This multicenter, cross-sectional study included individuals with PwMS and HCs. We adhered to the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE).

The inclusion criteria for PwMS were as follows: an MS diagnosis according to the 2017 McDonald criteria at sample extraction or during follow-up (24), a relapsing remitting MS form, an age between 18 and 65 years, an absence of other neurodegenerative or autoimmune diseases, a lack of treatment with any disease-modifying therapies (DMTs), a disease duration of less than 1 year, and a lack of corticosteroids treatment for at least 2 months prior to sample collection.

The inclusion criteria for HCs were as follows: an age between 18 and 65 years, no history or evidence of any neurodegenerative or autoimmune disease, no alcohol abuse or illicit drug use, and no current treatment with any immunosuppressive or chemotherapeutic agents. HCs were hospital employees who volunteered to participate by providing blood samples. Each HC underwent an interview conducted by a neurologist and showed normal sNfL values; however, no physical examination was performed.

Patients and HCs were recruited between January 2022 and December 2024 at three university hospitals in Spain. We included 253 PwMS recruited consecutively and 180 HCs. No participants were excluded from the final analysis.

We collected serum samples by drawing 10 ml of blood into dry tubes. The blood was then centrifuged at 3000×g for 5 minutes. After centrifugation, the serum was divided into 250 µl aliquots and stored at −80 °C until analysis. All of the samples were processed using standardized protocols by trained personnel, which reduced measurement bias.

sNfL and sGFAP were measured by single molecule array technique (SIMOA) using the SIMOA NF-light™ Advantage Plus Kit and the SIMOA™ GFAP Advantage Plus Kit, respectively (Quanterix, Billerica, MA, USA), on an HD-X instrument (Quanterix), according to the manufacturer’s instructions. sNfL concentrations were normalized using z-scores adjusted for age and body mass index (BMI), as previously described (4). PwMS were classified into NLGL (normal sNfL and sGFAP), NHGL (high sNfL/normal sGFAP), NHGH (high sNfL and sGFAP) and NLGH (normal sNfL/high sGFAP) according to recent literature (6, 7). Cut-off values were defined as previously reported (25). A z-score ≥1.5 was considered indicative of elevated sNfL levels. High sGFAP levels were defined as concentrations above 140 pg/ml for individuals under 55 years of age and above 280 pg/ml for individuals aged 55 years or older.

Serum soluble factors were quantified using the Olink™ Target 48 Cytokine panel on an Olink™ Signature Q100 instrument (Thermo Fisher, MA, USA). This platform allows for the simultaneous analysis of 45 immune-related factors, including cytokines, chemokines, and other pro- and anti-inflammatory proteins. Supplementary Table S1 provides a complete list of the soluble factors included in the panel.

The Olink^® Target 48 Cytokine assay uses Proximity Extension Assay (PEA) technology with a two-step normalization and strict quality control framework to ensure precision and cross-run comparability. Each well contains internal controls to monitor the immunoreaction, extension, and qPCR phases. Normalization involves correcting analyte Cq values by the Extension Control (ΔCq), then by the median Calibrator ΔCq across plates (ΔΔCq), and finally adjusting by a bridging factor to align different kit lots. External controls (calibrator, sample, and negative controls) were used to assess precision, accuracy, and background. Quality control criteria required intra- and inter-assay CVs < 30%, control deviations within ±0.3 values, and exclusion of samples failing these limits.

Missing data were generally very low, below 1% in 42 out of the 45 proteins analyzed. Only three proteins (MMP1, TSLP, and IL33) exhibited missing data levels above 3% (see Supplementary Table S2). No assumptions were made for missing values.

Categorical variables are presented as numbers and percentages [n (%)]. Differences between groups were analyzed using the chi-squared test and Fisher’s exact test.

We used the Mann-Whitney and Kruskal-Wallis tests to compare continuous variables. For the Kruskal-Wallis test, we conducted post-hoc analyses between groups using Dunn’s Multiple Comparison Test. All statistical tests were two-tailed, and a p-value of less than 0.05 was considered to indicate statistical significance. P-values were corrected for multiple comparisons using the False Discovery Rate (FDR) method. After correction q values lower than 0.05 were considered as significant. Spearman test correlation was used to assess the relationships between soluble factors and clinical parameters. Volcano plots were generated to visualize the differential distribution between groups.

