Evidence map›Paper›PMID 39867847›Full record

ReviewBrain, behavior, & immunity - health2025

Inflammatory proteins related to depression in multiple sclerosis: A systematic review and meta-analysis.

L A Kiropoulos, V Rozenblat, N Baes

Abstract readReview
In one paragraph

Review in Brain, behavior, & immunity - health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–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

7 citing papers in PubMed.

  1. Review
  2. 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

3 authors.

L A KiropoulosMood and Anxiety Disorders Lab, Melbourne School of Psychological Sciences, University of Melbourne, Victoria, Australia.
V RozenblatMood and Anxiety Disorders Lab, Melbourne School of Psychological Sciences, University of Melbourne, Victoria, Australia.
N BaesMood and Anxiety Disorders Lab, Melbourne School of Psychological Sciences, University of Melbourne, Victoria, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Up to 50% of individuals with multiple sclerosis (MS) experience depression. Depression has been accompanied by increases in inflammatory proteins. This meta-analysis summarized the data on inflammatory protein concentrations and level of depression in individuals with MS. Methods: We performed a meta-analysis of studies measuring inflammatory protein concentrations and level of depression in individuals with MS with a database search of the English literature (to October 2024) and a manual search of references. Quality of evidence was also assessed. Results: Fifteen studies involving measurements of inflammatory proteins and level of depression in 1102 individuals with MS were included in the meta-analysis: five for interleukin (IL)-10 (LPS and PHA), four for tumour necrosis factor (TNF)-α, four for interferon (IFN)-γ, and four for IL-6 (LPS and PHA). A meta-analysis showed that higher concentrations of TNF-α, IFN-γ, IL-6 and IL-10 were significantly associated with higher levels of depression in individuals with MS ( Conclusion: This meta-analysis reports significant associations between higher concentrations of TNF-α, IFN-γ, IL-6 and IL-10 and level of depresson in individuals with MS. Future longitudinal studies with improved reporting of data are needed to replicate these results and confirm the mechanisms through which these inflammatory proteins are present. Meta-analytic findings lend support to depression being associated with the activation of the inflammatory system in individuals with MS.

Indexed as

DepressionInflammatory proteinsMeta-analysisMultiple sclerosis

Identifiers

PMID39867847
PMCPMC11758135

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Registered trials

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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.