Evidence map›Paper›PMID 37520535›Full record

ArticleFrontiers in immunology2023

An immuno-lipidomic signature revealed by metabolomic and machine-learning approaches in labial salivary gland to diagnose primary Sjögren's syndrome.

Geoffrey Urbanski, Floris Chabrun, Estelle Delattre, Carole Lacout, Brittany Davidson, Odile Blanchet, Juan Manuel Chao de la Barca, Gilles Simard, Christian Lavigne, Pascal Reynier

Open access · goldAbstract read
In one paragraph

Article in Frontiers in immunology, 2023. 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
1.2field-weighted citation impact, top 21% 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

7 citing papers in PubMed, 7 citations in OpenAlex.

  1. Review
  2. Article
  3. Review
  4. Article
  5. Article
  6. Review
  7. 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.

Geoffrey UrbanskiDepartment of Internal Medicine and Clinical Immunology, University Hospital, Angers, France.
Floris ChabrunMitolab, MitoVasc Institute, CNRS 6015, INSERM U1083, University of Angers, Angers, France.
Estelle DelattreDepartment of Internal Medicine and Clinical Immunology, University Hospital, Angers, France.
Carole LacoutDepartment of Internal Medicine and Clinical Immunology, University Hospital, Angers, France.
Brittany DavidsonDepartment of Orofacial Sciences, University of California, San Francisco, San Francisco, CA, United States.
Odile BlanchetCentre de Ressources Biologiques, University Hospital, Angers, France.
Juan Manuel Chao de la BarcaMitolab, MitoVasc Institute, CNRS 6015, INSERM U1083, University of Angers, Angers, France.
Gilles SimardMitolab, MitoVasc Institute, CNRS 6015, INSERM U1083, University of Angers, Angers, France.
Christian LavigneDepartment of Internal Medicine and Clinical Immunology, University Hospital, Angers, France.
Pascal ReynierMitolab, MitoVasc Institute, CNRS 6015, INSERM U1083, University of Angers, Angers, France.
Université d'Angers · FRInserm · FRUniversity of California, San Francisco · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Assessing labial salivary gland exocrinopathy is a cornerstone in primary Sjögren's syndrome. Currently this relies on the histopathologic diagnosis of focal lymphocytic sialadenitis and computing a focus score by counting lym=phocyte foci. However, those lesions represent advanced stages of primary Sjögren's syndrome, although earlier recognition of primary Sjögren's syndrome and its effective treatment could prevent irreversible damage to labial salivary gland. This study aimed at finding early biomarkers of primary Sjögren's syndrome in labial salivary gland combining metabolomics and machine-learning approaches. Methods: We used a standardized targeted metabolomic approach involving high performance liquid chromatography coupled with mass spectrometry among newly diagnosed primary Sjögren's syndrome (n=40) and non- primary Sjögren's syndrome sicca (n=40) participants in a prospective cohort. A metabolic signature predictive of primary Sjögren's syndrome status was explored using linear (logistic regression with elastic-net regularization) and non-linear (random forests) machine learning architectures, after splitting the data set into training, validation, and test sets. Results: Among 126 metabolites accurately measured, we identified a discriminant signature composed of six metabolites with robust performances (ROC-AUC = 0.86) for predicting primary Sjögren's syndrome status. This signature included the well-known immune-metabolite kynurenine and five phospholipids (LysoPC C28:0; PCaa C26:0; PCaaC30:2; PCae C30:1, and PCaeC30:2). It was split into two main components: the first including the phospholipids was related to the intensity of lymphocytic infiltrates in salivary glands, while the second represented by kynurenine was independently associated with the presence of anti-SSA antibodies in participant serum. Conclusion: Our results reveal an immuno-lipidomic signature in labial salivary gland that accurately distinguishes early primary Sjögren's syndrome from other causes of sicca symptoms.

Indexed as

Sjogren's SyndromeHumansKynurenineProspective StudiesSalivary GlandsSalivary Glands, MinorKynureninebiomarkerkynureninemetabolomicsomicssalivary glandssiccaSjögren’s syndrome

Identifiers

PMID37520535
PMCPMC10375713
OpenAlexW4384297031

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.