Metabolomic and Lipid Alterations are Associated with Longitudinal Neurological Performance in Multiple Sclerosis

Metabolomic and Lipid Alterations are Associated with Longitudinal Neurological Performance in Multiple Sclerosis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Metabolomic and Lipid Alterations are Associated with Longitudinal Neurological Performance in Multiple Sclerosis Rose Noroozi, Hui-Hsin Tsai, Ketian Yu, Karunakar Samuel, Kien Trinh, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7078057/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Metabolomics incorporates information from multiple biological systems and is recognized as an emerging tool for identifying novel multiple sclerosis (MS) biomarkers. While prior studies have linked metabolic alterations to the disease severity in people with MS (pwMS), a longitudinal approach enables a significant advantage to study this relationship over disease progression. Therefore, this study aims to identify metabolomic signatures associated with longitudinal outcomes in pwMS. We performed a multi-site study, profiling the serum metabolome (Biocrates Inc.) from participants for the MS Partners Advancing Technology and Health Solutions (MS PATHS) network. Outcomes, including 25-foot walking speed, manual dexterity, and processing speed, were quantified using the iPad®-based Multiple Sclerosis Performance Test (MSPT). We applied generalized estimating equation regression models (adjusted for potential confounders) to assess the association of 517 metabolites at baseline with longitudinal assessments of MSPT component tests. We performed network, pathway enrichment, and MetaboIndicator analyses to infer biological insights from our findings. This study included 767 pwMS (mean age 44.9 [SD: 11.4]; 72.1% relapsing-remitting MS; 72.9% female; 11.8% non-white), who had an average of 7.0 (SD = 4.57) MSPT measures per person over an average follow-up period of 3.0 (SD = 1.25) years. Certain metabolites were associated with MSPT outcomes over time. For example, a 1 SD decrease in Phosphatidyl-choline aa C36:6 (PC aa 36:6) level was associated with a 9.3% decline in walking speed performance (95% CI: 6.7–11.9; FDR-adjusted p = 8.9E-09) and a 5.4% reduction in manual dexterity performance (95% CI: 3.3–7.4; FDR-adjusted p = 9.6E-05). Metabolite set enrichment and MetaboIndicator analyses pointed to pathways involved in polyunsaturated fatty acid (PUFA) metabolism and suggested altered enzymatic activities, such as increased phospholipase A2 (PLA2) activity. Leveraging a large longitudinal cohort, our findings suggest a potential role of altered lipid metabolism in the progression of MS. Multiple Sclerosis Metabolomics Lipidomics Neurological functioning Cognitive testing Biofluid Biomarkers Figures Figure 1 Figure 2 Figure 3 Introduction Multiple sclerosis (MS) is a complex neuroinflammatory disorder[ 1 , 2 ], characterized by varying disease progression trajectories. The underlying pathobiology of the heterogeneity in MS course is not well understood, with several different genetic and environmental factors suggested to affect MS outcomes. This complexity limits our ability to effectively predict long-term outcome evolution for people with MS (pwMS) using currently available biomarkers. Metabolomics, the comprehensive profiling of circulating metabolites, provides a promising approach to identifying candidate biomarkers in MS, as it captures a dynamic interplay of biological processes. Metabolite alterations, shaped by both genetic and environmental factors, may have the potential to serve as prognostic biomarkers and also provide insights into possible biological mechanisms contributing to the MS course. Previous studies have identified potential metabolites associated with MS risk and severity[ 3 , 4 ]; however, most were cross-sectional comparisons of the metabolome between pwMS and healthy people and often only considered a singular measure of MS disability. For example, in a previous study, our team reported the disruptions in aromatic amino acid (AAA) metabolism in pwMS and linked altered tyrosine metabolism with increased disability in MS[ 3 ]. Additionally, our previous cross-sectional analysis has identified lipidomic alterations linked to disease severity, including dysregulation in the glycerolipid metabolism-related pathways[ 5 ]. Therefore, longitudinal studies that include multiple outcome measures are essential for identifying how metabolic changes correlate with disease worsening. To address this gap, we examined changes in the serum levels of a broad set of metabolites and lipids as they relate to objective neuroperformance outcomes in a large multinational cohort of pwMS. Material and methods Participants and sampling We include pwMS from the Multiple Sclerosis Partners Advancing Technology and Health Solutions (MS PATHS, V.16.0) network, which is a consortium of MS centers in the United States (7) and Europe (3) [ 6 ]. MS PATHS was formerly sponsored by Biogen. MS PATHS was designed to model a learning health system in which data are collected on participants as a part of routine clinical care using an iPad-based questionnaire and objective assessment of neuroperformance via the MS Performance Test (MSPT). Participants provide their demographic information, such as age, sex, ethnicity, and race, as well as disease details, including age at symptom onset, MS type, use of disease-modifying therapies, and self-reported disability using the Patient Determined Disease Steps (PDDS) scale[ 7 ]. The MSPT is modeled after the MS functional composite (MSFC) and includes the following components: (1) processing speed test (PST), which is similar to the Symbol Digit Modalities Test (SDMT) where higher scores indicate better cognition; (2) the Manual Dexterity Test (MDT), a tablet-based timed 9-hole Peg test to assess the upper extremity motor function for which higher recorded time reflect worse dexterity; and (3) the Walking Speed Test (WST), which is a 25-foot timed walking test to assess lower extremity motor function where higher recorded time indicate slower, worse, walking speed performance. Other characteristics, such as height and weight, to calculate body mass index (BMI), as well as smoking status, are automatically abstracted from the electronic health records. Data collection efforts were approved by the institutional review boards at each participating institution, and participants provided informed consent for the collection and sharing of data. Assessment and quality control of metabolomic profiles As part of the MS PATHS network, participants are given the option to participate in an optional biobanking study. Non-fasted blood samples are collected and processed under a standardized protocol and shipped to a centralized repository for storage at -80°C. Time of sample collection and time of last meal are recorded for each draw. We then measured serum levels of 517 targeted metabolites from the first biobanked sample for a subset of MS PATHS participants using liquid chromatography-mass spectrometry (LC-MS) at Biocrates Inc. [ 8 ], applying the MxP® Quant 500 assay, eicosanoid assay, and oxysterol assay under a rigorous set of laboratory procedures (i.e., samples randomized across plates and inclusion of quality control [QC] samples to mitigate batch effects). The initial data processing and quantification were conducted with the MetIDQ™ software. QC samples were used to ensure comparability across runs. We confirmed the technical validity of the analysis using the coefficient of variation from duplicate samples. We established the limit of detection (LOD) for each analyte to ensure consistent analysis across the plates. Values falling below this limit were imputed using a uniform distribution between the LOD and LOD/2. Finally, the samples underwent outlier testing using principal component analysis (PCA), excluding those samples exceeding 3 standard deviations from the mean of PC1 and PC2. Metabolites passing QC were mean-normalized and log2-transformed. In addition to analyzing individual metabolites, we employed the MetaboINDICATOR™ software to calculate biologically informed metabolite sums and ratios. These derived metabolite measures, known as MetaboIndicators, are based on well-characterized metabolic pathways and can offer insights into underlying enzymatic activities that may be perturbed and contribute to alterations in metabolite levels. [ 9 ]. Statistical Analysis Individual Metabolite Analyses We included MS PATHS participants in which (1) blood samples used for metabolomics assessment were collected within 90 days of completion of the MSPT (e.g., MSPT baseline) and (2) had additional MSPT follow-up after the MSPT baseline assessment. We then assessed the association of individual metabolites and MetaboIndicator provided sums and ratios (as proxies of enzyme activity) with MSPT outcomes, including WST, MDT, and PST using a generalized estimating equation (GEE) model adjusted for age, sex, race, smoking status, BMI, the duration between the last meal and sample collection, disease course, disease duration, class of disease modifying therapy (DMT), and follow-up time duration. We categorized DMT use into “first line injectable” (interferon-beta and glatiramer acetate), oral (teriflunomide, sphingosine-1-phosphate inhibitors, and fumaric acid esters), “infusion or immune reconstitution” (anti-CD20 agents, natalizumab, alemtuzumab, and cladribine), “other”, and “untreated”. Smoking status was classified as never smoker, former smoker, current smoker, or unknown smoking history. BMI was categorized into four groups: underweight (< 18.5 kg/m²), healthy weight (18.5–24.9 kg/m²), overweight (25.0–29.9 kg/m²), and obese (≥ 30.0 kg/m²). Participants self-identified their race as White, Black, or Other races. Disease duration was categorized into quartiles based on the difference between chronological age and age at onset in years: short (≤ 3.72), medium (3.72 to 14.76), and long (≥ 14.76). We categorized the time gap between the last meal consumption and sample collection into approximate tertiles: short ( 380 minutes). Missing data rates were generally low and were accounted for using a missing indicator. We corrected for multiple testing using the false discovery rate (FDR), applying a threshold of FDR-corrected two-sided P-value < 0.05 for significance. We also conducted a series of pre-specified sensitivity analyses, additionally adjusting for center and country effects, and subgroup analyses using GEE models. Subgroups of interest include MS disease course [Relapsing-Remitting MS (RRMS) and Progressive MS (PMS)], DMT class (low, moderate, high efficacy therapy), and sex groups. Models were adjusted for a similar set of covariates. Network Analysis using