Comparative assessment of anthropometric and bioimpedence methods for determining adiposity

Abstract

Background Obesity is a risk factor for different chronic conditions. Over the years, obesity has become a pandemic and it is therefore important that effective diagnostic tools are developed. Obesity is a measure of adiposity and it has become increasingly evident that anthropometric measures such as body mass index (BMI) used to estimate adiposity are inadequate. This study therefore examined the ability of different anthropometric measurements to diagnose obesity within a cross-section of Ghanaian women. Methods We obtained anthropometric measurements and used that to generate derived measures of adiposity such as body adiposity index (BAI) and conicity index. Furthermore we also measured adiposity using a bioimpedance analyser. Associations between these measurements and percentage body fat (%BF) were drawn in order to determine the suitability of the various measures to predict obesity. The prevalence of obesity was determined using both %BF and BMI. Results BMI, Waist and hip circumference and visceral fat (VF) were positively correlated with % BF whereas skeletal muscle mass was negatively correlated. Prevalence of obesity was 16% and 31.6% using BMI and %BF respectively. Receiver operating characteristic (ROC) analysis showed that these differences in prevalence was due to BMI based misclassification of persons who have obesity as overweight. Similar, shortfalls were observed for the other anthropometric measurements using ROC. Conclusions No single measure investigated could adequately predict obesity as an accumulation of fat using current established cut-off points within our study population. Large scale epidemiological studies are therefore needed to define appropriate population based cut-off points if anthropometric measurements are to be employed in diagnosing obesity within a particular population.

Haematological Indices and Antioxidant Enzyme Activity in Ghanaian Stroke Patients

“…The ROC curve, area under the curve, sensitivity, specificity, and accuracy of prediction were the criteria used. Sensitivity measures the probability that a person has stroke given that the person actually has stroke, whilst specificity measures the probability that a person does not have stroke given that the person does not have stroke [ 22 ].…”

Influences of sex chromosome aneuploidy on height, weight, and body mass index in human childhood and adolescence

“…Finally, our current report only considers height and weight, and it will be important for future research to examine more granular measures of body composition in SCA. In particular it would be important to obtain: more diverse and more precise measures of body composition aside from BMI (e.g., bioimpedance or DEXA scans to derive measures of percentage of total body fat, lean mass, and bone density; Adedia et al, 2020;Dekkers et al, 2019;Jaffrin, 2009;Kettaneh et al, 2005); more dense longitudinal data to allow curve fitting of trajectories that may be nonlinear and to better capture rapid pubertal changes; data covering a wider age range to investigate longitudinal changes from childhood to adulthood to late life;…”

“…Finally, our current report only considers height and weight, and it will be important for future research to examine more granular measures of body composition in SCA. In particular it would be important to obtain: more diverse and more precise measures of body composition aside from BMI (e.g., bioimpedance or DEXA scans to derive measures of percentage of total body fat, lean mass, and bone density; Adedia et al, 2020; Dekkers et al, 2019; Jaffrin, 2009; Kettaneh et al, 2005); more dense longitudinal data to allow curve fitting of trajectories that may be nonlinear and to better capture rapid pubertal changes; data covering a wider age range to investigate longitudinal changes from childhood to adulthood to late life; and accompanying phenotypic measures across different body systems that are known to vary with body size and SCA (e.g., brain anatomy, metabolism, bone density) so as to allow analyses of the coordinated SCA effects. Finally, we compare our SCA sample to norms derived from the standard CDC growth charts that are used as part of standard clinical care within the US.…”

Comparative Assessment of the Utility of Anthropometric and Bioelectrical Impedance Indices as Potential Predictors of Hypertension within a Ghanaian Adult Population: A Cross-Sectional Study

“…The derived measures of adiposity, such as CI, AVI, and BAI, were calculated using the subsequent formulas as previously described [ 27 ]. Body Mass Index (BMI): Body Adiposity Index (BAI): Abdominal Volume Index (AVI): Conicity Index (CI): …”