Relationship of microglia-associated pro-inflammatory cytokines to clinical and radiological parameters in patients with multiple sclerosis - a single-centre study in a Polish population

Relationship of microglia-associated pro-inflammatory cytokines to clinical and radiological parameters in patients with multiple sclerosis - a single-centre study in a Polish population | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Relationship of microglia-associated pro-inflammatory cytokines to clinical and radiological parameters in patients with multiple sclerosis - a single-centre study in a Polish population Hubert Mado, Artur Stasiniewicz, Monika Adamczyk - Sowa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8782258/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction: Multiple sclerosis (MS) is a demyelinating disease of the central nervous system with a chronic course. Available data point to an autoimmune basis for MS. It is currently suggested that microglia-associated cytokines have a role that is especially relevant. Study objective: To determine the association between microglia cytokines: C-C motif chemokine ligand 2/ Monocyte Chemoattractant Protein-1 (CCL2/MCP-1), Interferon gamma (IFN-ɣ), Interleukin-1 (IL-1), Interleukin-6 (IL-6), Interleukin-18 (IL-18) and clinical and radiological parameters of MS patients. Material and methods The study involved 96 patients with MS diagnosed according to the 2017 McDonald’s criteria. The control group consisted of 73 healthy participants. Patients in the study group negated other immunological conditions. Results CCL2/MCP-1 levels were significantly increased in the study group. CCL/2MCP-1 levels were significantly higher in the other MS group compared to Relapse-remitting multiple sclerosis (RRMS). A significant positive correlation was identified between CCL2 (MCP-1) and disease duration. IFN-g levels were significantly higher in the control group. IFN-g concentrations exhibited a trend toward higher levels in the Expanded Disability Status Scale (EDSS) ≥ 4 score group. Significantly higher IFN-g levels were found in patients without active lesions on Magnetic resonance imaging (MRI). IL-18 levels were significantly increased in patients with no or single T2-weighted lesions. Conclusions The inverse correlation between IL-18 levels and MRI lesions suggests that this cytokine may serve as a predictive marker for monitoring the course of MS. IFN-g is a cytokine with an important role in the development of inflammation in MS. Higher plasma levels of CCL2 (MCP-1) with disease duration suggest progressive immune activation. Multiple sclerosis MS microglia cytokines interleukins MRI lesions Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Multiple sclerosis (MS) is a chronic disorder of the central nervous system (CNS) with a presumed autoimmune basis [ 1 ]. It is an inflammatory disease characterised by progressive neurodegeneration [ 1 , 2 ]. There are currently an estimated 2.8 million people worldwide with the disease, which amounts to 35.9 patients per 100,000 people. Annually, there are 2.1 new cases of MS per 100,000 people [ 3 ]. MS is therefore the most common demyelinating disease, and, at present, this is an incurable condition [ 4 , 5 ]. Fundamental to the pathogenesis of MS are disturbances in the control and balance of the immune response [ 6 ]. B lymphocytes, whose role includes the production of autoantibodies and cytokines, are involved in the pathophysiology of this disease [ 1 , 7 ]. B cells also mediate the effects exerted by antigen-presenting cells. The result is the activation of T cells, which is thought to be a key element in the pathomechanism of the disease [ 7 , 8 ]. This results in infiltration of the brain and spinal cord by inflammatory cells. A dual role for microglia has been demonstrated in the pathogenesis of MS, where it exerts both beneficial and negative effects [ 2 ]. As inflammation occurs in the CNS, as is the case in MS pathogenesis, microglia also have secretory and modulatory functions [ 2 , 9 – 13 ]. Soluble factors via which microglia participate in negative effects in MS pathogenesis include interferon gamma (IFN-ɣ), tumour necrosis factor alpha (TNF-ɑ), reactive oxygen species, interleukin 1β (IL-1β), IL-6, IL-18, IL-12 and IL-23, but also C-C motif chemokines ligands (CCLs) [ 2 , 10 , 11 ]. Thus, microglia are involved in pathological processes in both white matter and grey matter [ 2 , 14 ]. This aspect is all the more important because currently existing disease-modifying therapies (DMTs) only reduce white matter loss but have limited properties to significantly reduce or prevent grey matter neurodegeneration [ 2 , 14 ]. Current disease-modifying treatments have only a limited effect on this process. Due to this, determining the relationship between plasma levels of microglia-associated pro-inflammatory cytokines and clinical status and radiological findings could provide important data on the pathogenesis of MS, as well as contributing to the development of new treatments for the condition. The aim of this study is to determine the relationship between the levels of pro-inflammatory microglia cytokines and the clinical and radiological status of patients with MS. 2. Methodology Participants provided informed consent to participate in the study. The study group included 96 patients with MS diagnosed according to the 2017 McDonald’s criteria. Participants in the study group negated malignancies and other immunological diseases. The control group included 73 participants. These subjects negated the presence of neurological conditions, immunological diseases and malignancies. The study lasted from April 1st, 2023, to June 1st, 2024. It was approved by the Bioethics Committee Medical University of Silesia in Katowice, Poland in accordance with the Declaration of Helsinki, BNW/NWN/0052/KB1/132/I/22/23 (March 23rd, 2023). All participants have been given a consent for using clinical results we obtained to publish. Serum concentrations of CCL2/MCP-1, IL-1 beta/IL-1F2, IL-6, IFN-gamma and IL-18/IL-1F4 were determined in both control and study participants using Human Magnetic Luminex® Assays (R&D SYSTEMS, biotechne). Each drug was classified into a larger pharmacological category using the second level of the Anatomical Therapeutic Chemical (ATC) classification system (ATC2), which identifies the main therapeutic group of each substance (L01 - anticancer agents, L03 - immunostimulants, L04 - immunosuppressants). This was intended to group the drugs consistently. These ATC2 categories were used to organise the active substances into clinically meaningful groups relevant to immune modulation, particularly in the treatment of multiple sclerosis. No further subclassifications using more granular levels of the ATC system (such as ATC4) were performed. According to the ATC2 classification, the L01 category covered Cladribine and Ofatumumab, the L03 category included glatiramer acetate, interferon beta 1a and 1b, while the L04 category listed dimethyl fumarate, fingolimod, ocrelizumab, ozanimod, siponimod, teriflunomide. For the EDSS score, medical justification for level grouping was used [ 15 ]. Scores of 2.0-2.5 were classified as “Minimal/Mild”, 3.0-3.5 as “Moderate” and 4.0 or higher as “Significant”. This categorisation was intended to ensure both clinical consistency but also comparability between groups, where each group represented approximately one-third of the study group. The significance level was taken as p = 0.05, while p-values showing statistically significant findings are in bold. Statistical analysis calculations were performed using R software (R Core Team (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/ ), version 4.3.2. 3. Results 3.1 Baseline characteristics There were 169 participants in the study, with 56.8% (N = 96) being the study group and 43.2% (N = 73) being the control group. Gender data were available for 152 participants, with 61.2% (n = 93). The median age of participants was 34 years (IQR: 24–46), while the mean age was 36.35 years (SD = 14.45). It should be noted that participants in the study group were significantly older (median age = 43 years) than those in the control group (median age = 22.5 years). Marker levels were assessed in up to 160 individuals. For CCL2/MCP-1, the mean was 442.6 pg/ml (SD = 184.71), and the median was 418.65 pg/ml (IQR: 311.55–551.83). IL-1b had a median concentration of 0.52 pg/ml (IQR: 0.31–2.04) and a mean of 2.85 pg/ml (SD = 6.28), reflecting a right-skewed distribution. IFN-g and IL-18 also showed skewed distributions, with IL-18 presenting the highest median concentration of 792.22 pg/ml (IQR: 640.75–928.1). Detailed information on cytokine concentrations in the subgroups as well as for baseline characteristics is presented in Table 1 . Table 1 Baseline characteristics Variable Parameter Overall (N = 169) Study (N = 96) Control (N = 73) Group Study 56.8% (N = 96) - - Control 43.2% (N = 73) - - Missing data N = 0 - - Gender Male 38.8% (N = 59) 33.8% (N = 27) 44.4% (N = 32) Female 61.2% (N = 93) 66.2% (N = 53) 55.6% (N = 40) Missing data N = 17 N = 16 N = 1 Age (years) N 127 81 46 Mean (SD) 36.35 (14.45) 44.42 (11.97) 22.13 (2.47) Median (Q1-Q3) 34 (24–46) 43 (35–52) 22.5 (20–24) Range 18–72 19–72 18–27 Missing data N = 42 N = 15 N = 27 CCL2/MCP-1 [pg/ml] N 160 96 64 Mean (SD) 442.6 (184.71) 472.02 (207.67) 398.48 (133.33) Median (Q1-Q3) 418.65 (311.55–551.83) 429.71 (331.41–600.03) 395.64 (288.96–504.04) Range 2.88–1233.31 2.88–1233.31 127.53–681.29 Missing data N = 9 N = 0 N = 9 IL-1b [pg/ml] N 160 96 64 Mean (SD) 2.85 (6.28) 2.49 (6.2) 3.4 (6.41) Median (Q1-Q3) 0.52 (0.31–2.04) 0.41 (0.28–1.46) 0.73 (0.49–3.32) Range 0.08–51.3 0.19–51.3 0.08–32.95 Missing data N = 9 N = 0 N = 9 IFN-g [pg/ml] N 160 96 64 Mean (SD) 2.95 (4.64) 2.63 (4.67) 3.42 (4.58) Median (Q1-Q3) 1.55 (0.87–3.14) 1.2 (0.8–2.58) 1.77 (1.1–3.57) Range 0.06–41.68 0.11–41.68 0.06–27.73 Missing data N = 9 N = 0 N = 9 IL-18 [pg/ml] N 160 96 64 Mean (SD) 811.32 (270.56) 786.89 (266.89) 847.96 (273.97) Median (Q1-Q3) 792.22 (640.75–928.1) 772.73 (630.54–913.25) 826.12 (670.03–1036.67) Range 54.42–1800.93 54.42–1800.93 89.06–1773.45 Missing data N = 9 N = 0 N = 9 Body mass (kg) N 72 72 - Mean (SD) 71.78 (13.36) 71.78 (13.36) - Median (Q1-Q3) 69.5 (62–81.25) 69.5 (62–81.25) - Range 50–104 50–104 - Missing data N = 97 N = 24 - Height (cm) N 71 71 - Mean (SD) 170.31 (8.98) 170.31 (8.98) - Median (Q1-Q3) 170 (162.5–176) 170 (162.5–176) - Range 155–191 155–191 - Missing data N = 98 N = 25 - Body mass index (BMI) [kg/m^2] N 71 71 - Mean (SD) 24.75 (3.95) 24.75 (3.95) - Median (Q1-Q3) 24.03 (21.93–26.19) 24.03 (21.93–26.19) - Range 17.78–34.06 17.78–34.06 - Missing data N = 98 N = 25 - MS type PPMS 1.5% (N = 1) 1.5% (N = 1) - RRMS 87.9% (N = 58) 87.9% (N = 58) - SPMS 10.6% (N = 7) 10.6% (N = 7) - Missing data N = 103 N = 30 - MS type - category RRMS 87.9% (N = 58) 87.9% (N = 58) - Other 12.1% (N = 8) 12.1% (N = 8) - Missing data N = 103 N = 30 - Disease duration (years) N 80 80 - Mean (SD) 8.79 (7.58) 8.79 (7.58) - Median (Q1-Q3) 7 (2.88–12) 7 (2.88–12) - Range 0.58–37 0.58–37 - Missing data N = 89 N = 16 - Disease duration (years) - category Less than 3 25% (N = 20) 25% (N = 20) - (3–7) 23.8% (N = 19) 23.8% (N = 19) - (7–12) 22.5% (N = 18) 22.5% (N = 18) - At least 12 28.7% (N = 23) 28.7% (N = 23) - Missing data N = 89 N = 16 - Time from the first symptoms to the diagnose (years) N 72 72 - Mean (SD) 4.2 (6.02) 4.2 (6.02) - Median (Q1-Q3) 2 (0.5–5.25) 2 (0.5–5.25) - Range 0–26 0–26 - Missing data N = 97 N = 24 - Time from the first symptoms to the diagnose (years) - category Less than 0.5 23.6% (N = 17) 23.6% (N = 17) - (0.5-2) 22.2% (N = 16) 22.2% (N = 16) - (2-5.5) 29.2% (N = 21) 29.2% (N = 21) - At least 5.5 25% (N = 18) 25% (N = 18) - Missing data N = 97 N = 24 - Age of MS diagnose (years) N 80 80 - Mean (SD) 35.86 (9.91) 35.86 (9.91) - Median (Q1-Q3) 34 (29.5–43.25) 34 (29.5–43.25) - Range 17–65 17–65 - Missing data N = 89 N = 16 - Age of MS diagnose (years) - category Less than 30 25% (N = 20) 25% (N = 20) - (30–35) 27.5% (N = 22) 27.5% (N = 22) - (35–44) 22.5% (N = 18) 22.5% (N = 18) - At least 44 25% (N = 20) 25% (N = 20) - Missing data N = 89 N = 16 - Number of relapses (last year) N 78 78 - Mean (SD) 0.23 (0.51) 0.23 (0.51) - Median (Q1-Q3) 0 (0–0) 0 (0–0) - Range 0–2 0–2 - Missing data N = 91 N = 18 - Relapses in last year Any 19.2% (N = 15) 19.2% (N = 15) - None 80.8% (N = 63) 80.8% (N = 63) - Missing data N = 91 N = 18 - EDSS score 2 14.1% (N = 11) 14.1% (N = 11) - 2.5 20.5% (N = 16) 20.5% (N = 16) - 3 28.2% (N = 22) 28.2% (N = 22) - 3.5 6.4% (N = 5) 6.4% (N = 5) - 4 11.5% (N = 9) 11.5% (N = 9) - 4.5 6.4% (N = 5) 6.4% (N = 5) - 5 2.6% (N = 2) 2.6% (N = 2) - 5.5 5.1% (N = 4) 5.1% (N = 4) - 6 3.8% (N = 3) 3.8% (N = 3) - 6.5 1.3% (N = 