metabolite sets Since many metabolites are correlated and participate in similar biological pathways, we applied Weighted Gene Co-expression Network Analysis (WGCNA), which is an agnostic approach for metabolite groups [ 10 ]. WGCNA does not require prior knowledge of the correlation between measured metabolites to be specified and facilitates an unbiased construction of correlated metabolite networks. This approach offers a robust framework to identify highly correlated groups of metabolites and has been previously applied to metabolomics data[ 11 ]. Using WGCNA, we identified clusters of highly correlated metabolites, referred to as metabolite modules (Supplementary Fig. 2) . Within each module, the most important metabolites were defined as those with the highest intramodular connectivity, referred to as hub metabolites. For each module, an eigen-metabolite is calculated as the first principal component of the metabolites. We used eigen-metabolite scores in the analysis with MSPT outcomes over time. These analyses were conducted using GEE models, adjusting for a predefined list of covariates. Pathway Enrichment Analysis for Individual Metabolite Results We performed a follow-up metabolite set enrichment analysis (MSEA) to identify metabolic pathways associated with MSPT outcomes (Supplementary File). Results Participants The analysis included 767 individuals diagnosed with MS who provided serum samples within 90 days of completion of the baseline MSPT outcomes and had at least one follow-up MSPT. Participants were predominantly female (72.9%) and diagnosed with relapsing-remitting MS (72.1%), with a mean age of 44.9 years (SD = 11.4; range: 18–83) at baseline. Over an average follow-up of 3.0 years (SD = 1.25), participants completed a mean of 7.0 MSPT assessments (SD = 4.57). Baseline MSPT outcomes indicated a mean manual dexterity score of 25.4 (SD = 6.3), a walking speed score of 6.5 (SD = 3.5), and a processing speed score of 51.8 (SD = 13.7)(Table 1 ). Table 1 Summary statistics of characteristics of the study participants. Variable Baseline measures No. Samples 767 Age, yrs, mean (SD) 44.9 (11.4) Age range (min, max) 18.4, 83.2 Follow up, yrs, mean (SD) 3.0 (1.25) Sex, Female, n (%) 559 (72.9%) Race White 676 (88.1%) Black 40 (5.2%) Other 51 (6.7%) BMI Healthy 234 (30.5%) Overweight 213 (27.8%) Obese 226 (29.5%) Underweight 11 (1.4%) Unknown 83 (10.8%) Smoking status Never smoker 440 (57.3%) Former smoker 174 (22.7%) Current smoker 144 (18.8%) Unknown smoking history 9 (1.2%) Time from last meal to sample collection (mins) Short (≤ 180) 217 (28.3%) Medium (181 to 381) 235 (30.6%) Long (≥ 381) 250 (32.6%) Unknown 65 (8.5%) MSPT repeats, mean (range) 7.0 (2–26) Disease course RRMS 553 (72.1%) PMS 205 (26.7%) Unknown 9 (1.2%) Disease duration categories, yrs, No. (%) Short (≤ 3.72) 210 (27.4%) Medium (3.72 to 14.76) 353 (46.0%) Long (≥ 14.76) 181 (23.6%) Unknown 23 (3.0%) DMT class, efficacy, No. (%) No therapy 113 (14.7%) Low efficacy 113 (14.7%) Moderate efficacy 213 (27.8%) High efficacy 228 (29.7%) Unknown 100 (13.1%) RRMS: Relapsing-Remitting Multiple Sclerosis; PMS: Progressive Multiple Sclerosis; DMT: Disease Modifying Therapy; MSPT: Multiple Sclerosis Performance Test. Association of Baseline Serum Metabolites and Longitudinal MSPT Outcomes Levels of specific lipid metabolites were significantly associated with neuroperformance, as measured by MSPT outcomes over time. For example, a 1 SD decrease in phosphatidylcholine aa C36:6 (PC aa C36:6) was linked to a 9.3% decline in walking speed performance (beta = -0.10; 95% CI: 6.7–11.9; FDR-adjusted p = 8.9E-09) and a 5.4% reduction in manual dexterity (beta = -0.06; 95% CI: 3.3–7.4; FDR-adjusted p = 9.6E-05). Similarly, a 1 SD decrease in eicosapentaenoic acid (EPA) was associated with a 4.8% slower walking speed performance (beta = -0.05, 95% CI: 2.8–6.8; FDR-adjusted p = 3.4E-04) and a 3.5% decline in manual dexterity (beta = -0.04, 95% CI: 2.2–4.8; FDR-adjusted p = 8.3E-05) (Fig. 1 . A&B) . Summary of significant metabolites associated with MDT and WST at FDR-adjusted p < 0.05 is reported in Supplementary Data . Although no individual metabolites showed significant associations with PST after FDR correction, several metabolites were associated with a decline in PST using a nominal p-value threshold. Specifically, worse processing speed was associated with higher levels of p-Cresol sulfate (p-Cresol-SO₄) and lower levels of dehydroepiandrosterone sulfate (DHEAS) (Supplementary Fig. 1). Results were generally similar in subgroup analyses. Top metabolites (PC aa C36:5 and PC aa C36:6) showed no significant interaction with sex, disease course, or the class of DMT ( Fig. 1 . C) . We found potential interaction with sex for some hormone-related metabolites. For example, in men, a 1 SD increase in cortisol levels was nominally associated with a 7.45% decrease in processing speed (95% CI: 3.10 to 11.67; p = 1E-3), and in women a 1 SD decrease in DHEAS level was nominally associated with a 3.34% decline in processing speed (95% CI: -5.10 to -1.61; p = 1E-4). Summary of metabolites exclusively associated with specific subgroups is reported in Supplementary Table 2 . (A) The Manhattan plot summarizes the analyzed metabolites and significant results. The x-axis shows the metabolite classes, which are color-coded. The y-axis displays the − log₁₀ of FDR-adjusted p-values. The dashed blue and red horizontal lines indicate the cut-off p-values for FDR at 0.01 and 0.05, respectively. The most significant associations are labeled, while the full list of significant results is reported in the Supplementary Data . The volcano plot displays metabolites associated with the manual dexterity score. Each point represents an individual metabolite; the y-axis shows the − log₁₀ of the p-value, and the x-axis reflects the effect size, thereby indicating the directionality of the association. The dashed gray horizontal line marks the significance threshold at p = 0.05, and the most significant associations with FDR-adjusted p < 0.01 are labeled. (B) The Manhattan plot highlights associations between metabolites and walking speed scores, with results meeting FDR-adjusted p-value thresholds of < 0.05 and < 0.01. The volcano plot displays these significant associations as well, with the horizontal dashed line indicating a nominal p-value of 0.05. The x-axis represents effect size, while the y-axis shows − log₁₀ p-values. (C) The forest plot illustrates metabolites that were consistently significant across subgroups, displaying effect estimates alongside their 95% confidence intervals. Separate facets present results for MDT and WST outcomes. The x-axis represents the GEE coefficient, with a vertical dashed line at zero indicating no effect. Subgroups are shown along the y-axis, and distinct point shapes are used to distinguish the top two metabolites. A color gradient reflects the level of statistical significance, and error bars indicate the 95% confidence intervals. Association of Metabolite Sums and Ratios with Longitudinal MSPT Outcomes Composite sums and ratios (indicative of enzymatic activities) of metabolites of interest were also strongly associated with outcomes. The most significant results were for the ratio of EPA to Arachidonic Acid (AA) [MDT: beta = -0.05 (95% CI: -0.07,-0.03); FDR-adjusted.p = 7.38E-07; WST: beta = -0.07 (95% CI: -0.1,-0.04); FDR-adjusted.p = 6.89E-06], the ratio of Docosahexaenoic Acid (DHA) to AA [MDT: beta = -0.04 (95% CI: -0.06,-0.02); FDR-adjusted.p = 1.01E-03; WST: beta = -0.07 (95% CI: -0.1,-0.04); FDR-adjusted.p = 3.12E-05], and an indicator of Phospholipase A2 activity (PLA2 Activity 3) that was associated with an estimated 4.72% and 7.80% increase in MDT and WST scores, respectively, per unit increase in enzymatic activity [MDT: beta = 0.05 (95% CI: 0.03,0.07); FDR-adjusted.p = 3.00E-04; WST: beta = 0.08 (95% CI: 0.05,0.10); FDR-adjusted.p =, WST = 6.89E-06] (Fig. 2 ) . The coefficient plot displays GEE model estimates and 95% confidence intervals for the most significant MetaboIndicators associated with MSPT outcomes. Dot color indicates the direction and magnitude of the coefficients, while separate facets correspond to individual MSPT outcomes. The x-axis represents effect size, and the y-axis lists MetaboIndicators ordered by statistical significance. Ratio of EPA to AA: Ratio of Eicosapentaenoic Acid to Arachidonic Acid; Ratio of DHA to AA: Ratio of Docosahexaenoic Acid to Arachidonic Acid; PLA2 Activity (3): Phospholipase A2 Activity (3) [lysoPC a C16:1 + AA) / PC aa C36:5]; PLA2 Activity (4): Phospholipase A2 Activity (4) [(lysoPC a C18:0 + AA) / PC aa C38:4]; Sum of Measured Omega-3 Fatty Acids: [DHA + EPA]; Sarcosine Synthesis from Glycine: [Sarcosine / Gly]; DLD (NBS): Dihydrolipoamide Dehydrogenase Deficiency (NBS) [Pro / Phe]; Sum of MUFA-LysoPCs: Sum of Monounsaturated Fatty Acid Lysophosphatidylcholines; Ratio of Non-Essential to Essential Amino Acids: [(Ala + Arg + Asn + Asp + Cys + Gln + Glu + Gly + Pro + Ser + Tyr) / (His + Ile + Leu + Lys + Met + Phe + Thr + Trp + Val)]; Sum of Aromatic Amino Acids: [Phe + Trp + Tyr]; Sum of Long-Chain Fatty Acid Lysophosphatidylcholines: Sum of lysoPC a C14-20:x; Sum of LysoPCs: Sum of Lysophosphatidylcholines; Ratio of SM-OHs to SM-Non OHs: Ratio of Hydroxylated Sphingomyelins to Non-Hydroxylated Sphingomyelins; Sum of SFA-LysoPCs: Sum of Saturated Fatty Acid Lysophosphatidylcholines; HipAcid Synthesis: Hippuric Acid Synthesis [HipAcid / Gly]; Cys Synthesis: Cysteine Synthesis [Cys / (Ser + Met)]; Sum of Essential Amino Acids: [His + Ile + Leu + Lys + Met + Phe + Thr + Trp + Val ]; Betaine Synthesis: [Betaine / Choline]. The figure is a schematic representation of potential molecular mechanisms involved in cell activation, highlighting the aberrant activity of cytosolic PLA₂ and the generation of downstream byproducts that may trigger inflammatory responses. Module-wise association of correlated metabolites with MSPT outcomes In WGCNA analysis, several modules of correlated metabolites were associated with MSPT outcomes. Notably, Module-E, enriched in PCs, with PC aa C36:6 as a highly connected hub metabolite, was inversely associated with both MDT and WST. Other modules linked to WST, including Module-D and Module-H, were enriched in triglycerides (TGs) and cholesteryl esters, respectively. Module-F, enriched in diglycerides and TGs, was also significantly associated with MDT Fig. 3 . (A) The heatmap plot shows the Module-MSPT outcomes associations. The x-axis indicates MSPT outcomes, and the y-axis indicates labeled modules (ME) of clustered metabolites obtained from WGCNA. The number of stars inside the heatmap denotes statistical significance levels: one star (*) for FDR-adjusted p < 0.05, two stars (**) for FDR-adjusted p < 