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Charcot-Marie-Tooth 1A patients with low level of impairment have a higher energy cost of walking than healthy individuals
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Neuromuscular Disorders 21 (2011) 52–57 www.elsevier.com/locate/nmd
Charcot-Marie-Tooth 1A patients with low level of impairment have a higher energy cost of walking than healthy individuals Federica Menotti a, Francesco Felici a, Antonello Damiani b, Fortunato Mangiola b, Roberto Vannicelli b, Andrea Macaluso a,⇑ a
Department of Human Movement and Sport Sciences, University of Rome Foro Italico, Rome, Italy b Unione Italiana Lotta alla Distrofia Muscolare (UILDM), Sezione Laziale, Rome, Italy Received 30 July 2010; received in revised form 14 September 2010; accepted 16 September 2010
Abstract The study aimed at quantifying the walking energy cost of a group of Charcot-Marie-Tooth 1A patients (CMT1A), with low severity of walking impairment, in comparison with healthy individuals. Oxygen uptake was measured in 8 patients (age-range 20–48 years; Barthel >90; Tinetti >20) and 8 healthy individuals, matched for age and gender, when walking on a circuit for 5-min at their self-selected speeds (“slow”, “comfortable” and “fast”). Both comfortable and fast speeds were lower in patients than in the control group (0.92 ± 0.16 vs 1.16 ± 0.22 and 1.27 ± 0.27 vs 1.61 ± 0.22 m s 1, respectively; P < 0.05), whereas walking energy cost per unit of distance was higher in patients than in the control group (P < 0.05) at both “comfortable” (2.27 ± 0.35 vs 1.92 ± 0.21 J kg 1 m 1) and “fast” speed (3.05 ± 0.35 vs 2.37 ± 0.42 J kg 1 m 1). CMT1A patients, therefore, choose to walk slower but with higher metabolic cost compared to healthy individuals, despite no clinically evident walking impairment, which is likely due to altered walking patterns. Ó 2010 Elsevier B.V. All rights reserved. Keywords: Walking economy; Oxygen consumption; Gait; Hereditary neuromuscular disorder; Neuropathies
1. Introduction Charcot Marie Tooth disease (CMT), also referred to as hereditary motor and sensory neuropathy (HMSN), is a genetic and progressive neuropathy affecting from 10 to 30 per 100,000 people in the world [1,2]. CMT1A, which is the most frequent form of CMT1 (60–80% of total cases, [3]), is characterised by segmental demyelination, reduction of the nerve conduction velocity of peripheral nerves and consequent axonal degeneration that impair functions of the distal part of legs and arms [4,5]. CMT1A patients show a decline in motor performances due to loss of muscle ⇑ Corresponding author. Address: Department of Human Movement and Sport Sciences, Universita` degli Studi di Roma Foro Italico, Piazza Lauro De Bosis 6, 00135 Roma, Italy. Tel.: +39 06 36 733 242; fax: +39 06 36 733 214. E-mail address: [email protected] (A. Macaluso).
0960-8966/$ - see front matter Ó 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.nmd.2010.09.008
strength [6–9], fatigue or experienced fatigue [10–13], foot and ankle deformities and alteration of balance [1,2,5,14– 16], pain [17–20] reduction of functional aerobic capacity [6,21] and, as a consequence, low levels of daily activity [22–24]. It has been recently demonstrated by Padua and colleagues [20] that in CMT1A patients the ability to ambulate independently is the motor skill most positively affecting the perceived quality of life, as assessed by means of questionnaires. Patterns of walking in CMT patients have been extensively described by means of motion analysis especially on severely impaired patients. These studies highlighted that the principal limits of CMT walking are instability of the ankle on a sagittal plane [25], increase in plantar flexion during the initial contact [26,27], higher dorsi flexion during the stand phase [26,28] and loss of active push off [25–27], higher knee and hip flexion [26] and the asymmetric hip movement in medio-lateral plane,
F. Menotti et al. / Neuromuscular Disorders 21 (2011) 52–57
hip elevation [26,29,30] and decrease in hip adduction [26,27,30]. Don and colleagues [26] have attempted to estimate the energy expenditure based on mechanical behaviour of the body in terms of energy recovery and energy consumption in relation to the whole body centre of mass. They have shown that CMT patients with foot drop have a higher walking energy cost, which is related to the mechanical effort to perform a marked knee and hip flexion. On the other hand, despite a gait with hip abduction and pelvic elevation, the walking energy cost is lower in patients with plantar flexor failure. To the best of the authors’ knowledge there are no studies on the metabolic cost of walking in this population. The measurement of walking energy cost per unit of distance (WECd), also referred to as walking economy, is a valid indicator of walking performance. Individuals with good walking economy, from a practical perspective, walk faster and for longer with a lower level of physical effort [31,32]. Moreover, WECd is a quantitative and reliable method able to detect even minor walking impairment [33,34] and it has been used to determine walking economy on a number of patients with various pathological conditions [35–39]. Similarly, the heart rate per unit of distance (HRd), also referred to as cardiac cost, correlates with WECd and, although less accurate, has been used as an indicator of the cost of walking [31,32]. In CMT1A patients who are only moderately impaired, quantifying the level of walking impairment by means of WECd and HRd might be highly relevant for assessing the effect of specific interventions designed to improve their walking ability. The purpose of this study is therefore to quantify the walking energy and cardiac costs of patients with CMT1A, with low severity of walking impairment, in comparison with healthy individuals. It is hypothesised that patients with CMT1A have a higher metabolic and cardiac cost of walking with respect to healthy individuals despite the lack of clinically evident walking impairment. 