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| ORIGINAL ARTICLE |
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| Year : 2016 | Volume
: 53
| Issue : 2 | Page : 74-78 |
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Primary fatigue contributes to cognitive dysfunction in patients with multiple sclerosis
Mohamed S El-Tamawy1, Moshera H Darwish2, Sandra M Ahmed1, Ahmed M Abdelalim1, Engy B. S. Moustafa2
1 Department of Neurology, Faculty of Medicine, Cairo University, Cairo, Egypt
2 Department of Physical Therapy for Neuromuscular Disorders and its Surgery, Cairo University, Cairo, Egypt
| Date of Submission | 17-Jul-2015 |
| Date of Acceptance | 20-Jan-2016 |
| Date of Web Publication | 2-Jun-2016 |
Correspondence Address:
Sandra M Ahmed
MD, Department of Neurology, Faculty of Medicine, Cairo University, Cairo, 11562
Egypt
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/1110-1083.183406
Background
A rising concern about quality of life of multiple sclerosis (MS) patients has emerged. Cognitive dysfunction and primary fatigue have been largely related to each other.
Objective
The aim of the present study was to examine the relationship between primary fatigue, cognitive dysfunction, and inflammatory biomarkers for patients with MS.
Patients and methods
A total of 40 Egyptian MS patients (Expanded Disability Status Scale<5) were divided into two groups according to the Fatigue Severity Scale (FSS), into patients with fatigue (G1; FSS>36) and those without fatigue (G2; FSS<36). Patients with depression and sleep problems were excluded from the study. Cognitive functions were assessed for both groups using the computer-based 'RehaCom' software, using which the following tests were carried out: (a) attention/concentration tests and (b) reaction behavior tests. The serum levels of tumor necrosis factor-α (TNF-α) and interferon-γ (IFN-γ) were analyzed for all MS patients.
Results
A statistically significant decrease in cognitive functions was found in G1 compared with G2 (P < 0.001), as well as a statistically significant higher level of TNF-α and IFN-γ in G1 compared with G2. FSS was positively correlated with the attention/concentration test. Correlative study also indicated a strong relation between the level of cytokines and FSS but not cognitive dysfunction.
Conclusion
Primary fatigue contributes to cognitive dysfunction in patients with MS and is associated with elevated serum level of TNF-α and IFN-γ Keywords: Cognitive functions, interferon-γ, multiple sclerosis, primary fatigue, RehaCom, tumor necrosis factor-α
How to cite this article:
El-Tamawy MS, Darwish MH, Ahmed SM, Abdelalim AM, Moustafa EB. Primary fatigue contributes to cognitive dysfunction in patients with multiple sclerosis. Egypt J Neurol Psychiatry Neurosurg 2016;53:74-8 |
How to cite this URL:
El-Tamawy MS, Darwish MH, Ahmed SM, Abdelalim AM, Moustafa EB. Primary fatigue contributes to cognitive dysfunction in patients with multiple sclerosis. Egypt J Neurol Psychiatry Neurosurg [serial online] 2016 [cited 2023 Dec 10];53:74-8. Available from: http://www.ejnpn.eg.net/text.asp?2016/53/2/74/183406 |
| Introduction | |  |
Prevalence of multiple sclerosis (MS) in the middle-east has markedly increased in last decades [1] and, at the same time, a concern about the quality of life of these patients has appeared [2]. Cognitive dysfunction has been shown to affect the quality of life of MS patients and may lead to a change in vocational status years following the diagnosis of MS [3].
Cognitive affection of MS patients usually is present in the domain of complex attention, information processing speed, and executive functions, which largely affect the everyday functional activity and hence the quality of life [4],[5],[6].
Contradictory results indicate toward primary fatigue as a causative factor of cognitive dysfunction [7],[8]. The term 'cognitive fatigue' has been used to describe time-related maintenance of full functioning cognitive capacity during a single session testing rather than a global permanent decrease in cognitive functions [9],[10].
