No evidence for an association between alcohol consumption and Multiple Sclerosis risk: a UK Biobank study

Multiple Sclerosis (MS) has been linked to a variety of environmental risk factors, including smoking, Epstein-Barr Virus infection, and childhood obesity. There is some evidence to support a relationship between alcohol consumption and MS risk, but this finding has been inconsistent across cohorts. A protective link between alcohol consumption and MS risk is seen in Swedish and Danish cohorts, however evidence from other cohorts and mendelian randomisation studies have failed to support this relationship. We assessed the relationship between alcohol consumption (never vs. ever drinking) and MS in 409,228 individuals (2100 with MS) from UK Biobank (UKB). We used multivariable logistic regression models adjusted for age and sex. To determine whether there was evidence of statistical interaction between alcohol consumption and HLA-DRB1*15:01 genotype, we calculated interaction on the additive and multiplicative scales. We analysed data from 2100 individuals with MS (72.3% female, median age at recruitment 56) and 407,128 controls (53.9% female, median age at recruitment 58). We found no evidence for an association between alcohol consumption and MS risk (OR = 1.12, 95% CI 0.61–2.08, p = 0.314). As expected, the HLA-DRB1*15:01 allele was strongly associated with MS risk (OR = 2.72, 95% CI 2.72–2.72, p < 2 × 10(−16)). We found no evidence of statistical interaction between non-drinking and MS risk on either the multiplicative scale (p = 0.8) or on the additive scale (Attributable Proportion = 0.03, 95% CI − 0.43–0.29, P = 0.45). Empirical power calculations indicated reasonable statistical power (85%) to detect a protective effect of alcohol consumption of Relative Risk ≤ 0.7. We were thus unable to replicate findings from other cohorts within UK Biobank. The inconsistent association seen between studies may reflect limited statistical power to detect a weak effect, differences in population characteristics, or the lack of a true causal association

Development of the Fetal Vermis: New Biometry Reference Data and Comparison of 3 Diagnostic Modalities-3D Ultrasound, 2D Ultrasound, and MR Imaging

Normal biometry of the fetal posterior fossa rules out most major anomalies of the cerebellum and vermis. Our aim was to provide new reference data of the fetal vermis in 4 biometric parameters by using 3 imaging modalities, 2D ultrasound, 3D ultrasound, and MR imaging, and to assess the relation among these modalities. A retrospective study was conducted between June 2011 and June 2013. Three different imaging modalities were used to measure vermis biometry: 2D ultrasound, 3D ultrasound, and MR imaging. The vermian parameters evaluated were the maximum superoinferior diameter, maximum anteroposterior diameter, the perimeter, and the surface area. Statistical analysis was performed to calculate centiles for gestational age and to assess the agreement among the 3 imaging modalities. The number of fetuses in the study group was 193, 172, and 151 for 2D ultrasound, 3D ultrasound, and MR imaging, respectively. The mean and median gestational ages were 29.1 weeks, 29.5 weeks (range, 21-35 weeks); 28.2 weeks, 29.05 weeks (range, 21-35 weeks); and 32.1 weeks, 32.6 weeks (range, 27-35 weeks) for 2D ultrasound, 3D ultrasound, and MR imaging, respectively. In all 3 modalities, the biometric measurements of the vermis have shown a linear growth with gestational age. For all 4 biometric parameters, the lowest results were those measured by MR imaging, while the highest results were measured by 3D ultrasound. The inter- and intraobserver agreement was excellent for all measures and all imaging modalities. Limits of agreement were considered acceptable for clinical purposes for all parameters, with excellent or substantial agreement defined by the intraclass correlation coefficient. Imaging technique-specific reference data should be used for the assessment of the fetal vermis in pregnanc

Cerebellar volume as imaging outcome in progressive multiple sclerosis

Background and purpose: To assess whether cerebellar volumes changes could represent a sensitive outcome measure in primary-progressive MS. Material and methods: Changes in cerebellar volumes over one-year follow-up, estimated in 26 primary-progressive MS patients and 20 controls with Freesurfer longitudinal pipeline, were assessed using Wilcoxon test and tested for their correlation with disability worsening by a logistic regression. Clinical worsening was defined as EDSS score increase or change of &gt;20% for 25-foot walk test or 9-hole peg test scores at follow-up. Sample sizes for given treatment effects and power were calculated. The findings were validated in an independent cohort of 20 primary-progressive MS patients. Results: Significant changes were detected in brain T1 lesion volume (p&lt;0.01), cerebellar T2 and T1 lesion volume (p&lt;0.01 and p&lt;0.05), cerebellar volume, cerebellar cortex volume, and cerebellar WM volume (p&lt;0.001). Only cerebellar volume and cerebellar cortex volume percentage change were significantly reduced in clinically progressed patients when compared to patients who did not progress (p&lt;0.01; respectively AUC of 0.91 and 0.96). Cerebellar volume percentage changes were consistent in the exploration and validation cohorts (cerebellar volume -1.90±1.11% vs -1.47±2.30%; cerebellar cortex volume -1.68±1.41% vs -1.56±2.23%). Based on our results the numbers of patients required to detect a 30% effect are 81 per arm for cerebellar volume and 162 per arm for cerebellar cortex volume (90% power, type 1 error alpha = 0.05). Conclusions: Our results suggest a role for cerebellar cortex volume and cerebellar volume as potential short-term imaging metrics to monitor treatment effect in primary-progressive MS clinical trials

