OBJECTIVES: This study aimed to investigate relationships between cholesterol profile, brain volumetric MRI, and clinical measures in a large observational cohort of multiple sclerosis (MS) patients. MATERIALS AND METHODS: We included 1.505 patients with 4.966 time points including complete lipid, clinical, and imaging data. The time among lipid, brain MRI and clinical measures was under 90 days. Cross-sectional statistical analysis at baseline was performed using an adjusted linear regression and analysis of longitudinal lipid and MRI measures data was performed using adjusted linear mixed models. RESULTS: We found associations between higher high-density lipoprotein cholesterol (HDL-C) and lower brain parenchymal fraction (BPF) at cross-sectional analysis at baseline (B = -0.43, CI 95%: -0.73, -0.12, p = 0.005), as well as in longitudinal analysis over follow-up (B = -0.32 ± 0.072, χ2 = 36.6; p = < 0.001). Higher HDL-C was also associated with higher T2-lesion volume in longitudinal analysis (B = 0.11 ± 0.023; χ2 = 23.04; p = < 0.001). We observed a weak negative association between low-density lipoprotein cholesterol (LDL-C) levels and BPF at baseline (B = -0.26, CI 95%: -0.4, -0.11, p = < 0.001) as well as in longitudinal analysis (B = -0.06 ± 0.03, χ2 = 4.46; p = 0.03). T2-LV did not show an association with LDL-C. We did not find any association between lipid measures and disability. The effect of lipid levels on MRI measures and disability was minimal (Cohen f2 < 0.02). CONCLUSIONS: Our results contradict the previously described exclusively positive effect of HDL-C on brain atrophy in patients with MS. Higher LDL-C was weakly associated with higher brain atrophy but not with higher lesion burden.

• Keywords
• Brain atrophy, Cholesterol, HDL, LDL, Lesion volume, Lipid, MRI, Multiple sclerosis,
• MeSH
• Cholesterol blood   MeSH
• Adult MeSH
• Cholesterol, HDL * blood   MeSH
• Cohort Studies MeSH
• Cholesterol, LDL blood   MeSH
• Middle Aged MeSH
• Humans MeSH
• Longitudinal Studies MeSH
• Magnetic Resonance Imaging * MeSH
• Brain * diagnostic imaging   pathology   MeSH
• Cross-Sectional Studies MeSH
• Multiple Sclerosis * diagnostic imaging   blood   pathology   MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Observational Study MeSH
• Names of Substances
• Cholesterol MeSH
• Cholesterol, HDL * MeSH
• Cholesterol, LDL MeSH

There are no blood-based biomarkers distinguishing patients with relapsing-remitting (RRMS) from secondary progressive multiple sclerosis (SPMS) although evidence supports metabolomic changes according to MS disease severity. Here machine learning analysis of serum metabolomic data stratified patients with RRMS from SPMS with high accuracy and a putative score was developed that stratified MS patient subsets. The top differentially expressed metabolites between SPMS versus patients with RRMS included lipids and fatty acids, metabolites enriched in pathways related to cellular respiration, notably, elevated lactate and glutamine (gluconeogenesis-related) and acetoacetate and bOHbutyrate (ketone bodies), and reduced alanine and pyruvate (glycolysis-related). Serum metabolomic changes were recapitulated in the whole blood transcriptome, whereby differentially expressed genes were also enriched in cellular respiration pathways in patients with SPMS. The final gene-metabolite interaction network demonstrated a potential metabolic shift from glycolysis toward increased gluconeogenesis and ketogenesis in SPMS, indicating metabolic stress which may trigger stress response pathways and subsequent neurodegeneration.

• Keywords
• Classification Description, Machine learning, Metabolomics, Molecular network, Transcriptomics,
• Publication type
• Journal Article MeSH

Background: Neutralizing anti-drug antibodies (ADA) can greatly reduce the efficacy of biopharmaceuticals used to treat patients with multiple sclerosis (MS). However, the biological factors pre-disposing an individual to develop ADA are poorly characterized. Thus, there is an unmet clinical need for biomarkers to predict the development of immunogenicity, and subsequent treatment failure. Up to 35% of MS patients treated with beta interferons (IFNβ) develop ADA. Here we use machine learning to predict immunogenicity against IFNβ utilizing serum metabolomics data. Methods: Serum samples were collected from 89 MS patients as part of the ABIRISK consortium-a multi-center prospective study of ADA development. Metabolites and ADA were quantified prior to and after IFNβ treatment. Thirty patients became ADA positive during the first year of treatment (ADA+). We tested the efficacy of six binary classification models using 10-fold cross validation; k-nearest neighbors, decision tree, random forest, support vector machine and lasso (Least Absolute Shrinkage and Selection Operator) logistic regression with and without interactions. Results: We were able to predict future immunogenicity from baseline metabolomics data. Lasso logistic regression with/without interactions and support vector machines were the most successful at identifying ADA+ or ADA- cases, respectively. Furthermore, patients who become ADA+ had a distinct metabolic response to IFNβ in the first 3 months, with 29 differentially regulated metabolites. Machine learning algorithms could also predict ADA status based on metabolite concentrations at 3 months. Lasso logistic regressions had the greatest proportion of correct classifications [F1 score (accuracy measure) = 0.808, specificity = 0.913]. Finally, we hypothesized that serum lipids could contribute to ADA development by altering immune-cell lipid rafts. This was supported by experimental evidence demonstrating that, prior to IFNβ exposure, lipid raft-associated lipids were differentially expressed between MS patients who became ADA+ or remained ADA-. Conclusion: Serum metabolites are a promising biomarker for prediction of ADA development in MS patients treated with IFNβ, and could provide novel insight into mechanisms of immunogenicity.

• Keywords
• anti-drug antibodies, cholesterol, immunogenicity, machine learning, metabolomics, multiple sclerosis,
• MeSH
• Biomarkers blood   MeSH
• Interferon-beta adverse effects   therapeutic use   MeSH
• Leukocytes, Mononuclear immunology   metabolism   MeSH
• Humans MeSH
• Membrane Lipids metabolism   MeSH
• Membrane Microdomains MeSH
• Metabolome * MeSH
• Metabolomics * methods   MeSH
• Antibodies, Neutralizing blood   immunology   MeSH
• Prognosis MeSH
• Antibodies blood   immunology   MeSH
• Multiple Sclerosis blood   diagnosis   drug therapy   MeSH
• Check Tag
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Names of Substances
• Biomarkers MeSH
• Interferon-beta MeSH
• Membrane Lipids MeSH
• Antibodies, Neutralizing MeSH
• Antibodies MeSH