Evidence map›Paper›PMID 25238064›Full record

ArticlePloS one2014

Metabolomics analysis and modeling suggest a lysophosphocholines-PAF receptor interaction in fibromyalgia.

Pierluigi Caboni, Barbara Liori, Amit Kumar, Maria Laura Santoru, Shailendra Asthana, Enrico Pieroni, Antonella Fais, Benedetta Era, Enrico Cacace, Valeria Ruggiero and 1 more

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed, 1 pooled it
1.4field-weighted citation impact, top 20% 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

36 citing papers in PubMed, 1 synthesis or guideline pooled it, 65 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Article
  4. Article
  5. Article
  6. Article
  7. Redox reactions in chronic pain: mechanisms and relevance in fibromyalgia.Frontiers in pain research (Lausanne, Switzerland) · 2025
    Review
  8. Article
  9. Article
  10. Article
  11. Review
  12. Lysophosphatidylcholine: Potential Target for the Treatment of Chronic Pain.International journal of molecular sciences · 2022
    Review
  13. Observational
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Review
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 1 country.

Pierluigi CaboniDepartment of Life and Environmental Sciences, University of Cagliari, Cagliari, Italy.
Barbara LioriDepartment of Life and Environmental Sciences, University of Cagliari, Cagliari, Italy.
Amit KumarDepartment of Biomedical Sciences, University of Cagliari, Cagliari, Italy; Biomedicine Department, CRS4, Cagliari, Italy.
Maria Laura SantoruDepartment of Biomedical Sciences, University of Cagliari, Cagliari, Italy.
Shailendra AsthanaDepartment of Biomedical Sciences, University of Cagliari, Cagliari, Italy.
Enrico PieroniBiomedicine Department, CRS4, Cagliari, Italy.
Antonella FaisDepartment of Life and Environmental Sciences, University of Cagliari, Cagliari, Italy.
Benedetta EraDepartment of Life and Environmental Sciences, University of Cagliari, Cagliari, Italy.
Enrico CacaceDepartment of Medical Sciences "Mario Aresu", University of Cagliari, Cagliari, Italy.
Valeria RuggieroDepartment of Medical Sciences "Mario Aresu", University of Cagliari, Cagliari, Italy.
Luigi AtzoriDepartment of Biomedical Sciences, University of Cagliari, Cagliari, Italy.
University of Cagliari · ITCluster in Biomedicine · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Fibromyalgia Syndrome (FMS) is a chronic disease characterized by widespread pain, and difficult to diagnose and treat. We analyzed the plasma metabolic profile of patients with FMS by using a metabolomics approach combining Liquid Chromatography-Quadrupole-Time Of Flight/Mass Spectrometry (LC-Q-TOF/MS) with multivariate statistical analysis, aiming to discriminate patients and controls. LC-Q-TOF/MS analysis of plasma (FMS patients: n = 22 and controls: n = 21) identified many lipid compounds, mainly lysophosphocholines (lysoPCs), phosphocholines and ceramides. Multivariate statistical analysis was performed to identify the discriminating metabolites. A protein docking and molecular dynamic (MD) study was then performed, using the most discriminating lysoPCs, to validate the binding to Platelet Activating Factor (1-alkyl-2-acetyl-sn-glycero-3-phosphocholine, PAF) Receptor (PAFr). Discriminating metabolites between FMS patients and controls were identified as 1-tetradecanoyl-sn-glycero-3-phosphocholine [PC(14:0/0:0)] and 1-hexadecanoyl-sn-glycero-3-phosphocholine [PC(16:0/0:0)]. MD and docking indicate that the ligands investigated have similar potentialities to activate the PAFr receptor. The application of a metabolomic approach discriminated FMS patients from controls, with an over-representation of PC(14:0/0:0) and PC(16:0/0:0) compounds in the metabolic profiles. These results and the modeling of metabolite-PAFr interaction, allowed us to hypothesize that lipids oxidative fragmentation might generate lysoPCs in abundance, that in turn will act as PAF-like bioactivators. Overall results suggest disease biomarkers and potential therapeutical targets for FMS.

Indexed as

Models, BiologicalChromatography, LiquidFemaleFibromyalgiaHumansMass SpectrometryMetabolomicsMolecular Dynamics SimulationMultivariate AnalysisPhosphorylcholinePlatelet Membrane GlycoproteinsReceptors, G-Protein-CoupledPhosphorylcholineplatelet activating factor receptorPlatelet Membrane GlycoproteinsReceptors, G-Protein-Coupled

Identifiers

PMID25238064
PMCPMC4169547
OpenAlexW2038250774

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.