Percentile reference values for anthropometric body composition indices in European children from the IDEFICS study

INTRODUCTION: To characterise the nutritional status in children with obesity or wasting conditions, European anthropometric reference values for body composition measures beyond the body mass index (BMI) are needed. Differentiated assessment of body composition in children has long been hampered by the lack of appropriate references. OBJECTIVES: The aim of our study is to provide percentiles for body composition indices in normal weight European children, based on the IDEFICS cohort (Identification and prevention of Dietary-and lifestyle-induced health Effects in Children and infantS). METHODS: Overall 18 745 2.0-10.9-year-old children from eight countries participated in the study. Children classified as overweight/obese or underweight according to IOTF (N = 5915) were excluded from the analysis. Anthropometric measurements (BMI (N = 12 830); triceps, subscapular, fat mass and fat mass index (N = 11 845-11 901); biceps, suprailiac skinfolds, sum of skinfolds calculated from skinfold thicknesses (N = 8129-8205), neck circumference (N = 12 241); waist circumference and waist-to-height ratio (N = 12 381)) were analysed stratified by sex and smoothed 1st, 3rd, 10th, 25th, 50th, 75th, 90th, 97th and 99th percentile curves were calculated using GAMLSS. RESULTS: Percentile values of the most important anthropometric measures related to the degree of adiposity are depicted for European girls and boys. Age-and sex-specific differences were investigated for all measures. As an example, the 50th and 99th percentile values of waist circumference ranged from 50.7-59.2 cm and from 51.3-58.7 cm in 4.5-to < 5.0-year-old girls and boys, respectively, to 60.6-74.5 cm in girls and to 59.9-76.7 cm in boys at the age of 10.5-10.9 years. CONCLUSION: The presented percentile curves may aid a differentiated assessment of total and abdominal adiposity in European children

Meta-analysis of variation suggests that embracing variability improves both replicability and generalizability in preclinical research

The replicability of research results has been a cause of increasing concern to the scientific community. The long-held belief that experimental standardization begets replicability has also been recently challenged, with the observation that the reduction of variability within studies can lead to idiosyncratic, lab-specific results that cannot be replicated. An alternative approach is to, instead, deliberately introduce heterogeneity, known as "heterogenization" of experimental design. Here, we explore a novel perspective in the heterogenization program in a meta-analysis of variability in observed phenotypic outcomes in both control and experimental animal models of ischemic stroke. First, by quantifying interindividual variability across control groups, we illustrate that the amount of heterogeneity in disease state (infarct volume) differs according to methodological approach, for example, in disease induction methods and disease models. We argue that such methods may improve replicability by creating diverse and representative distribution of baseline disease state in the reference group, against which treatment efficacy is assessed. Second, we illustrate how meta-analysis can be used to simultaneously assess efficacy and stability (i.e., mean effect and among-individual variability). We identify treatments that have efficacy and are generalizable to the population level (i.e., low interindividual variability), as well as those where there is high interindividual variability in response; for these, latter treatments translation to a clinical setting may require nuance. We argue that by embracing rather than seeking to minimize variability in phenotypic outcomes, we can motivate the shift toward heterogenization and improve both the replicability and generalizability of preclinical research

Farming, foreign holidays, and vitamin D in Orkney

Orkney, north of mainland Scotland, has the world's highest prevalence of multiple sclerosis (MS); vitamin D deficiency, a marker of low UV exposure, is also common in Scotland. Strong associations have been identified between vitamin D deficiency and MS, and between UV exposure and MS independent of vitamin D, although causal relationships remain to be confirmed. We aimed to compare plasma 25-hydroxyvitamin D levels in Orkney and mainland Scotland, and establish the determinants of vitamin D status in Orkney. We compared mean vitamin D and prevalence of deficiency in cross-sectional study data from participants in the Orkney Complex Disease Study (ORCADES) and controls in the Scottish Colorectal Cancer Study (SOCCS). We used multivariable regression to identify factors associated with vitamin D levels in Orkney. Mean (standard deviation) vitamin D was significantly higher among ORCADES than SOCCS participants (35.3 (18.0) and 31.7 (21.2), respectively). Prevalence of severe vitamin D deficiency was lower in ORCADES than SOCCS participants (6.6% to 16.2% p = 1.1 x 10(-15)). Older age, farming occupations and foreign holidays were significantly associated with higher vitamin D in Orkney. Although mean vitamin D levels are higher in Orkney than mainland Scotland, this masks variation within the Orkney population which may influence MS risk

Methylphenidate during early consolidation affects long-term associative memory retrieval depending on baseline catecholamines

RATIONALE: Synaptic memory consolidation is thought to rely on catecholaminergic signaling. Eventually, it is followed by systems consolidation, which embeds memories in a neocortical network. Although this sequence was demonstrated in rodents, it is unclear how catecholamines affect memory consolidation in humans. OBJECTIVES: Here, we tested the effects of catecholaminergic modulation on synaptic and subsequent systems consolidation. We expected enhanced memory performance and increased neocortical engagement during delayed retrieval. Additionally, we tested if this effect was modulated by individual differences in a cognitive proxy measure of baseline catecholamine synthesis capacity. METHODS: Fifty-three healthy males underwent a between-subjects, double-blind, placebo-controlled procedure across 2 days. On day 1, subjects studied and retrieved object-location associations and received 20 mg of methylphenidate or placebo. Drug intake was timed so that methylphenidate was expected to affect early consolidation but not encoding or retrieval. Memory was tested again while subjects were scanned three days later. RESULTS: Methylphenidate did not facilitate memory performance, and there was no significant group difference in activation during delayed retrieval. However, memory representations differed between groups depending on baseline catecholamines. The placebo group showed increased activation in occipito-temporal regions but decreased connectivity with the hippocampus, associated with lower baseline catecholamine synthesis capacity. The methylphenidate group showed stronger activation in the postcentral gyrus, associated with higher baseline catecholamine synthesis capacity. CONCLUSIONS: Altogether, methylphenidate during early consolidation did not foster long-term memory performance, but it affected retrieval-related neural processes depending on individual levels of baseline catecholamines

A multiple-phenotype imputation method for genetic studies

Genetic association studies have yielded a wealth of biologic discoveries. However, these have mostly analyzed one trait and one SNP at a time, thus failing to capture the underlying complexity of these datasets. Joint genotypephenotype analyses of complex, high-dimensional datasets represent an important way to move beyond simple GWAS with great potential. The move to high-dimensional phenotypes will raise many new statistical problems. In this paper we address the central issue of missing phenotypes in studies with any level of relatedness between samples. We propose a multiple phenotype mixed model and use a computationally efficient variational Bayesian algorithm to fit the model. On a variety of simulated and real datasets from a range of organisms and trait types, we show that our method outperforms existing state-of-the-art methods from the statistics and machine learning literature and can boost signals of associatio