1. Introduction
Multiple sclerosis (MS) is a chronic, immune-mediated, demyelinating, and neurodegenerative disorder of the central nervous system that typically presents in young adults and is a leading cause of non-traumatic neurological disability worldwide. Global prevalence has risen steadily in recent decades [ 1 ]. In Saudi Arabia, MS prevalence has been estimated at 40.4 per 100,000 individuals, with disproportionately higher rates reported in the Western Region relative to other parts of the Kingdom, although still below rates observed in some neighboring countries [ 2 , 3 ].
MS pathogenesis reflects an interplay between genetic susceptibility, environmental exposure, and dysregulated innate and adaptive immunity, in which cytokines act as principal effectors and regulators of the balance between demyelinating and reparative processes [ 4 , 5 ]. Associations between circulating or intrathecal interleukin levels and MS disease activity, progression, and disability have already been demonstrated in several cohorts outside the Middle East. Martins et al. reported dysregulated serum cytokine profiles in patients with MS using a multiplexed immunoassay [ 6 ]. Kallaur et al. showed that progressive MS is associated with a more pronounced inflammatory imbalance and a greater disability burden than relapsing–remitting disease [ 7 ]. Khaibullin et al. reported elevated pro-inflammatory cytokines in the cerebrospinal fluid (CSF) of patients with MS, indicating that inflammatory signaling is present both intrathecally and systemically [ 8 ]. Itorralba et al. found that intrathecal IL-6 levels are associated with progressive disease and clinical severity [ 9 ]. These studies, however, were conducted in North American and European cohorts; comparatively little work has characterized cytokine profiles in Saudi Arabian or other Middle Eastern populations, despite genetic, environmental, and lifestyle features that distinguish these populations from those in which most cytokine studies have been conducted.
This study focused on four interleukins selected a priori to represent complementary, non-redundant inflammatory mechanisms relevant to MS, rather than an exhaustive interleukin panel. IL-6 is a pleiotropic cytokine involved in acute-phase signaling, Th17 polarization, B-cell activation, and blood–brain barrier disruption, and has previously been linked to MS disease activity and to CSF markers of severity [ 9 , 10 , 11 ]. IL-16 is a chemoattractant for CD4+ lymphocytes, thought to reflect leukocyte trafficking and sustained immune cell recruitment within inflammatory lesions. Previous work has reported both elevation in MS and a reduction in IL-16 following interferon β-1a therapy [ 12 , 13 ]. IL-18 is an inflammasome-associated, interferon-γ- inducing member of the IL-1 family implicated in innate and adaptive immune amplification and has been reported to be elevated in several independent MS cohorts [ 14 , 15 , 16 , 17 ]. IL-36, an IL-1-family cytokine increasingly recognized as a mediator of chronic inflammatory signaling and glial–immune crosstalk, has to date been examined in only one other MS cohort worldwide [ 5 , 18 ]. Because each of these interleukins occupies a distinct position in the inflammatory cascade, we reasoned that evaluating them together, rather than assuming they behave as a single coordinated signal, would provide a more informative picture of which components of systemic inflammation, if any, track with clinical disability; this study was not designed, and was not powered, to test a combined multivariable biomarker model, and no such model is presented.
Clinical assessment of MS disability commonly relies on the Expanded Disability Status Scale (EDSS). This clinician-rated ordinal scale places strong weight on ambulation at higher scores but has recognized limitations, including non-linear scaling and relative insensitivity in some functional domains [ 19 ]. The Timed 25-Foot Walk (T25FW) provides a complementary, quantitative, performance-based measure of ambulatory function and is now considered a core outcome measure in MS trials and longitudinal cohorts [ 20 , 21 ]. Linking peripheral interleukin levels to both EDSS and T25FW may help clarify which, if any, aspects of systemic inflammation correspond to clinically meaningful disability.
The present study therefore evaluated serum IL-6, IL-16, IL-18, and IL-36 in patients with MS from the Western Region of Saudi Arabia relative to matched healthy controls, and examined, in an exploratory manner, whether these interleukins, individually or as a broader pattern of inflammatory elevation, were associated with disease duration, clinical phenotype, treatment exposure, and disability as measured by EDSS and T25FW. Given the modest sample size and cross-sectional, hypothesis-generating design, the objective was not to develop, validate, or preferentially foreground any single interleukin as a predefined outcome, but to characterize this exploratory, complementary interleukin panel in an underrepresented population and to report, transparently, whichever associations with clinical disability were and were not observed.
2. Materials and Methods
2.1. Study Design and Setting
This single-center, cross-sectional, matched case–control study was conducted at the Multiple Sclerosis Clinic of King Abdulaziz University Hospital, Jeddah, Saudi Arabia, from January to June 2024. The study assessed whether serum concentrations of IL-6, IL-16, IL-18, and IL-36 differed between adults with MS and individually matched healthy controls, and whether these interleukins were associated with disability-related measures (EDSS and T25FW) within the MS cohort. All procedures were conducted in accordance with the Declaration of Helsinki.
2.2. Ethical Approval and Consent
The study protocol was approved by the Biomedical Ethics Research Committee of King Abdulaziz University, Jeddah, Saudi Arabia (NCBE Registration No. HA-02-J-008; Reference No. 129-24; date of approval: 9 May 2024). Written informed consent was obtained from all participants before enrollment. All data were de-identified before analysis; clinical/demographic data and laboratory results were linked only after completion of measurement and quality-control procedures.
2.3. Participants and Eligibility Criteria
A total of 52 adults were enrolled: 26 with clinically confirmed MS and 26 neurologically healthy controls, individually matched 1:1 by sex and within an approximate age range of ±3 years.
