Abstract
Introduction
Osteoarthritis (OA) is a common degenerative disorder of the synovial joints and is usually an age-related disease that occurs due to continuous wear and tear of the cartilage in the joints. Presently, there is no proven medical management to halt the progression of the disease in the early stages. The purpose of our systematic review is to analyze the possible metabolites and metabolic pathways that are specifically involved in OA pathogenesis and early treatment of the disease.
Materials and Methods
The articles were collected from PubMed, Cochrane, Google Scholar, Embase, and Scopus databases. “Knee”, “Osteoarthritis”, “Proteomics”, “Lipidomics”, “Metabolomics”, “Metabolic Methods”, and metabolic* were employed for finding the articles. Only original articles with human or animal OA models with healthy controls were included.
Results
From the initial screening, a total of 458 articles were identified from the 5 research databases. From these, 297 articles were selected in the end for screening, of which 53 papers were selected for full-text screening. Finally, 50 articles were taken for the review based on body fluid: 6 urine studies, 15 plasma studies, 16 synovial fluid studies, 11 serum studies, 4 joint tissue studies, and 1 fecal study. Many metabolites were found to be elevated in OA. Some of these metabolites can be used to stage the OA Three pathways that were found to be commonly involved are the TCA cycle, the glycolytic pathway, and the lipid metabolism.
Conclusion
All these studies showed a vast array of metabolites and metabolic pathways associated with OA. Metabolites like lysophospholipids, phospholipids, arginine, BCCA, and histidine were identified as potential biomarkers of OA but a definite association was not identified, Three pathways (glycolytic pathway, TCA cycle, and lipid metabolic pathways) have been found as highly significant in OA pathogenesis. These metabolic pathways could provide novel therapeutic targets for the prevention and progression of the disease.
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs43465-024-01169-5/MediaObjects/43465_2024_1169_Fig1_HTML.png)
Similar content being viewed by others
Data availability
All data is contained within the manuscript.
References
Lawrence, R. C., Felson, D. T., Helmick, C. G., Arnold, L. M., Choi, H., Deyo, R. A., et al. (2008). Estimates of the prevalence of arthritis and other rheumatic conditions in the United States: Part II. Arthritis and Rheumatism, 58(1), 26–35. https://doi.org/10.1002/art.23176
Kohn, M. D., Sassoon, A. A., & Fernando, N. D. (2016). Classifications in brief: Kellgren-Lawrence classification of osteoarthritis. Clinical Orthopaedics and Related Research, 474(8), 1886–1893. https://doi.org/10.1007/s11999-016-4732-4
Wu, Z.-X., Ren, W.-X., & Wang, Z.-Q. (2022). Proximal fibular osteotomy versus high tibial osteotomy for treating knee osteoarthritis: A systematic review and meta-analysis. Journal of Orthopaedic Surgery and Research, 17(1), 470. https://doi.org/10.1186/s13018-022-03299-8
Haartmans, M. J. J., Emanuel, K. S., Tuijthof, G. J. M., Heeren, R. M. A., Emans, P. J., & Cillero-Pastor, B. (2021). Mass spectrometry-based biomarkers for knee osteoarthritis: A systematic review. Expert Review of Proteomics, 18(8), 693–706. https://doi.org/10.1080/14789450.2021.1952868
Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. BMJ, 339, b2535. https://doi.org/10.1136/bmj.b2535
