Urinary biomarkers indicative of recovery from spinal cord injury: A pilot study

Abstract

Current assessments of recovery following spinal cord injury (SCI) focus on clinical outcome measures. These assessments bear an inherent risk of bias, emphasizing the need for more reliable prognostic biomarkers to measure SCI severity. This study evaluated fluid biomarkers as an objective tool to aid with prognosticating outcomes following SCI. Using a ¹H nuclear magnetic resonance (NMR)-based quantitative metabolomics approach of urine samples, the objectives were to determine (a) if alterations in metabolic profiles reflect the extent of recovery of individual SCI patients, (b) whether changes in urine metabolites correlate to patient outcomes, and (c) whether biological pathway analysis reflects mechanisms of neural damage and repair. An inception cohort exploratory pilot study collected morning urine samples from male SCI patients (n=6) following injury and again at 6-months post-injury. A 700 MHz Bruker Avance III HD NMR spectrometer was used to acquire the metabolic signatures of urine samples, which were used to derive metabolic pathways. Multivariate statistical analyses were used to identify changes in metabolic signatures, which were correlated to clinical outcomes in the Spinal Cord Independence Measure (SCIM). Among SCI-induced metabolic changes, biomarkers which significantly correlated to patient SCIM scores included caffeine (R = -0.76, p < 0.01), 3-hydroxymandelic acid (R= -0.85, p < 0.001), L-valine (R = 0.90, p < 0.001; R = -0.64, p < 0.05), and N-methylhydantoin (R = -0.90, p < 0.001). The most affected pathway was purine metabolism. These findings indicate that urinary metabolites reflect SCI lesion severity and recovery and provide potentially prognostic biomarkers of SCI outcome in precision medicine approaches.

Keywords: 1H NMR spectroscopy; Biomarkers; Functional recovery; Metabolomics; Neurorehabilitation; Spinal cord injury; Urine.

Fig. 1

Pearson R correlations showing the correlation between both initial metabolite concentration (A-C) and delta in the metabolite concentration (D, E) to the percentage difference in the patient SCIM scores. Improved patient recovery corresponds to a higher percentage difference in the SCIM score. The R and P-values for each correlation are provided in the top right of each figure.

Fig. 2

Principal Components Analysis (PCA) scores plot (left) and heat map (right) representing unsupervised separation and hierarchical clustering analysis of male SCI patients’ metabolic profiles. The legend indicates the class label: initially one week after SCI and 6 months post-injury. The heat maps illustrate up-regulation versus down-regulation of metabolites significant by the VIAVC best subset (n = 3 bins) and paired T-test/Wilcoxon Mann–Whitney Test (n = 44 bins). Supplementary Table 1 provides the name of the metabolite corresponding to each of the numbers provided to the right of the heat map.

Fig. 3

Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) score plot showing supervised separation between male SCI patients initially (red/squares) and 6 months post-injury (indigo/triangles). This analysis was carried out using a list of urinary metabolites found to be statistically significant by either the paired T-test/Mann–Whitney or VIAVC testing. The 95% confidence interval is indicated by the shaded ellipses. The x- and y-axis show the predictive (between group) and orthogonal (within group) variation, respectively. The following are the cross-validation and permutation measures for the OPLS-DA figures: R²Y = 0.991, p = 0.011; Q² = 0.808, p = 0.002.

Fig. 4

The Receiver Operator Characteristic (ROC) curve represents a high sensitivity and specificity of the group separation between initial and post-injury samples. The corresponding area under the curve (AUC) and confidence interval are indicated on each figure. The ROC curve was constructed using the metabolites determined to be significantly altered based on the VIAVC best subset, which corresponds to 3 bins.

Fig. 5

Metabolic pathway analysis, conducted based on spectral bins that are significant by either the VIAVC best subset or the paired T-test/Wilcoxon Mann-Whitney test. A higher value on the y-axis indicates a lower p-value for the pathway and the x-axis provides the pathway impact, which is a measure of how affected each pathway is by the metabolites identified as significantly altered. The color of each circle is an indication of the p-value, with darker colors being more significant. The size of the circle is proportional to the pathways impact factor. Only pathways with a p-value less than 0.05 are labeled. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)