Ocrelizumab alters the circulating metabolome in people with relapsing–remitting multiple sclerosis
Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
Abstract
Background
Circulating metabolite levels are altered in multiple sclerosis (MS) and are associated with MS severity. However, how metabolic profiles shift following highly efficacious therapies, like ocrelizumab remains unclear.
Objective
Circulating metabolite levels are altered in multiple sclerosis (MS) and are associated with MS severity. However, how metabolic profiles shift following highly efficacious therapies, like ocrelizumab remains unclear. To assess changes in the circulating metabolome produced by ocrelizumab treatment in people with relapsing–remitting MS (RRMS).
Methods
Thirty‐one individuals with RRMS eligible for beginning treatment with ocrelizumab were recruited and followed with demographic, clinical, quality‐of‐life, and global metabolomics data collected at each visit. Modules of highly correlated metabolites were identified using the weighted correlation network analysis approach. Changes in each module's eigenmetabolite values and individual metabolites during the study were evaluated using linear mixed‐effects models.
Results
Patients with a mean age of 40.8 (SD = 10.30) years, and median disease duration of 4.0 (IQR = 8.5) years, were monitored for a median of 3.36 (IQR = 1.43) years. Two out of twelve identified sets of metabolites were altered significantly. The first module mainly contained androgenic and pregnenolone steroids (p‐value <0.001, coefficient: −0.10). The second module primarily consisted of several lysophospholipids, arachidonic acid, some endocannabinoids, and monohydroxy fatty acid metabolites (p‐value = 0.016, coefficient: −0.12), which its reduction was significantly associated with improvement based on overall disability response score (OR 3.09e‐01, 95% CI: 6.83e‐02, 9.09e‐01, p‐value = 3.15E‐02).
Interpretation
In this longitudinal observational study, using a global untargeted metabolomics approach, we showed significant alteration in circulating metabolome in RRMS patients undergoing ocrelizumab treatment. In particular, we observed a significant reduction in metabolites involved in the lysophospholipid pathway, which was associated with patients' improvement.
Article notes
Untitled section
Revised 2024 Jul 9; Received 2024 Mar 21; Accepted 2024 Jul 12; Collection date 2024 Sep.
Introduction
Multiple sclerosis (MS) is a chronic demyelinating disorder of the central nervous system (CNS) with inflammatory and degenerative components. 1 The lack of definitive diagnostic and prognostic biomarkers makes it challenging to monitor disease progression and assess treatment efficacy, highlighting the need for additional biomarkers for early diagnosis and precise monitoring of MS. 2 Metabolomics, a relatively new “omics” field, involves the assessment of levels of small molecules in biological matrices and can reflect changes of the metagenomics, the proteome, the microbiome, and the exposome (environmental factors)—which can provide a better insight in MS pathophysiology.3, 4 Recent advances in metabolomics research have helped define distinct metabolic alterations in the circulating metabolome of people with MS (PwMS) compared to healthy individuals.5, 6 Furthermore, significant changes in the metabolome of MS patients following specific treatments have also been identified.7, 8 Further exploration of the metabolome in PwMS holds the potential to uncover novel insights into the underlying mechanisms of the disease and find diagnostic and prognostic biomarkers. 9
Ocrelizumab, a humanized monoclonal antibody targeting CD20‐positive B cells, has emerged as a highly effective disease‐modifying therapy for various MS subtypes, demonstrating efficacy in reducing relapse rates and delaying disability progression in individuals with relapsing–remitting and progressive forms of the disease.10, 11, 12, 13 While the clinical benefits of ocrelizumab are well‐established, a comprehensive understanding of its impact at the molecular level remains incomplete.
In this study, we aimed to provide a comprehensive analysis of the metabolomic changes that occur in RRMS patients starting ocrelizumab treatment and identify correlations between these changes and disease progression. The identification of the main metabolites influenced by ocrelizumab therapy advances our understanding of its mechanism of action and helps with identifying biomarkers that are indicative of treatment response and disease progression. Moreover, the results of our study can be utilized to develop a targeted assay that would be more feasible to deploy in large‐scale validation studies.
Materials and Methods
Participants
Enrolled participants were chosen from a pool already enrolled or willing to participate in the Johns Hopkins Precision Medicine Center Biobanking initiative, which requires the collection of serum and plasma at 6‐monthly intervals. Those who were diagnosed with relapsing–remitting MS based on the 2017 McDonald Criteria, 14 between the ages of 18 and 75, eligible for starting ocrelizumab treatment as determined by their treating neurologist, able to return for visits to Johns Hopkins MS center every 6 months, and willing to sign the informed consent form were recruited in the study.
Patients were excluded from the study if they had been diagnosed with any other neuroinflammatory or neurodegenerative disorder, had received corticosteroids within 30 days prior to the blood draw, had other significant metabolic comorbidities such as uncontrolled hypothyroidism or diabetes, had previously been treated with Rituximab or other chemotherapy agents, or were likely to switch therapy within the next year.
At baseline, patients completed a visit before starting ocrelizumab and had regular follow‐up visits after initiation. During each study visit, their demographic data and clinical characteristics were collected. Moreover, phlebotomy, and various assessments, including the Expanded Disability Status Scale (EDSS), 15 processing speed test,16, 17 Multiple Sclerosis Functional Composite (MSFC), and18, 19 short form Neuro‐QOL (quality of life) 20 were performed.
Informed consent was obtained from all study participants. Patients could withdraw from the study at any time. The reasons for withdrawal from the study could include but were not limited to patient request, investigator or sponsor discretion, and non‐compliance by the patient, which is defined as the failure to attend scheduled study visits. The research protocol followed in this study was approved by the Johns Hopkins Institutional Review Board.
