ArticleScientific reports2020
A blood-based metabolomics test to distinguish relapsing-remitting and secondary progressive multiple sclerosis: addressing practical considerations for clinical application.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Metabolomics in Multiple Sclerosis: Advances, Challenges, and Clinical Perspectives-A Systematic Review.International journal of molecular sciences · 2025Pooled it
- Role of B vitamins in modulating homocysteine and metabolic pathways linked to brain atrophy: Metabolomics insights from the VITACOG trial.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Trial
- Serum GFAP and NfL augment a metabolomics-driven strategy for long-term prediction of multiple sclerosis progression.Communications medicine · 2026Article
- PlasmaNeurology(R) neuroimmunology & neuroinflammation · 2025Article
- Formic Acid Pretreatment Enhances Untargeted Serum and Plasma Metabolomics.Analytical chemistry · 2025Article
- Integrating TSPO-PET imaging with metabolomics for enhanced prognostic accuracy in multiple sclerosis.BMJ neurology open · 2025Article
- Impact of Serotonin Transporter Absence on Brain Insulin Receptor Expression, Plasma Metabolome Changes, and ADHD-like Behavior in Mice fed a Western Diet.Biomolecules · 2024Article
- Blood metabolomic and transcriptomic signatures stratify patient subgroups in multiple sclerosis according to disease severity.iScience · 2024Article
- Disease phenotype prediction in multiple sclerosis.iScience · 2023Article
- Secondary Progressive Multiple Sclerosis: A Review of Clinical Characteristics, Definition, Prognostic Tools, and Disease-Modifying Therapies.Neurology(R) neuroimmunology & neuroinflammation · 2023Review
- Blood Metabolomics May Discriminate a Sub-Group of Patients with First Demyelinating Episode in the Context of RRMS with Increased Disability and MRI Characteristics Indicative of Poor Prognosis.International journal of molecular sciences · 2022Article
- Metabolomics detects clinically silent neuroinflammatory lesions earlier than neurofilament-light chain in a focal multiple sclerosis animal model.Journal of neuroinflammation · 2022Article
- Contribution of Metabolomics to Multiple Sclerosis Diagnosis, Prognosis and Treatment.International journal of molecular sciences · 2021Review
- Determination of CSF GFAP, CCN5, and vWF Levels Enhances the Diagnostic Accuracy of Clinically Defined MS From Non-MS Patients With CSF Oligoclonal Bands.Frontiers in immunology · 2021Article
- Objective biomarkers for clinical relapse in multiple sclerosis: a metabolomics approach.Brain communications · 2021Article
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Authors and funding
11 authors.
Funding
Abstract
The transition from relapsing-remitting multiple sclerosis (RRMS) to secondary progressive MS (SPMS) represents a huge clinical challenge. We previously demonstrated that serum metabolomics could distinguish RRMS from SPMS with high diagnostic accuracy. As differing sample-handling protocols can affect the blood metabolite profile, it is vital to understand which factors may influence the accuracy of this metabolomics-based test in a clinical setting. Herein, we aim to further validate the high accuracy of this metabolomics test and to determine if this is maintained in a 'real-life' clinical environment. Blood from 31 RRMS and 28 SPMS patients was subjected to different sample-handling protocols representing variations encountered in clinics. The effect of freeze-thaw cycles (0 or 1) and time to erythrocyte removal (30, 120, or 240 min) on the accuracy of the test was investigated. For test development, samples from the optimised protocol (30 min standing time, 0 freeze-thaw) were used, resulting in high diagnostic accuracy (mean ± SD, 91.0 ± 3.0%). This test remained able to discriminate RRMS and SPMS samples that had experienced additional freeze-thaw, and increased standing times of 120 and 240 min with accuracies ranging from 85.5 to 88.0%, because the top discriminatory metabolite biomarkers from the optimised protocol remained discriminatory between RRMS and SPMS despite these sample-handling variations. In conclusion, while strict sample-handling is essential for the development of metabolomics-based blood tests, the results confirmed that the RRMS vs. SPMS test is resistant to sample-handling variations and can distinguish these two MS stages in the clinics.
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