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Assessing skeletal muscle mass (SMM) is critical in studying and detecting sarcopenia. Direct measurements by MRI or computerized tomography are expensive or high in radiation exposure. Dual-energy X-ray absorptiometry (DXA) is promising for body composition assessments, but the validity of DXA for predicting SMM in the elderly is still under investigation. The objective of this study was to assess the relationship between DXA-derived measurements of lean soft tissue mass (LSTM) and SMM in older women. Study participants were postmenopausal women (n = 101) recruited in southern Arizona. Total and regional body composition was measured using MRI and DXA (QDR4500w). The participants' mean age was 70.7 +/- 6.4 y and their mean BMI was 27.4 +/- 5.1 kg/m2. DXA-derived LSTM was highly correlated with MRI-derived SMM for the whole body (r = 0.94; P < 0.001) and leg region (r = 0.91; P < 0.001). In multivariate models, adjusting for age and DXA-derived percent fat slightly increased the amount of variance in SMM that can be explained by the DXA-derived LSTM assessments for the leg region but not for the total body. In conclusion, although the relationships between DXA measures and MRI-derived SMM vary by region of interest, the overall prediction of SMM by DXA is excellent. We conclude that DXA is a reliable method for cross-sectional assessments of SMM in older women.

Background: Deuterium oxide (D2O) dilution is the criterion method for total body water (TBW) measurement, but results may vary depending on the specimen type, analysis method, and analyzing laboratory. Bioelectrical impedance (BIA) estimates TBW, but results may vary by device make and model.Objectives: We investigated the accuracy and precision of TBW estimates and how measurement conditions affected the accuracy of body composition using multicompartment body composition models.Methods: Eighty collegiate athletes received duplicate TBW measures acquired from 3 BIA devices (S10, SFB7, and SOZO) and from unique D2O combinations of specimen type (saliva, urine), analysis methodology [Fourier transform infrared spectrophotometry (FTIR), isotope-ratio mass spectrometry (IRMS)], and 3 different laboratories. TBW measures were substituted into 2-compartment (2C) and 5-compartment (5C) body composition models. Criterion measures were compared using Lin's concordance correlation coefficient cutoff of poor (<0.90), moderate (0.90-0.95), substantial (0.95-0.99), and almost perfect (>0.99).Results: Fifty-one participants (26 female) completed the protocol. Using IRMS saliva as the criterion TBW, all other measures produced a substantial or almost perfect agreement, except for SFB7 (poor) and SOZO (moderate). The 2C body composition measures using D2O and BIA produced poor agreement except for moderate agreement for lab 3 FTIR saliva. The 5C body composition measures using D2O produced a substantial agreement, whereas the BIA device S10 and SOZO had a moderate agreement, while the SFB7 had a poor agreement to the criterion. Test-retest precision varied between techniques from 0.3% to 1.2% for TBW.Conclusions: Small differences in TBW measurement led to significant differences in 2C models. The 5C models partially mitigate differences seen in 2C models when different TBW measures are used. Interchanging TBW measures in multicompartment models can be problematic and should be performed with these considerations. [ABSTRACT FROM AUTHOR]

Background: The aim of obesity treatment is to promote loss of fat relative to lean mass. However, body composition changes with calorie restriction differ among individuals.Objectives: The goal of this study was to test the hypothesis that insulin secretion predicts body composition changes among young and middle-age adults with high BMI (in kg/m2) following major weight loss.Methods: Exploratory analyses were conducted with pre-randomization data from 2 large feeding trials: the Framingham, Boston, Bloomington, Birmingham, and Baylor study (FB4; n = 82, 43.9% women, BMI ≥27) and the Framingham State Food Study [(FS)2; n = 161, 69.6% women, BMI ≥25]. Participants in the 2 trials consumed calorie-restricted moderate-carbohydrate or very-low-carbohydrate diets to produce 12-18% weight loss in ∼14 wk or 10-14% in ∼10 wk, respectively. We determined insulin concentration 30 min after a 75-g oral glucose load (insulin-30) as a measure of insulin secretion and HOMA-IR as a measure of insulin resistance at baseline. Body composition was determined by DXA at baseline and post-weight loss. Associations were analyzed using general linear models with adjustment for covariates.Results: In FB4, higher insulin-30 was associated with a smaller decrease in fat mass (0.441 kg per 100 μIU/mL increment in baseline insulin-30; P = 0.005; -1.20-kg mean difference between the first compared with the fifth group of insulin-30) and a larger decrease in lean mass (-0.465 kg per 100 μIU/mL; P = 0.004; 1.27-kg difference). Participants with higher insulin-30 lost a smaller proportion of weight loss as fat (-3.37% per 100 μIU/mL; P = 0.003; 9.20% difference). Greater HOMA-IR was also significantly associated with adverse body composition changes. Results from (FS)2 were qualitatively similar but of a smaller magnitude.Conclusions: Baseline insulin dynamics predict substantial individual differences in body composition following weight loss. These findings may inform understanding of the pathophysiological basis for weight regain and the design of more effective obesity treatment. Registered at clinicaltrials.gov as NCT03394664 and NCT02068885. [ABSTRACT FROM AUTHOR]

