Do Multiple Sclerosis and Neuromyelitis Optica Patients Have a Lower Chance of Developing Neurological Complications of COVID-19, Compared to Healthy People? The Role of ACE2

As COVID-19 spreads all around the world, it indicates various side effects and complications. Currently, we know that, this disease can affect other organs like brain. The growing number of neurological complications from this disease suggests that, the coronavirus is a neurotropic virus, and this neurotropicity has been attributed to the expression and presence of receptors of angiotensin-converting enzyme 2 (ACE2) in central nervous system (CNS). Unlike ACE itself, ACE2 converts angiotensin 2 to 1, and is present in lung alveolar epithelial cells. In this regard, the coronavirus is likely to use ACE2 as a receptor to enter and infect human cells. The virus causes disease in some other areas such as pancreas and colon with the same mechanism as that of ACE2 receptor. Moreover, the high presence of the corresponding receptor in the CNS has increased the likelihood of neurological involvement in this virus. The binding of the virus to this receptor (Figure 1), which is present in different areas of the brain such as the glial cells, neurons and astrocytes spreads the virus to the CNS and this induces a variety of neurological symptoms. One of the most important areas of the brain that causes high expressions of ACE and ACE2, angiotensinogen, and angiotensin II secretion in the CNS, is perivascular astrocytes. Neuromyelitis optica spectrum disorder (NMOSD) is an astrocytopathy in which a high rate of astrocyte destruction occurs. Some studies have also shown that, these perivascular astrocytes are largely eliminated in multiple sclerosis (MS), especially at chronic stages. This destruction could justify the studies, which have demonstrated the low levels of ACE2 in the cerebrospinal fluid of these patients. Matsushita et al. revealed that, angiotensin II, ACE, and ACE2 levels were lower in the cerebrospinal fluid of the patients with seropositive NMOSD compared to healthy individuals. Accordingly, the same was true for ACE2 levels in MS patients. Another study confirmed the low level of ACE2 concentration in the cerebrospinal fluid of the patients with MS. The destruction of astrocytes and low level of ACE2 concentration could theoretically predict the ACE2 receptor deficiency which might reduce the chance of entering the virus into the CNS, and consequently, decrease the neurological complications. This may suggest that, neurological complications are less likely to occur in the patients with NMOSD and MS in case of developing COVID-19. However, as with all diseases, it is not possible to simply predict the lower degree of neurological complications in these patients on the basis of one factor such as a lower expression of ACE2 in these patients. Thereafter, further investigations are required to shed light on how MS and NMOSD patients develop infectious diseases related to the CNS

Association of body mass index and physical activity with fatigue, depression, and anxiety among Iranian patients with multiple sclerosis

IntroductionDepression, fatigue, and anxiety are three common clinical comorbidities of multiple sclerosis (MS). We investigated the role of physical activity (PA) level and body mass index (BMI) as modifiable lifestyle factors in these three comorbidities.MethodsA cross-sectional study was conducted in the MS specialist clinic of Sina Hospital, Tehran, Iran. Demographic and clinical data were collected. BMI was categorized in accordance with the WHO’s standard classification. Physical activity (PA) level and sitting time per day were obtained using the short form of the International Physical Activity Questionnaire (IPAQ-SF). Fatigue, anxiety, and depression scores were measured using the Persian version of the Fatigue Severity Scale (FSS), Beck Anxiety Inventory (BAI), and Beck’s Depression Inventory II (BDI-II) questionnaires, respectively. The correlation between the metabolic equivalent of tasks (MET), BMI, and daily sitting hours with depression, anxiety, and fatigue were checked using the linear regression test. The normal BMI group was considered a reference, and the difference in quantitative variables between the reference and the other groups was assessed using an independent sample t-test. Physical activity was classified with tertiles, and the difference in depression, anxiety, and fatigue between the PA groups was evaluated by a one-way ANOVA test.ResultsIn total, 85 MS patients were recruited for the study. The mean ± SD age of the participants was 39.07 ± 8.84 years, and 72.9% (n: 62) of them were female. The fatigue score was directly correlated with BMI (P: 0.03; r: 0.23) and sitting hours per day (P: 0.01; r: 0.26) and indirectly correlated with PA level (P < 0.01; r: −0.33). Higher depression scores were significantly correlated with elevated daily sitting hours (P: 0.01; r: 0.27). However, the correlation between depression with PA and BMI was not meaningful (p > 0.05). Higher anxiety scores were correlated with BMI (P: 0.01; r: 0.27) and lower PA (P: 0.01; r: −0.26). The correlation between anxiety and sitting hours per day was not significant (p > 0.05). Patients in the type I obesity group had significantly higher depression scores than the normal weight group (23.67 ± 2.30 vs. 14.05 ± 9.12; P: 0.001). Fatigue (32.61 ± 14.18 vs. 52.40 ± 12.42; P: <0.01) and anxiety (14.66 ± 9.68 vs. 27.80 ± 15.48; P: 0.01) scores were significantly greater among participants in the type II obesity group in comparison with the normal weight group. Fatigue (P: 0.01) and anxiety (P: 0.03) scores were significantly different in the three levels of PA, but no significant difference was found in the depression score (P: 0.17).ConclusionOur data suggest that a physically active lifestyle and being in the normal weight category are possible factors that lead to lower depression, fatigue, and anxiety in patients with MS