MS-group eligibility required ages 18–53 years and a diagnosis established according to the 2017 revised McDonald criteria. The upper age limit was chosen to maintain a relatively homogeneous adult cohort available during the recruitment window, to reduce confounding by older age and age-related comorbidity, and to reflect the age distribution of clinically stable patients seen at the clinic during this period, rather than using a disease-based cutoff. Only clinically stable patients were included, defined operationally as the absence of relapse and no corticosteroid treatment within the 30 days preceding enrollment. Exclusion criteria were: any other central nervous system disorder; systemic autoimmune or inflammatory disease; acute infection; chronic liver or kidney disease; corticosteroid use within the previous 4 weeks; treatment with cytotoxic or other potent immunosuppressive agents; pregnancy; or lactation.
Healthy controls were recruited from hospital staff and community volunteers with no known neurological disease, autoimmune disorder, chronic inflammatory condition, or active infection. They were not receiving corticosteroids or immunomodulatory therapy at the time of sampling.
For each MS participant, the following were recorded: age, sex, disease duration from symptom onset, MS phenotype (relapsing–remitting [RRMS] or secondary progressive [SPMS]), current disease-modifying therapy (DMT), and comorbidities. Disease duration was dichotomized, for exploratory subgroup analysis only, as newly diagnosed (≤2 years from symptom onset) or long-standing (>2 years from symptom onset); this 2-year cutoff was chosen descriptively to separate the cohort at the cohort’s observed median disease duration and does not reflect a validated clinical threshold. DMT exposure was categorized into three mutually exclusive groups: anti-CD20 B-cell-depleting therapy (rituximab, ocrelizumab, or ofatumumab), oral therapy (dimethyl fumarate or teriflunomide), or no current DMT (untreated). No participants in this cohort were receiving injectable platform therapies (interferon-beta or glatiramer acetate) at the time of sampling, so this category does not appear in the analysis.
2.4. Clinical Assessment
2.4.1. Expanded Disability Status Scale (EDSS)
Disability was scored by a neurologist experienced in standardized EDSS assessment on a 0–10 scale in 0.5-point increments, performed on the same day as blood collection whenever feasible. The neurologist performing EDSS scoring was blinded to all serum interleukin results, which were generated subsequently as part of batched laboratory analysis [ 19 ].
2.4.2. Timed 25-Foot Walk (T25FW)
Participants walked a clearly marked 25-foot (7.62 m) distance as quickly and safely as possible; two trials were completed, and the faster time was used for analysis. Habitual walking aids were permitted and documented.
2.5. Blood Collection and Sample Processing
Fasting venous blood (3 mL) was collected between 08:00 and 12:00 to minimize circadian variation in cytokine measurements. Blood was drawn into plain serum tubes, allowed to clot at room temperature for approximately 30 min, then centrifuged at 1500–2000× g for 10 min. Serum was aliquoted into labeled polypropylene tubes and stored at −80 °C until analysis. All samples were processed within 1 h of collection and subjected to a single freeze–thaw cycle. Hemolyzed samples were excluded, and visibly lipemic samples were re-collected when feasible.
2.6. Cytokine Quantification by ELISA
Serum levels of IL-6, IL-16, IL-18, and IL-36 were measured in duplicate using commercial sandwich enzyme-linked immunosorbent assay kits (BT LAB, Shanghai, China; Cat. Nos. E0090Hu, E0096Hu, E0147Hu, and E7518Hu, respectively) according to the manufacturer’s instructions. Standards and diluted serum samples were added to pre-coated 96-well plates and incubated at 37 °C. Plates were then washed, incubated with biotinylated detection antibody and streptavidin–horseradish peroxidase, and developed with 3,3′,5,5′-tetramethylbenzidine substrate. The reaction was stopped with an acidic stop solution, and optical density was read at 450 nm on a calibrated microplate reader; concentrations were derived from four-parameter logistic standard curves. Samples with duplicate coefficients of variation > 15% were re-assayed, and samples that remained outside acceptance criteria after repeat analysis were excluded from the cytokine-specific analysis. Laboratory personnel were blinded to case–control status, MS phenotype, and treatment exposure throughout.
No serum sample had a missing or non-detectable cytokine concentration; all 52 participants had valid measurements for all four analytes within each assay’s working range. Manufacturer-reported assay characteristics (BT LAB, Shanghai, China) were: IL-6 (Cat. No. E0090Hu), standard curve range 2–600 ng/L, sensitivity 1.03 ng/L; IL-16 (Cat. No. E0096Hu), standard curve range 3–900 ng/L, sensitivity 1.47 ng/L; IL-18 (Cat. No. E0147Hu), standard curve range 0.5–100 ng/L, sensitivity 0.2 ng/L; IL-36 (Cat. No. E7518Hu), standard curve range 18.75–1200 pg/mL, sensitivity 10.13 pg/mL. The lower limit of quantification for each assay corresponded to the lowest point on the standard curve. Samples with a duplicate coefficient of variation exceeding 15% were re-assayed, and all were successfully re-assayed within acceptance criteria; none were excluded from the final analysis.
2.7. Statistical Analysis
Analyses were conducted in R (version 3.6.3; R Foundation for Statistical Computing, Vienna, Austria). Distributional characteristics of continuous variables were assessed using histograms, Q–Q plots, and the Shapiro–Wilk test. All continuous variables were non-normally distributed and are reported as medians with interquartile ranges (IQR).
Because healthy controls were individually matched to MS patients by sex and approximate age, the primary case–control comparisons of continuous variables (cytokine concentrations and demographic variables) used the Wilcoxon signed-rank test for paired data. Within the MS cohort, unpaired subgroup comparisons used the Wilcoxon rank-sum test for two-group comparisons (e.g., RRMS vs. SPMS; newly diagnosed vs. long-standing; treated vs. untreated) and the Kruskal–Wallis test for comparisons involving more than two groups (DMT category). Categorical variables were compared using the chi-square test or Fisher’s exact test, as appropriate. Associations between continuous variables (cytokine concentrations, age, EDSS, and T25FW) were assessed using Spearman’s rank correlation coefficient (r), reported with the corresponding coefficient of determination (r 2 ) and a two-sided p -value; r 2 is reported alongside r throughout because a coefficient in the moderate range can still leave most of the variance unexplained, and interpreting r together with r 2 and clinical context has been specifically recommended for correlation coefficients of this magnitude [ 22 ].
