Influence of expertise on the visual control strategies of athletes during competitive long jumping

Understanding performance of athletes in competition is required for enhancing the quality of how athletes co-adapt to the specific, changing constraints of those environments. In long jumping, for example, an athlete must co-adapt with these constraints while also meeting the challenging accuracy demands of the sport. Examining then how long jumpers with different levels of expertise navigate the competition environment is important. This analysis is necessary, given evidence from motor learning research showing that individuals with higher levels of expertise use different sources of information to guide their performance behaviors. In this study, key gait variables during the long jump run-up were recorded during performance at 8 competitions in the 2015 and 2016 Australian track and field seasons to examine the visual control strategies of athletes differing in expertise levels, when performing legal and foul jumps. No statistically significant differences were observed between jumpers differing in levels of expertise when comparing gait patterns in foul and legal jumps. However, different footfall variability curves did emerge that can advance current understanding of long jump run-ups. International-level athletes exhibited higher levels of functional variability during the initial phases of the run-up of legal jumps, with step adjustments spread across the whole of the run-up, compared to National-level athletes. Since athletes of lower levels of expertise exhibited a more stereotyped run-up profile, it is suggested that coaches and practitioners encourage more exploration in training of this group by incorporating increased levels of representative variability during practice. From a practical perspective, increasing variability in practice contexts could encourage National-level athletes to explore different movement solutions and (re)calibrate actions to changing environmental demands, providing more representative simulations of the competition environment

Moving toward earlier treatment of multiple sclerosis: Findings from a decade of clinical trials and implications for clinical practice.

The first clinical presentation of multiple sclerosis (MS) is usually a single episode of typical symptoms and signs and is designated a "first clinical demyelinating event" (FCDE) or a "clinically isolated syndrome". Patients with an FCDE who show 'silent' magnetic resonance imaging lesions are at high risk of further clinical events and therefore of meeting the criteria for the diagnosis of clinically definite MS (CDMS). Here we review five Phase III trials, in which treatment with the following disease-modifying drugs (DMDs) was initiated at this early stage: interferon beta (ETOMS, CHAMPS, BENEFIT, and REFLEX) and glatiramer acetate (PreCISe). Differences in the design of the trials and their patient inclusion criteria limit comparisons. However, the proportion of placebo-treated patients who developed CDMS within 2 years was 38–45% across studies, and this rate was significantly reduced by DMD treatment. Conversion t

MRI characteristics are predictive for CDMS in monofocal, but not in multifocal patients with a clinically isolated syndrome

BACKGROUND: To diagnose multiple sclerosis (MS), evidence for dissemination in space and time is required. There is no clear definition on how symptoms and signs of a patient indicate clinical dissemination in space. To provide a uniform approach on this subject, a clinical classification system was described recently differentiating patients with mono- and multifocal clinical presentation. Here we assess the predictive value of clinically defined dissemination in space at first presentation for time to clinically definite MS (CDMS). METHODS: Four hundred and sixty-eight patients with a first episode suggestive of MS were classified as clinically mono- or multifocal by two neurologists blinded to magnetic resonance imaging (MRI) results. These patients were part of the BENEFIT study in which 292 patients were randomized to interferon beta-1b (IFNB-1b) and 176 to placebo. By using Kaplan-Meier statistics the risk for CDMS was studied in mono- and multifocal patients of the placebo group, both with and without taking into account MRI measures of potential prognostic relevance. RESULTS: Time to CDMS was similar in monofocal and multifocal patients. In monofocal patients, the risk for CDMS over 2 years was significantly higher when <or= 9 T2 lesions or at least one Gd-enhancing lesion were present at the first event or 3 or 6 months after the first event. In patients with multifocal presentation, these MRI measures had no significant added value in predicting time to CDMS. CONCLUSION: These data indicate that a carefully performed neurological assessment of symptoms and signs, combined with lesions on MRI, is important for defining the risk of conversion to CDMS. TRIAL REGISTRATION: The Benefit trial has been registered under NCT00185211 http://www.clinicaltrials.gov

Physical and Cognitive Functioning After 3 Years Can Be Predicted Using Information From the Diagnostic Process in Recently Diagnosed Multiple Sclerosis

