Evidence map›Paper›PMID 31659123›Full record

ArticleNeurology(R) neuroimmunology & neuroinflammation2019

Classifying the antibody-negative NMO syndromes: Clinical, imaging, and metabolomic modeling.

Tianrong Yeo, Fay Probert, Maciej Jurynczyk, Megan Sealey, Ana Cavey, Timothy D W Claridge, Mark Woodhall, Patrick Waters, Maria Isabel Leite, Daniel C Anthony and 1 more

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

Article in Neurology(R) neuroimmunology & neuroinflammation, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 2 pooled it
2.0field-weighted citation impact, top 13% 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

21 citing papers in PubMed, 2 syntheses or guidelines pooled it, 32 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. PlasmaNeurology(R) neuroimmunology & neuroinflammation · 2025
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  13. Double-negative neuromyelitis optica spectrum disorder.Multiple sclerosis (Houndmills, Basingstoke, England) · 2023
    Review
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  16. Review
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  19. A novel serum metabolomic panel distinguishes IgG4-related sclerosing cholangitis from primary sclerosing cholangitis.Liver international : official journal of the International Association for the Study of the Liver · 2022
    Article
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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

11 authors at 2 institutions in 2 countries.

Tianrong YeoFrom the Department of Pharmacology (T.Y, F.P., M.S., D.C.A.), University of Oxford, UK; Department of Neurology (T.Y.), National Neuroscience Institute, Singapore; Nuffield Department of Clinical Neurosciences (M.J., A.C., M.W., P.W., M.I.L., J.P.), John Radcliffe Hospital, University of Oxford, UK; Department of Chemistry, (T.D.W.C.), Chemistry Research Laboratory, University of Oxford, UK.
Fay ProbertFrom the Department of Pharmacology (T.Y, F.P., M.S., D.C.A.), University of Oxford, UK; Department of Neurology (T.Y.), National Neuroscience Institute, Singapore; Nuffield Department of Clinical Neurosciences (M.J., A.C., M.W., P.W., M.I.L., J.P.), John Radcliffe Hospital, University of Oxford, UK; Department of Chemistry, (T.D.W.C.), Chemistry Research Laboratory, University of Oxford, UK.
Maciej JurynczykFrom the Department of Pharmacology (T.Y, F.P., M.S., D.C.A.), University of Oxford, UK; Department of Neurology (T.Y.), National Neuroscience Institute, Singapore; Nuffield Department of Clinical Neurosciences (M.J., A.C., M.W., P.W., M.I.L., J.P.), John Radcliffe Hospital, University of Oxford, UK; Department of Chemistry, (T.D.W.C.), Chemistry Research Laboratory, University of Oxford, UK.
Megan SealeyFrom the Department of Pharmacology (T.Y, F.P., M.S., D.C.A.), University of Oxford, UK; Department of Neurology (T.Y.), National Neuroscience Institute, Singapore; Nuffield Department of Clinical Neurosciences (M.J., A.C., M.W., P.W., M.I.L., J.P.), John Radcliffe Hospital, University of Oxford, UK; Department of Chemistry, (T.D.W.C.), Chemistry Research Laboratory, University of Oxford, UK.
Ana CaveyFrom the Department of Pharmacology (T.Y, F.P., M.S., D.C.A.), University of Oxford, UK; Department of Neurology (T.Y.), National Neuroscience Institute, Singapore; Nuffield Department of Clinical Neurosciences (M.J., A.C., M.W., P.W., M.I.L., J.P.), John Radcliffe Hospital, University of Oxford, UK; Department of Chemistry, (T.D.W.C.), Chemistry Research Laboratory, University of Oxford, UK.
Timothy D W ClaridgeFrom the Department of Pharmacology (T.Y, F.P., M.S., D.C.A.), University of Oxford, UK; Department of Neurology (T.Y.), National Neuroscience Institute, Singapore; Nuffield Department of Clinical Neurosciences (M.J., A.C., M.W., P.W., M.I.L., J.P.), John Radcliffe Hospital, University of Oxford, UK; Department of Chemistry, (T.D.W.C.), Chemistry Research Laboratory, University of Oxford, UK.
Mark WoodhallFrom the Department of Pharmacology (T.Y, F.P., M.S., D.C.A.), University of Oxford, UK; Department of Neurology (T.Y.), National Neuroscience Institute, Singapore; Nuffield Department of Clinical Neurosciences (M.J., A.C., M.W., P.W., M.I.L., J.P.), John Radcliffe Hospital, University of Oxford, UK; Department of Chemistry, (T.D.W.C.), Chemistry Research Laboratory, University of Oxford, UK.
Patrick WatersFrom the Department of Pharmacology (T.Y, F.P., M.S., D.C.A.), University of Oxford, UK; Department of Neurology (T.Y.), National Neuroscience Institute, Singapore; Nuffield Department of Clinical Neurosciences (M.J., A.C., M.W., P.W., M.I.L., J.P.), John Radcliffe Hospital, University of Oxford, UK; Department of Chemistry, (T.D.W.C.), Chemistry Research Laboratory, University of Oxford, UK.
Maria Isabel LeiteFrom the Department of Pharmacology (T.Y, F.P., M.S., D.C.A.), University of Oxford, UK; Department of Neurology (T.Y.), National Neuroscience Institute, Singapore; Nuffield Department of Clinical Neurosciences (M.J., A.C., M.W., P.W., M.I.L., J.P.), John Radcliffe Hospital, University of Oxford, UK; Department of Chemistry, (T.D.W.C.), Chemistry Research Laboratory, University of Oxford, UK.
Daniel C AnthonyFrom the Department of Pharmacology (T.Y, F.P., M.S., D.C.A.), University of Oxford, UK; Department of Neurology (T.Y.), National Neuroscience Institute, Singapore; Nuffield Department of Clinical Neurosciences (M.J., A.C., M.W., P.W., M.I.L., J.P.), John Radcliffe Hospital, University of Oxford, UK; Department of Chemistry, (T.D.W.C.), Chemistry Research Laboratory, University of Oxford, UK. jacqueline.palace@ndcn.ox.ac.uk daniel.anthony@pharm.ox.ac.uk.
Jacqueline PalaceFrom the Department of Pharmacology (T.Y, F.P., M.S., D.C.A.), University of Oxford, UK; Department of Neurology (T.Y.), National Neuroscience Institute, Singapore; Nuffield Department of Clinical Neurosciences (M.J., A.C., M.W., P.W., M.I.L., J.P.), John Radcliffe Hospital, University of Oxford, UK; Department of Chemistry, (T.D.W.C.), Chemistry Research Laboratory, University of Oxford, UK. jacqueline.palace@ndcn.ox.ac.uk daniel.anthony@pharm.ox.ac.uk.
University of Oxford · GBNational Neuroscience Institute · SG