Primary fatigue is not only caused by physical disability. Severe fatigue was found to be accompanied with peripheral secretion of interferon-γ (IFN-γ) [11]. Tumor necrosis factor-α (TNF-α) was found to be more generally a marker of disease activity and progression [12].
Aim
The aim of this study was to find a possible causal relation between primary fatigue and cognitive dysfunction in patients with MS and their relationship with the serum level of TNF-α and IFN-γ as indicators of disease progression.
| Patients and methods | | |
This case-control study included 40 Egyptian MS patients. They were recruited from the Multiple Sclerosis Research Unit, Neurology Department, Faculty of Medicine, Cairo University, Egypt, during the period from September 2013 to January 2014. All patients were diagnosed with definite MS on the basis of the McDonald criteria (2010) [13]. The age range of the patient was 20-40 years, with Expanded Disability Status Scale (EDSS) less than 5 [14], with no other significant medical problems. According to the Fatigue Severity Scale (FSS) [15], patients were divided into two groups: group 1 (G1, n = 20) included fatigued patients with FSS greater than or equal to 36 and group 2 (G2, n = 20) included nonfatigued patients with FSS less than 36. G1 was designed to include only primary fatigue patients by excluding the causes of secondary fatigue; depression using Beck's Depression Inventory [16], and sleepiness using the Epworth Sleepiness Scale [17]. Both groups were matched for age, sex, and duration of illness (P = 0.31, 0.75, and 0.3, respectively).
Cognitive assessment was carried out for all patients (G1 and G2) using the computer-based RehaCom software Hasomed, Magdeburg, Germany. It is an intensive cognitive rehabilitation test that includes 32 assessment tasks for attention, memory, logical reasoning, and executive function. RehaCom procedure is performed through a regular PC with at least a 19 inch screen, RehaCom panel, and a software (1990-1997) EN/ISO-13485 certified. Patients were subjected to two tests: (a) assessment of attention/concentration (A/C) and (b) assessment of reaction behavior (RB).
A/C tests consisted of 100 levels of difficulty. Each level has an average of 22 subtests. The maximum period of the session was about 60 min for each patient, with 5 min of rest between levels. The assessment of each patient started from level 'one' and the test progressed to the next level, which was more difficult. A grey performance bar presented on the left side of the screen changed according to the quality of patient performance. It grew up with every correct answer and shrank with every incorrect answer. As this performance bar grew up the patient completed the level and progressed to the more difficult level. If this performance bar shrank for more than three consecutive incorrect answers, the test was stopped and the patient's maximum level of achievement was recorded to the same level of difficulty. No limited solution time was preset during assessment. Maximum and minimum reaction times were assessed for each patient.
RB tests consisted of 16 levels of difficulties. Each level consisted of an average 50 stimuli. Average time of assessment was about 30 min. Time period between stimuli (interstimulus interval) was preset to the default of about 2000 ms. Maximum reaction time was preset to the default of 1200 ms. An answer was considered incorrect when the time taken to answer exceeded 1200 ms and the next stimulus appeared. Percentage of correct reactions was calculated as the percentage value of relevant to irrelevant stimuli. The patient was shifted to the next level of assessment, if the percentage of correct reactions was 75% or more. If the patient was unable to complete a certain level for a long period of time, the test was stopped and results were calculated according to the maximum reached performance level. After accomplishing maximum performance level in different tasks in RB tests for each patient, the results of the percentage of correct reactions and median reaction time were displayed in a table form with diagrams.
Analysis of inflammatory cytokines was carried out through blood sampling on the same day of confirmed fatigue and before performing cognitive assessment. Immunomodulatory therapy was postponed for 36 h before sampling. Analysis of inflammatory cytokines TNF-α and IFN-γ was carried out using the 'Quantikine Human TNF-α and IFN-γ Immunoassay Kit' R&D Systems, Inc., Minneapolis, MN, USA.
This study was approved by the scientific committee of the Faculty of Physical Therapy, Cairo University, Egypt. A written consent was obtained from each patient after they were provided with a thorough description of the test.
