Agreement of BMI-Based Equations and DXA in Determining Body-Fat Percentage in Adults With Down Syndrome

Abstract

The purpose of this investigation was to evaluate measurements of body-fat percentage (BF%) in 4 body-mass-index- (BMI) -based equations and dual-energy X-ray absorptiometry (DXA) in individuals with Down syndrome (DS). Ten male and 10 female adults with DS volunteered for this study. Four regression equations for estimating BF% based on BMI previously developed by Deurenberg et al. (DE(BMI-BF%)), Gallagher et al. (GA(BMI-BF%)), Womersley & Durnin (WO(BMI-BF%)), and Jackson et al. (JA(BMI-BF%)) were compared with DXA. There was no significant difference (p = .659) in mean BF% values between JA(BMI-BF%) (BF% = 40.80% ± 6.3%) and DXA (39.90% ± 11.1%), while DE(BMI-BF%) (34.40% ± 9.0%), WO(BMI-BF%) (35.10% ± 9.4%), and GA(BMI-BF%) (35.10% ± 9.4%) were significantly (p < .001) lower. The limits of agreement (1.96 SD of the constant error) varied from 9.80% to 16.20%. Therefore, BMI-based BF% equations should not be used in individuals with DS.

Body Composition in Adults With Intellectual Disabilities: Implications for Practice

“…Noninvasive body measurements such as height, weight, BMI, and WC are feasible measurements to obtain in adults with ID in a clinical setting (Verstraelen et al, 2009;Waninge et al, 2009;Waninge et al, 2010); however, individual patient compliance and ability to follow instructions can influence feasibility (Verstraelen et al, 2009;Waninge et al, 2009). BMI is a reasonable indicator of body fatness among adults with ID (Casey, 2013;Esco et al, 2016;Temple et al, 2010) but does not correlate well with fat-free mass (Esco et al, 2016;Temple et al, 2010). Since fat-free mass includes muscle, bone, blood, and water, using BMI as a health indicator in this population does not fully capture true body composition.…”

“…Across eight trials, body composition measurements have been studied among 299 community-dwelling and residential adults with ID (Esco, Nickerson, Bicard, Russell, & Bishop, 2016; Havinga-Top et al, 2015; Nickerson et al, 2015; Temple et al, 2010; Usera et al, 2005; Verstraelen et al, 2009; Waninge et al, 2009; Waninge et al, 2010). Table 2 provides a comparison of the field-based body composition measurements that have been studied in adults with ID.…”

“…gender and presence of Down syndrome (males: R 2 = 0.71, p < .001; females: R 2 = 0.89, p < .001; Down syndrome: R 2 = 0.88, p < .001; without Down syndrome: R 2 = 0.85; p < .001; Temple et al, 2010). This finding was later echoed by Esco et al (2016) who reported a significant relationship between BMI and fat mass (r = 0.84, p < .001) but not fat-free mass (r = 0.31, p = .17) among 20 community-dwelling adults with Down syndrome. Temple and colleagues also observed that a BMI ≥30.0 kg/m 2 had 57.1% sensitivity and 100.0% specificity in relation to obesity identified by DXA, with sensitivity improving with lower cutoff points (e.g., BMI ≥ 23.0 kg/m 2 had 100% sensitivity and 58.8% specificity).…”

“…The BMI cutoff value of ≥30.0 kg/m 2 accurately classified adults who are not obese but misclassified some individuals found to be obese by DXA as not having obesity (Temple et al, 2010). BMI-based equations and their ability to accurately predict body fat have also been examined in the literature with one study by Esco et al (2016) comparing four equations (Deurenberg, Weststrate, & Seidell, 1991;Gallagher et al, 2000;Jackson et al, 2002;Womersley, 1977) to DXA results among 20 community-dwelling adults with Down syndrome (Esco et al, 2016). While only one equation (Jackson et al, 2002) did not differ significantly from DXA results (p = .66), the correlation coefficients were large for all four equations (range of r = 0.68 to r = 0.89; Jackson et al, 2002).…”

“…Overall, all four equations provided wide limits of agreement with the standard error of estimates ranging from 5.1% to 8.4% and total errors ranging from 6.8% to 8.93%. The equations that had tighter limits of agreements and stronger correlations to DXA included age, gender, and race as prediction variables (Esco et al, 2016).…”