Synopsis

Quantitative MRI provides objective non-invasive biomarkers for muscle pathology in muscular dystrophy disorders. In this work we show that MR biomarkers for muscular fat infiltration and atrophy accurately reflect clinical outcomes for disease severity and physical capacity in myotonic dystrophy type 1 (DM1) patients. Furthermore, we found that 37% of the variation in fat infiltration in DM1 patients was explained by age. Interestingly, an additional 9.7% of the variation in fat infiltration was associated with the over life time increase in the DMPK CTG repeat length, i.e. the genetic defect causing DM1.

Purpose

Methods

Subjects: We included 33 DM1 patients (18 men) with an average age of 45 years (range: 19-72y).

Data acquisition

MR acquisition: Subjects were examined using a 3T Siemens MR system and spine/phased array coil combination placed around the right or left lower extremity. We acquired a 3D DIXON sequence (TR=10ms; FA=3°; FOV=256x192mm; voxel size=1x1x5mm; number slices=32; 2pt-Dixon: TE1/TE2=2.45/3.675ms; 3pt-Dixon: TE1/TE2/TE3=2.31/3.68/5.07ms). This resulted in a reconstructed fat (F) and water image (W).

Disease severity and physical capacity: Disease severity was assessed by the DM1-specific muscle impairment rating score (MIRS)⁸. Physical capacity was tested by performing a six minute walking test (6MWT) and in 21 DM1 patients it was also assessed objectively with an accelerometer that was worn for two weeks. This accelerometer recorded the average activity over 24 hours, the most active and the least active five hours.

DMPK CTG repeat length: Blood samples were taken to determine the average CTG repeat length at time of visit (CTG_visit) and inherited CTG repeat length (CTG_inherited) of the expanded allele.⁹ Somatic instability (CTG_diff) was determined as the difference between CTG_visit and CTG_inherited.

Post-processing MRI

A fat fraction map (FF_map) was calculated voxel-wise as F/(F+W). Twelve thigh and eight calf muscles were manually delineated on five distal, middle and proximal slices of the 3D Dixon (Fig1), avoiding contamination of subcutaneous fat. For each muscle, the fat fraction (FF) was calculated as the average over all voxels on the FF_map and muscle volume (MV) as the number of voxels multiplied by the voxel volume. Thereafter, contractile muscle volume (cMV), i.e. the remaining muscle tissue being still able to contract, was calculated as muscle volume times the muscle fraction (1-FF). For each subject, the individual muscle outcomes were combined to a value for the whole lower extremity. We determined the average over all muscle for FF, while we took the sum of all muscles for MV and cMV.

Statistics

FF and cMV were correlated with MIRS, 6MWT, and accelerometer data using a Pearson or Spearman correlation. Furthermore, multivariate linear regression was applied to explain the variation in FF using age, CTG_visit, CTG_inherited, and CTG_diff.

Results

Quantitative MRI vs. disease severity and physical capacity (Fig2): MIRS only correlated significantly with FF (r=0.537; p=0.005), while 6MWT correlated significantly with both FF (r=-0.553; p=0.003) and cMV (r=0.403; p=0.041). Interestingly, we observe one outlier, where the patient had a very low 6MWT (50m), with a relatively normal FF and cMV (12.5% and 938 cm3). For the accelerometer data, average activity over 24 hours and most active five hours correlated significantly with FF (24h: r=-0.802, p<0.001; 5h: r=-0.790, p<0.001) and cMV (24h: r=0.541, p=0.011; 5h: r=-0.545, p=0.011), while, as expected, no correlation was found with the least active 5 hours.

Quantitative MRI vs age and DMPK CTG repeat length (Fig3, Table 1): Linear regression models showed that age is the main predictor of FF (r²=0.377, p=0.001), while CTG_visit, CTG_inherited, and CTG_diff did not explain any variation in FF. However, if CTG_diff is added to age it significantly explains an additional 9.7% of variation in FF (p=0.043).

Conclusion

Acknowledgements

We would like to thank all patients for their time and effort. This work was funded by the European Community's Seventh Framework Programme (FP7/2007– 2013), grant number 305697.

References

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