Statistical methods
The mean value and SDs of FSS, A/C test, and the RB test from RehaCom procedure and also the results of blood analysis including the proinflammatory cytokines level (TNF-α, IFN-γ) were obtained and compared for both G1 and G2 using SPSS Statistical package (SPSS Statistics for Windows, Version 17.0; SPSS Inc., Chicago, Illinois, USA). Multivariate analysis of variance test was used to compare the mean values and SDs of the different results that were obtained from using RehaCom tests and proinflammatory cytokines (TNF-α and IFN-γ) between G1 and G2. Spearman's correlation coefficient (r) was used to correlate between level of fatigue represented by (FSS) scores, degree of cognitive decline represented by different variables of RehaCom, and the level of proinflammatory cytokines in the primary fatigued group (G1).
| Results | | |
Fatigued MS patients (G1) had a statistically significant higher EDSS score (4.17 ± 1.44) compared with nonfatigued MS patients (G2) (2.0 ± 0.74) (P < 0.001).
There was a statistically significant increase in both maximum reaction time and minimum reaction time in G1 compared with G2. There were statistically significant decreases in the percentage of correct reactions and increase in the median reaction time in fatigued MS patients (G1) [Table 1]. | Table 1: Comparison of RehaCom cognitive assessment and reaction behavior between the fatigued group (G1) and the nonfatigued group (G2) of multiple sclerosis patients
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There was a statistically significant higher level of TNF-α and IFN-γ in the fatigued group (G1) compared with the nonfatigued group (G2), as shown in [Table 2]. | Table 2: Comparison of proinflammatory cytokines between the fatigued group (G1) and the nonfatigued group (G2) of multiple sclerosis patients
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Positive correlation was found between FSS and A/C test but not with the RB test of RehaCom cognitive assessment [Table 3]. | Table 3: Correlation between Fatigue Severity Scale and RehaCom cognitive assessment tests in multiple sclerosis patients
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There was a statistically significant positive correlation between FSS and the level of proinflammatory cytokines unlike cognitive functions, which showed no correlation with cytokine levels [Table 4]. | Table 4 Correlation between tumor necrosis factor-α, interferon-γ, Fatigue Severity Scale, and RehaCom cognitive assessment tests in multiple sclerosis patients
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| Discussion | | |
The current study showed that primary fatigue in MS is accompanied with cognitive dysfunction in the domains of A/C and RB. This dysfunction is accompanied by an increased level of the proinflammatory cytokines TNF-α and IFN-γ, which positively correlate with fatigue severity but not with cognitive dysfunction.
Patients' selection criteria were designed to eliminate confounders that would affect cognitive functions regardless of primary fatigue. Patients were selected within the age range of 20-40 years to avoid the effects of normal aging on cognition [18]. Illiteracy [19], sleep disorders [20], and depression [21] were also among the exclusion criteria.
Drugs also play a role in cognitive decline (e.g. glucocorticoids and interferon-1β) [22]. For this, we chose patients during remission and those either on no disease-modifying drugs or not using the drug 36 h before the test and serum sampling for cytokines.
Patients, before grouping, were selected among those with EDSS less than 5. It was proven that the baseline level of physical fatigue was associated with a progression of disability status [23].
Primary fatigue was related to an increased level of proinflammatory cytokines, especially TNF-α and IFN-γ [24]. The fatigued group (G1) had a statistically significant elevation of these two cytokines compared with the nonfatigued group (G2) (TNF-α, P = 0.0009 and IFN-γ, P = 0.0007), a finding that supported the fact that patients in G1 had primary rather than secondary fatigue. TNF-α has previously been detected in MS patients' brains [25] and recognized as an indicator of disease progression causing excitotoxic neurodegeneration [12]. IFN-γ-stimulated peripheral production has more specifically being related to fatigue and depression in MS patients [11].
A/C test results showed a significant delay in both minimum and maximum reaction time in patients of the fatigued group (G1) compared with the nonfatigued group (G2) (P < 0.001). The RB test results showed as well a statistically significant decrease in the percentage of correct answers and a delay in the median reaction time in G1 compared with G2. These results are in agreement with the results of a study by Andreasen et al. (2010) [26], who reported reduced processing speed of information in patients with primary fatigue compared with patients with secondary fatigue or nonfatigued patients. Fatigue has been attributed to regional strategic brain atrophy rather than global brain affection [27]. Grey matter atrophy of the frontal cortex [28], especially the dorsolateral prefrontal cortex [29], was reported as one of the major causes relating fatigue to cognitive decline in MS patients.
Finally, we found a strong positive correlation between FSS of G1 and elevated serum level of TNF-α and IFN-γ serum levels (r = +0.719, P = 0.000 and r = + 0.532, P = 0.016; respectively). On the other hand, we found no significant correlation between the cytokine levels and either of the cognitive assessment tests (P > 0.005). These findings indicate that fatigue was the symptom mirroring internal disease activity rather than cognitive dysfunction in G1.
| Conclusion | | |
Primary fatigue contributes to cognitive dysfunction in MS patients. Elevated serum levels of TNF-α and IFN-γ are related to primary fatigue severity rather than to cognitive dysfunction.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
| References | | |
| 1. | Al Tahan AM, Alsharoqi I, Bohlega SA, Dahdaleh M, Daif A, Deleu D, et al. Characteristics of multiple sclerosis in the Middle East with special reference to the applicability of international guidelines to the region. Int J Neurosci 2014; 124(9):635-641.  |
| 2. | Al-Tahan AM, Al-Jumah MA, Bohlega SM, Al-Shammari SN, Al-Sharoqi IA, Dahdaleh MP, et al. The importance of quality-of-life assessment in the management of patients with multiple sclerosis. Recommendations from the Middle East MS Advisory Group. Neurosciences (Riyadh) 2011; 16(2):109-113. |
| 3. | Ruet A, Deloire M, Hamel D, Ouallet JC, Petry K, Brochet B. Cognitive impairment, health-related quality of life and vocational status at early stages of multiple sclerosis: a 7-year longitudinal study. J Neurol 2013; 260(3):776-784. |
| 4. | Chiaravalloti ND, DeLuca J. Cognitive impairment in multiple sclerosis. Lancet Neurol 2008; 7(12):1139-1151. |
| 5. | Kalmar JH, Gaudino EA, Moore NB, Halper J, Deluca J. The relationship between cognitive deficits and everyday functional activities in multiple sclerosis. Neuropsychology 2008; 22(4):442-449. |
| 6. | Benedict RH, Wahlig E, Bakshi R, Fishman I, Munschauer F, Zivadinov R, Weinstock-Guttman B. Predicting quality of life in multiple sclerosis: accounting for physical disability, fatigue, cognition, mood disorder, personality, and behavior change. J Neurol Sci 2005; 231(1-2):29-34. |
| 7. | Diamond BJ, Johnson SK, Kaufman M, Graves L. Relationships between information processing, depression, fatigue and cognition in multiple sclerosis. Arch Clin Neuropsychol 2008; 23(2):189-199. |
| 8. | Morrow SA, Weinstock-Guttman B, Munschauer FE, Hojnacki D, Benedict RH. Subjective fatigue is not associated with cognitive impairment in multiple sclerosis: cross-sectional and longitudinal analysis. Mult Scler 2009; 15:998-1005. |
| 9. | Krupp LB, Elkins LE. Fatigue and declines in cognitive functioning in multiple sclerosis. Neurology 2000; 55(7):934-939. |
| 10. | Schwid SR, Tyler CM, Scheid EA, Weinstein A, Goodman AD, McDermott MP. Cognitive fatigue during a test requiring sustained attention: a pilot study. Mult Scler 2003; 9(5):503-508. |
| 11. | Pokryszko-Dragan A, Frydecka I, Kosmaczewska A, Ciszak L, Biliñska M, Gruszka E, et al. Stimulated peripheral production of interferon-gamma is related to fatigue and depression in multiple sclerosis. Clin Neurol Neurosurg 2012; 114(8):1153-1158. |
| 12. | Rossi S, Motta C, Studer V, Barbieri F, Buttari F, Bergami A, et al. Tumor necrosis factor is elevated in progressive multiple sclerosis and causes excitotoxic neurodegeneration. Mult Scler 2014; 20(3):304-312. |
| 13. | Polman CH, Reingold SC, Banwell B, Clanet M, Cohen JA, Filippi M, et al. Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol 2011; 69(2):292-302. |
| 14. | Kurtzke JF. Rating neurologic impairment in multiple sclerosis: an Expanded Disability Status Scale (EDSS). Neurology 1983; 33(11):1444-1452. |
| 15. | Krupp LB, LaRocca NG, Muir-Nash J, Steinberg AD. The Fatigue Severity Scale. Application to patients with multiple sclerosis and systemic lupus erythematosus. Arch Neurol 1989; 46(10):1121-1123. |
| 16. | Beck AT, Guth D, Steer RA, Ball R. Screening for major depression disorders in medical inpatients with the Beck Depression Inventory for Primary Care. Behav Res Ther 1997; 35(8):785-791. |
| 17. | Johns MW. A new method for measuring daytime sleepiness: the Epworth Sleepiness Scale. Sleep 1991; 14(6):540-545. |
| 18. | Salthouse TA. When does age-related cognitive decline begin? Neurobiol Aging 2009; 30(4):507-514. |
| 19. | Kabir ZN, Herlitz A. The Bangla adaptation of Mini-Mental State Examination (BAMSE): an instrument to assess cognitive function in illiterate and literate individuals. Int J Geriatr Psychiatry 2000; 15(5):441-450. |
| 20. | Veauthier C, Paul F. Sleep disorders in multiple sclerosis and their relationship to fatigue. Sleep Med 2014; 15(1):5-14. |
| 21. | Watson TM, Ford E, Worthington E, Lincoln NB. Validation of mood measures for people with multiple sclerosis. Int J MS Care 2014; 16(2):105-109. |
| 22. | Thelen JM, Lynch SG, Bruce AS, Hancock LM, Bruce JM. Polypharmacy in multiple sclerosis: relationship with fatigue, perceived cognition, and objective cognitive performance. J Psychosom Res 2014; 76(5):400-404. |
| 23. | Debouverie M, Pittion-Vouyovitch S, Brissart H, Guillemin F. Physical dimension of fatigue correlated with disability change over time in patients with multiple sclerosis. J Neurol 2008; 255(5):633-636. |
| 24. | Heesen C, Nawrath L, Reich C, Bauer N, Schulz KH, Gold SM. Fatigue in multiple sclerosis: an example of cytokine mediated sickness behaviour? J Neurol Neurosurg Psychiatry 2006; 77(1):34-39. |
| 25. | Hofman FM, Hinton DR, Johnson K, Merrill JE. Tumor necrosis factor identified in multiple sclerosis brain. J Exp Med 1989; 170(2):607-612. |
| 26. | Andreasen AK, Spliid PE, Andersen H, Jakobsen J. Fatigue and processing speed are related in multiple sclerosis. Eur J Neurol 2010; 17(2):212-218. |
| 27. | Rocca MA, Parisi L, Pagani E, Copetti M, Rodegher M, Colombo B, et al. Regional but not global brain damage contributes to fatigue in multiple sclerosis. Radiology 2014;273:511-520. |
| 28. | Sepulcre J, Masdeu JC, Goñi J, Arrondo G, Vélez de Mendizábal N, Bejarano B, Villoslada P. Fatigue in multiple sclerosis is associated with the disruption of frontal and parietal pathways. Mult Scler 2009; 15(3):337-344. |
| 29. | Gobbi C, Rocca MA, Riccitelli G, Pagani E, Messina R, Preziosa P, et al. Influence of the topography of brain damage on depression and fatigue in patients with multiple sclerosis. Mult Scler 2014; 20(2):192-201. |
[Table 1], [Table 2], [Table 3], [Table 4]
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