2. Materials and methods 2.1. Participants Eight patients with Charcot-Marie-Tooth 1A (3 male and 5 female; mean age 35.2 ± 9.6 years, mean body mass 67.6 ± 10.6 kg) and 8 healthy adults (3 male and 5 female, mean age 34.5 ± 9.7 years; mean body mass 64.2 ± 11.3 kg) participated in the study. Volunteers with CMT1A were recruited from the UILDM Rehabilitation Centre in Rome. The inclusion criteria were: (1) history of CMT1A; (2) Barthel index >70 [40] and Tinetti score >20 [41] to ensure the ability to walk without walking assistance; (3) age between 20 and 50 years; (4) no clinical signs of heart or pulmonary disease. Selected patients had a mean Barthel Index of 96.3/ 100 ± 3.8 (mean ± SD) and a Tinetti score of 23/28 ± 4.3 (mean ± SD). Muscle strength around hip, knee and ankle joints was assessed according to the Medical Research Council (MRC) scale [42], as reported in Table 1. The range
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Table 1 MRC scores (mean ± SD) of patients’ lower limb muscles. Muscles
Mean ± SD
MRC score Range
Interquartile range
Hip extensors Hip flexors Knee extensors Knee flexors Ankle plantar flexors Ankle dorsi flexors
4.88 ± 0.4 4.75 ± 0.7 4.94 ± 0.2 4.88 ± 0.4 4.75 ± 0.7 3.31 ± 1.3
4–5 3–5 4–5 4–5 3–5 1–4
5–5 5–5 5–5 5–5 5–5 3–4
of motion of the ankle joint, clinically evaluated according to Kapandji [43], was 95 ± 10° for both limbs. The individuals of the control group were selected from the employees of the rehabilitation centre and were matched to the CMT patients for age, gender and body mass. With Ethics approval, the study was carried out in accordance with the Declaration of Helsinki and informed consent was obtained from all participants. 2.2. Instrumentation and measurements _ 2 ) and carbon dioxThe steady-state oxygen uptake (VO _ ide production (VCO ) were measured by means of a tele2 metric, portable system (K4b2, COSMED, Italy), of which validity, accuracy and reproducibility have been assessed during rest and exercises at various intensities [43,44]. Heart rate (HR) was recorded by means of a portable heart rate monitor (Polar, Finland) from which the output was telemetrically transmitted and recorded in the K4 system. _ 2 and HR were first measured during standing for VO 5 min to reach a steady-state condition. Participants were then requested to walk on an oval shaped 23-m walkway circuit (rectilinear for 8-m on each side) at their self-selected walking speeds (slow, comfortable and fast). Instructions to the participants on the three self-selected speeds were given by associating the speeds to the activities performed during day to day life: comfortable speed was described as the way the participants normally walk in a relaxed mood, fast speed as the way the participants walk when they are late for an appointment and slow speed as the way they walk during relaxed window-shopping. Each condition lasted 5 min in order to reach a steady-state, and 5 min were given for adequate recovery between each con_ 2 dition, which was verified by visually inspecting both VO and HR prior to beginning the next trial. The sequence of measurement conditions was randomised for each participant. The data obtained during the final minute were used for further analysis. Time was measured with a stop watch (Oregon Scientific, Hong Kong, China), and the total number of steps were counted by the experimenter. 2.3. Data analysis Average walking speed was obtained by dividing the total walking distance (m) by the time taken to cover it
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(s). Following Bernardi et al. [32] and Thomas et al. [31], the walking energy cost per unit of time (WECt) was calculated as the amount of oxygen uptake per unit of body mass and per unit of time (expressed in J kg 1 min 1). It was cal_ 2 ), where VO _ 2 is the energy cost culated as WECt = k (VO 1 1 (expressed in ml kg min ) and k is the energy (J) equivalent of oxygen. The respiratory gas-exchange ratio (RER) of the last minute was taken into account to adjust k [46]. The net walking energy cost per unit distance (WECd) was then calculated as the net energy cost per unit of body mass and per unit of distance (expressed in J kg 1 m 1). The following formula was used: WECd = (WECt SECt)/S, where WECt is the energy cost during walking in J kg 1 min 1, SECt is the energy cost during standing in J kg 1 min 1 and S is walking speed in m min 1. Heart rate (HR) was expressed in beats min 1. The net heart rate per unit of distance (HRd) was calculated as the number of heart beats per unit of distance (beats m 1) according to the formula: HRd = (wHR sHR)/S, where wHR is HR during walking in beats min 1 and sHR is HR during standing in beats min 1 and S is the average walking speed in m min 1. Step length, expressed in m, was computed as the total distance walked by the individual divided by the total number of steps counted by the experimenter. Step frequency, expressed in steps s 1, was computed as average walking speed divided by step length.