Longitudinal Assessment of Antisaccades in Patients with Multiple Sclerosis

We have previously demonstrated that assessment of antisaccades (AS) provides not only measures of motor function in multiple sclerosis (MS), but measures of cognitive control processes in particular, attention and working memory. This study sought to demonstrate the potential for AS measures to sensitively reflect change in functional status in MS. Twenty-four patients with relapsing-remitting MS and 12 age-matched controls were evaluated longitudinally using an AS saccade task. Compared to control subjects, a number of saccade parameters changed significantly over a two year period for MS patients. These included saccade error rates, latencies, and accuracy measures. Further, for MS patients, correlations were retained between OM measures and scores on the PASAT, which is considered the reference task for the cognitive evaluation of MS patients. Notably, EDSS scores for these patients did not change significantly over this period. These results demonstrate that OM measures may reflect disease evolution in MS, in the absence of clinically evident changes as measured using conventional techniques. With replication, these markers could ultimately be developed into a cost-effective, non-invasive, and well tolerated assessment tool to assist in confirming progression early in the disease process, and in measuring and predicting response to therapy

WAPM-World Association of Perinatal Medicine Practice Guidelines: Fetal central nervous system examination

These practice guidelines follow the mission of the World Association of Perinatal Medicine in collaboration with the Perinatal Medicine Foundation, bringing together groups and individuals throughout the world, with the goal of improving the ultrasound assessment of the fetal Central Nervous System (CNS) anatomy. In fact, this document provides further guidance for healthcare practitioners for the evaluation of the fetal CNS during the mid-trimester ultrasound scan with the aim to increase the ability in evaluating normal fetal anatomy. Therefore, it is not intended to establish a legal standard of care. This document is based on consensus among perinatal experts throughout the world, and serves as a guideline for use in clinical practice

Eleven fetal echocardiographic planes using 4-dimensional ultrasound with spatio-temporal image correlation (STIC): a logical approach to fetal heart volume analysis

<p>Abstract</p> <p>Background</p> <p>Theoretically, a cross-sectional image of any cardiac planes can be obtained from a STIC fetal heart volume dataset. We described a method to display 11 fetal echocardiographic planes from STIC volumes.</p> <p>Methods</p> <p>Fetal heart volume datasets were acquired by transverse acquisition from 200 normal fetuses at 15 to 40 weeks of gestation. Analysis of the volume datasets using the described technique to display 11 echocardiographic planes in the multiplanar display mode were performed offline.</p> <p>Results</p> <p>Volume datasets from 18 fetuses were excluded due to poor image resolution. The mean visualization rates for all echocardiographic planes at 15-17, 18-22, 23-27, 28-32 and 33-40 weeks of gestation fetuses were 85.6% (range 45.2-96.8%, N = 31), 92.9% (range 64.0-100%, N = 64), 93.4% (range 51.4-100%, N = 37), 88.7%(range 54.5-100%, N = 33) and 81.8% (range 23.5-100%, N = 17) respectively.</p> <p>Conclusions</p> <p>Overall, the applied technique can favorably display the pertinent echocardiographic planes. Description of the presented method provides a logical approach to explore the fetal heart volumes.</p

Prediction of acute multiple sclerosis relapses by transcription levels of peripheral blood cells

<p>Abstract</p> <p>Background</p> <p>The ability to predict the spatial frequency of relapses in multiple sclerosis (MS) would enable physicians to decide when to intervene more aggressively and to plan clinical trials more accurately.</p> <p>Methods</p> <p>In the current study our objective was to determine if subsets of genes can predict the time to the next acute relapse in patients with MS. Data-mining and predictive modeling tools were utilized to analyze a gene-expression dataset of 94 non-treated patients; 62 patients with definite MS and 32 patients with clinically isolated syndrome (CIS). The dataset included the expression levels of 10,594 genes and annotated sequences corresponding to 22,215 gene-transcripts that appear in the microarray.</p> <p>Results</p> <p>We designed a two stage predictor. The first stage predictor was based on the expression level of 10 genes, and predicted the time to next relapse with a resolution of 500 days (error rate 0.079, p < 0.001). If the predicted relapse was to occur in less than 500 days, a second stage predictor based on an additional different set of 9 genes was used to give a more accurate estimation of the time till the next relapse (in resolution of 50 days). The error rate of the second stage predictor was 2.3 fold lower than the error rate of random predictions (error rate = 0.35, p < 0.001). The predictors were further evaluated and found effective both for untreated MS patients and for MS patients that subsequently received immunomodulatory treatments after the initial testing (the error rate of the first level predictor was < 0.18 with p < 0.001 for all the patient groups).</p> <p>Conclusion</p> <p>We conclude that gene expression analysis is a valuable tool that can be used in clinical practice to predict future MS disease activity. Similar approach can be also useful for dealing with other autoimmune diseases that characterized by relapsing-remitting nature.</p

