Large Scale Metabolic Profiling identifies Novel Steroids linked to Rheumatoid Arthritis

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 unt...

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Autores principales:	Noha A. Yousri, Karim Bayoumy, Wessam Gad Elhaq, Robert P. Mohney, Samar Al Emadi, Mohammed Hammoudeh, Hussein Halabi, Basel Masri, Humeira Badsha, Imad Uthman, Robert Plenge, Richa Saxena, Karsten Suhre, Thurayya Arayssi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ba383362ef77417ab01af14566d56760
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spelling	oai:doaj.org-article:ba383362ef77417ab01af14566d567602021-12-02T16:06:50ZLarge Scale Metabolic Profiling identifies Novel Steroids linked to Rheumatoid Arthritis10.1038/s41598-017-05439-12045-2322https://doaj.org/article/ba383362ef77417ab01af14566d567602017-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-05439-1https://doaj.org/toc/2045-2322Abstract 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.Noha A. YousriKarim BayoumyWessam Gad ElhaqRobert P. MohneySamar Al EmadiMohammed HammoudehHussein HalabiBasel MasriHumeira BadshaImad UthmanRobert PlengeRicha SaxenaKarsten SuhreThurayya ArayssiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-9 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Noha A. Yousri Karim Bayoumy Wessam Gad Elhaq Robert P. Mohney Samar Al Emadi Mohammed Hammoudeh Hussein Halabi Basel Masri Humeira Badsha Imad Uthman Robert Plenge Richa Saxena Karsten Suhre Thurayya Arayssi Large Scale Metabolic Profiling identifies Novel Steroids linked to Rheumatoid Arthritis
description	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.
format	article
author	Noha A. Yousri Karim Bayoumy Wessam Gad Elhaq Robert P. Mohney Samar Al Emadi Mohammed Hammoudeh Hussein Halabi Basel Masri Humeira Badsha Imad Uthman Robert Plenge Richa Saxena Karsten Suhre Thurayya Arayssi
author_facet	Noha A. Yousri Karim Bayoumy Wessam Gad Elhaq Robert P. Mohney Samar Al Emadi Mohammed Hammoudeh Hussein Halabi Basel Masri Humeira Badsha Imad Uthman Robert Plenge Richa Saxena Karsten Suhre Thurayya Arayssi
author_sort	Noha A. Yousri
title	Large Scale Metabolic Profiling identifies Novel Steroids linked to Rheumatoid Arthritis
title_short	Large Scale Metabolic Profiling identifies Novel Steroids linked to Rheumatoid Arthritis
title_full	Large Scale Metabolic Profiling identifies Novel Steroids linked to Rheumatoid Arthritis
title_fullStr	Large Scale Metabolic Profiling identifies Novel Steroids linked to Rheumatoid Arthritis
title_full_unstemmed	Large Scale Metabolic Profiling identifies Novel Steroids linked to Rheumatoid Arthritis
title_sort	large scale metabolic profiling identifies novel steroids linked to rheumatoid arthritis
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/ba383362ef77417ab01af14566d56760
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Large Scale Metabolic Profiling identifies Novel Steroids linked to Rheumatoid Arthritis

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