Moola, S., Munn, Z., Sears, K., Sfetcu, R., Currie, M., Lisy, K., et al. (2015). Conducting systematic reviews of association (etiology): The Joanna Briggs Institute’s approach. International Journal of Evidence-Based Healthcare, 13(3), 163–169. https://doi.org/10.1097/XEB.0000000000000064
Hahn, A. K., Batushansky, A., Rawle, R. A., Prado Lopes, E. B., June, R. K., & Griffin, T. M. (2021). Effects of long-term exercise and a high-fat diet on synovial fluid metabolomics and joint structural phenotypes in mice: An integrated network analysis. Osteoarthritis and Cartilage, 29(11), 1549–1563. https://doi.org/10.1016/j.joca.2021.08.008
Van Pevenage, P. M., Birchmier, J. T., & June, R. K. (2023). Utilizing metabolomics to identify potential biomarkers and perturbed metabolic pathways in osteoarthritis: A systematic review. Seminars in Arthritis and Rheumatism, 59, 152163. https://doi.org/10.1016/j.semarthrit.2023.152163
Hu, Y., Wu, Q., Qiao, Y., Zhang, P., Dai, W., Tao, H., et al. (2021). Disturbances in metabolic pathways and the identification of a potential biomarker panel for early cartilage degeneration in a rabbit anterior cruciate ligament transection model. Cartilage, 13(2_suppl), 1376S-1387S. https://doi.org/10.1177/1947603520921434
Carlson, A. K., Rawle, R. A., Wallace, C. W., Brooks, E. G., Adams, E., Greenwood, M. C., et al. (2019). Characterization of synovial fluid metabolomic phenotypes of cartilage morphological changes associated with osteoarthritis. Osteoarthritis and Cartilage, 27(8), 1174–1184. https://doi.org/10.1016/j.joca.2019.04.007
Zhang, W., Sun, G., Likhodii, S., Aref-Eshghi, E., Harper, P., Randell, E., et al. (2016). Metabolomic analysis of human synovial fluid and plasma reveals that phosphatidylcholine metabolism is associated with both osteoarthritis and diabetes mellitus. Metabolomics, 12, 1–10. https://doi.org/10.1007/s11306-015-0937-x
Bocsa, D.-C., Socaciu, C., Iancu, S., Pelea, M.-A., Roșca, R.-I., Leopold, N., et al. (2022). Stage related metabolic profile of synovial fluid in patients with acute flares of knee osteoarthritis. Medicine and Pharmacy Reports, 95, 438–445. https://doi.org/10.15386/mpr-2454
Zhang, W., Likhodii, S., Zhang, Y., Aref-Eshghi, E., Harper, P. E., Randell, E., et al. (2014). Classification of osteoarthritis phenotypes by metabolomics analysis. British Medical Journal Open, 4(11), e006286. https://doi.org/10.1136/bmjopen-2014-006286
Anderson, J. R., Chokesuwattanaskul, S., Phelan, M. M., Welting, T. J. M., Lian, L.-Y., Peffers, M. J., et al. (2018). 1H NMR metabolomics identifies underlying inflammatory pathology in osteoarthritis and rheumatoid arthritis synovial joints. Journal of Proteome Research, 17(11), 3780–3790. https://doi.org/10.1021/acs.jproteome.8b00455
Kim, S., Hwang, J., Kim, J., Ahn, J. K., Cha, H.-S., & Kim, K. H. (2017). Metabolite profiles of synovial fluid change with the radiographic severity of knee osteoarthritis. Joint, Bone, Spine, 84(5), 605–610. https://doi.org/10.1016/j.jbspin.2016.05.018
Yin, H., Wang, L., Li, Q., Zhang, J., Zhang, L., & Wang, X. (2017). Metabolomic analysis of biochemical changes in urine of osteoarthritis rat and interventional effects of Bushen-Huoxue herb couple. Chinese Herbal Medicines, 9(4), 369–375. https://doi.org/10.1016/S1674-6384(17)60117-5
Jiang, H., Liu, J., Qin, X.-J., Chen, Y.-Y., Gao, J.-R., Meng, M., et al. (2018). Gas chromatography-time of flight/mass spectrometry-based metabonomics of changes in the urinary metabolic profile in osteoarthritic rats. Experimental and Therapeutic Medicine, 15(3), 2777–2785. https://doi.org/10.3892/etm.2018.5788