Objective\ud To predict functioning after 3 years in patients with recently diagnosed multiple sclerosis (MS).\ud \ud Design\ud Inception cohort with 3 years of follow-up. At baseline, predictors were obtained from medical history taking, neurologic examination, and magnetic resonance imaging (MRI).\ud \ud Setting\ud Neurology outpatient clinic.\ud \ud Participants\ud Patients with MS (N=156); 146 with complete follow-up.\ud \ud Interventions\ud Not applicable.\ud \ud Main Outcome Measures\ud Inability to walk at least 500m, impaired dexterity, cognitive impairments, incontinence, inability to drive a car or use public transportation, social dysfunction, and reliance on a disability pension.\ud \ud Results\ud Clinical prediction rules were constructed for the models that were well calibrated (sufficient agreement between predicted and observed outcomes, based on visual inspection of calibration curves) and that showed sufficient discrimination (area under the receiver operation characteristic curve >.70) after internal bootstrap validation. The models for the inability to walk at least 500m, impaired dexterity, and cognitive impairments were well calibrated. Discrimination was sufficient for all 7 models, except the one predicting social dysfunction (.67). The inability to walk at least 500m was predicted by the perceived ability to walk, impairment of the cerebellar tract, and the number of MRI lesions in the spinal cord. Impaired dexterity was predicted by the perceived ability to use the hands, impairments of the pyramidal, cerebellar, and sensory tracts, and the T2-weighted infratentorial lesion load. Cognitive impairment was predicted by age, gender, the perceived ability to concentrate, and the T2-weighted supratentorial lesion load.\ud \ud Conclusions\ud Inability to walk at least 500m, impaired dexterity, and cognitive impairments can be predicted with predictors that are derived from medical history taking, neurologic examination, and MRI shortly after a definite diagnosis of MS has been made.\ud \u

Modeling the cumulative genetic risk for multiple sclerosis from genome-wide association data

Background: Multiple sclerosis (MS) is the most common cause of chronic neurologic disability beginning in early to middle adult life. Results from recent genome-wide association studies (GWAS) have substantially lengthened the list of disease loci and provide convincing evidence supporting a multifactorial and polygenic model of inheritance. Nevertheless, the knowledge of MS genetics remains incomplete, with many risk alleles still to be revealed. Methods: We used a discovery GWAS dataset (8,844 samples, 2,124 cases and 6,720 controls) and a multi-step logistic regression protocol to identify novel genetic associations. The emerging genetic profile included 350 independent markers and was used to calculate and estimate the cumulative genetic risk in an independent validation dataset (3,606 samples). Analysis of covariance (ANCOVA) was implemented to compare clinical characteristics of individuals with various degrees of genetic risk. Gene ontology and pathway enrichment analysis was done using the DAVID functional annotation tool, the GO Tree Machine, and the Pathway-Express profiling tool. Results: In the discovery dataset, the median cumulative genetic risk (P-Hat) was 0.903 and 0.007 in the case and control groups, respectively, together with 79.9% classification sensitivity and 95.8% specificity. The identified profile shows a significant enrichment of genes involved in the immune response, cell adhesion, cell communication/ signaling, nervous system development, and neuronal signaling, including ionotropic glutamate receptors, which have been implicated in the pathological mechanism driving neurodegeneration. In the validation dataset, the median cumulative genetic risk was 0.59 and 0.32 in the case and control groups, respectively, with classification sensitivity 62.3% and specificity 75.9%. No differences in disease progression or T2-lesion volumes were observed among four levels of predicted genetic risk groups (high, medium, low, misclassified). On the other hand, a significant difference (F = 2.75, P = 0.04) was detected for age of disease onset between the affected misclassified as controls (mean = 36 years) and the other three groups (high, 33.5 years; medium, 33.4 years; low, 33.1 years). Conclusions: The results are consistent with the polygenic model of inheritance. The cumulative genetic risk established using currently available genome-wide association data provides important insights into disease heterogeneity and completeness of current knowledge in MS genetics

Modeling the Cumulative Genetic Risk for Multiple Sclerosis from Genome-Wide Association Data

