Dataset Information

Integration of magnetic resonance imaging and protein and metabolite CSF measurements to enable early diagnosis of secondary progressive multiple sclerosis

ABSTRACT:

Molecular networks in neurological diseases are complex. Despite this fact, contemporary biomarkers are in most cases interpreted in isolation, leading to a significant loss of information and power. We present an analytical approach to scrutinize and combine information from biomarkers originating from multiple sources with the aim of discovering a condensed set of biomarkers that in combination could distinguish the progressive degenerative phenotype of multiple sclerosis (SPMS) from the relapsing-remitting phenotype (RRMS).

METHODS: Clinical and magnetic resonance imaging (MRI) data were integrated with data from protein and metabolite measurements of cerebrospinal fluid, and a method was developed to sift through all the variables to establish a small set of highly informative measurements. This prospective study included 16 SPMS patients, 30 RRMS patients and 10 controls. Protein concentrations were quantitated with multiplexed fluorescent bead-based immunoassays and ELISA. The metabolome was recorded using liquid chromatography-mass spectrometry. Clinical follow-up data of the SPMS patients were used to assess disease progression and development of disability.

RESULTS: Eleven variables were in combination able to distinguish SPMS from RRMS patients with high confidence superior to any single measurement. The identified variables consisted of three MRI variables: the size of the spinal cord and the third ventricle and the total number of T1 hypointense lesions; six proteins: galectin-9, monocyte chemoattractant protein-1 (MCP-1), transforming growth factor alpha (TGF-α), tumor necrosis factor alpha (TNF-α), soluble CD40L (sCD40L) and platelet-derived growth factor AA (PDGF-AA); and two metabolites: 20β-dihydrocortisol (20β-DHF) and indolepyruvate.

The proteins myelin basic protein (MBP) and macrophage-derived chemokine (MDC), as well as the metabolites 20β-DHF and 5,6-dihydroxyprostaglandin F1a (5,6-DH-PGF1), were identified as potential biomarkers of disability progression.

CONCLUSION: Our study demonstrates, in a limited but well-defined and data-rich cohort, the importance and value of combining multiple biomarkers to aid diagnostics and track disease progression.

Cohort 2 assays are reported in the current study MTBLS558.

Cohort 1 assays are reported in MTBLS1464.

OTHER RELATED OMICS DATASETS IN: PXD018322PXD006154

INSTRUMENT(S): Exactive (Thermo Scientific)

SUBMITTER: Stephanie Herman

PROVIDER: MTBLS558 | MetaboLights | 2018-08-13

REPOSITORIES: MetaboLights

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Dataset's files

Source:

			Action	DRS
	MTBLS558	Other
	FILES	Other
	a_MTBLS558_ms_neg_csf_metabolite_profiling_mass_spectrometry.txt	Txt
	a_MTBLS558_ms_pos_csf_metabolite_profiling_mass_spectrometry.txt	Txt
	i_Investigation.txt	Txt

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Publications

Integration of magnetic resonance imaging and protein and metabolite CSF measurements to enable early diagnosis of secondary progressive multiple sclerosis.

Herman Stephanie S Khoonsari Payam Emami PE Tolf Andreas A Steinmetz Julia J Zetterberg Henrik H Åkerfeldt Torbjörn T Jakobsson Per-Johan PJ Larsson Anders A Spjuth Ola O Burman Joachim J Kultima Kim K

Theranostics 20180807 16

PMID: 30214633

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