Peripheral blood age-sensitive immune markers in multiple sclerosis: relation to sex, cytomegalovirus status, and treatment

Abstract

Background: Immunosenescence is accelerated by chronic infectious and autoimmune diseases and could contribute to the pathobiology of multiple sclerosis (MS). How MS and disease-modifying therapies (DMTs) impact age-sensitive immune biomarkers is only partially understood.

Methods: We analyzed 771 serum samples from 147 healthy controls and 289 people with MS (PwMS) by multiplex immunoassays. We determined cytomegalovirus (CMV) serostatus and collected retrospective clinical information. We performed unsupervised and multivariable analyses.

Findings: Unsupervised analyses revealed that MS immune profile was characterized by low relative levels of anti-inflammatory/neuroprotective factors IL-4, IL-10, TNF, and β-NGF but high levels of growth factors EGF and bFGF. Serum levels of IL-4, β-NGF, IL-27, BDNF, and leptin were significantly influenced by sex and/or CMV status. IL-4 and β-NGF levels were lower in untreated PwMS compared to controls, while EGF and bFGF levels were influenced by age and markedly elevated in PwMS in multivariable analysis. Samples from treated PwMS, but not untreated PwMS, showed lower levels of BDNF and TNF than controls. Initiation of high efficacy DMTs, but not low efficacy DMTs, was associated with reduced levels of bFGF and EGF. Samples associated with distinct DMTs exhibited specific profiles for age-sensitive immune markers. Finally, lower levels of IL-6, TNF, IL-10, and β-NGF were observed at baseline in PwMS who subsequently experienced clinical failure after DMTs initiation.

Interpretation: Age, sex, CMV status, and specific DMTs significantly influence levels of age-sensitive immune biomarkers associated with MS and must be considered when investigating inflammation-related biomarkers.

Funding: This work was supported by a Grant for Multiple Sclerosis Innovation by Merck KGaA (ID: 10.12039/100009945).

Keywords: Aging; Autoimmunity; Central nervous system; Immunosenescence; Neurological disease.

Fig. 1

Comparison of analyte levels in serum samples from HC, untreated, and treated MS. Violin plots of serum analyte levels in healthy controls (HC, gray), untreated MS (MS untx, blue), and treated MS (MS tx purple) samples. Each dot represents one sample. The median and p values for the Kruskal–Wallis (IL-4, IL-6, IL-10, β-NGF, IL-18, IL-27, TNF-α, bFGF, leptin) or ANOVA (BDNF, EGF) tests comparing the three groups are shown along with the false discovery rate (FDR)-adjusted p-value.

Fig. 2

Data-driven clustering of HC and MS samples based on serum marker levels. Unsupervised clustering of serum samples based on immune marker levels was performed using the 648 samples for which all analyte levels were available. a) Representation of hierarchal clustering; clusters 1–8. The number of samples in each cluster is indicated. b) Frequency of HC (green) and MS (brown) samples from <50 years old (light tone) or ≥50 years old. (dark tone) donors in each cluster. c) Frequency of female (pink) and male (blue) from HC (light tone) and MS (dark tone) samples in each cluster. d) Distribution of samples from HC, untreated MS (Untx MS), and treated MS (Tx MS) by cluster. e) Heatmap showing relative levels of serum markers for the different clusters (data-driven analysis). Color scale legends the z-score, across the clusters, of each analyte median level; analytes are indicated on the x-axis and clusters on the y-axis.

Fig. 3

Impact of MS status on the relation between age and analyte levels. Linear regression models for the relation between age and analyte levels were estimated for each group: HC (black), untreated MS patients (aqua), and DMT-treated MS patients (magenta). Generalized estimating equations (GEE) were used to correct for the clustering of samples within individuals. Non-transformed data were used for BDNF and EGF; log scale values were used for IL-4 and bFGF; square root was used for IL-18 and β-NGF; root of 3 for IL-27, TNF, and leptin; root of 4 was used for IL-6 and IL-10. For each analyte, the beta coefficient, indicating the transformed value of biomarker level change (untransformed for BDNF and EGF) with each increasing year of age, and the 95% confidence intervals (95% CI) are provided. 95% CI that do not cross zero are shown in bold. Graphs show predicted analyte levels (line) with range of predicted values based on the 95% CI (colored area) according to age in CMV-negative females for each group.

Fig. 4

Variations in the immune profile according to DMTs and DMT-response. a) Principal component analysis (PCA) based on analyte levels according to HC, untreated, low-efficacy, moderate efficacy, and high-efficacy DMT. b) Analyte levels (pg/ml) in samples from untreated, or DMT-treated PwMS. The median and p values for the Kruskal–Wallis (IL-4, IL-6, IL-10, β-NGF, IL-18, IL-27, TNF-α, bFGF, leptin) or ANOVA (BDNF, EGF) tests comparing the three groups are shown along with the false discovery rate (FDR)-adjusted p-value. c) Analyte levels (pg/ml) in samples from untreated PwMS who subsequently started a DMT and showed either no sign or sign of treatment failure in the 6–36 months post-DMT initiation. Each dot represents one sample. Adjusted p values ∗ < 0.05; ∗∗p < 0.01; ∗∗∗p < 0.001; ∗∗∗∗p < 0.0001.