Every statistical comparison performed in this study, including the variables involved, the groups compared, and the test used, is listed in Supplementary Table S1 , ensuring that all analyses, including non-significant results, are fully reported. No post hoc, data-derived cutoffs (e.g., median-split dichotomization of a continuous cytokine) were used anywhere in this study; IL-6 and the other interleukins were analyzed exclusively as continuous variables. Given the exploratory design, modest sample size, and the number of comparisons listn Supplementary Table S1 , no formal adjustment for multiple comparisons was applied; consequently, p -values throughout this manuscript are reported and should be read as a continuum of evidence from a hypothesis-generating study, not as confirmatory tests of statistical significance. Marginal p -values (for example, close to 0.05) are not treated as qualitatively different from p -values just above that threshold. A two-sided p < 0.05 is used only as a descriptive reference point in the tables below.
3. Results
3.1. Demographic and Clinical Characteristics
Fifty-two participants were enrolled: 26 MS patients and 26 healthy controls, individually matched 1:1 by sex and approximate age. Median age was 35 years (IQR, 25–42) in MS patients and 33 years (IQR, 25–40) in controls; women comprised 76.9% of each group ( Table 1 ). Smoking status, marital status, education level, and nationality did not differ significantly between groups ( Table 1 ).
3.2. Clinical Characteristics of MS Patients
Median disease duration since onset was 2 years (IQR: 1–5). Median BMI was 28.8 kg/m 2 (IQR, 21.8–30.0); most patients were overweight or obese (65.4%), 30.8% had normal weight, and 3.8% were underweight. A family history of MS was reported by 11.5% of patients. The majority were classified as RRMS (73.1%), with the remainder as SPMS (26.9%). At least one comorbidity was present in 58% of patients. The most common presenting symptoms were numbness (78.8%) and visual weakness (21.1%). On the T25FW, 46.2% of patients completed the walk in ≤4 s, 50% required >4 s, and 3.8% were unable to walk. Regarding treatment, 53.8% of patients received rituximab, 15.4% dimethyl fumarate, 7.7% ocrelizumab, 7.7% teriflunomide, and 3.8% ofatumumab, while 11.5% were untreated at the time of sampli ( Table 2 ).
3.3. Serum Cytokine Profile in MS Patients and Matched Controls
Serum IL-6, IL-16, IL-18, and IL-36 concentrations werh higher in MS patients than in matched controls (Wilcoxon signed-rank test, p < 0.001 for all four compariso; Figure 1 ). Median (IQR) concentrations were: IL-6, 288.52 (263.52–352.74) ng/L in MS patients versus 80.76 (76.02–88.95) ng/L in controls; IL-16, 375.22 (302.54–510.41) ng/L versus 45.59 (38.19–51.15) ng/L; IL-18, 36.11 (32.21–38.77) ng/L versus 4.55 (4.10–5.63) ng/L; and IL-36, 612.00 (567.56–687.00) ng/L versus 56.44 (39.78–73.11) ng/L.

Table 3
Serum interleukin concentrations in MS patients and matched controls.
| Cytokine | MS Patients, Median (IQR) | Controls, Median (IQR) | p |
| IL-6 | 288.52 (263.52–352.74) ng/L | 80.76 (76.02–88.95) ng/L | p < 0.001 |
| IL-16 | 375.22 (302.54–510.41) ng/L | 45.59 (38.19–51.15) ng/L | p < 0.001 |
| IL-18 | 36.11 (32.21–38.77) ng/L | 4.55 (4.10–5.63) ng/L | p < 0.001 |
| IL-36 | 612.00 (567.56–687.00) ng/L | 56.44 (39.78–73.11) ng/L | p < 0.001 |
Using the individual-level cytokine concentrations subsequently retained, the full observed range (minimum–maximum) for each group was: IL-6, 239.38–656.62 ng/L (MS) vs. 68.69–165.24 ng/L (controls); IL-16, 280.78–669.67 ng/L vs. 32.63–82.63 ng/L; IL-18, 27.40–67.90 ng/L vs. 3.19–8.17 ng/L; IL-36, 395.33–817.56 ng/L vs. 12.00–206.44 ng/L. For every interleukin, the full MS range lies entirely above the full control range, confirming complete, non-overlapping group separation (consistent with the ROC AUC = 1.00 result reported in Section Strengths and Limitations).
3.4. Subgroup Comparisons by MS Phenotype, Disease Duration, and Treatment
Table 4 summarizes all within-cohort subgroup comparisons of serum IL-6, IL-16, IL-18, and IL-36. No significant differences in any of the four interleukins were observed between RRMS and SPMS, between DMT categories (anti-CD20, oral, or untreated), or between smokers and non-smokers (all p > 0.505 Table 4 ). The untreated subgroup (n = 3) was too small to support a statistically meaningful two-group comparison against treated patients and was therefore excluded from that specific test (see Section Strengths and Limitations, Limitations); the DMT-category comparison above already includes the untreated group without altering the conclusion. IL-16 concentrations differed significantly by disease duration ( p = 0.012), with higher levels in newly diagnosed than in long-standing patients; no corresponding difference by disease duration was observed for IL-6, IL-18, or IL-36 ( Table 4 ).
