Evidence map›Paper›PMID 28831053›Full record

ArticleScientific reports2017

Large Scale Metabolic Profiling identifies Novel Steroids linked to Rheumatoid Arthritis.

Noha A Yousri, Karim Bayoumy, Wessam Gad Elhaq, Robert P Mohney, Samar Al Emadi, Mohammed Hammoudeh, Hussein Halabi, Basel Masri, Humeira Badsha, Imad Uthman and 4 more

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
2.9field-weighted citation impact, top 9% 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

17 citing papers in PubMed, 29 citations in OpenAlex.

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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

14 authors at 9 institutions in 7 countries.

Noha A YousriPhysiology and Biophysics, Weill Cornell Medical College - Qatar, Education City, Doha, Qatar. nay2005@qatar-med.cornell.edu.
Karim BayoumyResearch Division, Weill Cornell Medical College-Qatar, Education City, Doha, Qatar.
Wessam Gad ElhaqResearch Division, Weill Cornell Medical College-Qatar, Education City, Doha, Qatar.
Robert P MohneyMetabolon Inc., Durham, NC, USA.
Samar Al EmadiDivision of Rheumatology, Department of Medicine, Hamad Medical Corporation, Doha, Qatar.
Mohammed HammoudehDivision of Rheumatology, Department of Medicine, Hamad Medical Corporation, Doha, Qatar.
Hussein HalabiDepartment of Internal Medicine, Division of Rheumatology, King Faisal Specialist Hospital & Research center, Jeddah, Kingdom of Saudi Arabia.
Basel MasriJordan Hospital, Amman, Jordan.
Humeira BadshaDr. Humeira Badsha Medical Center, Dubai, UAE.
Imad UthmanAmerican University of Beirut, Faculty of Medicine, Beirut, Lebanon.
Robert PlengeImmunology & Inflammation, Celgene, Cambridge, MA, USA.
Richa SaxenaCenter for Genomic Medicine, Department of Anesthesia, Critical Care and Pain Medicine Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Karsten SuhrePhysiology and Biophysics, Weill Cornell Medical College - Qatar, Education City, Doha, Qatar.ORCID 0000-0001-9638-3912
Thurayya ArayssiDepartment of Medicine, Weill Cornell Medical College-Qatar, Education City, Doha, Qatar. Tha2002@qatar-med.cornell.edu.ORCID 0000-0003-2469-0272
Weill Cornell Medical College in Qatar · QAHamad Medical Corporation · QAAmerican University of Beirut · LBBroad Institute · USCornell University · USDr. Humeira Badsha Medical Center · AEJordan Hospital · JOKing Faisal Specialist Hospital & Research Centre · SAMetabolon (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent metabolomics studies of Rheumatoid Arthritis (RA) reported few metabolites that were associated with the disease, either due to small cohort sizes or limited coverage of metabolic pathways. Our objective is to identify metabolites associated with RA and its cofounders using a new untargeted metabolomics platform. Moreover, to investigate the pathomechanism of RA by identifying correlations between RA-associated metabolites. 132 RA patients and 104 controls were analyzed for 927 metabolites. Metabolites were tested for association with RA using linear regression. OPLS-DA was used to discriminate RA patients from controls. Gaussian Graphical Models (GGMs) were used to identify correlated metabolites. 32 metabolites are identified as significantly (Bonferroni) associated with RA, including the previously reported metabolites as DHEAS, cortisol and androstenedione and extending that to a larger set of metabolites in the steroid pathway. RA classification using metabolic profiles shows a sensitivity of 91% and specificity of 88%. Steroid levels show variation among the RA patients according to the corticosteroid treatment; lowest in those taking the treatment at the time of the study, higher in those who never took the treatment, and highest in those who took it in the past. Finally, the GGM reflects metabolite relations from the steroidogenesis pathway.

Indexed as

AdultArthritis, RheumatoidBiomarkersCase-Control StudiesFemaleHumansMaleMetabolomicsMiddle AgedNormal DistributionRegression AnalysisSensitivity and SpecificitySteroidsBiomarkersSteroids

Identifiers

PMID28831053
PMCPMC5567269
OpenAlexW2745445970

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.