Mohammed Salman Shazeeb1, Zeynep Vardar1, Ahmet Peker1, Anna Kuhn1, Jean Johnston2, Precilla D'Souza2, Maria T. Acosta2, and Cynthia J. Tifft2
1University of Massachusetts Medical School, Worcester, MA, United States, 2National Institutes of Health, Bethesda, MD, United States
Synopsis
GM1-gangliosidosis is a rare heritable lysosomal storage disorder caused
by accumulation of GM1-ganglioside due
to deficiency of the lysosomal enzyme b-galactosidase
required for sphingolipid degradation. Progressive accumulation of GM1-ganglioside
in the central nervous system induces hypomyelination that results in
progressive neurodegeneration. This study used quantitative diffusion tensor
imaging (DTI) in 20 type II GM1 patients to assess white matter tract changes
in the internal capsule and corpus callosum of juvenile and late infantile patients.
Several DTI parameters showed significant differences between the patient
groups and compared to healthy subjects that may be important to track the progression
of the disease.
Introduction
The gangliosidoses are rare heritable lysosomal storage disorders caused
by the accumulation of sphingolipid metabolites (GM1- or GM2-ganglioside) due to defects in the enzymes required for sphingolipid degradation1.
GM2 gangliosidosis affects the central nervous system (CNS) while GM1 gangliosidosis (GM-1) affects both the CNS and other
organ systems. Progressive accumulation of GM1- and GM2-ganglioside in the CNS
induces hypomyelination and results in progressive neurodegeneration2.
GM1 is a disease continuum but for convenience is divided into three groups
based on the age of onset and severity of the symptoms: type I (infantile),
type II (late infantile and juvenile sub-types), and type III (adult)3.
Type I (infantile) is the most severe, characterized by severe CNS dysfunction with
death by 2 to 4 years of age3. Late infantile has onset of symptoms
between 1 and 3 years of age and life expectancy into the second decade. The
juvenile subtype features symptom onset at 4-5 years and life expectancy can
reach the third decade. Type III is the less severe form of the disease with
symptom onset in teens to the third decade. For type III patients, life
expectancy is variable but usually shorter than unaffected relatives2.
Hypomyelination4,
basal ganglia signal intensity changes5, cerebellar and cerebral
atrophy6 have been reported on MRI in patients with GM1. Although
hypomyelination has been documented in GM1 patients with conventional MRI,
quantitative analyses like longitudinal diffusion tensor imaging (DTI) studies
are lacking. Fractional anisotropy (FA), mean diffusivity (MD), radial
diffusivity (RD), and axial diffusivity (AD) are DTI parameters associated with
maturational changes and myelin pathologies7. In this study, we
quantified and tracked brain DTI changes longitudinally on specific brain
structures at different time-points to assess disease progression in a natural
history cohort of GM1 patients. Quantitative DTI can be used as a non-invasive biomarker
of disease progression for treatment trials. Methods
Twenty
type II GM1 patients were included in this study. Thirteen juvenile patients (mean
age 14 ± 6 years) and 7 late infantile patients (mean age 6 ± 2 years). Brain
MRI was performed using a Philips Achieva 3T system (Philips Healthcare, Best, the
Netherlands) equipped with an 8-channel SENSE head coil (Philips Healthcare).
DTI images were acquired with the following parameters: TR/TE = 6400/100 ms, 30-gradient
directions, b-values = 0 and 1000 s/mm2 , slice thickness = 2.5 mm,
acquisition matrix = 128 × 128, NEX = 1, FOV = 24 cm. FA, MD, RD, and AD maps
were generated with DSI studio (http://dsi-studio.labsolver.org). To further
quantify the changes in sub-regional white matter structures, the following
regions of interest (ROIs) were selected: genu and splenium of the corpus
callosum, and the anterior and posterior limbs of the internal capsule. The
ROIs were manually delineated by 2 radiologists (Fig. 1). FA, MD, RD, and AD values
were quantified for each of the aforementioned structures and tracked over time
for patients that had multiple imaging time-points. Comparison of all DTI
parameters were made between the juvenile and late infantile patients (regardless
of time-point) for each of the DTI parameters using 2-tailed non-parametric
Mann-Whitney t-test. Significant differences were considered for p<0.05.
