International journal of body composition research最新文献

Objective: To investigate the effects of the atypical antipsychotics drugs (AADs), olanzapine and risperidone, on femoral bone characteristics in female C57BL/6J mice.

Methods: Mice were treated with placebo or AADs (olanzapine or risperidone) for 3-4 weeks. Femoral cortical and trabecular bone characteristics were determined using micro-computed tomography.

Results: Olanzapine-treated mice tended to have lower trabecular bone volume (P = 0.088) and connectivity (P = 0.057) but no significant differences in bone density (P = 0.521) relative to controls. Risperidone-treated mice had significantly lower trabecular bone density (P = 0.001) and volume (P = 0.008), bone volume/total volume (P = 0.001), connectivity (P = 0.007), and trabecular number (P = 0.003) relative to controls. Cortical bone was not significantly affected by olanzapine or risperidone treatment.

Conclusion: AADs inhibited trabecular bone accrual in C57BL/6J mice suggesting that alternative treatment options may need to be considered for the schizophrenia patient with potential osteoporosis risk.

Objective: Assess the utility of peripheral quantitative computed tomography (pQCT) for estimating whole body fat in adolescent girls.

Research methods and procedures: Our sample included 458 girls (aged 10.7 ± 1.1y, mean BMI = 18.5 ± 3.3 kg/m²) who had DXA scans for whole body percent fat (DXA %Fat). Soft tissue analysis of pQCT scans provided thigh and calf subcutaneous percent fat and thigh and calf muscle density (muscle fat content surrogates). Anthropometric variables included weight, height and BMI. Indices of maturity included age and maturity offset. The total sample was split into validation (VS; n = 304) and cross-validation (CS; n = 154) samples. Linear regression was used to develop prediction equations for estimating DXA %Fat from anthropometric variables and pQCT-derived soft tissue components in VS and the best prediction equation was applied to CS.

Results: Thigh and calf SFA %Fat were positively correlated with DXA %Fat (r = 0.84 to 0.85; p <0.001) and thigh and calf muscle densities were inversely related to DXA %Fat (r = -0.30 to -0.44; p < 0.001). The best equation for estimating %Fat included thigh and calf SFA %Fat and thigh and calf muscle density (adj. R² = 0.90; SEE = 2.7%). Bland-Altman analysis in CS showed accurate estimates of percent fat (adj. R² = 0.89; SEE = 2.7%) with no bias.

Discussion: Peripheral QCT derived indices of adiposity can be used to accurately estimate whole body percent fat in adolescent girls.

Objective: Age-specific body mass index (BMI) is commonly employed as an index of adiposity for pediatric clinical and research purposes. However, BMI fails to discriminate between fat and lean mass, making it an imperfect monitor for obesity. We hypothesized that simple anthropometry and organized non-aquatic physical activity assessment (PA) would provide superior explanatory value for pediatric body composition outcomes.

Research methods and procedures: In a cross-sectional analysis, whole body DXA assessed body composition in 120 pre-menarcheal girls. Questionnaires were used to record and generate annual means for PA. Age, Tanner breast self-stage, height, weight, BMI, skinfold thicknesses, girths and PA were examined as potential predictors of body composition outcomes, using backward stepwise multiple linear regression. A parsimonious regression model was developed in 75% and cross-validated in 25% of the total sample; models were rerun with the total sample.

Results: Subject means were as follows: age = 10.4±1.2 y; lean soft tissue (LST) = 24.4±4.2 kg; fat mass (FM) = 8.1±4.1 kg; BMI = 17.6±2.5 kg/m²; PA = 6.8±5.0 h/wk; Tanner breast self-stage ranged from 1 to 3. BMI for age Z scores ranged from -2 to 2.1. Age and BMI alone yielded adjusted model r²=0.44 to 0.78. The final model, including age, height, weight, biceps skinfold and PA, yielded adjusted r²=0.61 to 0.92, P <0.001. Prediction of LST and FM increased from r²=0.64 and 0.76 to r²=0.92 and 0.91, respectively.

