581437
research-article2015
MSJ0010.1177/1352458515581437Multiple Sclerosis JournalHeine et al.
MULTIPLE SCLEROSIS MSJ JOURNAL
Original Research Paper
Cardiopulmonary fitness is related to disease severity in multiple sclerosis Martin Heine, Inez Wens, Martin Langeskov-Christensen, Olaf Verschuren, Bert O Eijnde, Gert Kwakkel and Ulrik Dalgas
Abstract Background: In persons with MS (pwMS), a lower cardiopulmonary fitness has been associated with a higher risk for secondary disorders, decreased functional capacity, symptom worsening and reduced health-related quality of life. Objective: To investigate the association between disease severity and cardiopulmonary fitness. Methods: Data from cardiopulmonary exercise tests, previously conducted in three different countries, were pooled. The association between disease severity (Expanded Disability Status Scale (EDSS)) and cardiopulmonary fitness (peak oxygen uptake (VO2peak)) was adjusted for age, sex and the country of origin. Results: The combined sample comprised 116 ambulant pwMS having a mean (± SD) EDSS score of 2.7 ± 1.3. There was a significant correlation (r = -0.418, p < .01) between VO2peak and EDSS. A multiple regression model (R2 = 0.520, p < .01) was constructed to describe VO2peak (mL∙kg−1∙min−1); VO2peak = 36.622 – 5.433 (Sex (1=men)) – 0.124 (Age) – 2.082 (EDSS) + 2.737 (Belgium) + 8.674 (Denmark). Conclusion: There was a significant association between disease severity and cardiopulmonary fitness. The close relation between cardiopulmonary fitness and chronic conditions associated with physical inactivity, suggest a progressive increase in risk of secondary health conditions in pwMS
Keywords: Multiple sclerosis, Expanded Disability Status Scale, exercise test, cardiopulmonary fitness, aerobic capacity, comorbidity Date received 17 November 2014; revised 23 February 2015, 16 March 2015; accepted 17 March 2015
Introduction Multiple sclerosis (MS) is considered an autoimmunemediated chronic inflammatory and neurodegenerative disorder of the central nervous system (CNS).1 The majority of persons with MS (pwMS) will experience a progressive increase of disease severity and decline in physical and cognitive functioning.2 Due to the diffuse damage to the CNS, the disease is characterized by a variety of sensory, motor, cerebellar and cognitive dysfunctions. These dysfunctions limit physical activity behavior in pwMS, and may result in subsequent deconditioning.3,4 Hence, it is not surprising that MS has been associated with an increased risk for a variety of other chronic health conditions which have been related to physical inactivity and a sedentary lifestyle such as cardiovascular disease, pneumonia, cancer,
non-infectious respiratory diseases and mental comorbidities.5–12 These secondary comorbidities may increase the all-cause mortality rate of pwMS by as much as 1.7 fold,5 and are associated with increased disability progression, reduced health-related quality of life of pwMS, and are the primary reason for hospitalization.6,13,14 Consequently, improving or maintaining physical activity and reducing the risk of secondary chronic disorders is important in pwMS. Cardiopulmonary fitness is considered a key marker of health and performance, and is closely associated with the level of physical activity and sedentary time.15,16 Direct measurement of the whole body peak oxygen consumption determined by an incremental exercise test to exhaustion is considered the gold
Multiple Sclerosis Journal 1–8 DOI: 10.1177/ 1352458515581437 © The Author(s), 2015. Reprints and permissions: http://www.sagepub.co.uk/ journalsPermissions.nav
Correspondence to: Martin Heine Brain Center Rudolf Magnus Institute of Neuroscience and Center of Excellence for Rehabilitation Medicine, University Medical Center Utrecht and Rehabilitation Center De Hoogstraat, Rembrandtkade 10, 3583 TM, Utrecht, The Netherlands. [email protected] Martin Heine Olaf Verschuren Brain Center Rudolf Magnus and Center of Excellence for Rehabilitation Medicine, University Medical Center Utrecht and Rehabilitation Center De Hoogstraat, Utrecht, The Netherlands Inez Wens Bert O Eijnde Rehabilitation Research Center (REVAL), Biomedical Research Institute, Faculty of Medicine and Life Sciences, Hasselt University, Hasselt, Belgium Martin LangeskovChristensen Ulrik Dalgas Department of Public Health, Section of Sport Science, Aarhus University, Aarhus, Denmark Gert Kwakkel Department of Rehabilitation Medicine, MOVE Research Institute Amsterdam, VU University Medical Center, Amsterdam, The Netherlands; Department of Neurorehabilitation, Centre of Rehabilitation and Rheumatology READE, Amsterdam, The Netherlands
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Multiple Sclerosis Journal Table 1. Study characteristics and testing procedures of each contributing country. Belgium, N = 33 (clinicaltrials.gov NCT01845896) Recruited for: an intervention study to assess the effects of high-intensity exercise training on muscle contractile properties and endurance capacity in persons with MS. Test protocol: Men: 30 Watt + 15 Watt/min Women: 20 Watt + 10 Watt/min Cadence: 70 rotations/min Test equipment: Ergometer: eBike Basic (electro-mechanical braking system ), General Electric GmbH, Germany Gas exchange measurement: Jaeger Oxycon (breath-by-breath), Erich Jaeger GmbH, Germany Denmark, N = 20 (not registered) Recruited for: a validity and reliability study of maximal exercise testing in persons with MS and healthy controls23 Test protocol: 22.5–80 Watt + 11.25–42.5 Watt/1.5 min Cadence: self-chosen between 45 and 85 rotations/min Test equipment: Ergometer: Monark Ergomedic 828E (brake belt), Monark, Sweden Gas exchange measurement: AMIS 2001 (mixing-chamber), Innovision, Denmark The Netherlands, N = 63 (controlled-trials.com ISRCTN69520623) Recruited for: an intervention study to assess the effects of aerobic endurance training on fatigue in persons with MS. Test protocol: Men: 25 Watt + 15 Watt/min Women: 25 Watt + 10 Watt/min Cadence: between 60-80 rotations/min Test equipment: Ergometer: Kettler X7 (electro-mechanical braking system), Kettler, Germany Gas exchange measurement: Metamax 3B (mixing-chamber) Cortex Medical, Germany