Abdelrazig, S., Ortori, C. A., Doherty, M., Valdes, A. M., Chapman, V., & Barrett, D. A. (2021). Metabolic signatures of osteoarthritis in urine using liquid chromatography-high resolution tandem mass spectrometry. Metabolomics, 17(3), 29. https://doi.org/10.1007/s11306-021-01778-3
Li, X., Yang, S., Qiu, Y., Zhao, T., Chen, T., Su, M., et al. (2010). Urinary metabolomics as a potentially novel diagnostic and stratification tool for knee osteoarthritis. Metabolomics, 6(1), 109–118. https://doi.org/10.1007/s11306-009-0184-0
Loeser, R. F., Pathmasiri, W., Sumner, S. J., McRitchie, S., Beavers, D., Saxena, P., et al. (2016). Association of urinary metabolites with radiographic progression of knee osteoarthritis in overweight and obese adults: An exploratory study. Osteoarthritis and Cartilage, 24(8), 1479–1486. https://doi.org/10.1016/j.joca.2016.03.011
Rushing, B. R., McRitchie, S., Arbeeva, L., Nelson, A. E., Azcarate-Peril, M. A., Li, Y.-Y., et al. (2022). Fecal metabolomics reveals products of dysregulated proteolysis and altered microbial metabolism in obesity-related osteoarthritis. Osteoarthritis and Cartilage, 30(1), 81–91. https://doi.org/10.1016/j.joca.2021.10.006
Murillo-Saich, J. D., Coras, R., Meyer, R., Llorente, C., Lane, N. E., & Guma, M. (2022). Synovial tissue metabolomic profiling reveal biomarkers of synovial inflammation in patients with osteoarthritis. Osteoarthritis and Cartilage Open, 4(3), 100295. https://doi.org/10.1016/j.ocarto.2022.100295
Haudenschild, D. R., Carlson, A. K., Zignego, D. L., Yik, J. H. N., Hilmer, J. K., & June, R. K. (2019). Inhibition of early response genes prevents changes in global joint metabolomic profiles in mouse post-traumatic osteoarthritis. Osteoarthritis and Cartilage, 27(3), 504–512. https://doi.org/10.1016/j.joca.2018.11.006
Wang, S., Song, Y., Xu, F., Liu, H., Shen, Y., Hu, L., et al. (2023). Identification and validation of ferroptosis-related genes in lipopolysaccharide-induced acute lung injury. Cellular Signalling, 108, 110698. https://doi.org/10.1016/j.cellsig.2023.110698
Pousinis, P., Gowler, P. R. W., Burston, J. J., Ortori, C. A., Chapman, V., & Barrett, D. A. (2020). Lipidomic identification of plasma lipids associated with pain behaviour and pathology in a mouse model of osteoarthritis. Metabolomics, 16(3), 32. https://doi.org/10.1007/s11306-020-01652-8
Costello, C. A., Hu, T., Liu, M., Zhang, W., Furey, A., Fan, Z., et al. (2020). Differential correlation network analysis identified novel metabolomics signatures for non-responders to total joint replacement in primary osteoarthritis patients. Metabolomics, 16(5), 61. https://doi.org/10.1007/s11306-020-01683-1
Zhang, W., Sun, G., Likhodii, S., Liu, M., Aref-Eshghi, E., Harper, P. E., et al. (2016). Metabolomic analysis of human plasma reveals that arginine is depleted in knee osteoarthritis patients. Osteoarthritis and Cartilage, 24(5), 827–834. https://doi.org/10.1016/j.joca.2015.12.004
Zhai, G., Sun, X., Randell, E. W., Liu, M., Wang, N., Tolstykh, I., et al. (2021). Phenylalanine is a novel marker for radiographic knee osteoarthritis progression: The MOST study. The Journal of Rheumatology, 48(1), 123–128. https://doi.org/10.3899/jrheum.200054
