Reliable and validated biomarkers for osteoarthritis (OA) are currently lacking.
To develop an accurate and minimally invasive method to assess OA-affected horses and provide potential spectral markers indicative of disease.
Observational, cross-sectional study.
Our cohort consisted of 15 horses with OA and 48 without clinical signs of the disease, which were used as controls. Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy was used to investigate serum samples (50 μL) collected from these horses. Spectral processing and multivariate analysis revealed differences and similarities, allowing for detection of spectral biomarkers that discriminated between the two cohorts. A supervised classification algorithm, namely principal component analysis coupled with quadratic discriminant analysis (PCA-QDA), was applied to evaluate the diagnostic accuracy.
Segregation between the two different cohorts, OA-affected and controls, was achieved with 100% sensitivity and specificity. The six most discriminatory peaks were attributed to proteins and lipids. Four of the spectral peaks were elevated in OA horses, which could be potentially due to an increase in lipids, protein expression levels and collagen, all of which have been previously reported in OA. Two peaks were found decreased and were tentatively assigned to the reduction of proteoglycan content that is observed during OA.
The control group had a wide range of ages and breeds. Presymptomatic OA cases were not included. Therefore, it remains unknown whether this test could also be used as an early diagnostic tool.
This spectrochemical approach could provide an accurate and cost-effective blood test, facilitating point-of-care diagnosis of equine OA.