W Zhang1, G Sun2, S Likhodii3, M Liu1, E Aref-Eshghi1, P E Harper1, G Martin4, A Furey4, R Green1, E Randell3, P Rahman2, G Zhai5. 1. Discipline of Genetics, Faculty of Medicine, Memorial University of Newfoundland, St John's, NL, Canada. 2. Discipline of Medicine, Faculty of Medicine, Memorial University of Newfoundland, St John's, NL, Canada. 3. Department of Laboratory Medicine, Faculty of Medicine, Memorial University of Newfoundland, St John's, NL, Canada. 4. Department of Surgery, Faculty of Medicine, Memorial University of Newfoundland, St John's, NL, Canada. 5. Discipline of Genetics, Faculty of Medicine, Memorial University of Newfoundland, St John's, NL, Canada; Department of Twin Research & Genetic Epidemiology, King's College London, London, UK. Electronic address: guangju.zhai@med.mun.ca.
Abstract
OBJECTIVE: To identify novel biomarker(s) for knee osteoarthritis (OA) using a metabolomics approach. METHOD: We utilized a two-stage case-control study design. Plasma samples were collected from knee OA patients and healthy controls after 8-h fasting and metabolically profiled using a targeted metabolomics assay kit. Linear regression was used to identify novel metabolic markers for OA. Receiver operating characteristic (ROC) analysis was used to examine diagnostic values. Gene expression analysis was performed on human cartilage to explore the potential mechanism for the novel OA marker(s). RESULTS: Sixty-four knee OA patients and 45 controls were included in the discovery stage and 72 knee OA patients and 76 age and sex matched controls were included in the validation stage. We identified and confirmed six metabolites that were significantly associated with knee OA, of which arginine was the most significant metabolite (P < 3.5 × 10(-13)) with knee OA patients having on average 69 μM lower than that in controls. ROC analysis showed that arginine had the greatest diagnostic value with area under the curve (AUC) of 0.984. The optimal cutoff of arginine concentration was 57 μM with 98.3% sensitivity and 89% specificity. The depletion of arginine in OA patients was most likely due to the over activity of arginine to ornithine pathway, leading to imbalance between cartilage repair and degradation. CONCLUSION: Arginine is significantly depleted in refractory knee OA patients. Further studies within a longitudinal setting are required to examine whether arginine can predict early OA changes.
OBJECTIVE: To identify novel biomarker(s) for knee osteoarthritis (OA) using a metabolomics approach. METHOD: We utilized a two-stage case-control study design. Plasma samples were collected from knee OA patients and healthy controls after 8-h fasting and metabolically profiled using a targeted metabolomics assay kit. Linear regression was used to identify novel metabolic markers for OA. Receiver operating characteristic (ROC) analysis was used to examine diagnostic values. Gene expression analysis was performed on humancartilage to explore the potential mechanism for the novel OA marker(s). RESULTS: Sixty-four knee OA patients and 45 controls were included in the discovery stage and 72 knee OA patients and 76 age and sex matched controls were included in the validation stage. We identified and confirmed six metabolites that were significantly associated with knee OA, of which arginine was the most significant metabolite (P < 3.5 × 10(-13)) with knee OA patients having on average 69 μM lower than that in controls. ROC analysis showed that arginine had the greatest diagnostic value with area under the curve (AUC) of 0.984. The optimal cutoff of arginine concentration was 57 μM with 98.3% sensitivity and 89% specificity. The depletion of arginine in OA patients was most likely due to the over activity of arginine to ornithine pathway, leading to imbalance between cartilage repair and degradation. CONCLUSION:Arginine is significantly depleted in refractory knee OA patients. Further studies within a longitudinal setting are required to examine whether arginine can predict early OA changes.
Authors: Guangju Zhai; Xianbang Sun; Edward W Randell; Ming Liu; Na Wang; Irina Tolstykh; Proton Rahman; James Torner; Cora E Lewis; Michael C Nevitt; Ali Guermazi; Frank Roemer; David T Felson Journal: J Rheumatol Date: 2020-05-01 Impact factor: 4.666
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