Keywords
electromyography - kinematics - kinetics - mucopolysaccharidoses - three-dimensional
gait evaluation
Introduction
Mucopolysaccharidoses (MPS) are rare genetic syndromes constituting the largest group
of lysosomal storage diseases. Affected subjects present abnormal intracellular micromolecule
degradation by lysosomal enzymes, leading to the intracellular accumulation of semi-degraded
compounds called glycosaminoglycans (GAG).[1]
Its general incidence is estimated as 1 in every 25,000 live births, varying according
to country and ethnic origin. The most common MPS are the Morquio (MPS IV) and Hurler
(MPS I) syndromes.[2]
[3] In Brazil, the most common subtypes are MPS II (Hunter syndrome) and VI (Maroteaux-Lami
syndrome).[4]
There are specific mutations in each type of MPS[2]
[5] resulting in a characteristic accumulation of GAGs and allowing MPS classification
into seven types.
The clinical picture is quite diverse, involving multiple organs and systems, presenting
different phenotypes and a range of functionality and severity; it can be fatal in
some cases.[6]
[7] Limited ambulation and reduced resistance due to cardiopulmonary disease, joint
stiffness, contractures, pain, and skeletal deformities are significant and progressive
issues for many of these patients.
The diagnosis of MPS relies on GAG level, enzymatic activity, and genotype determination.[8] Chorionic villi and amniotic fluid cell samples establish prenatal diagnosis in
fetuses.[2]
Enzyme replacement therapy (ERT) and hematopoietic stem cell transplantation (HSCT)
are the most common treatment options. Furthermore, several surgical techniques are
used to treat musculoskeletal changes.
Few studies obtained appropriate functional measurements to monitor the MPS course
and evaluate the impact of current therapeutic options. These studies highlight the
difficulty in carrying out traditional performance tests in this population, including
resistance and strength tests using treadmills, exercise bikes, and dynamometry. This
partly occurs because MPS patients present reduced limb size and stature, joint diseases,
limited strength, and a broad clinical spectrum. Assessing these patients is more
effective using tests incorporating daily activities such as walking.[9] In these cases, an alternative is computerized gait analysis.[10]
The importance of this tool motivated the development of indices to synthesize gait
analysis data, facilitating its understanding.[11] Two of these data, the gait profile scale (GPS) and the gait deviation index (GDI)
are global measures of gait variability and are sensitive to detect relevant changes
in the deambulation of patients with orthopedic and neurological disorders.[12]
[13]
The objective of the present study was to evaluate and determine, through instrumented
three-dimensional (3D) gait analysis, the kinetic, kinematic, and electromyographic
(EMG) profile of patients with MPS IV and VI in a reference service for rare diseases.
Material and Methods
The Research Ethics Committee of the proposing Institution approved this cross-sectional,
descriptive, and analytical study under number CAAE 40847720.5.0000.5208. The 3D gait
analysis examination used the BTS-Gaitlab Hardware (BTS Bioengineering, Milan, Italy),
and data analysis employed Excel (Microsoft Corp. Redmond, WA, USA) version 2301 and
the Statistical Package Social Sciences (SPSS IBM Corp. Armonk, NY, USA) version 22.
The study included 11 patients with MPS IV and VI evaluated from June 2020 to January
2021. The choice for both types of MPS occurred because affected subjects typically
have better cognitive capacity, facilitating the test. Additionally, these are the
most frequent MPS types monitored at the partner service.
The inclusion criteria were patients aged 4 years and older, diagnosed with MPS IV
or VI, deambulatory, and treated at the partner service who agreed to participate
in the study after invitation, with the patient or legal guardian having signed the
informed consent form (ICF). Those with confirmation of other potential conditions,
severe heart diseases, and cognitive, visual, or auditory deficits that compromised
test application were excluded from the study.
We evaluated the following epidemiological variables: age, gender, disease classification,
other conditions, and use of orthoses. The instrumented 3D gait analysis occurred
per the following standardization. First, we collected anthropometric data, including
weight, height, distance between the anterior superior iliac spines, pelvic depth,
length of the lower limbs, and width of the knees and ankles. Next, we defined the
BTS-Gaitlab protocol to carry out the test. For convenience and suitability for patients,
especially concerning knee valgus deviations, we adopted the Helen Hayes or Modified
Helen Hayes protocol for some subjects and the Davis protocol for others ([Fig. 1]). Both protocols provide the same variables of interest.
