994).


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