The statistical analyses were conducted using GraphPad Prism 9.0 (GraphPad Prism Inc., San Diego, CA, USA) and R software (version 4.3.2; R Foundation for Statistical Computing, Vienna, Austria).

The study was approved by the Ethics Committee of the Ramon y Cajal University Hospital. At the meeting held on 09/26/2023 record 459, it was decided to issue the positive report corresponding to the study. All participants provided written informed consent to participate.

The original data will be accessible to any researcher in the field for 3 years upon request to the corresponding author.

We included 253 PwMS (74% women, median age 37.9 years [IQR: 28.64–45.95]) and 180 HCs (72% women, median age 38.1 years [IQR: 28.9–48.2]). No statistical differences in sex or age were found between PwMS and HCs.

We classified PwMS according to their sNfL and sGFAP levels as NLGL (93 patients), NHGL (n=71), NHGH (n=55) and NLGH (n=34). Values of sNfL and sGFAP in every patient group and in HCs are shown in Supplementary Table S3.

Demographic and baseline clinical characteristics of the different groups of patients are summarized in Table 1. The only significant differences were observed in the number of gadolinium (Gd+) enhancing lesions, that were higher in the NHGH group compared with the NLGL (p=0.007) and NLGH (p=0.02) groups.

We next explored the differences between the serum values of 45 soluble factors within the four groups of PwMS and with the HCs group. Results including Dunn’s corrected p values are shown in Supplementary Table S4. Differences were further corrected using The FDR test and variables showing significant differences are shown in Table 2.

Our analysis revealed distinct patterns in inflammation-related soluble factors, chemokines, and repair-associated molecules among the PwMS and the HCs groups.

First, we observed decreased values of TH2 cytokines in all groups of PwMS patients compared with the HCs. IL-4 levels were higher in the HCs compared with the NLGL group (q=0.02), the NHGL group (q=0.04), the NHGH group (q=0.008), and the NLGH group (q=0.006). Similarly, the HCs exhibited higher IL-13 values than the NLGL group (q=0.003), the NHGL group (q=0.04), the NHGH group (q=0.002), and the NLGH group (q=0.002) (Supplementary Figure S1). We did not find any other common differences across all MS groups compared with the HCs.

However, when comparing controls with different MS groups, we identified distinct differences within these groups. Notably, GM-CSF levels were reduced in the NLGL (q=0.009) and NHGL (q=0.002) groups compared to HC. Of note, we found clear differences that were mainly restricted to NLGH group. Compared with the HCs, individuals in the NLGH group exhibited decreased levels of FLT3LG (q=0.003), a cytokine essential for dendritic cell immune response; TNFSF12 (q=0.004), a regulator of tissue remodeling and B cell activity; and TNF-α (q=0.004), a key mediator of Th1 immune response (Figures 1A–D).

Additionally, patients in the NLGH group showed a clear decrease in various chemokines involved in immune cell recruitment compared with the HCs and other patients groups. Levels of CCL3 (q=0.009), CCL7 (q=0.02), and CCL4 (q=0.007) (Figures 2A–C). CCL2, showed the most clear differences, since its values in NLGH group were lower than those of all other groups (q<0.0001 vs HC, q=0.02 vs NLGL, q=0.04 vs NHGL, and q=0.04 vs NHGH, Figure 3A).

Finally, we analyzed soluble factors related to tissue repair; we found lower epidermal growth factor (EGF) levels in the NLGL (q=0.009), NHGH (q<0.0001), and NLGH (q<0.0001) groups compared with the HCs. In addition, the NHGH (q=0.03) and NLGH (q=0.009) groups exhibited decreased values compared with the NHGL group (Figure 3B).

To further visualize differences in the soluble factors between all groups, we performed Volcano plots. Differences with HC are shown in Figure 4.

Finally, we explored the correlation between key soluble factors and clinical markers including EDSS after the first relapse, sNfL and sGFAP. We found a moderate negative correlation between EGF and sGFAP (r=-0.32 and p<0.0001) thus suggesting that astroglial inflammation associates with lower ability for tissue repair.