Using the retained individual-level cytokine concentrations, exact subgroup medians (IQR) and a direct anti-CD20 vs. oral comparison are now available. By phenotype: IL-6 was 280.76 (260.93–344.55) ng/L in RRMS (n = 19) vs. 327.31 (297.14–498.86) ng/L in SPMS (n = 7); IL-16 was 375.22 (306.70–495.59) vs. 427.07 (303.93–531.70) ng/L; IL-18 was 36.23 (32.04–39.85) vs. 35.32 (32.95–36.34) ng/L; IL-36 was 589.78 (567.56–664.78) vs. 628.67 (581.44–703.67) ng/L ( p -values as in the table above). By DMT category (anti-CD20, n = 17; oral, n = 6; untreated, n = 3): IL-6 medians were 287.66, 284.21, and 327.31 ng/L; IL-16 were 375.22, 443.74, and 356.70 ng/L; IL-18 were 36.00, 36.57, and 36.23 ng/L; IL-36 were 606.44, 603.67, and 650.89 ng/L, respectively ( p -values as in the table above). A direct two-group comparison restricted to anti-CD20 vs. oral therapy, excluding the small untreated group, was then computed directly and confirmed no significant difference for any cytokine (Wilcoxon rank-sum test: IL-6 p = 0.944; IL-16 p = 0.248; IL-18 p = 0.726; IL-36 p = 0.674), consistent with the three-group result above.
3.5. Correlations Between Age, Interleukins, and Disability-Related Measures
Age correlated positively with both EDSS (r = 0.573, p = 0.002) and T25FW (r = 0.464, p = 0.014). EDSS and T25FW correlated with each other (r = 0.580, p = 0.002). Correlations between age or each interleukin and the two disability-related measures are shown in Table 5 , together with the corresponding coefficient of determination (r 2 ).
Median EDSS was 1.00 (Q1–Q3, 0.00–2.25) in RRMS versus 3.50 (Q1–Q3, 2.75–4.50) in SPMS patients, a significant difference ( p = 0.002). Median EDSS did not differ significantly between newly diagnosed and long-standing patients ( p = 0.096), across DMT categories (anti-CD20 2.50 [1.00–3.00], oral 1.75 [1.12–2.75], untreated 1.00 [0.50–1.50]; p = 0.580), or between treated (2.00, Q1–Q3 1.00–3.00) and untreated patients (1.00, Q1–Q3 0.50–1.50; p = 0.274).
4. Discussion
In this matched case–control study, serum IL-6, IL-16, IL-18, and IL-36 were each elevated in Saudi adults with MS relative to matched healthy controls, consistent with a broad literature showing that peripheral and intrathecal inflammatory mediators are altered in MS [ 6 , 7 , 8 , 15 , 16 , 17 , 18 ]. Within the MS cohort, however, the four interleukins did not behave alike with respect to disability: only IL-6 showed a correlation of note, with T25FW (r = 0.53, r 2 ≈ 0.28) and, more weakly, with EDSS (r = 0.38, r 2 ≈ 0.14). IL-16 and IL-36 explained less than 3% of the variance in either disability measure, and IL-18 (r = 0.35, p = 0.126) was not statistically significant; none of the three is discussed further as a disability correlate below.
This pattern is broadly consistent with and extends prior reports linking IL-6 specifically—rather than a generic interleukin panel—to MS severity. Chen et al. and Eslami et al. reported elevated serum IL-6 in patients with MS, including a possible relationship to disease progression from relapsing–remitting to secondary progressive status [ 10 , 11 ]. Itorralba et al. found that intrathecal IL-6 is associated with progressive disease and clinical severity, although that work measured IL-6 in CSF rather than serum [ 9 ]. Guzel et al. and Manouchehrinia et al. likewise reported that EDSS increases with age, consistent with the age–EDSS correlation observed here, and support age as a contributor to the accumulation of disability independent of cytokine levels [ 23 , 24 ]. Bethoux et al. reported a positive relationship between EDSS and T25FW performance, consistent with our finding that the two instruments, though methodologically distinct, capture a shared dimension of overall disease severity [ 25 ]. Martins et al., Kallaur et al., and Khaibullin et al. each reported broader panels of dysregulated serum or CSF cytokines in MS, and Berek et al. profiled 65 cytokines and chemokines in paired CSF and serum from patients with MS [ 6 , 7 , 8 , 26 , 27 ]. Takenn together with the present findings, these studies support the general concept that systemic and compartmentalized inflammatory signaling is altered in MS, while our data indicate that, of the four interleukins we measured, this signal tracked with clinical disability only for IL-6.
A plausible explanation for this divergence lies in the distinct signaling roles of these four cytokines. IL-6 is secreted by T cells, B cells, macrophages, microglia, and non-immune cells including endothelial cells and neurons, and plays a pleiotropic role across MS onset and progression [ 4 , 28 , 29 ]. At the blood–brain barrier, IL-6 is among the pro-inflammatory cytokines produced by resident and endothelial cells that facilitate immune-cell infiltration into the CNS [ 30 , 31 ], and elevated IL-6 in the cerebrospinal fluid has been linked to MS disease activity [ 29 ]. IL-6 produced by B cells further promotes Th17 cell differentiation while inhibiting regulatory T-cell generation, and B cells from MS patients exhibit an abnormal pro-inflammatory profile with elevated IL-6 [ 30 ]; Th17 cells, in turn, are highly prevalent in active MS lesions [ 32 ]. IL-16, by contrast, acts primarily as a CD4+ T-cell chemoattractant reflecting leukocyte trafficking rather than a driver of BBB breakdown or Th17 polarization [ 33 , 34 ]; IL-18 acts largely through the IL-18 receptor/STAT4 pathway to induce IFN-γ [ 14 ]; and IL-36 promotes IFN-γ, IL-4, and IL-17 production and dendritic-cell maturation without an established BBB-specific role [ 5 ]. This asymmetry in upstream biology—IL-6’s dual role in Th17 polarization and BBB permeability, versus the more circumscribed roles of IL-16, IL-18, and IL-36—offers one candidate explanation for why only IL-1, among the four interleukins measured here, showed a correlation with disability. This explanation is drawn from the broader immunology literature rather than tested directly in this study and remains speculative pending mechanistic follow-up.