Statistical analysis
The concentrations of metabolites were obtained from untargeted metabolomics analysis. All identified metabolites were included in the analysis. Metabolites with missing values over 30% were removed, and the remaining missing metabolite values were imputed using the K‐Nearest Neighbors method (10 neighbors were used for each imputation). Finally, the metabolite values were median‐scaled and log‐transformed for normalization.
To identify metabolic pathways associated with ocrelizumab treatment, we used two different types of pathway‐based analyses; first, we grouped metabolites into pathways (with a minimum of five metabolites) based on related biological functions, ranked the obtained p‐values in the linear mixed effect (LME) models of individual metabolites, and performed pathway enrichment analysis using gene set enrichment analysis (GSEA). GSEA was used to detect significantly enriched metabolic pathways over the time of study. To quantify the degree to which a predefined metabolic set is enriched we used normalized enrichment score (NES) which considers variations in metabolic pathway size and metabolomic data characteristics. 19
In an alternative approach, we used weighted gene‐expression correlation network analysis (WGCNA) to cluster highly correlated metabolites and define modules as branches using a correlation network. 20 For constructing the network of the WGCNA, the soft‐threshold power was set at 12. The first principal component of the identified modules known as eigen‐metabolite was used in subsequent analyses. Then we utilized the eigen‐metabolite of each module as the dependent variable, the duration from the baseline visit (measured in years) as the independent variable, in the LME model adjusting for age at the beginning of the study, sex, and DMT history, to assess the change in the identified metabolite modules. Considering that over 90% of participants in our study were Caucasian, we did not adjust our models for race and ethnicity. A detailed covariate selection process is described in Supplementary Methods.
To evaluate metabolites' changes over time in response to ocrelizumab treatment, for each metabolite, we utilized an LME model. To ensure consistency and simplify comparisons between the results of the network correlation analysis (models using module eigen‐metabolites) and individual metabolite analysis, we used the same set of covariates across all models using individual metabolites as response variables. We considered the metabolite level as the dependent variable, the duration from the baseline visit (measured in years) as the independent variable, and adjusted the model for age at baseline, sex, and prior history of disease‐modifying therapy.
To assess the change in disability outcome measures, we compared the baseline and final EDSS of patients using the Wilcoxon Signed‐Rank Test. Additionally, we evaluated the change in MSFC components, including the processing speed test, 9‐Hole Peg Test (9HPT), 25‐Foot Walk Test (25FWT), and neuro‐QOL items over time utilizing the LME model adjusted for variables including age at baseline, sex, and DMT history. Due to the exploratory nature of this analysis, adjustments for multiple comparisons were not made.
To determine if the changes in metabolites were associated with changes in disability outcomes, we used logistic regression models with improvement status as a dependent variable and change in each eigen‐metabolite per standard deviation (SD) as a covariate adjusting for age at baseline, sex, years of follow‐up, and DMT history. To validate our results, we categorized patients as improved and unimproved based on two different methods. In the first method, we utilized thresholds from the EDSS‐Plus method to classify improvement. Improvement was defined as at least a 1‐point decrease from baseline in EDSS score with a baseline EDSS score less than or equal to 5.5 or 0.5‐point decrease with a baseline EDSS score over 5.5 or at least 20% improvement from baseline in either T25FW, dominant or non‐dominant hand 9HPT (9HPT‐ND) at the last study visit compared to baseline. 21 In the second method, we used the Overall Disability Response Score (ODRS) method to define patients' improvement. 22 To calculate the ODRS at the end of the study, we evaluated the change in four components: EDSS, T25FW, dominant hand 9HPT (9HPT‐D), and 9HPT‐ND from the study baseline. For the EDSS, we used the same improvement or worsening threshold as the previous method. For T25FW, 9HPT‐D, and 9HPT‐ND, we considered a reduction of 15% or more and an increase of 15% or more in time from baseline as improvement and worsening thresholds, respectively. For each component, a change that met the improvement threshold was given a score of +1, while a change that met the worsening threshold received a score of −1. We then added the scores for all four components for each patient, resulting in a total score ranging from +4 to −4. A positive score indicates an overall improvement in disability from the baseline. The same logistic model was performed on individual metabolites to estimate the improvement per SD increase in individual metabolites.
To evaluate the relationship between metabolic pathways and patients' self‐reported quality of life, we did two separate analyses; We analyzed the correlation between the changes in eigen‐metabolites and neuro‐QOL items using partial Spearman's correlation adjusting for patients' age at baseline, years of follow‐up, sex, and DMT history. In addition, we used the logistic regression model with the improvement on each item of the neuro‐QOL questionnaire as a dependent variable, and change in each eigen‐metabolite per SD as an independent variable, adjusting for the same covariates. We used the values of previously determined conditional minimal detectable changes for the items of neuro‐QOL to define improvement. 23
Results
Demographic and clinical characteristics of the study cohort
In this study, 31 patients, of whom 21 were women, were monitored for a median (IQR) of 3.35(2.65–4.08) years. The patients had a mean age of 40.8 ± 10.3 years, median (IQR) disease duration of 4 (1–9.5) years, and a mean BMI of 28.1 ± 6.14 kg/m2 at the beginning of the study (Table 1). There were at least three samples per patient, with an average interval of 1 year and 3 months between each sample collection. All samples from all time points (a total of 112 samples) were included in the analysis.
