Development and Psychometric Evaluation of an Item Bank for Computerized Adaptive Testing of the EORTC Insomnia Dimension in Cancer Patients (EORTC CAT-SL)

To further advance assessment of patient-reported outcomes, the European Organisation of Research and Treatment of Cancer (EORTC) Quality of Life Group has developed computerized adaptive test (CAT) versions of all EORTC Quality of Life Core Questionnaire (QLQ-C30) scales/items. The aim of this study was to develop and evaluate an item bank for CAT measurement of insomnia (CAT-SL). In line with the EORTC guidelines, the developmental process comprised four phases: (I) defining the concept insomnia and literature search, (II) selection and formulation of new items, (III) pre-testing and (IV) field-testing, including psychometric analyses of the final item bank. In phase I, the literature search identified 155 items that were compatible with our conceptualisation of insomnia, including both quantity and quality of sleep. In phase II, following a multistep-approach, this number was reduced to 15 candidate items. Pre-testing of these items in cancer patients (phase III) resulted in an item list of 14 items, which were field-tested among 1094 patients in phase IV. Psychometric evaluations showed that eight items could be retained in a unidimensional model. The final item bank yielded greater measurement precision than the original QLQ-C30 insomnia item. It was estimated that administering two or more items from the insomnia item bank with CAT results in a saving in sample size between approximately 15–25%. The 8-item EORTC CAT-SL item bank facilitates precise and efficient measurement of insomnia as part of the EORTC CAT system of health-related quality life assessment in both clinical research and practice

Knee kinematics and kinetics in former soccer players with a 16-year-old ACL injury – the effects of twelve weeks of knee-specific training

BACKGROUND: Training of neuromuscular control has become increasingly important and plays a major role in rehabilitation of subjects with an injury to the anterior cruciate ligament (ACL). Little is known, however, of the influence of this training on knee stiffness during loading. Increased knee stiffness occurs as a loading strategy of ACL-injured subjects and is associated with increased joint contact forces. Increased or altered joint loads contribute to the development of osteoarthritis. The aim of the study was to determine if knee stiffness, defined by changes in knee kinetics and kinematics of gait, step activity and cross-over hop could be reduced through a knee-specific 12-week training programme. METHODS: A 3-dimensional motion analysis system (VICON) and a force plate (AMTI) were used to calculate knee kinetics and kinematics before and after 12 weeks of knee-specific training in 12 males recruited from a cohort with ACL injury 16 years earlier. Twelve uninjured males matched for age, sex, BMI and activity level served as a reference group. Self-reported patient-relevant data were obtained by the KOOS questionnaire. RESULTS: There were no significant changes in knee stiffness during gait and step activity after training. For the cross-over hop, increased peak knee flexion during landing (from 44 to 48 degrees, p = 0.031) and increased internal knee extensor moment (1.28 to 1.55 Nm/kg, p = 0.017) were seen after training, indicating reduced knee stiffness. The KOOS sport and recreation score improved from 70 to 77 (p = 0.005) and was significantly correlated with the changes in knee flexion during landing for the cross-over hop (r = 0.6, p = 0.039). CONCLUSION: Knee-specific training improved lower extremity kinetics and kinematics, indicating reduced knee stiffness during demanding hop activity. Self-reported sport and recreational function correlated positively with the biomechanical changes supporting a clinical importance of the findings. Further studies are needed to confirm these results in women and in other ACL injured populations

Physical inactivity:A risk factor and target for intervention in renal care

Regular physical activity is associated with an increased quality of life and reduced morbidity and mortality in the general population and in patients with chronic kidney disease (CKD). Physical activity, cardiorespiratory fitness, and muscle mass decrease even in the early stages of CKD, and continue to decrease with disease progression; notably, full recovery is generally not achieved with transplantation. The combined effects of uraemia and physical inactivity drive the loss of muscle mass. Regular physical activity benefits cardiometabolic, neuromuscular and cognitive function across all stages of CKD, and therefore provides an approach to address the multimorbidity of the CKD population. Interestingly, maintenance of muscle health is associated with renoprotective effects. Despite evidence of its benefits, physical activity and exercise management are not routinely addressed in the care of these patients. Although studies defining the optimum frequency, duration and intensity of physical activity are lacking, evidence from related fields can guide practical approaches to the care of patients with renal disease. Optimization of metabolic and nutritional status alongside promotion of physical activity is recommended. Behavioural approaches are now recognized as crucial in helping patients to adopt lifestyle changes and might prove valuable in integrating physical activity into renal care

Deciphering the cryptic genome: Genome-wide analyses of the rice pathogen <em>Fusarium fujikuroi</em> reveal complex regulation of secondary metabolism and novel metabolites.