Maerz, T., Sherman, E., Newton, M., Yilmaz, A., Kumar, P., Graham, S. F., et al. (2018). Metabolomic serum profiling after ACL injury in rats: A pilot study implicating inflammation and immune dysregulation in post-traumatic osteoarthritis. Journal of Orthopaedic Research, 36(7), 1969–1979. https://doi.org/10.1002/jor.23854
Maher, A. D., Coles, C., White, J., Bateman, J. F., Fuller, E. S., Burkhardt, D., et al. (2012). 1H NMR spectroscopy of serum reveals unique metabolic fingerprints associated with subtypes of surgically induced osteoarthritis in sheep. Journal of Proteome Research, 11(8), 4261–4268. https://doi.org/10.1021/pr300368h
Chen, R., Han, S., Liu, X., Wang, K., Zhou, Y., Yang, C., et al. (2018). Perturbations in amino acids and metabolic pathways in osteoarthritis patients determined by targeted metabolomics analysis. Journal of Chromatography B, Analytical Technologies in the Biomedical and Life Sciences, 1085, 54–62. https://doi.org/10.1016/j.jchromb.2018.03.047
Schadler, P., Lohberger, B., Thauerer, B., Faschingbauer, M., Kullich, W., Stradner, M. H., et al. (2022). The association of blood biomarkers and body mass index in knee osteoarthritis: A cross-sectional study. Cartilage, 13(1), 19476035211069252. https://doi.org/10.1177/19476035211069251
**e, Z., Aitken, D., Liu, M., Lei, G., Jones, G., Cicuttini, F., et al. (2022). Serum metabolomic signatures for knee cartilage volume loss over 10 years in community-dwelling older adults. Life, 12(6), 869. https://doi.org/10.3390/life12060869
Adams, S., Setton, L., & Nettles, D. (2013). The role of metabolomics in osteoarthritis research. The Journal of the American Academy of Orthopaedic Surgeons, 21, 63–64. https://doi.org/10.5435/JAAOS-21-01-63
Rockel, J. S., & Kapoor, M. (2018). The metabolome and osteoarthritis: Possible contributions to symptoms and pathology. Metabolites, 8(4), 92. https://doi.org/10.3390/metabo8040092
Berenguer, N. I., Canet, V. J. S., Soler-Canet, C., Segarra, S., García de Carellán, A., & Serra Aguado, C. I. (2024). Changes in the serum metabolome in an inflammatory model of osteoarthritis in rats. International Journal of Molecular Sciences, 25(6), 3158. https://doi.org/10.3390/ijms25063158
Zhai, G., Wang-Sattler, R., Hart, D. J., Arden, N. K., Hakim, A. J., Illig, T., et al. (2010). Serum branched-chain amino acid to histidine ratio: A novel metabolomic biomarker of knee osteoarthritis. Annals of the Rheumatic Diseases, 69(6), 1227–1231. https://doi.org/10.1136/ard.2009.120857
Piccionello, A., Sassaroli, S., Pennasilico, L., Rossi, G., Di Cerbo, A., Riccio, V., et al. (2023). Comparative study of 1H-NMR metabolomic profile of canine synovial fluid in patients affected by four progressive stages of spontaneous osteoarthritis. Scientific Reports, 14(1), 3627. https://doi.org/10.21203/rs.3.rs-3627758/v1
Xu, B., Su, H., Wang, R., Wang, Y., & Zhang, W. (2021). Metabolic networks of plasma and joint fluid base on differential correlation. PLoS ONE, 16(2), e0247191. https://doi.org/10.1371/journal.pone.0247191
Carlson, A. K., Rawle, R. A., Adams, E., Greenwood, M. C., Bothner, B., & June, R. K. (2018). Application of global metabolomic profiling of synovial fluid for osteoarthritis biomarkers. Biochemical and Biophysical Research Communications, 499(2), 182–188. https://doi.org/10.1016/j.bbrc.2018.03.117
Datta, P., Zhang, Y., Parousis, A., Sharma, A., Rossomacha, E., Endisha, H., et al. (2017). High-fat diet-induced acceleration of osteoarthritis is associated with a distinct and sustained plasma metabolite signature. Scientific Reports. https://doi.org/10.1038/s41598-017-07963-6
