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.

• Klíčová slova
• Brain atrophy, Cholesterol, HDL, LDL, Lesion volume, Lipid, MRI, Multiple sclerosis,
• MeSH
• cholesterol krev   MeSH
• dospělí MeSH
• HDL-cholesterol * krev   MeSH
• kohortové studie MeSH
• LDL-cholesterol krev   MeSH
• lidé středního věku MeSH
• lidé MeSH
• longitudinální studie MeSH
• magnetická rezonanční tomografie * MeSH
• mozek * diagnostické zobrazování   patologie   MeSH
• průřezové studie MeSH
• roztroušená skleróza * diagnostické zobrazování   krev   patologie   MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• pozorovací studie MeSH
• Názvy látek
• cholesterol MeSH
• HDL-cholesterol * MeSH
• LDL-cholesterol MeSH

Altered cholesterol, oxysterol, sphingolipid, and fatty acid concentrations are reported in blood, cerebrospinal fluid, and brain tissue of people with relapsing-remitting multiple sclerosis (RRMS) and are linked to disease progression and treatment responses. CD4 + T cells are pathogenic in RRMS, and defective T-cell function could be mediated in part by liver X receptors (LXRs)-nuclear receptors that regulate lipid homeostasis and immunity. RNA-sequencing and pathway analysis identified that genes within the 'lipid metabolism' and 'signalling of nuclear receptors' pathways were dysregulated in CD4 + T cells isolated from RRMS patients compared with healthy donors. While LXRB and genes associated with cholesterol metabolism were upregulated, other T-cell LXR-target genes, including genes involved in cellular lipid uptake (inducible degrader of the LDL receptor, IDOL), and the rate-limiting enzyme for glycosphingolipid biosynthesis (UDP-glucosylceramide synthase, UGCG) were downregulated in T cells from patients with RRMS compared to healthy donors. Correspondingly, plasma membrane glycosphingolipids were reduced, and cholesterol levels increased in RRMS CD4 + T cells, an effect partially recapitulated in healthy T cells by in vitro culture with T-cell receptor stimulation in the presence of serum from RRMS patients. Notably, stimulation with LXR-agonist GW3965 normalized membrane cholesterol levels, and reduced proliferation and IL17 cytokine production in RRMS CD4 + T-cells. Thus, LXR-mediated lipid metabolism pathways were dysregulated in T cells from patients with RRMS and could contribute to RRMS pathogenesis. Therapies that modify lipid metabolism could help restore immune cell function.

• Klíčová slova
• CD4 + T cells, lipid metabolism, lipid rafts, liver X receptor, multiple sclerosis,
• MeSH
• CD4-pozitivní T-lymfocyty * imunologie   metabolismus   MeSH
• cholesterol metabolismus   MeSH
• dospělí MeSH
• glykosfingolipidy metabolismus   MeSH
• jaterní receptor X * metabolismus   MeSH
• lidé středního věku MeSH
• lidé MeSH
• metabolismus lipidů * MeSH
• relabující-remitující roztroušená skleróza * imunologie   metabolismus   MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• cholesterol MeSH
• glykosfingolipidy MeSH
• jaterní receptor X * 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.

• Klíčová slova
• Classification Description, Machine learning, Metabolomics, Molecular network, Transcriptomics,
• Publikační typ
• časopisecké články MeSH