1) 1.3% (N = 1) - Missing data N = 91 N = 18 - EDSS score - categorization 2-2.5 34.6% (N = 27) 34.6% (N = 27) - 3-3.5 34.6% (N = 27) 34.6% (N = 27) - >=4 30.8% (N = 24) 30.8% (N = 24) - Missing data N = 91 N = 18 - Amount of MRI pathologic lesions (T2) - category Numerous 81.3% (N = 61) 81.3% (N = 61) - Single 17.3% (N = 13) 17.3% (N = 13) - None 1.3% (N = 1) 1.3% (N = 1) - Missing data N = 94 N = 21 - Amount of MRI pathologic lesions (T2) - categorization Numerous 81.3% (N = 61) 81.3% (N = 61) - Single or none 18.7% (N = 14) 18.7% (N = 14) - Missing data N = 94 N = 21 - Active lesion - category Yes 45.9% (N = 17) 45.9% (N = 17) - No 54.1% (N = 20) 54.1% (N = 20) - Missing data N = 132 N = 59 - Hyperintensive - category Yes 63.6% (N = 14) 63.6% (N = 14) - No 36.4% (N = 8) 36.4% (N = 8) - Missing data N = 147 N = 74 - Nonactive - category Yes 98% (N = 50) 98% (N = 50) - No 2% (N = 1) 2% (N = 1) - Missing data N = 118 N = 45 - Number of lesions in Gd+ - category Numerous 43.3% (N = 29) 43.3% (N = 29) - Single 7.5% (N = 5) 7.5% (N = 5) - None 49.3% (N = 33) 49.3% (N = 33) - Missing data N = 102 N = 29 - Number of lesions in Gd+ - categorization Numerous 43.3% (N = 29) 43.3% (N = 29) - Single or none 56.7% (N = 38) 56.7% (N = 38) - Missing data N = 102 N = 29 - Active substance Cladribine 1.2% (N = 1) 1.2% (N = 1) - Dimethyl fumarate 37% (N = 30) 37% (N = 30) - Fingolimod 6.2% (N = 5) 6.2% (N = 5) - Glatiramer acetate 2.5% (N = 2) 2.5% (N = 2) - Interferon beta 1a 2.5% (N = 2) 2.5% (N = 2) - Interferon beta 1b 6.2% (N = 5) 6.2% (N = 5) - Ocrelizumab 11.1% (N = 9) 11.1% (N = 9) - Ofatumumab 19.8% (N = 16) 19.8% (N = 16) - Ozanimod 1.2% (N = 1) 1.2% (N = 1) - Siponimod 8.6% (N = 7) 8.6% (N = 7) - Teriflunomide 3.7% (N = 3) 3.7% (N = 3) - Missing data N = 88 N = 15 - ATC2 L01 21% (N = 17) 21% (N = 17) - L03 11.1% (N = 9) 11.1% (N = 9) - L04 67.9% (N = 55) 67.9% (N = 55) - Missing data N = 88 N = 15 - White blood cells (WBC) [x10^3/µl] N 75 75 - Mean (SD) 5.85 (1.87) 5.85 (1.87) - Median (Q1-Q3) 5.41 (4.46–6.8) 5.41 (4.46–6.8) - Range 3.01–11.8 3.01–11.8 - Missing data N = 94 N = 21 - Red blood cells (RBC) [mln/µl] N 76 76 - Mean (SD) 4.77 (1.25) 4.77 (1.25) - Median (Q1-Q3) 4.6 (4.39–4.99) 4.6 (4.39–4.99) - Range 3.73–15 3.73–15 - Missing data N = 93 N = 20 - Hemoglobin (HG) [g/dl] N 76 76 - Mean (SD) 13.94 (1.33) 13.94 (1.33) - Median (Q1-Q3) 14 (13.1–14.7) 14 (13.1–14.7) - Range 9.6–17.1 9.6–17.1 - Missing data N = 93 N = 20 - Platelets (PLT) [x10^3/µl] N 76 76 - Mean (SD) 256.2 (63.31) 256.2 (63.31) - Median (Q1-Q3) 244.5 (218.25–281.5) 244.5 (218.25–281.5) - Range 75–455 75–455 - Missing data N = 93 N = 20 - Thyroid-stimulating hormone (TSH) [mIU/l] N 41 41 - Mean (SD) 1.8 (1.09) 1.8 (1.09) - Median (Q1-Q3) 1.57 (0.93–2.27) 1.57 (0.93–2.27) - Range 0.01–4.46 0.01–4.46 - Missing data N = 128 N = 55 - Aminotransferase Alanina (AlAT) [U/l] N 74 74 - Mean (SD) 23.93 (11.24) 23.93 (11.24) - Median (Q1-Q3) 20.25 (15.4–32.6) 20.25 (15.4–32.6) - Range 7.3–55.3 7.3–55.3 - Missing data N = 95 N = 22 - Aspartate aminotransferase (AspAT) [U/l] N 75 75 - Mean (SD) 22.92 (8.96) 22.92 (8.96) - Median (Q1-Q3) 20 (17.7–24.75) 20 (17.7–24.75) - Range 12.2–70.08 12.2–70.08 - Missing data N = 94 N = 21 - Creatinine [µmol/l] N 75 75 - Mean (SD) 69.62 (13.14) 69.62 (13.14) - Median (Q1-Q3) 68 (61–78.84) 68 (61–78.84) - Range 46–122 46–122 - Missing data N = 94 N = 21 - CRP [mg/l] N 14 14 - Mean (SD) 1.58 (1.78) 1.58 (1.78) - Median (Q1-Q3) 1.07 (0.46–1.64) 1.07 (0.46–1.64) - Range 0.2–6.35 0.2–6.35 - Missing data N = 155 N = 82 - Lymphocyte percent N 72 72 - Mean (SD) 26.01 (10.44) 26.01 (10.44) - Median (Q1-Q3) 25.85 (17.68–33.6) 25.85 (17.68–33.6) - Range 3.3–49.6 3.3–49.6 - Missing data N = 97 N = 24 - Lymphocytes [x10^3/µl] N 75 75 - Mean (SD) 1.54 (0.76) 1.54 (0.76) - Median (Q1-Q3) 1.35 (0.92–2.04) 1.35 (0.92–2.04) - Range 0.32–3.9 0.32–3.9 - Missing data N = 94 N = 21 - 3.2 Parameter Comparison Between Study Groups Statistically significant differences in the comparison between the study group (N = 96) and the control group (N = 64) were shown for the following cytokines: CCL2/MCP-1 concentrations were significantly higher in the study group (median: 429.71 pg/ml) than in controls (median: 395.64 pg/ml; p = 0.0377, r = 0.164), though not confirmed by the Wald test (p = 0.705). IL-1b levels were significantly elevated in the control group (median: 0.73 pg/ml) versus the study group (median: 0.41 pg/ml; p < 0.001, r = 0.263), with a non-significant Wald test (p = 0.472). IFN-g was also significantly higher in controls (median: 1.77 pg/ml) than in the study group (median: 1.2 pg/ml; p = 0.0121, r = 0.198), though not confirmed in the Wald test (p = 0.384). Other markers (IL-4, IL-13, IL-18) did not show statistically significant differences (p > 0.05). 3.3 Parameter Comparison Within Study Group MS type – category CCL2/MCP-1 was notably elevated in the "Other" MS group (median: 644.99 pg/ml) versus relapse-remitting multiple sclerosis (RRMS) (median: 417.33 pg/ml), with statistical significance (p = 0.0103, effect size = 0.316), though not confirmed by the Wald test (p = 0.418). Other markers, including IL-1b, IFN-g and IL-18, did not show statistically significant differences between RRMS and other MS subtypes (p > 0.05). EDSS score – categorisation IFN-g concentrations exhibited a trend toward higher levels in the EDSS ≥ 4 group (median: 1.50 pg/ml) relative to the 3–3.5 group (median: 0.82 pg/ml) ( Fig. 1 ). Although this trend did not reach significance in the unadjusted analysis (p = 0.0533, effect size = 0.001), it became statistically significant in the adjusted model (Wald test p = 0.046, R² = 0.158) ( Fig. 2 ) . No statistically significant differences were observed for the other cytokines across EDSS categories in either unadjusted or adjusted analyses (all p > 0.05). Amount of MRI pathologic lesions (T2) – categorisation Among the cytokines assessed in relation to the amount of MRI T2 lesions (N = 96), one marker showed a significant difference: IL-18 levels were significantly higher in patients with single or no T2 lesions (median: 933.6 pg/ml) compared to those with numerous lesions (median: 748.57 pg/ml), with p = 0.0194 in the unadjusted analysis (effect size = 0.859) ( Fig. 3 ). This result was confirmed after adjustment, with the estimated marginal mean (emmeans) of IL-18 for the "Numerous" group at 742.68 pg/ml (95% CI: 673.28–812.07), and for the "Single or none" group at 959.09 pg/ml (95% CI: 812.20–1105.98). The adjusted difference was statistically significant: − 216.41 pg/ml (95% CI: − 379.42 to − 53.40; Wald test p = 0.008, R² = 0.148), indicating a robust association between lower lesion burden and higher IL-18 levels ( Fig. 4 ). Other cytokines showed no statistically significant differences between lesion burden groups in both unadjusted and adjusted analyses (all p > 0.05). Active lesion – category Among the cytokines assessed in relation to the presence of active MRI lesions (N = 96), only one marker showed a statistically significant difference: IFN-g levels were significantly lower in patients with active lesions (median: 0.82 pg/ml) compared to those without active lesions (median: 1.57 pg/ml), with p = 0.0302 in the unadjusted non-parametric analysis (effect size = 0.356) ( Fig. 5 ) . This finding was confirmed in the adjusted analysis, where the estimated marginal mean (emmeans) for the "Active lesions" group was 0.918 pg/ml (95% CI: 0.623–1.351), and for the "No active lesions" group it was 1.962 pg/ml (95% CI: 1.402–2.743). The adjusted difference was 0.468 pg/ml (95% CI: 0.276–0.794), indicating significantly higher IFN-g levels in patients without active lesions (Wald test p = 0.003, R² = 0.426) ( Fig. 6 ). Other cytokines did not show statistically significant differences between the two groups in either unadjusted or adjusted analyses (all p > 0.05). Hyperintensive – category No statistically significant associations were observed between the analyzed cytokines and the hyperintensity occurrence. Number of lesions in Gd+ - categorisation Similarly, no statistically significant associations were observed between the analyzed cytokines and the number of lesions in Gd+. ATC2 CCL2/MCP-1 levels differed significantly between ATC2 groups (Kruskal-Wallis p = 0.0264, effect size = 0.001). Post-hoc Dunn’s tests revealed significantly higher MCP-1 levels in the L03 group compared to L01 (p = 0.0225) and L01 to L04 (p = 0.0368), but no significant difference between L03 and L04 (p = 0.4384) ( Table 2 ) ( Fig. 7 ) . This difference was not statistically significant in the adjusted model (Wald p = 0.719, R² = 0.037). Other markers did not show statistically significant differences between ATC2 groups in either unadjusted or adjusted analyses (all p > 0.05). Table 2 Dunn post-hoc tests – variable CCL2/MCP-1. Variable Comparison p-value CCL2/MCP-1 (pg/ml) L01 - L03 0.0225 L01 - L04 0.0368 L03 - L04 0.4384 Other categories There were no statistically significant differences in disease duration, time from symptom onset to diagnosis, age at diagnosis, or the presence of recent relapses for any of the cytokines assessed. Correlation analysis Within Study Group Spearman correlation analysis was conducted to evaluate the association between selected cytokines and clinical, biochemical and haematological parameters. IL-1b was most strongly correlated with IL-6 (r = 0.534, p < 0.001), followed by IL-6 and IFN-g (r = 0.526, p < 0.001). A significant positive correlation was identified between CCL2 (MCP-1) and disease duration (r = 0.271, p = 0.015), which may reflect progressive immune activation over time. Cytokine levels were also correlated with haematological parameters. IL-6, IL-1b, and IFN-g demonstrated weak but statistically significant positive correlations with total white blood cell (WBC) count (r = 0.262, p = 0.023; r = 0.243, p = 0.036; and r = 0.231, p = 0.046, respectively). No statistically significant correlations were found between cytokine levels and age, height, weight, or most anthropometric measures, apart from a weak but significant correlation between IL-6 and BMI (r = 0.245, p = 0.039). 4. Discussion According to available information, the study we conducted is one of the first aimed at comprehensively assessing the mutual relationships between the level of pro-inflammatory microglia-associated cytokines in the course of MS and the presence of lesions in neuroimaging studies. This highlights the significant innovative nature of this study. Considering the fact that diagnoses of this disease are increasingly being made in various geographic regions of the world (more prevalent in Europe and in Northern America - >100 per 100.000 people, less frequent in sub-Saharan Africa and East Asia – only 2 per 100.000 people), as well as the lack of possibilities for its effective cure, the search for new treatment methods or ways to monitor MS seems particularly relevant [ 4 ]. These therapies could potentially reduce not only white matter loss, but also grey matter neurodegeneration, which is crucial for disability in MS [ 14 ]. In some countries, including those in Europe, women have three times the incidence of men, however, this is only true for the relapsing-remitting form of MS (RRMS) [ 4 , 7 ]. In the comparison with ratio of members in our study, it is about 2:1 (66.2% of females and 33.8% of males), which does not much differ to the general trend. It is important to emphasize the aspect of microglia-associated cytokines participation in the processes of neurodegeneration in MS. Demyelination, affecting both grey and white matter, can be reversed by, among other things, remyelination, in which microglia also participate through secreted cytokines [ 2 , 14 ]. Neurodegeneration of the grey matter, on the other hand, is irreversible, occurs very early in the course of MS and is largely responsible for permanent disability [ 2 ]. Areas of CNS demyelination are also formed [ 9 ]. Initially, the inflammatory process, stimulated by pro-inflammatory cytokines predominates, while later the degenerative process takes over. There is neuronal, axonal and synaptic damage, as well as destruction of glial cells [ 9 ]. Resident macrophages in the CNS, which are called microglia, have been shown to play a significant role in these pathological processes [ 2 , 7 ]. In a way, microglia behave as a controller that monitors the processes of the microenvironment and the proper functioning of neuronal cells, dendrites and axons [ 2 ]. In addition, these cells phagocytose necrotic cells and their parts [ 2 ]. The nature of microglia is highly plastic and, depending on the situation, can exert different, often opposing effects [ 2 ]. Our results indicate that IFN-ɣ concentrations are elevated in patients with a higher degree of disability as reflected by the EDSS score. However, given