| Baseline | |
| Number of subjects | 31 |
| Age, year | |
| Mean ± SD | 40.84 ± 10.34 |
| Min, max | 19, 61 |
| Female, n (%) | 21 (67.74) |
| Race | |
| Black or African American n (%) | 3 (9.67%) |
| White n (%) | 28 (90.33%) |
| BMI, kg/m2 | |
| Mean ± SD | 28.06 ± 6.13 |
| Min, max | 18.03, 45.24 |
| Disease duration, year | |
| Median (IQR) | 4.0 (1–9.5) |
| Min, max | 1, 22 |
| Previously DMT‐treated patients, n (%) | 22 (70.96) |
| EDSS, median (IQR) | 2 (1.25–3) |
| Follow‐up | |
| Follow‐up duration, year | |
| Median (IQR) | 3.36 (2.65–4.08) |
| Min, max | 1.50, 5.45 |
| Number of improved patients based on ODRS, n (%) | 9 (29.03) |
Compared with pre‐treatment values, EDSS (p‐value = 0.291), MSFC components including the processing speed test (p‐value = 0.151), 25FWT (p‐value = 0.153), 9HPT‐D (p‐value = 0.564), and 9HPT‐ND (p‐value = 0.707) did not change during the study period and patients were overall stable based on disability outcome measures (Fig. 1). Based on the neuro‐QOL questionnaire, patients experienced an improvement in their overall quality of life. This improvement was reflected in various domains of the questionnaire such as a decrease in the level of stigma (p‐value = 0.001) and depression (p‐value = 0.030), and an increase in cognitive performance (p‐value = 0.018) and satisfaction in their social roles (p‐value = 0.039) (Table 2).
| Outcome a | Baseline | End of study | p‐value c |
|---|---|---|---|
| EDSS, median (IQR) d | 2 (1.25–3) | 2 (1.25–4.25) | 0.291 |
| Processing speed test e | 50.48 ± 12.38 | 51.6 ± 15.31 | 0.151 |
| Dominant 9HPT b , d | 28.59 ± 16.16 | 25.38 ± 10.21 | 0.564 |
| Non‐dominant 9HPT b , d | 28.17 ± 11.61 | 26.04 ± 7.25 | 0.707 |
| Twenty‐Five FWT b , d | 6.70 ± 3.50 | 6.48 ± 3.98 | 0.153 |
| Neuro‐QOL anxiety d | 51.93 ± 7.37 | 49.07 ± 6.05 | 0.067 |
| Neuro‐QOL cognitive function e | 45.97 ± 8.04 | 47.87 ± 7.42 | 0.018 |
| Neuro‐QOL depression d | 47.42 ± 7.10 | 45.10 ± 6.29 | 0.030 |
| Neuro‐QOL fatigue d | 48.49 ± 8.80 | 46.60 ± 9.29 | 0.063 |
| Neuro‐QOL ability to participate in social roles e | 47.07 ± 8.33 | 48.53 ± 8.99 | 0.665 |
| Neuro‐QOL satisfaction with social roles e | 47.99 ± 7.47 | 48.62 ± 7.19 | 0.039 |
| Neuro‐QOL sleep disturbance d | 50.30 ± 6.86 | 48.53 ± 7.96 | 0.231 |
| Neuro‐QOL stigma d | 48.84 ± 8.99 | 45.97 ± 9.11 | 0.001 |
| Neuro‐QOL lower extremity e | 49.18 ± 9.89 | 46.37 ± 12.59 | 0.183 |
| Neuro‐QOL upper extremity e | 45.77 ± 9.56 | 47.97 ± 9.47 | 0.962 |
Change in the circulating metabolome following ocrelizumab based on individual metabolites and metabolic pathways
In analyses using individual metabolites, we observed significant alterations in various pregnenolone and androgenic steroids, lysophospholipid (LPL) metabolites, benzoate metabolites, pyrimidine metabolites (uracil containing), serine and lactate, after false discovery rate (FDR)‐adjustment (Table 3). The results of changes in all individual metabolites are in the Table S1.
| Metabolic pathway | Metabolite | Estimate (95% CI) | p‐value b | FDR |
|---|---|---|---|---|
| Pregnenolone steroids | Pregnenediol disulfate (C21H34O8S2) | −9.61e‐02 (−1.29e‐01, −6.32e‐02) | 1.78E‐07 | 1.90E‐04 |
| Chemical | Perfluorooctanesulfonate (PFOS) | −1.45e‐01 (−1.99e‐01, −9.08e‐02) | 1.17E‐06 | 6.23E‐04 |
| Benzoate metabolism | Propyl 4‐hydroxybenzoate sulfate | 5.93e‐01 (3.46e‐01, 8.39e‐01) | 1.02E‐05 | 3.64E‐03 |
| Pyrimidine metabolism, uracil containing | 2′‐deoxyuridine | −6.59e‐02 (−9.41e‐02, −3.77e‐02) | 1.64E‐05 | 4.38E‐03 |
| Pregnenolone steroids | Pregnenetriol disulfate | −7.65e‐02 (−1.12e‐01, −4.14e‐02) | 5.29E‐05 | 1.13E‐02 |
| Pyrimidine metabolism, uracil containing | N‐acetyl‐beta‐alanine | 7.86e‐02 (4.19e‐02, 1.15e‐01) | 6.85E‐05 | 1.22E‐02 |
| Androgenic steroids | Androstenediol (3beta,17beta) disulfate (2) | −7.13e‐02 (−1.06e‐01, −3.68e‐02) | 1.15E‐04 | 1.76E‐02 |
| Glycine, serine and threonine metabolism | Serine | −3.95e‐02 (−5.89e‐02, −2.01e‐02) | 1.43E‐04 | 1.92E‐02 |
| Glycolysis, gluconeogenesis, and pyruvate metabolism | Lactate | −5.38e‐02 (−8.07e‐02, −2.69e‐02) | 1.87E‐04 | 2.16E‐02 |
| Chemical | Perfluorooctanoate (PFOA) | −1.11e‐01 (−1.68e‐01, −5.49e‐02) | 2.20E‐04 | 2.16E‐02 |
| Lysophospholipid | 1‐linoleoyl‐GPA (18:2) | −9.76e‐02 (−1.47e‐01, −4.79e‐02) | 2.39E‐04 | 2.16E‐02 |
| Secondary bile acid metabolism | Taurocholenate sulfate | −1.02e‐01 (−1.54e‐01, −5e‐02) | 2.43E‐04 | 2.16E‐02 |
Results were also consistent in pathway‐based analyses. Pathway enrichment analysis, identified benzoate metabolism, androgenic and pregnenolone steroids, xanthine metabolism, sphingomyelins, and fatty acid metabolism (Acyl choline, Acyl carnitine, monohydroxy, and Hexosylceramides) as the most significantly changed metabolic pathways (Fig. 2). Based on WGCNA analysis, metabolites were clustered into 12 modules, of which green and magenta modules were altered significantly following ocrelizumab treatment based on the LME model. The green module mainly contained androgenic and pregnenolone steroids (p‐value <0.001, coefficient: −0.10). The magenta module primarily consisted of several LPLs, some endocannabinoid metabolites, monohydroxy fatty acid metabolites, and arachidonic acid (p‐value = 0.016, coefficient: −0.12) (Table 4). The complete contents of the magenta and green modules are listed in the Table S2.
