The expensive-tissue hypothesis (ETH) posited a brain-gut trade-off to explain how humans evolved large, costly brains. Versions of the ETH interrogating gut or other body tissues have been tested in non-human animals, but not humans. We collected brain and body composition data in 70 South Asian women and used structural equation modelling with instrumental variables, an approach that handles threats to causal inference including measurement error, unmeasured confounding and reverse causality. We tested a negative, causal effect of the latent construct 'nutritional investment in brain tissues' (MRI-derived brain volumes) on the construct 'nutritional investment in lean body tissues' (organ volume and skeletal muscle). We also predicted a negative causal effect of the brain latent on fat mass. We found negative causal estimates for both brain and lean tissue (-0.41, 95% CI, -1.13, 0.23) and brain and fat (-0.56, 95% CI, -2.46, 2.28). These results, although inconclusive, are consistent with theory and prior evidence of the brain trading off with lean and fat tissues, and they are an important step in assessing empirical evidence for the ETH in humans. Analyses using larger datasets, genetic data and causal modelling are required to build on these findings and expand the evidence base.

Background and objectives:Several studies have linked longer legs with favorable adult metabolic health outcomes and greater offspring birth weight. A recent Mendelian randomization study suggested a causal link between height and cardiometabolic risk; however, the underlying reasons remain poorly understood. Methodology:Using a cross-sectional design, we tested in a convenience sample of 70 healthy young women whether birth weight and tibia length as markers of early-life conditions associated more strongly with metabolically beneficial traits like organ size and skeletal muscle mass (SMM) than a statistically derived height-residual variable indexing later, more canalized growth. Results:Consistent with the 'developmental origins of health and disease' hypothesis, we found relatively strong associations of tibia length-but not birth weight-with adult organ size, brain size, SMM and resting energy expenditure measured by magnetic resonance imaging (MRI), dual-energy X-ray absorptiometry and indirect calorimetry, respectively. Conclusions and implications:Building on prior work, these results suggest that leg length is a sensitive marker of traits directly impacting metabolic and reproductive health. Alongside findings in the same sample relating tibia length and height-residual to MRI-measured pelvic dimensions, we suggest there may exist a degree of coordination in the development of long bone, lean mass and pelvic traits, possibly centered on early, pre-pubertal growth periods. Such phenotypic coordination has important implications for fitness, serving to benefit both adult health and the health of offspring in subsequent generations.

Background: Most authorities define childhood overweight/obesity as a BMI exceeding the same high centile cutoff at all ages. but it seems unlikely that true obesity prevalence (excess body fat) is constant throughout childhood. Objectives: We investigated how fat mass (FM) and lean mass (LM). adjusted for height, relate to BMI and each other across childhood, using a uniquely large database of body composition measures, estimated using gold standard methods. Methods: Cross-sectional and cohort data were collated from representative samples of healthy children aged 6 wk to 20 y and children attending obesity clinics aged 7-16 y. Body composition was measured by deuterium dilution up to age 4 y, and by either deuterium or the criterion 4-component model from 4 to 20 y. FM and LM were expressed, respectively, as fat mass index (FMI; FM/height(2)) and lean mass index (LMI; LM/height(2)). Results: There were 2367 measurements of weight, height, and body composition from 1953 individuals. Before age 6 y, the variability in FMI, LMI, and BMI was much less than after; FMI was low (mainly <8 kg/m(2)) and FMI and LMI were weakly negatively correlated. From mid-childhood, upper limits for both BMI and FMI rose, but FMI in children with BMI <91st centile still rarely exceeded 8. With increasing age, the correlation of FMI with LMI rose to 0.5-0.7. driven mainly by children with a high FMI also having a high LMI. Conclusions: Raised fat levels are much less common at younger than older ages, and young children with a high BMI centile have lower FMI than older children with the same BMI centile. Current BMI centile cutoffs thus overdiagnose obesity in younger groups. More stringent cutoffs are required for children aged <6 y, matching the WHO recommendation for 0-5 y.