standard assessment for cardiopulmonary fitness.17 As such, the peak oxygen uptake (VO2peak) has been associated with a variety of outcomes covering all levels within the framework of the International Classification of Functioning (body function, activity, participation), underlining the importance of this parameter in pwMS.13 However, cardiopulmonary fitness is a sensitive measure and intuitively, one would expect the cardiopulmonary fitness to decrease as disability increases in pwMS. Several cross-sectional studies in small samples have reported correlations between the disability level in terms of the Expanded Disability Status Score (EDSS) and VO2peak in pwMS.18 Nevertheless, it is still unclear how the disability level of pwMS per se affects cardiopulmonary fitness, since none of the existing studies were adjusted for age and gender, which are known to affect cardiopulmonary fitness in healthy people.19,20 For such an analysis, it is important that the sample studied is of sufficient size to
provide the required statistical power to perform multivariable analysis. Accordingly, the objective of the present study was to assess the relation between disease severity and cardiopulmonary fitness in a large sample of ambulatory pwMS, adjusting for age and sex. For this purpose, a secondary analysis was done on data from three different European countries. It was hypothesized that disability per se would affect the cardiopulmonary fitness in pwMS. Methods Data from three different countries (Belgium, Denmark and The Netherlands) were pooled, and a sample of 116 ambulant pwMS was obtained. Table 1 provides an overview of the three different studies and their testing procedures. Each study assessed cardiopulmonary fitness using an incremental leg-cycling protocol until voluntary exhaustion. In addition, the level of
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M Heine, I Wens et al. disability was assessed by means of the EDSS by a certified physician/neurologist (http://www.neurostatus.net). The EDSS is a widely used clinician-administered assessment scale evaluating the functional systems of the CNS. It can be used to describe disease progression in pwMS as well as to assess the effectiveness of interventions during clinical trials. It consists of an ordinal rating scale ranging from 0 (normal neurological functioning) to 10 (death due to MS) with 0.5 increments. The lower scale values (< 4.5) of the EDSS are based on the neurological examination, the middle scale values (4.0–6.0) rely heavily on walking ability, whereas the upper scale values (> 6.0) measure the disabilities of pwMS.21 In all three countries only subjects with an EDSS ⩽ 6.0 with definite, stable MS were included. In addition, participants that had a contra-indication to perform maximal exercise were excluded. Specific for The Netherlands was that these participants had a predefined high level of subjective fatigue as indicated by a score ⩾ 35 on the Checklist Individual Strength fatigue subscale. Three different independent variables were considered as confounders of the association between disease severity and cardiopulmonary fitness: age, gender and the country of origin. In healthy people, cardiopulmonary fitness in known to be lower in women compared to men, and, moreover, that cardiopulmonary fitness declines with age. The country of origin was a third potential confounder as it was hypothesized that differences in sample characteristics, testing procedures, inclusion criteria of the respective study in which context the data was collected, or merely differences in cardiopulmonary fitness between countries may distort the association between disease severity and cardiopulmonary fitness. Statistical analysis Between country differences were assessed by means of independent t-tests, one-way analysis of variance or a chi-square test depending on the determinant and outcome. To control for multiple testing, during the one-way analysis of variance, a Bonferroni correction was applied. The bivariable association between the continuous independent variables (age and EDSS) and the dependent variable, peak oxygen uptake corrected for body weight (mL∙kg−1∙min−1), was assessed by means of Pearson correlation coefficients. Subsequently, a multiple linear regression analysis was performed to assess the adjusted association of disease severity (EDSS) with VO2peak. If the correlation between two independent variables exceeded 0.800, these were considered multicollinear and the variable with the lowest association
with the dependent variable was omitted from the multivariable regression model. Finally, it was studied how adding candidate covariates to the univariable model affected the association between EDSS and VO2peak. If the regression coefficient of EDSS with VO2peak changed >15% after the variable was added to the model, this variable was considered to be a covariate that confounded the relationship between EDSS and VO2peak. Analysis of the distribution of the dependent and independent variables showed a normal distribution. Hence, the parametric tests described above, and the use of the ordinal EDSS scale as a continuous outcome, can be considered appropriate. Outcomes are presented as mean ± standard deviation (SD) or mean difference (MD) ± standard error (SE). All tests were conducted with a significance level of p < 0.05 applying SPSS statistical software package 21.0. Results Table 2 shows the participant characteristics of the total sample as well as per country. Of the total 116 participants, 69.8% were women. The average age was 44.4 ± 9.7 years, and the level of disability was 2.7 ± 1.3 on the EDSS scale. The mean VO2peak was 1.784 ± 0.609 L∙min−1 or when corrected for bodyweight, 23.9 ± 8.0 mL∙kg−1∙min−1. The VO2peak of men was significantly higher compared to women (MD = 6.5 ± 1.5). One-way analysis of variance showed that, the participants in Denmark were significantly younger (MDBelgium = 6.4 ± 2.7 years; MDNetherlands = 6.4 ± 2.4 years), had a higher peak power (MDBelgium = 62 ± 14 Watt; MDNetherlands = 49 ± 13 Watt) output as well as VO2peak (MDBelgium = 7.1 ± 1.9 mL∙kg−1∙min−1; MDNetherlands = 11.2 ± 1.8 mL∙kg−1∙min−1), compared to Belgium and the Netherlands. In addition, the participants in Belgium had a significant higher VO2peak compared to the participants from the Netherlands (MDNetherlands = 4.2 ± 1.5 mL∙kg−1∙min−1). Analysis of the association between age, EDSS and VO2peak revealed no signs of multicollinearity. However, correlation analysis did reveal significant associations between age and EDSS (r = 0.399, p < .01), age and VO2peak (r = -0.371, p 15% (bold) was considered a confounding covariate. Model 3: multivariable analysis to determine the adjusted association between EDSS and VO2peak. a p < 0.05. b p < 0.01. c The Netherlands served as reference country.