the lack of significance in the primary (unadjusted) test and the non-significant pairwise comparisons using estimated marginal means (mmeans), this result should be interpreted with caution. Nevertheless, in the study by Trenova et al., IFN-ɣ concentrations, as in our study, were positively correlated with a higher score on the EDSS scale, but only in specific clinical situations, such as during therapy with interferon beta or during a relapse [ 16 ]. In 1987, a study was conducted in which patients with MS were given a trial of therapy with IFN-ɣ [ 17 ]. The consequence of this trial appeared to be an exacerbation of disease symptoms in less than half of the patients. This was due to an increase in the expression of MHC class II monocytes, thus indicating a deleterious adverse effect of this cytokine in MS [ 17 ]. These results agree with those obtained from our studies. One therapeutic strategy contributing to delaying MS progression may be to restore the balance between the pro-inflammatory and immunomodulatory actions of IFN-ɣ. This may be served by reducing the concentration of this cytokine and other pro-inflammatory molecules [ 17 , 18 , 19 ]. It is noteworthy that disruption of this balance is one of the proven causes underlying the development of the disease [ 20 , 21 ]. Our study also showed higher IFN-ɣ levels in patients without active lesions on brain MRI. Thus, the observation from a randomized trial in a group of patients with primary-progressive multiple sclerosis (PPMS), according to which the effect of neutralizing antibodies to IFN-ɣ significantly slows the development of disability, seems interesting. This was confirmed both by MRI studies, by showing fewer active lesions, and by the shift in the cytokine profile of activated blood cells found in biochemical analysis [ 22 , 23 ]. It caused, among other things, a reduction in the synthesis of IL-1β, TNF-α and IFN-ɣ. These results suggest that IFN-ɣ inhibition may represent a promising new therapeutic strategy, especially in the treatment of PPMS [ 22 , 23 ]. Thus, the findings in this manuscript confirm current knowledge of IFN-ɣ as a molecule with pro-inflammatory effects. Moreover, in agreement with this is another phenomenon we observed. Namely, a positive correlation between white blood cell counts and IFN-ɣ levels, as well as two other microglia-associated cytokines that aggravate MS, specifically IL-1b and IL-6 [ 24 , 25 ]. In terms of IL-18, we have shown that the level of this cytokine is significantly higher in patients with a lower number of T2-dependent lesions on MRI. This finding is supported by results from a paper by Losy et al. [ 26 ]. This study reported significantly higher levels of IL-18 in both CSF and serum among participants who were also characterized by the presence of active CNS lesions visualized by MRI with gadolinium enhancement [ 27 , 28 ]. Based on this, the authors concluded that IL-18 has a significant effect relative to MS immunopathogenesis, especially in the active phases of the disease [ 26 – 29 ]. Taking into account the fact that these lesions shown by MRI are indicative of disease activity in the course of MS, and IL-18 has a proven inflammation-promoting effect, it is reasonable to assume that this cytokine may in the future serve as a predictive marker for monitoring MS. Our study also showed a moderate positive correlation between IL1b and IL-6, as well as IL-6 and INF-g. The cytokines IL-1 and IL-6 show a pro-inflammatory character [ 29 – 34 ]. The correlation of IL-6 with IFN-ɣ, which is a cytokine that promotes the development of MS and worsens prognosis in later stages of the disease, seems understandable [ 29 – 32 ]. CCL2 is seen as a molecule with pro-inflammatory effects [ 35 – 38 ]. The interaction of CCL2 with the CCR2 receptor results in the attraction of monocytes, macrophages, dendritic cells, natural-killer (NK) cells and T lymphocytes to the CNS [ 35 – 37 ]. As a result of the activation of the aforementioned cells, pro-inflammatory molecules are released: IL-1β, IL-6 and TNF-α, exerting destructive effects on the myelin sheaths of neurons [ 37 , 38 ]. Moreover, it has been shown that one of the causes of neuronal cell inflammation and subsequent degeneration may be the effect that CCL2 has toward microglia activation [ 39 , 40 ]. Previously published reports have revealed that CCL2 levels in the cerebrospinal fluid (CSF) are elevated in patients, suffering from MS and show a correlation with a more severe disease course [ 41 ]. This does not differ with our results, where we showed a significant positive correlation between CCL2/MCP-1 and disease duration, which may reflect progressive immune activation over time [ 41 – 43 ]. This may lay the groundwork for potential exploration of CCL2 use in clinical practice, including as a marker. 5. Summary The aim of this study was to assess the relationship of pro-inflammatory microglia cytokines with clinical and radiological parameters of MS patients. IL-18 levels were significantly increased in patients with single or non-T2-weighted lesions on MRI, suggesting a possible inverse association with lesion burden. No significant differences in cytokine profiles were observed with respect to disease duration, time from symptom onset to diagnosis, age at diagnosis, or the presence of recent relapses. Additionally, correlations between cytokine levels and radiological or clinical metrics were generally weak, with only a few statistically significant associations. The results suggest that IFN-ɣ levels may be higher in patients with higher levels of disability, but this should be interpreted with caution. It was also shown that IFN-ɣ levels were higher in patients without active brain MRI lesions. These findings suggest the action of IFN-ɣ as a cytokine with important effects on the development of inflammation in the context of MS. Higher plasma levels of CCL2/MCP-1 with disease duration suggest progressive immune activation. This suggests the possibility of using this molecule as a marker of MS. Moreover, a positive correlation was obtained between white blood cell count and IFN-ɣ levels, as well as IL-1b and IL-6, which are microglia-associated cytokines already known to have a negative prognostic effect in MS. It seems that the findings obtained may provide an interesting starting point for further studies aimed at further clarifying the role of the indicated molecules, as well as establishing and developing methods to exploit their properties. Abbreviations MS Multiple sclerosis CCL2/MCP 1 -C-C motif chemokine ligand 2/ Monocyte Chemoattractant Protein-1 IFN ɣ -Interferon gamma IL 1 -Interleukin-1 IL 6 -Interleukin-6 IL 18 -Interleukin-18 IL 12 -Interleukin 12 IL 23 -Interleukin 23 Declarations Ethics approval and consent to participate: • A study was approved by the Bioethics Committee Medical University of Silesia in Katowice, Poland in accordance with the Declaration of Helsinki, BNW/NWN/0052/KB1/132/I/22/23 (March 23rd, 2023). All participants provided informed consent to participate in the study. Consent for publication: • All participants have been given an agreement for using clinical results we obtained to publish. Competing interests: • We declare that the authors have no competing interests as defined by BMC, or other interests that might be perceived to influence the results and/or discussion reported in this paper. Conflicts of Interest: • The authors declare no conflicts of interest. Funding: The study was funded by the Silesian Medical University in Katowice. Funding number: BNW-2-014/N/4/K Author Contribution Conceptualization: H.M., Data curation: H.M., Formal analysis: H.M., A.S., Funding acquisition: H.M., Investigation: H.M., A.S., Methodology: H.M., Project administration: H.M., M.A.-S., Resources: H.M., Software: H.M., Supervision: M.A.-S., Validation: H.M., Visualization: H.M., Writing – original draft: H.M., A.S., Writing – review and editing: H.M., M.A.-S. Data Availability All data supporting the findings of this study are available within the paper and its Supplementary Information. References Mado H, Kubicka-Bączyk K, Adamczyk-Sowa M. Anti-severe acute respiratory syndrome coronavirus-2 antibody responses following Pfizer-BioNTech vaccination in a patient with multiple sclerosis treated with ocrelizumab: a case report. J Int Med Res [Internet]. 2021;49(9):030006052110443. 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Prototypical’ proinflammatory cytokine (IL-1) in multiple sclerosis: role in pathogenesis and therapeutic targeting. Volume 24. Expert Opinion on Therapeutic Targets; 2020. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8782258","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":595930973,"identity":"68c6399a-9d8b-45c4-b91b-5b5e2739c843","order_by":0,"name":"Hubert Mado","email":"","orcid":"","institution":"Department of Neurology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia in Katowice","correspondingAuthor":false,"prefix":"","firstName":"Hubert","middleName":"","lastName":"Mado","suffix":""},{"id":595930976,"identity":"fcacb612-fe64-476a-abee-32c48228f912","order_by":1,"name":"Artur Stasiniewicz","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYFADZgaGAwwMEgwM7A0grgV+pQwMBkhaeA6ARCSI0AIHEgn4tfDPPn9MuuLPH3n5dt6DBz62WcjLz3xj9oBxB24tEueS2STPthkYbjjMl3BwZpuEYePsHHMDxjN4HHaGmU2yscGAcQMzj8Fh3m0SCczSudskGNtwa5EHaWn4Y2A/vxmo5S9QC9BS/FoMwFrYDBIbDgO1MAK18Ejw4tdieIbZ2LKxzTh5A1DLwd5/EoYzePK/GyTi8YvcGcaHNxv+yNnO7z9j/OHHmTpg0B1Le/Bxhw1u72MDbAyJDaTpAGphJFXLKBgFo2AUDGcAAHU1TXH0NgMBAAAAAElFTkSuQmCC","orcid":"","institution":"Department of Neurology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia in Katowice","correspondingAuthor":true,"prefix":"","firstName":"Artur","middleName":"","lastName":"Stasiniewicz","suffix":""},{"id":595930980,"identity":"cea508e1-e234-4ec3-9ffe-e5c7e1d5e4a5","order_by":2,"name":"Monika Adamczyk - Sowa","email":"","orcid":"","institution":"Department of Neurology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia in Katowice","correspondingAuthor":false,"prefix":"","firstName":"Monika","middleName":"Adamczyk -","lastName":"Sowa","suffix":""}],"badges":[],"createdAt":"2026-02-04 05:53:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8782258/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8782258/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103383340,"identity":"1ad74631-3737-4a3d-b476-ef2069c8c784","added_by":"auto","created_at":"2026-02-25 06:11:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":23582,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDependency between variables: IFN-g (pg/ml) and EDSS score - categorisation in study group \u003c/strong\u003e(“Overall”: N = 96; “2-2.5”: N = 27; “3-3.5”: N = 27; “\u0026gt;=4”: N = 24; median: 1.2 pg/ml; p = 0.004; effect size r = 0.227; Wald test)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8782258/v1/d47ff63b7694ccdc6999bdd0.png"},{"id":103383336,"identity":"f1afb23a-aa8a-4615-ac7a-52239caa3758","added_by":"auto","created_at":"2026-02-25 06:11:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":36491,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMarginal means for IFN-g (pg/ml) by EDSS score – categorisation \u003c/strong\u003e(“Overall”: N = 96; “2-2.5”: N = 27; “3-3.5”: N = 27; “\u0026gt;=4”: N = 24; median: 1.2 pg/ml; p = 0.004; effect size r = 0.227; Wald test)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8782258/v1/2d42ede85f9b54889b2aefed.png"},{"id":103383391,"identity":"28ed4460-df1b-4bb3-b01a-1240568b3f51","added_by":"auto","created_at":"2026-02-25 06:12:05","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":234316,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDependency between variables: IL-18 (pg/ml) and Amount of MRI pathologic lesions (T2) - categorisation in study group \u003c/strong\u003e(N = 96; “Numerous”: N = 61; “Single or none”: N = 14; median: 772.73 pg/ml; p = 0.0194; effect size r = 0.859; Wald test p = 0.008)\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8782258/v1/de38569423904f6443c08a5e.jpeg"},{"id":103383338,"identity":"e82321ec-bafc-438c-a116-871829f5daf3","added_by":"auto","created_at":"2026-02-25 06:11:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":46642,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMarginal means for IL-18 (pg/ml) by Amount of MRI pathologic lesions (T2) - categorisation \u003c/strong\u003e(N = 96; “Numerous”: N = 61; “Single or none”: N = 14; median: 772.73 pg/ml; p = 0.0194; effect size r = 0.859; Wald test p = 0.008)\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8782258/v1/addc42883db7908eb592b78b.png"},{"id":103383486,"identity":"5f311207-0856-471a-89ef-ecbfc0431be3","added_by":"auto","created_at":"2026-02-25 06:12:11","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":210201,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDependency between variables: IFN-g (pg/ml) and Active lesion – category in study group \u003c/strong\u003e(N = 96; “Yes”: N = 17; “No”: N = 20; median = 1.2 pg/ml; p = 0.302; r effect size = 0.356; U Mann – Whitney test)\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8782258/v1/e78d39be7fd313dc28fefe00.jpeg"},{"id":103383394,"identity":"2fa7a95f-c433-43b8-b180-58bcbea4a690","added_by":"auto","created_at":"2026-02-25 06:12:06","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":34274,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMarginal means for IFN-g (pg/ml) by Active lesion – category \u003c/strong\u003e(N = 96; “Yes”: N = 17; “No”: N = 20; median = 1.2 pg/ml; p = 0.302; r effect size = 0.356; U Mann - Whitney test)\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8782258/v1/f1cc7d99c3123e0cd0257ed2.png"},{"id":103383256,"identity":"d1362e32-376e-48ed-918c-fb74708fb06b","added_by":"auto","created_at":"2026-02-25 06:11:21","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":24659,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDependency between variables: CCL2/MCP-1 (pg/ml) and ATC2 in study group \u003c/strong\u003e(N = 96; L01: N = L03: N = 9; L04: N = 55; median: 429.71 pg/ml; p = 0.026; effect size r = 0.001; Kruskal – Wallis test)\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8782258/v1/903c41aa4300ff6b0a64d83a.png"},{"id":104506209,"identity":"099c5eb9-7893-49a0-a60c-c639628cbc88","added_by":"auto","created_at":"2026-03-12 14:56:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2186292,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8782258/v1/db4e025d-34f7-4717-b532-8e14c33e293c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Relationship of microglia-associated pro-inflammatory cytokines to clinical and radiological parameters in patients with multiple sclerosis - a single-centre study in a Polish population","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMultiple sclerosis (MS) is a chronic disorder of the central nervous system (CNS) with a presumed autoimmune basis [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It is an inflammatory disease characterised by progressive neurodegeneration [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. There are currently an estimated 2.8\u0026nbsp;million people worldwide with the disease, which amounts to 35.9 patients per 100,000 people. Annually, there are 2.1 new cases of MS per 100,000 people [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. MS is therefore the most common demyelinating disease, and, at present, this is an incurable condition [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Fundamental to the pathogenesis of MS are disturbances in the control and balance of the immune response [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. B lymphocytes, whose role includes the production of autoantibodies and cytokines, are involved in the pathophysiology of this disease [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. B cells also mediate the effects exerted by antigen-presenting cells. The result is the activation of T cells, which is thought to be a key element in the pathomechanism of the disease [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This results in infiltration of the brain and spinal cord by inflammatory cells. A dual role for microglia has been demonstrated in the pathogenesis of MS, where it exerts both beneficial and negative effects [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. As inflammation occurs in the CNS, as is the case in MS pathogenesis, microglia also have secretory and modulatory functions [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan additionalcitationids=\"CR10 CR11 CR12\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Soluble factors via which microglia participate in negative effects in MS pathogenesis include interferon gamma (IFN-ɣ), tumour necrosis factor alpha (TNF-ɑ), reactive oxygen species, interleukin 1β (IL-1β), IL-6, IL-18, IL-12 and IL-23, but also C-C motif chemokines ligands (CCLs) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Thus, microglia are involved in pathological processes in both white matter and grey matter [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This aspect is all the more important because currently existing disease-modifying therapies (DMTs) only reduce white matter loss but have limited properties to significantly reduce or prevent grey matter neurodegeneration [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Current disease-modifying treatments have only a limited effect on this process. Due to this, determining the relationship between plasma levels of microglia-associated pro-inflammatory cytokines and clinical status and radiological findings could provide important data on the pathogenesis of MS, as well as contributing to the development of new treatments for the condition.\u003c/p\u003e \u003cp\u003eThe aim of this study is to determine the relationship between the levels of pro-inflammatory microglia cytokines and the clinical and radiological status of patients with MS.\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cp\u003eParticipants provided informed consent to participate in the study. The study group included 96 patients with MS diagnosed according to the 2017 McDonald\u0026rsquo;s criteria. Participants in the study group negated malignancies and other immunological diseases. The control group included 73 participants. These subjects negated the presence of neurological conditions, immunological diseases and malignancies.\u003c/p\u003e \u003cp\u003eThe study lasted from April 1st, 2023, to June 1st, 2024. It was approved by the Bioethics Committee Medical University of Silesia in Katowice, Poland in accordance with the Declaration of Helsinki, BNW/NWN/0052/KB1/132/I/22/23 (March 23rd, 2023). All participants have been given a consent for using clinical results we obtained to publish.\u003c/p\u003e \u003cp\u003eSerum concentrations of CCL2/MCP-1, IL-1 beta/IL-1F2, IL-6, IFN-gamma and IL-18/IL-1F4 were determined in both control and study participants using Human Magnetic Luminex\u0026reg; Assays (R\u0026amp;D SYSTEMS, biotechne).\u003c/p\u003e \u003cp\u003eEach drug was classified into a larger pharmacological category using the second level of the Anatomical Therapeutic Chemical (ATC) classification system (ATC2), which identifies the main therapeutic group of each substance (L01 - anticancer agents, L03 - immunostimulants, L04 - immunosuppressants). This was intended to group the drugs consistently.\u003c/p\u003e \u003cp\u003eThese ATC2 categories were used to organise the active substances into clinically meaningful groups relevant to immune modulation, particularly in the treatment of multiple sclerosis. No further subclassifications using more granular levels of the ATC system (such as ATC4) were performed. According to the ATC2 classification, the L01 category covered Cladribine and Ofatumumab, the L03 category included glatiramer acetate, interferon beta 1a and 1b, while the L04 category listed dimethyl fumarate, fingolimod, ocrelizumab, ozanimod, siponimod, teriflunomide.\u003c/p\u003e \u003cp\u003eFor the EDSS score, medical justification for level grouping was used [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Scores of 2.0-2.5 were classified as \u0026ldquo;Minimal/Mild\u0026rdquo;, 3.0-3.5 as \u0026ldquo;Moderate\u0026rdquo; and 4.0 or higher as \u0026ldquo;Significant\u0026rdquo;.\u003c/p\u003e \u003cp\u003eThis categorisation was intended to ensure both clinical consistency but also comparability between groups, where each group represented approximately one-third of the study group.\u003c/p\u003e \u003cp\u003eThe significance level was taken as p\u0026thinsp;=\u0026thinsp;0.05, while p-values showing statistically significant findings are in bold.\u003c/p\u003e \u003cp\u003eStatistical analysis calculations were performed using R software (R Core Team (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.R-project.org/\u003c/span\u003e\u003cspan address=\"https://www.R-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), version 4.3.2.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Baseline characteristics\u003c/h2\u003e \u003cp\u003eThere were 169 participants in the study, with 56.8% (N\u0026thinsp;=\u0026thinsp;96) being the study group and 43.2% (N\u0026thinsp;=\u0026thinsp;73) being the control group. Gender data were available for 152 participants, with 61.2% (n\u0026thinsp;=\u0026thinsp;93). The median age of participants was 34 years (IQR: 24\u0026ndash;46), while the mean age was 36.35 years (SD\u0026thinsp;=\u0026thinsp;14.45). It should be noted that participants in the study group were significantly older (median age\u0026thinsp;=\u0026thinsp;43 years) than those in the control group (median age\u0026thinsp;=\u0026thinsp;22.5 years).\u003c/p\u003e \u003cp\u003eMarker levels were assessed in up to 160 individuals. For CCL2/MCP-1, the mean was 442.6 pg/ml (SD\u0026thinsp;=\u0026thinsp;184.71), and the median was 418.65 pg/ml (IQR: 311.55\u0026ndash;551.83). IL-1b had a median concentration of 0.52 pg/ml (IQR: 0.31\u0026ndash;2.04) and a mean of 2.85 pg/ml (SD\u0026thinsp;=\u0026thinsp;6.28), reflecting a right-skewed distribution. IFN-g and IL-18 also showed skewed distributions, with IL-18 presenting the highest median concentration of 792.22 pg/ml (IQR: 640.75\u0026ndash;928.1). Detailed information on cytokine concentrations in the subgroups as well as for baseline characteristics is presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOverall (N\u0026thinsp;=\u0026thinsp;169)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStudy (N\u0026thinsp;=\u0026thinsp;96)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eControl (N\u0026thinsp;=\u0026thinsp;73)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.8% (N\u0026thinsp;=\u0026thinsp;96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.2% (N\u0026thinsp;=\u0026thinsp;73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.8% (N\u0026thinsp;=\u0026thinsp;59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.8% (N\u0026thinsp;=\u0026thinsp;27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44.4% (N\u0026thinsp;=\u0026thinsp;32)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.2% (N\u0026thinsp;=\u0026thinsp;93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.2% (N\u0026thinsp;=\u0026thinsp;53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55.6% (N\u0026thinsp;=\u0026thinsp;40)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.35 (14.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.42 (11.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.13 (2.47)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (Q1-Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (24\u0026ndash;46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43 (35\u0026ndash;52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.5 (20\u0026ndash;24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u0026ndash;72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u0026ndash;72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18\u0026ndash;27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eCCL2/MCP-1 [pg/ml]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e442.6 (184.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e472.02 (207.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e398.48 (133.33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (Q1-Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e418.65 (311.55\u0026ndash;551.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e429.71 (331.41\u0026ndash;600.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e395.64 (288.96\u0026ndash;504.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.88\u0026ndash;1233.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.88\u0026ndash;1233.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e127.53\u0026ndash;681.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eIL-1b [pg/ml]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.85 (6.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.49 (6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.4 (6.41)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (Q1-Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.52 (0.31\u0026ndash;2.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.41 (0.28\u0026ndash;1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.73 (0.49\u0026ndash;3.32)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u0026ndash;51.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.19\u0026ndash;51.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.08\u0026ndash;32.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eIFN-g [pg/ml]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.95 (4.