Fig. 1 (a) Front and back view of the modified Helen Hayes protocol. (b) Helen Hayes protocol markers placed on the model. (c) Patient performing gait analysis using the Helen Hayes protocol. (d) Front and back view of the Davis Protocol. (e) Davis protocol markers placed on the model. (f) Patient performing gait analysis using the Davis protocol.
Reflective markers for movement capturing were placed in anatomical locations defined
by the chosen protocol. The same occurred for the EMG sensors, using the rectus femoris,
semitendinosus, gastrocnemius, and anterior tibialis muscles as anatomical references.
After placing the markers and sensors, we asked the patients to perform two different
tasks, for example, static and dynamic acquisition. For the static acquisition, patients
remained in an orthostatic position for approximately 5 seconds on the force platforms.
This protocol calculates joint angles and performs a 3D reconstruction of the patient
to provide static data. For the dynamic acquisition, the patients walked naturally
on the laboratory's track with six digital 3D force platforms to capture their ground
reaction forces. Ten high-resolution, high-frequency infrared cameras captured information
from the reflective markers to determine joint position and movement during gait.
The EMG sensors captured the activity of the chosen muscles. Each patient performed
approximately 18 repetitions. The data was recorded and transmitted to a computer
via Bluetooth for processing and generation of a final report. On average, each complete
exam lasted one hour.
The spatiotemporal variables of interest were cadence, speed (m/s), average speed
(percentage of height/s), step length, stride length (m), stride length (percentage
of height), width step, stance phase, swing phase, double stance, single stance, stride
time, stance time and swing time. The hip assessment included inclination (pelvic
tilt), obliquity, rotation, hip flexion-extension, and abduction-adduction angles.
The range of motion evaluated knee (flexion-extension angle) and ankle (ankle dorsal
plantar flexion) deviations. The progression angle determined foot deviations. Both
GPS and GDI analyzed the general condition and gait quality.
We compared some analyzed variables with normative data to better understand potential
movement compensation activities in different anatomical planes.
Regarding EMG data, we use its activation as a reference, i.e., whether the muscles
were active or not (on-off) and during which phases of the gait cycle, as proposed
by Sutherland.[13]
The initial data analysis consisted of descriptive statistics, mean, standard deviation,
absolute, and percentage frequency, as well as the validated gait analysis protocols
integrated into BTS-Gaitlab. Statistical tests identified significant differences
between the observed variables and the system's normality parameters. Non-parametric,
Wilcoxon, and Mann-Whitney tests were used as an exact method for small samples. The
5% significance value indicated statistically significant correlation coefficients.
Results
The present study included 11 patients with MPS ([Table 1]), of which 5 (45.5%) were female and 6 (54.5%) were male. None of the patients used
orthoses. Furthermore, 9 (81.8%) have MPS VI, and 2 (18.2%) IV. The patients' ages
ranged from 4 to 43 years, with an average of 14.6.
Table 1
|
Variable
|
N (%)
|
|
Gender
|
|
|
Female
|
6 (54.5)
|
|
Male
|
5 (45.5)
|
|
MPS
|
|
|
IV
|
2 (18.2)
|
|
VI
|
9 (81.8)
|
|
Protocol type
|
|
|
Helen Hayes
|
6 (54.5)
|
|
Davis Heel
|
5 (45.5)
|
|
Variable
|
Mean (SD)
|
|
Weight (kg)
|
31.5 (13.1)
|
|
Height (cm)
|
113.6 (12.5)
|
|
Age (years)
|
14.6 (11.7)
|
Regarding temporal parameters, the average speed (m/s) and average speed (percentage
of height/s) were below the normality parameter. The average values of the other temporal
parameters evaluated had little change or values within normal limits. As for spatial
parameters, a mean stride length lower than the reference value is worth mentioning.
[Fig. 2] and [Table 2] show this data.
Fig. 2 Mean values from each temporal parameter variable.
