Predicting in vivo soft tissue masses of the lower extremity using segment anthropometric measures and DXA

The purpose of this study was to derive and validate regression equations for the prediction of fat mass (FM), lean mass (LM), wobbling mass (WM), and bone mineral content (BMC) of the thigh, leg, and leg + foot segments of living people from easily measured segmental anthropometric measures. The segment masses of 68 university-age participants (26 M, 42 F) were obtained from full-body dual photon x-ray absorptiometry (DXA) scans, and were used as the criterion values against which predicted masses were compared. Comprehensive anthropometric measures (6 lengths, 6 circumferences, 8 breadths, 4 skinfolds) were taken bilaterally for the thigh and leg for each person. Stepwise multiple linear regression was used to derive a prediction equation for each mass type and segment. Prediction equations exhibited high adjusted R2 values in general (0.673 to 0.925), with higher correlations evident for the LM and WM equations than for FM and BMC. Predicted (equations) and measured (DXA) segment LM and WM were also found to be highly correlated (R2 = 0.85 to 0.96), and FM and BMC to a lesser extent (R2 = 0.49 to 0.78). Relative errors between predicted and measured masses ranged between 0.7% and -11.3% for all those in the validation sample (n = 16). These results on university-age men and women are encouraging and suggest that in vivo estimates of the soft tissue masses of the lower extremity can be made fairly accurately from simple segmental anthropometric measures.     

Aged-Related Changes in Body Composition and Association between Body Composition with Bone Mass Density by Body Mass Index in Chinese Han Men over 50-year-old

Objectives

Aging, body composition, and body mass index (BMI) are important factors in bone mineral density (BMD). Although several studies have investigated the various parameters and factors that differentially influence BMD, the results have been inconsistent. Thus, the primary goal of the present study was to further characterize the relationships of aging, body composition parameters, and BMI with BMD in Chinese Han males older than 50 years.

Methods

The present study was a retrospective analysis of the body composition, BMI, and BMD of 358 Chinese male outpatients between 50 and 89 years of age that were recruited from our hospital between 2009 and 2011. Qualified subjects were stratified according to age and BMI as follows: 50–59 (n = 35), 60–69 (n = 123), 70–79 (n = 93), and 80–89 (n = 107) years of age and low weight (BMI: < 20 kg/m²; n = 21), medium weight (20 ≤ BMI < 24 kg/m²; n = 118), overweight (24 ≤ BMI < 28 kg/m²; n = 178), and obese (BMI ≥ 28 kg/m²; n = 41). Dual-energy X-ray absorptiometry (DEXA) was used to assess bone mineral content (BMC), lean mass (LM), fat mass (FM), fat-free mass (FFM), lumbar spine (L1-L4) BMD, femoral neck BMD, and total hip BMD. Additionally, the FM index (FMI; FM/height²), LM index (LMI; LM/height²), FFM index (FFMI; [BMC+LM]/height²), percentage of BMC (%BMC; BMC/[BMC+FM+LM] × 100%), percentage of FM (%FM; FM/[BMC+FM+LM] × 100%), and percentage of LM (%LM; LM/(BMC+FM+LM) × 100%) were calculated. Osteopenia or osteoporosis was identified using the criteria and T-score of the World Health Organization.

Results

Although there were no significant differences in BMI among the age groups, there was a significant decline in height and weight according to age (p < 0.0001 and p = 0.0002, respectively). The LMI and FFMI also declined with age (both p < 0.0001) whereas the FMI exhibited a significant increase that peaked in the 80-89-years group (p = 0.0145). Although the absolute values of BMC and LM declined with age (p = 0.0031 and p < 0.0001, respectively), there was no significant difference in FM. In terms of body composition, there were no significant differences in %BMC but there was an increase in %FM (p < 0.0001) and a decrease in %LM (p < 0.0001) with age. The femoral neck and total hip BMD significantly declined with age (p < 0.0001 and p = 0.0027, respectively) but there were no differences in L1-L4. BMD increased at all sites (all p < 0.01) as BMI increased but there were declines in the detection rates of osteoporosis and osteopenia (both p < 0.001). A logistic regression revealed that when the medium weight group was given a BMI value of 1, a decline in BMI was an independent risk factor of osteoporosis or osteopenia, while an increase in BMI was a protective factor for BMD. At the same time, BMD in L1-L4 exhibited a significant positive association with FMI (p = 0.0003) and the femoral neck and total hip BMDs had significant positive associations with FFMI and LMI, respectively (both p < 0.0001).

