FREE SHIPPING over $40
OVER 9000 FIVE-STAR REVIEWS
5% OFF ALL SUBSCRIPTIONS
100% HAPPINESS GUARANTEE
Analyze Diet
0
Home / Equine Research Bank / Front Vet Sci / Metabolomic analysis of synovial fluid from healthy and path...
Frontiers in veterinary science2025; 12; 1671176; doi: 10.3389/fvets.2025.1671176

Metabolomic analysis of synovial fluid from healthy and pathological equine joints and tendon sheaths using high-resolution 1H Nuclear Magnetic Resonance.

Abstract: Joint and tendon sheath diseases are a major cause of lameness and reduced performance in horses. Synovial fluid composition changes in response to pathological processes and metabolomic profiling offers a promising approach to detect these alterations. While equine joint metabolomics has been explored, little is known about the metabolomic profile of tendon sheaths. This study aimed to characterize and compare the synovial fluid metabolomic profiles of healthy and pathological joints and tendon sheaths in horses using high-resolution H Nuclear Magnetic Resonance spectroscopy, and to identify potential biomarkers associated with musculoskeletal pathology. Unassigned: Synovial fluid samples were collected from healthy joints and tendon sheaths of routinely slaughtered animals, and from pathological joints and tendon sheaths from owned athletic horses affected by inflammatory or degenerative conditions. The samples were analyzed using H Nuclear Magnetic Resonance spectroscopy. Synovial fluid samples were collected from healthy joints and tendon sheaths of routinely slaughtered animals, and from pathological joints and tendon sheaths from owned athletic horses affected by inflammatory or degenerative conditions. The samples were analyzed using H Nuclear Magnetic Resonance spectroscopy. Unassigned: The metabolomic analysis of equine synovial fluid identified amino acids, organic acids, glucose isomers, and other metabolites. No significant differences were observed in the metabolic profiles of synovial fluid from healthy joints and tendon sheaths (PCA: RX = 0.761, Q2 = 0.372; OPLS-DA: RX = 0.48; RY = 0.292; Q2 = -0.143). In contrast, a clear separation with distinct clustering was observed between healthy and pathological synovial fluid joints and tendon sheaths (PCA: RX = 0.88, Q2 = 0.684; OPLS-DA: RX = 0.775; RY = 0.6772, Q2 = -0.432). Multivariate statistical analysis revealed distinct clustering of healthy joints samples grouping closely with pathological joints samples (OPLS-DA: RX = 0.662; RX = 0.859, Q2 = 0.786). These findings were supported by univariate analysis (t-test, p < 0.05). Similarly, multivariate statistical analysis showed strong discrimination between healthy and pathological tendon sheaths synovial fluid (OPLS-DA: RX = 0.742; RY = 0.892, Q2 = 0.842), also supported by univariate analysis (t-test,  < 0.05). Unassigned: Metabolomic profiling by H-NMR effectively distinguished healthy from pathological synovial fluid in joints and tendon sheaths, providing a clear metabolic fingerprint of disease-related alterations that may support earlier detection and a better understanding of equine musculoskeletal disorders. The main limitation of this study was the small sample size, particularly for tendon sheath samples. Additional synovial fluid specimens from both healthy and pathological joints and tendon sheaths would be needed to implement metabolomic data. High-resolution H Nuclear Magnetic Resonance spectroscopy proves to be a valuable tool for differentiating healthy from pathological equine synovial fluid. Metabolomic analysis revealed a specific metabolic fingerprint in diseased joints and tendon sheaths, supporting its potential role in the diagnosis and monitoring of orthopedic conditions in horses.
Copyright © 2025 Guadalupi, Girelli, Della Tommasa, Corte, Crovace, Fanizzi, Brehm and Lacitignola.
Get Full Text
Publication Date: 2025-12-16 PubMed ID: 41477162PubMed Central: PMC12747927DOI: 10.3389/fvets.2025.1671176Google Scholar: Lookup
The Equine Research Bank provides access to a large database of publicly available scientific literature. Inclusion in the Research Bank does not imply endorsement of study methods or findings by Mad Barn.
LinkedInCopy
  • Journal Article

Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

Overview

  • This study analyzed the chemical composition of synovial fluid from healthy and diseased equine joints and tendon sheaths using high-resolution proton nuclear magnetic resonance (¹H NMR) spectroscopy.
  • The goal was to identify metabolic differences that could serve as biomarkers for musculoskeletal diseases in horses, potentially aiding diagnosis and understanding of joint and tendon sheath disorders.

