Identification of Serum Metabolites as Prognostic Biomarkers Following Spinal Cord Injury: A Pilot Study

Author:

Bykowski Elani A.12,Petersson Jamie N.123,Dukelow Sean45,Ho Chester6,Debert Chantel T.45,Montina Tony23ORCID,Metz Gerlinde A. S.12ORCID

Affiliation:

1. Canadian Centre for Behavioural Neuroscience, Department of Neuroscience, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada

2. Southern Alberta Genome Sciences Centre, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada

3. Department of Chemistry and Biochemistry, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada

4. Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 1N4, Canada

5. Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 1N4, Canada

6. Division of Physical Medicine and Rehabilitation, University of Alberta, Edmonton, AB T6G 2R7, Canada

Abstract

The assessment, management, and prognostication of spinal cord injury (SCI) mainly rely upon observer-based ordinal scales measures. 1H nuclear magnetic resonance (NMR) spectroscopy provides an effective approach for the discovery of objective biomarkers from biofluids. These biomarkers have the potential to aid in understanding recovery following SCI. This proof-of-principle study determined: (a) If temporal changes in blood metabolites reflect the extent of recovery following SCI; (b) whether changes in blood-derived metabolites serve as prognostic indicators of patient outcomes based on the spinal cord independence measure (SCIM); and (c) whether metabolic pathways involved in recovery processes may provide insights into mechanisms that mediate neural damage and repair. Morning blood samples were collected from male complete and incomplete SCI patients (n = 7) following injury and at 6 months post-injury. Multivariate analyses were used to identify changes in serum metabolic profiles and were correlated to clinical outcomes. Specifically, acetyl phosphate, 1,3,7-trimethyluric acid, 1,9-dimethyluric acid, and acetic acid significantly related to SCIM scores. These preliminary findings suggest that specific metabolites may serve as proxy measures of the SCI phenotype and prognostic markers of recovery. Thus, serum metabolite analysis combined with machine learning holds promise in understanding the physiology of SCI and aiding in prognosticating outcomes following injury.

Funder

CIHR

NSERC

Hotchkiss Brain Institute

Publisher

MDPI AG

Subject

Molecular Biology,Biochemistry,Endocrinology, Diabetes and Metabolism

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