Table 2 Walking speeds, step lengths and frequencies (mean ± SD) recorded during slow, comfortable and fast conditions in patients and control group. Variables
Control group (mean ± SD)
P Value
Speed (m s 1) Slow 0.68 ± 0.17 Comfortable 0.92 ± 0.16 Fast 1.27 ± 0.27
0.79 ± 0.23 1.16 ± 0.22 1.61 ± 0.22
0.316 0.024* 0.014*
Step length (m) Slow 0.51 ± 0.63 Comfortable 0.57 ± 0.08 Fast 0.65 ± 0.11
0.56 ± 0.10 0.66 ± 0.11 0.78 ± 0.09
0.293 0.083 0.022*
Step frequency (step s 1) Slow 80.45 ± 15.06 Comfortable 95.71 ± 9.36 Fast 116.10 ± 12.36
83.71 ± 13.43 104.74 ± 6.21 123.71 ± 10.56
0.655 0.056 0.207
*
Patients (mean ± SD)
Significantly different from control group.
length showed a significant effect of group (P < 0.05) and a significant effect of the condition (P < 0.001), whilst there was no significant effect of group for step frequency. The post hoc analysis showed that step lengths were significantly lower in patients (P < 0.05) in comparison to the control group only at fast speed, whereas step frequencies of both groups were similar in all three conditions.
2.4. Statistics 3.2. Cost of walking All data were normally distributed in terms of skewness and kurtosis (all values <2). Statistical comparisons of the parameters (WECd, WECt, HRd, HR, step length, step frequency), between groups (patients and individuals of the control group) at the three conditions (slow, comfortable and fast speed) were carried out by Two-Way ANOVA for repeated measures, followed by One-Way ANOVA and Student’s t-tests with Bonferroni correction where appropriate. Moreover, second-order polynomial regression curves were calculated to fit data of WECd and HRd of each individual at a single speed, i.e. the average most comfortable speed of the control group, in order to carry out a comparison between groups at matched speed by means of a Students’s t-test. Statistical significance levels were set at P < 0.05.
As shown in Table 3, there were no statistical differences in WECt and HR between patients and control group in any of three conditions. However, as shown in Fig. 1, the ANOVA for WECd showed a significant effect of group (P < 0.05) and a significant effect of the condition (P < 0.001). The post hoc analysis showed that the WECd was significantly higher in patients with respect to the individuals in the control group at both comfortable (P < 0.05) and fast speed (P < 0.01). Similarly, the ANOVA for HRd during walking showed significant effect of group (P < 0.01) and a significant effect of the condition (P < 0.001). The post hoc analysis showed that HRd (Fig. 2) was higher in patients than in individuals
3. Results
Table 3 WECt and HRt (mean ± SD) recorded during slow, comfortable and fast conditions in patients and control group.
3.1. Gait parameters
Variables
Walking speeds, step lengths and frequencies measured during the three walking trials in both groups are presented in Table 2. The ANOVA for walking speed showed a significant effect of group (P < 0.05) and a significant effect of the condition (P < 0.001). The post hoc analysis showed that patients walked at significantly slower speeds than individuals in the control group (P < 0.05) at both comfortable and fast self-selected speeds. The ANOVA for step
WECt (J kg Slow Comfortable Fast
1
Patients (mean ± SD)
Control group (mean ± SD)
P Value
min 1) 1.41 ± 0.33 2.04 ± 0.48 3.83 ± 0.74
1.42 ± 0.49 2.29 ± 0.76 3.84 ± 1.02
0.969 0.499 0.968
12.23 ± 7.17 19.07 ± 11.76 33.39 ± 16.06
0.285 0.984 0.286
HRt (beats min 1) Slow 16.43 ± 7.41 Comfortable 19.17 ± 6.41 Fast 41.56 ± 11.61
F. Menotti et al. / Neuromuscular Disorders 21 (2011) 52–57
Fig. 1. WECd (mean ± SE) as a function of self-selected walking speed (slow, comfortable and fast speeds) in patients and control group. Secondorder polynomial regression curves were fitted through the data. Significantly different from control group (P < 0.05).
of the control group at slow (P < 0.05) and fast speed (P < 0.01). When the energy and cardiac costs were estimated in each individual at the speed of 1.16 m s 1, which corresponds to the average most comfortable speed of the healthy controls, by fitting second-order polynomial regression curves to the data of each individual, WECd was significantly higher in patients (2.78 ± 0.50 J kg 1 m 1) than individuals of the control group (1.89 ± 0.36 J kg 1 m 1; P < 0.05), as well as HRd (0.63 ± 0.39 vs 0.26 ± 0.08 beats m 1; P < 0.05). 4. Discussion The major finding of this study is that our CMT1A patients, with low level of impairment, have a greater
Fig. 2. HRd (mean ± SE) as a function of self-selected walking speed (slow, comfortable and fast speeds) in patients and control group. Secondorder polynomial regression curves were fitted through the data. Significantly different from control group (P < 0.05).
