Metabolic alterations in multiple sclerosis and the impact of vitamin D supplementation

Abstract

Background: Our goal was to identify changes in the metabolome in multiple sclerosis (MS) and how vitamin D supplementation alters metabolic profiles in MS patients and healthy controls.

Methods: We applied global untargeted metabolomics to plasma from a cross-sectional cohort of age- and sex-matched MS patients and controls and a second longitudinal cohort of MS patients and healthy controls who received 5,000 IU cholecalciferol daily for 90 days. We applied partial least squares discriminant analysis, weighted correlation network analysis (WGCNA), and pathway analysis to the metabolomics data. Generalized estimating equations models were used to assess change in WGCNA-identified module scores or metabolite pathways with vitamin D supplementation.

Results: Utilizing multiple analytical techniques, we identified metabolic alterations in oxidative stress (γ-glutamyl amino acid, glutathione) and xenobiotic metabolism (benzoate, caffeine) in MS patients compared with healthy controls in the first cohort. In the vitamin D supplementation cohort, we identified two sets of metabolites altered differentially between MS patients and healthy controls with vitamin D supplementation. The first included markers of oxidative stress and protein oxidation (P = 0.006), while the second contained lysolipids and fatty acids (P = 0.03).

Conclusions: Using metabolomics, we identified alterations in oxidative stress and xenobiotic metabolism in MS patients and subsequently demonstrated a reduction of oxidative stress markers with vitamin D supplementation in healthy controls but not in MS patients. We demonstrate the utility of metabolomics in identifying aberrant metabolic processes and in monitoring the ability of therapeutic interventions to correct these abnormalities.

Trial registration: ClinicalTrials.gov NCT01667796.

Funding: This study was supported by NIH grant K23 NS067055, grants from the Race to Erase MS, the National Multiple Sclerosis Society, the American Academy of Neurology, and North American Research Committee on Multiple Sclerosis.

Conflict of interest statement

Conflict of interest: P.A. Calabresi reports grants from MedImmune, grants from Biogen, grants from Novartis, grants from Teva, personal fees from Biogen, and personal fees from Vertex outside the submitted work. E.M. Mowry reports grants from Biogen, other support from UpToDate, nonfinancial support from Teva, other support from Sun Pharma, other support from Biogen, and grants from Sanofi Genzyme outside the submitted work.

Figures

Figure 1. Flow chart of study procedures.

This figure depicts design of the study and steps beginning with sample collection for the two cohorts, followed by metabolomics analysis, preprocessing of metabolomics data, and then the various statistical analyses employed: partial least squares discriminant analysis, pathway analysis, and weighted correlation network analysis.

Figure 2. Cross-sectional cohort — partial least squares discriminant analysis of metabolomics data.

(A) The plot depicts separation of the relapsing-remitting multiple sclerosis (RRMS) and healthy control (HC) groups (n = 27 in each group) utilizing the first two components of the partial least squares discriminant analysis (PLS-DA) model. (B) Overview of the PLS-DA model with four components; R2Y was 0.979 and Q2Y was 0.558. (C) Results of the 1,000-fold cross-validation of the PLS-DA model using permutation of the group labels to generate a distribution to derive P values. The P value for R2Y was 0.047 and for Q2Y was 0.001.

Figure 3. Cross-sectional cohort — results of pathway analyses using weighted correlation network analysis.

(A) Box plots of eigen-metabolite levels for green module and brown module that significantly differ between multiple sclerosis (MS) cases and healthy controls (HC) (P = 0.05 for both; based on linear models adjusted for age and sex). Bounds of the box itself represent interquartile range (IQR), while the central line within the box represents the median, and minimum and maximum whiskers represent Q1–Q1.5 × IQR (or the absolute maximum, if smaller) and Q3 + 1.5 × IQR (or the absolute minimum, if larger), respectively. (B) Correlations between metabolites belonging to the green and brown modules. Each pie represents one metabolite within the module, where darker or brighter green or brown hues denote more significant results from the individual tests (P values are listed in Table 2) assessing whether a given metabolite significantly differs between MS patients and HCs. The length of each pie is proportional to the number of correlations a given metabolite has with other metabolites in the green or brown module that exceed 0.45. A connecting line is drawn between the metabolite pies if the metabolite-metabolite correlation is at least 0.45. For example, weighted correlation network analysis (WGCNA) classifies the metabolite glutamate into the green module, and the correlation between maleate and glutamate (classified by WGCNA into the brown module) is ≥0.45. As a result, there is a line connecting the glutamate and maleate pies within the circos plot. We observe some potential intermodular correlation between brown and green modules, where green and brown eigen-metabolites are marginally correlated (Pearson’s r = 0.27; P = 0.045). The contents of these modules are listed in Table 3 and Supplemental Table 1. (C) Relation between metabolite module-membership scores and difference in mean for metabolites between MS cases and HCs. The hue of brown or green color denotes the degree of significance for a test of differences between mean metabolite levels between MS patients and HCs (the darker the hue of green or brown denotes the more significantly different a given metabolite is between MS patients and HCs). Metabolite module-membership scores are derived as the correlation between the overall metabolite module eigen-metabolite score and that of the individual metabolite.

Figure 4. Vitamin D supplementation cohort — results of pathway analyses using weighted correlation network analysis.

Difference in average green metabolite module (A) and red metabolite module (B) changes occurring between multiple sclerosis (MS) cases and healthy controls (HCs). P values are derived from tests of 3-way cross-product between vitamin D status, time, and disease status in generalized estimating equations (GEE) models. (C) Correlations between metabolites belonging to the green and red modules. Each pie represents one metabolite within each of these modules, where darker or brighter green or red hues denote how strongly metabolites changed over time between MS patients and HCs following vitamin D supplementation. The length of each pie is proportional to the number of correlations a given metabolite has with other metabolites in the green or red modules that exceed 0.45. A connecting line is drawn between the metabolite pies if the metabolite-metabolite correlation is at least 0.45. For example, weighted correlation network analysis (WGCNA) classifies the metabolite γ-glutamyl histidine into the green module, and its correlation with γ-glutamyl valine (classified by WGCNA into the green module) is ≥0.45. As a result, there is a line connecting the γ-glutamyl valine and γ-glutamyl histidine pies. In contrast to the modules derived in the cross-sectional cohort (B), we observe no intermodular correlation between green and red modules (Pearson’s r = 0.03; P = 0.71). The contents of these modules are listed in Table 4 and Supplemental Table 2. (D) Relation between metabolite module-membership scores and difference in change in means between MS patients and HCs for the individual metabolite models. The hue of green or red color denotes the degree of significance for a test of differences in the change between MS patients and HCs occurring after vitamin D supplementation for a given metabolite. The darker the red or green hue, the more strongly the metabolite changes differently between MS patients and HCs following vitamin D supplementation. Metabolite module-membership scores are derived as the correlation between the overall metabolite module eigen-metabolite score and that of the individual metabolite.