Zhai, G., Pelletier, J.-P., Liu, M., Randell, E. W., Rahman, P., & Martel-Pelletier, J. (2019). Serum lysophosphatidylcholines to phosphatidylcholines ratio is associated with symptomatic responders to symptomatic drugs in knee osteoarthritis patients. Arthritis Research & Therapy, 21, 224. https://doi.org/10.1186/s13075-019-2006-8
Meessen, J. M. T. A., Saberi-Hosnijeh, F., Bomer, N., den Hollander, W., van der Bom, J. G., van Hilten, J. A., et al. (2020). Serum fatty acid chain length associates with prevalent symptomatic end-stage osteoarthritis, independent of BMI. Scientific Reports, 10(1), 15459. https://doi.org/10.1038/s41598-020-71811-3
Choi, I., Son, H., & Baek, J.-H. (2021). Tricarboxylic acid (TCA) cycle intermediates: Regulators of immune responses. Life (Basel, Switzerland), 11(1), 69. https://doi.org/10.3390/life11010069
Tan, C., Li, L., Han, J., Xu, K., & Liu, X. (2022). A new strategy for osteoarthritis therapy: Inhibition of glycolysis. Frontiers in Pharmacology, 13, 1057229. https://doi.org/10.3389/fphar.2022.1057229
Song, Z., Li, X., **e, J., Han, F., Wang, N., Hou, Y., et al. (2024). Associations of inflammatory cytokines with inflammatory bowel disease: A Mendelian randomization study. Frontiers in Immunology. https://doi.org/10.3389/fimmu.2023.1327879
Cao, X., Cui, Z., Ding, Z., Chen, Y., Wu, S., Wang, X., et al. (2022). An osteoarthritis subtype characterized by synovial lipid metabolism disorder and fibroblast-like synoviocyte dysfunction. Journal of Orthopaedic Translation, 33, 142–152. https://doi.org/10.1016/j.jot.2022.02.007
Senol, O., Gundogdu, G., Gundogdu, K., & Miloglu, F. D. (2019). Investigation of the relationships between knee osteoarthritis and obesity via untargeted metabolomics analysis. Clinical Rheumatology, 38(5), 1351–1360. https://doi.org/10.1007/s10067-019-04428-1
Tootsi, K., Kals, J., Zilmer, M., Paapstel, K., Ottas, A., & Märtson, A. (2018). Medium- and long-chain acylcarnitines are associated with osteoarthritis severity and arterial stiffness in end-stage osteoarthritis patients: A case-control study. International Journal of Rheumatic Diseases, 21(6), 1211–1218. https://doi.org/10.1111/1756-185X.13251
Wen, D. Y. (2000). Intra-articular hyaluronic acid injections for knee osteoarthritis. American Family Physician, 62(3), 565–570.
Akhbari, P., Karamchandani, U., Jaggard, M. K. J., Graça, G., Bhattacharya, R., Lindon, J. C., et al. (2020). Can joint fluid metabolic profiling (or “metabonomics”) reveal biomarkers for osteoarthritis and inflammatory joint disease? Bone & Joint Research, 9(3), 108–119. https://doi.org/10.1302/2046-3758.93.BJR-2019-0167.R1
Lamers, R. J. A. N., van Nesselrooij, J. H. J., Kraus, V. B., Jordan, J. M., Renner, J. B., Dragomir, A. D., et al. (2005). Identification of an urinary metabolite profile associated with osteoarthritis. Osteoarthritis and Cartilage, 13(9), 762–768. https://doi.org/10.1016/j.joca.2005.04.005
Mickiewicz, B., Kelly, J. J., Ludwig, T. E., Weljie, A. M., Wiley, J. P., Schmidt, T. A., et al. (2015). Metabolic analysis of knee synovial fluid as a potential diagnostic approach for osteoarthritis. Journal of Orthopaedic Research, 33(11), 1631–1638. https://doi.org/10.1002/jor.22949
Kosinska, M. K., Liebisch, G., Lochnit, G., Wilhelm, J., Klein, H., Kaesser, U., et al. (2014). Sphingolipids in human synovial fluid—A lipidomic study. PLoS ONE, 9(3), e91769. https://doi.org/10.1371/journal.pone.0091769