0.01, and three stars (***) for FDR-adjusted p < 0.001. (B) The network of the 17 highly connected metabolites in the significantly associated module-E is visualized using Cytoscape V3.10.1 [ 12 ]. Each node represents a metabolite, with the top three most highly connected (hub) metabolites colored in red, orange, and yellow, respectively, based on intramodular connectivity. Edges indicate pairwise connections with a topological overlap (TOM) threshold of 0.05. (C) The stacked bar chart summarizes the classes of metabolites grouped in each module. The x-axis displays the module name, while the y-axis indicates the metabolite count in each module. The color key for the class of metabolites is shown on the right side of the chart. Metabolite Set Enrichment Analysis The MSEA performed based on apriori-defined pathways, consistently highlighted alterations in lipid metabolism in association with MSPT outcomes ( Supplementary Fig. 3) . Discussion In this study, we conducted a metabolomics analysis on a large, multi-site cohort of pwMS to evaluate the association between baseline serum metabolite levels and longitudinal changes in neurological function in pwMS. We used quantitative MSPT outcomes to assess walking speed performance, manual dexterity, and processing speed performance in pwMS. We found notable associations between altered lipid levels, particularly PCs, and the progressive decline in upper and lower extremity motor function in pwMS, as assessed longitudinally, over a mean follow-up period of three years. These metabolites demonstrated robust links in individual and subgroup analyses, aligning with set-wide association WGCNA results where modules, primarily enriched with PCs, showed consistent links to MS severity. To enhance the biological interpretation of our findings, we investigated the association of biologically meaningful metabolite sums or ratios of interest with longitudinal worsening of MSPT outcomes, which highlighted the disruption in LysoPC/PC-related enzymatic activities and aberrant ratios of omega-3 to omega-6 fatty acids. These findings were reinforced by pathway enrichment analyses highlighting disruptions in lipid metabolism linked to temporal motor decline. These results underscore the critical role of lipid metabolism in MS progression and highlight potential lipid pathways contributing to the worsening of neurological outcomes. Lipidomic signatures of MS severity Altered lipid metabolism is well-documented in pwMS[ 13 ], but its impact on MS severity and progression remains uncertain. Lipid dysregulation may contribute to MS progression via oxidative stress, inflammation, and disrupted immune signaling and energy metabolism. One notable finding of our study is a consistent association of two phosphatidylcholines (PC aa C36:5 and PC aa C36:6) with longitudinal MS severity across different sets of analyses. PCs are the predominant glycerophospholipids in cellular membranes and myelin. Oxidized PC deposition has been found in MS brain lesions and is toxic to oligodendrocytes and neurons in culture [ 14 ]. PCs are broken down by PLA2, resulting in lysoPC and AA. AA is a precursor of inflammatory eicosanoids, and AA-related pathways are suggested to play a role in the pathogenesis of MS[ 15 ]. Both PCs and lysoPCs are reported to be linked to MS pathogenesis by contributing to inflammation, oxidative stress, and demyelination. Altered lysoPC/PC ratio indicates enzyme-mediated inflammation [ 16 ]. In our MetaboIndicator analysis, we observed that worsened MDT and WST were associated with the ratio of lysoPC a C16:1 plus AA to PC aa C36:5, which is an indicator of increased PLA2 activity. While we found an association between PLA2 activity and longitudinal MS severity outcomes, prior studies have reported inconsistent findings regarding elevated PLA2 activity in pwMS compared to controls[ 17 ]. Studies in Experimental Autoimmune Encephalomyelitis (EAE) models implicated PLA2-driven mechanisms, including inflammation, myelin breakdown, and demyelination, in MS and EAE, and proposed PLA2 inhibiting therapeutic strategies to delay disease onset and progression, with evidence suggesting the need to modulate not only AA overproduction but also regulate other PLA2-derived metabolites, such as lysoPCs[ 18 , 19 ] The notion that multiple lipid dysregulated pathways contribute to MS progression is further supported by our findings of reduced levels of Polyunsaturated fatty acids (PUFAs) including the omega-3 (ω3) fatty acids, EPA and DHA and their altered ratios to AA (EPA/AA and DHA/AA), which were associated with worsening MSPT outcomes and, in a recent untargeted lipidomic study, with axonal injury in pediatric-onset MS [ 20 ]. Interestingly, the serum level of PC aa C36:6 has been linked to genome-wide significant variants in the FADS gene family, which encodes desaturase enzymes that, along with elongase enzymes encoded by ELOVL genes, are essential for the metabolism of PUFAs [ 21 ]. The protective role of PUFAs has been reported in observational studies, linking omega-3 fatty acids intake with reduced MS risk; however, the beneficial effects seem to be modulated by different genetic variants and methylation levels of the FADS and ELOVL genes[ 22 ]. The brain primarily obtains the necessary fatty acids, including PUFAs, through the blood-brain barrier (BBB), and the consumption of fish oil seems to improve the quality of life in pwMS, likely due to its anti-inflammatory properties[ 23 , 24 ]. However, clinical trials have shown inconsistent results for omega-3 supplementation in modifying MS progression[ 20 , 25 – 27 ]. This inconsistency may arise from the lack of knowledge about the magnitude of the required change in the serum PUFA levels to achieve a clinically meaningful effect on MS progression and activity. Additionally, the protective effects of omega-3 on MS progression could be limited by the brain’s ability to uptake these fatty acids. This uptake can potentially be improved by consuming other phospholipid-rich sources of PUFAs rather than fish oil[ 28 , 29 ] or by using supplements that contain lysophosphatidylcholines coupled with DHA that can enhance the absorption of PUFAs into the brain [ 30 , 31 ]. Besides, we found elevated serum levels of TGs in association with worse MDT and WST outcomes, which is in line with previous studies collectively confirming the pro-inflammatory effect of increased levels of TGs on the development of autoinflammatory diseases[ 32 – 34 ]. Elevated TG levels may exacerbate MS severity by disrupting BBB integrity[ 35 ]. Lipid-induced neurovascular damage and inflammation caused by increased BBB transfer have been associated with lipolysis and the production of triglyceride-rich lipoproteins. These products can also induce the formation of lipid droplets in astrocytes, leading to the activation of cellular stress pathways and the release of inflammatory cytokines[ 36 ]. Other studies also reported the effect of hypertriglyceridemia on increased vascular permeability, leukocyte adhesion, and inflammation-induced oxidative stress[ 37 , 38 ]. We also observed lower tryptophan levels linked to a worsening walking speed and manual dexterity. This is consistent with a large-scale untargeted metabolomics study that links altered AAAs to MS severity[ 3 ]. We also noticed decreased levels of asparagine, histidine, methionine, and leucine associated with worse walking speed over time. Changes in the serum levels of amino acids may influence their transport across the BBB, which can result in metabolic dysregulation and induce inflammation by activating the mTORC1 pathway[ 39 ]. Altered levels of branched-chain amino acids could affect the brain's uptake of monoamine precursors necessary for neurotransmitter synthesis, which can result in altered glutamate homeostasis, impact synaptic plasticity, induce neurotoxicity, and contribute to neurodegeneration and MS progression[ 40 – 42 ]. Strengths and Limitations The strength of our study lies in its large-scale, multi-center design, comprehensive metabolomic profiling, and longitudinal quantitative assessments of neurological function. Rigorous preprocessing, including strategies for batch effects management, stringent quality control, and absolute metabolite quantification, ensured data reliability. However, in our study, we did not have access to detailed information on dietary habits, physical activity, or gut microbiome composition, all of which may influence metabolomic profiles. It’s also possible that various DMTs (or indications for specific DMTs or classes) could have impacted the metabolome differentially. Additionally, only a limited number of samples had metabolic assessments at two time points, and repeated measures of metabolomic profiles were generally unavailable. Conclusion In summary, our analysis of multiple classes of metabolites, combined with standardized quantitative assessments of neurologic function in a large multinational cohort of pwMS, identifies key serum lipidomic changes, including reduced PCs and PUFAs and elevated TGs for MS severity. These changes are associated with the longitudinal decline in MS neuroperformance. Sensitivity analyses accounting for center effects, MS subtypes, and different classes of DMT further validated our findings. This research proposes PLA2-driven specific PC depletion as a potential biomarker of MS course and suggests dysregulation in multiple lipid pathways as contributors to disease worsening. Abbreviations pwMS people with multiple sclerosis RRMS relapsing-remitting multiple sclerosis PMS progressive multiple sclerosis DMT disease-modifying therapy MSPT multiple sclerosis performance test MDT manual dexterity test WST walking speed test PST processing speed test HCs healthy controls CNS central nervous system CSF cerebrospinal fluid AAA aromatic amino acid PC phosphatidylcholine TG triglyceride. Declarations Ethics approval and consent to participate As part of the MS PATHS, the patients/participants involved in this study provided written informed consent. The MS PATHS was approved by Ethics committees or institutional review boards at the following participating centers: Cleveland Clinic, Cleveland, OH, USA; Johns Hopkins University, Baltimore, MD, USA; New York University, New York, NY, USA; OhioHealth, Columbus, OH, USA; Washington University in St. Louis, St. Louis, MO, USA; University of Rochester Medical Center, Rochester, NY, USA; Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA; University Hospital of Giessen and Marburg, Marburg, Germany; Vall d'Hebron University Hospital, Barcelona, Spain; and University Hospital Carl Gustav Carus, Dresden, Germany. Consent for publication Not applicable Availability of data and material Requests to access the datasets presented in this article should be directed to the corresponding author. Competing interests K.Y, E.T, and HH.T are employees of and hold stock/stock options in Biogen. K.T. was an employee at the time of study conduction and held stock/stock options in Biogen. A Harry Weaver Neuroscience Scholar Award from the National MS Society supports P.B. Funding Biogen sponsored the MS PATHS project, the metabolic study, and serves as the hub for data sharing. This study was also supported in part by R01NS133005 to KCF. Authors' contributions Study conceptualization, HH.T, K.T., Study design, and statistical analysis plan, K.C.F., HH.T, F.B.B; data analysis and investigation, R.N, and K.C.F; resources, E.T; writing the original draft, R.N., and K.C.F.; review & editing, K.C.F, F.B.B, P.B, K.Y, and S.K; All Authors discussed and reviewed the drafts. visualization, R.N, K.C.F; supervision, K.C.F; funding acquisition, K.C.F, and HH.T. Acknowledgements Not applicable References Lucchinetti C, Hohlfeld R: Multiple Sclerosis 3, Volume 34 E-Book: Blue Books of Neurology Series . Elsevier Health Sciences; 2009. 