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WECd than healthy individuals when walking at both comfortable and fast, self-selected speeds. HRd was also higher in the patient group at slow and fast speeds. Therefore, CMT1A patients have a lower walking economy, which from a practical perspective, means they walk slower, for a shorter duration and with a higher level of physical effort. The self-selected speed of walking was slower in patients than in the control group at both comfortable (20%) and fast (21%) speeds, but not significantly different at slow speed. The lower comfortable speed of walking is consistent with the findings of others [27,29], whilst, to the best of the authors’ knowledge, no previous studies have monitored slow and fast speeds in CMT patients. Our findings suggest that the magnitude of difference between patients and the control group increases with walking speed. Therefore, the functional limit imposed by disability on walking pattern becomes more evident with the increase of movement complexity. As walking speed increased, there was a tendency for step length to be lower in patients, which only became significant at fast walking speed. Step frequency followed a similar trend, but with no statistically significant differences. Our results are consistent with the findings of Don et al. [26] and Ramdharry et al. [29], although in both investigations the authors reported a significant reduction in both step length and step frequency also at comfortable speed of walking. These differences could be ascribed to the small sample size of our study and to the fact that patients of the aforementioned studies covered a wide range of both subtypes of CMT (including 1A, 1B, 1X, 2A) and severity of the impairment. Whereas in our investigation we selectively recruited participants with CMT1A and low level of impairment who could still maintain an adequate control of the ankle joint and, as a consequence, a better walking performance. WECt was similar in both patients and the control group at all self-selected speeds of walking. However, WECd was higher at both comfortable and fast walking speeds in the CMT1A patients, and highlights the lower walking economy of this group. In addition, estimated WECd at matched speed was significantly higher in patients than in individuals of the control group. Indeed, CMT1A patients may decide to decrease their walking speed to avoid a higher energy consumption per unit of time, but they still maintain a lower gait economy. Although, to the best of the authors’ knowledge, there are no studies on the metabolic cost of walking in CMT1A patients, our findings are partially supported by previous observations of mechanical energy expenditure of CMT gait by Don et al. [26]. Considering the mechanical work during walking, they found CMT patients with foot drop to have a higher energy consumption with respect to healthy individuals. This could be attributed to the high mechanical effort required to perform their peculiar gait with exaggerated movement of hip flexor, knee extensors and ankle plantar flexor muscles for both support and propulsion. Although our patients were able to perform
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muscular contraction of ankle dorsi flexor muscles, their higher energy cost could, in part, be attributed to the moderate weakness of these muscles as assessed by the MRC scale. Other features of CMT could help us to explain the increase in WECd in our patients. Individuals with the disease lack large myelinated fibres in the distal limb and commonly experience deficits of sensory feedback and proprioception at ankle level, as shown by a longer latency of the stretch reflex of plantaflexor muscles [25], a reduction of anticipatory activity of biceps femoris muscle during perturbed gait [47], and a delay in head alignment during perturbed standing [16]. These factors can alter balance control in dynamic conditions and may require an increased muscular coactivation at knee and hip level to reduce the risk of falling in CMT patients. Moreover, coactivation could also be due to ataxia that affects some of the patients with CMT. Although muscular coactivation has never been investigated as a possible consequence of a compromised dynamic postural control in CMT1A patients, an activation of additional muscles would increase oxygen consumption, induce a stiff deambulation, and result in a reduction in walking economy. Moreover the typical deformity of the patient’s foot, known as ‘pes cavus’, leads to a modification of the dynamic plantar pressure distribution towards the fore-foot, referred to as ‘digitigrade’ walking [18]. This leads to a greater external mechanical work performed by the limbs, which is accompanied by an increased recruitment of major extensor muscles of the ankle, knee, hip, and back [45]. As for the WECt, CMT1A patients maintained a HR comparable with the control group in all conditions by reducing the speed of walking. Nevertheless, HRd was significantly higher in patients than the control group at slow and fast self-selected speeds of walking. In addition, estimated HRd at matched speed was significantly higher in patients than in individuals of the control group. These results are generally consistent with those of Ramdharry et al. [29] who reported a higher heart rate in CMT patients in comparison with healthy individuals, even when expressed as a percentage of the age-predicted maximum. HR depends on the training status of the individuals and it is likely that patients are less trained and have lower cardiopulmonary fitness as the disease itself limits their performance ability. Changes in HRd and HR during walking mirror WECd and WECt, which suggests that cardiac cost could be used as an indicator of the cost of walking. Measuring heart rate is simple and less expensive than measuring oxygen uptake. However, the results should be interpreted with caution as HR is affected by anxiety, irregular rhythm and the use of drugs, especially beta blockers [32]. In conclusion, the homogeneous group of CMT1A patients with low level of walking impairment selected for this study showed a greater metabolic and cardiac cost of walking per unit of distance when compared with healthy individuals. Although the measurement of metabolic cost
