ArticleMetabolomics : Official journal of the Metabolomic Society2020
Differential correlation network analysis identified novel metabolomics signatures for non-responders to total joint replacement in primary osteoarthritis patients.
Article in Metabolomics : Official journal of the Metabolomic Society, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.
- Utilizing metabolomics to identify potential biomarkers and perturbed metabolic pathways in osteoarthritis: A systematic review.Seminars in arthritis and rheumatism · 2023Pooled it
- Trial
- Advanced Machine Learning for Comparative Synovial Fluid Analysis in Osteoarthritis and Rheumatoid Arthritis.Metabolites · 2025Article
- Metabolomics in Osteoarthritis Knee: A Systematic Review of Literature.Indian journal of orthopaedics · 2024Review
- Changes in the Serum Metabolome in an Inflammatory Model of Osteoarthritis in Rats.International journal of molecular sciences · 2024Article
- Study on the clinical efficacy of Mongolian medicine in the treatment of osteoarthritis.American journal of translational research · 2023Article
- The role of metabolomics in precision medicine of osteoarthritis: How far are we?Osteoarthritis and cartilage open · 2021Article
- Network Analysis in Systems Epidemiology.Journal of preventive medicine and public health = Yebang Uihakhoe chi · 2021Review
- Seeing the forest for the trees: Retrieving plant secondary biochemical pathways from metabolome networks.Computational and structural biotechnology journal · 2021Review
- Article
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Authors and funding
9 authors at 1 institution in 2 countries.
Funding
Abstract
introductionUp to one third of total joint replacement patients (TJR) experience poor surgical outcome.
objectivesTo identify metabolomic signatures for non-responders to TJR in primary osteoarthritis (OA) patients.
methodsA 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.
resultsDifferential correlation networks involving 12 metabolites and 23 metabolites were identified for pain non-responders and function non-responders, respectively.
conclusionThe differential networks suggest that inflammation, muscle breakdown, wound healing, and metabolic syndrome may all play roles in TJR response, warranting further investigation.
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Registered trials
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