Complementary roles of grey matter MTR and T2 lesions in predicting progression in early PPMS.


Tur, C; Khaleeli, Z; Ciccarelli, O; Altmann, DR; Cercignani, M; Miller, DH; Thompson, AJ; (2010) Complementary roles of grey matter MTR and T2 lesions in predicting progression in early PPMS. Journal of neurology, neurosurgery, and psychiatry. ISSN 0022-3050 DOI: 10.1136/jnnp.2010.209890

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Abstract

Objective To investigate whether T2 lesion load and magnetisation transfer ratio (MTR) in the normal-appearing white matter (NAWM) and grey matter (GM) at study entry are independent predictors of progression and whether their changes correlate with the accrual of disability, over 5 years in early primary progressive multiple sclerosis (PPMS). Methods Forty-seven patients with early PPMS and 18 healthy controls were recruited at baseline and invited to attend clinical 6-monthly assessments for 3 years, and after 5 years. Patients were scored on the Expanded Disability Status Scale and multiple sclerosis functional composite subtests (25-foot timed walk test (TWT), nine-hole peg test and paced auditory serial addition test). At each time point, all subjects underwent brain MRI including T2-weighted, magnetisation transfer and volumetric sequences. T2 lesion load (T2LL), MTR histogram parameters and volumes for NAWM and GM were calculated. Statistical analyses identified predictors of progression and correlations between MRI changes and clinical changes over time. Results Baseline T2LL and GM peak location and peak height MTR were independent predictors of progression, as measured by TWT; a model including these three predictors explained 91% of the variance of the progression on TWT, a significantly higher percentage than that obtained when the predictors were modelled individually (80%, 74% and 68%, respectively). A greater progression rate correlated with a steeper increase in T2LL and a faster decline in GM mean and peak location MTR. Conclusions The combined assessment of both visible white matter damage and GM involvement is useful in predicting progression in PPMS.

Item Type: Article
Faculty and Department: Faculty of Epidemiology and Population Health > Dept of Medical Statistics
PubMed ID: 20974648
Web of Science ID: 288384100018
URI: http://researchonline.lshtm.ac.uk/id/eprint/2350

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