Background and Importance

  • Joint and tendon sheath diseases are significant causes of lameness and reduced performance in horses, impacting animal welfare and athletic function.
  • Synovial fluid, a lubricating liquid found inside joints and tendon sheaths, changes in its molecular makeup during disease, making it a target for diagnostic biomarker discovery.
  • Metabolomics—the comprehensive analysis of small molecules or metabolites—provides a promising approach to detect these biochemical alterations associated with pathology.
  • While previous research has examined equine joint metabolomics, little was known about the metabolomic profile of tendon sheaths, which are also critical structures affected in musculoskeletal diseases.

Study Design and Methods

  • Synovial fluid samples were collected from:
    • Healthy joints and tendon sheaths from horses slaughtered routinely (serving as controls).
    • Pathological joints and tendon sheaths from athletic horses diagnosed with inflammatory or degenerative conditions.
  • Samples were analyzed using high-resolution ¹H NMR spectroscopy, a technique that detects a range of small molecules based on their unique nuclear magnetic properties.
  • This method allows identification and quantification of multiple metabolites simultaneously, including amino acids, organic acids, and glucose isomers.
  • Multivariate statistical analyses such as Principal Component Analysis (PCA) and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) were used to discern patterns and differences in metabolomic profiles.
  • Univariate statistical tests (t-tests) were also applied to confirm significant differences in individual metabolites.

Key Findings

  • The synovial fluid metabolomic profile included various metabolite classes, notably amino acids, organic acids, and glucose isomers.
  • Comparing healthy joints with healthy tendon sheaths showed no significant metabolic differences, indicating similar baseline metabolite profiles between these tissue types.
  • A clear and statistically significant separation was found between healthy and pathological synovial fluid samples, both in joints and tendon sheaths, based on their metabolomic fingerprints.
  • Statistical analyses demonstrated:
    • Healthy and diseased samples formed distinct clusters, indicating that pathological conditions lead to characteristic metabolic changes.
    • Both PCA and OPLS-DA showed high values of explained variance (R2) and predictive ability (Q2), supporting the robustness of the metabolomic distinctions.
    • Univariate analysis further confirmed significant changes in specific metabolites (p < 0.05), reinforcing the results of multivariate models.

Interpretation and Implications

  • The study demonstrated that high-resolution ¹H NMR metabolomic profiling is effective in distinguishing healthy from pathological synovial fluid in equine joints and tendon sheaths.
  • The unique metabolic fingerprints identified in diseased samples reflect biochemical alterations related to inflammation or degeneration.
  • These metabolic signatures have the potential to:
    • Support earlier and more accurate detection of musculoskeletal diseases in horses.
    • Improve understanding of the underlying biological mechanisms of joint and tendon sheath pathology.
    • Possibly serve as biomarkers for monitoring disease progression or response to therapy.

Limitations and Future Directions

  • The study’s main limitation was the relatively small sample size, especially concerning tendon sheath specimens, which limits the generalizability of the findings.
  • Further research should include larger numbers of synovial fluid samples from both healthy and pathological states to validate and refine the identified metabolic markers.
  • Additional studies could explore longitudinal sampling to track metabolomic changes over time during disease progression or treatment.

Conclusions

  • High-resolution ¹H NMR spectroscopy is a valuable and non-destructive tool for metabolomic analysis of equine synovial fluid.
  • The technique effectively differentiates healthy from diseased joints and tendon sheaths by revealing specific metabolic alterations associated with musculoskeletal pathology.
  • Metabolomic profiling holds promise as a diagnostic aid and a means to enhance understanding of orthopedic conditions in horses, potentially leading to improved clinical outcomes.