Some DTI parameters were also compared to healthy controls in the published
literature.Results and Discussion
Juvenile
patients showed similar white matter tracts and DTI quantifications as healthy
subjects (Fig. 1A and Fig. 2) while late infantile patients even at the same
age showed different stages of white matter maturation as noted in the corpus
callosum and frontal white matter (Fig. 1B and 1C). Quantitative analysis also
demonstarted higher FA values in the corpus callosum region in patient 14 (Fig.
1B and Fig. 3) compared to patient 15 (Fig. 1C and Fig. 3). All the DTI
parameters were quantified juvenile (Fig. 2 ) and late infantile (Fig. 3)
patients. Comparison of FA and RD values in the internal capsule and corpus
callosum showed significantly higher FA and lower RD values in the juvenile
patients compared to the late infantile patients (Fig. 4); an indicator of
better myelinated fibers8 in the juvenile patients. AD and MD
(indicators of axonal function and overall diffusivity, respectively) also
showed significant differences in some of the measured regions between juvenile
and late infantile patients. When comparing the GM-1 patients to healthy
age-matched subjects9,10, late infantile patients exhibited lower FA
values while juvenile patients showed similar FA values in the anterior and
posterior limbs of the internal capsule and the genu and splenium of the corpus
callosum (Fig. 5).Conclusion
To date, most of
the MRI data on GM1-patients primarily rely on qualtitative assessments. Quantitative
measurements using DTI can better assess white matter changes not only to track
disease progression but also to evaluate the effects of treatment strategies
over time. To our knowledge, this study is the first study to demonstrate the
utility of DTI in assessing longitudinal changes in the white matter tracts of
internal capsule and corpus callosum in type II GM1-patients.Acknowledgements
This study was
partly funded by Sio Gene Therapies.References
1. Bisel B, Pavone FS, Calamai M. GM1 and GM2
gangliosides: recent developments. Biomol
Concepts. 2014;5(1):87-93.
2. Regier DS, Proia RL, D'Azzo A, et al. The GM1 and GM2
Gangliosidoses: Natural History and Progress toward Therapy. Pediatr Endocrinol Rev. 2016;13 Suppl
1(Suppl 1):663-73.
3. Regier DS, Tifft CJ, Rothermel CE. GLB1-Related Disorders. 2013 Oct
17 [updated 2021 Apr 22]. In: Adam MP, Ardinger HH, Pagon RA, et al, eds.
GeneReviews® [Internet]. Seattle (WA): University of
Washington, Seattle; 1993–2021.
4. Steenweg ME, Vanderver A, Blaser S, et al. Magnetic resonance
imaging pattern recognition in hypomyelinating disorders. Brain. 2010;133(10):2971-82.
5. De Grandis E, Di Rocco M, Pessagno A, et al. MR Imaging Findings in
2 Cases of Late Infantile GM1 Gangliosidosis. AJNR Am J Neuroradiol.
2009;30(7):1325.
6. Nestrasil I, Ahmed A, Utz JM, et al. Distinct progression patterns
of brain disease in infantile and juvenile gangliosidoses: Volumetric
quantitative MRI study. Mol Genet Metab. 2018;123(2):97-104.
7. Miller JH, McKinstry RC, Philip JV, et al. Diffusion-Tensor MR
Imaging of Normal Brain Maturation: A Guide to Structural Development and
Myelination. AJR Am J Roentgenol. 2003;180(3):851-859.
8. Chang EH, Argyelan M, Aggarwal M, et al. The role of myelination in
measures of white matter integrity: Combination of diffusion tensor imaging and
two-photon microscopy of CLARITY intact brains. Neuroimage. 2017;147:253-261.
9. Hermoye L, Saint-Martin C, Cosnard G, et al. Pediatric diffusion
tensor imaging: normal database and observation of the white matter maturation
in early childhood. Neuroimage. 2006;29(2):493-504.
10. Bonekamp D, Nagae LM, Degaonkar M, et al. Diffusion tensor imaging
in children and adolescents: reproducibility, hemispheric, and age-related
differences. Neuroimage. 2007;34(2):733-42.