Dataset Information
Disease phenotype prediction in multiple sclerosis
Ontology highlight
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
Dataset's files
| 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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Publications
Integration of magnetic resonance imaging and protein and metabolite CSF measurements to enable early diagnosis of secondary progressive multiple sclerosis.
Theranostics 20180807 16
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 relap ...[more]
