Dataset Information
Differential correlation network analysis identified novel metabolomics signatures for non-responders to total joint replacement in primary osteoarthritis patients.
Ontology highlight
ABSTRACT: Introduction
Up to one third of total joint replacement patients (TJR) experience poor surgical outcome.Objectives
To identify metabolomic signatures for non-responders to TJR in primary osteoarthritis (OA) patients.Methods
A newly developed differential correlation network analysis method was applied to our previously published metabolomic dataset to identify metabolomic network signatures for non-responders to TJR.Results
Differential correlation networks involving 12 metabolites and 23 metabolites were identified for pain non-responders and function non-responders, respectively.Conclusion
The differential networks suggest that inflammation, muscle breakdown, wound healing, and metabolic syndrome may all play roles in TJR response, warranting further investigation.
SUBMITTER: Costello CA
PROVIDER: S-EPMC7183485 | biostudies-literature | 2020 Apr
REPOSITORIES: biostudies-literature
Publications
Differential correlation network analysis identified novel metabolomics signatures for non-responders to total joint replacement in primary osteoarthritis patients.
Metabolomics : Official journal of the Metabolomic Society 20200425 5
<h4>Introduction</h4>Up to one third of total joint replacement patients (TJR) experience poor surgical outcome.<h4>Objectives</h4>To identify metabolomic signatures for non-responders to TJR in primary osteoarthritis (OA) patients.<h4>Methods</h4>A newly developed differential correlation network analysis method was applied to our previously published metabolomic dataset to identify metabolomic network signatures for non-responders to TJR.<h4>Results</h4>Differential correlation networks involv ...[more]