TY - JOUR
T1 - Quantitative muscle MRI as an assessment tool for monitoring disease progression in LGMD2I
T2 - A Multicentre Longitudinal Study
AU - Willis, Tracey A
AU - Hollingsworth, Kieren G
AU - Coombs, Anna
AU - Sveen, Marie-Louise
AU - Andersen, Søren Peter
AU - Stojkovic, Tanya
AU - Eagle, Michelle
AU - Mayhew, Anna
AU - de Sousa, Paulo L
AU - Dewar, Liz
AU - Morrow, Jasper M
AU - Sinclair, Christopher D J
AU - Thornton, John S
AU - Bushby, Kate
AU - Lochmüller, Hanns
AU - Hanna, Michael G
AU - Hogrel, Jean-Yves
AU - Carlier, Pierre G
AU - Vissing, John
AU - Straub, Volker
PY - 2013/8/14
Y1 - 2013/8/14
N2 - Background:Outcome measures for clinical trials in neuromuscular diseases are typically based on physical assessments which are dependent on patient effort, combine the effort of different muscle groups, and may not be sensitive to progression over short trial periods in slow-progressing diseases. We hypothesised that quantitative fat imaging by MRI (Dixon technique) could provide more discriminating quantitative, patient-independent measurements of the progress of muscle fat replacement within individual muscle groups.Objective:To determine whether quantitative fat imaging could measure disease progression in a cohort of limb-girdle muscular dystrophy 2I (LGMD2I) patients over a 12 month period.Methods:32 adult patients (17 male;15 female) from 4 European tertiary referral centres with the homozygous c.826C>A mutation in the fukutin-related protein gene (FKRP) completed baseline and follow up measurements 12 months later. Quantitative fat imaging was performed and muscle fat fraction change was compared with (i) muscle strength and function assessed using standardized physical tests and (ii) standard T1-weighted MRI graded on a 6 point scale.Results:There was a significant increase in muscle fat fraction in 9 of the 14 muscles analyzed using the quantitative MRI technique from baseline to 12 months follow up. Changes were not seen in the conventional longitudinal physical assessments or in qualitative scoring of the T1w images.Conclusions:Quantitative muscle MRI, using the Dixon technique, could be used as an important longitudinal outcome measure to assess muscle pathology and monitor therapeutic efficacy in patients with LGMD2I.
AB - Background:Outcome measures for clinical trials in neuromuscular diseases are typically based on physical assessments which are dependent on patient effort, combine the effort of different muscle groups, and may not be sensitive to progression over short trial periods in slow-progressing diseases. We hypothesised that quantitative fat imaging by MRI (Dixon technique) could provide more discriminating quantitative, patient-independent measurements of the progress of muscle fat replacement within individual muscle groups.Objective:To determine whether quantitative fat imaging could measure disease progression in a cohort of limb-girdle muscular dystrophy 2I (LGMD2I) patients over a 12 month period.Methods:32 adult patients (17 male;15 female) from 4 European tertiary referral centres with the homozygous c.826C>A mutation in the fukutin-related protein gene (FKRP) completed baseline and follow up measurements 12 months later. Quantitative fat imaging was performed and muscle fat fraction change was compared with (i) muscle strength and function assessed using standardized physical tests and (ii) standard T1-weighted MRI graded on a 6 point scale.Results:There was a significant increase in muscle fat fraction in 9 of the 14 muscles analyzed using the quantitative MRI technique from baseline to 12 months follow up. Changes were not seen in the conventional longitudinal physical assessments or in qualitative scoring of the T1w images.Conclusions:Quantitative muscle MRI, using the Dixon technique, could be used as an important longitudinal outcome measure to assess muscle pathology and monitor therapeutic efficacy in patients with LGMD2I.
U2 - 10.1371/journal.pone.0070993
DO - 10.1371/journal.pone.0070993
M3 - Journal article
C2 - 23967145
SN - 1932-6203
VL - 8
SP - 1
EP - 7
JO - PLoS Computational Biology
JF - PLoS Computational Biology
IS - 8
M1 - e70993
ER -