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Torkildsen Ø, Wergeland S, Bakke S, Beiske AG, Bjerve KS, Hovdal H, Midgard R, Lilleås F, Pedersen T, Bjørnarå B: ω-3 fatty acid treatment in multiple sclerosis (OFAMS Study): a randomized, double-blind, placebo-controlled trial. Archives of neurology 2012, 69:1044–1051. Weinstock-Guttman B, Baier M, Park Y, Feichter J, Lee-Kwen P, Gallagher E, Venkatraman J, Meksawan K, Deinehert S, Pendergast D: Low fat dietary intervention with ω-3 fatty acid supplementation in multiple sclerosis patients. Prostaglandins, Leukotrienes and Essential Fatty Acids 2005, 73:397–404. Latifi S, Tamayol A, Habibey R, Sabzevari R, Kahn C, Geny D, Eftekharpour E, Annabi N, Blau A, Linder M: Natural lecithin promotes neural network complexity and activity. Scientific reports 2016, 6:25777. Hoang T-V, Tran T-T, Mafruhah OR, Hsieh M-T, Ha H-A: Comparison of Omega-3 polyunsaturated fatty acids bioavailability in fish oil and krill oil: Network Meta-analyses. Food Chemistry: X 2024, 24:101880. Semba RD: Perspective: the potential role of circulating lysophosphatidylcholine in neuroprotection against Alzheimer disease. Advances in Nutrition 2020, 11:760–772. Yalagala PR, Sugasini D, Dasarathi S, Pahan K, Subbaiah PV: Dietary lysophosphatidylcholine-EPA enriches both EPA and DHA in the brain: potential treatment for depression. Journal of Lipid Research 2019, 60:566–578. Cho EB, Cho H-J, Choi M, Seok JM, Shin HY, Kim BJ, Min J-H: Low high-density lipoprotein cholesterol and high triglycerides lipid profile in neuromyelitis optica spectrum disorder: associations with disease activity and disability. Multiple Sclerosis and Related Disorders 2020, 40:101981. ÇOMOĞLU S, YARDIMCI S, OKÇU Z: Body fat distribution and plasma lipid profiles of patients with multiple sclerosis. Turkish Journal of Medical Sciences 2004, 34:43–48. Tettey P, Simpson Jr S, Taylor B, Blizzard L, Ponsonby A-L, Dwyer T, Kostner K, van der Mei I: An adverse lipid profile is associated with disability and progression in disability, in people with MS. Multiple Sclerosis Journal 2014, 20:1737–1744. Maggi P, Macri SMC, Gaitán MI, Leibovitch E, Wholer JE, Knight HL, Ellis M, Wu T, Silva AC, Massacesi L: The formation of inflammatory demyelinated lesions in cerebral white matter. Annals of neurology 2014, 76:594–608. Lee LL, Aung HH, Wilson DW, Anderson SE, Rutledge JC, Rutkowsky JM: Triglyceride-rich lipoprotein lipolysis products increase blood-brain barrier transfer coefficient and induce astrocyte lipid droplets and cell stress. American Journal of Physiology-Cell Physiology 2017. Scioli MG, Storti G, D’Amico F, Rodríguez Guzmán R, Centofanti F, Doldo E, Céspedes Miranda EM, Orlandi A: Oxidative stress and new pathogenetic mechanisms in endothelial dysfunction: potential diagnostic biomarkers and therapeutic targets. Journal of Clinical Medicine 2020, 9:1995. Sitia S, Tomasoni L, Atzeni F, Ambrosio G, Cordiano C, Catapano A, Tramontana S, Perticone F, Naccarato P, Camici P: From endothelial dysfunction to atherosclerosis. Autoimmunity reviews 2010, 9:830–834. Ikeda K, Kinoshita M, Kayama H, Nagamori S, Kongpracha P, Umemoto E, Okumura R, Kurakawa T, Murakami M, Mikami N: Slc3a2 mediates branched-chain amino-acid-dependent maintenance of regulatory T cells. Cell reports 2017, 21:1824–1838. Mandolesi G, Gentile A, Musella A, Fresegna D, De Vito F, Bullitta S, Sepman H, Marfia GA, Centonze D: Synaptopathy connects inflammation and neurodegeneration in multiple sclerosis. Nature Reviews Neurology 2015, 11:711–724. Garton T, Gadani SP, Gill AJ, Calabresi PA: Neurodegeneration and demyelination in multiple sclerosis. Neuron 2024. Sarchielli P, Greco L, Floridi A, Floridi A, Gallai V: Excitatory amino acids and multiple sclerosis: evidence from cerebrospinal fluid. Archives of neurology 2003, 60:1082–1088. Additional Declarations Competing interest reported. K.Y, E.T, and HH.T are employees of and hold stock/stock options in Biogen. K.T. was an employee at the time of study conduction and held stock/stock options in Biogen. A Harry Weaver Neuroscience Scholar Award from the National MS Society supports P.B. Supplementary Files Supplementarydata.xlsx SupplementaryFile.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 06 Oct, 2025 Reviews received at journal 06 Oct, 2025 Reviewers agreed at journal 06 Oct, 2025 Reviews received at journal 03 Oct, 2025 Reviewers agreed at journal 28 Sep, 2025 Reviewers invited by journal 14 Jul, 2025 Editor assigned by journal 10 Jul, 2025 Submission checks completed at journal 09 Jul, 2025 First submitted to journal 08 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7078057","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":485361167,"identity":"9ddd197d-0ec4-4437-a916-398df31a818b","order_by":0,"name":"Rose Noroozi","email":"","orcid":"","institution":"Department of Neurology, Johns Hopkins University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Rose","middleName":"","lastName":"Noroozi","suffix":""},{"id":485361168,"identity":"f937e0e7-fed0-4dcf-981b-eb6713c381d8","order_by":1,"name":"Hui-Hsin Tsai","email":"","orcid":"","institution":"MS\u0026I and AD Clinical Development, Biogen","correspondingAuthor":false,"prefix":"","firstName":"Hui-Hsin","middleName":"","lastName":"Tsai","suffix":""},{"id":485361169,"identity":"33ca70e9-8629-4080-9cca-fdceb8caa9a9","order_by":2,"name":"Ketian Yu","email":"","orcid":"","institution":"Biomarkers and Systems Biology, Biogen","correspondingAuthor":false,"prefix":"","firstName":"Ketian","middleName":"","lastName":"Yu","suffix":""},{"id":485361170,"identity":"ad268241-69c7-42d1-8f17-d3407a59f9d0","order_by":3,"name":"Karunakar Samuel","email":"","orcid":"","institution":"Department of Neuroimmunology, University of Rochester School of Medicine and Dentistry","correspondingAuthor":false,"prefix":"","firstName":"Karunakar","middleName":"","lastName":"Samuel","suffix":""},{"id":485361171,"identity":"1eddd6e4-53c6-4dc8-9ecb-4c1d7e32e4da","order_by":4,"name":"Kien Trinh","email":"","orcid":"","institution":"Biomarkers and Systems Biology, Biogen","correspondingAuthor":false,"prefix":"","firstName":"Kien","middleName":"","lastName":"Trinh","suffix":""},{"id":485361172,"identity":"6652ba4a-7d6e-48e1-bf71-208b471fbad2","order_by":5,"name":"Ellen A. 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K.Y, E.T, and HH.T are employees of and hold stock/stock options in Biogen. K.T. was an employee at the time of study conduction and held stock/stock options in Biogen. A Harry Weaver Neuroscience Scholar Award from the National MS Society supports P.B.","formattedTitle":"Metabolomic and Lipid Alterations are Associated with Longitudinal Neurological Performance in Multiple Sclerosis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMultiple sclerosis (MS) is a complex neuroinflammatory disorder[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], characterized by varying disease progression trajectories. The underlying pathobiology of the heterogeneity in MS course is not well understood, with several different genetic and environmental factors suggested to affect MS outcomes. This complexity limits our ability to effectively predict long-term outcome evolution for people with MS (pwMS) using currently available biomarkers.\u003c/p\u003e\u003cp\u003eMetabolomics, the comprehensive profiling of circulating metabolites, provides a promising approach to identifying candidate biomarkers in MS, as it captures a dynamic interplay of biological processes. Metabolite alterations, shaped by both genetic and environmental factors, may have the potential to serve as prognostic biomarkers and also provide insights into possible biological mechanisms contributing to the MS course.\u003c/p\u003e\u003cp\u003ePrevious studies have identified potential metabolites associated with MS risk and severity[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]; however, most were cross-sectional comparisons of the metabolome between pwMS and healthy people and often only considered a singular measure of MS disability. For example, in a previous study, our team reported the disruptions in aromatic amino acid (AAA) metabolism in pwMS and linked altered tyrosine metabolism with increased disability in MS[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Additionally, our previous cross-sectional analysis has identified lipidomic alterations linked to disease severity, including dysregulation in the glycerolipid metabolism-related pathways[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Therefore, longitudinal studies that include multiple outcome measures are essential for identifying how metabolic changes correlate with disease worsening.\u003c/p\u003e\u003cp\u003eTo address this gap, we examined changes in the serum levels of a broad set of metabolites and lipids as they relate to objective neuroperformance outcomes in a large multinational cohort of pwMS.