Correlation between fatigue and brain atrophy and lesion load in multiple sclerosis patients independent of disability

Journal of the Neurological Sciences 263 (2007) 15 – 19 www.elsevier.com/locate/jns

Correlation between fatigue and brain atrophy and lesion load in multiple sclerosis patients independent of disability Gioacchino Tedeschi a,b,⁎, Daria Dinacci a , Luigi Lavorgna a , Anna Prinster c , Giovanni Savettieri d , Aldo Quattrone e , Paolo Livrea f , Corrado Messina g , Arturo Reggio h , Giovanna Servillo a , Vincenzo Bresciamorra i , Giuseppe Orefice i , Marcantonio Paciello j , Arturo Brunetti c,k , Andrea Paolillo l , Gabriella Coniglio j , Simona Bonavita a,b , Alfonso Di Costanzo a , Alessandra Bellacosa f , Paola Valentino e , Mario Quarantelli c , Francesco Patti h , Giuseppe Salemi d , Enrico Cammarata d , Isabella Simone f , Marco Salvatore c,k , Vincenzo Bonavita b,i , Bruno Alfano c a

d

Department of Neurological Sciences, Second University of Naples, Naples, Italy b Institute Hermitage Capodimonte, Naples, Italy c Institute of Biostructure and Bioimaging, National Research Council, Naples, Italy Department of Neurology, Ophthalmology, Otorhinolaryngology and Psychiatry, University of Palermo, Palermo, Italy e Department of Neurology, University of Catanzaro, Catanzaro, Italy f Department of Neurological and Psychiatric Sciences, University of Bari, Bari, Italy g Department of Neurology, University of Messina, Messina, Italy h Department of Neurology, University of Catania, Catania, Italy i Department of Neurology, University of Naples “Federico II”, Naples, Italy j Department of Neurology, San Carlo Hospital, Potenza, Italy k Department of Diagnostic Imaging, University of Naples “Federico II”, Naples, Italy l Department of Neurological Sciences, University of Rome “La Sapienza”, Rome, Italy Received 5 March 2007; received in revised form 1 June 2007; accepted 3 July 2007 Available online 1 August 2007

Abstract Background: Fatigue is a major problem in multiple sclerosis (MS), and its association with MRI features is debated. Objective: To study the correlation between fatigue and lesion load, white matter (WM), and grey matter (GM), in MS patients independent of disability. Methods: We studied 222 relapsing remitting MS patients with low disability (scores ≤ 2 at the Kurtzke Expanded Disability Status Scale). Lesion load, WM and GM were measured by fully automated, operator-independent, multi-parametric segmentation method. T1 and T2 lesion volume were also measured by a semi-automated method. Fatigue was assessed by the Fatigue Severity Scale (FSS), and patients divided in high-fatigue (FSS ≥ 5; n = 197) and low-fatigue groups (FSS ≤ 4; n = 25). Results: High-fatigue patients showed significantly higher abnormal white matter fraction (AWM-f), T1 and T2 lesion loads, and significant lower WM-f, and GM-f. Multivariate analysis showed that high FSS was significantly associated with lower WM-f, and GM-f. Females and highly educated patients were significantly less fatigued.

⁎ Corresponding author. Department of Neurological Sciences Second University on Naples Piazza Miraglia 2, 80138 Naples, Italy. Tel.: +39 081 5665095; fax: +39 081 5665096. E-mail address: [email protected] (G. Tedeschi). 0022-510X/$ - see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.jns.2007.07.004

16

G. Tedeschi et al. / Journal of the Neurological Sciences 263 (2007) 15–19

Conclusion: These results suggest that among MS patients with low disability those with high-fatigue show higher WM and GM atrophy and higher lesion load, and that female sex and higher levels of education may play a protective role towards fatigue. Furthermore, they suggest that in MS, independent of disability, WM and GM atrophy is a risk factor to have fatigue. © 2007 Elsevier B.V. All rights reserved. Keywords: Multiple sclerosis; Fatigue; Atrophy; White matter; Grey matter; Disability

