Evidence map›Paper›PMID 40248672›Full record

ArticleBMJ neurology open2025

Integrating TSPO-PET imaging with metabolomics for enhanced prognostic accuracy in multiple sclerosis.

Daniel E Radford-Smith, Abi G Yates, Tereza Kacerova, Marjo Nylund, Marcus Sucksdorff, Markus Matilainen, Eline Willemse, Johanna Oechtering, Aleksandra Maleska Maceski, David Leppert and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMJ neurology open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. 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

14 authors.

Daniel E Radford-SmithDepartment of Pharmacology, University of Oxford, Oxford, UK.
Abi G YatesDepartment of Pharmacology, University of Oxford, Oxford, UK.
Tereza KacerovaDepartment of Chemistry, University of Oxford, Oxford, UK.ORCID 0000-0002-9477-0182
Marjo NylundTurku PET Centre, University of Turku, Turku University Hospital and Åbo Akademi University, Turku, Finland.
Marcus SucksdorffTurku PET Centre, University of Turku, Turku University Hospital and Åbo Akademi University, Turku, Finland.
Markus MatilainenTurku PET Centre, University of Turku, Turku University Hospital and Åbo Akademi University, Turku, Finland.
Eline WillemseMultiple Sclerosis Centre, Departments of Biomedicine and Clinical Research, University Hospital and University of Basel, Basel, Switzerland.
Johanna OechteringMultiple Sclerosis Centre, Departments of Biomedicine and Clinical Research, University Hospital and University of Basel, Basel, Switzerland.
Aleksandra Maleska MaceskiMultiple Sclerosis Centre, Departments of Biomedicine and Clinical Research, University Hospital and University of Basel, Basel, Switzerland.
David LeppertMultiple Sclerosis Centre, Departments of Biomedicine and Clinical Research, University Hospital and University of Basel, Basel, Switzerland.
Jens KuhleMultiple Sclerosis Centre, Departments of Biomedicine and Clinical Research, University Hospital and University of Basel, Basel, Switzerland.ORCID 0000-0002-6963-8892
Fay ProbertDepartment of Chemistry, University of Oxford, Oxford, UK.
Daniel C AnthonyDepartment of Pharmacology, University of Oxford, Oxford, UK.
Laura AirasTurku PET Centre, University of Turku, Turku University Hospital and Åbo Akademi University, Turku, Finland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Predicting disease progression in multiple sclerosis (MS) remains challenging. PET imaging with 18 kDa translocator protein (TSPO) radioligands can detect microglial and astrocyte activation beyond MRI-visible lesions, which has been shown to be highly predictive of disease progression. We previously demonstrated that nuclear magnetic resonance (NMR)-based metabolomics could accurately distinguish between relapsing-remitting (RRMS) and secondary progressive MS (SPMS). This study investigates whether combining TSPO imaging with metabolomics enhances predictive accuracy in a similar setting. Methods: Blood samples were collected from 87 MS patients undergoing PET imaging with the TSPO-binding radioligand Results: Greater TSPO availability in the normal-appearing white matter and perilesional regions correlated with higher EDSS. Serum metabolites glutamate (p=0.02), glutamine (p=0.006), and glucose (p=0.008), detected by NMR, effectively distinguished future progressors. These three metabolites alone predicted progression with the same accuracy as TSPO-PET imaging (AUC 0.78; p=0.0001), validated in an independent cohort. Combining serum metabolite data with PET imaging significantly improved predictive power, achieving an AUC of 0.98 (p<0.0001). Conclusion: Measuring three specific serum metabolites is as effective as TSPO imaging in predicting MS progression. However, integrating TSPO imaging with serum metabolite analysis substantially enhances predictive accuracy. Given the simplicity and affordability of NMR analysis, this approach could lead to more personalised, accessible treatment strategies and serve as a valuable tool for clinical trial stratification.

Indexed as

MULTIPLE SCLEROSISPET

Identifiers

PMID40248672
PMCPMC12004482

What OpenQuestion 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 OpenQuestion graph.