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Multiple sclerosis risk loci and disease severity in 7,125 individuals from 10 studies.

ABSTRACT: OBJECTIVE:We investigated the association between 52 risk variants identified through genome-wide association studies and disease severity in multiple sclerosis (MS). METHODS:Ten unique MS case data sets were analyzed. The Multiple Sclerosis Severity Score (MSSS) was calculated using the Expanded Disability Status Scale at study entry and disease duration. MSSS was considered as a continuous variable and as 2 dichotomous variables (median and extreme ends; MSSS of ?5 vs >5 and MSSS of <2.5 vs ?7.5, respectively). Single nucleotide polymorphisms (SNPs) were examined individually and as both combined weighted genetic risk score (wGRS) and unweighted genetic risk score (GRS) for association with disease severity. Random-effects meta-analyses were conducted and adjusted for cohort, sex, age at onset, and HLA-DRB1*15:01. RESULTS:A total of 7,125 MS cases were analyzed. The wGRS and GRS were not strongly associated with disease severity after accounting for cohort, sex, age at onset, and HLA-DRB1*15:01. After restricting analyses to cases with disease duration ?10 years, associations were null (p value ?0.05). No SNP was associated with disease severity after adjusting for multiple testing. CONCLUSIONS:The largest meta-analysis of established MS genetic risk variants and disease severity, to date, was performed. Results suggest that the investigated MS genetic risk variants are not associated with MSSS, even after controlling for potential confounders. Further research in large cohorts is needed to identify genetic determinants of disease severity using sensitive clinical and MRI measures, which are critical to understanding disease mechanisms and guiding development of effective treatments.

SUBMITTER: George MF

PROVIDER: S-EPMC4974846 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

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Multiple sclerosis risk loci and disease severity in 7,125 individuals from 10 studies.

George Michaela F MF Briggs Farren B S FB Shao Xiaorong X Gianfrancesco Milena A MA Kockum Ingrid I Harbo Hanne F HF Celius Elisabeth G EG Bos Steffan D SD Hedström Anna A Shen Ling L Bernstein Allan A Alfredsson Lars L Hillert Jan J Olsson Tomas T Patsopoulos Nikolaos A NA De Jager Philip L PL Oturai Annette B AB Søndergaard Helle B HB Sellebjerg Finn F Sorensen Per S PS Gomez Refujia R Caillier Stacy J SJ Cree Bruce A C BA Oksenberg Jorge R JR Hauser Stephen L SL D'Alfonso Sandra S Leone Maurizio A MA Martinelli Boneschi Filippo F Sorosina Melissa M van der Mei Ingrid I Taylor Bruce V BV Zhou Yuan Y Schaefer Catherine C Barcellos Lisa F LF

Neurology. Genetics 20160804 4

<h4>Objective</h4>We investigated the association between 52 risk variants identified through genome-wide association studies and disease severity in multiple sclerosis (MS).<h4>Methods</h4>Ten unique MS case data sets were analyzed. The Multiple Sclerosis Severity Score (MSSS) was calculated using the Expanded Disability Status Scale at study entry and disease duration. MSSS was considered as a continuous variable and as 2 dichotomous variables (median and extreme ends; MSSS of ≤5 vs >5 and MSS ...[more]

PMID: 27540591

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Project description:ObjectiveTo optimally leverage the scalability and unique features of the electronic health records (EHR) for research that would ultimately improve patient care, we need to accurately identify patients and extract clinically meaningful measures. Using multiple sclerosis (MS) as a proof of principle, we showcased how to leverage routinely collected EHR data to identify patients with a complex neurological disorder and derive an important surrogate measure of disease severity heretofore only available in research settings.MethodsIn a cross-sectional observational study, 5,495 MS patients were identified from the EHR systems of two major referral hospitals using an algorithm that includes codified and narrative information extracted using natural language processing. In the subset of patients who receive neurological care at a MS Center where disease measures have been collected, we used routinely collected EHR data to extract two aggregate indicators of MS severity of clinical relevance multiple sclerosis severity score (MSSS) and brain parenchymal fraction (BPF, a measure of whole brain volume).ResultsThe EHR algorithm that identifies MS patients has an area under the curve of 0.958, 83% sensitivity, 92% positive predictive value, and 89% negative predictive value when a 95% specificity threshold is used. The correlation between EHR-derived and true MSSS has a mean R(2) = 0.38±0.05, and that between EHR-derived and true BPF has a mean R(2) = 0.22±0.08. To illustrate its clinical relevance, derived MSSS captures the expected difference in disease severity between relapsing-remitting and progressive MS patients after adjusting for sex, age of symptom onset and disease duration (p = 1.56×10(-12)).ConclusionIncorporation of sophisticated codified and narrative EHR data accurately identifies MS patients and provides estimation of a well-accepted indicator of MS severity that is widely used in research settings but not part of the routine medical records. Similar approaches could be applied to other complex neurological disorders.