Conclusions

These data indicate that LMI and FFMI exhibited significant negative associations with aging in Chinese Han males older than 50 years, whereas FMI had a positive association. BMD in the femoral neck and total hip declined with age but an increased BMI was protective for BMD. LMI and FFMI were protective for BMD in the femoral neck and total hip.     

Relationship of handgrip strength with anthropometric and body composition variables in prepubertal children

The purpose of the present study was to examine the relationship of handgrip strength with basic anthropometric variables, hand anthropometric variables, total body and hand composition, total body and hand bone mineral density (BMD) and bone mineral content (BMC) in prepubertal children aged between 8 and 11 years (n=64, 27 boys, 37 girls). Height and body mass were measured and body mass index (BMI kg/m²) was calculated. Biceps and triceps skinfolds, arm relaxed, arm flexed, forearm and wrist girths, acromiale-radiale, radiale-stylion-radiale and midstylion-dactylion length and humerus breadth were measured. Specific hand anthropometric variables according to Visnapuu and Jürimäe [2007. Handgrip strength and hand dimensions in young handball and basketball players. J. Strength Cond. Res. 21, 923-929] were used. Five fingers’ spans, fingers’ lengths and perimeters of the hand were measured. Total body and right-hand fat percentage, fat mass and lean mass (LBM) were measured by dual-energy X-ray absorptiometry (DXA). Right-hand BMC and BMD were analysed from the bone variables. Maximal handgrip strength of the right hand was measured with the hand dynamometer. Stepwise multiple regression analysis indicated that the most important predictive value from the basic anthropometric variables was body height, explaining 76.1% (R²×100), 40.7% and 50.6% of the handgrip strength in boys, girls and total group, respectively. Measured skinfold thicknesses and breadths were not related to handgrip strength in any group. Forearm girths significantly predicted handgrip strength in boys (30.8%), girls (43.4%) and total group (43.4%). As a rule, handgrip strength was more dependent on the anthropometric and body composition variables in boys than girls. It was concluded that body height, forearm girth, midstylion-dactylion and acromiale-radiale length and hand LBM and BMC are the most limiting factors influencing handgrip strength in prepubertal children.     

Effect of high contents of dietary animal-derived protein or carbohydrates on canine faecal microbiota

Background

Few equations have been developed in veterinary medicine compared to human medicine to predict body composition. The present study was done to evaluate the influence of weight loss on biometry (BIO), bioimpedance analysis (BIA) and ultrasonography (US) in cats, proposing equations to estimate fat (FM) and lean (LM) body mass, as compared to dual energy x-ray absorptiometry (DXA) as the referenced method. For this were used 16 gonadectomized obese cats (8 males and 8 females) in a weight loss program. DXA, BIO, BIA and US were performed in the obese state (T0; obese animals), after 10% of weight loss (T1) and after 20% of weight loss (T2). Stepwise regression was used to analyze the relationship between the dependent variables (FM, LM) determined by DXA and the independent variables obtained by BIO, BIA and US. The better models chosen were evaluated by a simple regression analysis and means predicted vs. determined by DXA were compared to verify the accuracy of the equations.

Results

The independent variables determined by BIO, BIA and US that best correlated (p?<?0.005) with the dependent variables (FM and LM) were BW (body weight), TC (thoracic circumference), PC (pelvic circumference), R (resistance) and SFLT (subcutaneous fat layer thickness). Using Mallows??Cp statistics, p value and r ² , 19 equations were selected (12 for FM, 7 for LM); however, only 7 equations accurately predicted FM and one LM of cats.

