Development of a Systems Medicine Approach to Spinal Cord Injury

Journal of Neurotrauma, 2023 · DOI: 10.1089/neu.2023.0024 · Published: September 1, 2023

Simple Explanation

Traumatic spinal cord injury (SCI) causes a sudden onset multi-system disease, permanently altering homeostasis with multiple complications. Consequences include aberrant neuronal circuits, multiple organ system dysfunctions, and chronic phenotypes such as neuropathic pain and metabolic syndrome. Reductionist approaches are used to classify SCI patients based on residual neurological function. Still, recovery varies due to interacting variables, including individual biology, comorbidities, complications, therapeutic side effects, and socioeconomic inﬂuences for which data integration methods are lacking. To better understand the evolution from acute SCI to chronic SCI multi-system states, we propose a topological phenotype framework integrating bioinformatics, physiological data, and allostatic load tested against accepted established recovery metrics. This form of correlational phenotyping may reveal critical nodal points for intervention to improve recovery trajectories.

Study Duration

Not specified

Participants

Not specified

Evidence Level

Not specified

Key Findings

1
Acute injury outcome predictors, including blood and cerebrospinal ﬂuid biomarkers, neuroimaging signal changes, and autonomic system abnormalities, often do not predict chronic SCI syndrome phenotypes.
2
Interactions between interdependent systems produce emergent effects, such as resilience, that preclude single mechanism interpretations.
3
In systems medicine, network analysis of bioinformatics data is used to derive molecular control modules.

Research Summary

Acute SCI is a critically dysregulated dynamic multi-system condition characterized by multiple interacting molecular and physiologic modules, some supporting recovery and others producing chronic multi-system dysfunction. Current SCI classiﬁers are mainly ordinal, predate the advent of bioinformatics, and lack biomolecular grounding. Current data elements assign deﬁnitions to multi-factorial and complex phenomena such as ‘‘neuropathic pain’’ and ‘‘spasticity.’’ Considering the interactions among critical systems after SCI, a systems medicine approach can support models of individual change over time and identify critical transition states.

Practical Implications

Improved Classification of SCI

Moving towards more integrated models using clustered phenotype combinations to learn the critical events in their evolution.

Personalized Treatment Strategies

Integration of evolving individual injury data may enable the prediction of eventual neurological and complication phenotypes, health outcomes, and better inform treatment interventions.

Therapeutic Target Identification

The identification of critical nodal points for intervention to improve recovery trajectories through correlational phenotyping.

Study Limitations

1
Extrinsic factors like polypharmacy and the effects of antibiotics on inflammatory responses may affect models.
2
Sociodemographic factors contribute to allostatic stress and may be challenging to quantify accurately.
3
The systems biology approach is at a very early stage.

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