1. Introduction Fatigue is a common and disabling symptom in multiple sclerosis (MS) [1]. It affects more than 80% of patients, and in more than 40% it is considered the most disabling symptom [2–5]. Growing evidence suggest that fatigue in MS can be caused by cortico-subcortical interconnection damage in critical sites of the CNS, such as the cortico-spinal tract. Cerebral glucose metabolism was found reduced in a number of cortical and subcortical areas of fatigued MS patients [6]. Functional magnetic resonance imaging (MRI) studies suggest that fatigue in MS is related to impaired interactions between functionally related cortical and subcortical areas [7]. The correlation between fatigue and conventional MRI is debated [8–12], although recent studies based on advanced MRI techniques have reported a significant association of fatigue with brain atrophy [13] and diffuse periventricular reduction of the putative neuronal marker N-acetylaspartate (NAA) [14]. A major possible caveat when assessing the correlation between fatigue and MRI parameters is the influence of the overall physical disability on fatigue. Furthermore, a number of evidence [15–20] suggest that, although MS is a white matter (WM) disease, disability may be associated with the concomitant or even selective involvement of the grey matter (GM). Therefore, we sought to assess the association between fatigue and lesion load, and WM, GM and whole brain atrophy in a large cohort of MS patients independent of disability. 2. Methods 2.1. Subjects This work derives from a multi-centre, cross-sectional study based on 597 MS patients [15]. In each centre, the same neurologist assessed the Kurtzke Expanded Disability Status Scale (EDSS) [21] and administered the Fatigue Severity Scale (FSS) [1] questionnaire to assess subjective, general fatigue. The patients filled out the questionnaire by assigning a number between 1 and 7 to the single items, with higher scores indicating more fatigue. The answers were then averaged, resulting in a global fatigue score for each patient. The patients were divided into two groups on the basis of their FSS score: the lowfatigue group (FSS ≤ 4) and the high-fatigue group (FSS ≥ 5). To assess the correlation between FSS and MRI parameters

independent of disability we selected relapsing remitting (RR) MS patients [22] with a EDSS score ≤ 2. This cut off was chosen because statistical analysis showed that EDSS score ≤ 2 was not a significant predictor of high-fatigue (OR = 2.39 95% CI 0.85–6.74). Patients whose FSS score was between 4 and 5 were also excluded. The choice of these FSS parameters was based on previous works [6,9,23]. A total of 222 patients were included in the present study: 197 with low-fatigue and 25 with high-fatigue. Criteria for excluding patients were ongoing clinical relapse, other major medical illnesses, history of substance abuse and corticosteroid treatment within 12 weeks of the start of the study. We excluded patients taking antidepressants, anxiolytics, or fatigue modulating drugs, such as amantadine, modafinil, and L-carnitine or because they referred a previous history of depression. The protocol was approved by local ethic committees. All enrolled patients were examined on the same day as the MRI session. All participants gave written informed consent. 2.2. MRI studies The same MRI protocol was performed in all subjects using the same MRI machine (1.0 T Genesys Signa; GE Medical Systems, Milwaukee, USA). For each study two interleaved sets of 15 slices (4 mm thick) covering the entire brain were acquired using two conventional spin-echo sequences (TR/TE 600/15 m/s) for each set, two averages; TR/ TE 2300/15, 90 m/s dual echo sequence, one average; both with 90° flip angle, 256 × 192 matrix; FOV (240 mm). All the studies were segmented using a multi-spectral fully automated method, based on relaxometric characterization of brain tissues [24,25]. The program furnishes complete sets of multi-feature images [R1(= 1 / T1), R2(= 1 / T2), proton density (N(H))-based] and segmented images, and calculates the volumes of the following intracranial tissues: abnormal WM (AWM), WM (including the AWM and the normal appearing WM), and GM. To normalize for head size variability, the volumes of intracranial tissues were expressed as fractions (f) of the intracranial volume, which were calculated for each subject as the sum of all intracranial tissues. AWM-f is a measure of lesion load as determined by the R1, R2 and N(H) information and morphological characteristics, the reduction of WM-f indicates WM atrophy, and the reduction of GM-f indicates GM atrophy. Furthermore, to differentiate the major components of AWM, an expert radiologist blinded to patients' clinical conditions, defined the T2-weighted hyperintense and

G. Tedeschi et al. / Journal of the Neurological Sciences 263 (2007) 15–19 Table 1 Comparison of demographic and clinical variables using Student t test for independent samples (low FSS vs high FSS)