Conclusions

The equations with two variables are better to use because they are effective and will be an alternative method to estimate body composition in the clinical routine. For estimated lean mass the equations using body weight associated with biometrics measures can be proposed. For estimated fat mass the equations using body weight associated with bioimpedance analysis can be proposed.     

Validation of a dual energy X-ray absorptiometer: measurement of bone mass and soft tissue composition

We investigated the reproducibility of total and regional body composition measurements performed on a dual energy X-ray absorptiometer (DXA). A group of 38 women aged 21–81 (mean 52. 4) years was scanned twice with repositioning to determine intra-observer reproducibility of measurements of bone mineral density (BMD, g · cm⁻²), bone mineral content (BMC, g), lean mass (LM, kg) and fat mass (FM, kg) of the total body and of the major subregions of the body. In addition, the ability of the DXA machine to detect changes in LM and FM (simulated by placing 11.1 and 22.3 kg porcine lard on the body of 11 subjects) was examined. Coefficients of variations calculated from the root mean square averages of individual standard deviations were as follows (BMD, BMC, FM, LM): 1.4%, 1.1%, 1.4%, 1.7% (total body), 2.2%, 2.1%,-,- (head), 2.8%, 2.8%, 2.0%, 2.2% (trunk), 3.6%, 3.9%, 4.0%, 4.9% (arms), 2.7%, 1.3%, 2.6%, 2.8% (legs). Percentage fat (%fat) of exogenous lard was 81.3 (SD 3.5)% as assessed by the absorptiometer which corresponded well with the result of chemical analysis (82.8%). Estimated %fat of exogenous lard was not influenced by initial body mass or percentage body fat. Percentages of expected mean values with 11.1 kg lard placed on the body were 99.9 (SD 0.3) for body mass, 100.5 (SD 2.1) for LM, and 99.5 (SD 3.5) for FM. BMD was overestimated by 3.2% (P < 0.005) with 11.1 kg lard on the body. BMD as well as BMC increased significantly with 22.3␣kg lard on the body (P < 0.005). The results showed that BMD, BMC, LM, and FM of the total body were precisely estimated by the DXA machine used. Regional measurements were less precise. Changes in total body soft tissue composition were precisely and accurately estimated. The lard placed on the body falsely affected BMD and BMC measurements. Changes in body mass could have a similar effect. Accepted: 6 January 1997     

Accuracy and precision of dual-energy X-ray absorptiometry for body composition measurements in rhesus monkeys

Accuracy of body composition measurements by dual-energy X-ray absorptiometry (DXA) was compared with direct chemical analysis in 10 adult rhesus monkeys. DXA was highly correlated (r-values > 0.95) with direct analyses of body fat mass (FM), lean mass (LM) and lumbar spine bone mineral content (BMC). DXA measurements of total body BMC were not as strongly correlated (r-value = 0.58) with total carcass ash content. DXA measurements of body FM, LM and lumbar spine BMC were not different from data obtained by direct analyses (P-values > 0.30). In contrast, DXA determinations of total BMC (TBMC) averaged 15%, less than total carcass ash measurements (P = 0.002). In conclusion, this study confirms the accurate measurement of fat and lean tissue mass by DXA in rhesus monkeys. DXA also accurately measured lumbar spine BMC but underestimated total body BMC as compared with carcass ash determinations.     

