Predicting Total Body Fat from Waist Circumference and Triceps Skinfold

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Han and Lean (20) have observed a systematic underestimation of body fat by equations using subcutaneous skinfold thicknesses (2) in subjects with increased intra-abdominal fat mass, reflected by a high waist circumference or waist-to-hip ratio, including the elderly and those with type 2 diabetes. Waist circumference (Figure 4.4) has been found to correlate highly with both intra-abdominal and total fat masses (6,23), and was used on its own and with skinfold thicknesses to develop new regression equations to correct for the intra-abdominal fat mass (6). These equations were validated in a large Dutch sample from previous study of body fat distribution (3).

Equations using waist circumference alone, adjusted for age (equation 3), showed good prediction of body fat in the independent Dutch sample (r2 = 78%) with similar error of prediction as other equations. These equations are particularly good for estimating body fat in the elderly without the systematic underestimation of body fat that occurs in the subcutaneous skinfold method (Figure 4.6).

Body fat % (men) = [0.567 x Waist circumference (cm)] + [0.101 x Age (years)] - 31.8 (3)

Body fat % (women) = [0.439 x Waist circumference (cm)] + [0.221 x Age (years)] — 9.4

Equations combining waist circumference and triceps skinfold, adjusted for age (equation 4), have been shown to improve predictive power of body fat estimation without systematic errors over equations employing subcutaneous skinfolds alone in subjects with type 2 diabetes who had increased intra-abdominal fat mass (20).

Body fat % (men) = [0.353 x Waist (cm)] + [0.756 x Triceps (mm)] + 0.235 x Age (years)]

— 26.4 (4) Body fat % (women) = [0.232 x Waist (cm)] + [0.657 x Triceps (mm)] + [0.215 x Age (years)]

Skinfold Prediction Equations
Figure 4.6 Plots of errors of predicting body fat by underwater weighing from equations using waist circumference (—•—, Lean et al. (6) and subcutaneous skinfolds (-- - □ ---, Durnin and Womersley (2) against age in men (a) and women (b) aged 18 to 83 years

Calculations of Body Mass Index (Quetelet Index), and its Use to Predict Body Fat

Bigger people—both taller and fatter—are heavier than small people. Body weight includes fat, muscle and all other organs. For people of the same height, most of the variation in weight is accounted for by different amounts of body fat. BMI aims to describe weight for height in a way which will relate maximally to body weight (or body fat) with minimal relation to height (24). BMI is calculated as the ratio of weight in kilograms divided by height squared (m2). Since BMI uses height, the height measurement needs to be very accurate. Classification of BMI (Table 4.2) uses the same criteria for both men and women is now adopted by both the NIH (8) and WHO (25). A BMI of 18.5 to 24.9 kg/m2 is considered as in the normal range, above 25 kg/m2 as overweight and above 30kg/m2 as obese. For some purposes, the obese category is subclassified by the

WHO (25) as 30-34.9 (moderately obese), 35-39.9 (severely obese), and greater than 40 (very severely obese) kg/m2.

BMI can be used to predict body fat from underwater weight (r2 = 79%) with age and sex corrections (3). Our derived equations using BMI to predict body fat were validated in the independent sample provided by Deurenberg et al. (3) and showed similarly good prediction of body fat as other equations currently in use (equation 5).

Body fat % (men) = [1.33 x BMI (kg/m2)] + [0.236 x Age (years)] — 20.2 (5)

Body fat % (women) = [1.21 x BMI (kg/m2)] + [0.262 x Age (years)] — 6.7

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Diabetes 2

Diabetes 2

Diabetes is a disease that affects the way your body uses food. Normally, your body converts sugars, starches and other foods into a form of sugar called glucose. Your body uses glucose for fuel. The cells receive the glucose through the bloodstream. They then use insulin a hormone made by the pancreas to absorb the glucose, convert it into energy, and either use it or store it for later use. Learn more...

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