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo determine whether unsupervised principal component analysis (PCA) of comprehensive clinico-radiologic data can identify phenotypic subgroups within antibody-negative patients with overlapping features of multiple sclerosis (MS) and neuromyelitis optica spectrum disorders (NMOSDs), and to validate the phenotypic classifications using high-resolution nuclear magnetic resonance (NMR) plasma metabolomics with inference to underlying pathologies.

methodsForty-one antibody-negative patients were recruited from the Oxford NMO Service. Thirty-six clinico-radiologic parameters, focusing on features known to distinguish NMOSD and MS, were collected to build an unbiased PCA model identifying phenotypic subgroups within antibody-negative patients. Metabolomics data from patients with relapsing-remitting MS (RRMS) (n = 34) and antibody-positive NMOSD (Ab-NMOSD) (aquaporin-4 antibody n = 54, myelin oligodendrocyte glycoprotein antibody n = 20) were used to identify discriminatory plasma metabolites separating RRMS and Ab-NMOSD.

resultsPCA of the 36 clinico-radiologic parameters revealed 3 phenotypic subgroups within antibody-negative patients: an MS-like subgroup, an NMOSD-like subgroup, and a low brain lesion subgroup. Supervised multivariate analysis of metabolomics data from patients with RRMS and Ab-NMOSD identified myoinositol and formate as the most discriminatory metabolites (both higher in RRMS). Within antibody-negative patients, myoinositol and formate were significantly higher in the MS-like vs NMOSD-like subgroup; myoinositol (mean [SD], 0.0023 [0.0002] vs 0.0019 [0.0003] arbitrary units [AU];

conclusionsPCA identifies 3 phenotypic subgroups within antibody-negative patients and that the metabolite discriminators of RRMS and Ab-NMOSD suggest that these groupings have some pathogenic meaning. Thus, the identified clinico-radiologic discriminators may provide useful diagnostic clues when seeing antibody-negative patients in the clinic.

Indexed as

AdultAutoantibodiesCohort StudiesFemaleHumansMagnetic Resonance ImagingMaleMetabolomicsMiddle AgedMultiple Sclerosis, Relapsing-RemittingNeuromyelitis OpticaSyndromeAutoantibodies

Identifiers

PMID31659123
PMCPMC6865851
OpenAlexW2982428157

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