Evaluation of longevity modeling censored records in Nellore

The aim of the present study was to evaluate the prediction ability of models that cope with longevity phenotypic expression as uncensored and censored in Nellore cattle. Longevity was defined as the difference between the dates of last weaned calf and cow birth. There were information of 77 353 females, being 61 097 cows with uncensored phenotypic information and 16 256 cows with censored records. These data were analyzed considering three different models: (1) Gaussian linear model (LM), in which only uncensored records were considered; and two models that consider both uncensored and censored records: (2) Censored Gaussian linear model (CLM); and (3) Weibull frailty hazard model (WM). For the model prediction ability comparisons, the data set was randomly divided into training and validation sets, containing 80% and 20% of the records, respectively. There were considered 10 repetitions applying the following restrictions: (a) at least three animals per contemporary group in the training set; and (b) sires with more than 10 progenies with uncensored records (352 sires) should have daughters in the training and validation sets. The variance components estimated using the whole data set in each model were used as true values in the prediction of breeding values of the animals in the training set. The WM model showed the best prediction ability, providing the lowest χ² average and the highest number of sets in which a model had the smallest value of χ² statistics. The CLM and LM models showed prediction abilities 2.6% and 3.7% less efficient than WM, respectively. In addition, the accuracies of sire breeding values for LM and CLM were lower than those obtained for WM. The percentages of bulls in common, considering only 10% of sires with the highest breeding values, were around 75% and 54%, respectively, between LM–CLM and LM–WM models, considering all sires, and 75% between LM–CLM and LM–WM, when only sires with more than 10 progenies with uncensored records were taken into account. These results are indicative of reranking of animals in terms of genetic merit between LM, CLM and WM. The model in which censored records of longevity were excluded from the analysis showed the lowest prediction ability. The WM provides the best predictive performance, therefore this model would be recommended to perform genetic evaluation of longevity in this population.     

Reliability of upper and lower extremity anthropometric measurements and the effect on tissue mass predictions

Accurate modeling of soft tissue motion effects relative to bone during impact requires knowledge of the mass of soft and rigid tissues in living people. Holmes et al., [2005. Predicting in vivo soft tissue masses of the lower extremity using segment anthropometric measures and DXA. Journal of Applied Biomechanics, 21, 371–382] developed and validated regression equations to predict the individual tissue masses of lower extremity segments of young healthy adults, based on simple anthropometric measurements. However, the reliability of these measurements and the effect on predicted tissue mass estimates from the equations has yet to be determined. In the current study, two measurers were responsible for collecting two sets of unilateral measurements (25 male and 25 female subjects) for the right upper and lower extremities. These included 6 lengths, 6 circumferences, 8 breadths, and 4 skinfold thicknesses. Significant differences were found between measurers and between sexes, but these differences were relatively small in general (75–80% of between-measurer differences were <1 cm). Within-measurer measurement differences were smaller and more consistent than those between measurers in most cases. Good to excellent reliability was demonstrated for all measurement types, with intra-class correlation coefficients of 0.79, 0.86, 0.85 and 0.86 for lengths, circumferences, breadth and skinfolds, respectively. Predicted tissue mass magnitudes were moderately affected by the measurement differences. The maximum mean errors between measurers ranged from 3.2% to 24.2% for bone mineral content and fat mass, for the leg and foot, and the leg segments, respectively.     

Magnetic resonance tomography measurement of the thigh adipose tissue volume in men

The volume of the thigh adipose tissue was estimated using magnetic resonance tomography (MRT) and anthropometric measurements. Eighty-seven physically well-developed men aged 18–45 years participated in the experiment. The MRT estimate of the thigh fat volume was 2206 ± 882 cm³. The results were used to derive two multiple linear regression equations for calculating the thigh adipose tissue volume from anthropometric parameters. The correlation coefficient between the thigh adipose tissue volumes calculated from the equation and measured by MRT was r = 0.97.     

