Predicting composition of leg sections with anthropometry and bioelectrical impedance analysis using magnetic resonance imaging as reference

Magnetic resonance imaging (MRI) was used to evaluate and compare with anthropometry a fundamental bioelectrical impedance analysis (BIA) method for predicting muscle and adipose tissue composition in the lower limb. Healthy volunteers (eight men and eight women), aged 41 to 62 years, with mean (S.D.) body mass indices of 28.6 (5.4) kg/m(2) and 25.1 (5.4) kg/m(2) respectively, were subjected to MRI leg scans, from which 20-cm sections of thigh and IO-cm sections of lower leg (calf) were analysed for muscle and adipose tissue content, using specifically developed software. Muscle and adipose tissue were also predicted from anthropometric measurements of circumferences and skinfold thicknesses, and by use of fundamental BIA equations involving section impedance at 50 kHz and tissue-specific resistivities. Anthropometric assessments of circumferences, cross-sectional areas and volumes for total constituent tissues matched closely MRI estimates. Muscle volume was substantially overestimated (bias: thigh, -40%; calf, -18%) and adipose tissue underestimated (bias: thigh, 43%; calf, 8%) by anthropometry, in contrast to generally better predictions by the fundamental BIA approach for muscle (bias:thigh, -12%; calf, 5%) and adipose tissue (bias:thigh, 17%; calf, -28%). However, both methods demonstrated considerable individual variability (95% limits of agreement 20-77%). In general, there was similar reproducibility for anthropometric and fundamental BIA methods in the thigh (inter-observer residual coefficient of variation for muscle 3.5% versus 3.8%), but the latter was better in the calf (inter-observer residual coefficient of variation for muscle 8.2% versus 4.5%). This study suggests that the fundamental BIA method has advantages over anthropometry for measuring lower limb tissue composition in healthy individuals.

Assessment of limb muscle and adipose tissue by dual-energy X-ray absorptiometry using magnetic resonance imaging for comparison

OBJECTIVE: To use magnetic resonance imaging (MRI) to validate estimates of muscle and adipose tissue (AT) in lower limb sections obtained by dual-energy X-ray absorptiometry (DXA) modelling. DESIGN: MRI measurements were used as reference for validating limb muscle and AT estimates obtained by DXA models that assume fat-free soft tissue (FFST) comprised mainly muscle: model A accounted for bone hydration only; model B also applied constants for FFST in bone and skin and fat in muscle and AT; model C was as model B but allowing for variable fat in muscle and AT. SUBJECTS: Healthy men (n = 8) and women (n = 8), ages 41 - 62 y; mean (s.d.) body mass indices (BMIs) of 28.6 (5.4) kg/m(2) and 25.1 (5.4) kg/m2, respectively. MEASUREMENTS: MRI scans of the legs and whole body DXA scans were analysed for muscle and AT content of thigh (20 cm) and lower leg (10 cm) sections; 24 h creatinine excretion was measured. RESULTS: Model A overestimated thigh muscle volume (MRI mean, 2.3 l) substantially (bias 0.36 l), whereas model B underestimated it by only 2% (bias 0.045 l). Lower leg muscle (MRI mean, 0.6 l) was better predicted using model A (bias 0.04 l, 7% overestimate) than model B (bias 0.1 l, 17% underestimate). The 95% limits of agreement were high for these models (thigh,+/- 20%; lower leg,+/- 47%). Model C predictions were more discrepant than those of model B. There was generally less agreement between MRI and all DXA models for AT. Measurement variability was generally less for DXA measurements of FFST (coefficient of variation 0.7 - 1.8%) and fat (0.8 - 3.3%) than model B estimates of muscle (0.5-2.6%) and AT (3.3 - 6.8%), respectively. Despite strong relationships between them, muscle mass was overestimated by creatinine excretion with highly variable predictability. CONCLUSION: This study has shown the value of DXA models for assessment of muscle and AT in leg sections, but suggests the need to re-evaluate some of the assumptions upon which they are based.

Congenital thyrotoxicosis in premature infants

OBJECTIVES Graves' disease (GD) complicates 0.1% to 0.2% of pregnancies, but congenital thyrotoxicosis is rare occurring in one in 70 of these pregnancies independent of maternal disease status. Antenatal prediction of affected infants is imprecise; however, maternal history, coupled with a high maternal serum TSH receptor binding immunoglobulin index (TBII) predict adverse neonatal outcome. Mortality is reported to be as high as 25% in affected infants and would therefore be expected to be higher in premature infants. This study illustrates that in sick, premature, extreme low birth weight (ELBW) or intrauterine growth retarded (IUGR) infants, the diagnosis maybe overlooked especially in the absence of antenatal risk assessment and management of thyrotoxicosis in this setting is complex. DESIGN and PATIENTS The records of premature neonates born at the three main maternity units in Brisbane, between January 1996 and July 1998 diagnosed with congenital thyrotoxicosis were reviewed. Data were recorded on gestational age, birth weight (B Wt), maternal thyroid history and current status, and neonatal course. Thyroid function and TBII status was assessed using standard biochemical assays. RESULTS Seven neonates from five pregnancies were identified (four female, three male). Mean gestational age was 30 week (25-36 week) and median B Wt was 1.96 kg (0.50-2.62 kg). Only one mother received formal antenatal counselling by a paediatric endocrine service and had a TBII (54%) measured prior to delivery. Three of five mothers had elevated TBII measured after diagnosis in their offspring (57%, 65%, 83%) and in one mother, a TBII was not performed. All mothers were biochemically euthyroid at delivery. Mean age at diagnosis was 9 days (1-16 days) and mean age at commencement of treatment was 12 days (7-26 days). Two infants received propylthiouracil and five received a combination of carbimazole and propranolol. Pour became biochemically hypothyroid, in three this resolved with cessation of the antithyroid drug (ATD), and one required ongoing T4 supplementation. Only one infant required treatment for cardiac failure and there were no deaths in this cohort. CONCLUSIONS This is a large series of extremely small and premature infants with neonatal thyrotoxicosis. Presentation was nonspecific. The diagnosis was delayed because of low birth weight, prematurity, multiple birth and/or an unrecognized maternal history of Graves' disease. The treatment of neonatal thyrotoxicosis was difficult in these extreme law birth weight infants yet no infant died and significant morbidity was confined to high output cardiac failure in one infant. With antenatal recognition of past or active Graves' disease, assessment of maternal TSH receptor binding immunoglobulin index prior to delivery and postnatal monitoring of cord TSH and venous fT4 and TSH on days 4 and 7 rapid treatment of affected infants may have further reduced neonatal morbidity.

Recoverable reserves and support effects when optimizing open pit mine designs

Orebody modelling, support effects and the estimation of recoverable reserves are key parts of open pit optimization studies. A case study is presented on the estimation of recoverable reserves using an implementation of indicator kriging where metal quantity is used to select cutoffs, and support corrections founded on a conditional simulation approach. Mining selectivity is explored in the subsequent optimization study to compare results from indicator kriging of grade estimates on a regular size blocks and indicator kriging estimates on small size blocks. The use of indicator kriging models adjusted for a given selectivity and the use of grade proportions in each block for the optimization study, provide a presentation of the expected ore recovery for a predefined level of selectivity. The case study shows that indicator kriging estimation with full accounting of block grade distributions generates substantially better results in the pit optimization study. In addition, the adverse effects of small blocks and over-smoothing on optimization results are illustrated.