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Neuromuscular Disorders 21 (2011) 52–57 www.elsevier.com/locate/nmd
Charcot-Marie-Tooth 1A patients with low level of impairment have a higher energy cost of walking than healthy individuals Federica Menotti a, Francesco Felici a, Antonello Damiani b, Fortunato Mangiola b, Roberto Vannicelli b, Andrea Macaluso a,⇑ a
Department of Human Movement and Sport Sciences, University of Rome Foro Italico, Rome, Italy b Unione Italiana Lotta alla Distrofia Muscolare (UILDM), Sezione Laziale, Rome, Italy Received 30 July 2010; received in revised form 14 September 2010; accepted 16 September 2010
Abstract The study aimed at quantifying the walking energy cost of a group of Charcot-Marie-Tooth 1A patients (CMT1A), with low severity of walking impairment, in comparison with healthy individuals. Oxygen uptake was measured in 8 patients (age-range 20–48 years; Barthel >90; Tinetti >20) and 8 healthy individuals, matched for age and gender, when walking on a circuit for 5-min at their self-selected speeds (“slow”, “comfortable” and “fast”). Both comfortable and fast speeds were lower in patients than in the control group (0.92 ± 0.16 vs 1.16 ± 0.22 and 1.27 ± 0.27 vs 1.61 ± 0.22 m s 1, respectively; P < 0.05), whereas walking energy cost per unit of distance was higher in patients than in the control group (P < 0.05) at both “comfortable” (2.27 ± 0.35 vs 1.92 ± 0.21 J kg 1 m 1) and “fast” speed (3.05 ± 0.35 vs 2.37 ± 0.42 J kg 1 m 1). CMT1A patients, therefore, choose to walk slower but with higher metabolic cost compared to healthy individuals, despite no clinically evident walking impairment, which is likely due to altered walking patterns. Ó 2010 Elsevier B.V. All rights reserved. Keywords: Walking economy; Oxygen consumption; Gait; Hereditary neuromuscular disorder; Neuropathies
1. Introduction Charcot Marie Tooth disease (CMT), also referred to as hereditary motor and sensory neuropathy (HMSN), is a genetic and progressive neuropathy affecting from 10 to 30 per 100,000 people in the world [1,2]. CMT1A, which is the most frequent form of CMT1 (60–80% of total cases, [3]), is characterised by segmental demyelination, reduction of the nerve conduction velocity of peripheral nerves and consequent axonal degeneration that impair functions of the distal part of legs and arms [4,5]. CMT1A patients show a decline in motor performances due to loss of muscle ⇑ Corresponding author. Address: Department of Human Movement and Sport Sciences, Universita` degli Studi di Roma Foro Italico, Piazza Lauro De Bosis 6, 00135 Roma, Italy. Tel.: +39 06 36 733 242; fax: +39 06 36 733 214. E-mail address: [email protected] (A. Macaluso).
0960-8966/$ - see front matter Ó 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.nmd.2010.09.008
strength [6–9], fatigue or experienced fatigue [10–13], foot and ankle deformities and alteration of balance [1,2,5,14– 16], pain [17–20] reduction of functional aerobic capacity [6,21] and, as a consequence, low levels of daily activity [22–24]. It has been recently demonstrated by Padua and colleagues [20] that in CMT1A patients the ability to ambulate independently is the motor skill most positively affecting the perceived quality of life, as assessed by means of questionnaires. Patterns of walking in CMT patients have been extensively described by means of motion analysis especially on severely impaired patients. These studies highlighted that the principal limits of CMT walking are instability of the ankle on a sagittal plane [25], increase in plantar flexion during the initial contact [26,27], higher dorsi flexion during the stand phase [26,28] and loss of active push off [25–27], higher knee and hip flexion [26] and the asymmetric hip movement in medio-lateral plane,
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hip elevation [26,29,30] and decrease in hip adduction [26,27,30]. Don and colleagues [26] have attempted to estimate the energy expenditure based on mechanical behaviour of the body in terms of energy recovery and energy consumption in relation to the whole body centre of mass. They have shown that CMT patients with foot drop have a higher walking energy cost, which is related to the mechanical effort to perform a marked knee and hip flexion. On the other hand, despite a gait with hip abduction and pelvic elevation, the walking energy cost is lower in patients with plantar flexor failure. To the best of the authors’ knowledge there are no studies on the metabolic cost of walking in this population. The measurement of walking energy cost per unit of distance (WECd), also referred to as walking economy, is a valid indicator of walking performance. Individuals with good walking economy, from a practical perspective, walk faster and for longer with a lower level of physical effort [31,32]. Moreover, WECd is a quantitative and reliable method able to detect even minor walking impairment [33,34] and it has been used to determine walking economy on a number of patients with various pathological conditions [35–39]. Similarly, the heart rate per unit of distance (HRd), also referred to as cardiac cost, correlates with WECd and, although less accurate, has been used as an indicator of the cost of walking [31,32]. In CMT1A patients who are only moderately impaired, quantifying the level of walking impairment by means of WECd and HRd might be highly relevant for assessing the effect of specific interventions designed to improve their walking ability. The purpose of this study is therefore to quantify the walking energy and cardiac costs of patients with CMT1A, with low severity of walking impairment, in comparison with healthy individuals. It is hypothesised that patients with CMT1A have a higher metabolic and cardiac cost of walking with respect to healthy individuals despite the lack of clinically evident walking impairment. 