IL-16 was elevated in our MS cohort compared with controls, consistent with findings from Nischwitz et al. and Kouchaki et al. [ 12 , 13 ]. IL-16 also differed by disease duration, with higher concentrations in newly diagnosed than in long-standing patients; because this was one of many exploratory subgroup comparisons performed without correction for multiple testing, and our cross-sectional design cannot distinguish a true duration-related pattern from a chance finding in a modest sample, we regard it as hypothesis-generating and recommend confirmation in a longitudinal design. One possible interpretation of higher IL-16 in newly diagnosed than in long-standing disease is a shift from an early phase of active CD4+ T-cell recruitment into CNS lesions, for which IL-16, as a chemoattractant acting through CD4 [ 33 , 34 , 35 ], would be expected to be relatively elevated, toward a later phase in which sustained antigen exposure and chronic activation lead to a contraction of trafficking-associated signaling, sometimes described as an immune-exhaustion-like state. A broadly analogous temporal pattern has been reported for another interleukin in MS: IL-38 was found at significantly higher serum levels in newly diagnosed than in established MS and systemic sclerosis patients [ 36 , 37 , 38 ], supporting the general plausibility that interleukin levels track disease stage rather than remain static. However, the mechanisms need not be identical across cytokines. This interpretation is necessarily post hoc, as our cross-sectional design captured a single time point per patient rather than a within-patient trajectory, and should be tested longitudinally before being treated as established. It may also explain why IL-16, unlike IL-6, tracked disease duration but not disability: leukocyte trafficking intensity and cumulative disability accrual are related but not equivalent processes.
IL-18 was elevated in patients relative to controls, consistent with Jahanbani-Ardakani et al., Nicoletti et al., Chen et al., and Losy and Niezgoda [ 14 , 15 , 16 , 17 ]. As in Nicoletti et al. and Chen et al., we found no significant correlation between IL-18 and either disability measure [ 16 , 17 ]. IL-36 was likewise elevated relative to controls; to our knowledge, only one other cohort has reported serum IL-36 in MS, similarly finding elevation without a relationship to disease duration, a pattern our data reproduce [ 18 ].
Several factors limit how these associations should be interpreted. In this cohort, age correlated with both EDSS and T25FW, and MS phenotype was strongly associated with EDSS ( p = 0.002). Because no multivariable or partial-correlation analyses were performed, and given the modest overall sample size (post hoc power 48–81% for the IL-6 correlations reported here; Section Strengths and Limitations) and the small SPMS subgroup (n = 7), we cannot determine whether the IL-6–disability association is independent of age, phenotype, or disease duration, or is instead partly or wholly confounded by them. Using the raw ELISA measurements subsequently retained for all 26 MS patients, a partial correlation of IL-6 with EDSS controlling for age and phenotype (RRMS/SPMS) attenuated the association from an unadjusted Pearson r = 0.40 ( p = 0.043; Spearman r = 0.39, p = 0.052) to a partial r = 0.25 ( p = 0.228, n = 26). In a multivariable linear model (EDSS ~ IL-6 + age + phenotype, R 2 = 0.56), the IL-6 coefficient was not statistically significant ( p = 0.248) once age ( p = 0.036) and phenotype ( p = 0.014) were included, indicating the unadjusted IL-6–EDSS correlation is at least partly attributable to age and phenotype rather than an independent IL-6 effect. Disease duration could not be added as a covariate: the per-patient duration recomputed from diagnosis dates did not reconcile with the newly diagnosed/long-standing grouping used elsewhere in this study (see Section Strengths and Limitations), so we did not adjust for a duration variable of uncertain provenance. We therefore describe the IL-6 finding as an observed, unadjusted correlation rather than as evidence of an independent biomarker effect. We did not attempt to construct or report a combined interleukin ‘signature’ or ‘profile’: the study measured four individual analytes without composite scoring, clustering, or dimensionality reduction, and only one of them, IL-6, showed a notable correlation with either disability measure. Subgroup comparisons by phenotype, disease duration, DMT category, and treatment status were also based on small and, in the case of treatment, markedly imbalanced groups (17 anti-CD20, 6 oral DMT, 3 untreated); the absence of a significant difference in these comparisons reflects limited statistical power in a modest sample and should not be interpreted as evidence of equivalence or of no underlying biological effect.
From a clinical perspective, serum-based inflammatory markers remain of interest because they are minimally invasive and more feasible for repeated sampling than CSF, particularly where access to advanced imaging or CSF analysis is limited [ 7 , 8 , 26 ]. However, this cross-sectional, unadjusted, single-center study of 26 patients does not establish diagnostic accuracy, prognostic performance, or readiness for clinical stratification for IL-6 or any other interleukin measured here; at most, these findings support further, larger, and adjusted study of IL-6 as a candidate adjunctive biomarker. Using the individual-level cytokine concentrations subsequently retained, ROC analysis confirmed perfect discrimination between patients and controls for all four interleukins (AUC = 1.00 each; see Limitations), consistent with the pronounced, non-overlapping group separation in Table 3 ; this reflects strong case–control discriminatory value at the group level and should not be read as evidence of individual-level diagnostic utility in an undiagnosed population.
Strengths and Limitations
This study benefits from an individually age- and sex-matched control group, statistical methods that account for that matching, concurrent assessment of two complementary disability measures, and blinding of EDSS scoring and laboratory analysis to case–control status. To our knowledge, it is the second dataset worldwide reporting serum IL-36 in MS. Its principal limitations are as follows: Modest sample size, particularly for subgroup comparisons (e.g., n = 7 for SPMS; post hoc power 48–81% for the IL-6 correlations reported here). Cross-sectional design, which precludes inferring a causal temporal relationship between cytokine elevation and disability onset and cannot establish whether interleukin elevations precede, accompany, or follow disability. Using the individual-level ELISA concentrations subsequently retained, ROC analysis showed perfect discrimination between MS patients and controls for all four interleukins (AUC = 1.00 for IL-6, IL-16, IL-18, and IL-36; Mann–Whitney p < 10 −9 for each), reflecting their fully non-overlapping concentration ranges ( Table 3 ); this is a strong group-level result but does not by itself establish individual diagnostic performance in an undifferentiated clinical population. A partial correlation adjusting the IL-6–EDSS association for age and phenotype was also performed (Discussion, above) and attenuated the association to non-significance (partial r = 0.25, p = 0.228); adjustment for disease duration specifically remains a priority for future studies, as the per-patient duration data available to us could not be reconciled with the categorical grouping used elsewhere in this study ( Figure 2 ). Lack of correction for multiple comparisons across a substantial number of statistical tests ( Supplementary Table S1 ), which may increase the probability of Type I errors; results should be read as hypothesis-generating rather than confirmatory. Use of serum rather than CSF, which may not fully reflect intrathecal inflammatory activity. A severe imbalance in treatment-group sample sizes, with anti-CD20 therapy comprising the vast majority of treated patients (17 of 23; 6 oral, 3 untreated), which prevents effective evaluation of the specific impact of individual DMTs on cytokine levels.