Discussion: Compared to BMI and age alone, models including biceps skinfold, PA, height, weight and age had superior explanatory value for clinically-relevant body composition outcomes, and are feasible for clinical use.

Purpose: Adipocyte cell size varies among individuals and importantly, is inversely correlated with insulin sensitivity, and modifiable by weight loss or pharmaceutical agents. However, there are no non-invasive, in vivo methods for adipocyte cell size determination. Here we apply Chemical-Shift Water-Fat MRI to in vivo measures of subcutaneous (inguinal) and visceral (gonadal) white adipose tissue (WAT) to determine whether the fat-signal fraction (FF) is a sensitive indicator of adipocyte cell size.

Materials and methods: C57BL/6J male mice (8 weeks old) were singly housed and fed a low-fat diet, high-fat diet or very high-fat diet (n = 16 or 15/group) for 8 weeks. Food intake, body weight and composition were measured; CS-MRI was performed on a 9.4 Tesla Bruker magnet with respiratory gating and anesthesia. Histology was acquired for gonadal WAT; both gonadal and inguinal WAT were fixed with osmium tetroxide and then measured through Image J for cell size.

Results: Mice fed with higher fat content diets gained significantly more body weight, fat and lean mass while maintaining higher energy intakes over the 8 weeks. There was no significant difference in fat fraction for either gonadal (P = 0.1295) or inguinal (P = 0.4704) WAT among the three groups, despite significantly larger adipocytes (P <0.0001) in mice on high fat diets.

Conclusion: Although diet-induced obesity significantly increased the amount of fat mass, as well as mean and overall white adipocyte cell size, the CS-MRI measured fat fraction between groups were not significantly different. These results do not support the utility of CS-MRI measured FF for in vivo determination of adipocyte cell size.

Objective: Anthropometrics are commonly used indices of total and central adiposity. No study has compared anthropometric measurements to dual-energy X-ray absorptiometry (DXA) measurements as correlates of cardiovascular risks in a nationally representative sample of youth. We aimed to evaluate the validity of anthropometrics compared to DXA-assessed adiposity in relation to cardiovascular risks in youth aged 8-19 years.

Methods: Data were from the National Health and Nutrition Examination Survey 1999-2004 (n=7013). We examined the correlations between anthropometric and DXA measures of adiposity (i.e., body mass index (BMI) versus percent fat mass (%FM) and fat mass index, and waist circumference (WC) and waist-to-height ratio (WHtR) versus percent trunk fat mass (%TFM)) with nine cardiovascular risks, stratified by sex and age, or race-ethnicity.

Results: Anthropometric and DXA adiposity measures were significantly correlated with insulin (r: 0.48 to 0.66), C-reactive protein (r: 0.47 to 0.58), triglycerides (r: 0.15 to 0.41), high-density lipoprotein cholesterol (HDL-C, r: -0.44 to -0.22), systolic blood pressure (SBP, r: 0.10 to 0.31), low-density lipoprotein cholesterol (r: 0.09 to 0.30), total cholesterol (TC, r: 0.01 to 0.29) and glucose (r: 0.05 to 0.20). Only in all youth, BMI was more strongly correlated with SBP (0.22 vs. 0.12, P<0.0001) and HDL-C (-0.34 vs. -0.25, P<0.0001) than %FM; WC but not WHtR was more strongly correlated with HDL-C (-0.37 vs. -0.30, P<0.0001) but less strongly associated with TC (0.12 vs. 0.21, P<0.0001) than %TFM.

Conclusions: DXA adiposity measures do not produce stronger associations with cardiovascular risk factors in youth than BMI or WC.

Objective: Accumulation of intra-abdominal (visceral) adipose tissue, independent of total adiposity, is associated with development of metabolic abnormalities such as insulin resistance and type-2 diabetes in children and adults. The objective of this study was to develop prediction equations for estimating visceral adiposity (VAT) measured by magnetic resonance imaging (MRI) using anthropometric variables and measures of abdominal fat mass from DXA in adolescents and young adults.