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cp\u003e\u003cb\u003eParticipants and sampling\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe include pwMS from the Multiple Sclerosis Partners Advancing Technology and Health Solutions (MS PATHS, V.16.0) network, which is a consortium of MS centers in the United States (7) and Europe (3) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. MS PATHS was formerly sponsored by Biogen. MS PATHS was designed to model a learning health system in which data are collected on participants as a part of routine clinical care using an iPad-based questionnaire and objective assessment of neuroperformance via the MS Performance Test (MSPT). Participants provide their demographic information, such as age, sex, ethnicity, and race, as well as disease details, including age at symptom onset, MS type, use of disease-modifying therapies, and self-reported disability using the Patient Determined Disease Steps (PDDS) scale[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe MSPT is modeled after the MS functional composite (MSFC) and includes the following components: (1) processing speed test (PST), which is similar to the Symbol Digit Modalities Test (SDMT) where higher scores indicate better cognition; (2) the Manual Dexterity Test (MDT), a tablet-based timed 9-hole Peg test to assess the upper extremity motor function for which higher recorded time reflect worse dexterity; and (3) the Walking Speed Test (WST), which is a 25-foot timed walking test to assess lower extremity motor function where higher recorded time indicate slower, worse, walking speed performance. Other characteristics, such as height and weight, to calculate body mass index (BMI), as well as smoking status, are automatically abstracted from the electronic health records. Data collection efforts were approved by the institutional review boards at each participating institution, and participants provided informed consent for the collection and sharing of data.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAssessment and quality control of metabolomic profiles\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAs part of the MS PATHS network, participants are given the option to participate in an optional biobanking study. Non-fasted blood samples are collected and processed under a standardized protocol and shipped to a centralized repository for storage at -80\u0026deg;C. Time of sample collection and time of last meal are recorded for each draw. We then measured serum levels of 517 targeted metabolites from the first biobanked sample for a subset of MS PATHS participants using liquid chromatography-mass spectrometry (LC-MS) at Biocrates Inc. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], applying the MxP\u0026reg; Quant 500 assay, eicosanoid assay, and oxysterol assay under a rigorous set of laboratory procedures (i.e., samples randomized across plates and inclusion of quality control [QC] samples to mitigate batch effects). The initial data processing and quantification were conducted with the MetIDQ\u0026trade; software. QC samples were used to ensure comparability across runs. We confirmed the technical validity of the analysis using the coefficient of variation from duplicate samples. We established the limit of detection (LOD) for each analyte to ensure consistent analysis across the plates. Values falling below this limit were imputed using a uniform distribution between the LOD and LOD/2. Finally, the samples underwent outlier testing using principal component analysis (PCA), excluding those samples exceeding 3 standard deviations from the mean of PC1 and PC2. Metabolites passing QC were mean-normalized and log2-transformed. In addition to analyzing individual metabolites, we employed the MetaboINDICATOR\u0026trade; software to calculate biologically informed metabolite sums and ratios. These derived metabolite measures, known as MetaboIndicators, are based on well-characterized metabolic pathways and can offer insights into underlying enzymatic activities that may be perturbed and contribute to alterations in metabolite levels. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003e\u003cb\u003eIndividual Metabolite Analyses\u003c/b\u003e\u003c/p\u003e\u003cp\u003e We included MS PATHS participants in which (1) blood samples used for metabolomics assessment were collected within 90 days of completion of the MSPT (e.g., MSPT baseline) and (2) had additional MSPT follow-up after the MSPT baseline assessment. We then assessed the association of individual metabolites and MetaboIndicator provided sums and ratios (as proxies of enzyme activity) with MSPT outcomes, including WST, MDT, and PST using a generalized estimating equation (GEE) model adjusted for age, sex, race, smoking status, BMI, the duration between the last meal and sample collection, disease course, disease duration, class of disease modifying therapy (DMT), and follow-up time duration. We categorized DMT use into \u0026ldquo;first line injectable\u0026rdquo; (interferon-beta and glatiramer acetate), oral (teriflunomide, sphingosine-1-phosphate inhibitors, and fumaric acid esters), \u0026ldquo;infusion or immune reconstitution\u0026rdquo; (anti-CD20 agents, natalizumab, alemtuzumab, and cladribine), \u0026ldquo;other\u0026rdquo;, and \u0026ldquo;untreated\u0026rdquo;. Smoking status was classified as never smoker, former smoker, current smoker, or unknown smoking history. BMI was categorized into four groups: underweight (\u0026lt;\u0026thinsp;18.5 kg/m\u0026sup2;), healthy weight (18.5\u0026ndash;24.9 kg/m\u0026sup2;), overweight (25.0\u0026ndash;29.9 kg/m\u0026sup2;), and obese (\u0026ge;\u0026thinsp;30.0 kg/m\u0026sup2;). Participants self-identified their race as White, Black, or Other races. Disease duration was categorized into quartiles based on the difference between chronological age and age at onset in years: short (\u0026le;\u0026thinsp;3.72), medium (3.72 to 14.76), and long (\u0026ge;\u0026thinsp;14.76). We categorized the time gap between the last meal consumption and sample collection into approximate tertiles: short (\u0026lt;\u0026thinsp;185 minutes), medium (185\u0026ndash;380 minutes), and long (\u0026gt;\u0026thinsp;380 minutes). Missing data rates were generally low and were accounted for using a missing indicator. We corrected for multiple testing using the false discovery rate (FDR), applying a threshold of FDR-corrected two-sided P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for significance. We also conducted a series of pre-specified sensitivity analyses, additionally adjusting for center and country effects, and subgroup analyses using GEE models. Subgroups of interest include MS disease course [Relapsing-Remitting MS (RRMS) and Progressive MS (PMS)], DMT class (low, moderate, high efficacy therapy), and sex groups. Models were adjusted for a similar set of covariates.\u003c/p\u003e\u003cp\u003e\u003cb\u003eNetwork Analysis using metabolite sets\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSince many metabolites are correlated and participate in similar biological pathways, we applied Weighted Gene Co-expression Network Analysis (WGCNA), which is an agnostic approach for metabolite groups [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. WGCNA does not require prior knowledge of the correlation between measured metabolites to be specified and facilitates an unbiased construction of correlated metabolite networks. This approach offers a robust framework to identify highly correlated groups of metabolites and has been previously applied to metabolomics data[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Using WGCNA, we identified clusters of highly correlated metabolites, referred to as metabolite modules \u003cb\u003e(Supplementary Fig.\u0026nbsp;2)\u003c/b\u003e. Within each module, the most important metabolites were defined as those with the highest intramodular connectivity, referred to as hub metabolites. For each module, an eigen-metabolite is calculated as the first principal component of the metabolites. We used eigen-metabolite scores in the analysis with MSPT outcomes over time. These analyses were conducted using GEE models, adjusting for a predefined list of covariates.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePathway Enrichment Analysis for Individual Metabolite Results\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe performed a follow-up metabolite set enrichment analysis (MSEA) to identify metabolic pathways associated with MSPT outcomes \u003cb\u003e(Supplementary File).\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eParticipants\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe analysis included 767 individuals diagnosed with MS who provided serum samples within 90 days of completion of the baseline MSPT outcomes and had at least one follow-up MSPT. Participants were predominantly female (72.9%) and diagnosed with relapsing-remitting MS (72.1%), with a mean age of 44.9 years (SD\u0026thinsp;=\u0026thinsp;11.4; range: 18\u0026ndash;83) at baseline. Over an average follow-up of 3.0 years (SD\u0026thinsp;=\u0026thinsp;1.25), participants completed a mean of 7.0 MSPT assessments (SD\u0026thinsp;=\u0026thinsp;4.57). Baseline MSPT outcomes indicated a mean manual dexterity score of 25.4 (SD\u0026thinsp;=\u0026thinsp;6.3), a walking speed score of 6.5 (SD\u0026thinsp;=\u0026thinsp;3.5), and a processing speed score of 51.8 (SD\u0026thinsp;=\u0026thinsp;13.7)(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary statistics of characteristics of the study participants.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBaseline measures\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo. Samples\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e767\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge, yrs, mean (SD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44.9 (11.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge range (min, max)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18.4, 83.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFollow up, yrs, mean (SD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.0 (1.25)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex, Female, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e559 (72.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRace\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e676 (88.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlack\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40 (5.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51 (6.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealthy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e234 (30.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e213 (27.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObese\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e226 (29.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnderweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11 (1.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e83 (10.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSmoking status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e440 (57.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFormer smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e174 (22.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e144 (18.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown smoking history\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9 (1.