Background: In adults, weight loss and sarcopenia are prognostic indicators of poor outcomes for patients awaiting liver transplant (LT). We tested the hypothesis that sarcopenia in children awaiting LT was related to poor outcomes. Methods: Children with end-stage chronic liver disease undergoing assessment for LT were recruited into an observational longitudinal study. Anthropometry and body composition (BC; whole-body dual-energy x-ray absorptiometry scan) were assessed before and, on average, 1 year after LT. Results: Eleven children (6 females:5 males) were assessed (4.7 to 17.2 years; median, 9.9) at baseline. Nine children went on to have an LT. The aspartate aminotransferase-to-platelet ratio index had a significant positive correlation with trunk lean mass and trunk lean mass index (LMI) SD score (SDS). At baseline, 4 patients were sarcopenic with appendicular LMI SDS less than −1.96. All fat mass and fat mass index (FMI) SDSs were within the normal range (above −1.96). There was a strong negative correlation between FMI SDS and height SDS. After transplant, there was a significant reduction in trunk LMI from 1.20 to −0.51 (95% CI, 1.03-2.4; P < 0.01). Body mass index SDS had a negative correlation with days to discharge after transplant. The majority of patients discharged after 16 days were sarcopenic. One year after transplantation, all patients were alive with normal graft function regardless of BC before LT. Conclusion: FMIs were normal regardless of LMIs and correlated negatively with height. BC was related to days to discharge after LT but not to outcomes a year after LT.

OBJECTIVES:Imaging methods to measure the human pelvis in vivo provide opportunities to better understand pelvic variation and adaptation. Magnetic resonance imaging (MRI) provides high-resolution images, but is more expensive than dual-energy X-ray absorptiometry (DXA). We sought to compare pelvic breadth measurements collected from the same individuals using both methods, to investigate if there are systematic differences in pelvic measurement between these imaging methods. METHODS:Three pelvic breadth dimensions (bi-iliac breadth, bi-acetabular breadth, medio-lateral inlet breadth) were collected from MRI and DXA scans of a cross-sectional sample of healthy, nulliparous adult women of South Asian ancestry (n = 63). Measurements of MRI and DXA pelvic dimensions were collected four times in total, with one baseline data collection session and three replications. Data collected from these sessions were averaged, used to calculate technical error of measurement and entered into a Bland-Altman analysis. Linear regression models were fitted with a given MRI pelvic measurement regressed on the same measurement collected from DXA scans, as well as MRI mean bias regressed on DXA mean bias. RESULTS:Technical error of measurement was higher in DXA measurements of bi-iliac breadth and medio-lateral pelvic inlet breadth and higher for MRI measurements of bi-acetabular breadth. Bland Altman analyses showed no statistically significant relationship between the mean bias of MRI and DXA, and the differences between MRI and DXA pelvic measurements. CONCLUSIONS:DXA measurements of pelvic breadth are comparable to MRI measurements of pelvic breadth. DXA is a less costly imaging technique than MRI and can be used to collect measurements of skeletal elements in living people.

BACKGROUND & AIMS:Bioelectrical impedance analysis (BIA) is widely considered a body composition technique suitable for routine application. However, its utility in sick or malnourished children is complicated by variability in hydration. A BIA variant termed vector analysis (BIVA) aims to resolve this, by differentiating hydration from cell mass. However, the model was only partially supported by children's data. To improve accuracy, further adjustment for body shape variability has been proposed, known as specific BIVA (BIVAspecific). METHODS:We re-analysed body composition data from 281 children and adolescents (46% male) aged 4-20 years of European ancestry. Measurements included anthropometry, conventional BIA, BIVA outcomes adjusted either for height (BIVAconventional), or for height and body cross-sectional area (BIVAspecific), and fat-free mass (FFM) and fat mass (FM) by the criterion 4-component model. Graphic analysis and regression analysis were used to evaluate different BIA models for predicting FFM and FM. RESULTS:Age was strongly correlated with BIVAconventional parameters, but weakly with BIVAspecific parameters. FFM correlated more strongly with BIVAconventional than with BIVAspecific parameters, whereas the opposite pattern was found for FM. In multiple regression analyses, the best prediction models combined conventional BIA with BIVAspecific parameters, explaining 97.0% and 89.8% of the variance in FFM and FM respectively. These models could be further improved by incorporating body weight. CONCLUSIONS:The prediction of body composition can be improved by combining two different theoretical models, each of which appears to provide different information about the two components FFM and FM. Further work should test the utility of this approach in pediatric patients.

