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Metabolic signatures of osteoarthritis in urine using liquid chromatography‐high resolution tandem mass spectrometry

INTRODUCTION: Osteoarthritis (OA) is a common cause of disability in older people, but its aetiology is not yet fully understood. Biomarkers of OA from metabolomics studies have shown potential use in understanding the progression and pathophysiology of OA. OBJECTIVES: To investigate possible surrog...

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Detalles Bibliográficos
Autores principales: Abdelrazig, Salah, Ortori, Catharine A., Doherty, Michael, Valdes, Ana M., Chapman, Victoria, Barrett, David A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7925472/
https://www.ncbi.nlm.nih.gov/pubmed/33655418
http://dx.doi.org/10.1007/s11306-021-01778-3
Descripción
Sumario:INTRODUCTION: Osteoarthritis (OA) is a common cause of disability in older people, but its aetiology is not yet fully understood. Biomarkers of OA from metabolomics studies have shown potential use in understanding the progression and pathophysiology of OA. OBJECTIVES: To investigate possible surrogate biomarkers of knee OA in urine using metabolomics to contribute towards a better understanding of OA progression and possible targeted treatment. METHOD: Liquid chromatography-high resolution mass spectrometry (LC-HRMS) was applied in a case–control approach to explore the possible metabolic differences between the urinary profiles of symptomatic knee OA patients (n = 74) (subclassified into inflammatory OA, n = 22 and non-inflammatory OA, n = 52) and non-OA controls (n = 68). Univariate, multivariate and pathway analyses were performed with a rigorous validation including cross-validation, permutation test, prediction and receiver operating characteristic curve to identify significantly altered metabolites and pathways in OA. RESULTS: OA datasets generated 7405 variables and multivariate analysis showed clear separation of inflammatory OA, but not non-inflammatory OA, from non-OA controls. Adequate cross-validation (R(2)Y = 0.874, Q(2) = 0.465) was obtained. The prediction model and the ROC curve showed satisfactory results with a sensitivity of 88%, specificity of 71% and accuracy of 77%. 26 metabolites were identified as potential biomarkers of inflammatory OA using HMDB, authentic standards and/or MS/MS database. CONCLUSION: Urinary metabolic profiles were altered in inflammatory knee OA subjects compared to those with non-inflammatory OA and non-OA controls. These altered profiles associated with perturbed activity of the TCA cycle, pyruvate and amino acid metabolism linked to inflammation, oxidative stress and collagen destruction. Of note, 2-keto-glutaramic acid level was > eightfold higher in the inflammatory OA patients compared to non-OA control, signalling a possible perturbation in glutamine metabolism related to OA progression. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11306-021-01778-3.