VO2peak. The relation between EDSS and VO2peak was confounded by age. Based on the final multiple regression model, VO2peak can be calculated as:
(
VO 2 peak mL ⋅ kg −1 ⋅ min −1
)
= 36.622 − 5.433
(SEX; 0 = male, 1 = female ) − 0.124 ( AGE ) − 2.082 ( EDSS) + 2.737 ( Belgium ) + 8.674 ( Denmark ) Based on the R2 the model explains 52% of the variance in VO2peak, showing that VO2peak decreases with increasing EDSS. Discussion The present study pooled data from three different European countries to assess the relation between disease severity and cardiopulmonary fitness in pwMS, adjusting for age, sex, and country of origin. Based on the multivariable regression analysis, disease severity is significantly associated with a decrease in VO2peak, the gold standard measure of cardiopulmonary fitness.22,23 Reductions in cardiopulmonary fitness have been associated with numerous health complications and decreased physical performance.6,24 To our knowledge, this is the first sufficiently powered study to assess the association between disease severity and cardiopulmonary fitness in pwMS adjusting for natural changes with respect to aging and differences between men and women. This is of importance when
trying to improve our understanding of cardiopulmonary fitness in pwMS, and (secondary) health risks with increasing disease severity. Kodama et al.24 showed, in healthy people, that a 1-metabolic equivalent (1 MET = 3.5 mL∙kg−1∙min−1) lower level of cardiopulmonary fitness corresponded to a relative risk of 1.15 (95%CI 1.11–1.19) for allrisk mortality, and 1.18 (95%CI 1.14–1.22) for coronary heart disease (CHD)/cardiovascular disease (CVD), respectively. Based on the present study, pwMS with an EDSS of 6.0 have a 3.6 MET lower VO2peak, which theoretically induces a 4.1 fold higher risk at all-cause mortality and a 4.3 fold higher risk of CHD/CVD compared to pwMS with no neurological symptoms (EDSS = 0). In addition, Kodama et al.24 distinguished between low fitness (< 7.9 MET), moderate fitness (7.9 – 10.8 MET) and high fitness (> 10.8 MET). In the present study, and according to these definitions, 76.7% of pwMS had low fitness, only 17.2% had moderate fitness, and 6.0% had high fitness.25 However, it is unclear if this distribution between low, moderate, and high fitness is different from healthy people. Moreover, one may question if VO2peak derived from a CPET is actually a measure of cardiopulmonary fitness in pwMS having an EDSS score > 4.0, as these patients may often not reach the recommend criteria indicative of maximal exercise.22 Hence, in these pwMS the test may reflect functional capacity rather than cardiopulmonary fitness, which may bias the 76.7% of pwMS that had low fitness. Nonetheless, the results of the present study are in line with a recent review that suggests that cardiopulmonary fitness in pwMS is generally poor as
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Multiple Sclerosis Journal compared to the healthy population, and affected by disease progression such that it elicits significant health risks as well as being associated with a variety of MS symptoms.13 The question remains to what extent the decline in cardiopulmonary fitness with increasing disability can be prevented. A potential cause for the lower cardiopulmonary fitness with higher levels of disease severity can be related to changes in physical activity. Durstine et al. described a circular process whereby persons with a chronic illness or disability experience less physical activity, which then leads to a cycle of deconditioning and further physical deterioration and reduction in activity and cardiopulmonary fitness.26 As seen in this study, PwMS with low levels of disease severity already have lower VO2peak values compared to typically developing sedentary peers (39.0 ± 6.8 mL∙kg−1∙min−1 for men and 30.0 ± 5.4 mL∙kg−1∙min−1 for women).27 The low fitness levels seen in pwMS at low disability levels may also be related to the underlying MS pathology, for instance due to altered immune function, autonomic function, or substrate metabolism.28,29 Nonetheless, introduction to an active lifestyle that includes physical fitness promotion might be beneficial for future health and function. Though, disease progression may limit the ability of pwMS to maintain an appropriate physical activity level both from a physical viewpoint as well as a pragmatic (i.e. mobility, independence, etc.) viewpoint. Exercise therapy, and specifically aerobic training, may elicit significant improvements in cardiopulmonary fitness in healthy persons as well as in pwMS. A meta-analysis showed that cardiopulmonary fitness in pwMS on average improved 3.5 mL∙kg−1∙min−1 (1 MET) in the existing aerobic exercise intervention studies.13 However, the duration of the exercise interventions included for this meta-analysis were in general too short to expect significant disease progression over the same period of time. Hence, the estimated improvement of 3.5 mL∙kg−1∙min−1 in VO2peak would suggest a ‘better in – better out’ principle rather than a reduced decline in cardiopulmonary fitness with an increase in disease severity. On the other hand, disease progression may also be exaggerated by reductions in physical activity or a sedentary lifestyle, thereby suggesting exercise therapy as a disease-modifying treatment. Exercise or physical activity may have an anti-inflammatory and neuroprotective function preserving gray matter volume, white matter integrity and reduce lesion load.3,30,31 Sumowski and colleagues added to this conception by showing that cognitive and brain reserve at baseline protect against cognitive decline
