Evidence map›Paper›PMID 38433900›Full record

ArticleiScience2024

Blood metabolomic and transcriptomic signatures stratify patient subgroups in multiple sclerosis according to disease severity.

Alexandra E Oppong, Leda Coelewij, Georgia Robertson, Lucia Martin-Gutierrez, Kirsty E Waddington, Pierre Dönnes, Petra Nytrova, Rachel Farrell, Inés Pineda-Torra, Elizabeth C Jury

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
8.9field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Article
  4. Review
  5. Article
  6. Review
  7. PlasmaNeurology(R) neuroimmunology & neuroinflammation · 2025
    Article
  8. Article
  9. Review
  10. Metabolic and lipid alterations in multiple sclerosis linked to disease severity.Multiple sclerosis (Houndmills, Basingstoke, England) · 2025
    Article
  11. Article
  12. Article
  13. Article
  14. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 3 institutions in 2 countries.

Alexandra E OppongDivision of Medicine, Department of Inflammation, University College London, London WC1E 6JF, UK.
Leda CoelewijDivision of Medicine, Department of Inflammation, University College London, London WC1E 6JF, UK.
Georgia RobertsonDivision of Medicine, Department of Inflammation, University College London, London WC1E 6JF, UK.
Lucia Martin-GutierrezDivision of Medicine, Department of Inflammation, University College London, London WC1E 6JF, UK.
Kirsty E WaddingtonDivision of Medicine, Department of Inflammation, University College London, London WC1E 6JF, UK.
Pierre DönnesDivision of Medicine, Department of Inflammation, University College London, London WC1E 6JF, UK.
Petra NytrovaDepartment of Neurology and Centre of Clinical, Neuroscience, First Faculty of Medicine, General University Hospital and First Faculty of Medicine, Charles University in Prague, 500 03 Prague, Czech Republic.
Rachel FarrellDepartment of Neuroinflammation, University College London and Institute of Neurology and National Hospital of Neurology and Neurosurgery, London WC1N 3BG, UK.
Inés Pineda-TorraDivision of Medicine, Department of Inflammation, University College London, London WC1E 6JF, UK.
Elizabeth C JuryDivision of Medicine, Department of Inflammation, University College London, London WC1E 6JF, UK.
Centre for Inflammation Research · GBCharles University · CZNational Hospital for Neurology and Neurosurgery · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

There are no blood-based biomarkers distinguishing patients with relapsing-remitting (RRMS) from secondary progressive multiple sclerosis (SPMS) although evidence supports metabolomic changes according to MS disease severity. Here machine learning analysis of serum metabolomic data stratified patients with RRMS from SPMS with high accuracy and a putative score was developed that stratified MS patient subsets. The top differentially expressed metabolites between SPMS versus patients with RRMS included lipids and fatty acids, metabolites enriched in pathways related to cellular respiration, notably, elevated lactate and glutamine (gluconeogenesis-related) and acetoacetate and bOHbutyrate (ketone bodies), and reduced alanine and pyruvate (glycolysis-related). Serum metabolomic changes were recapitulated in the whole blood transcriptome, whereby differentially expressed genes were also enriched in cellular respiration pathways in patients with SPMS. The final gene-metabolite interaction network demonstrated a potential metabolic shift from glycolysis toward increased gluconeogenesis and ketogenesis in SPMS, indicating metabolic stress which may trigger stress response pathways and subsequent neurodegeneration.

Indexed as

Classification DescriptionMachine learningMetabolomicsMolecular networkTranscriptomics

Identifiers

PMID38433900
PMCPMC10907838
OpenAlexW4391838546

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.