Metabolomic profiles in relapsing-remitting and progressive multiple sclerosis compared to healthy controls: a five-year follow-up study.
Amino acids
Lipids
Metabolomics
Multiple sclerosis
Pathways
Journal
Metabolomics : Official journal of the Metabolomic Society
ISSN: 1573-3890
Titre abrégé: Metabolomics
Pays: United States
ID NLM: 101274889
Informations de publication
Date de publication:
20 04 2023
20 04 2023
Historique:
received:
13
12
2022
accepted:
11
04
2023
medline:
24
4
2023
pubmed:
20
4
2023
entrez:
20
04
2023
Statut:
epublish
Résumé
Multiple sclerosis (MS) is a disease of the central nervous system associated with immune dysfunction, demyelination, and neurodegeneration. The disease has heterogeneous clinical phenotypes such as relapsing-remitting MS (RRMS) and progressive multiple sclerosis (PMS), each with unique pathogenesis. Metabolomics research has shown promise in understanding the etiologies of MS disease. However, there is a paucity of clinical studies with follow-up metabolomics analyses. This 5-year follow-up (5YFU) cohort study aimed to investigate the metabolomics alterations over time between different courses of MS patients and healthy controls and provide insights into metabolic and physiological mechanisms of MS disease progression. A cohort containing 108 MS patients (37 PMS and 71 RRMS) and 42 controls were followed up for a median of 5 years. Liquid chromatography-mass spectrometry (LC-MS) was applied for untargeted metabolomics profiling of serum samples of the cohort at both baseline and 5YFU. Univariate analyses with mixed-effect ANCOVA models, clustering, and pathway enrichment analyses were performed to identify patterns of metabolites and pathway changes across the time effects and patient groups. Out of 592 identified metabolites, the PMS group exhibited the most changes, with 219 (37%) metabolites changed over time and 132 (22%) changed within the RRMS group (Bonferroni adjusted P < 0.05). Compared to the baseline, there were more significant metabolite differences detected between PMS and RRMS classes at 5YFU. Pathway enrichment analysis detected seven pathways perturbed significantly during 5YFU in MS groups compared to controls. PMS showed more pathway changes compared to the RRMS group.
Identifiants
pubmed: 37079261
doi: 10.1007/s11306-023-02010-0
pii: 10.1007/s11306-023-02010-0
doi:
Types de publication
Journal Article
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
44Informations de copyright
© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Références
Aasly, J., Gårseth, M., Sonnewald, U., Zwart, J. A., White, L., & Unsgård, G. (1997). Cerebrospinal fluid lactate and glutamine are reduced in multiple sclerosis. Acta Neurologica Scandinavica, 95(1), 9–12. https://doi.org/10.1111/j.1600-0404.1997.tb00060.x?sid=nlm/3Apubmed
doi: 10.1111/j.1600-0404.1997.tb00060.x?sid=nlm/3Apubmed
pubmed: 9048978
Bates, D., Sarkar, D., Bates, M. D., & Matrix, L. (2007). The lme4 package. R Package Version, 2(1), 74.
Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B (methodological), 57(1), 289–300.
Cicalini, I., Rossi, C., Pieragostino, D., Agnifili, L., Mastropasqua, L., di Ioia, M., De Luca, G., Onofrj, M., Federici, L., & Del Boccio, P. (2019). Integrated lipidomics and metabolomics analysis of tears in multiple sclerosis: An insight into diagnostic potential of lacrimal fluid. International Journal of Molecular Sciences, 20(6), 1265.
doi: 10.3390/ijms20061265
pubmed: 30871169
pmcid: 6471885
Confavreux, C., & Vukusic, S. (2006). Natural history of multiple sclerosis: A unifying concept. Brain, 129(3), 606–616.
