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

Metabolites, 2023 · DOI: 10.3390/metabo13050605 · Published: April 28, 2023

Simple Explanation

This study explores the potential of using blood metabolites as biomarkers to predict recovery after spinal cord injury (SCI). It uses NMR spectroscopy to analyze blood samples from SCI patients and correlates the metabolite changes with clinical outcomes. The study found that specific metabolites, such as acetyl phosphate, 1,3,7-trimethyluric acid, 1,9-dimethyluric acid, and acetic acid, were significantly related to SCIM scores, suggesting they could be proxy measures of the SCI phenotype and prognostic markers of recovery. The study suggests that analyzing serum metabolites along with machine learning techniques holds promise for understanding the physiology of SCI and predicting outcomes after injury.

Study Duration

6 Months

Participants

7 male patients with complete and incomplete SCI

Evidence Level

Pilot Study

Key Findings

1
Specific metabolites, including acetyl phosphate, 1,3,7-trimethyluric acid and citric acid, showed significant changes in SCI patients.
2
Changes in 1,3,7-trimethyluric acid, 1,9-dimethyluric acid, and acetic acid levels correlated with the degree of functional improvement as measured by SCIM scores.
3
Metabolic pathways significantly affected included pyruvate metabolism, the citrate cycle, glycolysis/gluconeogenesis, alanine, aspartate, the glutamate metabolism, and glyoxylate and dicarboxylate metabolism.

Research Summary

This pilot study investigated whether blood-derived metabolites could serve as prognostic biomarkers for recovery following spinal cord injury (SCI). NMR spectroscopy was used to identify changes in serum metabolic profiles of male SCI patients at initial injury and 6 months post-injury, correlating these changes to clinical outcomes measured by the Spinal Cord Independence Measure (SCIM). The findings suggest that specific metabolites, such as acetyl phosphate, 1,3,7-trimethyluric acid, 1,9-dimethyluric acid and acetic acid, may serve as proxy measures and prognostic markers of SCI recovery, highlighting the potential of metabolomics combined with machine learning in understanding SCI physiology and predicting outcomes.

Practical Implications

Personalized Prognosis

Metabolite profiles could help predict individual recovery trajectories, enabling tailored rehabilitation strategies.

Therapeutic Targets

Identified metabolic pathways may represent novel targets for interventions to promote recovery after SCI.

Clinical Monitoring

Serum metabolite analysis could serve as an objective tool for monitoring treatment effectiveness and patient progress.

Study Limitations

1
Small sample size, limiting generalizability.
2
Lack of female participants, restricting the findings to male SCI patients.
3
Absence of a strict diet regimen and standardized rehabilitation/exercise protocols among participants.

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