over a 4.5 year period.32 A similar principle may be true for motor reserve in relation to motor function and fatigability.31 A study by Motl and colleagues showed, for instance, that the level of physical activity prior to the diagnosis of MS significantly predicted the linear change in disability scores, which suggests a potential preventative function, in terms of disease progression, of physical activity in pwMS, and a repressive function for a sedentary lifestyle.3,31 However, a recent review evaluating whether exercise can influence disease progression in MS concluded that data exist that indicate a possible disease-modifying effect of exercise, but that the strength of the evidence limits definite conclusions at present.3 An unexpected finding in the present study is the large differences in cardiopulmonary fitness between the different European countries. There are multiple factors that can explain these differences. First, it is possible that the different purposes for which these pwMS were recruited, and the inclusion criteria that were used, induced a substantial selection bias. Second, cardiopulmonary fitness may indeed be different due to differences in the health care structure, which may result in a different availability of (para) medical resources (e.g. free physiotherapy once a week in Denmark). A third explanation relates to the differences in testing protocol and equipment used between the different countries. For instance, the Cortex Metamax 3B, used in The Netherlands has been shown to overestimate VO2peak by 10–17% compared to the Jaeger Oxycon used in Belgium.33 To our knowledge, no study has compared different maximal exercise protocols or gas exchange measurement equipment in pwMS. Hence, the difference between countries, as shown in the multivariable regression model, should not be interpreted as a true difference in cardiopulmonary fitness, but rather a ‘black box’ in which all the above explanations may contribute to some extent. Limitations One limitation of the present study is the cross-sectional design. The present study shows that pwMS with a high EDSS level have a lower VO2peak. However, this study does not show the longitudinal construct validity between a high cardiopulmonary fitness and EDSS over time (i.e. better in – better out). Moreover, even though treated as a continuous outcome in the present study, the EDSS scale is an ordinal scale, meaning that a 1-point difference on the EDSS scale, and the associated change in VO2peak, may have a different clinical meaning at different stages of the EDSS scale. A second limitation is the
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M Heine, I Wens et al. slightly different protocols used for maximal exercise testing. We were, however, able to correct for these differences by including country as an independent variable in the multivariable regression analysis. Nonetheless, the underlying components leading to the differences between countries, which could be related to testing procedures, use of equipment, inclusion criteria and patient characteristics, were essentially unexplored. Third, even though all three related studies included pwMS up to an EDSS of 6.0, the majority of participants had an EDSS score ⩽ 4.0. This restricts the generalization of the results to ambulant pwMS able to walk > 500 metres. Conclusion Ambulant pwMS with a higher level of disease severity, as measured by the EDSS scale, have lower levels of cardiopulmonary fitness. The close relation between cardiopulmonary fitness and chronic conditions associated with physical inactivity, suggest a progressive increase in risk of secondary health conditions in pwMS.
conditions, and health behaviors. Continuum (Minneap Minn) 2013; 19: 1046–1057. 7. Wens I, Dalgas U, Stenager E, et al. Risk factors related to cardiovascular diseases and the metabolic syndrome in multiple sclerosis – a systematic review. Mult Scler 2013; 19: 1556–1564. 8. Marrie RA, Reider N, Stuve O, et al. The incidence and prevalence of comorbid gastrointestinal, musculoskeletal, ocular, pulmonary, and renal disorders in multiple sclerosis: a systematic review. Mult Scler 2015; 21(3): 332–341. 9. Marrie RA, Reider N, Cohen J, et al. A systematic review of the incidence and prevalence of cancer in multiple sclerosis. Mult Scler 2015; 21(3): 294–304. 10. Marrie RA, Reider N, Cohen J, et al. A systematic review of the incidence and prevalence of sleep disorders and seizure disorders in multiple sclerosis. Mult Scler 2015; 21(3): 342–349. 11. Marrie RA, Reider N, Cohen J, et al. A systematic review of the incidence and prevalence of cardiac, cerebrovascular, and peripheral vascular disease in multiple sclerosis. Mult Scler 2015; 21(3): 318–331. 12. Marrie RA, Reider N, Cohen J, et al. A systematic review of the incidence and prevalence of autoimmune disease in multiple sclerosis. Mult Scler 2015; 21(3): 282–293.
Conflict of interest None declared. Funding This research received no specific grant from any funding agency in the public, commercial or not-forprofit sectors.
13. Langeskov-Christensen M, Heine M and Kwakkel G, et al. Aerobic capacity in persons with multiple sclerosis: a systematic review and meta-analysis. Sports Med. Epub ahead of print 5 March 2015. DOI: 10.1007/s40279-015-0307-x. 14. Marrie RA, Elliott L, Marriott J, et al. Dramatically changing rates and reasons for hospitalization in multiple sclerosis. Neurology 2014; 83: 929–937.