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.63 (4.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.42 (4.58)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (Q1-Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.55 (0.87\u0026ndash;3.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.2 (0.8\u0026ndash;2.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.77 (1.1\u0026ndash;3.57)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u0026ndash;41.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.11\u0026ndash;41.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.06\u0026ndash;27.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eIL-18 [pg/ml]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e811.32 (270.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e786.89 (266.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e847.96 (273.97)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (Q1-Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e792.22 (640.75\u0026ndash;928.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e772.73 (630.54\u0026ndash;913.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e826.12 (670.03\u0026ndash;1036.67)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.42\u0026ndash;1800.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54.42\u0026ndash;1800.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e89.06\u0026ndash;1773.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eBody mass (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71.78 (13.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.78 (13.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (Q1-Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.5 (62\u0026ndash;81.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69.5 (62\u0026ndash;81.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50\u0026ndash;104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50\u0026ndash;104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eHeight (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e170.31 (8.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e170.31 (8.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (Q1-Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e170 (162.5\u0026ndash;176)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e170 (162.5\u0026ndash;176)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e155\u0026ndash;191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e155\u0026ndash;191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eBody mass index (BMI) [kg/m^2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.75 (3.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.75 (3.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (Q1-Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.03 (21.93\u0026ndash;26.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.03 (21.93\u0026ndash;26.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.78\u0026ndash;34.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.78\u0026ndash;34.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eMS type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePPMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5% (N\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5% (N\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRRMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87.9% (N\u0026thinsp;=\u0026thinsp;58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87.9% (N\u0026thinsp;=\u0026thinsp;58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSPMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.6% (N\u0026thinsp;=\u0026thinsp;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.6% (N\u0026thinsp;=\u0026thinsp;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMS type - category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRRMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87.9% (N\u0026thinsp;=\u0026thinsp;58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87.9% (N\u0026thinsp;=\u0026thinsp;58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.1% (N\u0026thinsp;=\u0026thinsp;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.1% (N\u0026thinsp;=\u0026thinsp;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eDisease duration (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.79 (7.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.79 (7.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (Q1-Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (2.88\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (2.88\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.58\u0026ndash;37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.58\u0026ndash;37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eDisease duration (years) - category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLess than 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25% (N\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25% (N\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(3\u0026ndash;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.8% (N\u0026thinsp;=\u0026thinsp;19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.8% (N\u0026thinsp;=\u0026thinsp;19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(7\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.5% (N\u0026thinsp;=\u0026thinsp;18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.5% (N\u0026thinsp;=\u0026thinsp;18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAt least 12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.7% (N\u0026thinsp;=\u0026thinsp;23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.7% (N\u0026thinsp;=\u0026thinsp;23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eTime from the first symptoms to the diagnose (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.2 (6.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.2 (6.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (Q1-Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (0.5\u0026ndash;5.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (0.5\u0026ndash;5.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eTime from the first symptoms to the diagnose (years) - category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLess than 0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.6% (N\u0026thinsp;=\u0026thinsp;17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.6% (N\u0026thinsp;=\u0026thinsp;17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.5-2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.2% (N\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.2% (N\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2-5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.2% (N\u0026thinsp;=\u0026thinsp;21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.2% (N\u0026thinsp;=\u0026thinsp;21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAt least 5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25% (N\u0026thinsp;=\u0026thinsp;18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25% (N\u0026thinsp;=\u0026thinsp;18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eAge of MS diagnose (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.86 (9.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.86 (9.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (Q1-Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (29.5\u0026ndash;43.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34 (29.5\u0026ndash;43.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u0026ndash;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17\u0026ndash;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eAge of MS diagnose (years) - category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLess than 30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25% (N\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25% (N\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(30\u0026ndash;35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.5% (N\u0026thinsp;=\u0026thinsp;22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.5% (N\u0026thinsp;=\u0026thinsp;22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(35\u0026ndash;44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.5% (N\u0026thinsp;=\u0026thinsp;18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.5% (N\u0026thinsp;=\u0026thinsp;18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAt least 44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25% (N\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25% (N\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eNumber of relapses (last year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.23 (0.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.23 (0.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (Q1-Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0\u0026ndash;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0\u0026ndash;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eRelapses in last year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAny\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.2% (N\u0026thinsp;=\u0026thinsp;15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.2% (N\u0026thinsp;=\u0026thinsp;15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.8% (N\u0026thinsp;=\u0026thinsp;63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80.8% (N\u0026thinsp;=\u0026thinsp;63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"10\" rowspan=\"11\"\u003e \u003cp\u003eEDSS score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.1% (N\u0026thinsp;=\u0026thinsp;11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.1% (N\u0026thinsp;=\u0026thinsp;11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.5% (N\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.5% (N\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.2% (N\u0026thinsp;=\u0026thinsp;22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.2% (N\u0026thinsp;=\u0026thinsp;22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.4% (N\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.4% (N\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.5% (N\u0026thinsp;=\u0026thinsp;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.5% (N\u0026thinsp;=\u0026thinsp;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.4% (N\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.4% (N\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.6% (N\u0026thinsp;=\u0026thinsp;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.6% (N\u0026thinsp;=\u0026thinsp;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.1% (N\u0026thinsp;=\u0026thinsp;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.1% (N\u0026thinsp;=\u0026thinsp;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.8% (N\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.8% (N\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3% (N\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.3% (N\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eEDSS score - categorization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2-2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.6% (N\u0026thinsp;=\u0026thinsp;27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.6% (N\u0026thinsp;=\u0026thinsp;27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3-3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.6% (N\u0026thinsp;=\u0026thinsp;27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.6% (N\u0026thinsp;=\u0026thinsp;27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;=4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.8% (N\u0026thinsp;=\u0026thinsp;24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.8% (N\u0026thinsp;=\u0026thinsp;24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAmount of MRI pathologic lesions (T2) - category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumerous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81.3% (N\u0026thinsp;=\u0026thinsp;61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81.3% (N\u0026thinsp;=\u0026thinsp;61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.3% (N\u0026thinsp;=\u0026thinsp;13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.3% (N\u0026thinsp;=\u0026thinsp;13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3% (N\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.3% (N\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAmount of MRI pathologic lesions (T2) - categorization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumerous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81.3% (N\u0026thinsp;=\u0026thinsp;61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81.3% (N\u0026thinsp;=\u0026thinsp;61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle