UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

A Risk Score for Predicting Multiple Sclerosis

Dobson, R; Ramagopalan, S; Topping, J; Smith, P; Solanky, B; Schmierer, K; Chard, D; (2016) A Risk Score for Predicting Multiple Sclerosis. PLoS ONE , 11 (11) , Article e0164992. 10.1371/journal.pone.0164992. Green open access

[img]
Preview
Text
Dobson_Risk_Score_Predicting_Multiple_Sclerosis.PDF - Published version

Download (1MB) | Preview

Abstract

OBJECTIVE: Multiple sclerosis (MS) develops as a result of environmental influences on the genetically susceptible. Siblings of people with MS have an increased risk of both MS and demonstrating asymptomatic changes in keeping with MS. We set out to develop an MS risk score integrating both genetic and environmental risk factors. We used this score to identify siblings at extremes of MS risk and attempted to validate the score using brain MRI. METHODS: 78 probands with MS, 121 of their unaffected siblings and 103 healthy controls were studied. Personal history was taken, and serological and genetic analysis using the illumina immunochip was performed. Odds ratios for MS associated with each risk factor were derived from existing literature, and the log values of the odds ratios from each of the risk factors were combined in an additive model to provide an overall score. Scores were initially calculated using log odds ratio from the HLA-DRB1*1501 allele only, secondly using data from all MS-associated SNPs identified in the 2011 GWAS. Subjects with extreme risk scores underwent validation studies. MRI was performed on selected individuals. RESULTS: There was a significant difference in the both risk scores between people with MS, their unaffected siblings and healthy controls (p<0.0005). Unaffected siblings had a risk score intermediate to people with MS and controls (p<0.0005). The best performing risk score generated an AUC of 0.82 (95%CI 0.75–0.88). INTERPRETATIONS: The risk score demonstrates an AUC on the threshold for clinical utility. Our score enables the identification of a high-risk sibling group to inform pre-symptomatic longitudinal studies.

Type: Article
Title: A Risk Score for Predicting Multiple Sclerosis
Open access status: An open access version is available from UCL Discovery
DOI: 10.1371/journal.pone.0164992
Publisher version: http://dx.doi.org/10.1371/journal.pone.0164992
Language: English
Additional information: Copyright © 2016 Dobson et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Neuroinflammation
URI: https://discovery.ucl.ac.uk/id/eprint/1528436
Downloads since deposit
43Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item