References 1. Compston A and Coles A. Multiple sclerosis. Lancet 2008; 372: 1502–1517. 2. Browne P, Chandraratna D, Angood C, et al. Atlas of Multiple Sclerosis 2013: A growing global problem with widespread inequity. Neurology 2014; 83: 1022–1024. 3. Dalgas U and Stenager E. Exercise and disease progression in multiple sclerosis: can exercise slow down the progression of multiple sclerosis? Ther Adv Neurolog Dis 2012; 5: 81–95. 4. Marck CH, Hadgkiss EJ, Weiland TJ, et al. Physical activity and associated levels of disability and quality of life in people with multiple sclerosis: a large international survey. BMC Neurol 2014; 14: 143. 5. Jick SS, Li L, Falcone GJ, et al. Mortality of patients with multiple sclerosis: a cohort study in UK primary care. J Neurol. 2014; 261: 1508–1517. 6. Marrie RA and Hanwell H. General health issues in multiple sclerosis: comorbidities, secondary
15. Eriksen L, Curtis T, Gronbaek M, et al. The association between physical activity, cardiorespiratory fitness and self-rated health. Prev Med 2013; 57: 900–902. 16. Motl RW, McAuley E, Wynn D, et al. Physical activity, self-efficacy, and health-related quality of life in persons with multiple sclerosis: analysis of associations between individual-level changes over one year. Qual Life Res 2013; 22: 253–261. 17. Stickland MK, Butcher SJ, Marciniuk DD, et al. Assessing exercise limitation using cardiopulmonary exercise testing. Pulmonary Med 2012; 2012: 824091. 18. Romberg A, Virtanen A, Aunola S, et al. Exercise capacity, disability and leisure physical activity of subjects with multiple sclerosis. Mult Scler 2004; 10: 212–218. 19. Hakola L, Komulainen P, Hassinen M, et al. Cardiorespiratory fitness in aging men and women: the DR’s EXTRA study. Scand J Med Sci Sport 2011; 21: 679–687.
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research-article2015
MSJ0010.1177/1352458515581437Multiple Sclerosis JournalHeine et al.
MULTIPLE SCLEROSIS MSJ JOURNAL
Original Research Paper
Cardiopulmonary fitness is related to disease severity in multiple sclerosis Martin Heine, Inez Wens, Martin Langeskov-Christensen, Olaf Verschuren, Bert O Eijnde, Gert Kwakkel and Ulrik Dalgas
Abstract Background: In persons with MS (pwMS), a lower cardiopulmonary fitness has been associated with a higher risk for secondary disorders, decreased functional capacity, symptom worsening and reduced health-related quality of life. Objective: To investigate the association between disease severity and cardiopulmonary fitness. Methods: Data from cardiopulmonary exercise tests, previously conducted in three different countries, were pooled. The association between disease severity (Expanded Disability Status Scale (EDSS)) and cardiopulmonary fitness (peak oxygen uptake (VO2peak)) was adjusted for age, sex and the country of origin. Results: The combined sample comprised 116 ambulant pwMS having a mean (± SD) EDSS score of 2.7 ± 1.3. There was a significant correlation (r = -0.418, p < .01) between VO2peak and EDSS. A multiple regression model (R2 = 0.520, p < .01) was constructed to describe VO2peak (mL∙kg−1∙min−1); VO2peak = 36.622 – 5.433 (Sex (1=men)) – 0.124 (Age) – 2.082 (EDSS) + 2.737 (Belgium) + 8.674 (Denmark). Conclusion: There was a significant association between disease severity and cardiopulmonary fitness. The close relation between cardiopulmonary fitness and chronic conditions associated with physical inactivity, suggest a progressive increase in risk of secondary health conditions in pwMS
Keywords: Multiple sclerosis, Expanded Disability Status Scale, exercise test, cardiopulmonary fitness, aerobic capacity, comorbidity Date received 17 November 2014; revised 23 February 2015, 16 March 2015; accepted 17 March 2015
Introduction Multiple sclerosis (MS) is considered an autoimmunemediated chronic inflammatory and neurodegenerative disorder of the central nervous system (CNS).1 The majority of persons with MS (pwMS) will experience a progressive increase of disease severity and decline in physical and cognitive functioning.2 Due to the diffuse damage to the CNS, the disease is characterized by a variety of sensory, motor, cerebellar and cognitive dysfunctions. These dysfunctions limit physical activity behavior in pwMS, and may result in subsequent deconditioning.3,4 Hence, it is not surprising that MS has been associated with an increased risk for a variety of other chronic health conditions which have been related to physical inactivity and a sedentary lifestyle such as cardiovascular disease, pneumonia, cancer,
non-infectious respiratory diseases and mental comorbidities.5–12 These secondary comorbidities may increase the all-cause mortality rate of pwMS by as much as 1.7 fold,5 and are associated with increased disability progression, reduced health-related quality of life of pwMS, and are the primary reason for hospitalization.6,13,14 Consequently, improving or maintaining physical activity and reducing the risk of secondary chronic disorders is important in pwMS. Cardiopulmonary fitness is considered a key marker of health and performance, and is closely associated with the level of physical activity and sedentary time.15,16 Direct measurement of the whole body peak oxygen consumption determined by an incremental exercise test to exhaustion is considered the gold
Multiple Sclerosis Journal 1–8 DOI: 10.1177/ 1352458515581437 © The Author(s), 2015. Reprints and permissions: http://www.sagepub.co.uk/ journalsPermissions.nav
Correspondence to: Martin Heine Brain Center Rudolf Magnus Institute of Neuroscience and Center of Excellence for Rehabilitation Medicine, University Medical Center Utrecht and Rehabilitation Center De Hoogstraat, Rembrandtkade 10, 3583 TM, Utrecht, The Netherlands. [email protected] Martin Heine Olaf Verschuren Brain Center Rudolf Magnus and Center of Excellence for Rehabilitation Medicine, University Medical Center Utrecht and Rehabilitation Center De Hoogstraat, Utrecht, The Netherlands Inez Wens Bert O Eijnde Rehabilitation Research Center (REVAL), Biomedical Research Institute, Faculty of Medicine and Life Sciences, Hasselt University, Hasselt, Belgium Martin LangeskovChristensen Ulrik Dalgas Department of Public Health, Section of Sport Science, Aarhus University, Aarhus, Denmark Gert Kwakkel Department of Rehabilitation Medicine, MOVE Research Institute Amsterdam, VU University Medical Center, Amsterdam, The Netherlands; Department of Neurorehabilitation, Centre of Rehabilitation and Rheumatology READE, Amsterdam, The Netherlands
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Multiple Sclerosis Journal Table 1. Study characteristics and testing procedures of each contributing country. Belgium, N = 33 (clinicaltrials.gov NCT01845896) Recruited for: an intervention study to assess the effects of high-intensity exercise training on muscle contractile properties and endurance capacity in persons with MS. Test protocol: Men: 30 Watt + 15 Watt/min Women: 20 Watt + 10 Watt/min Cadence: 70 rotations/min Test equipment: Ergometer: eBike Basic (electro-mechanical braking system ), General Electric GmbH, Germany Gas exchange measurement: Jaeger Oxycon (breath-by-breath), Erich Jaeger GmbH, Germany Denmark, N = 20 (not registered) Recruited for: a validity and reliability study of maximal exercise testing in persons with MS and healthy controls23 Test protocol: 22.5–80 Watt + 11.25–42.5 Watt/1.5 min Cadence: self-chosen between 45 and 85 rotations/min Test equipment: Ergometer: Monark Ergomedic 828E (brake belt), Monark, Sweden Gas exchange measurement: AMIS 2001 (mixing-chamber), Innovision, Denmark The Netherlands, N = 63 (controlled-trials.com ISRCTN69520623) Recruited for: an intervention study to assess the effects of aerobic endurance training on fatigue in persons with MS. Test protocol: Men: 25 Watt + 15 Watt/min Women: 25 Watt + 10 Watt/min Cadence: between 60-80 rotations/min Test equipment: Ergometer: Kettler X7 (electro-mechanical braking system), Kettler, Germany Gas exchange measurement: Metamax 3B (mixing-chamber) Cortex Medical, Germany