Chandra, D., Ashraf, D., Yadav, P., & Raghuvanshi, V. (2023). Synovial fluid proteomics and serum metabolomics reveal molecular and metabolic changes in osteoarthritis. Molecular Biology and Biochemistry, 1, 1–9. https://doi.org/10.5281/zenodo.10255533
Rockel, J. S., Layeghifard, M., Rampersaud, Y. R., Perruccio, A. V., Mahomed, N. N., Davey, J. R., et al. (2022). Identification of a differential metabolite-based signature in patients with late-stage knee osteoarthritis. Osteoarthritis and Cartilage Open, 4(3), 100258. https://doi.org/10.1016/j.ocarto.2022.100258
Rockel, J. S., Zhang, W., Shestopaloff, K., Likhodii, S., Sun, G., Furey, A., et al. (2018). Metabolomics with severity of radiographic knee osteoarthritis and early phase synovitis in middle-aged women from the Iwaki Health Promotion Project: A cross-sectional study. PLoS ONE, 13(6), e0199618. https://doi.org/10.1371/journal.pone.0199618
Sasaki, E., Yamamoto, H., Asari, T., Matsuta, R., Ota, S., Kimura, Y., et al. (2022). Metabolomics with severity of radiographic knee osteoarthritis and early phase synovitis in middle-aged women from the Iwaki Health Promotion Project: A cross-sectional study. Arthritis Research & Therapy, 24(1), 145. https://doi.org/10.1186/s13075-022-02830-w
Werdyani, S., Liu, M., Sun, G., Furey, A., Randell, E., Rahman, P., et al. (2020). Plasma metabolomics identified three distinct endotypes of primary osteoarthritis patients. Osteoarthritis and Cartilage, 28, S23–S24. https://doi.org/10.1016/j.joca.2020.02.036
Huang, Z., He, Z., Kong, Y., Liu, Z., & Gong, L. (2020). Insight into osteoarthritis through integrative analysis of metabolomics and transcriptomics. Clinica Chimica Acta, 510, 323–329. https://doi.org/10.1016/j.cca.2020.07.010
Sun, S., Chen, M., Zhang, T., Wang, Y., Shen, W., Zhang, T., et al. (2024). Identification of key factors in cartilage tissue during the progression of osteoarthritis using a non-targeted metabolomics strategy. Phenomics. https://doi.org/10.1007/s43657-023-00123-z
Welhaven, H. D., Welfley, A. H., Brahmachary, P., Bergstrom, A. R., Houske, E., Glimm, M., et al. (2024). Metabolomic profiles and pathways in osteoarthritic human cartilage: A comparative analysis with healthy cartilage. Metabolites, 14(4), 183. https://doi.org/10.1101/2024.01.25.577269
Tootsi, K., Vilba, K., Märtson, A., Kals, J., Paapstel, K., & Zilmer, M. (2020). Metabolomic signature of amino acids, biogenic amines and lipids in blood serum of patients with severe osteoarthritis. Metabolites, 10(8), 323. https://doi.org/10.3390/metabo10080323
Zhang, Q., Li, H., Zhang, Z., Yang, F., & Chen, J. (2015). Serum metabolites as potential biomarkers for diagnosis of knee osteoarthritis. Disease Markers, 2015, 684794. https://doi.org/10.1155/2015/684794
Acknowledgements
Nil.
Funding
Nil.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
The authors declare that they have no conflict of interest.
Ethical Standard Statement
This article does not contain any studies with human or animal subjects performed by the any of the authors.
Informed Consent
For this type of study informed consent is not required.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Arjun, A., Chellamuthu, G., Jeyaraman, N. et al. Metabolomics in Osteoarthritis Knee: A Systematic Review of Literature. JOIO 58, 813–828 (2024). https://doi.org/10.1007/s43465-024-01169-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s43465-024-01169-5