Heritability of Body Composition Measured by DXA in the Diabetes Heart Study

Objective: The purpose of this study was to investigate the heritability of body composition measured by DXA in the Diabetes Heart Study (DHS). Research Methods and Procedures: Participants were 292 women and 262 men (age, 38 to 86 years; BMI, 17 to 57 kg/m²) from 244 families. There were 492 white and 49 African‐American sibling pairs. DXA measurements of percentage fat mass (FM), whole body FM, and lean mass (LM), as well as regional measurements of trunk fat mass (TFM) and appendicular lean mass (ALM), were obtained. Heritability of FM, LM, and BMI were estimated using Sequential Oligogenic Linkage Analysis Routines. Results: After adjusting for age, gender, ethnicity, and height, the heritability estimates of various compositional attributes were %FM = 0.64, whole body FM = 0.71, TFM = 0.63, whole body LM = 0.60, ALM = 0.66, and BMI = 0.64 (all p < 0.0001). Additional adjustment for diabetes status, smoking, dietary intake, and physical activity resulted in only minor changes in the heritability estimates (?² = 0.63 to 0.72, all p < 0.0001). Furthermore, heritability of TFM after additional adjustment for whole body FM was significant (?² = 0.55, p < 0.0001), and heritability of ALM after additional adjustment for whole body LM was also significant (?² = 0.51, p < 0.0001). Discussion: These data suggest that FM and LM measured by DXA are highly heritable and can be effectively used in designing linkage studies to locate genes governing body composition. In addition, regional distribution of FM and LM may be genetically determined.     

Validation of BIA in Obese Children and Adolescents and Re‐evaluation in a Longitudinal Study

Decrease in fat mass (FM) is a one of the aims of pediatric obesity treatment; however, measurement techniques suitable for routine clinical assessment are lacking. The objective of this study was to validate whole‐body bioelectrical impedance analysis (BIA; TANITA BC‐418MA) against the three‐component (3C) model of body composition in obese children and adolescents, and to test the accuracy of our new equations in an independent sample studied longitudinally. A total of 77 white obese subjects (30 males) aged 5–22 years, BMI‐standard deviation score (SDS) 1.6–3.9, had measurements of weight, height (HT), body volume, total body water (TBW), and impedance (Z). FM and fat‐free mass (FFM) were calculated using the 3C model or predicted from TANITA. FFM was predicted from HT²/Z. This equation was then evaluated in 17 other obese children (5 males) aged 9–13 years. Compared to the 3C model, TANITA manufacturer's equations overestimated FFM by 2.7 kg (P < 0.001). We derived a new equation: FFM = ?2.211 + 1.115 (HT²/Z), with r² of 0.96, standard error of the estimate 2.3 kg. Use of this equation in the independent sample showed no significant bias in FM or FFM (mean bias 0.5 ± 2.4 kg; P = 0.4), and no significant bias in change in FM or FFM (mean bias 0.2 ± 1.8 kg; P = 0.7), accounting for 58% (P < 0.001) and 55% (P = 0.001) of the change in FM and FFM, respectively. Our derived BIA equation, shown to be reliable for longitudinal assessment in white obese children, will aid routine clinical monitoring of body composition in this population.     

Manual segmentation of DXA scan images results in reliable upper and lower extremity soft and rigid tissue mass estimates

Quantification of segment soft and rigid tissue masses in living people is important for a variety of clinical and biomechanical research applications including wobbling mass modeling. Although Dual-energy X-ray Absorptiometry (DXA) is widely accepted as a valid method for this purpose, the reliability of manual segmentation from DXA scans using custom regions of interest (ROIs) has not been evaluated to date. Upper and lower extremity images of 100 healthy adults who underwent a full body DXA scan in the supine position were manually segmented by 3 measurers independently using custom ROIs. Actual tissue masses (fat mass, lean mass, bone mineral content) of the arm, arm with shoulder, forearm, forearm and hand, thigh, leg, and leg and foot segments were quantified bilaterally from the ROIs. There were significant differences between-measurers, however, percentage errors were relatively small overall (<1–5.98%). Intraclass correlation coefficients (ICCs) were very high between and within-measurers, ranging from 0.990 to 0.999 and 0.990 to 1.00 for the upper and lower extremities, respectively, suggesting excellent reliability. Between and within-measurer errors were comparable in general, and differences between the tissue types were small on average (maximum of 42 and 53 g for upper and lower extremities, respectively). These results suggest that manual segmentation of DXA images using ROIs is a reliable method of estimating soft and rigid tissues in living people.     

Mass, center of mass, and moment of inertia estimates for infant limb segments.