Cite This Article

APA
Guadalupi M, Girelli CR, Della Tommasa S, Corte FD, Crovace AM, Fanizzi FP, Brehm W, Lacitignola L. (2025). Metabolomic analysis of synovial fluid from healthy and pathological equine joints and tendon sheaths using high-resolution 1H Nuclear Magnetic Resonance. Front Vet Sci, 12, 1671176. https://doi.org/10.3389/fvets.2025.1671176

Publication

Frontiers in veterinary science
ISSN: 2297-1769
NlmUniqueID: 101666658
Country: Switzerland
Language: English
Volume: 12
Pages: 1671176
PII: 1671176

Researcher Affiliations

Guadalupi, Marta
  • Dipartimento di Medicina di Precisione e Rigenerativa e Area Jonica, Università degli Studi di Bari Aldo Moro, Bari, Italy.
Girelli, Chiara Roberta
  • Department of Biological and Environmental Sciences and Technologies, University of Salento, Lecce, Italy.
Della Tommasa, Simone
  • Department for Horse, University of Leipzig, Leipzig, Germany.
Corte, Federica Della
  • Dipartimento di Medicina di Precisione e Rigenerativa e Area Jonica, Università degli Studi di Bari Aldo Moro, Bari, Italy.
Crovace, Alberto Maria
  • Dipartimento di Medicina Veterinaria, Università di Sassari, Sassari, Italy.
Fanizzi, Francesco Paolo
  • Department of Biological and Environmental Sciences and Technologies, University of Salento, Lecce, Italy.
Brehm, Walter
  • Department for Horse, University of Leipzig, Leipzig, Germany.
Lacitignola, Luca
  • Dipartimento di Medicina di Precisione e Rigenerativa e Area Jonica, Università degli Studi di Bari Aldo Moro, Bari, Italy.

