Evidence map›Paper›PMID 31842967›Full record

ArticleArthritis research & therapy2019

Performance of cytokine models in predicting SLE activity.

Nopparat Ruchakorn, Pintip Ngamjanyaporn, Thanitta Suangtamai, Thanuchporn Kafaksom, Charin Polpanumas, Veerachat Petpisit, Trairak Pisitkun, Prapaporn Pisitkun

Open access · goldAbstract read
In one paragraph

Article in Arthritis research & therapy, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
41citing papers in PubMed, 1 pooled it
7.9field-weighted citation impact, top 2% 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

41 citing papers in PubMed, 1 synthesis or guideline pooled it, 82 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Review
  9. Article
  10. Article
  11. Article
  12. Article
  13. Observational
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Observational
  20. 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

8 authors at 3 institutions in 1 country.

Nopparat RuchakornDivision of Rheumatology, Department of Medicine, Faculty of Medicine, Srinakharinwirot University Ongkharak Campus, Nakorn Nayok, 26120, Thailand.
Pintip NgamjanyapornDivision of Allergy, Immunology, and Rheumatology, Department of Medicine, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, 10400, Thailand.
Thanitta SuangtamaiDivision of Allergy, Immunology, and Rheumatology, Department of Medicine, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, 10400, Thailand.
Thanuchporn KafaksomDivision of Allergy, Immunology, and Rheumatology, Department of Medicine, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, 10400, Thailand.
Charin PolpanumasBridgeAsia Healthcare Ventures, Bangkok, 10330, Thailand.
Veerachat PetpisitBridgeAsia Healthcare Ventures, Bangkok, 10330, Thailand.
Trairak PisitkunCenter of Excellence in Systems Biology, Faculty of Medicine, Chulalongkorn University, Bangkok, 10330, Thailand.
Prapaporn PisitkunDivision of Allergy, Immunology, and Rheumatology, Department of Medicine, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, 10400, Thailand. prapaporn.pis@mahidol.ac.th.ORCID 0000-0001-5749-1267
Mahidol University · THChulalongkorn University · THSrinakharinwirot University · TH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIdentification of universal biomarkers to predict systemic lupus erythematosus (SLE) flares is challenging due to the heterogeneity of the disease. Several biomarkers have been reported. However, the data of validated biomarkers to use as a predictor for lupus flares show variation. This study aimed to identify the biomarkers that are sensitive and specific to predict lupus flares.

methodsOne hundred and twenty-four SLE patients enrolled in this study and were prospectively followed up. The evaluation of disease activity achieved by the SLE disease activity index (SLEDAI-2K) and clinical SLEDAI (modified SLEDAI). Patients with active SLE were categorized into renal or non-renal flares. Serum cytokines were measured by multiplex bead-based flow cytometry. The correlation and logistic regression analysis were performed.

resultsLevels of IFN-α, MCP-1, IL-6, IL-8, and IL-18 significantly increased in active SLE and correlated with clinical SLEDAI. Complement C3 showed a weakly negative relationship with IFN-α and IL-18. IL-18 showed the highest positive likelihood ratios for active SLE. Multiple logistic regression analysis showed that IL-6, IL-8, and IL-18 significantly increased odds ratio (OR) for active SLE at baseline while complement C3 and IL-18 increased OR for active SLE at 12 weeks. IL-18 and IL-6 yielded higher sensitivity and specificity than anti-dsDNA and C3 to predict active renal and active non-renal, respectively.

conclusionThe heterogeneity of SLE pathogenesis leads to different signaling mechanisms and mediates through several cytokines. The monitoring of cytokines increases the sensitivity and specificity to determine SLE disease activity. IL-18 predicts the risk of active renal SLE while IL-6 and IL-8 predict the risk of active non-renal. The sensitivity and specificity of these cytokines are higher than the anti-dsDNA or C3. We propose to use the serum level of IL-18, IL-6, and IL-8 to monitor SLE disease activity in clinical practice.

Indexed as

AdultBiomarkersCytokinesFemaleHumansInflammation MediatorsInterleukin-18Interleukin-6Interleukin-8Lupus Erythematosus, SystemicMaleMiddle AgedPrognosisSensitivity and SpecificitySeverity of Illness IndexYoung AdultBiomarkersCytokinesInflammation MediatorsInterleukin-18Interleukin-6Interleukin-8BiomarkerCytokinesDisease activityInterleukinSystemic lupus erythematosus

Identifiers

PMID31842967
PMCPMC6915901
OpenAlexW2996276729

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.