Untargeted Metabolomics Analysis using LC-MSQTOF for Metabolite Profile Comparison between Patients with Myofascial Pain of Upper Trapezius Muscle versus Controls
Issued Date
2022-11-01
Resource Type
eISSN
22288082
Scopus ID
2-s2.0-85141779793
Journal Title
Siriraj Medical Journal
Volume
74
Issue
11
Start Page
792
End Page
803
Rights Holder(s)
SCOPUS
Bibliographic Citation
Siriraj Medical Journal Vol.74 No.11 (2022) , 792-803
Suggested Citation
Vannabhum M., Harnphadungkit K., Akarasereenont P., Tapechum S. Untargeted Metabolomics Analysis using LC-MSQTOF for Metabolite Profile Comparison between Patients with Myofascial Pain of Upper Trapezius Muscle versus Controls. Siriraj Medical Journal Vol.74 No.11 (2022) , 792-803. 803. doi:10.33192/Smj.2022.94 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/85404
Title
Untargeted Metabolomics Analysis using LC-MSQTOF for Metabolite Profile Comparison between Patients with Myofascial Pain of Upper Trapezius Muscle versus Controls
Author's Affiliation
Other Contributor(s)
Abstract
Objective: This study aims to identify different biomarkers of Myofascial pain syndrome (MPS) using untargeted metabolomics screening. Materials and Methods: In a case-control study, serum samples from MPS patients (n = 19) and healthy controls (n = 10) were analyzed using reverse-phase liquid chromatography and mass spectrometry quadrupole time-of -flight (MS-QTOF). The resulted raw data was processed with Progenesis QI data analysis software. The HMBD database was used to identify the metabolites based on their fold change (>1.2), variable importance plot (>1) with P < 0.05. MetaboAnalyst 5.0 was used to generate metabolic network analysis for all identified metabolites. Results: The MPS group reported significantly higher pain on visual analog scale when compared with control while most of the other routine blood chemical profiles were not different. Twenty-seven metabolites were analyzed and identified with untargeted metabolomics analysis which could distinguish MPS patients from healthy controls. Inosine and chenodeoxycholic acid were abundant in the MPS group, whereas the others were low. Metabolites were divided into three categories: lipids, nucleotides, and organic compounds. Possible MPS metabolites included lysoSM (sphingomyelin), lysoPC (lysophosphatidylcholine), lysoPE (lysophosphatidylethanolamine), triglyceride, and inosine. Conclusion: These metabolite profiles, including glycerophospholipids mechanism and purine metabolism, indicate that the inflammatory process might be related to the mechanisms of MPS. A larger sample size, a different trigger point location, and modifications in therapy afterward should all be further explored.