Conflict of Interest Statement

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

This article includes 61 references
  1. McIlwraith CW, Frisbie DD, Kawcak CE, van Weeren R. Joint disease in the horse, Philadelphia, US: Elsevier Health Sciences. (2015).
  2. Martel-Pelletier J, Barr AJ, Cicuttini FM, Conaghan PG, Cooper C, Goldring MB. Osteoarthritis. Nat Rev Dis Primers (2016) 2:16072.
    doi: 10.1038/nrdp.2016.72pubmed: 27734845google scholar: lookup
  3. McIlwraith CW, Kawcak CE, Frisbie DD, Little CB, Clegg PD, Peffers MJ. Biomarkers for equine joint injury and osteoarthritis. J Orthop Res (2018) 36:823–31.
    doi: 10.1002/jor.23738pubmed: 28921609google scholar: lookup
  4. Ortved KF, Nixon AJ. Cell-based cartilage repair strategies in the horse. Vet J (2016) 208:1–12.
    doi: 10.1016/j.tvjl.2015.10.027pubmed: 26702950google scholar: lookup
  5. McIlwraith CW. Use of synovial fluid and serum biomarkers in equine bone and joint disease: a review. Equine Vet J (2005) 37:473–82.
    doi: 10.2746/042516405774480102pubmed: 16163952google scholar: lookup
  6. McIlwraith CW, Kawcak C, Baxter GM, Goodrich LR, Valberg SJ. Principles of musculoskeletal disease. In: Adams and Stashak's lameness in horses, Hoboken, New Jersey, US. (2020) 801–74.
  7. Swami PN, Andriamifidy HF, Haque S, Reed T, Khan A, Grande DA. Exosomes from the synovial microenvironment in joint homeostasis and osteoarthritis. J. Cartil. Joint Preserv. (2024) 4:100220.
    doi: 10.1016/j.jcjp.2024.100220google scholar: lookup
  8. Nguyen LT, Sharma AR, Chakraborty C, Saibaba B, Ahn ME, Lee SS. Review of prospects of biological fluid biomarkers in osteoarthritis. Int J Mol Sci (2017) 18:601.
    doi: 10.3390/ijms18030601pmc: PMC5372617pubmed: 28287489google scholar: lookup
  9. Munjal A, Bapat S, Hubbard D, Hunter M, Kolhe R, Fulzele S. Advances in molecular biomarker for early diagnosis of osteoarthritis. Biomol Concepts (2019) 10:111–9.
    doi: 10.1515/bmc-2019-0014pubmed: 31401621google scholar: lookup
  10. Altobelli E, Angeletti PM, Piccolo D, De Angelis R. Synovial fluid and serum concentrations of inflammatory markers in rheumatoid arthritis, psoriatic arthritis and Osteoarthitis: a systematic review. Curr Rheumatol Rev (2017) 13:170–9.
    doi: 10.2174/1573397113666170427125918pubmed: 28460627google scholar: lookup
  11. Lee H, Lee A, Seo N, Oh J, Kweon OK, An HJ. Discovery of N-glycan biomarkers for the canine osteoarthritis. Life (Basel) (2020) 10:199.
    doi: 10.3390/life10090199pmc: PMC7555374pubmed: 32937769google scholar: lookup
  12. Malemud CJ. Markers of osteoarthritis and cartilage research in animal models. Curr Opin Rheumatol (1993) 5:494–502.
    doi: 10.1097/00002281-199305040-00015pubmed: 8251023google scholar: lookup
  13. de Bakker E, Stroobants V, VanDael F, Ryssen BV, Meyer E. Canine synovial fluid biomarkers for early detection and monitoring of osteoarthritis. Vet Rec (2017) 180:328–9.
    doi: 10.1136/vr.103982pubmed: 28364073google scholar: lookup
  14. Perera TRW, Skerrett-Byrne DA, Gibb Z, Nixon B, Swegen A. The future of biomarkers in veterinary medicine: emerging approaches and associated challenges. Animals Basel (2022) 12:2194.
    doi: 10.3390/ani12172194pmc: PMC9454634pubmed: 36077913google scholar: lookup
  15. Smolinska A, Blanchet L, Buydens LM, Wijmenga SS. NMR and pattern recognition methods in metabolomics: from data acquisition to biomarker discovery: a review. Anal Chim Acta (2012) 750:82–97.
    doi: 10.1016/j.aca.2012.05.049pubmed: 23062430google scholar: lookup
  16. de Sousa EB, Dos Santos GCJ, Duarte MEL, Moura VN, Aguiar DP. Metabolomics as a promising tool for early osteoarthritis diagnosis. Braz J Med Biol Res (2017) 50:e6485.
    doi: 10.1590/1414-431x20176485pmc: PMC5609603pubmed: 28953990google scholar: lookup
  17. Lafeber FP, van Spil WE. Osteoarthritis year 2013 in review: biomarkers; reflecting before moving forward, one step at a time. Osteoarthr Cartil (2013) 21:1452–64.
    doi: 10.1016/j.joca.2013.08.012pubmed: 23954702google scholar: lookup