doi: 10.1093/brain/awl007
pubmed: 16415308
Correale, J. (2020). Immunosuppressive amino-acid catabolizing enzymes in multiple sclerosis. Frontiers in Immunology, 11, 600428. https://doi.org/10.3389/fimmu.2020.600428
doi: 10.3389/fimmu.2020.600428
pubmed: 33552055
Davis, I., & Liu, A. (2015). What is the tryptophan kynurenine pathway and why is it important to neurotherapeutics? Expert Review of Neurotherapeutics, 15(7), 719–721. https://doi.org/10.1586/14737175.2015.1049999
doi: 10.1586/14737175.2015.1049999
pubmed: 26004930
pmcid: 4482796
Del Boccio, P., Rossi, C., di Ioia, M., Cicalini, I., Sacchetta, P., & Pieragostino, D. (2016). Integration of metabolomics and proteomics in multiple sclerosis: From biomarkers discovery to personalized medicine. PROTEOMICS—Clinical Applications, 10(4), 470–484.
doi: 10.1002/prca.201500083
pubmed: 27061322
Dickens, A. M., Larkin, J. R., Griffin, J. L., Cavey, A., Matthews, L., Turner, M. R., Wilcock, G. K., Davis, B. G., Claridge, T. D., & Palace, J. (2014). A type 2 biomarker separates relapsing-remitting from secondary progressive multiple sclerosis. Neurology, 83(17), 1492–1499.
doi: 10.1212/WNL.0000000000000905
pubmed: 25253748
pmcid: 4222850
Doneanu, C. E., Chen, W., & Mazzeo, J. R. (2011). UPLC-MS monitoring of water-soluble vitamin Bs in cell culture media in minutes. Water Application Note, 2011, 1–7.
Fiehn, O. (2002). Metabolomics—The link between genotypes and phenotypes. Functional genomics (pp. 155–171). Springer.
doi: 10.1007/978-94-010-0448-0_11
Hauser, S. L., & Cree, B. A. (2020). Treatment of multiple sclerosis: A review. The American Journal of Medicine, 133(12), 1380-1390 e2.
doi: 10.1016/j.amjmed.2020.05.049
pubmed: 32682869
pmcid: 7704606
Herman, S., Khoonsari, P. E., Tolf, A., Steinmetz, J., Zetterberg, H., Åkerfeldt, T., Jakobsson, P.-J., Larsson, A., Spjuth, O., & Burman, J. (2018). Integration of magnetic resonance imaging and protein and metabolite CSF measurements to enable early diagnosis of secondary progressive multiple sclerosis. Theranostics, 8(16), 4477.
doi: 10.7150/thno.26249
pubmed: 30214633
pmcid: 6134925
Hu, W., Sun, L., Gong, Y., Zhou, Y., Yang, P., Ye, Z., Fu, J., Huang, A., Fu, Z., Yu, W., Zhao, Y., Yang, T., & Zhou, H. (2016). Relationship between branched-chain amino acids, metabolic syndrome, and cardiovascular risk profile in a Chinese population: A cross-sectional study. International Journal of Endocrinology, 2016, 8173905. https://doi.org/10.1155/2016/8173905
doi: 10.1155/2016/8173905
pubmed: 27528871
pmcid: 4977397
Hum, S., Lapierre, Y., Scott, S. C., Duquette, P., & Mayo, N. E. (2017). Trajectory of MS disease course for men and women over three eras. Multiple Sclerosis Journal, 23(4), 534–545.
doi: 10.1177/1352458516655478
pubmed: 27364326
Jakimovski, D., Guan, Y., Ramanathan, M., Weinstock-Guttman, B., & Zivadinov, R. (2019). Lifestyle-based modifiable risk factors in multiple sclerosis: Review of experimental and clinical findings. Neurodegenerative Disease Management, 9(3), 149–172. https://doi.org/10.2217/nmt-2018-0046
doi: 10.2217/nmt-2018-0046
pubmed: 31116081
Jia, Y., Wu, T., Jelinek, C. A., Bielekova, B., Chang, L., Newsome, S., Gnanapavan, S., Giovannoni, G., Chen, D., & Calabresi, P. A. (2012). Development of protein biomarkers in cerebrospinal fluid for secondary progressive multiple sclerosis using selected reaction monitoring mass spectrometry (SRM-MS). Clinical Proteomics, 9(1), 1–9.