Age (years) Age at onset (years) Disease duration (years) Education (years) Number of relapses AWM-f WM-f GM-f T2 lesion T1 lesion

FSS

N

Mean

SD

Low High Low High Low High Low High Low High Low High Low High Low High Low High Low High

197 25 197 25 197 25 197 25 197 25 197 25 197 25 197 25 108 20 108 20

34 39 27 29 6 10 12 10 0.79 0.92 0.01 0.02 0.35 0.34 0.52 0.49 7119.75 17485.97 868.03 2560.47

9.05 9.18 8.35 9.44 5.69 6.56 3.49 3.45 0.99 0.95 0.01 0.01 0.03 0.03 0.03 0.04 6571.47 16736.48 1704.94 3797.60

p 0.004 0.219 0.006 0.004 0.526 0.001 0.147 b0.001 b0.001

17

Results of univariate and multivariate logistic regression are showed in Table 2. For each brain MRI fraction we considered the median value and divided patients into two groups, higher or lower than the median value and inserted this variable into our regression models to investigate the relationship between MRI fraction and FSS. Univariate logistic regression results show that sex, age, disease duration, education and MRI parameters fraction are related to high FSS. Multivariate logistic regression was performed only for those variables that showed a significant correlation at the univariate regression analysis, and shows that high FSS was significantly associated with lower WM-f and GM-f (OR = 3.07 and 4.30 respectively). No significant association was found between FSS and lesion load. Females (OR = 0.30) and patients with higher levels of education (OR = 0.88) were significantly less affected by MS related fatigue. Area under ROC curve of the model is 0.798, thus suggesting that the model has good discriminative ability. 4. Discussion

0.002

FSS: Fatigue Severity Scale; AWM-f: abnormal white matter fraction; WM-f: white matter fraction; GM-f: grey matter fraction.

T1-weighted hypointense areas. Then, to calculate lesion volume, a semi-automated quantification method running on a LINUX Debian workstation was used (D-Image, Dilogix S.p.A., Italy) [26]. 2.3. Statistics In order to evaluate statistically significant differences between higher and lower FSS we used Pearson's chi square test for categorical variables and Student t test for independent sample to compare quantitative variables. Univariate and multivariate logistic regression were performed to determine risk factors associated with higher FSS. Area under receiver operating characteristic (ROC) curve indicated ability of multivariate logistic model to discriminate, with values between 0.70 and 0.90 indicating a good level of accuracy [27]. 3. Results Patients were divided into two groups: low FSS and high FSS. Comparing these two groups in terms of gender we observed that men had higher FSS than women (p value, chi square= 0.014, data not shown). There were no statistically significant differences in terms of type of treatment (never treated, discontinued therapy, interferone, other, p = 0.095). Table 1 shows the comparison of demographic and clinical variables using Student t test for independent samples: there are no statistically significant differences in terms of age at onset, number of relapses and WM-f (p = 0.147). On the other hand, patients with higher FSS are statistically older, and have significantly lower level of education, longer disease duration, lower GM-f, higher AWM-f and higher T1 and T2 lesion loads.

This study investigated the association between fatigue and the possible selective involvement of WM and GM in a large population of MS patients independent of disability. We sought to assess the correlation between fatigue and MRI parameters in MS patients independent of disability as a number of evidence showed that fatigue and physical disability are significantly associated in MS patients [4,28]. Furthermore, since the potentially substantial destructive or degenerative changes in both WM and GM have been emphasized by a number of studies based on advanced MRI techniques [15–19], we assessed the correlation of fatigue with both WM (lesion load and atrophy) and GM involvement. The cumulative effect of diffuse WM and GM damage may result in brain atrophy, which may be visible on conventional MRI or may be better Table 2 Univariate and multivariate regression models for having high FSS Predictors

Sex

Univariate

Multivariate

OR

Adj OR a 95%CI

95%CI

Male 1.00 Female 0.36 0.15–0.83 Age (years) 1.06 1.02–1.11 Age at onset (years) 1.03 0.98–1.08 Disease duration 1.08 1.02–1.15 (years) Education (years) 0.84 0.74–0.95 Number of relapses 1.13 0.77–1.67 AWM-f (median) Higher 1.00 Lower 0.35 0.14–0.87 WM-f (median) Higher 1.00 Lower 2.88 1.15–7.19 GM-f (median) Higher 1.00 Lower 4.57 1.65–12.65 T2 lesion 1.00 1.00–1.00 T1 lesion 1.00 1.00–1.00

