Neurobiology of Disease & Regeneration | Methods for Diagnostic & Therapeutic Studies | Multiple Sclerosis & Related Disorders
Frequent early relapses and long term disease evolution of multiple sclerosis
Antonio Scalfari*, Anneke Neuhaus, Martin Daumer, Paolo A Muraro, Paolo Giannetti, George Ebers
*Corresponding author: Antonio Scalfari
Centre for Neuroscience, Division of Experimental Medicine, Imperial College, London, UK
F1000Posters 2011, 2: 831 (poster) [ENGLISH]
Poster [655.82 KB]
21st Meeting of the European Neurological Society 2011, 28 - 31 May 2011, P419
The predictive effect of relapses is limited to the first 2 years of the disease (early relapses). Here we assessed the long-term evolution of those with high frequency (>/- 3 attacks).
We evaluated the risk of becoming disabled according to the duration of the relapsing remitting (RR) phase (latency to progression). Those with a shorter duration of the RR phase attained disability endpoints in significantly shorter times. This effect largely disappeared when tested from onset of progression.
Time to onset of SP exerted a similar predictive effect even in groups with the same number of early relapses, accounting for the variability of the outcome among patients sharing the same clinical features. Patients with frequent early relapses (≥ 3 attacks; N= 158) streamed into two 2 sub populations: 103 entered the SP phase and reached DSS 6-8-10 on average in 8.9, 15.2 and 20.5 years respectively. However, the remaining 55 patients, despite having the same early relapse frequency (1.92 attack/year), never converted to SP MS. The latency of progression strongly predicts late disability accumulation.
Two extreme disability outcomes among patients with high early relapse frequency suggest that selective vulnerability of axons might be controlled by other factors than inflammatory mechanisms.
No relevant conflicts of interest declared.
Please note that most posters on this site present work that is preliminary in nature and has not been peer reviewed.
This poster is open access subject to the CC BY-NC Creative Commons 3.0 License