This study did not include an a priori sample-size calculation because of its exploratory, hypothesis-generating design. Post hoc power, computed for the Spearman correlations using Fisher’s z-transformation (α = 0.05, two-sided), was approximately 81% to detect the observed IL-6–T25FW correlation (r = 0.53, n = 26), 48% for the IL-6–EDSS correlation (r = 0.38, n = 26), and 42% for the IL-18–T25FW correlation (r = 0.35, n = 26); the study was well powered (≥87%) to detect the age–EDSS correlation (r = 0.573) but only moderately powered (67%) for age–T25FW (r = 0.464). These estimates should be read as approximate, as post hoc power calculations based on an observed effect size are known to be biased and to yield limited estimates of true power; they are reported here to characterize rather than justify the modest sample size. Power for the subgroup comparisons was even lower, particularly for the SPMS subgroup (n = 7) and the untreated subgroup (n = 3), and null results from these comparisons should not be interpreted as evidence of equivalence.

5. Conclusions
In this matched case–control study of Saudi adults with MS, serum levels of IL-6, IL-16, IL-18, and IL-36 were elevated relative to those of matched healthy controls. Among these four interleukins, only IL-6 showed a relevant, though modest and unadjusted, correlation with a disability-related measure, most notably T25FW. IL-16 additionally differed by disease duration in this exploratory cohort, a finding that requires longitudinal confirmation. These results provide region-specific, hypothesis-generating evidence on peripheral interleukin biology in an underrepresented Middle Eastern MS population and support prioritizing IL-6s; specifically, for further evaluation in larger, longitudinal, covariate-adjusted studies.
Acknowledgments
The authors thank the Department of Biology at King Abdulaziz University and King Abdulaziz University Hospital for institutional support, and are grateful to the patients and healthy volunteers who participated in this study. The authors acknowledge with thanks WAQF and the Deanship of Scientific Research (DSR) for their technical and financial support. The authors used Grammarly (online version, Pro 2026) solely for English-language editing (grammar, spelling, and readability). No generative AI or large language model was used to generate the scientific content, analyses, interpretations, or conclusions of this manuscript.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/neurolint18090168/s1 , Table S1: Summary of all statistical analyses performed in this study.
Author Contributions
Conceptualization, J.A., Y.M. and H.A.A.; methodology, Y.M.; software, Y.M., A.A. and K.Z.; validation, J.A., Y.M., K.Z. and H.A.A.; formal analysis, A.A. and K.Z.; investigation, J.A., Y.M., A.A., K.Z. and H.A.A.; resources, J.A., Y.M. and H.A.A.; data curation, J.A., Y.M., K.Z., A.A. and H.A.A.; writing—original draft preparation, J.A., Y.M. and H.A.A.; writing—review and editing, A.A. and K.Z.; visualization, J.A., Y.M., K.Z. and H.A.A.; supervision, J.A. and H.A.A.; project administration, J.A. and H.A.A.; funding acquisition, J.A. and H.A.A. All authors have read and agreed to the published version of the manuscript.
Institutional Review Board Statement
The study was conducted in accordance with the Declaration of Helsinki and approved by the Biomedical Ethics Research Committee of King Abdulaziz University, Jeddah, Saudi Arabia (NCBE Registration No. HA-02-J-008; protocol/reference code 129-24; approval date: 9 May 2024).
Informed Consent Statement
Informed consent was obtained from all subjects involved in the study.
Data Availability Statement
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to institutional privacy restrictions on patient-level clinical data.
Conflicts of Interest
The authors declare no conflicts of interest.