Diurnal variation in glucose tolerance: cyclic suppression of insulin action and insulin secretion in normal-weight, but not obese, subjects. Booth ([1]) evidently questions whether insulin secretion affects body composition or body composition affects insulin secretion. High insulin secretion may exist with low or high levels of insulin resistance. [Extracted from the article]

Assessing skeletal muscle mass (SMM) is critical in studying and detecting sarcopenia. Direct measurements by MRI or computerized tomography are expensive or high in radiation exposure. Dual-energy X-ray absorptiometry (DXA) is promising for body composition assessments, but the validity of DXA for predicting SMM in the elderly is still under investigation. The objective of this study was to assess the relationship between DXA-derived measurements of lean soft tissue mass (LSTM) and SMM in older women. Study participants were postmenopausal women (n = 101) recruited in southern Arizona. Total and regional body composition was measured using MRI and DXA (QDR4500w). The participants' mean age was 70.7 ± 6.4 y and their mean BMI was 27.4 ± 5.1 kg/m². DXA-derived LSTM was highly correlated with MRI-derived SMM for the whole body (r = 0.94; P < 0.001) and leg region (r = 0.91: P < 0.001). In multivariate models, adjusting for age and DXA-derived percent fat slightly increased the amount of variance in SMM that can be explained by the DXA-derived LSTM assessments for the leg region but not for the total body. In conclusion, although the relationships between DXA measures and MRI-derived SMM vary by region of interest, the overall prediction of SMM by DXA is excellent. We conclude that DXA is a reliable method for cross-sectional assessments of SMM in older women. [ABSTRACT FROM AUTHOR]

A whole body skeletal muscle [(SM); kg] mass estimation model, based on total body potassium [(TBK); mmol] measured by whole body (40)K counting (WBC) was developed (SM = 0.0082.TBK) and validated in adults in a previous study. It is unknown whether the adult TBK SM prediction model is applicable for pediatric use. The aim of this study was to derive and validate a pediatric TBK SM prediction equation. SM measured by MRI was used as the criterion and TBK was measured by WBC. The protocol was completed in 116 healthy children, 66 males and 50 females, 11.7 +/- 3.5 y (mean +/- SD, range = 5-17 y). A strong linear correlation was observed between TBK and SM (r = 0.984; P < 0.001). The SM:TBK ratio was 0.0071 +/- 0.0008 kg/mmol in the children studied, much lower than the corresponding value of 0.0082 kg/mmol in adults. An empirical SM prediction equation was developed using TBK alone: SM = 0.0085.TBK - 2.83, r(2) = 0.97, SEE = 1.39 kg. Bland-Altman analysis did not disclose a significant bias in the prediction of SM. When biological factors entered along with TBK in the general linear model, another prediction equation was developed: SM = 5.52 + 0.001.TBK (mmol) + 0.081.weight (kg) - 0.049.height (cm) + 0.00004.TBK . height + race (-0.60 for Caucasian, 0.49 for African-American, and 0 for Hispanic). Because the adult TBK SM prediction model is not applicable for pediatric use, this study provides new empirical TBK SM prediction equations that should prove useful for studies on nutrition, growth, and development in children. [ABSTRACT FROM AUTHOR]

Protein is an important body component for monitoring growth, development, and nutritional status. We previously developed a total body potassium (TBK, in mmol) and bone mineral (Mo, in kg) model for predicting total body protein (TBPro, in kg) in adults (TBPro = 0.00252 × TBK + 0.732 × Mo). However, the applicability of the TBK-Mo model for children is unknown. The study aims were to develop a TBK-independent 6-component (6-C) TBPro approach as the criterion, and then to validate the TBK-Mo model in children. The following measurements were made in adolescents and children (n = 62,38 boys and 24 girls, aged 5–;17 y): body weight (BW, in kg), body volume (By, in liters) by air displacement plethysmography, total body water (TBW, in kg) by 2H2O dilution, Mo by dual-energy X-ray absorptiometry, and TBK by whole-body counting. A 6-C model was derived as TBPro = 2.922 × BW - 0.301 × TBW - 2.039 × Mo - 2.632 × BV. The TBPro estimates did not differ between the 6-C and TBK-Mo models (mean ± SD, 0.20 ± 0.86 kg). There was a significant correlation between TBPro by the 6-C and TBK-Mo models (r = 0.94, P < 0.001). Bland-Altman analysis indicated that the differences between TBPro by 6-C and TBK-Mo models were not significantly correlated with the mean TBPro estimates by the 2 models (r= 0.032, P> 0.05). The TBK-Mo model can thus be used to estimate TBPro in healthy adults, adolescents, and children >5 y old. [ABSTRACT FROM AUTHOR]