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTime from last meal to sample collection (mins)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShort (\u0026le;\u0026thinsp;180)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e217 (28.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedium (181 to 381)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e235 (30.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLong (\u0026ge;\u0026thinsp;381)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e250 (32.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65 (8.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMSPT repeats, mean (range)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.0 (2\u0026ndash;26)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDisease course\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRRMS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e553 (72.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePMS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e205 (26.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9 (1.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDisease duration categories, yrs, No. (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShort (\u0026le;\u0026thinsp;3.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e210 (27.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedium (3.72 to 14.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e353 (46.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLong (\u0026ge;\u0026thinsp;14.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e181 (23.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23 (3.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDMT class, efficacy, No. (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo therapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e113 (14.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow efficacy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e113 (14.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate efficacy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e213 (27.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh efficacy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e228 (29.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e100 (13.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eRRMS: Relapsing-Remitting Multiple Sclerosis; PMS: Progressive Multiple Sclerosis; DMT: Disease Modifying Therapy; MSPT: Multiple Sclerosis Performance Test.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAssociation of Baseline Serum Metabolites and Longitudinal MSPT Outcomes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eLevels of specific lipid metabolites were significantly associated with neuroperformance, as measured by MSPT outcomes over time. For example, a 1 SD decrease in phosphatidylcholine aa C36:6 (PC aa C36:6) was linked to a 9.3% decline in walking speed performance (beta = -0.10; 95% CI: 6.7\u0026ndash;11.9; FDR-adjusted p\u0026thinsp;=\u0026thinsp;8.9E-09) and a 5.4% reduction in manual dexterity (beta = -0.06; 95% CI: 3.3\u0026ndash;7.4; FDR-adjusted p\u0026thinsp;=\u0026thinsp;9.6E-05). Similarly, a 1 SD decrease in eicosapentaenoic acid (EPA) was associated with a 4.8% slower walking speed performance (beta = -0.05, 95% CI: 2.8\u0026ndash;6.8; FDR-adjusted p\u0026thinsp;=\u0026thinsp;3.4E-04) and a 3.5% decline in manual dexterity (beta = -0.04, 95% CI: 2.2\u0026ndash;4.8; FDR-adjusted p\u0026thinsp;=\u0026thinsp;8.3E-05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003cb\u003eA\u0026amp;B)\u003c/b\u003e. Summary of significant metabolites associated with MDT and WST at FDR-adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 is reported in \u003cb\u003eSupplementary Data\u003c/b\u003e. Although no individual metabolites showed significant associations with PST after FDR correction, several metabolites were associated with a decline in PST using a nominal p-value threshold. Specifically, worse processing speed was associated with higher levels of p-Cresol sulfate (p-Cresol-SO₄) and lower levels of dehydroepiandrosterone sulfate (DHEAS)\u003cb\u003e(Supplementary Fig.\u0026nbsp;1).\u003c/b\u003e Results were generally similar in subgroup analyses. Top metabolites (PC aa C36:5 and PC aa C36:6) showed no significant interaction with sex, disease course, or the class of DMT \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003cb\u003eC)\u003c/b\u003e. We found potential interaction with sex for some hormone-related metabolites. For example, in men, a 1 SD increase in cortisol levels was nominally associated with a 7.45% decrease in processing speed (95% CI: 3.10 to 11.67; p\u0026thinsp;=\u0026thinsp;1E-3), and in women a 1 SD decrease in DHEAS level was nominally associated with a 3.34% decline in processing speed (95% CI: -5.10 to -1.61; p\u0026thinsp;=\u0026thinsp;1E-4). Summary of metabolites exclusively associated with specific subgroups is reported in \u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(A)\u003c/b\u003e The Manhattan plot summarizes the analyzed metabolites and significant results. The x-axis shows the metabolite classes, which are color-coded. The y-axis displays the \u0026minus;\u0026thinsp;log₁₀ of FDR-adjusted p-values. The dashed blue and red horizontal lines indicate the cut-off p-values for FDR at 0.01 and 0.05, respectively. The most significant associations are labeled, while the full list of significant results is reported in the \u003cb\u003eSupplementary Data\u003c/b\u003e. The volcano plot displays metabolites associated with the manual dexterity score. Each point represents an individual metabolite; the y-axis shows the \u0026minus;\u0026thinsp;log₁₀ of the p-value, and the x-axis reflects the effect size, thereby indicating the directionality of the association. The dashed gray horizontal line marks the significance threshold at p\u0026thinsp;=\u0026thinsp;0.05, and the most significant associations with FDR-adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 are labeled. \u003cb\u003e(B)\u003c/b\u003e The Manhattan plot highlights associations between metabolites and walking speed scores, with results meeting FDR-adjusted p-value thresholds of \u0026lt;\u0026thinsp;0.05 and \u0026lt;\u0026thinsp;0.01. The volcano plot displays these significant associations as well, with the horizontal dashed line indicating a nominal p-value of 0.05. The x-axis represents effect size, while the y-axis shows\u0026thinsp;\u0026minus;\u0026thinsp;log₁₀ p-values. \u003cb\u003e(C)\u003c/b\u003e The forest plot illustrates metabolites that were consistently significant across subgroups, displaying effect estimates alongside their 95% confidence intervals. Separate facets present results for MDT and WST outcomes. The x-axis represents the GEE coefficient, with a vertical dashed line at zero indicating no effect. Subgroups are shown along the y-axis, and distinct point shapes are used to distinguish the top two metabolites. A color gradient reflects the level of statistical significance, and error bars indicate the 95% confidence intervals.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAssociation of Metabolite Sums and Ratios with Longitudinal MSPT Outcomes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eComposite sums and ratios (indicative of enzymatic activities) of metabolites of interest were also strongly associated with outcomes. The most significant results were for the ratio of EPA to Arachidonic Acid (AA) [MDT: beta = -0.05 (95% CI: -0.07,-0.03); FDR-adjusted.p\u0026thinsp;=\u0026thinsp;7.38E-07; WST: beta = -0.07 (95% CI: -0.1,-0.04); FDR-adjusted.p\u0026thinsp;=\u0026thinsp;6.89E-06], the ratio of Docosahexaenoic Acid (DHA) to AA [MDT: beta = -0.04 (95% CI: -0.06,-0.02); FDR-adjusted.p\u0026thinsp;=\u0026thinsp;1.01E-03; WST: beta = -0.07 (95% CI: -0.1,-0.04); FDR-adjusted.p\u0026thinsp;=\u0026thinsp;3.12E-05], and an indicator of Phospholipase A2 activity (PLA2 Activity 3) that was associated with an estimated 4.72% and 7.80% increase in MDT and WST scores, respectively, per unit increase in enzymatic activity [MDT: beta\u0026thinsp;=\u0026thinsp;0.05 (95% CI: 0.03,0.07); FDR-adjusted.p\u0026thinsp;=\u0026thinsp;3.00E-04; WST: beta\u0026thinsp;=\u0026thinsp;0.08 (95% CI: 0.05,0.10); FDR-adjusted.p =, WST\u0026thinsp;=\u0026thinsp;6.89E-06] (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe coefficient plot displays GEE model estimates and 95% confidence intervals for the most significant MetaboIndicators associated with MSPT outcomes. Dot color indicates the direction and magnitude of the coefficients, while separate facets correspond to individual MSPT outcomes. The x-axis represents effect size, and the y-axis lists MetaboIndicators ordered by statistical significance. Ratio of EPA to AA: Ratio of Eicosapentaenoic Acid to Arachidonic Acid; Ratio of DHA to AA: Ratio of Docosahexaenoic Acid to Arachidonic Acid; PLA2 Activity (3): Phospholipase A2 Activity (3) [lysoPC a C16:1\u0026thinsp;+\u0026thinsp;AA) / PC aa C36:5]; PLA2 Activity (4): Phospholipase A2 Activity (4) [(lysoPC a C18:0\u0026thinsp;+\u0026thinsp;AA) / PC aa C38:4]; Sum of Measured Omega-3 Fatty Acids: [DHA\u0026thinsp;+\u0026thinsp;EPA]; Sarcosine Synthesis from Glycine: [Sarcosine / Gly]; DLD (NBS): Dihydrolipoamide Dehydrogenase Deficiency (NBS) [Pro / Phe]; Sum of MUFA-LysoPCs: Sum of Monounsaturated Fatty Acid Lysophosphatidylcholines; Ratio of Non-Essential to Essential Amino Acids: [(Ala\u0026thinsp;+\u0026thinsp;Arg\u0026thinsp;+\u0026thinsp;Asn\u0026thinsp;+\u0026thinsp;Asp\u0026thinsp;+\u0026thinsp;Cys\u0026thinsp;+\u0026thinsp;Gln\u0026thinsp;+\u0026thinsp;Glu\u0026thinsp;+\u0026thinsp;Gly\u0026thinsp;+\u0026thinsp;Pro\u0026thinsp;+\u0026thinsp;Ser\u0026thinsp;+\u0026thinsp;Tyr) / (His\u0026thinsp;+\u0026thinsp;Ile\u0026thinsp;+\u0026thinsp;Leu\u0026thinsp;+\u0026thinsp;Lys\u0026thinsp;+\u0026thinsp;Met\u0026thinsp;+\u0026thinsp;Phe\u0026thinsp;+\u0026thinsp;Thr\u0026thinsp;+\u0026thinsp;Trp\u0026thinsp;+\u0026thinsp;Val)]; Sum of Aromatic Amino Acids: [Phe\u0026thinsp;+\u0026thinsp;Trp\u0026thinsp;+\u0026thinsp;Tyr]; Sum of Long-Chain Fatty Acid Lysophosphatidylcholines: Sum of lysoPC a C14-20:x; Sum of LysoPCs: Sum of Lysophosphatidylcholines; Ratio of SM-OHs to SM-Non OHs: Ratio of Hydroxylated Sphingomyelins to Non-Hydroxylated Sphingomyelins; Sum of SFA-LysoPCs: Sum of Saturated Fatty Acid Lysophosphatidylcholines; HipAcid Synthesis: Hippuric Acid Synthesis [HipAcid / Gly]; Cys Synthesis: Cysteine Synthesis [Cys / (Ser\u0026thinsp;+\u0026thinsp;Met)]; Sum of Essential Amino Acids: [His\u0026thinsp;+\u0026thinsp;Ile\u0026thinsp;+\u0026thinsp;Leu\u0026thinsp;+\u0026thinsp;Lys\u0026thinsp;+\u0026thinsp;Met\u0026thinsp;+\u0026thinsp;Phe\u0026thinsp;+\u0026thinsp;Thr\u0026thinsp;+\u0026thinsp;Trp\u0026thinsp;+\u0026thinsp;Val ]; Betaine Synthesis: [Betaine / Choline].