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

Salah Abdelrazig, Catharine A Ortori, Michael Doherty, Ana M Valdes, Victoria Chapman, David A Barrett, Salah Abdelrazig, Catharine A Ortori, Michael Doherty, Ana M Valdes, Victoria Chapman, David A Barrett

Abstract

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 (R2Y = 0.874, Q2 = 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.

Keywords: Biomarkers; HILIC; LC-HRMS; Osteoarthritis; Untargeted metabolomics.

Conflict of interest statement

The authors declare no competing financial interest. All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.

Figures

Fig. 1
Fig. 1
PCA and OPLS-DA score plots obtained from all OA participants and non-OA controls. a PCA of non-OA controls (n = 68), inflammatory OA (n = 22), non-inflammatory OA (n = 52) participants and pooled QCs (n = 15), whereas b OPLS-DA of inflammatory OA participants and non-OA controls and c OPLS-DA of non-inflammatory OA participants and non-OA controls analysed by LC-HRMS. d The Significantly altered metabolites were selected using VIP vs p(corr) of OPLS-DA of inflammatory OA participants and non-OA controls
Fig. 2
Fig. 2
PCA-class analysis score plots obtained from a non-OA controls (n = 68, R2X = 0.39, Q2 = − 0.001), b inflammatory OA (n = 22, R2X = 0.35, Q2 = − 0.014) and c non-inflammatory OA (n = 52, R2X = 0.42, Q2 = − 0.006) participants’ urine samples analysed by LC-HRMS
Fig. 3
Fig. 3
Workflow for balancing class size of non-OA control with inflammatory OA for biomarker analysis. a Non-OA control (n = 68) were sub-divided into 3 subsets using multivariate design based on PCA single class analysis. 3 OPLS-DA models were generated from each dataset against inflammatory OA patients’ dataset. SUS plot was used to monitor the similarity of the generated OPLS-DA models. SUS plots were generated for 2 models at a time. This procedure was repeated until the selected subsets of the healthy controls generated adequately similar OPLS-DA models with inflammatory OA patients. b OPLS-DA score plot obtained from inflammatory OA patients (OA active) and the balanced non-OA controls urine samples
Fig. 4
Fig. 4
Pathway analysis of the significantly altered metabolites in inflammatory OA participants compared to non-OA controls. a Pathway analysis, b pathway enrichment analysis and c pathway network map highlighting significantly changed pathways and interactions between the significantly altered metabolites in inflammatory OA participants compared to non-OA controls

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