Weidong Zhang1, Sergei Likhodii1, Erfan Aref-Eshghi1, Yuhua Zhang1, Patricia E Harper1, Edward Randell1, Roger Green1, Glynn Martin1, Andrew Furey1, Guang Sun1, Proton Rahman1, Guangju Zhai2. 1. From the Discipline of Genetics, Department of Laboratory Medicine, Department of Surgery, Discipline of Medicine, Faculty of Medicine, Memorial University of Newfoundland (MUN), St. John's, Newfoundland, Canada; Department of Twin Research and Genetic Epidemiology, King's College London, London, UK.W. Zhang, PhD, Postdoctoral Fellow, Discipline of Genetics; S. Likhodii, PhD, Assistant Professor, Department of Laboratory Medicine; E. Aref-Eshghi, MD, PhD student; Y. Zhang, MSc, Statistician; P.E. Harper, BSC, MSc student, Discipline of Genetics; E. Randell, PhD, Associate Professor, Department of Laboratory Medicine; R. Green, PhD, Professor, Discipline of Genetics; G. Martin, MD, Orthopedic Fellow; A. Furey, MD, Associate Professor, Department of Surgery; G. Sun, PhD, Professor; P. Rahman, MD, Professor, Discipline of Medicine, Faculty of Medicine, MUN; G. Zhai, PhD, Associate Professor, Discipline of Genetics, MUN, and Department of Twin Research and Genetic Epidemiology, King's College London. 2. From the Discipline of Genetics, Department of Laboratory Medicine, Department of Surgery, Discipline of Medicine, Faculty of Medicine, Memorial University of Newfoundland (MUN), St. John's, Newfoundland, Canada; Department of Twin Research and Genetic Epidemiology, King's College London, London, UK.W. Zhang, PhD, Postdoctoral Fellow, Discipline of Genetics; S. Likhodii, PhD, Assistant Professor, Department of Laboratory Medicine; E. Aref-Eshghi, MD, PhD student; Y. Zhang, MSc, Statistician; P.E. Harper, BSC, MSc student, Discipline of Genetics; E. Randell, PhD, Associate Professor, Department of Laboratory Medicine; R. Green, PhD, Professor, Discipline of Genetics; G. Martin, MD, Orthopedic Fellow; A. Furey, MD, Associate Professor, Department of Surgery; G. Sun, PhD, Professor; P. Rahman, MD, Professor, Discipline of Medicine, Faculty of Medicine, MUN; G. Zhai, PhD, Associate Professor, Discipline of Genetics, MUN, and Department of Twin Research and Genetic Epidemiology, King's College London. guangju.zhai@med.mun.ca.
Abstract
OBJECTIVE: To investigate the relationship between plasma and synovial fluid (SF) metabolite concentrations in patients with osteoarthritis (OA). METHODS: Blood plasma and SF samples were collected from patients with primary knee OA undergoing total knee arthroplasty. Metabolic profiling was performed by electrospray ionization tandem mass spectrometry using the AbsoluteIDQ kit. The profiling yielded 168 metabolite concentrations. Correlation analysis between SF and plasma metabolite concentrations was done on absolute concentrations as well as metabolite concentration ratios using Spearman's rank correlation (ρ) method. RESULTS: A total of 69 patients with knee OA were included, 30 men and 39 women, with an average age of 66 ± 8 years. For the absolute metabolite concentrations, the average ρ was 0.23 ± 0.13. Only 8 out of 168 metabolite concentrations had a ρ ≥ 0.45, with a p value ≤ 2.98 × 10(-4), statistically significant after correcting multiple testing with the Bonferroni method. For the metabolite ratios (n = 28,056), the average ρ was 0.29 ± 0.20. There were 4018 metabolite ratios with a ρ ≥ 0.52 and a p value ≤ 1.78 × 10(-6), significant after correcting multiple testing. Sex-separate analyses found no difference in ρ between men and women. Similarly, there was no difference in ρ between people younger and older than 65 years. CONCLUSION: Correlation between blood plasma and SF metabolite concentrations are modest. Metabolite ratios, which are considered proxies for enzymatic reaction rates and have higher correlations, should be considered when using blood plasma as a surrogate of SF in OA biomarker identification.
OBJECTIVE: To investigate the relationship between plasma and synovial fluid (SF) metabolite concentrations in patients with osteoarthritis (OA). METHODS: Blood plasma and SF samples were collected from patients with primary knee OA undergoing total knee arthroplasty. Metabolic profiling was performed by electrospray ionization tandem mass spectrometry using the AbsoluteIDQ kit. The profiling yielded 168 metabolite concentrations. Correlation analysis between SF and plasma metabolite concentrations was done on absolute concentrations as well as metabolite concentration ratios using Spearman's rank correlation (ρ) method. RESULTS: A total of 69 patients with knee OA were included, 30 men and 39 women, with an average age of 66 ± 8 years. For the absolute metabolite concentrations, the average ρ was 0.23 ± 0.13. Only 8 out of 168 metabolite concentrations had a ρ ≥ 0.45, with a p value ≤ 2.98 × 10(-4), statistically significant after correcting multiple testing with the Bonferroni method. For the metabolite ratios (n = 28,056), the average ρ was 0.29 ± 0.20. There were 4018 metabolite ratios with a ρ ≥ 0.52 and a p value ≤ 1.78 × 10(-6), significant after correcting multiple testing. Sex-separate analyses found no difference in ρ between men and women. Similarly, there was no difference in ρ between people younger and older than 65 years. CONCLUSION: Correlation between blood plasma and SF metabolite concentrations are modest. Metabolite ratios, which are considered proxies for enzymatic reaction rates and have higher correlations, should be considered when using blood plasma as a surrogate of SF in OA biomarker identification.
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