standard assessment for cardiopulmonary fitness.17 As such, the peak oxygen uptake (VO2peak) has been associated with a variety of outcomes covering all levels within the framework of the International Classification of Functioning (body function, activity, participation), underlining the importance of this parameter in pwMS.13 However, cardiopulmonary fitness is a sensitive measure and intuitively, one would expect the cardiopulmonary fitness to decrease as disability increases in pwMS. Several cross-sectional studies in small samples have reported correlations between the disability level in terms of the Expanded Disability Status Score (EDSS) and VO2peak in pwMS.18 Nevertheless, it is still unclear how the disability level of pwMS per se affects cardiopulmonary fitness, since none of the existing studies were adjusted for age and gender, which are known to affect cardiopulmonary fitness in healthy people.19,20 For such an analysis, it is important that the sample studied is of sufficient size to
provide the required statistical power to perform multivariable analysis. Accordingly, the objective of the present study was to assess the relation between disease severity and cardiopulmonary fitness in a large sample of ambulatory pwMS, adjusting for age and sex. For this purpose, a secondary analysis was done on data from three different European countries. It was hypothesized that disability per se would affect the cardiopulmonary fitness in pwMS. Methods Data from three different countries (Belgium, Denmark and The Netherlands) were pooled, and a sample of 116 ambulant pwMS was obtained. Table 1 provides an overview of the three different studies and their testing procedures. Each study assessed cardiopulmonary fitness using an incremental leg-cycling protocol until voluntary exhaustion. In addition, the level of
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M Heine, I Wens et al. disability was assessed by means of the EDSS by a certified physician/neurologist (http://www.neurostatus.net). The EDSS is a widely used clinician-administered assessment scale evaluating the functional systems of the CNS. It can be used to describe disease progression in pwMS as well as to assess the effectiveness of interventions during clinical trials. It consists of an ordinal rating scale ranging from 0 (normal neurological functioning) to 10 (death due to MS) with 0.5 increments. The lower scale values (< 4.5) of the EDSS are based on the neurological examination, the middle scale values (4.0–6.0) rely heavily on walking ability, whereas the upper scale values (> 6.0) measure the disabilities of pwMS.21 In all three countries only subjects with an EDSS ⩽ 6.0 with definite, stable MS were included. In addition, participants that had a contra-indication to perform maximal exercise were excluded. Specific for The Netherlands was that these participants had a predefined high level of subjective fatigue as indicated by a score ⩾ 35 on the Checklist Individual Strength fatigue subscale. Three different independent variables were considered as confounders of the association between disease severity and cardiopulmonary fitness: age, gender and the country of origin. In healthy people, cardiopulmonary fitness in known to be lower in women compared to men, and, moreover, that cardiopulmonary fitness declines with age. The country of origin was a third potential confounder as it was hypothesized that differences in sample characteristics, testing procedures, inclusion criteria of the respective study in which context the data was collected, or merely differences in cardiopulmonary fitness between countries may distort the association between disease severity and cardiopulmonary fitness. Statistical analysis Between country differences were assessed by means of independent t-tests, one-way analysis of variance or a chi-square test depending on the determinant and outcome. To control for multiple testing, during the one-way analysis of variance, a Bonferroni correction was applied. The bivariable association between the continuous independent variables (age and EDSS) and the dependent variable, peak oxygen uptake corrected for body weight (mL∙kg−1∙min−1), was assessed by means of Pearson correlation coefficients. Subsequently, a multiple linear regression analysis was performed to assess the adjusted association of disease severity (EDSS) with VO2peak. If the correlation between two independent variables exceeded 0.800, these were considered multicollinear and the variable with the lowest association
with the dependent variable was omitted from the multivariable regression model. Finally, it was studied how adding candidate covariates to the univariable model affected the association between EDSS and VO2peak. If the regression coefficient of EDSS with VO2peak changed >15% after the variable was added to the model, this variable was considered to be a covariate that confounded the relationship between EDSS and VO2peak. Analysis of the distribution of the dependent and independent variables showed a normal distribution. Hence, the parametric tests described above, and the use of the ordinal EDSS scale as a continuous outcome, can be considered appropriate. Outcomes are presented as mean ± standard deviation (SD) or mean difference (MD) ± standard error (SE). All tests were conducted with a significance level of p < 0.05 applying SPSS statistical software package 21.0. Results Table 2 shows the participant characteristics of the total sample as well as per country. Of the total 116 participants, 69.8% were women. The average age was 44.4 ± 9.7 years, and the level of disability was 2.7 ± 1.3 on the EDSS scale. The mean VO2peak was 1.784 ± 0.609 L∙min−1 or when corrected for bodyweight, 23.9 ± 8.0 mL∙kg−1∙min−1. The VO2peak of men was significantly higher compared to women (MD = 6.5 ± 1.5). One-way analysis of variance showed that, the participants in Denmark were significantly younger (MDBelgium = 6.4 ± 2.7 years; MDNetherlands = 6.4 ± 2.4 years), had a higher peak power (MDBelgium = 62 ± 14 Watt; MDNetherlands = 49 ± 13 Watt) output as well as VO2peak (MDBelgium = 7.1 ± 1.9 mL∙kg−1∙min−1; MDNetherlands = 11.2 ± 1.8 mL∙kg−1∙min−1), compared to Belgium and the Netherlands. In addition, the participants in Belgium had a significant higher VO2peak compared to the participants from the Netherlands (MDNetherlands = 4.2 ± 1.5 mL∙kg−1∙min−1). Analysis of the association between age, EDSS and VO2peak revealed no signs of multicollinearity. However, correlation analysis did reveal significant associations between age and EDSS (r = 0.399, p < .01), age and VO2peak (r = -0.371, p 15% (bold) was considered a confounding covariate. Model 3: multivariable analysis to determine the adjusted association between EDSS and VO2peak. a p < 0.05. b p < 0.01. c The Netherlands served as reference country.