Methods: Cross-sectional data was collected from a multiethnic population of seventy males and females, aged 12-25 years, with BMI ranging from 14.5-38.1 kg/m². Android (AFM; android region as defined by manufacturers instruction) and lumbar L1-L4 regional fat masses were assessed using DXA (GE Lunar Prodigy; GE Lunar Corp, Madison, WI, USA). Criterion measures of intra-abdominal visceral fat were obtained using single-slice MRI (General Electric Signa Model 5x 1.5T) and VAT area was analyzed at the level OF L4-L5. Image analysis was carried out using ZedView 3.1.

Results: DXA measures of AFM (r=0.76) and L1-L4 (r=0.71) were significantly (P<0.0001) correlated with MRI-measured VAT. DXA AFM, together with gender and weight, explained 62% of the variance in VAT (SEE=10.06 cm²). DXA L1-L4 fat mass with gender explained 54% of the variance in VAT (SEE=11.08 cm²). Addition of the significant interaction, gender × DXA fat mass, improved prediction of VAT from AFM (R_adj²=0.61, SEE=10.10cm²) and L1-L4 (R_adj²=0.59, SEE=10.39cm²).

Conclusion: These results demonstrate that VAT is accurately estimated from regional fat masses measured by DXA in adolescents and young adults.

OBJECTIVE: Body Adiposity Index (BAI), a new surrogate measure of body fat (hip circumference/[height 1.5-18]), has been proposed as a more accurate alternative to BMI. We compared BAI with BMI and their correlations with measures of body fat, waist circumference (WC), and indirect indices of fat pre- and post-Roux-en-Y gastric bypass (RYGB). METHODS: Sixteen clinically severe obese (CSO) non-diabetic women (age = 33.9± 7.9 SD; BMI = 46.5±9.5 kg/m(2)) were assessed pre-surgery, and at 2 (n=9) and 5 mo (n=8) post-surgery. Body fat percentage (% fat) was estimated with bioimpedance analysis (BIA), air displacement plethysmography (ADP), and dual-energy x-ray absorptiometry (DXA). WC, an indicator of central fat, and both plasma leptin (ng/ml) and insulin (mU/l) concentrations were measured as indirect body fat indices. Pre- and post-surgery values were analyzed with Pearson correlations and linear regressions. RESULTS: BAI and BMI correlated significantly with each other pre-surgery and at each time point post surgery. BAI and BMI also correlated significantly with % fat from BIA and ADP; however, only BMI correlated significantly with % fat from DXA pre- and post-RYGB. BMI was the single best predictor of WC and leptin at 2 and 5 mo post-surgery and had significant longitudinal changes correlating with % fat from BIA and DXA as well as with leptin. DISCUSSION: Both BAI and BMI were good surrogates of % fat as estimated from BIA and ADP, but only BMI was a good surrogate of % fat from DXA in CSO women. Thus, BAI may not be a better alternative to BMI.

Mass-specific basal metabolic rate (mass-specific BMR), defined as the resting energy expenditure per unit body mass per day, is an important parameter in energy metabolism research. However, a mechanistic explanation for magnitude of mass-specific BMR remains lacking. The objective of the present study was to validate the applicability of a proposed mass-specific BMR model in healthy adults. A mechanistic model was developed at the organ-tissue level, mass-specific BMR = Σ(K_i × F_i), where Fi is the fraction of body mass as individual organs and tissues, and Ki is the specific resting metabolic rate of major organs and tissues. The Fi values were measured by multiple MRI scans and the Ki values were suggested by Elia in 1992. A database of healthy non-elderly non-obese adults (age 20 - 49 yrs, BMI <30 kg/m²) included 49 men and 57 women. Measured and predicted mass-specific BMR of all subjects was 21.6 ± 1.9 (mean ± SD) and 21.7 ± 1.6 kcal/kg per day, respectively. The measured mass-specific BMR was correlated with the predicted mass-specific BMR (r = 0.82, P <0.001). A Bland-Altman plot showed no significant trend (r = 0.022, P = 0.50) between the measured and predicted mass-specific BMR, versus the average of measured and predicted mass-specific BMR. In conclusion, the proposed mechanistic model was validated in non-elderly non-obese adults and can help to understand the inherent relationship between mass-specific BMR and body composition.