Abbreviations
The following abbreviations are used in this manuscript: BMI Body mass index CSF Cerebrospinal fluid DMT Disease-modifying therapy EDSS Expanded Disability Status Scale IL Interleukin IQR Interquartile range MS Multiple sclerosis RRMS Relapsing–remitting multiple sclerosis SPMS Secondary progressive multiple sclerosis T25FW Timed 25-Foot Walk Th17 T helper 17
References
1. Walton C. King R. Rechtman L. Kaye W. Leray E. Marrie R.A. Robertson N. La Rocca N. Uitdehaag B. van der Mei I. Rising Prevalence of Multiple Sclerosis Worldwide: Insights from the Atlas of MS, Third Edition Mult. Scler. J. 2020 26 1816 1821 10.1177/1352458520970841 PMC7720355 33174475
2. Saeedi J.A. AlYafeai R.H. AlAbdulSalam A.M. Al-Dihan A.Y. AlDwaihi A.A. Al Harbi A.A. Aljadhai Y.I. Al-Jedai A.H. AlKhawajah N.M. Al-Luqmani M.M. Saudi Consensus Recommendations on the Management of Multiple Sclerosis: Diagnosis and Radiology/Imaging Clin. Transl. Neurosci. 2023 7 5 10.3390/ctn7010005
3. Aljumah M. Bunyan R. Al Otaibi H. Al Towaijri G. Karim A. Al Malik Y. Kalakatawi M. Alrajeh S. Al Mejally M. Algahtani H. Rising Prevalence of Multiple Sclerosis in Saudi Arabia, a Descriptive Study BMC Neurol. 2020 20 49 10.1186/s12883-020-1629-3 PMC7007659 32035478
4. Grunwald C. Krętowska-Grunwald A. Adamska-Patruno E. Kochanowicz J. Kułakowska A. Chorąży M. The Role of Selected Interleukins in the Development and Progression of Multiple Sclerosis—A Systematic Review Int. J. Mol. Sci. 2024 25 2589 10.3390/ijms25052589 PMC10932438 38473835
5. Byrne J. Baker K. Houston A. Brint E. IL-36 Cytokines in Inflammatory and Malignant Diseases Front. Immunol. 2021 12 639706 10.1007/s00018-021-03909-4 PMC8429149 34365521
6. Martins T.B. Rose J.W. Jaskowski T.D. Wilson A.R. Husebye D. Seraj H.S. Hill H.R. Analysis of Proinflammatory and Anti-Inflammatory Cytokine Serum Concentrations in Patients with Multiple Sclerosis by Using a Multiplexed Immunoassay Am. J. Clin. Pathol. 2011 136 696 704 10.1309/ajcp7ubk8ibvmvnr 22031307
7. Kallaur A.P. Oliveira S.R. Simão A.N.C. Alfieri D.F. Flauzino T. Lopes J. de Carvalho Jennings Pereira W.L. de Meleck Proença C. Borelli S.D. Kaimen-Maciel D.R. Cytokine Profile in Patients with Progressive Multiple Sclerosis and Its Association with Disease Progression and Disability Mol. Neurobiol. 2017 54 2950 2960 10.1007/s12035-016-9846-x 27023227
8. Khaibullin T. Ivanova V. Martynova E. Cherepnev G. Khabirov F. Granatov E. Rizvanov A. Khaiboullina S. Elevated Levels of Proinflammatory Cytokines in Cerebrospinal Fluid of Multiple Sclerosis Patients Front. Immunol. 2017 8 531 10.3389/fimmu.2017.00531 PMC5435759 28572801
9. Itorralba J. Brand-Arzamendi K. Saab G. Muccilli A. Schneider R. Intrathecal Interleukin-6 Levels Are Associated with Progressive Disease and Clinical Severity in Multiple Sclerosis BMC Neurol. 2025 25 145 10.1186/s12883-025-04145-0 PMC11963510 40175894
10. Chen Y.C. Yang X. Miao L. Liu Z.G. Li W. Zhao Z.X. Sun X.J. Jiang G.X. Chen S.D. Cheng Q. Serum Level of Interleukin-6 in Chinese Patients with Multiple Sclerosis J. Neuroimmunol. 2012 249 109 111 10.1016/j.jneuroim.2012.04.015 22633195
11. Eslami M. Mirabi A.M. Baghbanian M. Rafiei A. Interleukin 6 but not Interleukin 8 Might Be an Indicator of Multiple Sclerosis Progression from Relapse Remitting to Secondary Progressive Status Res. Mol. Med. 2020 8 1 8 10.32598/rmm.8.1.1
12. Nischwitz S. Faber H. Sämann P.G. Domingues H.S. Krishnamoorthy G. Knop M. Müller-Sarnowski F. Yassouridis A. Weber F. Interferon β-1a Reduces Increased Interleukin-16 Levels in Multiple Sclerosis Patients Acta Neurol. Scand. 2014 130 46 52 10.1111/ane.12215 24571587
13. Kouchaki E. Akbari H. Mahmoudi F. Salehi M. Naim E. Nikoueinejad H. Correlation of Serum Levels of Interleukin-16, CCL27, Tumor Necrosis Factor-Related Apoptosis-Inducing Ligand, and B-Cell Activating Factor with Multiple Sclerosis Severity Iran. J. Allergy Asthma Immunol. 2022 21 27 34 10.18502/ijaai.v21i1.8610 35524375
14. Ihim S.A. Abubakar S.D. Zian Z. Sasaki T. Saffarioun M. Maleknia S. Azizi G. Interleukin-18 Cytokine in Immunity, Inflammation, and Autoimmunity: Biological Role in Induction, Regulation, and Treatment Front. Immunol. 2022 13 919973 10.3389/fimmu.2022.919973 PMC9410767 36032110
15. Jahanbani-Ardakani H. Alsahebfosoul F. Etemadifar M. Abtahi S.H. Interleukin 18 Polymorphisms and Its Serum Level in Patients with Multiple Sclerosis Ann. Indian Acad. Neurol. 2019 22 474 476 10.4103/aian.aian_515_18 PMC6839318 31736573
16. Nicoletti F. Di Marco R. Mangano K. Patti F. Reggio E. Nicoletti A. Bendtzen K. Reggio A. Increased Serum Levels of Interleukin-18 in Patients with Multiple Sclerosis Neurology 2001 57 342 344 10.1212/wnl.57.2.342 11468327
17. Losy J. Niezgoda A. IL-18 in Patients with Multiple Sclerosis Acta Neurol. Scand. 2001 104 171 173 10.1034/j.1600-0404.2001.00356.x 11551238