\u003c/p\u003e\u003cp\u003eThe figure is a schematic representation of potential molecular mechanisms involved in cell activation, highlighting the aberrant activity of cytosolic PLA₂ and the generation of downstream byproducts that may trigger inflammatory responses.\u003c/p\u003e\u003cp\u003e\u003cb\u003eModule-wise association of correlated metabolites with MSPT outcomes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn WGCNA analysis, several modules of correlated metabolites were associated with MSPT outcomes. Notably, Module-E, enriched in PCs, with PC aa C36:6 as a highly connected hub metabolite, was inversely associated with both MDT and WST. Other modules linked to WST, including Module-D and Module-H, were enriched in triglycerides (TGs) and cholesteryl esters, respectively. Module-F, enriched in diglycerides and TGs, was also significantly associated with MDT Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(A)\u003c/b\u003e The heatmap plot shows the Module-MSPT outcomes associations. The x-axis indicates MSPT outcomes, and the y-axis indicates labeled modules (ME) of clustered metabolites obtained from WGCNA. The number of stars inside the heatmap denotes statistical significance levels: one star (*) for FDR-adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, two stars (**) for FDR-adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, and three stars (***) for FDR-adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.001. \u003cb\u003e(B)\u003c/b\u003e The network of the 17 highly connected metabolites in the significantly associated module-E is visualized using Cytoscape V3.10.1 [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Each node represents a metabolite, with the top three most highly connected (hub) metabolites colored in red, orange, and yellow, respectively, based on intramodular connectivity. Edges indicate pairwise connections with a topological overlap (TOM) threshold of 0.05. \u003cb\u003e(C)\u003c/b\u003e The stacked bar chart summarizes the classes of metabolites grouped in each module. The x-axis displays the module name, while the y-axis indicates the metabolite count in each module. The color key for the class of metabolites is shown on the right side of the chart.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMetabolite Set Enrichment Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe MSEA performed based on apriori-defined pathways, consistently highlighted alterations in lipid metabolism in association with MSPT outcomes (\u003cb\u003eSupplementary Fig.\u0026nbsp;3)\u003c/b\u003e.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we conducted a metabolomics analysis on a large, multi-site cohort of pwMS to evaluate the association between baseline serum metabolite levels and longitudinal changes in neurological function in pwMS. We used quantitative MSPT outcomes to assess walking speed performance, manual dexterity, and processing speed performance in pwMS.\u003c/p\u003e\u003cp\u003eWe found notable associations between altered lipid levels, particularly PCs, and the progressive decline in upper and lower extremity motor function in pwMS, as assessed longitudinally, over a mean follow-up period of three years. These metabolites demonstrated robust links in individual and subgroup analyses, aligning with set-wide association WGCNA results where modules, primarily enriched with PCs, showed consistent links to MS severity. To enhance the biological interpretation of our findings, we investigated the association of biologically meaningful metabolite sums or ratios of interest with longitudinal worsening of MSPT outcomes, which highlighted the disruption in LysoPC/PC-related enzymatic activities and aberrant ratios of omega-3 to omega-6 fatty acids. These findings were reinforced by pathway enrichment analyses highlighting disruptions in lipid metabolism linked to temporal motor decline. These results underscore the critical role of lipid metabolism in MS progression and highlight potential lipid pathways contributing to the worsening of neurological outcomes.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLipidomic signatures of MS severity\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAltered lipid metabolism is well-documented in pwMS[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], but its impact on MS severity and progression remains uncertain. Lipid dysregulation may contribute to MS progression via oxidative stress, inflammation, and disrupted immune signaling and energy metabolism. One notable finding of our study is a consistent association of two phosphatidylcholines (PC aa C36:5 and PC aa C36:6) with longitudinal MS severity across different sets of analyses.\u003c/p\u003e\u003cp\u003ePCs are the predominant glycerophospholipids in cellular membranes and myelin. Oxidized PC deposition has been found in MS brain lesions and is toxic to oligodendrocytes and neurons in culture [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. PCs are broken down by PLA2, resulting in lysoPC and AA. AA is a precursor of inflammatory eicosanoids, and AA-related pathways are suggested to play a role in the pathogenesis of MS[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Both PCs and lysoPCs are reported to be linked to MS pathogenesis by contributing to inflammation, oxidative stress, and demyelination. Altered lysoPC/PC ratio indicates enzyme-mediated inflammation [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In our MetaboIndicator analysis, we observed that worsened MDT and WST were associated with the ratio of lysoPC a C16:1 plus AA to PC aa C36:5, which is an indicator of increased PLA2 activity.\u003c/p\u003e\u003cp\u003eWhile we found an association between PLA2 activity and longitudinal MS severity outcomes, prior studies have reported inconsistent findings regarding elevated PLA2 activity in pwMS compared to controls[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Studies in Experimental Autoimmune Encephalomyelitis (EAE) models implicated PLA2-driven mechanisms, including inflammation, myelin breakdown, and demyelination, in MS and EAE, and proposed PLA2 inhibiting therapeutic strategies to delay disease onset and progression, with evidence suggesting the need to modulate not only AA overproduction but also regulate other PLA2-derived metabolites, such as lysoPCs[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eThe notion that multiple lipid dysregulated pathways contribute to MS progression is further supported by our findings of reduced levels of Polyunsaturated fatty acids (PUFAs) including the omega-3 (ω3) fatty acids, EPA and DHA and their altered ratios to AA (EPA/AA and DHA/AA), which were associated with worsening MSPT outcomes and, in a recent untargeted lipidomic study, with axonal injury in pediatric-onset MS [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eInterestingly, the serum level of PC aa C36:6 has been linked to genome-wide significant variants in the \u003cem\u003eFADS\u003c/em\u003e gene family, which encodes desaturase enzymes that, along with elongase enzymes encoded by \u003cem\u003eELOVL\u003c/em\u003e genes, are essential for the metabolism of PUFAs [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The protective role of PUFAs has been reported in observational studies, linking omega-3 fatty acids intake with reduced MS risk; however, the beneficial effects seem to be modulated by different genetic variants and methylation levels of the \u003cem\u003eFADS\u003c/em\u003e and \u003cem\u003eELOVL\u003c/em\u003e genes[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The brain primarily obtains the necessary fatty acids, including PUFAs, through the blood-brain barrier (BBB), and the consumption of fish oil seems to improve the quality of life in pwMS, likely due to its anti-inflammatory properties[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. However, clinical trials have shown inconsistent results for omega-3 supplementation in modifying MS progression[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This inconsistency may arise from the lack of knowledge about the magnitude of the required change in the serum PUFA levels to achieve a clinically meaningful effect on MS progression and activity. Additionally, the protective effects of omega-3 on MS progression could be limited by the brain\u0026rsquo;s ability to uptake these fatty acids. This uptake can potentially be improved by consuming other phospholipid-rich sources of PUFAs rather than fish oil[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] or by using supplements that contain lysophosphatidylcholines coupled with DHA that can enhance the absorption of PUFAs into the brain [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBesides, we found elevated serum levels of TGs in association with worse MDT and WST outcomes, which is in line with previous studies collectively confirming the pro-inflammatory effect of increased levels of TGs on the development of autoinflammatory diseases[\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Elevated TG levels may exacerbate MS severity by disrupting BBB integrity[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Lipid-induced neurovascular damage and inflammation caused by increased BBB transfer have been associated with lipolysis and the production of triglyceride-rich lipoproteins. These products can also induce the formation of lipid droplets in astrocytes, leading to the activation of cellular stress pathways and the release of inflammatory cytokines[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Other studies also reported the effect of hypertriglyceridemia on increased vascular permeability, leukocyte adhesion, and inflammation-induced oxidative stress[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWe also observed lower tryptophan levels linked to a worsening walking speed and manual dexterity. This is consistent with a large-scale untargeted metabolomics study that links altered AAAs to MS severity[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. We also noticed decreased levels of asparagine, histidine, methionine, and leucine associated with worse walking speed over time. Changes in the serum levels of amino acids may influence their transport across the BBB, which can result in metabolic dysregulation and induce inflammation by activating the mTORC1 pathway[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Altered levels of branched-chain amino acids could affect the brain's uptake of monoamine precursors necessary for neurotransmitter synthesis, which can result in altered glutamate homeostasis, impact synaptic plasticity, induce neurotoxicity, and contribute to neurodegeneration and MS progression[\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eStrengths and Limitations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe strength of our study lies in its large-scale, multi-center design, comprehensive metabolomic profiling, and longitudinal quantitative assessments of neurological function. Rigorous preprocessing, including strategies for batch effects management, stringent quality control, and absolute metabolite quantification, ensured data reliability. However, in our study, we did not have access to detailed information on dietary habits, physical activity, or gut microbiome composition, all of which may influence metabolomic profiles. It\u0026rsquo;s also possible that various DMTs (or indications for specific DMTs or classes) could have impacted the metabolome differentially. Additionally, only a limited number of samples had metabolic assessments at two time points, and repeated measures of metabolomic profiles were generally unavailable.