Background: Bioelectrical impedance analysis (BIA) is a simple tool to assess total body water (TBW), from which body composition can be estimated using statistical equations. However, standard BIA equations have not been sufficiently validated during pregnancy, in HIV infection, or in sub-Saharan Africa. We therefore compared TBW estimates from multifrequency BIA with those from the reference method deuterium isotope dilution (Deut) in a cohort of 30 HIV-uninfected and 30 HIV-infected pregnant women from Tanzania.

Methods: We enrolled pregnant women presenting for routine antenatal care and collected data on pregnancy outcomes. At each trimester of gestation and once at 10-wk post-partum, we measured maternal anthropometry, TBW_BIA, and TBW_Deut.

Results: TBW_BIA was highly correlated at each time point with TBW_Deut among HIV-infected (all P ≤0.001) and HIV-uninfected women (all P <0.0001). During pregnancy, mean TBW_BIA progressively underestimated TBW_Deut in the overall cohort; trimester-specific differences (mean ±SD) were -1.02 ±2.36 kg, -1.47 ±2.43 kg, and -2.42 ±2.63 kg, respectively. The difference at 10-wk postpartum was small (-0.24 ±2.07 kg). In Bland-Altman and regression models, TBW_BIA was subject to a systematic predictive bias at each antenatal and postnatal time point (all P ≤0.038). Among HIV-positive women, TBW_Deut measured during the first (P =0.02) and second trimester (P =0.03) was positively related to birthweight.

Conclusions: The validity of current BIA equations to assess TBW during pregnancy and in the postpartum period among women from sub-Saharan Africa remains uncertain. Deuterium dilution may assess aspects of maternal body composition relevant for pregnancy outcomes among HIV-infected women.

OBJECTIVE: To develop a magnetic resonance imaging (MRI)-based approach for quantifying absolute fat mass in organs, muscles, and adipose tissues, and to validate its accuracy against reference chemical analysis (CA). METHODS: Chemical-shift imaging can accurately decompose water and fat signals from the acquired MRI data. A proton density fat fraction (PDFF) can be computed from the separated images, and reflects the relative fat content on a voxel-by-voxel basis. The PDFF is mathematically closely related to the fat mass fraction and can be converted to absolute fat mass in grams by multiplying by the voxel volume and the mass density of fat. In this validation study, 97 freshly excised and unique samples from four pigs, comprising of organs, muscles, and adipose and lean tissues were imaged by MRI and then analyzed independently by CA. Linear regression was used to assess correlation, agreement, and measurement differences between MRI and CA. RESULTS: Considering all 97 samples, a strong correlation and agreement was obtained between MRI and CA-derived fat mass (slope = 1.01, intercept = 1.99g, r(2) = 0.98, p < 0.01). The mean difference d between MRI and CA was 2.17±3.40g. MRI did not exhibit any tendency to under or overestimate CA (p > 0.05). When considering samples from each pig separately, the results were (slope = 1.05, intercept = 1.11g, r(2) = 0.98, d = 2.66±4.36g), (slope = 0.99, intercept = 2.33g, r(2) = 0.99, d = 1.88±2.68g), (slope = 1.07, intercept = 1.52g, r(2) = 0.96, d = 2.73±2.50g), and (slope=0.92, intercept=2.84g, r(2) = 0.97, d = 1.18±3.90g), respectively. CONCLUSION: Chemical-shift MRI and PDFF provides an accurate means of determining absolute fat mass in organs, muscles, and adipose and lean tissues.