doi: 10.1186/1559-0275-9-9
Kampman, M. T., Wilsgaard, T., & Mellgren, S. I. (2007). Outdoor activities and diet in childhood and adolescence relate to MS risk above the Arctic Circle. Journal of Neurology, 254(4), 471–477. https://doi.org/10.1007/s00415-006-0395-5
doi: 10.1007/s00415-006-0395-5
pubmed: 17377831
Kanehisa, M., & Goto, S. (2000). KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Research, 28(1), 27–30.
doi: 10.1093/nar/28.1.27
pubmed: 10592173
pmcid: 102409
Keller, J., Zackowski, K., Kim, S., Chidobem, I., Smith, M., Farhadi, F., & Bhargava, P. (2021). Exercise leads to metabolic changes associated with improved strength and fatigue in people with MS. Annals of Clinical and Translational Neurology, 8, 1308.
doi: 10.1002/acn3.51368
pubmed: 33955210
pmcid: 8164856
Lenth, R., Singmann, H., Love, J., Buerkner, P., & Herve, M. (2018). Emmeans: Estimated marginal means, aka least-squares means. R Package Version, 1(1), 3.
Ligouri, M., Marrosu, M., Pugliatti, M., Giuliani, F., De Robertis, F., Cocco, E., Zimatore, G., Livrea, P., & Trojano, M. (2000). Age at onset in multiple sclerosis. Neurological Sciences, 21(2), S825–S829.
doi: 10.1007/s100720070020
Lim, C. K., Bilgin, A., Lovejoy, D. B., Tan, V., Bustamante, S., Taylor, B. V., Bessede, A., Brew, B. J., & Guillemin, G. J. (2017). Kynurenine pathway metabolomics predicts and provides mechanistic insight into multiple sclerosis progression. Scientific Reports, 7(1), 1–9.
Lorefice, L., Murgia, F., Fenu, G., Frau, J., Coghe, G., Murru, M. R., Tranquilli, S., Visconti, A., Marrosu, M. G., & Atzori, L. (2019). Assessing the metabolomic profile of multiple sclerosis patients treated with interferon beta 1a by 1 H-NMR spectroscopy. Neurotherapeutics, 16(3), 797–807.
doi: 10.1007/s13311-019-00721-8
pubmed: 30820880
pmcid: 6694336
Lublin, F. D., Reingold, S. C., Cohen, J. A., Cutter, G. R., Sørensen, P. S., Thompson, A. J., Wolinsky, J. S., Balcer, L. J., Banwell, B., & Barkhof, F. (2014). Defining the clinical course of multiple sclerosis: The 2013 revisions. Neurology, 83(3), 278–286.
doi: 10.1212/WNL.0000000000000560
pubmed: 24871874
pmcid: 4117366
Lucchinetti, C. F., Brück, W., Rodriguez, M., & Lassmann, H. (1996). Distinct patterns of multiple sclerosis pathology indicates heterogeneity in pathogenesis. Brain Pathology, 6(3), 259–274.
doi: 10.1111/j.1750-3639.1996.tb00854.x
pubmed: 8864283
Lutz, N. W., Viola, A., Malikova, I., Confort-Gouny, S., Audoin, B., Ranjeva, J.-P., Pelletier, J., & Cozzone, P. J. (2007). Inflammatory multiple-sclerosis plaques generate characteristic metabolic profiles in cerebrospinal fluid. PLoS ONE, 2(7), e595.