Project description:The search for the genetic foundation of multiple sclerosis (MS) severity remains elusive. It is, in fact, controversial whether MS severity is a stable feature that predicts future disability progression. If MS severity is not stable, it is unlikely that genotype decisively determines disability progression. An alternative explanation tested here is that the apparent instability of MS severity is caused by inaccuracies of its current measurement. We applied statistical learning techniques to a 902 patient-years longitudinal cohort of MS patients, divided into training (n?=?133) and validation (n?=?68) sub-cohorts, to test four hypotheses: (1) there is intra-individual stability in the rate of accumulation of MS-related disability, which is also influenced by extrinsic factors. (2) Previous results from observational studies are negatively affected by the insensitive nature of the Expanded Disability Status Scale (EDSS). The EDSS-based MS Severity Score (MSSS) is further disadvantaged by the inability to reliably measure MS onset and, consequently, disease duration (DD). (3) Replacing EDSS with a sensitive scale, i.e., Combinatorial Weight-Adjusted Disability Score (CombiWISE), and substituting age for DD will significantly improve predictions of future accumulation of disability. (4) Adjusting measured disability for the efficacy of administered therapies and other relevant external features will further strengthen predictions of future MS course. The result is a MS disease severity scale (MS-DSS) derived by conceptual advancements of MSSS and a statistical learning method called gradient boosting machines (GBM). MS-DSS greatly outperforms MSSS and the recently developed Age Related MS Severity Score in predicting future disability progression. In an independent validation cohort, MS-DSS measured at the first clinic visit correlated significantly with subsequent therapy-adjusted progression slopes (r?=?0.5448, p?=?1.56e-06) measured by CombiWISE. To facilitate widespread use of MS-DSS, we developed a free, interactive web application that calculates all aspects of MS-DSS and its contributing scales from user-provided raw data. MS-DSS represents a much-needed tool for genotype-phenotype correlations, for identifying biological processes that underlie MS progression, and for aiding therapeutic decisions.

Project description:BACKGROUND: Although there is substantial anecdotal evidence that clutter is common among people with multiple sclerosis (MS), the literature contains no reports of studies on the actual prevalence of the problem or its impact on functional performance in this population. Clutter promotes confusion and places individuals in potentially dangerous situations by increasing their risks of falling, losing medications, and misplacing important documents. In addition, it may negatively affect activities of daily living (ADLs). Many common MS symptoms such as decreased mobility, visual or cognitive changes, fatigue, and depression can exacerbate clutter accumulation, which in turn can have detrimental effects on physical, financial, emotional, cognitive, and social functioning. It is critical for MS clinicians to address clutter management in order to improve patients' overall functional independence and participation. METHODS: A clutter reduction protocol was developed and implemented at our institution for individuals with MS. Our group program addresses psychosocial issues preventing organization and offers practical strategies for clutter removal and management to improve performance in ADLs. A clutter questionnaire is administered to individuals before and after their participation in the group program. RESULTS: Anecdotal reports indicate that the intervention helped to reduce clutter, promote a more realistic attitude toward "possessions," and establish a sense of accomplishment in controlling one's environment. Participants also reported fewer falls, feeling less isolated, increased ease in finding their medications, and a general sense of cognitive clarity in accomplishing ADLs. Outcome assessments are now being developed to objectively measure these effects as well as the prevalence of clutter within the MS population. CONCLUSIONS: Clutter management is an important area for MS clinicians to address because it can significantly affect patients' functioning, safety, performance of ADLs, and quality of life.

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Multiple sclerosis risk loci and disease severity in 7,125 individuals from 10 studies.

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Multiple sclerosis risk loci and disease severity in 7,125 individuals from 10 studies.

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