Table 2
|
Standard value
|
Right limb
|
Left limb
|
|
Temporal parameters
|
|
|
|
|
Stride time (s)
|
1.10 (0.09)
|
1.01* (0.17)
|
1.02* (0.16)
|
|
Stance time (s)
|
0.65 (0.07)
|
0.64 (0.11)
|
0.63 (0.13)
|
|
Swing time (s)
|
0.44 (0.05)
|
0.38* (0.06)
|
0.40* (0.04)
|
|
Stance phase (%)
|
58.98 (1.97)
|
64.11* (5.48)
|
61.62* (3.41)
|
|
Swing phase (%)
|
40.03 (3.56)
|
38.75 (6.49)
|
39.38 (3.11)
|
|
Single stance (%)
|
38.87 (2.57)
|
40.41 (5.72)
|
37.72 (4.02)
|
|
Double stance (%)
|
10.27 (3.09)
|
11.83 (2.35)
|
13.60* (4.40)
|
|
Mean speed (m/s)
|
1.20 (0.20)
|
0.68* (0.21)
|
|
Mean speed (height %/s)
|
80.00 (5.00)
|
60.05* (19.98)
|
|
Cadence (steps/min):
|
114.00 (4.20)
|
121.85* (18.02)
|
|
Spatial parameters ciais
|
|
|
|
Stride length (m)
|
1.36 (0.11)
|
0.66* (0.15)
|
0.67* (0.17)
|
|
Stride length (height %)
|
80.00 (10.00)
|
58.29* (14.20)
|
59.27* (15.32)
|
|
Step length (m)
|
0.62 (0.05)
|
0.35* (0.09)
|
0.33* (0.09)
|
|
Step width (m)
|
0.08 (0.05)
|
0.14* (0.07)
|
|
Static angles
|
|
|
|
Pelvic obliquity (°)
|
0.00 (1.00)
|
4.74* (12.60)
|
−0.46* (4.00)
|
|
Pelvic tilt (°)
|
10.00 (4.00)
|
13.85* (7.60)
|
13.85* (7.60)
|
|
Pelvic rotation (°)
|
0.00 (5.00)
|
1.23 (5.13)
|
−1.23 (5.13)
|
|
Hip abduction-adduction (°)
|
0.00 (3.00)
|
−6.51* (8.46)
|
−8.74* (7.64)
|
|
Hip flexion-extension (°)
|
10.00 (4.00)
|
17.25* (13.65)
|
20.32* (14.72)
|
|
Hip rotation (°)
|
0.00 (5.00)
|
3.45 (12.02)
|
3.05 (9.71)
|
|
Knee flexion-extension (°)
|
5.00 (5.00)
|
13.25 (17.17)
|
17.28* (14.31)
|
|
Ankle dorsal-plantar flexion (°)
|
0.00 (5.00)
|
−2.46* (38.64)
|
−6.61* (41.77)
|
|
Foot progression (°)
|
−10.00 (5.00)
|
−26.59* (15.40)
|
−16.55* (13.51)
|
|
GPS
|
|
|
|
|
GPS (°)
|
< 7
|
14.85* (6.72)
|
11.71* (3.39)
|
|
GVS
|
|
|
|
Pelvic obliquity (°)
|
|
4.50 (1.87)
|
4.94 (1.86)
|
|
Pelvic tilt (°)
|
|
7.86 (6.57)
|
7.85 (6.58)
|
|
Pelvic rotation (°)
|
|
6.06 (4.33)
|
6.34 (4.03)
|
|
Hip abduction-adduction (°)
|
|
8.22 (4.41)
|
9.57 (5.86)
|
|
Hip flexion-extension (°)
|
|
13.18 (5.87)
|
13.65 (6.89)
|
|
Hip rotation (°)
|
|
9.75 (5.36)
|
9.90 (4.74)
|
|
Knee flexion-extension (°)
|
|
15.38 (7.73)
|
12.99 (7.02)
|
|
Ankle dorsal-plantar flexion (°)
|
|
13.19 (13.65)
|
13.33 (11.87)
|
|
Foot progression (°)
|
|
14.04 (8.80)
|
10.64 (5.68)
|
|
GDI
|
> 100
|
73.23* (14.50)
|
80.45* (17.05)
|
In the evaluation of static angles ([Table 2]), the hips showed an average flexion above the normal value ([Fig. 3]). The range of motion of the knees also demonstrated important changes, reaching
a maximum individual flexion value of 56.8° in the right knee and 48.9° in the left
one. Most patients (54.5%) presented a gait pattern with increased hip flexion-adduction
and knee flexion.
Fig. 3 (a) Patient during the static examination (standing) with Helen Hayes MM protocol in
the gait laboratory; (b) The 3D reconstruction at BTS SMART-Clinic. Note the pattern of hip flexion.
The mean GPS showed a significant deviation from the normality parameter. Regarding
the gait variable score (GVS), hip flexion-extension and rotation, the range of movement
of the knees and ankles, and the foot progression angle were the highest average values
and were the major contributors to the elevation of the GPS value. All patients had
a GDI below 100, and the lowest individual value was 59.67 ([Table 2] and [Fig. 4]).
Fig. 4 The BTS motion analysis graphs of different patients from the study. Note the deviation
of the lower limb curves concerning the normality parameter in all images.
