Comparative assessment of methods for determining adiposity and a model for obesity index

Background Obesity is increasingly becoming a pandemic considering the many risks it pose to other disease conditions. Obesity is largely a measure of adiposity, however, adiposity is not centralized in the human body. This makes it difficult for any single method to adequately represent obesity and by extension the risks specific areas of adipose accumulation pose to specific disease conditions. Subjects/Methods We evaluated the prevalence of obesity in a cohort of Ghanaian women using the body mass index (BMI) and further sought to evaluate how it compares to other methods of estimating adiposity and the suitability of any particular methods representing obesity in general. We used anthropometry and bioimpedance derived measures of adiposity and derived other indices such as the abdominal volume index (AVI), body adiposity index (BAI) and conicity index (CI). Results Waist and hip circumference, body fat (%BF) and visceral fat (VF) were positively correlated to BMI whereas skeletal muscle mass was negatively correlated. Physical activity indices did not show any significant correlation with BMI. Prevalence of obesity was 16% and 31% using BMI and %BF respectively. Receiver operating characteristic analysis showed that whereas BMI is effective in predicting underweight, normal weight and obesity it was a poor predictor of overweight. Conclusions There was also no single measure that could adequately predict obesity as an accumulation fat. Hence, we developed and propose a model as a factor of BAI, %BF, VF and BMI. This model should correctly represent a person’s adiposity status and hence should be evaluated in large cohort studies.


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Background: Obesity is increasingly becoming a pandemic considering the many risks it pose to other 29 disease conditions. Obesity is largely a measure of adiposity, however, adiposity is not centralized in 30 the human body. This makes it difficult for any single method to adequately represent obesity and by 31 extension the risks specific areas of adipose accumulation pose to specific disease conditions. 32 Subjects/Methods: We evaluated the prevalence of obesity in a cohort of Ghanaian women using the 33 body mass index (BMI) and further sought to evaluate how it compares to other methods of estimating 34 adiposity and the suitability of any particular methods representing obesity in general. We used 35 anthropometry and bioimpedance derived measures of adiposity and derived other indices such as the 36 abdominal volume index (AVI), body adiposity index (BAI) and conicity index (CI).

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Results: Waist and hip circumference, body fat (%BF) and visceral fat (VF) were positively correlated 38 to BMI whereas skeletal muscle mass was negatively correlated. Physical activity indices did not show 39 any significant correlation with BMI. Prevalence of obesity was 16% and 31% using BMI and %BF 40 respectively. Receiver operating characteristic analysis showed that whereas BMI is effective in 41 predicting underweight, normal weight and obesity it was a poor predictor of overweight.

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Chronic diseases such as diabetes, hypertension, and metabolic syndrome are rapidly taking over as 53 the major causes of morbidity and mortality in sub-Saharan Africa [1,2]. The chronic disease burden is 54 attributed to lifestyle changes such as diet, tobacco use and urbanization [2]. In sub-Saharan Africa, 55 the prevalence of infectious diseases such as malaria, HIV, tuberculosis and neglected tropical 56 diseases remains sturdy thereby inflicting a heavy blow on health systems [3,4]. With the rapidly 57 increasing prevalence of chronic diseases, the health systems will be affected by the rise in infectious 58 diseases co-existing with chronic diseases such as diabetes, hypertension, and metabolic syndrome 59 [  (  to hip ratio all others showed moderate predictive abilities (Fig 3). Table 3   that some of the measures have strong association with BMI which is an indication that they could be 228 used as well or in place of BMI for assessing obesity. Generally, these measures showed an increasing 229 trend as one progresses from underweight to obesity except for skeletal muscle mass that showed a 8 230 decreasing trend and this trend was similar to what had been observed previously [16] . Of particular 231 interest is the fact that for measures like WHR, VAI and CI the underweight individuals showed higher 232 levels than normal weight individual even though it did not achieve statistical significance. Although we 233 cannot assign a particular reason to this observation it may be a reflection that although obesity 234 representing an "overfed state" poses a significant health problem undernutrition represented by 235 underweight is also a significant health risk.

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The lack of association between physical activity and obesity in our study cohort could be due to the

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Based on these observations we wanted to see how BMI will fare against the gold standard which is 252 %BF. From the AUC values we see that BMI is an excellent predictor of underweight, normal weight 253 and obesity however it was a bad predictor of overweight. This is particularly due to the low specificity 254 even though it has a high sensitivity (

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In conclusion our study determined the various adiposity measures that could be used individually or 276 together to assess obesity: Body mass index, body adiposity index, visceral fat and body fat. From the 277 confirmatory factor analysis, visceral fat contributed the highest in the obesity index, followed by body 278 mass index, body fat and body adiposity index. We also observed that although BMI was an excellent 279 predictor of underweight, normal weight and obesity it wasn't a good predictor of underweight thus we 280 recommend that %BF be used in predicting obesity. We also suggest that ethnic specific cut-off for BMI 281 be developed.