  18. Machado TS, Correia da Silva LC, Baccarin RY, Michelacci YM. Synovial fluid chondroitin sulphate indicates abnormal joint metabolism in asymptomatic osteochondritic horses. Equine Vet J (2012) 44:404–11.
    doi: 10.1111/j.2042-3306.2011.00539.xpubmed: 22256903google scholar: lookup
  19. Williams RB, Harkins LS, Hammond CJ, Wood JL. Racehorse injuries, clinical problems and fatalities recorded on British racecourses from flat racing and National Hunt racing during 1996, 1997 and 1998. Equine Vet J (2001) 33:478–86.
    doi: 10.2746/042516401776254808pubmed: 11558743google scholar: lookup
  20. Jackson BE, Smith RK, Price JS. A molecular marker of type I collagen metabolism reflects changes in connective tissue remodelling associated with injury to the equine superficial digital flexor tendon. Equine Vet J (2003) 35:211–3.
    doi: 10.2746/042516403776114135pubmed: 12638801google scholar: lookup
  21. Smith RK, Heinegård D. Cartilage oligomeric matrix protein (COMP) levels in digital sheath synovial fluid and serum with tendon injury. Equine Vet J (2000) 32:52–8.
    doi: 10.2746/042516400777612053pubmed: 10661386google scholar: lookup
  22. Smith R, Önnerfjord P, Holmgren K, di Grado S, Dudhia J. Development of a cartilage oligomeric matrix protein neo-epitope assay for the detection of intra-thecal tendon disease. Int J Mol Sci (2020) 21:2155.
    doi: 10.3390/ijms21062155pmc: PMC7139564pubmed: 32245107google scholar: lookup
  23. Smith MR, Wright IM, Minshall GJ, Dudhia J, Verheyen K, Heinegård D. Increased cartilage oligomeric matrix protein concentrations in equine digital flexor tendon sheath synovial fluid predicts intrathecal tendon damage. Vet Surg (2011) 40:54–8.
    doi: 10.1111/j.1532-950X.2010.00751.xpubmed: 21077920google scholar: lookup
  24. Mickiewicz B, Kelly JJ, Ludwig TE, Weljie AM, Wiley JP, Schmidt TA. Metabolic analysis of knee synovial fluid as a potential diagnostic approach for osteoarthritis. J Orthop Res (2015) 33:1631–8.
    doi: 10.1002/jor.22949pubmed: 26010167google scholar: lookup
  25. Balakrishnan L, Nirujogi RS, Ahmad S, Bhattacharjee M, Manda SS, Renuse S. Proteomic analysis of human osteoarthritis synovial fluid. Clin Proteomics (2014) 11:6.
    doi: 10.1186/1559-0275-11-6pmc: PMC3942106pubmed: 24533825google scholar: lookup
  26. Overmyer KA, Muir P, Coon JJ. Discovery metabolomics and lipidomics of canine synovial fluid and serum. Osteoarthr Cartil (2018) 26:S172.
    doi: 10.1016/j.joca.2018.02.374google scholar: lookup
  27. Sa B, Kalinowski HO, Berger S. 150 and more basic NMR experiments. (1998).
  28. Cai W, Yang L, Li J, Li Q. (1)HNMR-based serum metabolomic profiling for patients with diffuse-type tenosynovial giant cell tumor. Asian J Surg (2023) 46:6033–4.
    doi: 10.1016/j.asjsur.2023.09.027pubmed: 37777406google scholar: lookup
  29. Meshitsuka S, Yamazaki E, Inoue M, Hagino H, Teshima R, Yamamoto K. Nuclear magnetic resonance studies of synovial fluids from patients with rheumatoid arthritis and osteoarthritis. Clin Chim Acta (1999) 281:163–7.
    doi: 10.1016/S0009-8981(98)00200-9pubmed: 10217637google scholar: lookup
  30. Volchenkov R, Dung Cao M, Elgstøen KB, Goll GL, Eikvar K, Bjørneboe O. Metabolic profiling of synovial tissue shows altered glucose and choline metabolism in rheumatoid arthritis samples. Scand J Rheumatol (2017) 46:160–1.
    doi: 10.3109/03009742.2016.1164242pubmed: 27098118google scholar: lookup
  31. Van Pevenage PM, Birchmier JT, June RK. Utilizing metabolomics to identify potential biomarkers and perturbed metabolic pathways in osteoarthritis: a systematic review. Semin Arthritis Rheum (2023) 59:152163.
    doi: 10.1016/j.semarthrit.2023.152163pmc: PMC9992342pubmed: 36736024google scholar: lookup
  32. Piccionello AP, Sassaroli S, Pennasilico L, Rossi G, Di Cerbo A, Riccio V. Comparative study of H-NMR metabolomic profile of canine synovial fluid in patients affected by four progressive stages of spontaneous osteoarthritis. Sci Rep (2024) 14:3627.