1.00 0.30

0.12–0.77

0.88

0.77–1.00

1.00 3.07 1.00 4.30

1.12–8.43 1.47–12.54

FSS: Fatigue Severity Scale; AWM-f: abnormal white matter fraction; WMf: white matter fraction; GM-f: grey matter fraction. a All variables are adjusted.

18

G. Tedeschi et al. / Journal of the Neurological Sciences 263 (2007) 15–19

detected by advanced MRI techniques. These have evolved from semi-automated, operator-dependent segmentation techniques to automated, operator-independent ones (for review see Pellettier et al. [29]), like the one used in the present study [24,25]. The results of this study suggest that fatigue is correlated to the diffuse CNS involvement even in MS patients with low disability, as patients with high-fatigue showed significantly higher lesion load, and WM, and GM atrophy than patients with low-fatigue. Although patients with higher FSS had higher lesion load, the univariate and multivariate statistical analysis did not identify lesion load as a risk factor to be fatigued. Conversely, our data suggest that fatigue, independent of disability, is significantly related to a destructive pathological process involving both WM and GM (as measured by WM and GW atrophy). Furthermore, they suggest that higher levels of education and female sex may play a protective role towards fatigue. In the present study, we could not ascertain the possible role of adaptive functional cortical–subcortical reorganization in influencing the level of fatigue. Indeed, a recent functional MRI study [30] showed that, in the execution of simple motor tasks, fatigued MS patients had a different pattern of movement associated with cortical and subcortical activation. This pattern could be associated with a major effort's perception during motor activity, thus leading to the symptom of fatigue. The correlation between MRI data and fatigue is debated. Positive findings were found in a study conducted in two groups of patients with MS with (15 cases) and without fatigue (15 cases), matched for sex, age, disease duration, depression score, and EDSS. Parietal lobe, internal capsule and periventricular trigone lesion loads were significantly higher in patients with fatigue than in those without [8]. Other previous MRI studies, based on smaller patients' samples, have unsuccessfully attempted to correlate fatigue and MRI findings as T2 lesion load, brain atrophy, monthly gadolinium enhancing lesions and magnetization transfer ratios [9–12]. Recently, in a longitudinal study performed in a cohort of 134 patients with MS [13] no correlation was found between fatigue (measured by the Sickness Impact Profile's Sleep and Rest Scale) and brain atrophy at baseline. However, there was a significant association between worsening fatigue during the initial 2 years and progressive brain atrophy over the next 6 years, when comparing patients who showed an increased fatigue with those who showed a decreased fatigue. Finally, in a proton MR spectroscopy [14] study performed on 73 patients with MS, the putative neuronal marker NAA, independent of the EDSS, was significantly lower in the high-fatigue group than in the lowfatigue group. Although the correlation between depression and fatigue in MS is not fully elucidated, we are aware that a possible methodological caveat of the present study is the lack of formal depression evaluation. There are reports showing no relationship between fatigue and depression in patients with MS [2,31], while a significant correlation was reported in a more recent study [32]. In the present study, depression was not formally assessed, as clinical and laboratory evaluations were