Background Children with cystic fibrosis (CF) are at risk of altered body composition (BC). Newborn screening (NBS) may lead to improved BC outcomes. We investigated BC and its relationship with lung function in prepubertal children diagnosed with CF by NBS. Secondary aims explored predictors of fat-free mass (FFM) and lung function. Methods Thirty-seven screened (non-meconium ileus) children with CF (20 boys) born 2007-2012 had a dual-energy x-ray absorptiometry scan at 5-8 years to determine whole-body (WB) and appendicular BC. Anthropometry was performed and routine spirometry recorded. Results were converted to z-scores, height-adjusted (fat mass index [FMI] and FFM index [FFMI]) and compared with population mean values. Predictors of forced expiratory volume in 1 second (FEV1) were assessed using linear regression. Results Height, body mass index (BMI), and FEV1 were within normal limits, however, weight and BC were significantly low compared with reference data (weight, P = .03; WB FMI, P = .001; WB FFMI, P = .009). Gender differences were detected, with lower appendicular BC in boys and lower weight, BMI, and BC in girls. The association between FEV1 and WB FFMI (r = 0.38; P = .02) was stronger than with BMI (r = 0.29; P = .08). WB FFMI was the only significant predictor of FEV1 in a multivariable model (95% CI, 0.11-0.99; P = .016). Conclusion In this NBS CF population, gender differences in growth and BC were apparent despite preserved lung function. These results support BC assessment in prepubertal children, particularly girls, with an opportunity to direct interventions to optimize FFM.

Background: Better tools are needed to diagnose and identify children at risk of clinical malnutrition. Objectives: We aimed to compare body composition (BC) and malnutrition screening tools (MSTs) for detecting malnutrition on admission; and examine their ability to predict adverse clinical outcomes [increased length of stay (LOS) and complications] in complex pediatric patients. Methods: This was a prospective study in children 5-18 y old admitted to a tertiary pediatric hospital (n = 152). MSTs [Pediatric Yorkhill Malnutrition Score (PYMS), Screening Tool for the Assessment of Malnutrition in Pediatrics (STAMP), and Screening Tool for Risk of Impaired Nutritional Status and Growth (STRONGkids)] were completed on admission. Weight, height, and BC [fat mass (FM) and lean mass (LM) by DXA] were measured (n = 118). Anthropometry/BC and MSTs were compared with each other and with clinical outcomes. Results: Subjects were significantly shorter with low LM compared to reference data. Depending on the diagnostic criteria used, 3%-17% were classified as malnourished. Agreement between BC/anthropometric parameters and MSTs was poor. STAMP and STRONGkids identified children with low weight, LM, and height. PYMS, and to a lesser degree STRONGkids, identified children with increased LOS, as did LM compared with weight or height. Patients with complications had lower mean +/- SD LM SD scores (-1.38 +/- 1.03 compared with -0.74 +/- 1.40, P < 0.05). In multivariable models, PYMS high risk and low LM were independent predictors of increased LOS (OR: 3.76; 95% CI: 1.36, 10.35 and OR: 3.69; 95% CI: 1.24, 10.98, respectively). BMI did not predict increased LOS or complications. Conclusions: LM appears better than weight and height for predicting adverse clinical outcomes in this population. BMI was a poor diagnostic parameter. MSTs performed differently in associations to BC/anthropometry and clinical outcomes. PYMS and LM provided complementary information regarding LOS. Studies on specific patient populations may further clarify the use of these tools and measurements.

Background Most body composition techniques assume constant properties of fat free mass (FFM) (hydration and density) regardless of nutritional status, which may lead to biased values. Aim To evaluate the interactive associations of age and body mass index (BMI) with hydration and density of FFM. Methods Data from subjects aged between 4 and 22 years old from several studies conducted in London, UK were assessed. Hydration (H FFM ) and density (D FFM ) of FFM obtained from the four-component model in 936 and 905 individuals, respectively, were assessed. BMI was converted in to z -scores, and categorised into five groups using z -score cut-offs (thin, normal weight, overweight, obese, and severely obese). Linear regression models for H FFM and D FFM were developed using age, sex, and BMI group as predictors. Results Nearly 30% of the variability in H FFM was explained by models including age and BMI groups, showing increasing H FFM values in heavier BMI groups. On the other hand, ∼40% of variability in D FFM was explained by age, sex, and BMI groups, with D FFM values decreasing in association with higher BMI group. Conclusion Nutritional status should be considered when assessing body composition using two-component methods, and reference data for H FFM and D FFM is needed for higher BMI groups to avoid bias. Further research is needed to explain intra-individual variability in FFM properties.

