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Animals : an open access journal from MDPI2024; 14(7); 986; doi: 10.3390/ani14070986

Infrared Spectroscopy of Synovial Fluid Shows Accuracy as an Early Biomarker in an Equine Model of Traumatic Osteoarthritis.

Abstract: Osteoarthritis is a leading cause of lameness and joint disease in horses. A simple, economical, and accurate diagnostic test is required for routine screening for OA. This study aimed to evaluate infrared (IR)-based synovial fluid biomarker profiling to detect early changes associated with a traumatically induced model of equine carpal osteoarthritis (OA). Unilateral carpal OA was induced arthroscopically in 9 of 17 healthy thoroughbred fillies; the remainder served as Sham-operated controls. The median age of both groups was 2 years. Synovial fluid (SF) was obtained before surgical induction of OA (Day 0) and weekly until Day 63. IR absorbance spectra were acquired from dried SF films. Following spectral pre-processing, predictive models using random forests were used to differentiate OA, Sham, and Control samples. The accuracy for distinguishing between OA and any other joint group was 80%. The classification accuracy by sampling day was 87%. For paired classification tasks, the accuracies by joint were 75% for OA vs. OA Control and 70% for OA vs. Sham. The accuracy for separating horses by group (OA vs. Sham) was 68%. In conclusion, SF IR spectroscopy accurately discriminates traumatically induced OA joints from controls.
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Publication Date: 2024-03-22 PubMed ID: 38612225PubMed Central: PMC11011100DOI: 10.3390/ani14070986Google Scholar: Lookup
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  • 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.

This research aims to verify the accuracy of Infrared (IR)-based synovial fluid testing as a potential early indicator for osteoarthritis (OA) in racing horses. It concludes that this method can differentiate between healthy and osteoarthritic joints with an overall accuracy of 80%.

Research Methodology

  • The study included 17 thoroughbred fillies, a type of horse, with a median age of 2 years.
  • Unilateral carpal osteoarthritis (OA), a type of arthritis affecting a specific joint in the horses’ front leg, was surgically induced in 9 out of the 17 horses. The rest served as sham-operated controls, meaning they underwent a fake surgery to account for the psychological aspects of surgery.
  • Synovial fluid (SF) was collected from all horses before the surgical induction, as well as every week until day 63 after the surgery.
  • The collected fluid was tested using infrared (IR) spectroscopy. This technique identifies molecules based on their IR absorption characteristics. The researchers dried the fluid on slides to create films, from which they derived IR absorbance spectra.

Data Processing and Analysis

  • After obtaining the spectra, the team conducted pre-processing to prepare the data for analysis.
  • Random forests, a machine learning algorithm, was used to build predictive models. These models classified the data samples into respective categories: OA, Sham, and Control.
  • The models’ performance was validated using classification accuracy, a metric indicating the percentage of correctly predicted classes.

Results

  • The study showed an overall accuracy of 80% in distinguishing between OA and any other joint group, indicating the method’s high reliability.
  • For separate data groupings, the accuracy of the model was 87% based on the day of sample collection, 75% for OA vs OA Control, and 70% for OA vs Sham.
  • The study also managed to correctly classify horses into groups (OA vs Sham) with an accuracy of 68%.

Conclusion

  • The study concluded that synovial fluid IR spectroscopy could be a reliable and accurate method to diagnose early signs of osteoarthritis in horses.
  • This method can help in early detection and treatment of the disease, potentially preventing severe damage and pain for the horses.

Cite This Article

APA
Panizzi L, Vignes M, Dittmer KE, Waterland MR, Rogers CW, Sano H, McIlwraith CW, Riley CB. (2024). Infrared Spectroscopy of Synovial Fluid Shows Accuracy as an Early Biomarker in an Equine Model of Traumatic Osteoarthritis. Animals (Basel), 14(7), 986. https://doi.org/10.3390/ani14070986

Publication

Animals : an open access journal from MDPI
ISSN: 2076-2615
NlmUniqueID: 101635614
Country: Switzerland
Language: English
Volume: 14
Issue: 7
PII: 986

Researcher Affiliations

Panizzi, Luca
  • School of Veterinary Science, Massey University, Palmerston North 4442, New Zealand.
Vignes, Matthieu
  • School of Mathematical and Computational Sciences, Massey University, Palmerston North 4442, New Zealand.
Dittmer, Keren E
  • School of Veterinary Science, Massey University, Palmerston North 4442, New Zealand.
Waterland, Mark R
  • School of Natural Sciences, Massey University, Palmerston North 4442, New Zealand.
Rogers, Chris W
  • School of Veterinary Science, Massey University, Palmerston North 4442, New Zealand.
  • School of Agriculture and Environment, Massey University, Palmerston North 4442, New Zealand.
Sano, Hiroki
  • Veterinary Specialty Hospital Hong Kong, G/F-2/F 165-171 Wan Chai Road, Wan Chai, Hong Kong, China.
McIlwraith, C Wayne
  • Orthopaedic Research Center, C. Wayne McIlwraith Translational Medicine Institute, Colorado State University, Fort Collins, CO 80523, USA.
Riley, Christopher B
  • Department of Clinical Studies, Ontario Veterinary College, Guelph, ON N1G 2W1, Canada.

Grant Funding

  • 3000023006 / New Zealand Equine Trust, Massey University

Conflict of Interest Statement

C.W.M. is the New Zealand Equine Trust Chair. He provided his expertise with the model of equine OA used in this study. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. C.W.M. reviewed the manuscript before submission but was not involved in writing, study design, data collection, or analysis. The authors declare no other conflicts of interest.

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Citations

This article has been cited 1 times.
  1. Kumar R, Sporn K, Borole A, Khanna A, Gowda C, Paladugu P, Ngo A, Jagadeesan R, Zaman N, Tavakkoli A. Biomarker-Guided Imaging and AI-Augmented Diagnosis of Degenerative Joint Disease. Diagnostics (Basel) 2025 Jun 3;15(11).
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