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, our analysis of multiple classes of metabolites, combined with standardized quantitative assessments of neurologic function in a large multinational cohort of pwMS, identifies key serum lipidomic changes, including reduced PCs and PUFAs and elevated TGs for MS severity. These changes are associated with the longitudinal decline in MS neuroperformance. Sensitivity analyses accounting for center effects, MS subtypes, and different classes of DMT further validated our findings. This research proposes PLA2-driven specific PC depletion as a potential biomarker of MS course and suggests dysregulation in multiple lipid pathways as contributors to disease worsening.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003epwMS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003epeople with multiple sclerosis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRRMS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003erelapsing-remitting multiple sclerosis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePMS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eprogressive multiple sclerosis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDMT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003edisease-modifying therapy\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMSPT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003emultiple sclerosis performance test\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMDT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003emanual dexterity test\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eWST\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ewalking speed test\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePST\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eprocessing speed test\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHCs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ehealthy controls\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCNS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ecentral nervous system\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCSF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ecerebrospinal fluid\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAAA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003earomatic amino acid\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ephosphatidylcholine\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003etriglyceride.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs part of the MS PATHS, the patients/participants involved in this study provided written informed consent. The MS PATHS was approved by Ethics committees or institutional review boards at the following participating centers: Cleveland Clinic, Cleveland, OH, USA; Johns Hopkins University, Baltimore, MD, USA; New York University, New York, NY, USA; OhioHealth, Columbus, OH, USA; Washington University in St. Louis, St. Louis, MO, USA; University of Rochester Medical Center, Rochester, NY, USA; Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA; University Hospital of Giessen and Marburg, Marburg, Germany; Vall d'Hebron University Hospital, Barcelona, Spain; and University Hospital Carl Gustav Carus, Dresden, Germany.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRequests to access the datasets presented in this article should be directed to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eK.Y, E.T, and HH.T are employees of and hold stock/stock options in Biogen. K.T. was an employee at the time of study conduction and held stock/stock options in Biogen.\u0026nbsp;A Harry Weaver Neuroscience Scholar Award from the National MS Society supports P.B.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBiogen sponsored the MS PATHS project, the metabolic study, and serves as the hub for data sharing. This study was also supported in part by R01NS133005 to KCF.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy conceptualization, HH.T, K.T., Study design, and statistical analysis plan, K.C.F., HH.T, F.B.B; data analysis and investigation, R.N, and K.C.F; resources, E.T; writing the original draft, R.N., and K.C.F.; review \u0026amp; editing, K.C.F, F.B.B, P.B, K.Y, and S.K;\u0026nbsp;All Authors discussed and reviewed the drafts. visualization, R.N, K.C.F; supervision, K.C.F; funding acquisition, K.C.F, and HH.T.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLucchinetti C, Hohlfeld R: \u003cem\u003eMultiple Sclerosis 3, Volume 34 E-Book: Blue Books of Neurology Series\u003c/em\u003e. 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11:711\u0026ndash;724.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGarton T, Gadani SP, Gill AJ, Calabresi PA: Neurodegeneration and demyelination in multiple sclerosis. \u003cem\u003eNeuron\u003c/em\u003e 2024.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSarchielli P, Greco L, Floridi A, Floridi A, Gallai V: Excitatory amino acids and multiple sclerosis: evidence from cerebrospinal fluid. \u003cem\u003eArchives of neurology\u003c/em\u003e 2003, 60:1082\u0026ndash;1088.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"molecular-neurodegeneration-advances","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Molecular Neurodegeneration Advances](https://mnadvances.biomedcentral.com/)","snPcode":"44477","submissionUrl":"https://submission.springernature.com/new-submission/44477/3?","title":"Molecular Neurodegeneration Advances","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Multiple Sclerosis, Metabolomics, Lipidomics, Neurological functioning, Cognitive testing, Biofluid Biomarkers","lastPublishedDoi":"10.21203/rs.3.rs-7078057/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7078057/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMetabolomics incorporates information from multiple biological systems and is recognized as an emerging tool for identifying novel multiple sclerosis (MS) biomarkers. While prior studies have linked metabolic alterations to the disease severity in people with MS (pwMS), a longitudinal approach enables a significant advantage to study this relationship over disease progression. Therefore, this study aims to identify metabolomic signatures associated with longitudinal outcomes in pwMS. We performed a multi-site study, profiling the serum metabolome (Biocrates Inc.) from participants for the MS Partners Advancing Technology and Health Solutions (MS PATHS) network. Outcomes, including 25-foot walking speed, manual dexterity, and processing speed, were quantified using the iPad\u0026reg;-based Multiple Sclerosis Performance Test (MSPT). We applied generalized estimating equation regression models (adjusted for potential confounders) to assess the association of 517 metabolites at baseline with longitudinal assessments of MSPT component tests. We performed network, pathway enrichment, and MetaboIndicator analyses to infer biological insights from our findings. This study included 767 pwMS (mean age 44.9 [SD: 11.4]; 72.1% relapsing-remitting MS; 72.9% female; 11.8% non-white), who had an average of 7.0 (SD\u0026thinsp;=\u0026thinsp;4.57) MSPT measures per person over an average follow-up period of 3.0 (SD\u0026thinsp;=\u0026thinsp;1.25) years. Certain metabolites were associated with MSPT outcomes over time. For example, a 1 SD decrease in Phosphatidyl-choline aa C36:6 (PC aa 36:6) level was associated with a 9.3% decline in walking speed performance (95% CI: 6.7\u0026ndash;11.9; FDR-adjusted p\u0026thinsp;=\u0026thinsp;8.9E-09) and a 5.4% reduction in manual dexterity performance (95% CI: 3.3\u0026ndash;7.4; FDR-adjusted p\u0026thinsp;=\u0026thinsp;9.6E-05). Metabolite set enrichment and MetaboIndicator analyses pointed to pathways involved in polyunsaturated fatty acid (PUFA) metabolism and suggested altered enzymatic activities, such as increased phospholipase A2 (PLA2) activity. Leveraging a large longitudinal cohort, our findings suggest a potential role of altered lipid metabolism in the progression of MS.\u003c/p\u003e","manuscriptTitle":"Metabolomic and Lipid Alterations are Associated with Longitudinal Neurological Performance in Multiple Sclerosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-22 08:11:19","doi":"10.21203/rs.3.rs-7078057/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-06T14:59:25+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-06T14:52:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"197044855218975690283746046600215024028","date":"2025-10-06T14:47:53+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-03T21:18:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"126146356166669964993132918519400677421","date":"2025-09-28T15:41:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-14T19:48:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-10T16:53:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-09T04:20:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Neurodegeneration Advances","date":"2025-07-08T21:09:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-neurodegeneration-advances","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Molecular Neurodegeneration Advances](https://mnadvances.biomedcentral.com/)","snPcode":"44477","submissionUrl":"https://submission.springernature.com/new-submission/44477/3?","title":"Molecular Neurodegeneration Advances","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1a180561-47a3-4eb2-8c17-cc02ee574e35","owner":[],"postedDate":"July 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-18T21:38:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-22 08:11:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7078057","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7078057","identity":"rs-7078057","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}