VO2peak. The relation between EDSS and VO2peak was confounded by age. Based on the final multiple regression model, VO2peak can be calculated as:
(
VO 2 peak mL ⋅ kg −1 ⋅ min −1
)
= 36.622 − 5.433
(SEX; 0 = male, 1 = female ) − 0.124 ( AGE ) − 2.082 ( EDSS) + 2.737 ( Belgium ) + 8.674 ( Denmark ) Based on the R2 the model explains 52% of the variance in VO2peak, showing that VO2peak decreases with increasing EDSS. Discussion The present study pooled data from three different European countries to assess the relation between disease severity and cardiopulmonary fitness in pwMS, adjusting for age, sex, and country of origin. Based on the multivariable regression analysis, disease severity is significantly associated with a decrease in VO2peak, the gold standard measure of cardiopulmonary fitness.22,23 Reductions in cardiopulmonary fitness have been associated with numerous health complications and decreased physical performance.6,24 To our knowledge, this is the first sufficiently powered study to assess the association between disease severity and cardiopulmonary fitness in pwMS adjusting for natural changes with respect to aging and differences between men and women. This is of importance when
trying to improve our understanding of cardiopulmonary fitness in pwMS, and (secondary) health risks with increasing disease severity. Kodama et al.24 showed, in healthy people, that a 1-metabolic equivalent (1 MET = 3.5 mL∙kg−1∙min−1) lower level of cardiopulmonary fitness corresponded to a relative risk of 1.15 (95%CI 1.11–1.19) for allrisk mortality, and 1.18 (95%CI 1.14–1.22) for coronary heart disease (CHD)/cardiovascular disease (CVD), respectively. Based on the present study, pwMS with an EDSS of 6.0 have a 3.6 MET lower VO2peak, which theoretically induces a 4.1 fold higher risk at all-cause mortality and a 4.3 fold higher risk of CHD/CVD compared to pwMS with no neurological symptoms (EDSS = 0). In addition, Kodama et al.24 distinguished between low fitness (< 7.9 MET), moderate fitness (7.9 – 10.8 MET) and high fitness (> 10.8 MET). In the present study, and according to these definitions, 76.7% of pwMS had low fitness, only 17.2% had moderate fitness, and 6.0% had high fitness.25 However, it is unclear if this distribution between low, moderate, and high fitness is different from healthy people. Moreover, one may question if VO2peak derived from a CPET is actually a measure of cardiopulmonary fitness in pwMS having an EDSS score > 4.0, as these patients may often not reach the recommend criteria indicative of maximal exercise.22 Hence, in these pwMS the test may reflect functional capacity rather than cardiopulmonary fitness, which may bias the 76.7% of pwMS that had low fitness. Nonetheless, the results of the present study are in line with a recent review that suggests that cardiopulmonary fitness in pwMS is generally poor as
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Multiple Sclerosis Journal compared to the healthy population, and affected by disease progression such that it elicits significant health risks as well as being associated with a variety of MS symptoms.13 The question remains to what extent the decline in cardiopulmonary fitness with increasing disability can be prevented. A potential cause for the lower cardiopulmonary fitness with higher levels of disease severity can be related to changes in physical activity. Durstine et al. described a circular process whereby persons with a chronic illness or disability experience less physical activity, which then leads to a cycle of deconditioning and further physical deterioration and reduction in activity and cardiopulmonary fitness.26 As seen in this study, PwMS with low levels of disease severity already have lower VO2peak values compared to typically developing sedentary peers (39.0 ± 6.8 mL∙kg−1∙min−1 for men and 30.0 ± 5.4 mL∙kg−1∙min−1 for women).27 The low fitness levels seen in pwMS at low disability levels may also be related to the underlying MS pathology, for instance due to altered immune function, autonomic function, or substrate metabolism.28,29 Nonetheless, introduction to an active lifestyle that includes physical fitness promotion might be beneficial for future health and function. Though, disease progression may limit the ability of pwMS to maintain an appropriate physical activity level both from a physical viewpoint as well as a pragmatic (i.e. mobility, independence, etc.) viewpoint. Exercise therapy, and specifically aerobic training, may elicit significant improvements in cardiopulmonary fitness in healthy persons as well as in pwMS. A meta-analysis showed that cardiopulmonary fitness in pwMS on average improved 3.5 mL∙kg−1∙min−1 (1 MET) in the existing aerobic exercise intervention studies.13 However, the duration of the exercise interventions included for this meta-analysis were in general too short to expect significant disease progression over the same period of time. Hence, the estimated improvement of 3.5 mL∙kg−1∙min−1 in VO2peak would suggest a ‘better in – better out’ principle rather than a reduced decline in cardiopulmonary fitness with an increase in disease severity. On the other hand, disease progression may also be exaggerated by reductions in physical activity or a sedentary lifestyle, thereby suggesting exercise therapy as a disease-modifying treatment. Exercise or physical activity may have an anti-inflammatory and neuroprotective function preserving gray matter volume, white matter integrity and reduce lesion load.3,30,31 Sumowski and colleagues added to this conception by showing that cognitive and brain reserve at baseline protect against cognitive decline