Gene Expression Signature in Peripheral Blood Detects Thoracic Aortic Aneurysm

BACKGROUND: Thoracic aortic aneurysm (TAA) is usually asymptomatic and associated with high mortality. Adverse clinical outcome of TAA is preventable by elective surgical repair; however, identifying at-risk individuals is difficult. We hypothesized that gene expression patterns in peripheral blood cells may correlate with TAA disease status. Our goal was to identify a distinct gene expression signature in peripheral blood that may identify individuals at risk for TAA. METHODS AND FINDINGS: Whole genome gene expression profiles from 94 peripheral blood samples (collected from 58 individuals with TAA and 36 controls) were analyzed. Significance Analysis of Microarray (SAM) identified potential signature genes characterizing TAA vs. normal, ascending vs. descending TAA, and sporadic vs. familial TAA. Using a training set containing 36 TAA patients and 25 controls, a 41-gene classification model was constructed for detecting TAA status and an overall accuracy of 78+/-6% was achieved. Testing this classifier on an independent validation set containing 22 TAA samples and 11 controls yielded an overall classification accuracy of 78%. These 41 classifier genes were further validated by TaqMan real-time PCR assays. Classification based on the TaqMan data replicated the microarray results and achieved 80% classification accuracy on the testing set. CONCLUSIONS: This study identified informative gene expression signatures in peripheral blood cells that can characterize TAA status and subtypes of TAA. Moreover, a 41-gene classifier based on expression signature can identify TAA patients with high accuracy. The transcriptional programs in peripheral blood leading to the identification of these markers also provide insights into the mechanism of development of aortic aneurysms and highlight potential targets for therapeutic intervention. The classifier genes identified in this study, and validated by TaqMan real-time PCR, define a set of promising potential diagnostic markers, setting the stage for a blood-based gene expression test to facilitate early detection of TAA

Is the Concept of Quality of Life Relevant for Multiple Sclerosis Patients with Cognitive Impairment? Preliminary Results of a Cross-Sectional Study

Background: Cognitive impairment occurs in about 50 % of multiple sclerosis (MS) patients, and the use of self-reported outcomes for evaluating treatment and managing care among subjects with cognitive dysfunction has been questioned. The aim of this study was to provide new evidence about the suitability of self-reported outcomes for use in this specific population by exploring the internal structure, reliability and external validity of a specific quality of life (QoL) instrument, the Multiple Sclerosis International Quality of Life questionnaire (MusiQoL). Methods: Design: cross-sectional study. Inclusion criteria: MS patients of any disease subtype. Data collection: sociodemographic (age, gender, marital status, education level, and occupational activity) and clinical data (MS subtype, Expanded Disability Status Scale, disease duration); QoL (MusiQoL and SF36); and neuropsychological performance (Stroop color-word test). Statistical analysis: confirmatory factor analysis, item-dimension correlations, Cronbach’s alpha coefficients, Rasch statistics, relationships between MusiQoL dimensions and other parameters. Principal Findings: One hundred and twenty-four consecutive patients were enrolled. QoL scores did not differ between the 69 cognitively non-impaired patients and the 55 cognitively impaired patients, except for the symptoms dimension. The confirmatory factor analysis performed among the impaired subjects showed that the structure of the questionnaire matched with the initial structure of the MusiQoL. The unidimensionality of the MusiQoL dimensions was preserved, and th

Detecting early-warning signals for sudden deterioration of complex diseases by dynamical network biomarkers

Considerable evidence suggests that during the progression of complex diseases, the deteriorations are not necessarily smooth but are abrupt, and may cause a critical transition from one state to another at a tipping point. Here, we develop a model-free method to detect early-warning signals of such critical transitions, even with only a small number of samples. Specifically, we theoretically derive an index based on a dynamical network biomarker (DNB) that serves as a general early-warning signal indicating an imminent bifurcation or sudden deterioration before the critical transition occurs. Based on theoretical analyses, we show that predicting a sudden transition from small samples is achievable provided that there are a large number of measurements for each sample, e.g., high-throughput data. We employ microarray data of three diseases to demonstrate the effectiveness of our method. The relevance of DNBs with the diseases was also validated by related experimental data and functional analysis