or none\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.7% (N\u0026thinsp;=\u0026thinsp;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.7% (N\u0026thinsp;=\u0026thinsp;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eActive lesion - category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.9% (N\u0026thinsp;=\u0026thinsp;17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.9% (N\u0026thinsp;=\u0026thinsp;17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.1% (N\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54.1% (N\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eHyperintensive - category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.6% (N\u0026thinsp;=\u0026thinsp;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63.6% (N\u0026thinsp;=\u0026thinsp;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.4% (N\u0026thinsp;=\u0026thinsp;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.4% (N\u0026thinsp;=\u0026thinsp;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNonactive - category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98% (N\u0026thinsp;=\u0026thinsp;50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98% (N\u0026thinsp;=\u0026thinsp;50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2% (N\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2% (N\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eNumber of lesions in Gd+ - category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumerous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.3% (N\u0026thinsp;=\u0026thinsp;29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43.3% (N\u0026thinsp;=\u0026thinsp;29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.5% (N\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.5% (N\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.3% (N\u0026thinsp;=\u0026thinsp;33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.3% (N\u0026thinsp;=\u0026thinsp;33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNumber of lesions in Gd+ - categorization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumerous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.3% (N\u0026thinsp;=\u0026thinsp;29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43.3% (N\u0026thinsp;=\u0026thinsp;29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle or none\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.7% (N\u0026thinsp;=\u0026thinsp;38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.7% (N\u0026thinsp;=\u0026thinsp;38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"11\" rowspan=\"12\"\u003e \u003cp\u003eActive substance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCladribine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2% (N\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.2% (N\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDimethyl fumarate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37% (N\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37% (N\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFingolimod\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.2% (N\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.2% (N\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlatiramer acetate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.5% (N\u0026thinsp;=\u0026thinsp;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.5% (N\u0026thinsp;=\u0026thinsp;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInterferon beta 1a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.5% (N\u0026thinsp;=\u0026thinsp;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.5% (N\u0026thinsp;=\u0026thinsp;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInterferon beta 1b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.2% (N\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.2% (N\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOcrelizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.1% (N\u0026thinsp;=\u0026thinsp;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.1% (N\u0026thinsp;=\u0026thinsp;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOfatumumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.8% (N\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.8% (N\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOzanimod\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2% (N\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.2% (N\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSiponimod\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.6% (N\u0026thinsp;=\u0026thinsp;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.6% (N\u0026thinsp;=\u0026thinsp;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTeriflunomide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.7% (N\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.7% (N\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eATC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21% (N\u0026thinsp;=\u0026thinsp;17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21% (N\u0026thinsp;=\u0026thinsp;17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.1% (N\u0026thinsp;=\u0026thinsp;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.1% (N\u0026thinsp;=\u0026thinsp;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.9% (N\u0026thinsp;=\u0026thinsp;55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67.9% (N\u0026thinsp;=\u0026thinsp;55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eWhite blood cells (WBC) [x10^3/\u0026micro;l]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.85 (1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.85 (1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (Q1-Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.41 (4.46\u0026ndash;6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.41 (4.46\u0026ndash;6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.01\u0026ndash;11.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.01\u0026ndash;11.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eRed blood cells (RBC) [mln/\u0026micro;l]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.77 (1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.77 (1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (Q1-Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.6 (4.39\u0026ndash;4.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.6 (4.39\u0026ndash;4.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.73\u0026ndash;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.73\u0026ndash;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eHemoglobin (HG) [g/dl]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.94 (1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.94 (1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (Q1-Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (13.1\u0026ndash;14.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (13.1\u0026ndash;14.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.6\u0026ndash;17.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.6\u0026ndash;17.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003ePlatelets (PLT) [x10^3/\u0026micro;l]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e256.2 (63.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e256.2 (63.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (Q1-Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e244.5 (218.25\u0026ndash;281.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e244.5 (218.25\u0026ndash;281.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75\u0026ndash;455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75\u0026ndash;455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eThyroid-stimulating hormone (TSH) [mIU/l]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.8 (1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.8 (1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (Q1-Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.57 (0.93\u0026ndash;2.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.57 (0.93\u0026ndash;2.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u0026ndash;4.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u0026ndash;4.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eAminotransferase Alanina (AlAT) [U/l]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.93 (11.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.93 (11.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (Q1-Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.25 (15.4\u0026ndash;32.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.25 (15.4\u0026ndash;32.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.3\u0026ndash;55.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.3\u0026ndash;55.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eAspartate aminotransferase (AspAT) [U/l]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.92 (8.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.92 (8.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (Q1-Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (17.7\u0026ndash;24.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (17.7\u0026ndash;24.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.2\u0026ndash;70.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.2\u0026ndash;70.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eCreatinine [\u0026micro;mol/l]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.62 (13.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69.62 (13.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (Q1-Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68 (61\u0026ndash;78.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68 (61\u0026ndash;78.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46\u0026ndash;122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46\u0026ndash;122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eCRP [mg/l]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.58 (1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.58 (1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (Q1-Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.07 (0.46\u0026ndash;1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.07 (0.46\u0026ndash;1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2\u0026ndash;6.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2\u0026ndash;6.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eLymphocyte percent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.01 (10.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.01 (10.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (Q1-Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.85 (17.68\u0026ndash;33.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.85 (17.68\u0026ndash;33.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.3\u0026ndash;49.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.3\u0026ndash;49.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eLymphocytes [x10^3/\u0026micro;l]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.54 (0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.54 (0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (Q1-Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.35 (0.92\u0026ndash;2.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.35 (0.92\u0026ndash;2.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.32\u0026ndash;3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.32\u0026ndash;3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Parameter Comparison Between Study Groups\u003c/h2\u003e \u003cp\u003eStatistically significant differences in the comparison between the study group (N\u0026thinsp;=\u0026thinsp;96) and the control group (N\u0026thinsp;=\u0026thinsp;64) were shown for the following cytokines:\u003c/p\u003e \u003cp\u003eCCL2/MCP-1 concentrations were significantly higher in the study group (median: 429.71 pg/ml) than in controls (median: 395.64 pg/ml; p\u0026thinsp;=\u0026thinsp;0.0377, r\u0026thinsp;=\u0026thinsp;0.164), though not confirmed by the Wald test (p\u0026thinsp;=\u0026thinsp;0.705).\u003c/p\u003e \u003cp\u003eIL-1b levels were significantly elevated in the control group (median: 0.73 pg/ml) versus the study group (median: 0.41 pg/ml; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, r\u0026thinsp;=\u0026thinsp;0.263), with a non-significant Wald test (p\u0026thinsp;=\u0026thinsp;0.472).