over a 4.5 year period.32 A similar principle may be true for motor reserve in relation to motor function and fatigability.31 A study by Motl and colleagues showed, for instance, that the level of physical activity prior to the diagnosis of MS significantly predicted the linear change in disability scores, which suggests a potential preventative function, in terms of disease progression, of physical activity in pwMS, and a repressive function for a sedentary lifestyle.3,31 However, a recent review evaluating whether exercise can influence disease progression in MS concluded that data exist that indicate a possible disease-modifying effect of exercise, but that the strength of the evidence limits definite conclusions at present.3 An unexpected finding in the present study is the large differences in cardiopulmonary fitness between the different European countries. There are multiple factors that can explain these differences. First, it is possible that the different purposes for which these pwMS were recruited, and the inclusion criteria that were used, induced a substantial selection bias. Second, cardiopulmonary fitness may indeed be different due to differences in the health care structure, which may result in a different availability of (para) medical resources (e.g. free physiotherapy once a week in Denmark). A third explanation relates to the differences in testing protocol and equipment used between the different countries. For instance, the Cortex Metamax 3B, used in The Netherlands has been shown to overestimate VO2peak by 10–17% compared to the Jaeger Oxycon used in Belgium.33 To our knowledge, no study has compared different maximal exercise protocols or gas exchange measurement equipment in pwMS. Hence, the difference between countries, as shown in the multivariable regression model, should not be interpreted as a true difference in cardiopulmonary fitness, but rather a ‘black box’ in which all the above explanations may contribute to some extent. Limitations One limitation of the present study is the cross-sectional design. The present study shows that pwMS with a high EDSS level have a lower VO2peak. However, this study does not show the longitudinal construct validity between a high cardiopulmonary fitness and EDSS over time (i.e. better in – better out). Moreover, even though treated as a continuous outcome in the present study, the EDSS scale is an ordinal scale, meaning that a 1-point difference on the EDSS scale, and the associated change in VO2peak, may have a different clinical meaning at different stages of the EDSS scale. A second limitation is the
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M Heine, I Wens et al. slightly different protocols used for maximal exercise testing. We were, however, able to correct for these differences by including country as an independent variable in the multivariable regression analysis. Nonetheless, the underlying components leading to the differences between countries, which could be related to testing procedures, use of equipment, inclusion criteria and patient characteristics, were essentially unexplored. Third, even though all three related studies included pwMS up to an EDSS of 6.0, the majority of participants had an EDSS score ⩽ 4.0. This restricts the generalization of the results to ambulant pwMS able to walk > 500 metres. Conclusion Ambulant pwMS with a higher level of disease severity, as measured by the EDSS scale, have lower levels of cardiopulmonary fitness. The close relation between cardiopulmonary fitness and chronic conditions associated with physical inactivity, suggest a progressive increase in risk of secondary health conditions in pwMS.
conditions, and health behaviors. Continuum (Minneap Minn) 2013; 19: 1046–1057. 7. Wens I, Dalgas U, Stenager E, et al. Risk factors related to cardiovascular diseases and the metabolic syndrome in multiple sclerosis – a systematic review. Mult Scler 2013; 19: 1556–1564. 8. Marrie RA, Reider N, Stuve O, et al. The incidence and prevalence of comorbid gastrointestinal, musculoskeletal, ocular, pulmonary, and renal disorders in multiple sclerosis: a systematic review. Mult Scler 2015; 21(3): 332–341. 9. Marrie RA, Reider N, Cohen J, et al. A systematic review of the incidence and prevalence of cancer in multiple sclerosis. Mult Scler 2015; 21(3): 294–304. 10. Marrie RA, Reider N, Cohen J, et al. A systematic review of the incidence and prevalence of sleep disorders and seizure disorders in multiple sclerosis. Mult Scler 2015; 21(3): 342–349. 11. Marrie RA, Reider N, Cohen J, et al. A systematic review of the incidence and prevalence of cardiac, cerebrovascular, and peripheral vascular disease in multiple sclerosis. Mult Scler 2015; 21(3): 318–331. 12. Marrie RA, Reider N, Cohen J, et al. A systematic review of the incidence and prevalence of autoimmune disease in multiple sclerosis. Mult Scler 2015; 21(3): 282–293.
Conflict of interest None declared. Funding This research received no specific grant from any funding agency in the public, commercial or not-forprofit sectors.
13. Langeskov-Christensen M, Heine M and Kwakkel G, et al. Aerobic capacity in persons with multiple sclerosis: a systematic review and meta-analysis. Sports Med. Epub ahead of print 5 March 2015. DOI: 10.1007/s40279-015-0307-x. 14. Marrie RA, Elliott L, Marriott J, et al. Dramatically changing rates and reasons for hospitalization in multiple sclerosis. Neurology 2014; 83: 929–937.
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