To quantify limb dynamics, accurate estimates are needed of anthropometric inertia parameters (mass, center-of-mass location, and moments of inertia). These estimates, however, are not available for human infants; therefore, the movement dynamics of infants have not been studied extensively. Here, regression equations for the masses, center-of-mass locations, and transverse moments of inertia of upper and lower limb segments (upper arm, forearm, and hand; thigh, leg, and foot) of 0.04 to 1.50 yr old infants are provided. A mathematical model of the human body was used to determine the anthropometric inertia parameters for upper limbs in 44 infants and for lower limbs in 70 infants. Stepwise linear regressions were used to fit the distributions of the anthropometric inertia parameters. The regression equations accounted for significant amounts of the variance (64-98%), and the R2-values compared favorably when our equations were cross-validated. Consequently, these regression equations can provide, for infants of similar ages, reasonable estimates of upper and lower limb anthropometric inertia parameters, suitable for equations of motion in the analysis of limb dynamics in human infants.     

Predictive equations for body fat and abdominal fat with DXA and MRI as reference in Asian Indians

Objective: To develop accurate and reliable equations from simple anthropometric parameters that would predict percentage of total body fat (%BF), total abdominal fat (TAF), subcutaneous abdominal adipose tissue (SCAT), and intra‐abdominal adipose tissue (IAAT) with a fair degree of accuracy. Methods and Procedures: Anthropometry, %BF by dual‐energy X‐ray absorptiometry (DXA) in 171 healthy subjects (95 men and 76 women) and TAF, IAAT, and SCAT by single slice magnetic resonance imaging (MRI) at L3–4 intervertebral level in 100 healthy subjects were measured. Mean age and BMI were 32.2 years and 22.9 kg/m², respectively. Multiple regression analysis was used on the training data set (70%) to develop equations, by taking anthropometric and demographic variables as potential predictors. Predicted equations were applied on validation data set (30%). Results: Multiple regression analysis revealed the best equation for predicting %BF to be: %BF = 42.42 + 0.003 × age (years) + 7.04 × gender (M = 1, F = 2) + 0.42 × triceps skinfold (mm) + 0.29 × waist circumference (cm) ? 0.22 × weight (kg) ? 0.42 × height (cm) (R ² = 86.4%). The most precise predictive equation for estimating IAAT was: IAAT (mm²) = ?238.7 + 16.9 × age (years) + 934.18 × gender (M = 1, F = 2) + 578.09 × BMI (kg/m²) ? 441.06 × hip circumference (cm) + 434.2 × waist circumference (cm) (R ² = 52.1%). SCAT was best predicted by: SCAT (mm²) = ?49,376.4 ? 17.15 × age (years) + 1,016.5 × gender (M = 1, F = 2) +783.3 × BMI (kg/m²) + 466 × hip circumference (cm) (R ² = 67.1). Discussion: We present predictive equations to quantify body fat and abdominal adipose tissue sub‐compartments in healthy Asian Indians. These equations could be used for clinical and research purposes.     

Conjugal Transfer of Lactose-Fermenting Ability Among Streptococcus cremoris and Streptococcus lactis Strains

Streptococcus cremoris C3 was found to transfer lactose-fermenting ability to LM2301, a Streptococcus lactis C2 lactose-negative streptomycin-resistant (Lac⁻ Str^r) derivative which is devoid of plasmid deoxyribonucleic acid (DNA); to LM3302, a Lac⁻ erythromycin-resistant (Ery^r) derivative of S. lactis ML3; and to BC102, an S. cremoris B₁ Lac⁻ Ery^r derivative which is devoid of plasmid DNA. S. cremoris strains R1, EB₇, and Z8 were able to transfer lactose-fermenting ability to LM3302 in solid-surface matings. Transduction and transformation were ruled out as mechanisms of genetic transfer. Chloroform treatment of donor cells prevented the appearance of recombinant clones, indicating that viable cell-to-cell contact was responsible for genetic transfer. Transfer of plasmid DNA was confirmed by agarose gel electrophoresis. Transconjugants recovered from EB₇ and Z8 matings with LM3302 exhibited plasmid sizes not observed in the donor strains. Transconjugants recovered from R1, EB₇, and Z8 matings with LM3302 were able to donate lactose-fermenting ability at a high frequency to LM2301. In S. cremoris R1, EB₇, and Z8 matings with LM2301, streptomycin resistance was transferred from LM2301 to the S. cremoris strains. The results confirm genetic transfer resembling conjugation between S. cremoris and S. lactis strains and present presumptive evidence for plasmid linkage of lactose metabolism in S. cremoris.     