\u003c/p\u003e \u003cp\u003eIFN-g was also significantly higher in controls (median: 1.77 pg/ml) than in the study group (median: 1.2 pg/ml; p\u0026thinsp;=\u0026thinsp;0.0121, r\u0026thinsp;=\u0026thinsp;0.198), though not confirmed in the Wald test (p\u0026thinsp;=\u0026thinsp;0.384). Other markers (IL-4, IL-13, IL-18) did not show statistically significant differences (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Parameter Comparison Within Study Group\u003c/h2\u003e \u003cp\u003e \u003cem\u003eMS type \u0026ndash; category\u003c/em\u003e \u003c/p\u003e \u003cp\u003eCCL2/MCP-1 was notably elevated in the \"Other\" MS group (median: 644.99 pg/ml) versus relapse-remitting multiple sclerosis (RRMS) (median: 417.33 pg/ml), with statistical significance (p\u0026thinsp;=\u0026thinsp;0.0103, effect size\u0026thinsp;=\u0026thinsp;0.316), though not confirmed by the Wald test (p\u0026thinsp;=\u0026thinsp;0.418).\u003c/p\u003e \u003cp\u003eOther markers, including IL-1b, IFN-g and IL-18, did not show statistically significant differences between RRMS and other MS subtypes (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cem\u003eEDSS score \u0026ndash; categorisation\u003c/em\u003e \u003c/p\u003e \u003cp\u003eIFN-g concentrations exhibited a trend toward higher levels in the EDSS\u0026thinsp;\u0026ge;\u0026thinsp;4 group (median: 1.50 pg/ml) relative to the 3\u0026ndash;3.5 group (median: 0.82 pg/ml) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Although this trend did not reach significance in the unadjusted analysis (p\u0026thinsp;=\u0026thinsp;0.0533, effect size\u0026thinsp;=\u0026thinsp;0.001), it became statistically significant in the adjusted model (Wald test p\u0026thinsp;=\u0026thinsp;0.046, R\u0026sup2; = 0.158) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eNo statistically significant differences were observed for the other cytokines across EDSS categories in either unadjusted or adjusted analyses (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eAmount of MRI pathologic lesions (T2) \u0026ndash; categorisation\u003c/em\u003e \u003c/p\u003e \u003cp\u003eAmong the cytokines assessed in relation to the amount of MRI T2 lesions (N\u0026thinsp;=\u0026thinsp;96), one marker showed a significant difference:\u003c/p\u003e \u003cp\u003eIL-18 levels were significantly higher in patients with single or no T2 lesions (median: 933.6 pg/ml) compared to those with numerous lesions (median: 748.57 pg/ml), with p\u0026thinsp;=\u0026thinsp;0.0194 in the unadjusted analysis (effect size\u0026thinsp;=\u0026thinsp;0.859) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This result was confirmed after adjustment, with the estimated marginal mean (emmeans) of IL-18 for the \"Numerous\" group at 742.68 pg/ml (95% CI: 673.28\u0026ndash;812.07), and for the \"Single or none\" group at 959.09 pg/ml (95% CI: 812.20\u0026ndash;1105.98). The adjusted difference was statistically significant: \u0026minus;\u0026thinsp;216.41 pg/ml (95% CI: \u0026minus;\u0026thinsp;379.42 to \u0026minus;\u0026thinsp;53.40; Wald test p\u0026thinsp;=\u0026thinsp;0.008, R\u0026sup2; = 0.148), indicating a robust association between lower lesion burden and higher IL-18 levels \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOther cytokines showed no statistically significant differences between lesion burden groups in both unadjusted and adjusted analyses (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eActive lesion \u0026ndash; category\u003c/em\u003e \u003c/p\u003e \u003cp\u003eAmong the cytokines assessed in relation to the presence of active MRI lesions (N\u0026thinsp;=\u0026thinsp;96), only one marker showed a statistically significant difference:\u003c/p\u003e \u003cp\u003eIFN-g levels were significantly lower in patients with active lesions (median: 0.82 pg/ml) compared to those without active lesions (median: 1.57 pg/ml), with p\u0026thinsp;=\u0026thinsp;0.0302 in the unadjusted non-parametric analysis (effect size\u0026thinsp;=\u0026thinsp;0.356) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. This finding was confirmed in the adjusted analysis, where the estimated marginal mean (emmeans) for the \"Active lesions\" group was 0.918 pg/ml (95% CI: 0.623\u0026ndash;1.351), and for the \"No active lesions\" group it was 1.962 pg/ml (95% CI: 1.402\u0026ndash;2.743). The adjusted difference was 0.468 pg/ml (95% CI: 0.276\u0026ndash;0.794), indicating significantly higher IFN-g levels in patients without active lesions (Wald test p\u0026thinsp;=\u0026thinsp;0.003, R\u0026sup2; = 0.426) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOther cytokines did not show statistically significant differences between the two groups in either unadjusted or adjusted analyses (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eHyperintensive \u0026ndash; category\u003c/em\u003e \u003c/p\u003e \u003cp\u003eNo statistically significant associations were observed between the analyzed cytokines and the hyperintensity occurrence.\u003c/p\u003e \u003cp\u003e \u003cem\u003eNumber of lesions in Gd+ - categorisation\u003c/em\u003e \u003c/p\u003e \u003cp\u003eSimilarly, no statistically significant associations were observed between the analyzed cytokines and the number of lesions in Gd+.\u003c/p\u003e \u003cp\u003e \u003cem\u003eATC2\u003c/em\u003e \u003c/p\u003e \u003cp\u003eCCL2/MCP-1 levels differed significantly between ATC2 groups (Kruskal-Wallis p\u0026thinsp;=\u0026thinsp;0.0264, effect size\u0026thinsp;=\u0026thinsp;0.001). Post-hoc Dunn\u0026rsquo;s tests revealed significantly higher MCP-1 levels in the L03 group compared to L01 (p\u0026thinsp;=\u0026thinsp;0.0225) and L01 to L04 (p\u0026thinsp;=\u0026thinsp;0.0368), but no significant difference between L03 and L04 (p\u0026thinsp;=\u0026thinsp;0.4384) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. This difference was not statistically significant in the adjusted model (Wald p\u0026thinsp;=\u0026thinsp;0.719, R\u0026sup2; = 0.037).\u003c/p\u003e \u003cp\u003eOther markers did not show statistically significant differences between ATC2 groups in either unadjusted or adjusted analyses (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDunn post-hoc tests \u0026ndash; variable CCL2/MCP-1.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComparison\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCCL2/MCP-1 (pg/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL01 - L03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.0225\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL01 - L04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.0368\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL03 - L04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4384\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eOther categories\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThere were no statistically significant differences in disease duration, time from symptom onset to diagnosis, age at diagnosis, or the presence of recent relapses for any of the cytokines assessed.\u003c/p\u003e \u003cp\u003eCorrelation analysis Within Study Group\u003c/p\u003e \u003cp\u003eSpearman correlation analysis was conducted to evaluate the association between selected cytokines and clinical, biochemical and haematological parameters.\u003c/p\u003e \u003cp\u003eIL-1b was most strongly correlated with IL-6 (r\u0026thinsp;=\u0026thinsp;0.534, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), followed by IL-6 and IFN-g (r\u0026thinsp;=\u0026thinsp;0.526, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eA significant positive correlation was identified between CCL2 (MCP-1) and disease duration (r\u0026thinsp;=\u0026thinsp;0.271, p\u0026thinsp;=\u0026thinsp;0.015), which may reflect progressive immune activation over time.\u003c/p\u003e \u003cp\u003eCytokine levels were also correlated with haematological parameters. IL-6, IL-1b, and IFN-g demonstrated weak but statistically significant positive correlations with total white blood cell (WBC) count (r\u0026thinsp;=\u0026thinsp;0.262, p\u0026thinsp;=\u0026thinsp;0.023; r\u0026thinsp;=\u0026thinsp;0.243, p\u0026thinsp;=\u0026thinsp;0.036; and r\u0026thinsp;=\u0026thinsp;0.231, p\u0026thinsp;=\u0026thinsp;0.046, respectively).\u003c/p\u003e \u003cp\u003eNo statistically significant correlations were found between cytokine levels and age, height, weight, or most anthropometric measures, apart from a weak but significant correlation between IL-6 and BMI (r\u0026thinsp;=\u0026thinsp;0.245, p\u0026thinsp;=\u0026thinsp;0.039).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eAccording to available information, the study we conducted is one of the first aimed at comprehensively assessing the mutual relationships between the level of pro-inflammatory microglia-associated cytokines in the course of MS and the presence of lesions in neuroimaging studies. This highlights the significant innovative nature of this study.\u003c/p\u003e \u003cp\u003eConsidering the fact that diagnoses of this disease are increasingly being made in various geographic regions of the world (more prevalent in Europe and in Northern America - \u0026gt;100 per 100.000 people, less frequent in sub-Saharan Africa and East Asia \u0026ndash; only 2 per 100.000 people), as well as the lack of possibilities for its effective cure, the search for new treatment methods or ways to monitor MS seems particularly relevant [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. These therapies could potentially reduce not only white matter loss, but also grey matter neurodegeneration, which is crucial for disability in MS [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn some countries, including those in Europe, women have three times the incidence of men, however, this is only true for the relapsing-remitting form of MS (RRMS) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In the comparison with ratio of members in our study, it is about 2:1 (66.2% of females and 33.8% of males), which does not much differ to the general trend.\u003c/p\u003e \u003cp\u003eIt is important to emphasize the aspect of microglia-associated cytokines participation in the processes of neurodegeneration in MS. Demyelination, affecting both grey and white matter, can be reversed by, among other things, remyelination, in which microglia also participate through secreted cytokines [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Neurodegeneration of the grey matter, on the other hand, is irreversible, occurs very early in the course of MS and is largely responsible for permanent disability [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAreas of CNS demyelination are also formed [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Initially, the inflammatory process, stimulated by pro-inflammatory cytokines predominates, while later the degenerative process takes over. There is neuronal, axonal and synaptic damage, as well as destruction of glial cells [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Resident macrophages in the CNS, which are called microglia, have been shown to play a significant role in these pathological processes [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In a way, microglia behave as a controller that monitors the processes of the microenvironment and the proper functioning of neuronal cells, dendrites and axons [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In addition, these cells phagocytose necrotic cells and their parts [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The nature of microglia is highly plastic and, depending on the situation, can exert different, often opposing effects [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur results indicate that IFN-ɣ concentrations are elevated in patients with a higher degree of disability as reflected by the EDSS score. However, given the lack of significance in the primary (unadjusted) test and the non-significant pairwise comparisons using estimated marginal means (mmeans), this result should be interpreted with caution. Nevertheless, in the study by Trenova et al., IFN-ɣ concentrations, as in our study, were positively correlated with a higher score on the EDSS scale, but only in specific clinical situations, such as during therapy with interferon beta or during a relapse [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn 1987, a study was conducted in which patients with MS were given a trial of therapy with IFN-ɣ [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The consequence of this trial appeared to be an exacerbation of disease symptoms in less than half of the patients. This was due to an increase in the expression of MHC class II monocytes, thus indicating a deleterious adverse effect of this cytokine in MS [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. These results agree with those obtained from our studies.