    doi: 10.1038/s41598-024-54144-3pmc: PMC10864333pubmed: 38351089google scholar: lookup
  33. Pye CR, Green DC, Anderson JR, Phelan MM, Fitzgerald MM, Comerford EJ. Determining predictive metabolomic biomarkers of meniscal injury in dogs with cranial cruciate ligament rupture. J Small Anim Pract (2024) 65:90–103.
    doi: 10.1111/jsap.13688pubmed: 38013167google scholar: lookup
  34. Stabile M, Girelli CR, Lacitignola L, Samarelli R, Crovace A, Fanizzi FP. (1)H-NMR metabolomic profile of healthy and osteoarthritic canine synovial fluid before and after UC-II supplementation. Sci Rep (2022) 12:19716.
    doi: 10.1038/s41598-022-23977-1pmc: PMC9669020pubmed: 36385297google scholar: lookup
  35. Laus F, Gialletti R, Bazzano M, Laghi L, Dini F, Marchegiani A. Synovial fluid metabolome can differentiate between healthy joints and joints affected by osteoarthritis in horses. Meta (2023) 13:913.
    doi: 10.3390/metabo13080913pmc: PMC10456394pubmed: 37623857google scholar: lookup
  36. Lacitignola L, Fanizzi FP, Francioso E, Crovace A. 1H NMR investigation of normal and osteo-arthritic synovial fluid in the horse. Vet Comp Orthop Traumatol (2008) 21:85–8.
    doi: 10.3415/VCOT-06-12-0101pubmed: 18288348google scholar: lookup
  37. Anderson JR, Phelan MM, Caamaño-Gutiérrez E, Clegg PD, Rubio-Martinez LM, Peffers MJ. Metabolomic and proteomic stratification of equine osteoarthritis. Equine Vet J (2025) 57:1204–18.
    doi: 10.1111/evj.14490pmc: PMC12326899pubmed: 39972657google scholar: lookup
  38. Henneke DR, Potter GD, Kreider JL, Yeates BF. Relationship between condition score, physical measurements and body fat percentage in mares. Equine Vet J (1983) 15:371–2.
    doi: 10.1111/j.2042-3306.1983.tb01826.xpubmed: 6641685google scholar: lookup
  39. Lacitignola L, Imperante A, Staffieri F, De Siena R, De Luca P, Muci A. Assessment of intra- and inter-observer measurement variability in a radiographic metacarpophalangeal joint osteophytosis scoring system for the horse. Vet Sci (2020) 7:39.
    doi: 10.3390/vetsci7020039pmc: PMC7357069pubmed: 32268589google scholar: lookup
  40. Laus F, Bazzano M, Spaterna A, Laghi L, Marchegiani A. Nuclear magnetic resonance (nmr) metabolomics: current applications in equine health assessment. Metabolites (2024) 14:269.
    doi: 10.3390/metabo14050269pmc: PMC11123227pubmed: 38786746google scholar: lookup
  41. Brugaletta G, De Cesare A, Laghi L, Manfreda G, Zampiga M, Oliveri C. A multi-omics approach to elucidate the mechanisms of action of a dietary muramidase administered to broiler chickens. Sci Rep (2022) 12:5559.
    doi: 10.1038/s41598-022-09546-6pmc: PMC8976025pubmed: 35365750google scholar: lookup
  42. van den Berg RA, Hoefsloot HCJ, Westerhuis JA, Smilde AK, van der Werf MJ. Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC Genomics (2006) 7:142.
    doi: 10.1186/1471-2164-7-142pmc: PMC1534033pubmed: 16762068google scholar: lookup
  43. Eriksson L, Byrne T, Johansson E, Trygg J, Vikström C. Multi- and Megavariate data analysis basic principles and applications. (2013).
  44. Xia J, Psychogios N, Young N, Wishart DS. MetaboAnalyst: a web server for metabolomic data analysis and interpretation. Nucleic Acids Res (2009) 37:W652–60.
    doi: 10.1093/nar/gkp356pmc: PMC2703878pubmed: 19429898google scholar: lookup
  45. Damyanovich AZ, Staples JR, Marshall KW. 1H NMR investigation of changes in the metabolic profile of synovial fluid in bilateral canine osteoarthritis with unilateral joint denervation. Osteoarthr Cartil (1999) 7:165–72.
    doi: 10.1053/joca.1998.0205pubmed: 10222215google scholar: lookup
  46. Carlson AK, Rawle RA, Wallace CW, Brooks EG, Adams E, Greenwood MC. Characterization of synovial fluid metabolomic phenotypes of cartilage morphological changes associated with osteoarthritis. Osteoarthr Cartil (2019) 27:1174–84.
    doi: 10.1016/j.joca.2019.04.007pmc: PMC6646055pubmed: 31028882google scholar: lookup
  47. Naughton DP, Haywood R, Blake DR, Edmonds S, Hawkes GE, Grootveld M. A comparative evaluation of the metabolic profiles of normal and inflammatory knee-joint synovial fluids by high resolution proton NMR spectroscopy. FEBS Lett (1993) 332:221–5.