Although specialized cortical pathways that process specific sensory stimuli and/or execute cognitive functions have been identified, the neuro-specificity for food-related stimuli has not been clearly demonstrated. We employed functional magnetic resonance imaging (fMRl) to compare neural systems associated with the appreciation of foods and nonfoods. Healthy, normal weight, right-handed men and women (n = 12; age 29.8 ± 1.8 y, BMI 21.8 ± 0.8 kg/m²) were imaged by fMRI while fasting. Real food and nonfood items were presented to subjects both visually and tactilely, during scanning. Subjects were instructed to pay attention to the items. A randomized 2 × 2 block design consisted of 4 conditions: visual food, visual nonfood, tactile food, and tactile nonfood. Brain regions that were significantly activated to a greater extent during the presentation of foods compared with nonfood items included the anterior cingulate, superior temporal gyrus, parahippocampal gyrus, hippocampus, and the insula. These findings support the claim that the presence of food (either seen or felt) elicits a unique cortical response that is differentiated from nonfood items. This neural substrate specialized for processing of foods informs models of food-related behavior. [ABSTRACT FROM AUTHOR]

The relationship between resting energy expenditure (REE) (kJ/d) and body mass (M) (kg) is a cornerstone in the study of energy physiology. By expressing REE as a function of body mass observed across mammals, Kleiber formulated the now classic equation: REE = 293M(0.75). The biological processes underlying Kleiber's law have been a topic of long-standing interest and speculation. In the present report we develop a new perspective of Kleiber's law by developing an organ-tissue level REE model consisting of five components: liver, brain, kidneys, heart and remaining tissues. The resting thermal output of each component is the product of the component's specific resting metabolic rate (K) and mass (T). With increasing body size, the K values for all five components had negative exponents and were directly proportional to M(-0.08--0.27), and all component T values were directly proportional to M(0.76-1.01). The resulting exponents of the product (K x T) were M(0.60-0.86) for the five components. Although the (K x T) values of individual components do not scale equally, their combined formula (286M(0.76)) is similar to that observed by Kleiber on the whole-body level. Modeling mammalian REE at the organ-tissue level provides new insights and pathways for future mechanistic explorations of REE-body composition relationships. [ABSTRACT FROM AUTHOR]

Although elements are the foundation of the human body, information concerning the atomic level of body composition is still limited. The aim of this study was to explore potentially constant relationships among elements found in vivo. Based on the known stoichiometries of relevant chemical components, a theoretical model was derived, suggesting the existence of a relatively constant ratio of total body oxygen to carbon-free body mass (TBO/CFM) in men. Eight elements (C, H, N, Ca, P, K, Na and Cl ) were measured in 22 healthy male subjects by using in vivo neutron activation-40K whole-body counting, and TBO was calculated as the difference between body mass and the sum of the eight measured elements. TBO (in kg) was significantly correlated with CFM (in kg): TBO = 0.829 x CFM - 1.8; r = 0.998, P < 0.001, standard error of estimate = 0.4 kg. The ratio of TBO to CFM was relatively constant, mean +/- SD at 0. 800 +/- 0.009 with a CV of 1.1%. Oxygen and carbon are the two most abundant elements in the human body. The discovery of a constant relationship between oxygen and carbon is not only helpful for understanding the atomic level of body composition, but also provides the possibility of estimating the content of specific elements in vivo. [ABSTRACT FROM AUTHOR]

Protein is an important body component for monitoring growth, development, and nutritional status. We previously developed a total body potassium (TBK, in mmol) and bone mineral (Mo, in kg) model for predicting total body protein (TBPro, in kg) in adults (TBPro = 0.00252 x TBK + 0.732 x Mo). However, the applicability of the TBK-Mo model for children is unknown. The study aims were to develop a TBK-independent 6-component (6-C) TBPro approach as the criterion, and then to validate the TBK-Mo model in children. The following measurements were made in adolescents and children (n = 62, 38 boys and 24 girls, aged 5-17 y): body weight (BW, in kg), body volume (BV, in liters) by air displacement plethysmography, total body water (TBW, in kg) by 2H2O dilution, Mo by dual-energy X-ray absorptiometry, and TBK by whole-body counting. A 6-C model was derived as TBPro = 2.922 x BW - 0.301 x TBW - 2.039 x Mo - 2.632 x BV. The TBPro estimates did not differ between the 6-C and TBK-Mo models (mean +/- SD, 0.20 +/- 0.86 kg). There was a significant correlation between TBPro by the 6-C and TBK-Mo models (r = 0.94, P < 0.001). Bland-Altman analysis indicated that the differences between TBPro by 6-C and TBK-Mo models were not significantly correlated with the mean TBPro estimates by the 2 models (r = 0.032, P > 0.05). The TBK-Mo model can thus be used to estimate TBPro in healthy adults, adolescents, and children > 5 y old.