| Module | Metabolite | Linear mixed effect model results for individual metabolite change following treatment | ||
|---|---|---|---|---|
| MM a | Estimate (95% CI) | p‐value b | ||
| Magenta | 2‐palmitoyl‐GPC (16:0) | 0.85 | −4.2e‐02 (−7.61e‐02, −7.82e‐03) | 1.83E‐02 |
| 2‐hydroxystearate | 0.78 | −3.06e‐02 (−5.64e‐02, −4.92e‐03) | 2.21E‐02 | |
| 1‐stearoyl−2‐arachidonoyl‐GPI (18:0/20:4) | 0.70 | −2.33e‐02 (−3.87e‐02, −7.89e‐03) | 4.00E‐03 | |
| N‐stearoyltaurine | 0.65 | −6.32e‐02 (−1.05e‐01, −2.11e‐02) | 4.29E‐03 | |
| Spermidine | 0.44 | −6.6e‐02 (−1.12e‐01, −2.03e‐02) | 5.91E‐03 | |
| Glutamine | 0.43 | −2.04e‐02 (−3.63e‐02, −4.51e‐03) | 1.39E‐02 | |
| 2‐arachidonoylglycerol (20:4) | 0.38 | −6.22e‐02 (−1.16e‐01, −8.76e‐03) | 2.52E‐02 | |
| S‐carboxymethyl‐cysteine | 0.36 | −5.13e‐02 (−9.8e‐02, −4.65e‐03) | 3.42E‐02 | |
| 2‐hydroxyarachidate | 0.34 | −7.35e‐02 (−1.22e‐01, −2.5e‐02) | 3.94E‐03 | |
| Green | Androstenediol (3beta,17beta) disulfate (2) | 0.93 | −7.13e‐02 (−1.06e‐01, −3.68e‐02) | 1.15E‐04 |
| 21‐hydroxypregnenolone disulfate | 0.92 | −5.96e‐02 (−9.15e‐02, −2.78e‐02) | 4.39E‐04 | |
| Pregnenediol sulfate (C21H34O5S) | 0.92 | −5e‐02 (−9.11e‐02, −8.84e‐03) | 1.96E‐02 | |
| Dehydroepiandrosterone sulfate (DHEA‐S) | 0.91 | −6.2e‐02 (−1.05e‐01, −1.93e‐02) | 5.62E‐03 | |
| Pregnenetriol sulfate | 0.91 | −5.59e‐02 (−1.05e‐01, −6.53e‐03) | 2.93E‐02 | |
| Pregnenolone sulfate | 0.89 | −7.68e‐02 (−1.23e‐01, −3.06e‐02) | 1.64E‐03 | |
| Pregnenediol disulfate (C21H34O8S2) | 0.87 | −9.61e‐02 (−1.29e‐01, −6.32e‐02) | 1.78E‐07 | |
| Androstenediol (3beta,17beta) disulfate (1) | 0.86 | −7.91e‐02 (−1.2e‐01, −3.76e‐02) | 3.44E‐04 | |
| Androstenediol (3beta,17beta) monosulfate (1) | 0.85 | −6.97e‐02 (−1.2e‐01, −1.9e‐02) | 8.58E‐03 | |
| Epiandrosterone sulfate | 0.84 | −6.84e‐02 (−1.35e‐01, −1.63e‐03) | 4.80E‐02 | |
| 5alpha‐androstan‐3beta,17beta‐diol disulfate | 0.82 | −7.98e‐02 (−1.48e‐01, −1.11e‐02) | 2.55E‐02 | |
| Androsterone glucuronide | 0.82 | −5.62e‐02 (−1.04e‐01, −8.64e‐03) | 2.31E‐02 | |
Association of metabolome alteration with disability outcome measures and quality of life
To investigate the relationship between alterations in the circulating metabolome and patients' disease status, individuals were classified into improved and unimproved (containing both stable and deteriorating patients) groups. Nine patients were classified as improved based on the ODRS method. The result of the logistic regression model showed that only a change in the magenta eigen‐metabolite was significantly associated with the improvement of patients (OR 3.09e‐01, 95% CI: 6.83e‐02, 9.09e‐01, p‐value = 3.15E‐02) (Table S3). The same result was obtained when improvement was defined using the EDSS‐Plus, with eight patients being classified as improved (OR 6.11e‐02, 95% CI: 2.2e‐03, 4.46e‐01, p‐value = 2.68e‐02). The green eigen‐metabolite value was correlated with age (Coefficient: −0.58, p‐value <0.001), and its reduction was seen in both improved and unimproved patients, regardless of the disease status during the study. However, the reduction of the magenta eigen‐metabolite was exclusively observed in improved patients (Fig. 3, p‐value = 0.012). Using similar analyses assessing the association between individual metabolites and improvement, we observed that reductions in several LPL metabolites were strongly associated with improvement independent of age, sex, DMT history, and years of follow‐up (Table S4, Fig. 4).
