What measure of adiposity to use?
Obesity is defined as an excess of body fat, which can be measured directly using dual energy X-ray absorptiometry and isotopic dilution techniques. However, these are costly and their limited availability makes it difficult to perform such measurements in large numbers of subjects. In some studies fat mass has been measured indirectly using bioelectrical impedance or skin-fold thickness both of which correlate reasonably well within the normal range but less so in the very lean or the obese. The most commonly used marker of adiposity is BMI (weight in kg/height in m2) which is a measure of heaviness that can be performed in large epidemiological studies and correlates reasonably well with body fat content. Body fat distribution provides an additional risk that is not given by simply the amount of body fat. Abdominal fat mass, for example, can vary significantly within a narrow range of total body fat or BMI. Furthermore, within a given BMI, men have on average twice the amount of abdominal fat compared to premenopausal women. There are several methods to accurately localize body fat distribution in humans (computer tomography, ultrasound and magnetic resource imaging), but these methods are often impractical and expensive and difficult to perform in large numbers of subjects. The waist to hip ratio (WHR) has been used to identify subjects with abdominal fat accumulation (WHR of >1.0 in men and >0.85 in women) as has waist circumference alone which is a convenient and simple measurement and correlates well with BMI, WHR and most importantly with risk factors for cardiovascular disease. A waist circumference of >102 cm (^40 inches) in men and >88 cm (~35 inch) in women is consistent with abdominal obesity and provides a substantial increased risk for metabolic complications. Surrogate markers of adipose tissue mass such as the adipocyte derived protein leptin (which correlates positively and closely with fat mass, r = 0.8) have been used in some studies (Comuzzie, 2002).
Some studies either focus on, or incorporate ''intermediate phenotypes'' in their analyses. Such traits have the theoretical advantage that they may be more proximally related to the function of the gene under study. Thus, a gene which influences energy expenditure might be easier to identify if one studied resting metabolic rate as the outcome variable. Intermediate phenotypes that are frequently used include resting metabolic rate, respiratory quotient (RQ), insulin sensitivity, and food intake and preferences measured using questionnaire-based methods.
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