Elisabeth G. Celius

Multiple Sclerosis Severity Score Using disability and disease duration to rate disease severity

Background: There is no consensus method for determining progression of disability in patients with multiple sclerosis (MS) when each patient has had only a single assessment in the course of the disease. Methods: Using data from two large longitudinal databases, the authors tested whether cross-sectional disability assessments are representative of disease severity as a whole. An algorithm, the Multiple Sclerosis Severity Score (MSSS), which relates scores on the Expanded Disability Status Scale (EDSS) to the distribution of disability in patients with comparable disease durations, was devised and then applied to a collection of 9,892 patients from 11 countries to create the Global MSSS. In order to compare different methods of detecting such effects the authors simulated the effects of a genetic factor on disability. Results: Cross-sectional EDSS measurements made after the first year were representative of overall disease severity. The MSSS was more powerful than the other methods the authors tested for detecting different rates of disease progression. Conclusion: The Multiple Sclerosis Severity Score (MSSS) is a powerful method for comparing disease progression using single assessment data. The Global MSSS can be used as a reference table for future disability comparisons. While useful for comparing groups of patients, disease fluctuation precludes its use as a predictor of future disability in an individual.

Variation in interleukin 7 receptor alpha chain (IL7R) influences risk of multiple sclerosis

Multiple sclerosis is a chronic, often disabling, disease of the central nervous system affecting more than 1 in 1,000 people in most western countries. The inflammatory lesions typical of multiple sclerosis show autoimmune features and depend partly on genetic factors. Of these genetic factors, only the HLA gene complex has been repeatedly confirmed to be associated with multiple sclerosis, despite considerable efforts. Polymorphisms in a number of non-HLA genes have been reported to be associated with multiple sclerosis, but so far confirmation has been difficult. Here, we report compelling evidence that polymorphisms in IL7R, which encodes the interleukin 7 receptor α chain (IL7Rα), indeed contribute to the non-HLA genetic risk in multiple sclerosis, demonstrating a role for this pathway in the pathophysiology of this disease. In addition, we report altered expression of the genes encoding IL7Rα and its ligand, IL7, in the cerebrospinal fluid compartment of individuals with multiple sclerosis.

The expanding genetic overlap between multiple sclerosis and type I diabetes

Familial clustering of autoimmune disease is well recognized and raises the possibility that some susceptibility genes may predispose to autoimmunity in general. In light of this observation, it might be expected that some of the variants of established relevance in one autoimmune disease may also be relevant in other related conditions. On the basis of this hypothesis, we tested seven single nucleotide polymorphisms (SNPs) that are known to be associated with type I diabetes in a large multiple sclerosis data set consisting of 2369 trio families, 5737 cases and 10 296 unrelated controls. Two of these seven SNPs showed evidence of association with multiple sclerosis; that is rs12708716 from the CLEC16A gene (P=1.6 × 10−16) and rs763361 from the CD226 gene (P=5.4 × 10−8). These findings thereby identify two additional multiple sclerosis susceptibility genes and lend support to the notion of autoimmune susceptibility genes.

Network-Based Multiple Sclerosis Pathway Analysis with GWAS Data from 15,000 Cases and 30,000 Controls

Multiple sclerosis (MS) is an inflammatory CNS disease with a substantial genetic component, originally mapped to only the human leukocyte antigen (HLA) region. In the last 5 years, a total of seven genome-wide association studies and one meta-analysis successfully identified 57 non-HLA susceptibility loci. Here, we merged nominal statistical evidence of association and physical evidence of interaction to conduct a protein-interaction-network-based pathway analysis (PINBPA) on two large genetic MS studies comprising a total of 15,317 cases and 29,529 controls. The distribution of nominally significant loci at the gene level matched the patterns of extended linkage disequilibrium in regions of interest. We found that products of genome-wide significantly associated genes are more likely to interact physically and belong to the same or related pathways. We next searched for subnetworks (modules) of genes (and their encoded proteins) enriched with nominally associated loci within each study and identified those modules in common between the two studies. We demonstrate that these modules are more likely to contain genes with bona fide susceptibility variants and, in addition, identify several high-confidence candidates (including BCL10, CD48, REL, TRAF3, and TEC). PINBPA is a powerful approach to gaining further insights into the biology of associated genes and to prioritizing candidates for subsequent genetic studies of complex traits.

Sex and age at diagnosis are correlated with the HLA-DR2, DQ6 haplotype in multiple sclerosis

The HLA-DR2, DQ6 (i.e., HLA-DRB1*1501, DQA1*0102, DQB1*0602) haplotype contributes to the risk of developing multiple sclerosis (MS) in Caucasoids of Northern European heritage. A correlation between the clinical expression of MS and the presence of HLA-DR2, DQ6 has, however, not convincingly been shown. In this study conventional bivariate analysis and logistic regression analysis were used to study the relationship between HLA-DR2, DQ6 and four disease variables in a cohort of 286 Norwegian MS patients from the Oslo area. Logistic regression analysis showed that HLA-DR2, DQ6 was significantly more frequent among female than male patients (P=0. 0251), and was negatively correlated with age at diagnosis regardless of sex (P=0.0254). No significant correlation was observed between HLA-DR2, DQ6 and type of disease (relapsing-remitting versus primary chronic progressive MS) or presence/absence of oligoclonal bands in the cerebrospinal fluid.