Clinical use of bioelectric impedance is limited by variability in hydration. Analysis of raw bioelectric impedance vectors (BIVA), resistance (R), reactance (Xc) and phase angle (PA) may be an alternative for monitoring disease progression/treatment. Clinical experience of BIVA in children is limited. We investigated predictors of BIVA and their ability to predict clinical outcomes in children with complex diagnoses. R, Xc and PA were measured (BODYSTAT Quadscan 4000) on admission in 108 patients (4.6–16.8 years, mean 10.0). R and Xc were indexed by height (H) and BIVA-SDS for age and sex calculated using data from healthy children. Potential predictors and clinical outcomes (greater-than-expected length-of-stay (LOS), complications) were recorded. Mean R/H-SDS was significantly higher (0.99 (SD 1.32)) and PA-SDS lower (−1.22 (1.68))) than expected, with a wide range for all parameters. In multivariate models, the Strongkids risk category predicted R/H-SDS (adjusted mean for low, medium and high risk = 0.49, 1.28, 2.17, p = 0.009) and PA-SDS (adjusted mean −0.52, −1.53, −2.36, p = 0.01). BIVA-SDS were not significantly different in patients with or without adverse outcomes. These complex patients had abnormal mean BIVA-SDS suggestive of reduced hydration and poor cellular health according to conventional interpretation. R/H-SDS was higher and PA-SDS lower in those classified as higher malnutrition risk by the StrongKids tool. Further investigation in specific patient groups, including those with acute fluid shifts and using disease-specific outcomes, may better define the clinical role of BIV.

OBJECTIVES:Our objective was to test the hypothesis that children with end-stage chronic liver disease (ESCLD) are hypermetabolic when compared to healthy children, and that this hypermetabolism persists for at least 6 months after liver transplant.METHODS:Seventeen patients with end-stage chronic liver disease and 14 healthy controls had their resting energy expenditure measured (mREE) by indirect calorimetry. Weight, height, and body mass index were converted to standard deviation (SD) scores. Children older than 5 years had air displacement plethysmography and patients older than 5 years also had whole body dual-energy X-ray absorptiometry with characterization of fat mass (FM), fat-free mass (FFM), and bone-free fat free (lean) mass.RESULTS:When compared to the prediction equation 44% of the patients and 50% of the healthy controls were hypermetabolic. The younger patients (0-5 years) had a lower mREE than the healthy controls but were significantly lighter and shorter than their healthy counterparts. mREE correlated strongly for all children with age, weight, height, and FFM. There was a strong negative correlation between age and mREE/kg in both patients (rs = -0.94, P < 0.01) and controls (rs = -0.91, P < 0.01). Almost 84% of the variance in mREE was explained by age (P < 0.001). There were no significant differences between resting energy expenditure (REE)/FFM between the 2 groups. mREE/kg before liver transplant correlated with mREE/kg after transplant (Pearson r = 0.83, P < 0.01).CONCLUSIONS:REE mostly reflected the size of the child. The patients were not hypermetabolic when compared to the healthy children. The main determinant of REE/kg after transplant was REE/kg before transplant.