2. Materials and methods 2.1. Participants Eight patients with Charcot-Marie-Tooth 1A (3 male and 5 female; mean age 35.2 ± 9.6 years, mean body mass 67.6 ± 10.6 kg) and 8 healthy adults (3 male and 5 female, mean age 34.5 ± 9.7 years; mean body mass 64.2 ± 11.3 kg) participated in the study. Volunteers with CMT1A were recruited from the UILDM Rehabilitation Centre in Rome. The inclusion criteria were: (1) history of CMT1A; (2) Barthel index >70 [40] and Tinetti score >20 [41] to ensure the ability to walk without walking assistance; (3) age between 20 and 50 years; (4) no clinical signs of heart or pulmonary disease. Selected patients had a mean Barthel Index of 96.3/ 100 ± 3.8 (mean ± SD) and a Tinetti score of 23/28 ± 4.3 (mean ± SD). Muscle strength around hip, knee and ankle joints was assessed according to the Medical Research Council (MRC) scale [42], as reported in Table 1. The range
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Table 1 MRC scores (mean ± SD) of patients’ lower limb muscles. Muscles
Mean ± SD
MRC score Range
Interquartile range
Hip extensors Hip flexors Knee extensors Knee flexors Ankle plantar flexors Ankle dorsi flexors
4.88 ± 0.4 4.75 ± 0.7 4.94 ± 0.2 4.88 ± 0.4 4.75 ± 0.7 3.31 ± 1.3
4–5 3–5 4–5 4–5 3–5 1–4
5–5 5–5 5–5 5–5 5–5 3–4
of motion of the ankle joint, clinically evaluated according to Kapandji [43], was 95 ± 10° for both limbs. The individuals of the control group were selected from the employees of the rehabilitation centre and were matched to the CMT patients for age, gender and body mass. With Ethics approval, the study was carried out in accordance with the Declaration of Helsinki and informed consent was obtained from all participants. 2.2. Instrumentation and measurements _ 2 ) and carbon dioxThe steady-state oxygen uptake (VO _ ide production (VCO ) were measured by means of a tele2 metric, portable system (K4b2, COSMED, Italy), of which validity, accuracy and reproducibility have been assessed during rest and exercises at various intensities [43,44]. Heart rate (HR) was recorded by means of a portable heart rate monitor (Polar, Finland) from which the output was telemetrically transmitted and recorded in the K4 system. _ 2 and HR were first measured during standing for VO 5 min to reach a steady-state condition. Participants were then requested to walk on an oval shaped 23-m walkway circuit (rectilinear for 8-m on each side) at their self-selected walking speeds (slow, comfortable and fast). Instructions to the participants on the three self-selected speeds were given by associating the speeds to the activities performed during day to day life: comfortable speed was described as the way the participants normally walk in a relaxed mood, fast speed as the way the participants walk when they are late for an appointment and slow speed as the way they walk during relaxed window-shopping. Each condition lasted 5 min in order to reach a steady-state, and 5 min were given for adequate recovery between each con_ 2 dition, which was verified by visually inspecting both VO and HR prior to beginning the next trial. The sequence of measurement conditions was randomised for each participant. The data obtained during the final minute were used for further analysis. Time was measured with a stop watch (Oregon Scientific, Hong Kong, China), and the total number of steps were counted by the experimenter. 2.3. Data analysis Average walking speed was obtained by dividing the total walking distance (m) by the time taken to cover it
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(s). Following Bernardi et al. [32] and Thomas et al. [31], the walking energy cost per unit of time (WECt) was calculated as the amount of oxygen uptake per unit of body mass and per unit of time (expressed in J kg 1 min 1). It was cal_ 2 ), where VO _ 2 is the energy cost culated as WECt = k (VO 1 1 (expressed in ml kg min ) and k is the energy (J) equivalent of oxygen. The respiratory gas-exchange ratio (RER) of the last minute was taken into account to adjust k [46]. The net walking energy cost per unit distance (WECd) was then calculated as the net energy cost per unit of body mass and per unit of distance (expressed in J kg 1 m 1). The following formula was used: WECd = (WECt SECt)/S, where WECt is the energy cost during walking in J kg 1 min 1, SECt is the energy cost during standing in J kg 1 min 1 and S is walking speed in m min 1. Heart rate (HR) was expressed in beats min 1. The net heart rate per unit of distance (HRd) was calculated as the number of heart beats per unit of distance (beats m 1) according to the formula: HRd = (wHR sHR)/S, where wHR is HR during walking in beats min 1 and sHR is HR during standing in beats min 1 and S is the average walking speed in m min 1. Step length, expressed in m, was computed as the total distance walked by the individual divided by the total number of steps counted by the experimenter. Step frequency, expressed in steps s 1, was computed as average walking speed divided by step length.