Analysis of neuro‐QOL results aiming to investigate the relationship between quality of life and metabolic pathways showed that changes in the tan module (containing phosphatidylinositols and phosphatidylcholines) and turquoise module (containing long‐chain saturated and unsaturated fatty acids) eigen‐metabolites were correlated with change in patients' reported quality of life (Figure 5).
Analyses using logistic regression to identify associations with quality of life improvement demonstrated an increase in the tan module eigen‐metabolite was associated with the improvement of anxiety (OR 7.54e+00, 95% CI: 1.14e+00, 1.06e+02, p‐value = 3.44e‐02) and fatigue (OR 5.23e+00, 95% CI: 1.19e+00, 4.14e+01, p‐value = 2.62e‐02) in patients. Turquoise module (OR 2.2e+00, 95% CI: 1.04e+00, 6.26e+00, p‐value = 3.9e‐02), green module (OR 7.96e+00, 95% CI: 1.14e+00, 1.07e+02, p‐value = 3.48e‐02), and black module (mainly contained purine, pyrimidine, and amino acids metabolisms) (OR 3.14e+00, 95% CI: 1.12e+00, 1.24e+01, p‐value = 2.78e‐02) eigen‐metabolite were other factors associated with fatigue improvement.
Discussion
This longitudinal observational study demonstrated significant alterations in the metabolome following ocrelizumab treatment in patients with RRMS using analyses based on either individual metabolites or metabolic pathways. The results of both the WGCNA pathway analysis and the individual metabolite analysis revealed significant changes in steroid metabolites and metabolites involved in LPL and monohydroxy fatty acid metabolism. Changes in lipid metabolism were significantly associated with changes in the quality of life and disability status of patients. In particular, the concentration of LPL metabolites was found to be a potential factor associated with improvement in patients' disease status.
Our primary analysis demonstrated that ocrelizumab treatment reduced the levels of multiple LPLs and monohydroxy fatty acids. Altered levels of various metabolites in lipid metabolism have been observed in PwMS.24, 25, 26 Notably, distinct changes in lipid metabolites were observed with dimethyl fumarate (DMF) compared to ocrelizumab treatment in patients with RRMS. Specifically, reduced circulating levels of fatty acid metabolites following DMF treatment were correlated with immune alterations. Additionally, increased levels of phospholipids, plasmalogens, and LPLs were observed, but these changes did not show any significant association with immune alterations. 7 LPLs, especially lysophosphatidylcholines (LPC) have pro‐inflammatory, and cytotoxic characteristics by several mechanisms27, 28; They increase the production of inflammatory mediators, 29 suppress the function of naturally occurring regulatory T cells (nTregs),30, 31 enhance the production of reactive oxygen species (ROS), 32 and induce the chemotaxis in T cells and natural killer cells.33, 34 The production of chemotactic factors and increased generation of ROS was found to be associated with saturated LPC16:0 and LPC18:032, 35; Notably, the reduction in LPC16:0 was associated with patients' improvement in our study. Given the demonstrated pro‐inflammatory properties of LPLs and their role in exacerbating the inflammatory cascade, decreased inflammation and stability in patients of our study could be partially attributed to the reduction of LPLs.
Increased activation of phospholipase A2 (PLA2) during inflammation leads to LPC and arachidonic acid (AA) production from phospholipids of the myelin sheath.36, 37, 38 LPCs induce oligodendrocyte demyelination leading to the exacerbation of demyelination.39, 40 Therefore, it is logical to see the reduction of LPCs together with the reduction of inflammation and demyelination. LPCs can induce proinflammatory activation of monocytes by initiating two signaling pathways including cPLA2, leading to increased release of AA.41, 42 Given that WGCNA analysis clusters metabolites that are related functionally, AA being clustered with other LPLs in the magenta module could be due to this important pathway. AA which is a polyunsaturated fatty acid that exists abundantly in the CNS and its pathway and derivative lipid mediators are shown to be associated with MS severity by their inflammatory and demyelinating properties.43, 44, 45, 46 The reduction of arachidonic acid together with several arachidonoyl LPCs highlights the important role of these metabolites in potentially exacerbating inflammation and progression in MS.
The treatment also impacted other metabolic pathways that were previously shown to be altered in MS. We saw increased levels of benzoate metabolites following treatment, and this increase was associated with patients' improvement. Reduced benzoate metabolites in PwMS compared to healthy people were observed in previous studies.5, 18 Although the exact role of benzoate and its metabolites is not clear, the neuroprotective property of sodium benzoate was shown in other neurological disorders including Parkinson's disease, Alzheimer's disease, and traumatic brain injury.47, 48, 49, 50 Moreover, higher sodium benzoate levels have recently been shown to be associated with the enrichment of proteins involved in lipid metabolism and immune responses in Alzheimer's disease. 51
We also saw a notable reduction in androgenic and pregnenolone steroids. Many of the metabolites belonging to this group are neurosteroids which are known to possess immunomodulatory and neuroprotective properties. 52 Impaired neurosteroid synthesis and its possible role in the neuroinflammatory process of MS have been demonstrated in studies.52, 53, 54 Since we hypothesized that the metabolome potentially normalizes following ocrelizumab, in this case, ocrelizumab was not effective in correcting the impaired steroidogenesis observed in MS. In our primary analysis, we observed a negative correlation between steroid levels and age. Given that most of our participants were females in their 40s, in their perimenopause stage, and our study had a long follow‐up duration, another likely explanation for the observed reduction in steroid levels could be attributed to the natural aging process, particularly as individuals approach menopausal age.55, 56
Two modules within our study, containing phospholipids and fatty acids, were correlated with improvement in patients' quality of life measures. This correlation could be mediated through various metabolites in these modules, known for their established anti‐inflammatory effects. For instance, docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA), and docosapentaenoic acid (DPA) and their metabolites in these modules are recognized for anti‐inflammatory and neuroprotective properties,57, 58, 59, 60 which could be associated with improved quality of life. However, since these modules contain several kinds of phospholipids and fatty acids, making a precise determination of how their increase was associated with quality of life is challenging. The apparent dissociation between the metabolites associated with quality of life and those associated with disability measures could arise from two possible sources. The first is that there is a distinct biological basis for these relationships with different metabolites being linked to domains measured in quality of life like cognition and mood. Alternatively, given our limited sample size, this could be a function of inadequate power to detect a relationship of the tan and turquoise modules with disability outcomes or to detect a relationship between the magenta module and quality of life outcomes.
