Cerebrospinal fluid and serum proteomic profiles accurately distinguish neuroaxonal dystrophy from cervical vertebral compressive myelopathy in horses.

BACKGROUND

Cervical vertebral compressive myelopathy (CVCM) and equine neuroaxonal dystrophy/degenerative myeloencephalopathy (eNAD/EDM) are leading causes of spinal ataxia in horses. The conditions can be difficult to differentiate, and there is currently no diagnostic modality that offers a definitive antemortem diagnosis.

OBJECTIVE

Evaluate novel proteomic techniques and machine learning algorithms to predict biomarkers that can aid in the antemortem diagnosis of noninfectious spinal ataxia in horses.

ANIMALS

Banked serum and cerebrospinal fluid (CSF) samples from necropsy-confirmed adult eNAD/EDM (n = 47) and CVCM (n = 25) horses and neurologically normal adult horses (n = 45).

METHODS

. A subset of serum and CSF samples from eNAD/EDM (n = 5) and normal (n = 5) horses was used to evaluate the proximity extension assay (PEA). All samples were assayed by PEA for 368 neurologically relevant proteins. Data were analyzed using machine learning algorithms to define potential diagnostic biomarkers.

RESULTS

Of the 368 proteins, 84 were detected in CSF and 146 in serum. Eighteen of 84 proteins in CSF and 30/146 in serum were differentially abundant among the 3 groups, after correction for multiple testing. Modeling indicated that a 2-protein test using CSF had the highest accuracy for discriminating among all 3 groups. Cerebrospinal fluid R-spondin 1 (RSPO1) and neurofilament-light (NEFL), in parallel, predicted normal horses with an accuracy of 87.18%, CVCM with 84.62%, and eNAD/EDM with 73.5%.

MAIN LIMITATIONS

Cross-species platform. Uneven sample size.

CONCLUSIONS AND CLINICAL IMPORTANCE

Proximity extension assay technology allows for rapid screening of equine biologic matrices for potential protein biomarkers. Machine learning analysis allows for unbiased selection of highly accurate biomarkers from high-dimensional data.