18. Alsahebfosoul F. Jahanbani-Ardakani H. Ghavimi R. Sedaghat N. Etemadifar M. Serum Level of Interleukin 36 in Patients with Multiple Sclerosis J. Immunoass. Immunochem. 2018 39 558 564 10.1080/15321819.2018.1520128 30252593
19. Kurtzke J.F. Rating Neurologic Impairment in Multiple Sclerosis: An Expanded Disability Status Scale (EDSS) Neurology 1983 33 1444 1452 10.1212/wnl.33.11.1444 6685237
20. Cutter G.R. Baier M.L. Rudick R.A. Cookfair D.L. Fischer J.S. Petkau J. Syndulko K. Weinshenker B.G. Antel J.P. Confavreux C. Development of a Multiple Sclerosis Functional Composite as a Clinical Trial Outcome Measure Brain 1999 122 871 882 10.1093/brain/122.5.871 10355672
21. Helmlinger B. Pinter D. Hechenberger S. Bachmaier G. Khalil M. Heschl B. Damulina A. Enzinger C. Evaluation of the Timed 25-Foot Walk in Minimally Disabled People with Multiple Sclerosis J. Neurol. Sci. 2024 454 120635 10.1016/j.jns.2024.123073 38852261
22. Guzel I. Mungan S. Oztekin Z.N. Ak F. Is There an Association Between the Expanded Disability Status Scale and Inflammatory Markers in Multiple Sclerosis? J. Chin. Med. Assoc. 2016 79 54 57 10.1016/j.jcma.2015.08.010 26589195
23. Manouchehrinia A. Westerlind H. Kingwell E. Zhu F. Carruthers R. Ramanujam R. Ban M. Glaser A. Sawcer S. Tremlett H. Age-Related Multiple Sclerosis Severity Score: Disability Ranked by Age Mult. Scler. 2016 23 1938 1946 10.1177/1352458517690618 PMC5700773 28155580
24. Bethoux F.A. Palfy D.M. Plow M.A. Correlates of the Timed 25-Foot Walk in a Multiple Sclerosis Outpatient Rehabilitation Clinic Int. J. Rehabil. Res. 2016 39 134 139 10.1097/mrr.0000000000000157 PMC4850097 26926380
25. Schober P. Boer C. Schwarte L.A. Correlation Coefficients: Appropriate Use and Interpretation Anesth. Analg. 2018 126 1763 1768 10.1213/ane.0000000000002864 29481436
26. Zhu W. Chen C. Zhang L. Hoyt T. Walker E. Venkatesh S. Deykin A. Kieseier B. Association Between Serum Multi-Protein Biomarker Profile and Real-World Disability in Multiple Sclerosis Brain Commun. 2023 5 fcad300 10.1093/braincomms/fcad300 PMC10773609 38192492
27. Berek K. Bauer A. Rudzki D. Auer M. Barket R. Zinganell A. Hofer L. Grams A. Poskaite P. Rejc Z. Immune Profiling in Multiple Sclerosis: A Single-Center Study of 65 Cytokines and Chemokines in Cerebrospinal Fluid and Serum Front. Immunol. 2023 14 1200146 10.3389/fimmu.2023.1200146 PMC10294231 37383229
28. Rothaug M. Becker-Pauly C. Rose-John S. The Role of Interleukin-6 Signaling in Nervous Tissue Biochim. Biophys. Acta Mol. Cell Res. 2016 1863 1218 1227 10.1016/j.bbamcr.2016.03.018 27016501
29. Stampanoni Bassi M. Iezzi E. Drulovic J. Pekmezovic T. Gilio L. Furlan R. Finardi A. Marfia G.A. Sica F. Centonze D. IL-6 in the Cerebrospinal Fluid Signals Disease Activity in Multiple Sclerosis Front. Cell. Neurosci. 2020 14 120 10.3389/fncel.2020.00120 PMC7324533 32655367
30. Häusser-Kinzel S. Weber M.S. The Role of B Cells and Antibodies in Multiple Sclerosis, Neuromyelitis Optica, and Related Disorders Front. Immunol. 2019 10 201 10.3389/fimmu.2019.00201 PMC6375838 30800132
31. Filippi M. Bar-Or A. Piehl F. Preziosa P. Solari A. Vukusic S. Rocca M.A. Multiple Sclerosis Nat. Rev. Dis. Primers 2018 4 43 10.1038/s41572-018-0041-4 30410033
32. Fletcher J.M. Lalor S.J. Sweeney C.M. Tubridy N. Mills K.H.G. T Cells in Multiple Sclerosis and Experimental Autoimmune Encephalomyelitis Clin. Exp. Immunol. 2010 162 1 11 10.1111/j.1365-2249.2010.04143.x PMC2990924 20682002
33. Skundric D.S. Cruikshank W.W. Montgomery P.C. Lisak R.P. Tse H.Y. Emerging Role of IL-16 in Cytokine-Mediated Regulation of Multiple Sclerosis Cytokine 2015 75 234 248 10.1016/j.cyto.2015.01.005 25703787
34. Wilson K.C. Center D.M. Cruikshank W.W. The Effect of Interleukin-16 and Its Precursor on T Lymphocyte Activation and Growth Growth Factors 2004 22 97 104 10.1080/08977190410001704679 15253385
35. Yoon T. Pyo J.Y. Ahn S.S. Song J.J. Park Y.B. Lee S.W. Serum Interleukin-16 Significantly Correlates with the Vasculitis Damage Index in Antineutrophil Cytoplasmic Antibody-Associated Vasculitis Arthritis Res. Ther. 2020 22 73 10.1186/s13075-020-02172-5 PMC7140543 32264927
36. Zarrabi M. Nazarinia M. Rahimi Jaberi A. Gholijani N. Amirghofran Z. Elevated IL-38 Serum Levels in Newly Diagnosed Multiple Sclerosis and Systemic Sclerosis Patients Med. Princ. Pract. 2021 30 146 153 10.1159/000510915 PMC8114062 33080590
37. Hridi S.U. Barbour M. Wilson C. Franssen A.J.P.M. Harte T. Jiang H.R. Bushell T.J. Increased Levels of IL-16 in the Central Nervous System during Neuroinflammation Are Associated with Infiltrating Immune Cells and Resident Glial Cells Biology 2021 10 472 10.3390/biology10060472 PMC8229350 34071825
38. Mado H. Stasiniewicz A. Adamczyk-Sowa M. Sowa P. Selected Interleukins Relevant to Multiple Sclerosis: New Direc-tions, Potential Targets and Therapeutic Perspectives Int. J. Mol. Sci. 2024 25 10931 10.3390/ijms252010931 PMC11506881 39456713