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% . 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 , 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 .
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 , 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 . 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 .
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 . Previous studies in this regard have shown no association between depression and decreased cognitive function in patients with MS . 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  and is considered the worst symptom occurring in more than two-thirds of individuals with MS . Contrary to this study, the results of Morrow et al.’s study showed no association between fatigue and cognitive dysfunction in MS . 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.