Investigating the association between dairy intake and migraine odds among pediatrics and adolescents: A case-control study

Abstract Objective Migraine is recognized as a disease with unknown etiology andvarious pathophysiologic pathways which are not fully understood. Due to the relation between dairy intake and various chronic conditions in children and also the paucity of data on the probable role of dairy intake on pediatrics’ odds of having migraine, this studywas designed. Materials & Methods The present study was a population-based case-control design that was accomplished in a tertiary headache clinic.290 child (aged from7 to 14 years old) was included in this study. A definite diagnosis of migraine was performed by a neurologist; concerning the 2018 international classification of headache disorder 3 (ICHD3) criteria. Also, demographic and anthropometric characteristics were obtained. In addition, the usual dietary intake of participants was evaluated using a validated semi-quantitative food frequency questionnaire (FFQ). Results Those children in the case group significantly had higher age and BMI means (P.value:0.000). In the second regression model, odds of migraine were 48% (OR: 0.52; 95%CI:0.27-1.00) diminished in the second tertile and 53% (OR:0.47;95%CI:0.24-0.92) in the third tertile of low-fat dairy intake (P-trend:0.03). In the fully adjustedmodel, the achieved migraine ORs were as followings:0.48 (95% CI:0.240.95) in the second tertile and 0.46 (95% CI:0.21-0.96) in the third tertile (P-trend:0.04), respectively. Children with more high fat dairy intake also consumed higher amounts of energy, pastries, simple sugar, unhealthy snacks, and hydrogenated oil (P<0.05). Conclusion This study results proposed that a greater amount of lowfat dairy intake may attenuate the odds of having migraine attacks in pediatrics and adolescents who might be at risk of headache, which can be attributed to the micronutrient and also to the bioactive content of these dietary components

Association of body mass index and physical activity with fatigue, depression, and anxiety among Iranian patients with multiple sclerosis

Introduction: Depression, fatigue, and anxiety are three common clinical comorbidities of multiple sclerosis (MS). We investigated the role of physical activity (PA) level and body mass index (BMI) as modifiable lifestyle factors in these three comorbidities. Methods: A cross-sectional study was conducted in the MS specialist clinic of Sina Hospital, Tehran, Iran. Demographic and clinical data were collected. BMI was categorized in accordance with the WHO’s standard classification. Physical activity (PA) level and sitting time per day were obtained using the short form of the International Physical Activity Questionnaire (IPAQ-SF). Fatigue, anxiety, and depression scores were measured using the Persian version of the Fatigue Severity Scale (FSS), Beck Anxiety Inventory (BAI), and Beck’s Depression Inventory II (BDI-II) questionnaires, respectively. The correlation between the metabolic equivalent of tasks (MET), BMI, and daily sitting hours with depression, anxiety, and fatigue were checked using the linear regression test. The normal BMI group was considered a reference, and the difference in quantitative variables between the reference and the other groups was assessed using an independent sample t-test. Physical activity was classified with tertiles, and the difference in depression, anxiety, and fatigue between the PA groups was evaluated by a one-way ANOVA test. Results: In total, 85 MS patients were recruited for the study. The mean ± SD age of the participants was 39.07 ± 8.84 years, and 72.9% (n: 62) of them were female. The fatigue score was directly correlated with BMI (P: 0.03; r: 0.23) and sitting hours per day (P: 0.01; r: 0.26) and indirectly correlated with PA level (P 0.05). Higher anxiety scores were correlated with BMI (P: 0.01; r: 0.27) and lower PA (P: 0.01; r: −0.26). The correlation between anxiety and sitting hours per day was not significant (p > 0.05). Patients in the type I obesity group had significantly higher depression scores than the normal weight group (23.67 ± 2.30 vs. 14.05 ± 9.12; P: 0.001). Fatigue (32.61 ± 14.18 vs. 52.40 ± 12.42; P