OBJECTIVES To develop and validate a prediction model for fat mass in children aged 4-15 years using routinely available risk factors of height, weight, and demographic information without the need for more complex forms of assessment. DESIGN Individual participant data meta-analysis. Setting Four population based cross sectional studies and a fifth study for external validation, United Kingdom. PARTICIPANTS A pooled derivation dataset (four studies) of 2375 children and an external validation dataset of 176 children with complete data on anthropometric measurements and deuterium dilution assessments of fat mass. MAIN OUTCOME measure Multivariable linear regression analysis, using backwards selection for inclusion of predictor variables and allowing non-linear relations, was used to develop a prediction model for fat-free mass (and subsequently fat mass by subtracting resulting estimates from weight) based on the four studies. Internal validation and then internal-external cross validation were used to examine overfitting and generalisability of the model's predictive performance within the four development studies; external validation followed using the fifth dataset. RESULTS Model derivation was based on a multi-ethnic population of 2375 children (47.8% boys, n=1136) aged 4-15 years. The final model containing predictor variables of height, weight, age, sex, and ethnicity had extremely high predictive ability (optimism adjusted R-2: 94.8%, 95% confidence interval 94.4% to 95.2%) with excellent calibration of observed and predicted values. The internal validation showed minimal overfitting and good model generalisability, with excellent calibration and predictive performance. External validation in 176 children aged 11-12 years showed promising generalisability of the model (R-2: 90.0%, 95% confidence interval 87.2% to 92.8%) with good calibration of observed and predicted fat mass (slope: 1.02, 95% confidence interval 0.97 to 1.07). The mean difference between observed and predicted fat mass was -1.29 kg (95% confidence interval -1.62 to -0.96 kg). CONCLUSION The developed model accurately predicted levels of fat mass in children aged 4-15 years. The prediction model is based on simple anthropometric measures without the need for more complex forms of assessment and could improve the accuracy of assessments for body fatness in children (compared with those provided by body mass index) for effective surveillance, prevention, and management of clinical and public health obesity.

BACKGROUND/OBJECTIVES:Bio-electrical impedance (BI) analysis is a simple body composition method ideal for children. However, its utility in sick or malnourished children is complicated by variability in hydration. BI vector analysis (BIVA) potentially resolves this, using a theoretical model that differentiates hydration from cell mass. We tested this model against reference methods in healthy children varying widely in age and nutritional status.SUBJECTS/METHODS:We compiled body composition data from 291 children and adolescents (50% male) aged 4-20 years of European ancestry. Measurements included anthropometry, BIVA outcomes (height-adjusted resistance (R/H) and reactance (Xc/H); phase angle (PA)), and fat-free mass (FFM), fat mass (FM) and FFM-hydration (HFFM) by the criterion 4-component model. All outcomes were converted to age- and sex-standardised standard deviation scores (SDS). Graphic analysis and regression analysis were used to evaluate the BIVA model.RESULTS:R/H and Xc/H declined with age in curvilinear manner, whereas PA increased linearly with age. R/H-SDS and Xc-SDS were negatively correlated with FFM-SDS, HFFM-SDS. and FM-SDS. PA was positively correlated with FFM-SDS but unrelated to HFFM-SDS and FM-SDS.CONCLUSIONS:While previous studies of adults with major fluid perturbations support the BIVA model, it is less successful in predicting variability in FFM in healthy children and adolescents. BIVA outcomes varied as predicted by the model with HFFM, but not as predicted with FFM. Variability in adiposity also explains some of the variability in BIVA traits. Further work is needed to develop a theoretical BIVA model for application in paediatric patients without major fluid disturbances.

Background/Objectives Major organ-specific and tissue-specific metabolic rate (K-i) values were initially estimated using in vivo methods, and values reported by Elia (Energy metabolism: tissue determinants and cellular corollaries, Raven Press, New York, 1992) were subsequently supported by statistical analysis. However, the majority of work to date on this topic has addressed individuals of European descent, whereas population variability in resting energy metabolism has been reported. We aimed to estimate K-i values in South Asian females. Subjects/Methods This cross-sectional study recruited 70 healthy young women of South Asian ancestry. Brain and organs were measured using magnetic resonance imaging, skeletal muscle mass by dual-energy X-ray absorptiometry, fat mass by the 4-component model, and whole-body resting energy expenditure by indirect calorimetry. Organ and tissue K-i values were estimated indirectly using regression analysis through the origin. Preliminary analysis suggested overestimation of heart mass, hence the modeling was repeated with a literature-based 22.5% heart mass reduction. Results The pattern of derived K-i values across organs and tissues matched that previously estimated in vivo, but the values were systematically lower. However, adjusting for the overestimation of heart mass markedly improved the agreement. Conclusions Our results support variability in K-i values among organs and tissues, where some are more metabolically "expensive" than others. Initial findings suggesting lower organ/tissue K-i values in South Asian women were likely influenced by heart mass estimation bias. The question of potential ethnic variability in organ-specific and tissue-specific energy metabolism requires further investigation.