Table 2 Walking speeds, step lengths and frequencies (mean ± SD) recorded during slow, comfortable and fast conditions in patients and control group. Variables
Control group (mean ± SD)
P Value
Speed (m s 1) Slow 0.68 ± 0.17 Comfortable 0.92 ± 0.16 Fast 1.27 ± 0.27
0.79 ± 0.23 1.16 ± 0.22 1.61 ± 0.22
0.316 0.024* 0.014*
Step length (m) Slow 0.51 ± 0.63 Comfortable 0.57 ± 0.08 Fast 0.65 ± 0.11
0.56 ± 0.10 0.66 ± 0.11 0.78 ± 0.09
0.293 0.083 0.022*
Step frequency (step s 1) Slow 80.45 ± 15.06 Comfortable 95.71 ± 9.36 Fast 116.10 ± 12.36
83.71 ± 13.43 104.74 ± 6.21 123.71 ± 10.56
0.655 0.056 0.207
*
Patients (mean ± SD)
Significantly different from control group.
length showed a significant effect of group (P < 0.05) and a significant effect of the condition (P < 0.001), whilst there was no significant effect of group for step frequency. The post hoc analysis showed that step lengths were significantly lower in patients (P < 0.05) in comparison to the control group only at fast speed, whereas step frequencies of both groups were similar in all three conditions.
2.4. Statistics 3.2. Cost of walking All data were normally distributed in terms of skewness and kurtosis (all values <2). Statistical comparisons of the parameters (WECd, WECt, HRd, HR, step length, step frequency), between groups (patients and individuals of the control group) at the three conditions (slow, comfortable and fast speed) were carried out by Two-Way ANOVA for repeated measures, followed by One-Way ANOVA and Student’s t-tests with Bonferroni correction where appropriate. Moreover, second-order polynomial regression curves were calculated to fit data of WECd and HRd of each individual at a single speed, i.e. the average most comfortable speed of the control group, in order to carry out a comparison between groups at matched speed by means of a Students’s t-test. Statistical significance levels were set at P < 0.05.
As shown in Table 3, there were no statistical differences in WECt and HR between patients and control group in any of three conditions. However, as shown in Fig. 1, the ANOVA for WECd showed a significant effect of group (P < 0.05) and a significant effect of the condition (P < 0.001). The post hoc analysis showed that the WECd was significantly higher in patients with respect to the individuals in the control group at both comfortable (P < 0.05) and fast speed (P < 0.01). Similarly, the ANOVA for HRd during walking showed significant effect of group (P < 0.01) and a significant effect of the condition (P < 0.001). The post hoc analysis showed that HRd (Fig. 2) was higher in patients than in individuals
3. Results
Table 3 WECt and HRt (mean ± SD) recorded during slow, comfortable and fast conditions in patients and control group.
3.1. Gait parameters
Variables
Walking speeds, step lengths and frequencies measured during the three walking trials in both groups are presented in Table 2. The ANOVA for walking speed showed a significant effect of group (P < 0.05) and a significant effect of the condition (P < 0.001). The post hoc analysis showed that patients walked at significantly slower speeds than individuals in the control group (P < 0.05) at both comfortable and fast self-selected speeds. The ANOVA for step
WECt (J kg Slow Comfortable Fast
1
Patients (mean ± SD)
Control group (mean ± SD)
P Value
min 1) 1.41 ± 0.33 2.04 ± 0.48 3.83 ± 0.74
1.42 ± 0.49 2.29 ± 0.76 3.84 ± 1.02
0.969 0.499 0.968
12.23 ± 7.17 19.07 ± 11.76 33.39 ± 16.06
0.285 0.984 0.286
HRt (beats min 1) Slow 16.43 ± 7.41 Comfortable 19.17 ± 6.41 Fast 41.56 ± 11.61
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Fig. 1. WECd (mean ± SE) as a function of self-selected walking speed (slow, comfortable and fast speeds) in patients and control group. Secondorder polynomial regression curves were fitted through the data. Significantly different from control group (P < 0.05).
of the control group at slow (P < 0.05) and fast speed (P < 0.01). When the energy and cardiac costs were estimated in each individual at the speed of 1.16 m s 1, which corresponds to the average most comfortable speed of the healthy controls, by fitting second-order polynomial regression curves to the data of each individual, WECd was significantly higher in patients (2.78 ± 0.50 J kg 1 m 1) than individuals of the control group (1.89 ± 0.36 J kg 1 m 1; P < 0.05), as well as HRd (0.63 ± 0.39 vs 0.26 ± 0.08 beats m 1; P < 0.05). 4. Discussion The major finding of this study is that our CMT1A patients, with low level of impairment, have a greater
Fig. 2. HRd (mean ± SE) as a function of self-selected walking speed (slow, comfortable and fast speeds) in patients and control group. Secondorder polynomial regression curves were fitted through the data. Significantly different from control group (P < 0.05).
