Virginia L. Hicks

Validation of near-infrared interactance and skinfold methods for estimating body composition of American Indian women.

PURPOSE This study tested the predictive accuracy of the Jackson et al. skinfold (SKF) equations (sigma7SKF and sigma3SKF), a multi-site near-infrared interactance (NIR) prediction equation, and the Futrex-5000 NMR equation in estimating body composition of American Indian women (N = 151, aged 18-60 yr). METHODS Criterion body density (Db) was obtained from hydrodensitometry at residual lung volume. RESULTS Sigma7SKF significantly underestimated Db (P < 0.05). Sigma3SKF and Heywards NIR equations significantly overestimated Db (P < 0.05). The Futrex-5000 NIR equation significantly underestimated percent of body fat (%BF) (P < 0.05). Prediction errors for SKF and multi-site NIR exceeded 0.0080 g x cc(-1). The SEE for Futrex-5000 was 5.5%BF. Thus, ethnic-specific SKF and NIR equations were developed. For the SKF model, the sigma3SKF (triceps, axilla, and suprailium) and age explained 67.3% of the variance in Db:Db = 1.06198316 -0.00038496(sigma3SKF) -0.00020362(age). Cross-validation analysis yielded r = 0.88, SEE = 0.0068 g x cc(-1), E = 0.0070 g x cc(-1), and no significant difference between predicted and criterion Db. For the NIR model, the hip circumference, sigma2AdeltaOD2 (biceps and chest), FIT index, age, and height explained 73.9% of the variance in Db:Db = 1.0707606 -0.0009865(hip circumference) -0.0369861(sigma2deltaOD2) + 0.0004167(height) + 0.0000866(FIT index) -0.0001894(age). Cross-validation yielded r = 0.85, SEE = 0.0076 g x cc(-1), E = 0.0079 g x cc(-1), and a small, but significant, difference between predicted and criterion Db. CONCLUSIONS We recommend using the ethnic-specific SKF and NIR equations developed in this study to estimate Db of American Indian women.

Validity of single‐site and multi‐site models for estimating body composition of women using near‐infrared interactance

The purpose of this study was to develop a multi‐site near‐infrared (NIR) model (Model I) and compare its predictive accuracy to single‐site models (IIA and IIB). In Model I, the sum of two optical density (OD) measures (Σ2OD), age, body weight, height, and physical activity level were used as potential predictors of body density (Db). In Model IIA, the variables used in the manufacturers NIR equation (biceps OD1 and OD2, body weight, height, gender, and physical activity level) were the potential predictors. This model was modified by including age as an additional potential predictor in Model IIB. We also examined the test‐retest reliability and interrelationships of OD measures taken at 10 anatomical sites, as well as the validity of the manufacturers NIR equation, for estimating body composition of women. The subjects, 148 women between 20 and 72 years, were hydrostatically weighed to determine criterion Db. The Futrex‐5000 was used to measure OD1 and OD2 at 10 anatomical sites. Only two sites (pectoral OD2 and biceps OD2) contributed significantly to the variance in Db. Thus, the sum of these two ODs (Σ2OD), was used as a potential predictor in the multi‐site model. Test‐retest reliability was high, with intraclass correlation coefficients ≥0.85 for many of the OD measurements. Intercorrelations of ODs ranged from 0.22 to 0.91. In the multi‐site model (I), ΣOD, body weight, age, and height were significant predictors, accounting for 85.7% of the variance in Db. The SEE was 0.0076 g/ml or 3.3% BF. In the manufacturers model (IIA), biceps OD2, body weight, and height accounted for 76.3% of the variance in Db, and the SEE was 0.0094 g/ml (4.1% BF). When age was included as a predictor (Model IIB), the R2 increased (86.0%) and the SEE (0.0073 g/ml or 3.1% BF) decreased substantially. Cross‐validation of the three equations yielded r2s ranging between 0.688 (Model IIA) and 0.748 (Model I) and slightly larger SEEs (0.0094–0.001048 g/ml). There were no significant differences between average criterion Db and predicted Db for each equation. The manufacturers equation programmed in the Futrex‐5000 yielded a lower r2 (0.55), higher SEE (5.61% BF), and significantly underestimated criterion % BF by an average of 3% BF. Either the multi‐site (model I) or single‐site (Model IIB) equations is recommended to estimate body composition of this population.

Body Composition of Native-American Women Estimated by Dual-Energy X-ray Absorptiometry and Hydrodensitometry

In the present sample, the Native-American women varied in age (18-60 y) and fatness (23.0-57.4% BF). The cross-validation analysis for %BF estimated by DXA for this sample yielded a high validity coefficient (r = 0.89), and the average %BFDXA (37.3%) and %BFHW (37.6%) did not differ significantly. The prediction error (3.28% BF) was less than the theoretical expected value, given the wide range in age and fatness in this sample. Thus, it appears that DXA may be a viable alternative method for estimating the %BF of a diverse group of Native-American women. The DXA method is more practical than hydrostatic weighing, especially for subjects who are uncomfortable in the water. Also, DXA estimates of bone mineral may lead to improved estimates of FFB density for different ethnic populations.