considered to be already too long for the patients. Nevertheless, we attempted to control for the effect of depression by excluding patients taking antidepressants, anxiolytics, or fatigue modulating drugs, or referring a previous history of depression. The significant negative correlation between fatigue and education in RR MS patients with low disability is in agreement with a previous work where FSS was assessed in 368 MS by a mailed questionnaire [33]. This finding is particularly complex to explain, and we believe that the hypotheses suggested by Lerdal et al. [33] are all equally valuable, although we prefer the one suggesting that patient's level of education reflects their ability to learn, and that this higher learning capacity makes them more adaptable to fatigue. In the present study we found a significant inverse correlation between fatigue and female sex. On line with previous studies [34], which showed that females have less degeneration and more inflammation than males, we found that T1 lesion volume was significantly smaller in female than in male patients (mean 813 mm3 vs 1786 mm3; p = 0.020), while there was no significant difference in T2 volume among sexes. In conclusion, our results suggest that among MS patients with low disability those with high-fatigue show higher WM and GM atrophy and higher lesion load, and that female sex and higher levels of education may play a protective role towards fatigue. Furthermore, they suggest that in MS, independent of disability, WM and GM atrophy is a risk factor to have fatigue. Acknowledgments The authors wish to thank “Fondazione Cesare Serono” for financial support in renting the mobile MRI machine and Dr. Orietta Picconi for statistical advice. References [1] Krupp LB, Alvarez LA, LaRocca NG, Scheinberg LC. Fatigue in multiple sclerosis. Arch Neurol 1988;45:435–7. [2] Fisk JD, Pontefract A, Ritvo PG, Archibald CJ, Murray TJ. The impact of fatigue on patients with multiple sclerosis. Can J Neurol Sci 1994;21:9–14. [3] Rolak LA. Fatigue and multiple sclerosis. In: Dawson DM, Sabin TD, editors. Chronic fatigue syndrome. Boston: Little, Brown; 1993. p. 153–60. [4] Colosimo C, Millefiorini E, Grasso MG, Vinci F, Fiorelli M, Koudriavtseva T, et al. Fatigue in MS is associated with specific clinical features. Acta Neurol Scand 1995;92:353–5. [5] Lobentanz IS, Asenbaum S, Vass K, Sauter C, Klosch G, Kollegger H, et al. Factor influencing quality of life in multiple sclerosis patients: disability, depressive mood, fatigue and sleep quality. Acta Neurol Scand 2004;110:6–13. [6] Roelcke U, Kappos L, Lechner-Scott J, Brunnschweiler H, Huber S, Ammann W, et al. Reduced glucose metabolism in the frontal cortex and basal ganglia of multiple sclerosis patients with fatigue: a 18Ffluorodeoxyglucose positron emission tomography study. Neurology 1997;48:1566–71. [7] Filippi M, Rocca MA, Colombo B, Falini A, Codella M, Scotti G, et al. Functional magnetic resonance imaging correlates of fatigue in multiple sclerosis. NeuroImage 2002;15:559–67. [8] Colombo B, Martinelli Boneschi F, Rossi P, Rovaris M, Maderna L, Filippi M, et al. MRI and motor evoked potential findings in

G. Tedeschi et al. / Journal of the Neurological Sciences 263 (2007) 15–19

[9]

[10]

[11]

[12]

[13] [14]

[15]

[16] [17]

[18]

[19]

[20]

[21] [22]

nondisabled multiple sclerosis patients with and without symptoms of fatigue. J Neurol 2000;247:506–9. van der Werf SP, Jongen PJH, Lycklama GJ, Barkhof F, Hommes OR, Bleijenberg G. Fatigue in multiple sclerosis: interrelations between fatigue complaints, cerebral MRI abnormalities and neurological disability. J Neurol Sci 1998;160:174–170. Bakshi R, Miletich RS, Henschel K, Shaikh ZA, Janardan V, Wasay M, et al. Fatigue in multiple sclerosis: cross-sectional correlation with brain MRI findings in 71 patients. Neurology 1999;53:1151–3. Mainero C, Faroni J, Gasperini C, Filippi M, Giugni E, Ciccarelli O, et al. Fatigue and magnetic resonance imaging activity in multiple sclerosis. J Neurol 1999;246:454–8. Codella M, Rocca MA, Colombo B, Martinelli-Boneschi F, Comi G, Filippi M. Cerebral grey matter pathology and fatigue in patients with multiple sclerosis: a preliminary study. J Neurol Sci 2002;194:71–4. Marrie RA, Fisher E, Miller DM, Lee J, Rudick RA. Association of fatigue and brain atrophy in multiple sclerosis. J Neurol Sci 2005;228:161–6. Tartaglia MC, Narayanan S, Francis SJ, Santos AC, De Stefano N, Lapierre Y, et al. The relationship between diffuse axonal damage and fatigue in multiple sclerosis. Arch Neurol 2004;61:201–7. Tedeschi G, Lavorgna L, Russo P, Prinster A, Dinacci D, Savettieri G, et al. Brain atrophy and lesion load in a large population of patients with multiple sclerosis. Neurology 2005;26(65):280–5. Kidd D, Barkof F, McConnell R, Algra PR, Allen IV, Revesz T. Cortical lesions in multiple sclerosis. Brain 1999;122:17–26. Miller DH, Barkhof F, Frank JA, Parker GJM, Thompson AJ. Measurement of atrophy in multiple sclerosis: pathological basis, methodological aspects and clinical relevance. Brain 2002;125:1676–95. Liu C, Edwards S, Gong Q, Roberts N, Blumhardt LD. Three dimensional MRI estimates of brain and spinal cord atrophy in multiple sclerosis. J Neurol Neurosurg Psychiatry 1999;66:323–30. De Stefano N, Narayanan S, Arnaoutelis R, Francis GS, Arnaoutelis R, Tartaglia MC, Antel JP, et al. Evidence of axonal damage in the early stage of multiple sclerosis and its relevance to disability. Arch Neurol 2001;58:65–70. Kutzelnigg A, Lucchinetti CF, Stadelmann C, Bruck W, Rauschka H, Bergmann M, et al. Cortical demyelination and diffuse white mater injury in multiple sclerosis. Brain 2005;128:2705–12. Kurtzke JF. Rating neurological impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology 1983;33:1444–52. McDonald WI, Compston A, Edan G, Goodkin D, Hartung HP, Lublin FD, et al. Recommended diagnostic criteria for multiple sclerosis:

