A Risk Score for Predicting Multiple Sclerosis.
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).


CONCLUSIONS
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.
Queen Mary University London; Blizard Institute, Barts and the London School of Medicine and Dentistry, London, United Kingdom.
Ruth
R
Dobson
Queen Mary University London; Blizard Institute, Barts and the London School of Medicine and Dentistry, London, United Kingdom.
Sreeram
S
Ramagopalan
Queen Mary University London; Blizard Institute, Barts and the London School of Medicine and Dentistry, London, United Kingdom.
Joanne
J
Topping
Royal London Hospital, Barts Health NHS Trust, London, United Kingdom.
Paul
P
Smith
NMR Research Unit, Queen Square Multiple Sclerosis Centre, University College London (UCL) Institute of Neurology, London, United Kingdom.
Bhavana
B
Solanky
Queen Mary University London; Blizard Institute, Barts and the London School of Medicine and Dentistry, London, United Kingdom.|Royal London Hospital, Barts Health NHS Trust, London, United Kingdom.
Klaus
K
Schmierer
NMR Research Unit, Queen Square Multiple Sclerosis Centre, University College London (UCL) Institute of Neurology, London, United Kingdom.|National Institute for Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre, London, United Kingdom.
Declan
D
Chard
Queen Mary University London; Blizard Institute, Barts and the London School of Medicine and Dentistry, London, United Kingdom.|Royal London Hospital, Barts Health NHS Trust, London, United Kingdom.
Gavin
G
Giovannoni
D000293:Adolescent; D000328:Adult; D000368:Aged; D000483:Alleles; D005260:Female; D005787:Gene Frequency / Q000235:genetics; D020022:Genetic Predisposition to Disease / Q000235:genetics*; D005820:Genetic Testing / Q000379:methods; D005838:Genotype; D059811:HLA-DRB1 Chains / Q000235:genetics; D006801:Humans; D008297:Male; D008875:Middle Aged; D009103:Multiple Sclerosis / Q000235:genetics*; D016017:Odds Ratio; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D012307:Risk Factors; D055815:Young Adult
D059811:HLA-DRB1 Chains