Dataset Information

Disease phenotype prediction in multiple sclerosis

ABSTRACT:

Progressive multiple sclerosis (PMS) is currently diagnosed retrospectively. Here, we work towards a set of biomarkers that could assist in early diagnosis of PMS. A selection of cerebrospinal fluid metabolites (n=15) was shown to differentiate between PMS and its preceding phenotype in an independent cohort (AUC=0.93). Complementing the classifier with conformal prediction showed that highly confident predictions could be made, and that three out of eight patients developing PMS within three years of sample collection were predicted as PMS at that time point. Finally, this methodology was applied to PMS patients part of a clinical trial for intrathecal treatment with rituximab. The methodology showed that 68% of the patients decreased their similarity to the PMS phenotype one year after treatment. In conclusion, the inclusion of confidence predictors contributes with more information compared to traditional machine learning, and this information is relevant for disease monitoring.

Cohort 1 assays are reported in the current study MTBLS1464.

Cohort 2 assays are reported in MTBLS558.

INSTRUMENT(S): Liquid Chromatography MS - negative - reverse phase, Liquid Chromatography MS - positive - reverse phase

SUBMITTER: Stephanie Herman

PROVIDER: MTBLS1464 | MetaboLights | 2023-05-31

REPOSITORIES: MetaboLights

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Dataset's files

Source:

			Action	DRS
	MTBLS1464	Other
	FILES	Other
	a_MTBLS1464_LC-MS_negative_reverse-phase_metabolite_profiling.txt	Txt
	a_MTBLS1464_LC-MS_positive_reverse-phase_metabolite_profiling.txt	Txt
	files-all.json	Other

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Project description: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 <a href='https://www.ebi.ac.uk/metabolights/MTBLS558' rel='noopener noreferrer' target='_blank'>MTBLS558</a>.Cohort 1 assays are reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS1464' rel='noopener noreferrer' target='_blank'>MTBLS1464</a>.

Dataset Information

Disease phenotype prediction in multiple sclerosis

Dataset's files

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