Blood-Derived Metabolic Signatures as Biomarkers of Injury Severity in Traumatic Brain Injury: A Pilot Study

Author:

Bykowski Elani A.12ORCID,Petersson Jamie N.123,Dukelow Sean P.45,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

Metabolomic biomarkers hold promise in aiding the diagnosis and prognostication of traumatic brain injury. In Canada, over 165,000 individuals annually suffer from a traumatic brain injury (TBI), making it one of the most prevalent neurological conditions. In this pilot investigation, we examined blood-derived biomarkers as proxy measures that can provide an objective approach to TBI diagnosis and monitoring. Using a 1H nuclear magnetic resonance (NMR)-based quantitative metabolic profiling approach, this study determined whether (1) blood-derived metabolites change during recovery in male participants with mild to severe TBI; (2) biological pathway analysis reflects mechanisms that mediate neural damage/repair throughout TBI recovery; and (3) changes in metabolites correlate to initial injury severity. Eight male participants with mild to severe TBI (with intracranial lesions) provided morning blood samples within 1–4 days and again 6 months post-TBI. Following NMR analysis, the samples were subjected to multivariate statistical and machine learning-based analyses. Statistical modelling displayed metabolic changes during recovery through group separation, and eight significant metabolic pathways were affected by TBI. Metabolic changes were correlated to injury severity. L-alanine (R= −0.63, p < 0.01) displayed a negative relationship with the Glasgow Coma Scale. This study provides pilot data to support the feasibility of using blood-derived metabolites to better understand changes in biochemistry following TBI.

Funder

The Hotchkiss Brain Institute at the University of Calgary

CIHR Project Scheme

NSERC Discovery

CIHR CGS-M studentship

Publisher

MDPI AG

Subject

Molecular Biology,Biochemistry,Endocrinology, Diabetes and Metabolism

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