The EMG data consisted of the activities of the rectus femoris, anterior tibiae, gastrocnemius
(medial head), and semitendinosus muscles of 10 patients. One subject decided to undergo
the examination without EMG.
Our results demonstrated intervals of muscular activity similar to the usual pattern.
However, there was high variability between patients. Different activation modalities
were captured and observed even in consecutive strides of a same subject. There were
variations in the number of activations, onset time, and signal displacement. The
muscles most activated during the gait cycle were the tibialis anterior (more active
in 60% of patients) and the gastrocnemius. Muscle activation was asymmetric when comparing
limbs from the same patient in 10% of cases.
Discussion
Skeletal deformities are striking features present in different types of MPS. Angular
defects, such as knee and ankle valgus, progressive hip dysplasia, and joint laxity/hypermobility
are examples of changes resulting in gait dysfunction in these patients,[14] making walking and balance difficult and causing greater functional dependence in
daily activities.[15]
Matos et al.[16] studied 19 patients with MPS and showed that gait dysfunctions are the main complaints
reported by this group and one of the major causes of functional disability in this
population.
Human mobility represents a fundamental requirement for an adequate quality of life.
The ability to walk is a general health indicator as it denotes autonomy.[17] Since walking is a basic requirement for many daily activities, gait analysis provides
critical information about people's functional capacity.
The gait pattern of subjects has become an area of great interest. Gait analysis is
widely used to diagnose diseases and evaluate therapeutic plans and the prognosis
of several conditions.[18] Deviations from the typical gait pattern are frequent in neurological, muscular,
and skeletal diseases.[19]
Each MPS type has a wide range of clinical manifestations.[4]
[20] Furthermore, the same subtype can present several abnormal osteoarticular patterns
and, therefore, gait patterns. The 3D assessment of these patients allows for a better
understanding of the natural history of the disease in its different phenotypes.
The average speed and, mainly, stride length were below normal in our study, consistent
with Salazar-Torres.[21] This finding indicates a higher metabolic demand for gait, as demonstrated by Kimoto
et al.[22]
Support time shows a fundamental correlation with stability and balance. High stance
times represent a decrease in patients' ability to balance.[23] The average stance time in our study was normal, demonstrating that these patients
have good stability during walking.
Another key aspect observed was the asymmetry between the values obtained for each
member. The parameters with the most differences between limbs during walking were
the knees' range of movement and the foot's progression angle. Asymmetry was also
noted in temporal values and, to a lesser extent, in EMG data. It is known that the
greater the asymmetry of the lower limbs during walking, the greater the energy consumption.[24]
Most of our patients presented a pattern of flexion and adduction of the hips and
flexion of the knees. Changes in these joints are well described in both the Maroteaux-Lamy
and Morquio syndromes. It is known that, especially in the hips and knees of these
patients, there may be impairment from the first years of life.[25]
The GPS and GDI indices represent a summary of the general gait quality, facilitating
the comparison between pathological and normal deambulation. Massad et al.[26] described GDI as a reliable measure for gait assessment even in a single session.
In our study, both these score values showed an important deviation from normality.
The EMG data demonstrated intervals of muscular activity approximately following the
current model. However, it is worth highlighting the high variability of this tests'
patterns between patients, consistent with Agostini et al.[27] A limiting factor in the evaluation of these data in our study was that 54.5% of
the patients were children, of which 45.5% ranged from 4 to 8-years-old. Although
subjects from this age group have mature deambulation, Granata et al.[28] demonstrated that intrasession EMG variability in children aged 6 to 8 years is
twice as high as in adults. Agostini et al.[27] stated that this exam's activity can vary drastically between children, with less
than 50% showing a similar pattern. The large variability in children may indicate
more responsive stabilization control than in adults.[27]
A study by Fleming et al.[29] evaluated the level of functionality in 15 children with MPS using the International
Classification of Functioning, Disability, and Health. These authors reported mild
impairment of bodily functions and severe impairment of joint mobility and gait, which
is in line with our study.
The limitation of this study was the small number of subjects. In genetic storage
syndromes, involving different systems, research must deal with the high variability
between patients. As such, these studies must include a large number of subjects.
Therefore, further studies are required with a larger sample of patients.
Conclusion
Patients presented a decreased average speed and stride length. Most cases presented
a gait pattern of flexion and adduction of the hips and flexion of the knees. Both
GPS and GDI showed an important deviation from normality. The analysis of EMG data
revealed intervals of muscular activity approximately following the current model,
highlighting the high variability of muscle activation patterns between patients.