The fungus Fusarium fujikuroi causes &ldquo;bakanae&rdquo; disease of rice due to its ability to produce gibberellins (GAs), but it is also known for producing harmful mycotoxins. However, the genetic capacity for the whole arsenal of natural compounds and their role in the fungus&#39; interaction with rice remained unknown. Here, we present a high-quality genome sequence of F. fujikuroi that was assembled into 12 scaffolds corresponding to the 12 chromosomes described for the fungus. We used the genome sequence along with ChIP-seq, transcriptome, proteome, and HPLC-FTMS-based metabolome analyses to identify the potential secondary metabolite biosynthetic gene clusters and to examine their regulation in response to nitrogen availability and plant signals. The results indicate that expression of most but not all gene clusters correlate with proteome and ChIP-seq data. Comparison of the F. fujikuroi genome to those of six other fusaria revealed that only a small number of gene clusters are conserved among these species, thus providing new insights into the divergence of secondary metabolism in the genus Fusarium. Noteworthy, GA biosynthetic genes are present in some related species, but GA biosynthesis is limited to F. fujikuroi, suggesting that this provides a selective advantage during infection of the preferred host plant rice. Among the genome sequences analyzed, one cluster that includes a polyketide synthase gene (PKS19) and another that includes a non-ribosomal peptide synthetase gene (NRPS31) are unique to F. fujikuroi. The metabolites derived from these clusters were identified by HPLC-FTMS-based analyses of engineered F. fujikuroi strains overexpressing cluster genes. In planta expression studies suggest a specific role for the PKS19-derived product during rice infection. Thus, our results indicate that combined comparative genomics and genome-wide experimental analyses identified novel genes and secondary metabolites that contribute to the evolutionary success of F. fujikuroi as a rice pathogen

Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure underpinning obesity

Genome-wide association studies (GWAS) have identified >250 loci for body mass index (BMI), implicating pathways related to neuronal biology. Most GWAS loci represent clusters of common, non-coding variants from which pinpointing causal genes remains challenging. Here, we combined data from 718,734 individuals to discover rare and low-frequency (MAF<5%) coding variants associated with BMI. We identified 14 coding variants in 13 genes, of which eight in genes (ZBTB7B, ACHE, RAPGEF3, RAB21, ZFHX3, ENTPD6, ZFR2, ZNF169) newly implicated in human obesity, two (MC4R, KSR2) previously observed in extreme obesity, and two variants in GIPR. Effect sizes of rare variants are ~10 times larger than of common variants, with the largest effect observed in carriers of an MC4R stop-codon (p.Tyr35Ter, MAF=0.01%), weighing ~7kg more than non-carriers. Pathway analyses confirmed enrichment of neuronal genes and provide new evidence for adipocyte and energy expenditure biology, widening the potential of genetically-supported therapeutic targets to treat obesity

Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity.

Genome-wide association studies (GWAS) have identified >250 loci for body mass index (BMI), implicating pathways related to neuronal biology. Most GWAS loci represent clusters of common, noncoding variants from which pinpointing causal genes remains challenging. Here we combined data from 718,734 individuals to discover rare and low-frequency (minor allele frequency (MAF) < 5%) coding variants associated with BMI. We identified 14 coding variants in 13 genes, of which 8 variants were in genes (ZBTB7B, ACHE, RAPGEF3, RAB21, ZFHX3, ENTPD6, ZFR2 and ZNF169) newly implicated in human obesity, 2 variants were in genes (MC4R and KSR2) previously observed to be mutated in extreme obesity and 2 variants were in GIPR. The effect sizes of rare variants are ~10 times larger than those of common variants, with the largest effect observed in carriers of an MC4R mutation introducing a stop codon (p.Tyr35Ter, MAF = 0.01%), who weighed ~7 kg more than non-carriers. Pathway analyses based on the variants associated with BMI confirm enrichment of neuronal genes and provide new evidence for adipocyte and energy expenditure biology, widening the potential of genetically supported therapeutic targets in obesity

A saturated map of common genetic variants associated with human height.

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes1. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel2) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries