Background

Multiple sclerosis (MS) is a common disease in the central nervous system caused by the degeneration of myelin axons. This impairment can disturb the ability of some parts of the nervous system that are responsible for communication and create many physical and cognitive signs and symptoms [1, 2]. MS appears in several forms, and its symptoms emerge either as staged recurrence (in relapsing forms) or over time (in intermittent forms) [3]. This disease often appears at a young age, and its onset is usually between the ages of 20 and 40 [4].

Physicians are more inclined to examine the physical disabilities of MS; however, over the past few decades, there has been a lot of awareness regarding cognitive dysfunctions in patients with MS. The involvement of various cognitive domains such as memory, processing speed, attention, and executive function can develop in the early stages of the disease, and even despite the lack of progress of physical dysfunction, MS-induced cognitive dysfunctions can accelerate over time [4, 5].

Many hypotheses have been proposed regarding the factors affecting the development of MS-induced cognitive dysfunctions [6, 7]. For example, fatigue and depression in patients with MS are among the factors whose effects on cognitive function can often be discussed and challenged. Individuals with MS may experience extreme fatigue after performing cognitive tasks [4, 8]. Moreover, the type or stage of MS can create a variety of cognitive dysfunctions of varying severity [9]. Other factors such as anxiety, disability, education, and age are also among other areas of interest for researchers [10, 11]. Even gender may play a role in this issue. Some studies have shown that gender may affect cognitive function, and men with MS may exhibit poorer cognitive abilities, especially in auditory-verbal memory [12]. In contrast, another study showed that women had lower scores on cognitive tests (the Selective Reminding Test-Long Term Storage (SRT-LTS) and the Paced Auditory Serial Addition Test-3 (PASAT-3) than men [13]. If the factors affecting cognitive dysfunctions are identified, they can be treated using psychotherapy. In addition, cognition can be improved through rehabilitation and other treatments, but the choice of appropriate treatment depends on the proper diagnosis of the underlying cause of cognitive dysfunction [4].

Few studies have been performed on the determinants of cognitive dysfunction in patients with multiple sclerosis [4, 7, 14]. Most of the published studies were not performed on large samples of patients with MS, so there is limited research on large sample sizes to reach a consensus in this domain. This study aimed to investigate the determinants of cognitive dysfunctions in a relatively large sample of patients with MS for screening of these patients. In the present study, the authors hypothesized a relationship between the type of MS disease and cognitive dysfunction. Based on this, it was also assumed that there is a relationship between clinical and demographic factors affecting cognitive dysfunction and the type of multiple sclerosis.

Methods

This study investigated 186 patients with MS who were referred to the MS Clinic of Booali Hospital in Qazvin for follow-up and were under care in this clinic from April 2017 to January 2018. Concerning MS status, 32 patients (17.6%) were classified as individuals with progressive MS (PMS), and 150 patients (82.4%) were categorized as individuals with relapsing-remitting MS (RRMS). This study was performed according to the research priorities of Qazvin University of Medical Sciences, had no ethical problems, and was approved by the ethics committee of the university.

The inclusion criteria in this study included the diagnosis of MS by McDonald’s revised diagnostic criteria [15], not having underlying diseases, not using psychotropic drugs, not having previous cognitive learning disabilities, not having serious vision and hearing problems, no previous history of psychiatric dysfunctions, complete familiarity with the Persian language, and no history of drug or alcohol use. Patients who did not have enough tendency and motivation to participate in the study were also excluded from the research. Of the 186 patients under study, 4 patients did not have the motivation to participate in the study, so they were excluded. To compare cognitive dysfunction between patients with MS and the general population, the assessment was performed in healthy individuals, as well. One hundred eighty-six healthy individuals were selected as the control group. These individuals were selected from the local community through advertising. All of them were Persian speakers, and 126 individuals (70%) were female. Control group participants were screened based on clinical interviews. The screening criteria in this study were the absence of neurological and psychiatric disorders and alcohol and drug abuse. In addition, those who had serious medical diseases such as diabetes and hypertension were also excluded from the study. It should be noted that 6 individuals in the control group did not have the motivation to participate in the study, so they were excluded.

Data collection method

The data for the present study can be classified into three general groups. The first group included data on demographic and clinical information, including age, gender, education level, and duration of illness. The second group involved cognitive data (Wechsler Memory Scale – 3rd Edition (WMS-III)), and the third group included the data related to the evaluation of neuropsychological status, including the Beck Depression and Anxiety Scales, the Fatigue Severity Scale (FSS), and the Expanded Disability Status Scale (EDSS).

Cognitive data

This study used the WMS-III to assess patients’ cognitive function. Due to its characteristics, this scale is considered the main component of any complete cognitive assessment. This scale was designed for people 16–89 years of age and took 15–20 min to complete [16]. A study in Iran showed an internal consistency of 0.85 for the WMS-III subscales and Cronbach’s alpha of 0.76 to 0.83 for the WMS-III indices [16]. According to the criteria in previous articles, in this study, we used a score lower than 70 on the WMS to indicate the presence of cognitive dysfunction.

The assessment of neuropsychological status

To evaluate the effect of fatigue severity on cognitive dysfunction stemmed from MS, the FSS was used [17]. This questionnaire consists of 9 items that examine the symptoms related to daily life activities such as physical function, work, and family and social life. Each item is scored on a 1- to 7-point scale and assesses fatigue in general. A score of 4 or higher indicates severe fatigue. A study by Shahvaroughi et al. investigated the reliability of the Persian version of the FSS in subjects with multiple sclerosis. All the participants found the FSS–P item precise in pilot testing. The intraclass correlation coefficient (ICC) was reported at 0.93 for the total score that showed high repeatability of (FSS), and Cronbach’s alpha was reported at 0.96 [18]. Mental health status was assessed using the Beck Depression Inventory (BDI) and the Beck Anxiety Inventory (BAI). The reliability and validity of the Persian version of the questionnaire, which is a powerful tool for measuring depression and anxiety, have been determined in previous studies [19]. Finally, the disability status was assessed using the EDSS, which estimates the degree of disability in patients with MS [20]. Persian version of this scale is widely used in national and internal literature [20, 21].

Statistical analysis

After data collection, the results were analyzed using SPSS. The Kolmogorov-Smirnov test was used to check the normality of the data. Frequency and percentage were calculated for stratified variables, and mean and standard deviation were calculated for continuous variables. To consider the different characteristics of the disease, patients were divided into two subgroups: RRMS and PMS. These three groups (PMS, RRMS, and control) were compared according to clinical and demographic variables using the analysis of variance (ANOVA) test. Post hoc tests (Tukey tests) were also used, where appropriate. The chi-square test was used to compare the stratified variables, and the independent t-test and ANOVA test were used to compare the continuous variables. To determine the factors affecting the WMS-III test scores in patients with MS, a multiple regression analysis was performed to identify significant predictors of the sensitive cognitive dysfunction test in MS. Logistic regression analysis was used to determine the effect of significant demographic and clinical variables on cognitive dysfunctions. The logistic regression models used in this study were entry methods. Due to the different clinical features between RRMS and PMS, regression analysis was divided into two different analyses. A P value < 0.05 was utilized as the significance level.

Results

Among the patients referred to the clinic, 53 patients with MS (33.6%) had cognitive dysfunction, and 126 patients (64.7%) had no cognitive dysfunction. The demographic and clinical characteristics of participants in the PMS, RRMS, and control groups are shown in Table 1. The groups were different regarding education level (P < 0.001) so the education level of the PMS group was significantly lower than that of the control group (F = 6.13, P < 0.001). PMS and RRMS groups had more fatigue compared to the control group (F = 49.02, P < 0.001). Concerning depression, both PMS and RRMS groups were higher than the control group (P < 0.001). In addition, the mean depression of the PMS group was significantly higher than the RRMS group (F = 6.13, P < 0.001). Regarding other variables (gender, age, and anxiety), no significant differences were observed between the different groups (P > 0.5).

Table 1 The demographic and clinical characteristics of participants
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Concerning the WMS-III, patients with RRMS and PMS had significantly lower scores compared to the control group, as patients with RRMS compared to the control group were 76.73 ± 8.05 versus 105.58 ± 8.71 (P < 0.01), and patients with PMS compared to the control group were 72.56 ± 6.64 versus 105.58 ± 8.71 (P < 0.01). Moreover, patients with RRMS also had significantly lower WMS-III scores than those with PMS (72.56 ± 6.64 versus 76.73 ± 8.05, P < 0.001) (Fig. 1).

Fig. 1
figure 1

Comparison of different groups in terms of WMS-III

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Regression analysis regarding the WMS-III score as the dependent variable showed that the significant predictors included education (B = − 0.95, P = 0.04), disability (B = − 0.98, P = 0.04), fatigue (B = − 0.34, P < 0.001), depression (B = − 0.11, P = 0.015), and duration of illness (B = − 0.04, P = 0.004), with an adjusted R2 = 0.608.

Table 2 presents the logistic regression analysis to determine the factors affecting cognitive dysfunctions in patients with RRMS. Among the potential predictors, the variables of disability, fatigue, depression, and duration of illness were associated with the presence or absence of cognitive dysfunctions in patients with MS. After adjusting the regression model by age, gender, and education, despite a slight change in the odds ratio of the mentioned variables, no significant difference was observed in their significance pattern.

Table 2 Logistic regression analysis to determine the factors affecting cognitive dysfunctions in patients with RRMS
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Table 3 presents the logistic regression analysis to determine the factors affecting cognitive dysfunction in patients with PMS. After the entry of potential predictors into the regression model, only disability was related to the presence or absence of cognitive dysfunction. After adjusting the regression model by age, gender, and education, despite a slight change in the odds ratio of the mentioned variables, no significant difference was observed in their significance pattern.

Table 3 Logistic regression analysis to determine the factors affecting cognitive dysfunctions in patients with PMS
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Discussion

To date, most published studies on the factors affecting cognitive dysfunction in patients with MS have not included large samples of patients with this disease. This is one issue that has led to the lack of clear evidence in this regard [4, 7, 14]. In this study, a relatively large sample of patients with MS was examined concerning cognitive dysfunction and the factors affecting the presence of these dysfunctions. The results showed that among patients with MS, 53 patients (33.6%) had cognitive dysfunction. Studies have shown that the prevalence rate of cognitive dysfunction in patients with MS varies between 30 and 70% [13]. Differences in the prevalence rate of cognitive dysfunction can be due to the different statistical populations in various studies. Another reason is that cognitive dysfunctions in this domain are not usually evaluated overall but are merely measured in various areas such as attention, processing speed, or information.

Since patients with MS are usually divided into two separate groups due to different clinical profiles [22], the analysis was performed accordingly. After comparing the three groups (PMS, RRMS, and control), the results showed that cognitive dysfunction was higher in patients with PMS compared to those with RRMS and the control group. In a study, Eijlers et al. also obtained similar results [7].

The comparisons between the three groups concerning demographic variables showed that patients in the PMS group had lower education levels than the control group. Regarding other demographic variables (age and gender), no significant difference was observed among the three groups. Regarding clinical variables, patients in the PMS and the RRMS groups had higher levels of fatigue and depression compared to the control group. Therefore, patients with PMS in this study had more clinical issues and more severe cognitive dysfunction. Additionally, a significant portion of the WMS-III score was related to education, fatigue, depression, and duration of illness. As expected, education level, fatigue, depression, and duration of illness had inverse relationships to the WMS-III score; however, age and gender had no significant relationship to WMS-III. In another study, Sandi et al. investigated demographic and clinical variables in patients with MS and the resultant cognitive dysfunction [23], showing that the difference between males and females was significant (P < 0.001). They found that except for gender, no significant predictor determines cognitive dysfunction in males. However, in females, both the EDSS and education level had decisive roles in the rate of cognitive dysfunction. In a study by Ruano et al., the effect of age on cognitive dysfunctions in the different groups of patients with MS was examined [24]. The results indicated that patients with severe cognitive dysfunction were older. However, this relationship was not statistically significant, and from this point of view, its results are in line with the present study. Studies performed on the relationship between demographic criteria and cognitive dysfunction in MS have discrepant results [25, 26]. Part of this discrepancy may be due to the small sample size or differences in the individuals under study. Examining the effects of demographic criteria such as age, gender, and education level can give us a more complete picture of the causes of cognitive dysfunction [4].

The results concerning the effect of depression on cognitive dysfunction are contradictory, although it is believed that healthy individuals with depression are prone to cognitive dysfunction [25]. Previous studies in this regard have shown no association between depression and decreased cognitive function in patients with MS [27]. However, in line with the present research, new studies have provided reasons to justify these findings [4, 28].

To investigate the role of each factor in the presence or absence of cognitive dysfunctions, logistic regression analysis was used. Because of the different clinical profiles of the two groups RRMS and PMS, the final analysis was performed for the two groups separately. Logistic regression analysis for the RRMS group showed that significant predictors for cognitive dysfunctions included disability, fatigue, depression, and duration of illness. Therefore, these variables can be used as a screening tool to screen cognitive dysfunctions in patients with MS. Interestingly, when we performed these analyses with PMS patients, the only significant variable in the model was disability that had no significant change after adjusting for age, gender, and education. Perhaps the reason is due to the different clinical profiles between the two groups. Research has shown that, regardless of disability and depression, among the symptoms of MS, fatigue is one of the most common symptoms that has been reported in more than 90% of patients [29] and is considered the worst symptom occurring in more than two-thirds of individuals with MS [30]. Contrary to this study, the results of Morrow et al.’s study showed no association between fatigue and cognitive dysfunction in MS [31]. Perhaps one of the reasons for the non-significance of clinical variables in these studies is that they have serious methodological problems (drawbacks) such as a small statistical sample and the use of inappropriate tools.

The limitation of the current study is related to its design. This analysis is an observational study in which no neuroimaging or genetic analysis has been performed. Therefore, performing studies involving neuroimaging and genetic factors, along with demographic and clinical data, can be very valuable.

Conclusions

This study shows that the type of MS and its duration and the level of depression, fatigue, and disability are the underlying factors related to cognitive dysfunction in this population. Proper identification of these factors can be effective in the screening of cognitive dysfunction in patients with MS and also will be effective in choosing the appropriate treatment and thus, through screening, will facilitate the treatment process of patients.