Replication analysis identifies TYK2 as a multiple sclerosis susceptibility factor

In a recent genome-wide association study (GWAS) based on 12 374 non-synonymous single nucleotide polymorphisms we identified a number of candidate multiple sclerosis susceptibility genes. Here, we describe the extended analysis of 17 of these loci undertaken using an additional 4234 patients, 2983 controls and 2053 trio families. In the final analysis combining all available data, we found that evidence for association was substantially increased for one of the 17 loci, rs34536443 from the tyrosine kinase 2 (TYK2) gene (P=2.7 × 10−6, odds ratio=1.32 (1.17–1.47)). This single nucleotide polymorphism results in an amino acid substitution (proline to alanine) in the kinase domain of TYK2, which is predicted to influence the levels of phosphorylation and therefore activity of the protein and so is likely to have a functional role in multiple sclerosis.

Multiple sclerosis in Oslo, Norway: prevalence on 1 January 1995 and incidence over a 25-year period

The Oslo Multiple Sclerosis (MS) Registry was established in 1990, and this is the first report on the prevalence and incidence of MS in the city of Oslo, Norway. The prevalence rate of definite MS on 1 January 1995 was 120.4/105. Inclusion of patients of native Norwegian ancestry only and exclusion of non‐Norwegian immigrants yielded a prevalence rate of 136.0/105. A similar prevalence rate (136.5/105) was found when patients and immigrants from the other Nordic countries (Finland, Sweden, Denmark) were included. Segregation of the native Norwegian patients according to the counties where they were born showed no significant differences except for a disproportionate increase of patients born in the inland county of Oppland.  A total of 794 cases were resident in Oslo at the time of a diagnosis of definite MS in the period 1972–99. The crude average annual incidence rate for each 5‐year period, between 1972 and 1996, increased significantly from 3.7/105 in the 1972–76 to 8.7/105 in the 1992–96 period. The increase was more marked in relapsing‐remitting (RR) than in primary progressive disease and in female cases.

Refining genetic associations in multiple sclerosis

Genome-wide association studies involve several hundred thousand markers and, even when quality control is scrupulous, are invariably confounded by residual uncorrected errors that can falsely inflate the apparent difference between cases and controls (so-called genomic inflation). As a consequence such studies inevitably generate false positives alongside genuine associations. By use of Bayesian logic and empirical data, the Wellcome Trust Case Control Consortium suggested that association studies in complex disease should involve at least 2000 cases and 2000 controls, at which level they predicted that p values of less than 5×10 −7 would more commonly signify true positives than false positives.

Class II HLA interactions modulate genetic risk for multiple sclerosis.

Association studies have greatly refined the understanding of how variation within the human leukocyte antigen (HLA) genes influences risk of multiple sclerosis. However, the extent to which major effects are modulated by interactions is poorly characterized. We analyzed high-density SNP data on 17,465 cases and 30,385 controls from 11 cohorts of European ancestry, in combination with imputation of classical HLA alleles, to build a high-resolution map of HLA genetic risk and assess the evidence for interactions involving classical HLA alleles. Among new and previously identified class II risk alleles (HLA-DRB1*15:01, HLA-DRB1*13:03, HLA-DRB1*03:01, HLA-DRB1*08:01 and HLA-DQB1*03:02) and class I protective alleles (HLA-A*02:01, HLA-B*44:02, HLA-B*38:01 and HLA-B*55:01), we find evidence for two interactions involving pairs of class II alleles: HLA-DQA1*01:01–HLA-DRB1*15:01 and HLA-DQB1*03:01–HLA-DQB1*03:02. We find no evidence for interactions between classical HLA alleles and non-HLA risk-associated variants and estimate a minimal effect of polygenic epistasis in modulating major risk alleles.

Fatigue and its association with sociodemographic variables among multiple sclerosis patients

Objective: To explore the relationship between fatigue, sociodemographic and clinical variables in a population of patients with multiple sclerosis (MS). Rationale: There is a need to identify empirical relationships with possible antecedents of fatigue among patients with MS. Methods: A mailed questionnaire designed to survey sociodemographic variables and the Fatigue Severity Scale (FSS) was mailed to 502 individuals from the population of patients with definite MS in the city of O slo. A total of 368 (73%) responded. C linical data were collected from the O slo C ity MS-Registry. Results: The prevalence of fatigue in this population was 60.1%. The FSS score showed a negative correlation with education (r =-0.15, P <0.01) and a positive correlation with age (r =0.20, P B-0.001) and time since disease onset (r =0.11, P B-0.05). When controlled for gender, level of education and time since disease onset, the data showed a positive relationship between fatigue and age (P B-0.001) among patients with primary progressive (PP) disease. This relationship between age and fatigue was not found among patients with relapsing-remitting/secondary progressive (RR/SP) disease. Conclusion: The negative relationship between level of formal educatio n (FE) and fatigue among individuals with RR/SP disease suggests that behavioral factors may be among the antecedents of fatigue in this patient group. In contrast to normative data from the general population, our findings revealed no differences in fatigue related to gender. Thus, this study supports the hypothesis that there are disease-specific antecedents of fatigue among patients with MS.