Although we conducted a longitudinal assessment of the effect ocrelizumab had on the circulating metabolome and addressed the limitation of cross‐sectional analyses, there are a few noteworthy limitations to our study. One significant limitation of our study is the absence of a control group, specifically the lack of a control group comprising PwMS using DMTs other than ocrelizumab, and/or healthy individuals. The inclusion of such a control group could have helped detect age, and treatment‐related alterations in the metabolome, as well as to differentiate metabolomic changes attributed specifically to the impact of peripheral B cell depletion versus a non‐specific decrease in MS disease activity. Moreover, at the time of starting our study, ocrelizumab was the only B‐cell depleting therapy approved for multiple sclerosis, and hence we could not compare metabolomic changes across different B‐cell depleting agents. Based on similar mechanisms of action of B‐cell depleting therapies, we would not expect significant differences in the overall impact on circulating metabolites between these agents. However, future research should explore the metabolomic effects of other B‐cell depleting therapies to validate and expand upon our findings.
Additionally, since we only included patients with RRMS and did not investigate the progressive forms of MS, we are unable to generalize our results on all MS types. Another limitation is that although we included all identified metabolites that were robustly detected in our analyses, we were unable to assess the association of short‐chain fatty acids (SCFAs) with disease status. This limitation arises because the global untargeted metabolomics method we employed is not optimized for detecting SCFAs, which requires a targeted methodology for accurate identification and quantification. Moreover, the relatively small sample size limited our ability to conduct sensitivity analyses. As a result, we were unable to separately evaluate the effect of the history of each type of DMT prior to ocrelizumab initiation on metabolome changes. A larger sample size would also enable us to perform longitudinal mediation analysis, providing valuable insights into the underlying mechanisms of the effect of ocrelizumab on the metabolome and outcomes, as well as a sensitive analysis by classifying patients as deteriorating or otherwise (stable plus improving). To address these concerns properly, future longitudinal studies with control groups and larger sample sizes are necessary.
Conclusion
In this longitudinal observational study, we demonstrated significant circulating metabolome alterations in RRMS patients as a result of ocrelizumab treatment using a global untargeted metabolomics approach. In particular, metabolites involved in the lysophospholipid pathway were reduced significantly, which was associated with improvement in patients' disability measures. The findings of our study serve as a rationale for future follow‐up studies to develop biomarkers for prognosis and treatment response to B cell‐depleting therapies.
Conflict of Interest
Dr. Bhargava has received honoraria from EMD‐Serono and Genentech, and funding support from EMD‐Serono, Genentech, GSK, and Amylyx Pharmaceuticals outside the submitted work. Dr. Nourbakhsh has received grants from the US Department of Defense, Genentech, the National Multiple Sclerosis Society, and personal fees for Alkermes, honoraria from TG Therapeutics outside the submitted work. Dr. Siavoshi, Dr. Ladakis, and Ms. Muller report no disclosures.
Supporting information
Acknowledgments
We gratefully acknowledge funding support for this work provided by an Investigator Initiated Grant from Genentech to Pavan Bhargava and a Harry Weaver Neuroscience Scholar Award from the National Multiple Sclerosis Society to Pavan Bhargava.
Funding Statement
This work was funded by Genentech ; National Multiple Sclerosis Society .
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.
References
Untitled section
References
- 1. Reich DS, Lucchinetti CF, Calabresi PA. Multiple Sclerosis. N Engl J Med. 2018;378(2):169‐180.
- 2. Gill AJ, Schorr EM, Gadani SP, Calabresi PA. Emerging imaging and liquid biomarkers in multiple sclerosis. Eur J Immunol. 2023;53(8):2250228.
- 3. Bhargava P, Calabresi PA. Metabolomics in multiple sclerosis. Mult Scler. 2016;22(4):451‐460.
- 4. Bhargava P, Anthony DC. Metabolomics in multiple sclerosis disease course and progression. Mult Scler J. 2020;26(5):591‐598.
- 5. Fitzgerald KC, Smith MD, Kim S, et al. Multi‐omic evaluation of metabolic alterations in multiple sclerosis identifies shifts in aromatic amino acid metabolism. Cell Rep Med. 2021;2(10):100424.
- 6. Shi T, Browne RW, Tamaño‐Blanco M, et al. Metabolomic profiles in relapsing–remitting and progressive multiple sclerosis compared to healthy controls: a five‐year follow‐up study. Metabolomics. 2023;19(5):44. doi: 10.1007/s11306-023-02010-0
- 7. Bhargava P, Fitzgerald KC, Venkata SLV, et al. Dimethyl fumarate treatment induces lipid metabolism alterations that are linked to immunological changes. Ann Clin Transl Neurol. 2019;6(1):33‐45.
- 8. Bhargava P, Steele SU, Waubant E, et al. Multiple sclerosis patients have a diminished serologic response to vitamin D supplementation compared to healthy controls. Mult Scler. 2016;22(6):753‐760.
- 9. Zahoor I, Rui B, Khan J, Datta I, Giri S. An emerging potential of metabolomics in multiple sclerosis: a comprehensive overview. Cell Mol Life Sci. 2021;78(7):3181‐3203.
- 10. Capasso N, Palladino R, Cerbone V, et al. Ocrelizumab effect on humoral and cellular immunity in multiple sclerosis and its clinical correlates: a 3‐year observational study. J Neurol. 2023;270(1):272‐282.