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WECd than healthy individuals when walking at both comfortable and fast, self-selected speeds. HRd was also higher in the patient group at slow and fast speeds. Therefore, CMT1A patients have a lower walking economy, which from a practical perspective, means they walk slower, for a shorter duration and with a higher level of physical effort. The self-selected speed of walking was slower in patients than in the control group at both comfortable (20%) and fast (21%) speeds, but not significantly different at slow speed. The lower comfortable speed of walking is consistent with the findings of others [27,29], whilst, to the best of the authors’ knowledge, no previous studies have monitored slow and fast speeds in CMT patients. Our findings suggest that the magnitude of difference between patients and the control group increases with walking speed. Therefore, the functional limit imposed by disability on walking pattern becomes more evident with the increase of movement complexity. As walking speed increased, there was a tendency for step length to be lower in patients, which only became significant at fast walking speed. Step frequency followed a similar trend, but with no statistically significant differences. Our results are consistent with the findings of Don et al. [26] and Ramdharry et al. [29], although in both investigations the authors reported a significant reduction in both step length and step frequency also at comfortable speed of walking. These differences could be ascribed to the small sample size of our study and to the fact that patients of the aforementioned studies covered a wide range of both subtypes of CMT (including 1A, 1B, 1X, 2A) and severity of the impairment. Whereas in our investigation we selectively recruited participants with CMT1A and low level of impairment who could still maintain an adequate control of the ankle joint and, as a consequence, a better walking performance. WECt was similar in both patients and the control group at all self-selected speeds of walking. However, WECd was higher at both comfortable and fast walking speeds in the CMT1A patients, and highlights the lower walking economy of this group. In addition, estimated WECd at matched speed was significantly higher in patients than in individuals of the control group. Indeed, CMT1A patients may decide to decrease their walking speed to avoid a higher energy consumption per unit of time, but they still maintain a lower gait economy. Although, to the best of the authors’ knowledge, there are no studies on the metabolic cost of walking in CMT1A patients, our findings are partially supported by previous observations of mechanical energy expenditure of CMT gait by Don et al. [26]. Considering the mechanical work during walking, they found CMT patients with foot drop to have a higher energy consumption with respect to healthy individuals. This could be attributed to the high mechanical effort required to perform their peculiar gait with exaggerated movement of hip flexor, knee extensors and ankle plantar flexor muscles for both support and propulsion. Although our patients were able to perform
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muscular contraction of ankle dorsi flexor muscles, their higher energy cost could, in part, be attributed to the moderate weakness of these muscles as assessed by the MRC scale. Other features of CMT could help us to explain the increase in WECd in our patients. Individuals with the disease lack large myelinated fibres in the distal limb and commonly experience deficits of sensory feedback and proprioception at ankle level, as shown by a longer latency of the stretch reflex of plantaflexor muscles [25], a reduction of anticipatory activity of biceps femoris muscle during perturbed gait [47], and a delay in head alignment during perturbed standing [16]. These factors can alter balance control in dynamic conditions and may require an increased muscular coactivation at knee and hip level to reduce the risk of falling in CMT patients. Moreover, coactivation could also be due to ataxia that affects some of the patients with CMT. Although muscular coactivation has never been investigated as a possible consequence of a compromised dynamic postural control in CMT1A patients, an activation of additional muscles would increase oxygen consumption, induce a stiff deambulation, and result in a reduction in walking economy. Moreover the typical deformity of the patient’s foot, known as ‘pes cavus’, leads to a modification of the dynamic plantar pressure distribution towards the fore-foot, referred to as ‘digitigrade’ walking [18]. This leads to a greater external mechanical work performed by the limbs, which is accompanied by an increased recruitment of major extensor muscles of the ankle, knee, hip, and back [45]. As for the WECt, CMT1A patients maintained a HR comparable with the control group in all conditions by reducing the speed of walking. Nevertheless, HRd was significantly higher in patients than the control group at slow and fast self-selected speeds of walking. In addition, estimated HRd at matched speed was significantly higher in patients than in individuals of the control group. These results are generally consistent with those of Ramdharry et al. [29] who reported a higher heart rate in CMT patients in comparison with healthy individuals, even when expressed as a percentage of the age-predicted maximum. HR depends on the training status of the individuals and it is likely that patients are less trained and have lower cardiopulmonary fitness as the disease itself limits their performance ability. Changes in HRd and HR during walking mirror WECd and WECt, which suggests that cardiac cost could be used as an indicator of the cost of walking. Measuring heart rate is simple and less expensive than measuring oxygen uptake. However, the results should be interpreted with caution as HR is affected by anxiety, irregular rhythm and the use of drugs, especially beta blockers [32]. In conclusion, the homogeneous group of CMT1A patients with low level of walking impairment selected for this study showed a greater metabolic and cardiac cost of walking per unit of distance when compared with healthy individuals. Although the measurement of metabolic cost
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