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Although total joint replacement (TJR) surgery is considered as the most effective treatment for advanced osteoarthritis (OA) patients, up to one‐third of patients reported unfavorable long‐term post‐operative pain outcomes. We aimed to identify metabolic biomarkers to predict non‐responders to TJR using a metabolomics approach. TJR patients were recruited and followed‐up at least 1‐year post‐surgery; TJR outcomes were assessed by Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain and function subscales. Targeted metabolomic profiling was performed on plasma samples collected pre‐surgery and pairwise metabolite ratios, as proxies for enzymatic reactions, were calculated. Association tests were performed between each metabolite ratio and non‐responders. The metabolome‐wide significance was defined as p < 2 × 10−5. A total of 461 TJR patients due to primary OA were included in the analysis. Fifteen percent of patients were classified as pain non‐responders; 16% were classified as function non‐responders. Lower baseline WOMAC pain and function scores were significantly associated with pain and function non‐responders, respectively (both p < 0.03). Two metabolite ratios were significantly associated with pain non‐responders; acetylcarnitine (C2) to phosphatidylcholine acyl‐alkyl C40:1 (PC ae C40:1) was five times higher in pain non‐responders whereas phosphatidylcholine diacyl C36:4 (PC aa C36:4) to isoleucine was twenty one times lower in pain non‐responders than responders (all p ≤ 1.93 × 10−5). One metabolite ratio, glutamine to isoleucine, was significantly lower in function non‐responders than responders (eight times lower; p = 1.08 × 10−5). Three metabolite ratios (C2 to PC ae C40:1, PC aa C36:4, and glutamine to isoleucine) related to inflammation and muscle breakdown could be considered as novel plasma markers for predicting non‐responders to TJR and warrant further investigation. © 2019 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 38:793‐802, 2020 [ABSTRACT FROM AUTHOR]

Osteoarthritis (OA) significantly compromises the life quality of affected individuals and imposes a substantial economic burden on our society. Unfortunately the pathogenesis of the disease is till poorly understood and no effective medications have been developed. OA is a complex disease that involves both genetic and environmental influences. To elucidate the complex interlinked structure of metabolic processes associated with OA, we developed a differential correlation network approach to detecting the interconnection of metabolite pairs whose relationships are significantly altered due to the diseased process. Through topological analysis of such a differential network, we identified key metabolites that played an important role in governing the connectivity and information flow of the network. Identification of these key metabolites suggests the association of their underlying cellular processes with OA and may help elucidate the pathogenesis of the disease and the development of novel targeted therapies.