- 11. Hauser SL, Bar‐Or A, Comi G, et al. Ocrelizumab versus interferon Beta‐1a in relapsing multiple sclerosis. N Engl J Med. 2017;376(3):221‐234.
- 12. Ellwardt E, Rolfes L, Klein J, et al. Ocrelizumab initiation in patients with MS: a multicenter observational study. Neurol Neuroimmunol Neuroinflamm. 2020;7(4):e719.
- 13. Montalban X, Hauser SL, Kappos L, et al. Ocrelizumab versus placebo in primary progressive multiple sclerosis. N Engl J Med. 2017;376(3):209‐220.
- 14. Thompson AJ, Banwell BL, Barkhof F, et al. Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol. 2018;17(2):162‐173. https://linkinghub.elsevier.com/retrieve/pii/S1474442217304702
- 15. Kurtzke JF. Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology. 1983;33(11):1444‐1452.
- 16. Rao SM, Losinski G, Mourany L, et al. Processing speed test: validation of a self‐administered, iPad®‐based tool for screening cognitive dysfunction in a clinic setting. Mult Scler. 2017;23(14):1929‐1937.
- 17. Fischer JS, Rudick RA, Cutter GR, Reingold SC, National MS Society Clinical Outcomes Assessment Task Force . The multiple sclerosis functional composite measure (MSFC): an integrated approach to MS clinical outcome assessment. Mult Scler J. 1999;5(4):244‐250.
- 18. Bhargava P, Fitzgerald KC, Calabresi PA, Mowry EM. Metabolic alterations in multiple sclerosis and the impact of vitamin D supplementation. JCI Insight. 2017;2(19):e95302.
- 19. Subramanian A, Tamayo P, Mootha VK, et al. Gene set enrichment analysis: a knowledge‐based approach for interpreting genome‐wide expression profiles. Proc Natl Acad Sci. 2005;102(43):15545‐15550.
- 20. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9:1‐13.
- 21. Cadavid D, Cohen JA, Freedman MS, et al. The EDSS‐plus, an improved endpoint for disability progression in secondary progressive multiple sclerosis. Mult Scler. 2017;23(1):94‐105.
- 22. Chang I, Kappos L, Giovannoni G, et al. Overall disability response score: an integrated endpoint to assess disability improvement and worsening over time in patients with multiple sclerosis. Mult Scler J. 2022;28(14):2263‐2273.
- 23. Kozlowski AJ, Cella D, Nitsch KP, Heinemann AW. Evaluating individual change with the quality of life in neurological disorders (neuro‐QoL) short forms. Arch Phys Med Rehabil. 2016;97(4):650‐654.e8.
- 24. Porter L, Shoushtarizadeh A, Jelinek GA, et al. Metabolomic biomarkers of multiple sclerosis: a systematic review. Front Mol Biosci. 2020;7:574133.
- 25. Ferreira HB, Melo T, Monteiro A, Paiva A, Domingues P, Domingues MR. Serum phospholipidomics reveals altered lipid profile and promising biomarkers in multiple sclerosis. Arch Biochem Biophys. 2021;697:108672.
- 26. Zahoor I, Suhail H, Datta I, et al. Blood‐based untargeted metabolomics in relapsing‐remitting multiple sclerosis revealed the testable therapeutic target. Proc Natl Acad Sci. 2022;119(25):e2123265119.
- 27. Tan ST, Ramesh T, Toh XR, Nguyen LN. Emerging roles of lysophospholipids in health and disease. Prog Lipid Res. 2020;80:101068.
- 28. Aiyar N, Disa J, Ao Z, et al. Lysophosphatidylcholine induces inflammatory activation of human coronary artery smooth muscle cells. Mol Cell Biochem. 2007;295:113‐120.
- 29. Sevastou I, Kaffe E, Mouratis MA, Aidinis V. Lysoglycerophospholipids in chronic inflammatory disorders: the PLA 2/LPC and ATX/LPA axes. Biochim Biophys Acta Mol Cell Biol Lipids. 2013;1831(1):42‐60.
- 30. Li X, Wang L, Fang P, et al. Lysophospholipids induce innate immune transdifferentiation of endothelial cells, resulting in prolonged endothelial activation. J Biol Chem. 2018;293(28):11033‐11045.
- 31. Hasegawa H, Lei J, Matsumoto T, Onishi S, Suemori K, Yasukawa M. Lysophosphatidylcholine enhances the suppressive function of human naturally occurring regulatory T cells through TGF‐β production. Biochem Biophys Res Commun. 2011;415(3):526‐531.
- 32. Ojala PJ, Hirvonen TE, Hermansson M, Somerharju P, Parkkinen J. Acyl chain‐dependent effect of lysophosphatidylcholine on human neutrophils. J Leukoc Biol. 2007;82(6):1501‐1509.
- 33. Park KS, Lee HY, Lee SY, et al. Lysophosphatidylethanolamine stimulates chemotactic migration and cellular invasion in SK‐OV3 human ovarian cancer cells: involvement of pertussis toxin‐sensitive G‐protein coupled receptor. FEBS Lett. 2007;581(23):4411‐4416.
- 34. Radu CG, Yang LV, Riedinger M, Au M, Witte ON. T cell chemotaxis to lysophosphatidylcholine through the G2A receptor. Proc Natl Acad Sci. 2004;101(1):245‐250.
- 35. Li X, Fang P, Li Y, et al. Mitochondrial reactive oxygen species mediate lysophosphatidylcholine‐induced endothelial cell activation. Arterioscler Thromb Vasc Biol. 2016;36(6):1090‐1100.
- 36. De S, Trigueros MA, Kalyvas A, David S. Phospholipase A2 plays an important role in myelin breakdown and phagocytosis during wallerian degeneration. Mol Cell Neurosci. 2003;24(3):753‐765.