Precision and Accuracy of Dual‐Energy X‐ray Absorptiometry for Determining in Vivo Body Composition of Mice

Objective: To evaluate the precision and accuracy of dual‐energy X‐ray absorptiometry (DXA) for the measurement of total‐bone mineral density (TBMD), total‐body bone mineral (TBBM), fat mass (FM), and bone‐free lean tissue mass (LTM) in mice. Research Methods and Procedures: Twenty‐five male C57BL/6J mice (6 to 11 weeks old; 19 to 29 g) were anesthetized and scanned three times (with repositioning between scans) using a peripheral densitometer (Lunar PIXImus). Gravimetric and chemical extraction techniques (Soxhlet) were used as the criterion method for the determination of body composition; ash content was determined by burning at 600°C for 8 hours. Results: The mean intraindividual coefficients of variation (CV) for the repeated DXA analyses were: TBMD, 0.84%; TBBM, 1.60%; FM, 2.20%; and LTM, 0.86%. Accuracy was determined by comparing the DXA‐derived data from the first scan with the chemical carcass analysis data. DXA accurately measured bone ash content (p = 0.942), underestimated LTM (0.59 ± 0.05g, p < 0.001), and overestimated FM (2.19 ± 0.06g, p < 0.001). Thus, DXA estimated 100% of bone ash content, 97% of carcass LTM, and 209% of carcass FM. DXA‐derived values were then used to predict chemical values of FM and LTM. Chemically extracted FM was best predicted by DXA FM and DXA LTM [FM = ?0.50 + 1.09(DXA FM) ? 0.11(DXA LTM), model r² = 0.86, root mean square error (RMSE) = 0.233 g] and chemically determined LTM by DXA LTM [LTM = ?0.14 + 1.04(DXA LTM), r² = 0.99, RMSE = 0.238 g]. Discussion: These data show that the precision of DXA for measuring TBMD, TBBM, FM, and LTM in mice ranges from a low of 0.84% to a high of 2.20% (CV). DXA accurately measured bone ash content but overestimated carcass FM and underestimated LTM. However, because of the close relationship between DXA‐derived data and chemical carcass analysis for FM and LTM, prediction equations can be derived to more accurately predict body composition.     

Sex difference in the effect of puberty on the relationship between fat mass and bone mass in 926 healthy subjects, 6 to 18 years old

Objective: Understanding factors influencing bone mineral accrual is critical to optimize peak bone mass during childhood. The epidemic of pediatric obesity and reported higher incident of fracture risk in obese children led us to study the influence of fat mass on bone mineral content (BMC) in children. Research Methods and Procedures: Height; weight; pubertal stage; and BMC, non‐bone fat‐free mass (nbFFM), and fat mass (FM) by DXA were obtained in a multiethnic group of healthy children (444 girls/482 boys; 6 to 18 years old) recruited in the New York metropolitan area. Regression techniques were used to explore the relationship between BMC and FM, with age, height, nbFFM, pubertal stage, sex, and ethnicity as covariates. Results: Because there were significant sex interactions, separate regression analyses were performed for girls and boys. Although ln(nbFFM) was the greatest predictor of ln(BMC), ln(FM) was also a significant predictor in prepubertal boys and all girls but not in pubertal boys. This effect was independent of ethnicity. Discussion: FM was a determinant of BMC in all girls but in only prepubertal boys. Our study confirms nbFFM as the greatest predictor of BMC but is the first to find a sex difference in the effect of puberty on the relationship of FM to BMC. Our results suggest that, in two individuals of the same sex and weight, the one with greater fat mass will have lower BMC, especially pubertal boys. The implications of these findings for achievement of optimal peak bone mass in a pediatric population with an unprecedented incidence of overweight and “overfat” status remain to be seen.     