\u003c/p\u003e \u003cp\u003eOne therapeutic strategy contributing to delaying MS progression may be to restore the balance between the pro-inflammatory and immunomodulatory actions of IFN-ɣ. This may be served by reducing the concentration of this cytokine and other pro-inflammatory molecules [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. It is noteworthy that disruption of this balance is one of the proven causes underlying the development of the disease [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Our study also showed higher IFN-ɣ levels in patients without active lesions on brain MRI. Thus, the observation from a randomized trial in a group of patients with primary-progressive multiple sclerosis (PPMS), according to which the effect of neutralizing antibodies to IFN-ɣ significantly slows the development of disability, seems interesting. This was confirmed both by MRI studies, by showing fewer active lesions, and by the shift in the cytokine profile of activated blood cells found in biochemical analysis [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. It caused, among other things, a reduction in the synthesis of IL-1β, TNF-α and IFN-ɣ. These results suggest that IFN-ɣ inhibition may represent a promising new therapeutic strategy, especially in the treatment of PPMS [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThus, the findings in this manuscript confirm current knowledge of IFN-ɣ as a molecule with pro-inflammatory effects. Moreover, in agreement with this is another phenomenon we observed. Namely, a positive correlation between white blood cell counts and IFN-ɣ levels, as well as two other microglia-associated cytokines that aggravate MS, specifically IL-1b and IL-6 [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn terms of IL-18, we have shown that the level of this cytokine is significantly higher in patients with a lower number of T2-dependent lesions on MRI. This finding is supported by results from a paper by Losy et al. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. This study reported significantly higher levels of IL-18 in both CSF and serum among participants who were also characterized by the presence of active CNS lesions visualized by MRI with gadolinium enhancement [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Based on this, the authors concluded that IL-18 has a significant effect relative to MS immunopathogenesis, especially in the active phases of the disease [\u003cspan additionalcitationids=\"CR27 CR28\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTaking into account the fact that these lesions shown by MRI are indicative of disease activity in the course of MS, and IL-18 has a proven inflammation-promoting effect, it is reasonable to assume that this cytokine may in the future serve as a predictive marker for monitoring MS.\u003c/p\u003e \u003cp\u003eOur study also showed a moderate positive correlation between IL1b and IL-6, as well as IL-6 and INF-g. The cytokines IL-1 and IL-6 show a pro-inflammatory character [\u003cspan additionalcitationids=\"CR30 CR31 CR32 CR33\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The correlation of IL-6 with IFN-ɣ, which is a cytokine that promotes the development of MS and worsens prognosis in later stages of the disease, seems understandable [\u003cspan additionalcitationids=\"CR30 CR31\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCCL2 is seen as a molecule with pro-inflammatory effects [\u003cspan additionalcitationids=\"CR36 CR37\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The interaction of CCL2 with the CCR2 receptor results in the attraction of monocytes, macrophages, dendritic cells, natural-killer (NK) cells and T lymphocytes to the CNS [\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. As a result of the activation of the aforementioned cells, pro-inflammatory molecules are released: IL-1β, IL-6 and TNF-α, exerting destructive effects on the myelin sheaths of neurons [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Moreover, it has been shown that one of the causes of neuronal cell inflammation and subsequent degeneration may be the effect that CCL2 has toward microglia activation [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Previously published reports have revealed that CCL2 levels in the cerebrospinal fluid (CSF) are elevated in patients, suffering from MS and show a correlation with a more severe disease course [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. This does not differ with our results, where we showed a significant positive correlation between CCL2/MCP-1 and disease duration, which may reflect progressive immune activation over time [\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. This may lay the groundwork for potential exploration of CCL2 use in clinical practice, including as a marker.\u003c/p\u003e"},{"header":"5. Summary","content":"\u003cp\u003eThe aim of this study was to assess the relationship of pro-inflammatory microglia cytokines with clinical and radiological parameters of MS patients.\u003c/p\u003e \u003cp\u003eIL-18 levels were significantly increased in patients with single or non-T2-weighted lesions on MRI, suggesting a possible inverse association with lesion burden.\u003c/p\u003e \u003cp\u003eNo significant differences in cytokine profiles were observed with respect to disease duration, time from symptom onset to diagnosis, age at diagnosis, or the presence of recent relapses. Additionally, correlations between cytokine levels and radiological or clinical metrics were generally weak, with only a few statistically significant associations.\u003c/p\u003e \u003cp\u003eThe results suggest that IFN-ɣ levels may be higher in patients with higher levels of disability, but this should be interpreted with caution. It was also shown that IFN-ɣ levels were higher in patients without active brain MRI lesions. These findings suggest the action of IFN-ɣ as a cytokine with important effects on the development of inflammation in the context of MS.\u003c/p\u003e \u003cp\u003eHigher plasma levels of CCL2/MCP-1 with disease duration suggest progressive immune activation. This suggests the possibility of using this molecule as a marker of MS.\u003c/p\u003e \u003cp\u003eMoreover, a positive correlation was obtained between white blood cell count and IFN-ɣ levels, as well as IL-1b and IL-6, which are microglia-associated cytokines already known to have a negative prognostic effect in MS.\u003c/p\u003e \u003cp\u003eIt seems that the findings obtained may provide an interesting starting point for further studies aimed at further clarifying the role of the indicated molecules, as well as establishing and developing methods to exploit their properties.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMultiple sclerosis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCCL2/MCP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cb\u003e1\u003c/b\u003e-C-C motif chemokine ligand 2/ Monocyte Chemoattractant Protein-1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eIFN\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cb\u003eɣ\u003c/b\u003e-Interferon gamma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eIL\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cb\u003e1\u003c/b\u003e-Interleukin-1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eIL\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cb\u003e6\u003c/b\u003e-Interleukin-6\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eIL\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cb\u003e18\u003c/b\u003e-Interleukin-18\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eIL\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cb\u003e12\u003c/b\u003e-Interleukin 12\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eIL\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cb\u003e23\u003c/b\u003e-Interleukin 23\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics approval and consent to participate:\u003c/h2\u003e \u003cp\u003e \u0026bull; A study was approved by the Bioethics Committee Medical University of Silesia in Katowice, Poland in accordance with the Declaration of Helsinki, BNW/NWN/0052/KB1/132/I/22/23 (March 23rd, 2023). All participants provided informed consent to participate in the study.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication:\u003c/strong\u003e \u003cp\u003e\u0026bull; All participants have been given an agreement for using clinical results we obtained to publish.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests:\u003c/h2\u003e \u003cp\u003e\u0026bull; We declare that the authors have no competing interests as defined by BMC, or other interests that might be perceived to influence the results and/or discussion reported in this paper.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConflicts of Interest:\u003c/h2\u003e \u003cp\u003e\u0026bull; The authors declare no conflicts of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThe study was funded by the Silesian Medical University in Katowice.\u003c/p\u003e \u003cp\u003eFunding number: BNW-2-014/N/4/K\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: H.M., Data curation: H.M., Formal analysis: H.M., A.S., Funding acquisition: H.M., Investigation: H.M., A.S., Methodology: H.M., Project administration: H.M., M.A.-S., Resources: H.M., Software: H.M., Supervision: M.A.-S., Validation: H.M., Visualization: H.M., Writing \u0026ndash; original draft: H.M., A.S., Writing \u0026ndash; review and editing: H.M., M.A.-S.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data supporting the findings of this study are available within the paper and its Supplementary Information.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMado H, Kubicka-Bączyk K, Adamczyk-Sowa M. 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Prototypical\u0026rsquo; proinflammatory cytokine (IL-1) in multiple sclerosis: role in pathogenesis and therapeutic targeting. Volume 24. Expert Opinion on Therapeutic Targets; 2020.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Multiple sclerosis, MS, microglia, cytokines, interleukins, MRI lesions","lastPublishedDoi":"10.21203/rs.3.rs-8782258/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8782258/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction:\u003c/h2\u003e \u003cp\u003eMultiple sclerosis (MS) is a demyelinating disease of the central nervous system with a chronic course. Available data point to an autoimmune basis for MS. It is currently suggested that microglia-associated cytokines have a role that is especially relevant.\u003c/p\u003e\u003ch2\u003eStudy objective:\u003c/h2\u003e \u003cp\u003eTo determine the association between microglia cytokines: C-C motif chemokine ligand 2/ Monocyte Chemoattractant Protein-1 (CCL2/MCP-1), Interferon gamma (IFN-ɣ), Interleukin-1 (IL-1), Interleukin-6 (IL-6), Interleukin-18 (IL-18) and clinical and radiological parameters of MS patients.\u003c/p\u003e\u003ch2\u003eMaterial and methods\u003c/h2\u003e \u003cp\u003eThe study involved 96 patients with MS diagnosed according to the 2017 McDonald\u0026rsquo;s criteria. The control group consisted of 73 healthy participants. Patients in the study group negated other immunological conditions.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCCL2/MCP-1 levels were significantly increased in the study group. CCL/2MCP-1 levels were significantly higher in the other MS group compared to Relapse-remitting multiple sclerosis (RRMS). A significant positive correlation was identified between CCL2 (MCP-1) and disease duration. IFN-g levels were significantly higher in the control group. IFN-g concentrations exhibited a trend toward higher levels in the Expanded Disability Status Scale (EDSS)\u0026thinsp;\u0026ge;\u0026thinsp;4 score group. Significantly higher IFN-g levels were found in patients without active lesions on Magnetic resonance imaging (MRI). IL-18 levels were significantly increased in patients with no or single T2-weighted lesions.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe inverse correlation between IL-18 levels and MRI lesions suggests that this cytokine may serve as a predictive marker for monitoring the course of MS. IFN-g is a cytokine with an important role in the development of inflammation in MS. Higher plasma levels of CCL2 (MCP-1) with disease duration suggest progressive immune activation.\u003c/p\u003e","manuscriptTitle":"Relationship of microglia-associated pro-inflammatory cytokines to clinical and radiological parameters in patients with multiple sclerosis - a single-centre study in a Polish population","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-25 06:08:47","doi":"10.21203/rs.3.rs-8782258/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"85fd8da5-35be-4d0e-af08-66d9ab3a4238","owner":[],"postedDate":"February 25th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-12T14:54:27+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-25 06:08:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8782258","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8782258","identity":"rs-8782258","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}