doi: 10.1371/journal.pone.0000595
pubmed: 17611627
pmcid: 1899231
McGarrah, R. W., & White, P. J. (2022). Branched-chain amino acids in cardiovascular disease. Nature Reviews Cardiology, 20, 77–89. https://doi.org/10.1038/s41569-022-00760-3
doi: 10.1038/s41569-022-00760-3
pubmed: 36064969
McGinley, M. P., Goldschmidt, C. H., & Rae-Grant, A. D. (2021). Diagnosis and treatment of multiple sclerosis: A review. JAMA, 325(8), 765–779.
doi: 10.1001/jama.2020.26858
pubmed: 33620411
Mo, M. L., Jamshidi, N., & Palsson, B. Ø. (2007). A genome-scale, constraint-based approach to systems biology of human metabolism. Molecular Biosystems, 3(9), 598–603.
doi: 10.1039/b705597h
pubmed: 17700859
Monaco, F., Fumero, S., Mondino, A., & Mutani, R. (1979). Plasma and cerebrospinal fluid tryptophan in multiple sclerosis and degenerative diseases. Journal of Neurology, Neurosurgery & Psychiatry, 42(7), 640–641.
doi: 10.1136/jnnp.42.7.640
Montalban, X., Hauser, S. L., Kappos, L., Arnold, D. L., Bar-Or, A., Comi, G., De Seze, J., Giovannoni, G., Hartung, H.-P., & Hemmer, B. (2017). Ocrelizumab versus placebo in primary progressive multiple sclerosis. New England Journal of Medicine, 376(3), 209–220.
doi: 10.1056/NEJMoa1606468
pubmed: 28002688
Murgia, F., Lorefice, L., Poddighe, S., Fenu, G., Secci, M. A., Marrosu, M. G., Cocco, E., & Atzori, L. (2020). Multi-platform characterization of cerebrospinal fluid and serum metabolome of patients affected by relapsing-remitting and primary progressive multiple sclerosis. Journal of Clinical Medicine, 9(3), 863. https://doi.org/10.3390/jcm9030863
doi: 10.3390/jcm9030863
pubmed: 32245176
pmcid: 7141510
Pang, Z., Chong, J., Zhou, G., de Lima Morais, D. A., Chang, L., Barrette, M., Gauthier, C., Jacques, P. -É., Li, S., & Xia, J. (2021). MetaboAnalyst 5.0: Narrowing the gap between raw spectra and functional insights. Nucleic Acids Research, 49, W388–W396.
doi: 10.1093/nar/gkab382
pubmed: 34019663
pmcid: 8265181
Regenold, W. T., Phatak, P., Makley, M. J., Stone, R. D., & Kling, M. A. (2008). Cerebrospinal fluid evidence of increased extra-mitochondrial glucose metabolism implicates mitochondrial dysfunction in multiple sclerosis disease progression. Journal of the Neurological Sciences, 275(1–2), 106–112.
doi: 10.1016/j.jns.2008.07.032
pubmed: 18783801
pmcid: 2584157
Reimand, J., Isserlin, R., Voisin, V., Kucera, M., Tannus-Lopes, C., Rostamianfar, A., Wadi, L., Meyer, M., Wong, J., & Xu, C. (2019). Pathway enrichment analysis and visualization of omics data using g: Profiler, GSEA Cytoscape and EnrichmentMap. Nature Protocols, 14(2), 482–517.
doi: 10.1038/s41596-018-0103-9
pubmed: 30664679
pmcid: 6607905
Rokach, L., & Maimon, O. (2005). Clustering methods. In O. Maimon & L. Rokach (Eds.), Data mining and knowledge discovery handbook (pp. 321–352). New York: Springer.