    doi: 10.1016/0014-5793(93)80636-9pubmed: 7691662google scholar: lookup
  48. Jiang D, Guo J, Liu Y, Li W, Lu D. Glycolysis: an emerging regulator of osteoarthritis. Front Immunol (2023) 14:1327852.
    doi: 10.3389/fimmu.2023.1327852pmc: PMC10803532pubmed: 38264652google scholar: lookup
  49. Dunham J, Dodds RA, Nahir AM, Frost GT, Catterall A, Bitensky L. Aerobic glycolysis of bone and cartilage: the possible involvement of fatty acid oxidation. Cell Biochem Funct (1983) 1:168–72.
    doi: 10.1002/cbf.290010309pubmed: 6678623google scholar: lookup
  50. Nahir AM. Aerobic glycolysis: a study of human articular cartilage. Cell Biochem Funct (1987) 5:109–12.
    doi: 10.1002/cbf.290050205pubmed: 3581435google scholar: lookup
  51. Zheng L, Zhang Z, Sheng P, Mobasheri A. The role of metabolism in chondrocyte dysfunction and the progression of osteoarthritis. Ageing Res Rev (2021) 66:101249.
    doi: 10.1016/j.arr.2020.101249pubmed: 33383189google scholar: lookup
  52. Pi P, Zeng L, Zeng Z, Zong K, Han B, Bai X. The role of targeting glucose metabolism in chondrocytes in the pathogenesis and therapeutic mechanisms of osteoarthritis: a narrative review. Front Endocrinol (Lausanne) (2024) 15:1319827.
    doi: 10.3389/fendo.2024.1319827pmc: PMC10951080pubmed: 38510704google scholar: lookup
  53. Arjun A, Chellamuthu G, Jeyaraman N, Jeyaraman M, Khanna M. Metabolomics in osteoarthritis knee: a systematic review of literature. Indian J Orthop (2024) 58:813–28.
    doi: 10.1007/s43465-024-01169-5pmc: PMC11208384pubmed: 38948380google scholar: lookup
  54. de Bakker E, Broeckx B, Demeyere K, Stroobants V, Van Ryssen B, Meyer E. Detection of osteoarthritis in dogs by metabolic, pro-inflammatory and degenerative synovial fluid biomarkers and traditional radiographic screening: a pilot study. Vet Immunol Immunopathol (2021) 237:110252.
    doi: 10.1016/j.vetimm.2021.110252pubmed: 34023616google scholar: lookup
  55. Marshall KW, Manolopoulos V, Mancer K, Staples J, Damyanovich A. Amelioration of disease severity by intraarticular hylan therapy in bilateral canine osteoarthritis. J Orthop Res (2000) 18:416–25.
    doi: 10.1002/jor.1100180313pubmed: 10937628google scholar: lookup
  56. Mobasheri A, Rayman MP, Gualillo O, Sellam J, van der Kraan P, Fearon U. The role of metabolism in the pathogenesis of osteoarthritis. Nat Rev Rheumatol (2017) 13:302–11.
    doi: 10.1038/nrrheum.2017.50pubmed: 28381830google scholar: lookup
  57. Peffers M, Riggs C, Phelan M, Clegg P. Identification of disease specific metabolic fingerprints in early osteoarthritis. Equine Vet J (2015) 47:13.
    doi: 10.1111/evj.12486_28google scholar: lookup
  58. Riley C, Vijarnsorn M, Siyuan H, Shaw A. Detection of biochemical variations among normal equine carpal and tarsocrural joint fluids based on infrared spectral characteristics and a modified approach to partial least squares discriminant analysis (2013). doi: 10.5176/2382-5685_VETSCI13.55
    doi: 10.5176/2382-5685_VETSCI13.55google scholar: lookup
  59. Anderson JR, Phelan MM, Clegg PD, Peffers MJ, Rubio-Martinez LM. Synovial fluid metabolites differentiate between septic and nonseptic joint pathologies. J Proteome Res (2018) 17:2735–43.
    doi: 10.1021/acs.jproteome.8b00190pmc: PMC6092013pubmed: 29969035google scholar: lookup
  60. Laus F, Bazzano M, Spaterna A, Laghi L, Marchegiani A. Nuclear magnetic resonance (NMR) metabolomics: current applications in equine health assessment. Meta (2024) 14.
    doi: 10.3390/metabo14050269pmc: PMC11123227pubmed: 38786746google scholar: lookup
  61. Garner BC, Stoker AM, Kuroki K, Evans R, Cook CR, Cook JL. Using animal models in osteoarthritis biomarker research. J Knee Surg (2011) 24:251–64.
    doi: 10.1055/s-0031-1297361pubmed: 22303754google scholar: lookup

Citations

This article has been cited 0 times.
Subscribe to our newsletter
Join 99,005 horse owners like you who receive our email updates on horse health and nutrition.