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Classification of osteoarthritis phenotypes by metabolomics analysis

OBJECTIVES: To identify metabolic markers that can classify patients with osteoarthritis (OA) into subgroups. DESIGN: A case-only study design was utilised. PARTICIPANTS: Patients were recruited from those who underwent total knee or hip replacement surgery due to primary OA between November 2011 an...

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Autores principales: Zhang, Weidong, Likhodii, Sergei, Zhang, Yuhua, Aref-Eshghi, Erfan, Harper, Patricia E, Randell, Edward, Green, Roger, Martin, Glynn, Furey, Andrew, Sun, Guang, Rahman, Proton, Zhai, Guangju
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4244434/
https://www.ncbi.nlm.nih.gov/pubmed/25410606
http://dx.doi.org/10.1136/bmjopen-2014-006286
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author Zhang, Weidong
Likhodii, Sergei
Zhang, Yuhua
Aref-Eshghi, Erfan
Harper, Patricia E
Randell, Edward
Green, Roger
Martin, Glynn
Furey, Andrew
Sun, Guang
Rahman, Proton
Zhai, Guangju
author_facet Zhang, Weidong
Likhodii, Sergei
Zhang, Yuhua
Aref-Eshghi, Erfan
Harper, Patricia E
Randell, Edward
Green, Roger
Martin, Glynn
Furey, Andrew
Sun, Guang
Rahman, Proton
Zhai, Guangju
author_sort Zhang, Weidong
collection PubMed
description OBJECTIVES: To identify metabolic markers that can classify patients with osteoarthritis (OA) into subgroups. DESIGN: A case-only study design was utilised. PARTICIPANTS: Patients were recruited from those who underwent total knee or hip replacement surgery due to primary OA between November 2011 and December 2013 in St. Clare's Mercy Hospital and Health Science Centre General Hospital in St. John's, capital of Newfoundland and Labrador (NL), Canada. 38 men and 42 women were included in the study. The mean age was 65.2±8.7 years. OUTCOME MEASURES: Synovial fluid samples were collected at the time of their joint surgeries. Metabolic profiling was performed on the synovial fluid samples by the targeted metabolomics approach, and various analytic methods were utilised to identify metabolic markers for classifying subgroups of patients with OA. Potential confounders such as age, sex, body mass index (BMI) and comorbidities were considered in the analysis. RESULTS: Two distinct patient groups, A and B, were clearly identified in the 80 patients with OA. Patients in group A had a significantly higher concentration on 37 of 39 acylcarnitines, but the free carnitine was significantly lower in their synovial fluids than in those of patients in group B. The latter group was further subdivided into two subgroups, that is, B1 and B2. The corresponding metabolites that contributed to the grouping were 86 metabolites including 75 glycerophospholipids (6 lysophosphatidylcholines, 69 phosphatidylcholines), 9 sphingolipids, 1 biogenic amine and 1 acylcarnitine. The grouping was not associated with any known confounders including age, sex, BMI and comorbidities. The possible biological processes involved in these clusters are carnitine, lipid and collagen metabolism, respectively. CONCLUSIONS: The study demonstrated that OA consists of metabolically distinct subgroups. Identification of these distinct subgroups will help to unravel the pathogenesis and develop targeted therapies for OA.
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spelling pubmed-42444342014-11-28 Classification of osteoarthritis phenotypes by metabolomics analysis Zhang, Weidong Likhodii, Sergei Zhang, Yuhua Aref-Eshghi, Erfan Harper, Patricia E Randell, Edward Green, Roger Martin, Glynn Furey, Andrew Sun, Guang Rahman, Proton Zhai, Guangju BMJ Open Rheumatology OBJECTIVES: To identify metabolic markers that can classify patients with osteoarthritis (OA) into subgroups. DESIGN: A case-only study design was utilised. PARTICIPANTS: Patients were recruited from those who underwent total knee or hip replacement surgery due to primary OA between November 2011 and December 2013 in St. Clare's Mercy Hospital and Health Science Centre General Hospital in St. John's, capital of Newfoundland and Labrador (NL), Canada. 38 men and 42 women were included in the study. The mean age was 65.2±8.7 years. OUTCOME MEASURES: Synovial fluid samples were collected at the time of their joint surgeries. Metabolic profiling was performed on the synovial fluid samples by the targeted metabolomics approach, and various analytic methods were utilised to identify metabolic markers for classifying subgroups of patients with OA. Potential confounders such as age, sex, body mass index (BMI) and comorbidities were considered in the analysis. RESULTS: Two distinct patient groups, A and B, were clearly identified in the 80 patients with OA. Patients in group A had a significantly higher concentration on 37 of 39 acylcarnitines, but the free carnitine was significantly lower in their synovial fluids than in those of patients in group B. The latter group was further subdivided into two subgroups, that is, B1 and B2. The corresponding metabolites that contributed to the grouping were 86 metabolites including 75 glycerophospholipids (6 lysophosphatidylcholines, 69 phosphatidylcholines), 9 sphingolipids, 1 biogenic amine and 1 acylcarnitine. The grouping was not associated with any known confounders including age, sex, BMI and comorbidities. The possible biological processes involved in these clusters are carnitine, lipid and collagen metabolism, respectively. CONCLUSIONS: The study demonstrated that OA consists of metabolically distinct subgroups. Identification of these distinct subgroups will help to unravel the pathogenesis and develop targeted therapies for OA. BMJ Publishing Group 2014-11-19 /pmc/articles/PMC4244434/ /pubmed/25410606 http://dx.doi.org/10.1136/bmjopen-2014-006286 Text en Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
spellingShingle Rheumatology
Zhang, Weidong
Likhodii, Sergei
Zhang, Yuhua
Aref-Eshghi, Erfan
Harper, Patricia E
Randell, Edward
Green, Roger
Martin, Glynn
Furey, Andrew
Sun, Guang
Rahman, Proton
Zhai, Guangju
Classification of osteoarthritis phenotypes by metabolomics analysis
title Classification of osteoarthritis phenotypes by metabolomics analysis
title_full Classification of osteoarthritis phenotypes by metabolomics analysis
title_fullStr Classification of osteoarthritis phenotypes by metabolomics analysis
title_full_unstemmed Classification of osteoarthritis phenotypes by metabolomics analysis
title_short Classification of osteoarthritis phenotypes by metabolomics analysis
title_sort classification of osteoarthritis phenotypes by metabolomics analysis
topic Rheumatology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4244434/
https://www.ncbi.nlm.nih.gov/pubmed/25410606
http://dx.doi.org/10.1136/bmjopen-2014-006286
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