Background: Multiple sclerosis (MS) is the most common cause of chronic neurologic disability beginning in early to middle adult life. Results from recent genome-wide association studies (GWAS) have substantially lengthened the list of disease loci and provide convincing evidence supporting a multifactorial and polygenic model of inheritance. Nevertheless, the knowledge of MS genetics remains incomplete, with many risk alleles still to be revealed. Methods: We used a discovery GWAS dataset (8,844 samples, 2,124 cases and 6,720 controls) and a multi-step logistic regression protocol to identify novel genetic associations. The emerging genetic profile included 350 independent markers and was used to calculate and estimate the cumulative genetic risk in an independent validation dataset (3,606 samples). Analysis of covariance (ANCOVA) was implemented to compare clinical characteristics of individuals with various degrees of genetic risk. Gene ontology and pathway enrichment analysis was done using the DAVID functional annotation tool, the GO Tree Machine, and the Pathway-Express profiling tool. Results: In the discovery dataset, the median cumulative genetic risk (P-Hat) was 0.903 and 0.007 in the case and control groups, respectively, together with 79.9% classification sensitivity and 95.8% specificity. The identified profile shows a significant enrichment of genes involved in the immune response, cell adhesion, cell communication/signaling, nervous system development, and neuronal signaling, including ionotropic glutamate receptors, which have been implicated in the pathological mechanism driving neurodegeneration. In the validation dataset, the median cumulative genetic risk was 0.59 and 0.32 in the case and control groups, respectively, with classification sensitivity 62.3% and specificity 75.9%. No differences in disease progression or T2-lesion volumes were observed among four levels of predicted genetic risk groups (high, medium, low, misclassified). On the other hand, a significant difference (F = 2.75, P = 0.04) was detected for age of disease onset between the affected misclassified as controls (mean = 36 years) and the other three groups (high, 33.5 years; medium, 33.4 years; low, 33.1 years). Conclusions: The results are consistent with the polygenic model of inheritance. The cumulative genetic risk established using currently available genome-wide association data provides important insights into disease heterogeneity and completeness of current knowledge in MS genetics

Molecular mechanism underlying the impact of vitamin D on disease activity of MS

Objective: Some previous studies suggest modest to strong effects of 25-hydroxyvitamin D (25(OH)D) on multiple sclerosis (MS) activity. The objective of this study was to explore the mechanistic rationale that may explain potential clinical effects of 25(OH)D. Methods: This study measured serum 25(OH)D levels and global gene expression profiles over a course of up to 2 years in patients starting treatment with interferon beta-1b (IFNB-1b) after a clinically isolated syndrome. MS disease activity was assessed by the number of gadolinium-enhancing lesions present on repeated magnetic resonance imaging (MRIs). Results: The number of gadolinium-enhancing lesions was highly significantly associated with 25(OH)D levels. Conducting various systems-level analyses on the molecular level, multiple lines of evidence indicated that 25(OH)D regulates expression dynamics of a large gene–gene interaction system which primarily regulates immune modulatory processes modulating MS activity. The vitamin D response element was significantly enriched in this system, indicating a direct regulation of this gene interaction network through the vitamin D receptor. With increasing 25(OH)D levels, resulting regulation of this system was associated with a decrease in MS activity. Within the complex network of genes that are regulated by 25(OH)D, well-described targets of IFNB-1b and a regulator of sphingosine-1-phosphate bioavailability were found. The 25(OH)D effects on MS activity were additively enhanced by IFNB-1b. Interpretation Here, we provide mechanistic evidence that an unbalanced 25(OH)D gene expression system may affect MS activity. Our findings support a potential benefit of monitoring and managing vitamin D levels (e.g., through supplementation) in early MS patients treated with IFN-beta-1b

Pathway and network-based analysis of genome-wide association studies in multiple sclerosis

Genome-wide association studies (GWAS) testing several hundred thousand SNPs have been performed in multiple sclerosis (MS) and other complex diseases. Typically, the number of markers in which the evidence for association exceeds the genome-wide significance threshold is very small, and markers that do not exceed this threshold are generally neglected. Classical statistical analysis of these datasets in MS revealed genes with known immunological functions. However, many of the markers showing modest association may represent false negatives. We hypothesize that certain combinations of genes flagged by these markers can be identified if they belong to a common biological pathway. Here we conduct a pathway-oriented analysis of two GWAS in MS that takes into account all SNPs with nominal evidence of association (P < 0.05). Gene-wise P-values were superimposed on a human protein interaction network and searches were conducted to identify sub-networks containing a higher proportion of genes associated with MS than expected by chance. These sub-networks, and others generated at random as a control, were categorized for membership of biological pathways. GWAS from eight other diseases were analyzed to assess the specificity of the pathways identified. In the MS datasets, we identified sub-networks of genes from several immunological pathways including cell adhesion, communication and signaling. Remarkably, neural pathways, namely axon-guidance and synaptic potentiation, were also over-represented in MS. In addition to the immunological pathways previously identified, we report here for the first time the potential involvement of neural pathways in MS susceptibilit