Nutritional behavior of cyclists during a 24-hour team relay race: a field study report

Background Information about behavior of energy intake in ultra-endurance cyclists during a 24-hour team relay race is scarce. The nutritional strategy during such an event is an important factor which athletes should plan carefully before the race. The purpose of this study was to examine and compare the nutritional intake of ultra-endurance cyclists during a 24-hour team relay race with the current nutritional guidelines for endurance events. Additionally, we analyzed the relationship among the nutritional and performance variables. Methods Using a observational design, nutritional intake of eight males (mean ± SD: 36.7 ± 4.7 years; 71.6 ± 4.9 kg; 174.6 ± 7.3 cm; BMI 23.5 ± 0.5 kg/m2) participating in a 24-hour team relay cycling race was assessed. All food and fluid intake by athletes were weighed and recorded. Additionally, distance and speed performed by each rider were also recorded. Furthermore, before to the race, all subjects carried out an incremental exercise test to determine two heart rate-VO2 regression equations which were used to estimate the energy expenditure. Results The mean ingestion of macronutrients during the event was 943 ± 245 g (13.1 ± 4.0 g/kg) of carbohydrates, 174 ± 146 g (2.4 ± 1.9 g/kg) of proteins and 107 ± 56 g (1.5 ± 0.7 g/kg) of lipids, respectively. This amount of nutrients reported an average nutrient intake of 22.8 ± 8.9 MJ which were significantly lower compared with energy expenditure 42.9 ± 6.8 MJ (P = 0.012). Average fluid consumption corresponded to 10497 ± 2654 mL. Mean caffeine ingestion was 142 ± 76 mg. Additionally, there was no relationship between the main nutritional variables (i.e. energy intake, carbohydrates, proteins, fluids and caffeine ingestion) and the main performance variables (i.e. distance and speed). Conclusions A 24-hour hours cycling competition in a team relay format elicited high energy demands which were not compensated by energy intake of the athletes despite that dietary consumption of macronutrients did not differ to the nutritional guidelines for longer events

A genome-wide meta-analysis of genetic variants associated with allergic rhinitis and grass sensitization and their interaction with birth order

BACKGROUND: Hay fever or seasonal allergic rhinitis (AR) is a chronic disorder associated with IgE sensitization to grass. The underlying genetic variants have not been studied comprehensively. There is overwhelming evidence that those who have older siblings have less AR, although the mechanism for this remains unclear. OBJECTIVE: We sought to identify common genetic variant associations with prevalent AR and grass sensitization using existing genome-wide association study (GWAS) data and to determine whether genetic variants modify the protective effect of older siblings. METHOD: Approximately 2.2 million genotyped or imputed single nucleotide polymorphisms were investigated in 4 large European adult cohorts for AR (3,933 self-reported cases vs 8,965 control subjects) and grass sensitization (2,315 cases vs 10,032 control subjects). RESULTS: Three loci reached genome-wide significance for either phenotype. The HLA variant rs7775228, which cis-regulates HLA-DRB4, was strongly associated with grass sensitization and weakly with AR (P(grass) = 1.6 × 10(-9); P(AR) = 8.0 × 10(-3)). Variants in a locus near chromosome 11 open reading frame 30 (C11orf30) and leucine-rich repeat containing 32 (LRRC32), which was previously associated with atopic dermatitis and eczema, were also strongly associated with both phenotypes (rs2155219; P(grass) = 9.4 × 10(-9); P(AR) = 3.8 × 10(-8)). The third genome-wide significant variant was rs17513503 (P(grass) = 1.2 × 10(-8); PAR = 7.4 × 10(-7)) which was located near transmembrane protein 232 (TMEM232) and solute carrier family 25, member 46 (SLC25A46). Twelve further loci with suggestive associations were also identified. Using a candidate gene approach, where we considered variants within 164 genes previously thought to be important, we found variants in 3 further genes that may be of interest: thymic stromal lymphopoietin (TSLP), Toll-like receptor 6 (TLR6) and nucleotide-binding oligomerization domain containing 1 (NOD1/CARD4). We found no evidence for variants that modified the effect of birth order on either phenotype. CONCLUSIONS: This relatively large meta-analysis of GWASs identified few loci associated with AR and grass sensitization. No birth order interaction was identified in the current analyses

Big data analytics in healthcare: A cloud based framework for generating insights

With exabytes of data being generated from genome sequencing, a whole new science behind genomic big data has emerged. As technology improves, the cost of sequencing a human genome has gone down considerably increasing the number of genomes being sequenced. Huge amounts of genomic data along with a vast variety of clinical data cannot be handled using existing frameworks and techniques. It is to be efficiently stored in a warehouse where a number of things have to be taken into account. Firstly, the genome data is to be integrated effectively and correctly with clinical data. The other data sources along with their formats have to be identified. Required data is then extracted from these other sources (such as clinical datasets) and integrated with the genome. The main challenge here is to be able to handle the integration complexity as a large number of datasets are being integrated with huge amounts of genome. Secondly, since the data is captured at disparate locations individually by clinicians and scientists, it brings the challenge of data consistency. It has to be made sure that the data consistency is not compromised as it is passed along the warehouse. Checks have to be put in place to make sure the data remains consistent from start to finish. Thirdly, to carry this out effectively, the data infrastructure has to be in the correct order. How frequently the data is accessed plays a crucial role here. Data in frequent use will be handled differently than data which is not in frequent use. Lastly, efficient browsing mechanisms have to put in place to allow the data to be quickly retrieved. The data is then iteratively analysed to get meaningful insights. The challenge here is to perform analysis very quickly. Cloud Computing plays an important role as it is used to provide scalability.N/