- 37. Zhang F, Sha J, Wood TG, et al. Alteration in the activation state of new inflammation‐associated targets by phospholipase A2‐activating protein (PLAA). Cell Signal. 2008;20(5):844‐861.
- 38. Dinnes DLM, Paul Santerre J, Labow RS. Phospholipase a 2 pathway association with macrophage‐mediated polycarbonate‐urethane biodegradation. Biomaterials. 2005;26(18):3881‐3889.
- 39. Plemel JR, Michaels NJ, Weishaupt N, et al. Mechanisms of lysophosphatidylcholine‐induced demyelination: a primary lipid disrupting myelinopathy. Glia. 2018;66(2):327‐347.
- 40. Hall SM. The effect of injections of lysophosphatidyl choline into white matter of the adult mouse spinal cord. J Cell Sci. 1972;10(2):535‐546.
- 41. Oestvang J, Anthonsen MW, Johansen B. LysoPC and PAF trigger arachidonic acid release by divergent signaling mechanisms in monocytes. J Lipids. 2011;2011:1‐11.
- 42. Wong JT, Tran K, Pierce GN, Chan AC, O K, Choy PC. Lysophosphtidylcholine stimulates the release of arachidonic acid in human endothelial cells. J Biol Chem. 1998;273(12):6830‐6836.
- 43. Broos JY, Loonstra FC, De Ruiter LRJ, et al. Association of arachidonic acid‐derived lipid mediators with disease severity in patients with relapsing and progressive multiple sclerosis. Neurology. 2023;101(5):E533‐E545.
- 44. Zhang Y, Liu Y, Sun J, Zhang W, Guo Z, Ma Q. Arachidonic acid metabolism in health and disease. MedComm (Beijing). 2023;4(5):e363.
- 45. Palumbo S. Pathogenesis and progression of multiple sclerosis: the role of arachidonic acid‐mediated neuroinflammation. Multiple Sclerosis: Perspectives in Treatment and Pathogenesis. Exon Publications; 2017:111‐123.
- 46. Broos JY, van der Burgt RTM, Konings J, et al. Arachidonic acid‐derived lipid mediators in multiple sclerosis pathogenesis: fueling or dampening disease progression? J Neuroinflammation. 2024;21(1):21.
- 47. Khasnavis S, Pahan K. Cinnamon treatment upregulates neuroprotective proteins Parkin and DJ‐1 and protects dopaminergic neurons in a mouse model of Parkinson's disease. J Neuroimmune Pharmacol. 2014;9(4):569‐581.
- 48. Xu W, Li T, Gao L, et al. Sodium benzoate attenuates secondary brain injury by inhibiting neuronal apoptosis and reducing mitochondria‐mediated oxidative stress in a rat model of intracerebral hemorrhage: possible involvement of DJ‐1/Akt/IKK/NFκB pathway. Front Mol Neurosci. 2019;12:105.
- 49. Lin C‐H, Chen P‐K, Wang S‐H, Lane H‐Y. Effect of sodium benzoate on cognitive function among patients with behavioral and psychological symptoms of dementia: secondary analysis of a randomized clinical trial. JAMA Netw Open. 2021;4(4):e216156.
- 50. Rangasamy SB, Raha S, Dasarathy S, Pahan K. Sodium benzoate, a metabolite of cinnamon and a food additive, improves cognitive functions in mice after controlled cortical impact injury. Int J Mol Sci. 2021;23(1):192.
- 51. Lin CH, Liao HY, Lane HY, Chen CJ. Elucidating the mechanisms of sodium benzoate in Alzheimer disease: insights from quantitative proteomics analysis of serum samples. Int J Neuropsychopharmacol. 2023;26(12):856‐866.
- 52. Yilmaz C, Karali K, Fodelianaki G, et al. Neurosteroids as regulators of neuroinflammation. Front Neuroendocrinol. 2019;55:100788.
- 53. Garay LI, Meyer M, Guennoun R, Schumacher M, Gonzalez Deniselle MC, de Nicola AF. Sex steroids, neurosteroidogenesis, and inflammation in multiple sclerosis and related animal models. Curr Opin Endocr Metab Res. 2021;21:100286.
- 54. Noorbakhsh F, Ellestad KK, Maingat F, et al. Impaired neurosteroid synthesis in multiple sclerosis. Brain. 2011;134(9):2703‐2721.
- 55. Fernandez N, Petit A, Pianos A, et al. Aging is associated with lower neuroactive steroids and worsened outcomes following cerebral ischemia in male mice. Endocrinology. 2023;164(1):bqac183.
- 56. Hill M, Lukáč D, Lapčík O, et al. Age relationships and sex differences in serum levels of pregnenolone and 17‐hydroxypregnenolone in normal subjects. Clin Chem Lab Med. 1999;37(4):439‐447. doi: 10.1515/CCLM.1999.072
- 57. Si TL, Liu Q, Ren YF, et al. Enhanced anti‐inflammatory effects of DHA and quercetin in lipopolysaccharide‐induced RAW264.7 macrophages by inhibiting NF‐κB and MAPK activation. Mol Med Rep. 2016;14(1):499‐508.
- 58. Zheng Z, Dai Z, Cao Y, Shen Q, Zhang Y. Docosapentaenoic acid (DPA, 22:5n‐3) ameliorates inflammation in an ulcerative colitis model. Food Funct. 2019;10(7):4199‐4209.
- 59. Crupi R, Cuzzocrea S. Role of EPA in inflammation: mechanisms, effects, and clinical relevance. Biomolecules. 2022;12(2):242.
- 60. Tian Y, Katsuki A, Romanazzi D, et al. Docosapentaenoic acid (22: 5n‐3) downregulates mRNA expression of pro‐inflammatory factors in LPS‐activated murine macrophage like RAW264. 7 cells. J Oleo Sci. 2017;66(10):1149‐1156.
Associated Data
Supplementary Materials
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.