Relationship of optical density and skinfold measurements : effects of age and level of body fatness

We examined relationships between skinfold (SKF) and optical density (delta OD) measurements across age and levels of body fatness (%BF) for 151 women, 20 to 72 years. There were significant (p < .05) relationships between delta ODs and SKFs at all sites, except the thigh. The interaction (SKF x Age) was significant (p < .05) for pectoral and biceps delta ODs. Slope comparisons indicated the relationships for younger (29 years) and older (59 years) women differed significantly from zero and each other (p < .05). Analysis of SKF x %BF interactions revealed that relationships between SKFs and delta ODs at the pectoral and biceps sites for leaner (22% BF) women differed significantly from zero (p < .05) and were larger than those for obese (39% BF) women (p < or = .05). Thus, the relationship between SKFs and delta ODs is stronger for younger and leaner women compared to older and fatter women. These findings may reflect differences in fat layering due to age or body fatness and provide insight as to why the manufacturers near-infrared (NIR) equation significantly underestimates the %BF of obese women.

Predictive accuracy of bioelectrical impedance equations for women

This study assessed the predictive accuracy of age‐ and fatness‐specific BIA equations in estimating the fat‐free mass (FFM) of a heterogeneous sample (N = 152) of women, 20–72 years (yr), with 11–57% body fat (BF). The criterion method was hydrostatic weighing (HW) at residual volume. The Siri (Siri [1961] Natl. Acad. Sci., pp. 78–89) two‐component model was used to convert body density into relative body fat (% BF) for calculation of criterion FFWHW. Average FFMHW and predicted FFMBIA did not differ significantly (P > .05) for the Lohman (Lohman [1981] Hum. Biol. 53:181–225) (20–29 yr), Van Loan and Mayclin (Van Loan and Mayclin [1987] Hum, Biol. 59:299–309) (18–64 yr), Gray (Gray [1989] Am. J. Clin. Nutr. 50:225–260) (19–59% BF), and Segal (Segal et al. [1988] Am. J. Clin. Nutr. 47:7–14) (<30% BF and ≥30% BF) equations. The SEE for these equations ranged from 2.11 to 2.65 kg. All other equations (Lohman 30–49 yr and 50–70 yr; Durenberg (Durenberg et al. [1990] Am. J. Clin. Nutr. 51:3–6) 20–40 yr and 60–83 yr) significantly underestimated (P < .05) FFMHW by as much as 5 kg, with the SEEs ranging from 2.12 to 2.82 kg., The prediction error of equations developed specifically for young (Lohman, 20–29 yr) and non‐obese (Segal, <30% BF) women was less than that for older (Van Loan and Mayclin, 18–64 yr) and obese (Segal, ≥30% BF; Gray, 19–59% BF) women. In conclusion, Lohmans equation for older (30–49 yr) women or Durenbergs equations for younger (20–40 yr) and older (60–83 yr) women are not recommended.

Bone Mineral in Southwest Native American Women

There is little, if any, information on bone mineral densities (BMD) in Native Americans. This information is valuable not only for assessing the risk of osteoporosis and fractures in this population, but with regard to estimating levels of obesity (%body fat), since bone mineral is a major factor influencing the density of the fat-free mass.1

Predictive accuracy of bioelectrical impedance equations corrected for fat‐free body size

This study examined whether the predictive accuracy of age‐specific bioelectrical impedance (BIA) equations was improved when estimated fat‐free body (FEBBIA) was corrected for the influence of FFB size on whole body resistance (WBR) and residual errors of prediction for 152 women, ages 20–72 years. The criterion measure of FFB (FFBHD) was obtained from hydrostatic weighing at residual volume (RV). FFBBIA was predicted from age‐specific equations. Each subjects FFBBIA was then adjusted for the relationship between FFB and residual scores using the Lohman et al. (1990) correction factor (Lohman TG, Going SB, Hewitt MJ, Williams DP [1990] Med. Sci. Sports Exerc. 22:S109 [abstract]): FFBADJ = (mean FFB reference sample – FFBBIA) × 0.18 + FFBBIA. Sample reference means were 45.0, 45.3, and 38.8 kg, respectively, for women 20–29 years, 30–49 years, and 50–70 years of age. The predictive accuracy of the unadjusted (FFBBIA) and adjusted (FFBADJ) BIA estimates was analyzed for the total sample and each age group. For the total sample, r2, standard error of estimate (SEE), and root mean square error (RMSE) were, respectively, 0.82, 2.5 kg, and 2.5 kg for FFBBIA. Corresponding values for FFBADJ were, respectively, 0.81, 2.5 kg, and 2.6 kg. Across age groups, r2s ranged from 0.66–0.88, and the SEEs and RMSEs were between 2.0 kg–2.8 kg. The relationship between FFBHD and residual scores (ry,res) was significant (P < 0.05) for all age groups. The ry,res for FFBBIA ranged from 0.37–0.41. For FFBADJ, the ry,res was higher (0.62–0.75). Thus, the overall predictive accuracy and systematic prediction error of the age‐specific BIA equations were not improved by adjusting BIA estimates for the relationship between FFB size and residual scores.