Otx2 and Otx1 protect diencephalon and mesencephalon from caudalization into metencephalon during early brain regionalization

Otx2 is expressed in each step and site of head development. To dissect each Otx2 function we have identified a series of Otx2 enhancers. The Otx2 expression in the anterior neuroectoderm is regulated by the AN enhancer and the subsequent expression in forebrain and midbrain later than E8.5 by FM1 and FM2 enhancers; the Otx1 expression takes place at E8.0. In telencephalon later than E9.5 Otx1 continues to be expressed in the entire pallium, while the Otx2 expression is confined to the most medial pallium. To determine the Otx functions in forebrain and midbrain development we have generated mouse mutants that lack both FM1 and FM2 enhancers (DKO: Otx2^{ΔFM1ΔFM2/ΔFM1ΔFM2}) and examined the TKO (Otx1^−/−Otx2^{ΔFM1ΔFM2/ΔFM1ΔFM2}) phenotype. The mutants develop normally until E8.0, but subsequently by E9.5 the diencephalon, including thalamic eminence and prethalamus, and the mesencephalon are caudalized into metencephalon consisting of isthmus and rhombomere 1; the caudalization does not extend to rhombomere 2 and more caudal rhombomeres. In rostral forebrain, neopallium, ganglionic eminences and hypothalamus in front of prethalamus develop; we propose that they become insensitive to the caudalization with the switch from the Otx2 expression under the AN enhancer to that under FM1 and FM2 enhancers. In contrast, the medial pallium requires Otx1 and Otx2 for its development later than E9.5, and the Otx2 expression in diencepalon and mesencephalon later than E9.5 is also directed by an enhancer other than FM1 and FM2 enhancers.     

Predicting changes of body weight, body fat, energy expenditure and metabolic fuel selection in C57BL/6 mice

The mouse is an important model organism for investigating the molecular mechanisms of body weight regulation, but a quantitative understanding of mouse energy metabolism remains lacking. Therefore, we created a mathematical model of mouse energy metabolism to predict dynamic changes of body weight, body fat, energy expenditure, and metabolic fuel selection. Based on the principle of energy balance, we constructed ordinary differential equations representing the dynamics of body fat mass (FM) and fat-free mass (FFM) as a function of dietary intake and energy expenditure (EE). The EE model included the cost of tissue deposition, physical activity, diet-induced thermogenesis, and the influence of FM and FFM on metabolic rate. The model was calibrated using previously published data and validated by comparing its predictions to measurements in five groups of male C57/BL6 mice (N = 30) provided ad libitum access to either chow or high fat diets for varying time periods. The mathematical model accurately predicted the observed body weight and FM changes. Physical activity was predicted to decrease immediately upon switching from the chow to the high fat diet and the model coefficients relating EE to FM and FFM agreed with previous independent estimates. Metabolic fuel selection was predicted to depend on a complex interplay between diet composition, the degree of energy imbalance, and body composition. This is the first validated mathematical model of mouse energy metabolism and it provides a quantitative framework for investigating energy balance relationships in mouse models of obesity and diabetes.     

Tubby-tagged balancers for the Drosophila X and second chromosomes

We generated FM7a and CyO balancer chromosomes bearing a Tubby¹ (Tb¹) dominant transgene. Flies heterozygous for these FM7a and CyO derivatives exhibit a phenotype undistinguishable from that elicited by the Tb¹ mutation associated with the TM6B balancer. We tested two of these Tb-bearing balancers (FM7-TbA and CyO-TbA) for more than 30 generations and found that the Tb¹ transgene they carry is stable. Thus, these new Tb-tagged balancers are particularly useful for balancing lethal mutations and distinguish homozygous mutant larvae from their heterozygous siblings.