doi: 10.1007/0-387-25465-X_15
Schwarcz, R. (2016). Kynurenines and glutamate: Multiple links and therapeutic implications. Advances in Pharmacology, 76, 13–37. https://doi.org/10.1016/bs.apha.2016.01.005
doi: 10.1016/bs.apha.2016.01.005
pubmed: 27288072
Schwarcz, R., Bruno, J. P., Muchowski, P. J., & Wu, H. Q. (2012). Kynurenines in the mammalian brain: When physiology meets pathology. Nature Reviews Neuroscience, 13(7), 465–477. https://doi.org/10.1038/nrn3257
doi: 10.1038/nrn3257
pubmed: 22678511
pmcid: 3681811
Senanayake, V. K., Jin, W., Mochizuki, A., Chitou, B., & Goodenowe, D. B. (2015). Metabolic dysfunctions in multiple sclerosis: Implications as to causation, early detection, and treatment, a case control study. BMC Neurology, 15(1), 1–10.
doi: 10.1186/s12883-015-0411-4
Smith, K. J., & Lassmann, H. (2002). The role of nitric oxide in multiple sclerosis. The Lancet Neurology, 1(4), 232–241. https://doi.org/10.1016/s1474-4422(02)00102-3
doi: 10.1016/s1474-4422(02)00102-3
pubmed: 12849456
Smolinska, A., Blanchet, L., Coulier, L., Ampt, K. A., Luider, T., Hintzen, R. Q., Wijmenga, S. S., & Buydens, L. M. (2012). Interpretation and visualization of non-linear data fusion in kernel space: Study on metabolomic characterization of progression of multiple sclerosis. PLoS ONE, 7(6), e38163.
doi: 10.1371/journal.pone.0038163
pubmed: 22715376
pmcid: 3371049
Sospedra, M., & Martin, R. (2005). Immunology of multiple sclerosis. Annual Review of Immunology, 23, 683–747.
doi: 10.1146/annurev.immunol.23.021704.115707
pubmed: 15771584
Stoessel, D., Stellmann, J.-P., Willing, A., Behrens, B., Rosenkranz, S. C., Hodecker, S. C., Stürner, K. H., Reinhardt, S., Fleischer, S., & Deuschle, C. (2018). Metabolomic profiles for primary progressive multiple sclerosis stratification and disease course monitoring. Frontiers in Human Neuroscience, 12, 226.
doi: 10.3389/fnhum.2018.00226
pubmed: 29915533
pmcid: 5994544
Swank, R. L. (1950). Multiple sclerosis; a correlation of its incidence with dietary fat. The American Journal of the Medical Sciences, 220(4), 421–430.
doi: 10.1097/00000441-195022040-00011
pubmed: 14771073
Torkildsen, O., Wergeland, S., Bakke, S., Beiske, A. G., Bjerve, K. S., Hovdal, H., Midgard, R., Lilleas, F., Pedersen, T., Bjornara, B., Dalene, F., Kleveland, G., Schepel, J., Olsen, I. C., & Myhr, K. M. (2012). omega-3 fatty acid treatment in multiple sclerosis (OFAMS Study): A randomized, double-blind, placebo-controlled trial. Archives of Neurology, 69(8), 1044–1051. https://doi.org/10.1001/archneurol.2012.283
doi: 10.1001/archneurol.2012.283
pubmed: 22507886
Tremlett, H., Zhao, Y., Rieckmann, P., & Hutchinson, M. (2010). New perspectives in the natural history of multiple sclerosis. Neurology, 74(24), 2004–2015.
doi: 10.1212/WNL.0b013e3181e3973f
pubmed: 20548045
Wishart, D. S., Feunang, Y. D., Marcu, A., Guo, A. C., Liang, K., Vázquez-Fresno, R., Sajed, T., Johnson, D., Li, C., & Karu, N. (2018). HMDB 4.0: The human metabolome database for 2018. Nucleic Acids Research, 46(D1), D608–D617.
doi: 10.1093/nar/gkx1089
pubmed: 29140435
Zahoor, I., Rui, B., Khan, J., Datta, I., & Giri, S. (2021). An emerging potential of metabolomics in multiple sclerosis: A comprehensive overview. Cellular and Molecular Life Sciences, 78, 3181–3203.
doi: 10.1007/s00018-020-03733-2
pubmed: 33449145
pmcid: 8038957