[23] [24]

[25]

[26]

[27] [28]

[29] [30]

[31]

[32]

[33]

[34]

19

guidelines from the International Panel on the diagnosis of multiple sclerosis. Ann Neurol 2001;50:121–7. Janardhan V, Bakshi R. Quality of life in patients with multiple sclerosis: the impact of fatigue and depression. J Neurol Sci 2002;205:51–8. Alfano B, Brunetti A, Covelli EM, Quarantelli M, Panico MR, Ciarmiello A, et al. Unsupervised, automated segmentation of the normal brain using a multispectral relaxometric magnetic resonance approach. Magn Reson Med 1997;37:84–93. Alfano B, Brunetti A, Larobina M, Quarantelli M, Tedeschi E, Ciarmiello A, et al. Automated segmentation and measurement of global white matter lesion volume in patients with multiple sclerosis. J Magn Reson Imaging 2000;12:799–807. Bonavita S, Dinacci D, Lavorgna L, Savettieri G, Quattrone A, Livrea P, et al. Treatment of multiple sclerosis with interferon beta in clinical practice: 2-year follow-up data from the South Italy Mobile MRI project. Neurol Sci 2006;27:S365–8. Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 1982:29–143. Flachenecker P, Kumpfel T, Kallmann B, Gottschalk M, Grauer O, Rieckmann P, et al. Fatigue in multiple sclerosis: a comparison of different rating scales and correlation to clinical parameters. Mult Scler 2002;8: 523–6. Pelletier D, Garrison K, Henry R. Measurements of whole-brain atrophy in multiple sclerosis. J Neuroimaging 2004;14:11S–9S. Rocca MA, Agosta F, Colombo B, Mezzapesa D, Falini A, Comi G, et al. fMRI changes in relapsing–remitting multiple sclerosis patients complaining of fatigue after IFN-1A injection. Hum Brain Mapp 2007;28:373–82. Vercoulen JHMM, Hommes OR, Swanink CMA, Jongen PJH, Fennis JFM, Galama JMD, et al. The measurement of fatigue in patients with multiple sclerosis. Arch Neurol 1996;53:642–9. Bakshi R, Shaikh ZA, Miletich RS, Czarnecki D, Dmochowski J, Henschel K. Fatigue in multiple sclerosis and its relationship to depression and neurological disability. Mult Scler 2000;6:181–5. Lerdal A, Celius EG, Moum T. Fatigue and its association with sociodemographic variables among multiple sclesoris patients. Mult Scler 2003;9:509–14. Pozzilli C, Tomassini V, Marinelli F, Paolillo A, Gasperini C, Bastianello S. “Gender gap” in multiple sclerosis: a magnetic resonance imaging evidence. Eur J Neurol 2003;10:95–7.