Cross-sectional comparison of body composition and resting metabolic rate in Premier League academy soccer players: Implications for growth and maturation

For the first time we aimed to: (1) assess fat-free mass (FFM) and RMR in youth soccer players, (2) compare measured RMR to estimated RMR using previously published prediction equations, and (3) develop a novel population-specific prediction equation. In a cross-sectional design, 99 males from a Premier League academy underwent assessments of body composition (DXA) and RMR (indirect-calorimetry). Measured RMR was compared to estimated values from five prediction equations. A novel RMR prediction equation was developed using stepwise multiple regression. FFM increased (P0.05). RMR in the U12s (1655±195 kcal.day−1), U13s (1720±205 kcal.day−1) and U14s (1846±218kcal.day−1) was significantly lower than the U15s (1957±128 kcal.day−1), U16s (2042±155 kcal.day−1), U18s (1875±180 kcal.day−1) and U23s (1941±197 kcal.day−1) squads (P>0.05). FFM was the single best predictor of RMR (r2=0.43; P<0.01) and was subsequently included in the novel prediction equation: RMR (kcal.day−1) = 1315 + (11.1 x FFM in kg). Both FFM and RMR increase from 12-16 years old, thus highlighting the requirement to adjust daily energy intake to support growth and maturation. The novel prediction RMR equation developed may help to inform daily energy requirements

VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines

BACKGROUND: Vaccine development in the post-genomic era often begins with the in silico screening of genome information, with the most probable protective antigens being predicted rather than requiring causative microorganisms to be grown. Despite the obvious advantages of this approach – such as speed and cost efficiency – its success remains dependent on the accuracy of antigen prediction. Most approaches use sequence alignment to identify antigens. This is problematic for several reasons. Some proteins lack obvious sequence similarity, although they may share similar structures and biological properties. The antigenicity of a sequence may be encoded in a subtle and recondite manner not amendable to direct identification by sequence alignment. The discovery of truly novel antigens will be frustrated by their lack of similarity to antigens of known provenance. To overcome the limitations of alignment-dependent methods, we propose a new alignment-free approach for antigen prediction, which is based on auto cross covariance (ACC) transformation of protein sequences into uniform vectors of principal amino acid properties. RESULTS: Bacterial, viral and tumour protein datasets were used to derive models for prediction of whole protein antigenicity. Every set consisted of 100 known antigens and 100 non-antigens. The derived models were tested by internal leave-one-out cross-validation and external validation using test sets. An additional five training sets for each class of antigens were used to test the stability of the discrimination between antigens and non-antigens. The models performed well in both validations showing prediction accuracy of 70% to 89%. The models were implemented in a server, which we call VaxiJen. CONCLUSION: VaxiJen is the first server for alignment-independent prediction of protective antigens. It was developed to allow antigen classification solely based on the physicochemical properties of proteins without recourse to sequence alignment. The server can be used on its own or in combination with alignment-based prediction methods. It is freely-available online at the URL:

Metabolic Regulation in Progression to Autoimmune Diabetes

Recent evidence from serum metabolomics indicates that specific metabolic disturbances precede β-cell autoimmunity in humans and can be used to identify those children who subsequently progress to type 1 diabetes. The mechanisms behind these disturbances are unknown. Here we show the specificity of the pre-autoimmune metabolic changes, as indicated by their conservation in a murine model of type 1 diabetes. We performed a study in non-obese prediabetic (NOD) mice which recapitulated the design of the human study and derived the metabolic states from longitudinal lipidomics data. We show that female NOD mice who later progress to autoimmune diabetes exhibit the same lipidomic pattern as prediabetic children. These metabolic changes are accompanied by enhanced glucose-stimulated insulin secretion, normoglycemia, upregulation of insulinotropic amino acids in islets, elevated plasma leptin and adiponectin, and diminished gut microbial diversity of the Clostridium leptum group. Together, the findings indicate that autoimmune diabetes is preceded by a state of increased metabolic demands on the islets resulting in elevated insulin secretion and suggest alternative metabolic related pathways as therapeutic targets to prevent diabetes

The potential benefits of low-molecular-weight heparins in cancer patients

Cancer patients are at increased risk of venous thromboembolism due to a range of factors directly related to their disease and its treatment. Given the high incidence of post-surgical venous thromboembolism in cancer patients and the poor outcomes associated with its development, thromboprophylaxis is warranted. A number of evidence-based guidelines delineate anticoagulation regimens for venous thromboembolism treatment, primary and secondary prophylaxis, and long-term anticoagulation in cancer patients. However, many give equal weight to several different drugs and do not make specific recommendations regarding duration of therapy. In terms of their efficacy and safety profiles, practicality of use, and cost-effectiveness the low-molecular-weight heparins are at least comparable to, and offer several advantages over, other available antithrombotics in cancer patients. In addition, data are emerging that the antithrombotics, and particularly low-molecular-weight heparins, may exert an antitumor effect which could contribute to improved survival in cancer patients when given for long-term prophylaxis. Such findings reinforce the importance of thromboprophylaxis with low-molecular-weight heparin in cancer patients

Exploration of Shared Genetic Architecture Between Subcortical Brain Volumes and Anorexia Nervosa

In MRI scans of patients with anorexia nervosa (AN), reductions in brain volume are often apparent. However, it is unknown whether such brain abnormalities are influenced by genetic determinants that partially overlap with those underlying AN. Here, we used a battery of methods (LD score regression, genetic risk scores, sign test, SNP effect concordance analysis, and Mendelian randomization) to investigate the genetic covariation between subcortical brain volumes and risk for AN based on summary measures retrieved from genome-wide association studies of regional brain volumes (ENIGMA consortium, n = 13,170) and genetic risk for AN (PGC-ED consortium, n = 14,477). Genetic correlations ranged from − 0.10 to 0.23 (all p > 0.05). There were some signs of an inverse concordance between greater thalamus volume and risk for AN (permuted p = 0.009, 95% CI: [0.005, 0.017]). A genetic variant in the vicinity of ZW10, a gene involved in cell division, and neurotransmitter and immune system relevant genes, in particular DRD2, was significantly associated with AN only after conditioning on its association with caudate volume (pFDR = 0.025). Another genetic variant linked to LRRC4C, important in axonal and synaptic development, reached significance after conditioning on hippocampal volume (pFDR = 0.021). In this comprehensive set of analyses and based on the largest available sample sizes to date, there was weak evidence for associations between risk for AN and risk for abnormal subcortical brain volumes at a global level (that is, common variant genetic architecture), but suggestive evidence for effects of single genetic markers. Highly powered multimodal brain- and disorder-related genome-wide studies are needed to further dissect the shared genetic influences on brain structure and risk for AN