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  4. Leveraging genetics to optimize rehabilitation outcomes after spinal cord injury: contemporary challenges and future opportunities

Leveraging genetics to optimize rehabilitation outcomes after spinal cord injury: contemporary challenges and future opportunities

Front. Genet., 2024 · DOI: 10.3389/fgene.2024.1350422 · Published: February 13, 2024

Spinal Cord InjuryGeneticsRehabilitation

Simple Explanation

Genetic analysis has revolutionized medicine, leading to targeted treatments and better patient outcomes. Rehabilitation medicine is now exploring genetics for personalized treatments. In spinal cord injury (SCI) medicine, a pivotal study that changes clinical care is lacking. The challenges in SCI medicine include heterogeneity and small sample sizes. Pooling resources and collaboration among clinical and research sites is a necessary effort. Collaborative efforts must expand to the meaningful collection, storage, and distribution of biosamples.

Study Duration
Not specified
Participants
Not specified
Evidence Level
Opinion

Key Findings

  • 1
    Lack of significant progress in clinical SCI genetics studies is due to inadequate genetic data and lack of uniform endpoints.
  • 2
    Challenges in SCI research include heterogeneity, small sample sizes, non-standardized outcome recordings, and confounding secondary medical complications.
  • 3
    Pooling resources, deep collaborations, and biobanking efforts are potential solutions to advance genetics studies in SCI medicine.

Research Summary

Genetic analyses have transformed medicine, leading to personalized treatments and enhanced patient outcomes. Rehabilitation medicine is now incorporating genetics, with initial successes in stroke and traumatic brain injury. Spinal cord injury (SCI) medicine faces challenges, including heterogeneity, limited sample sizes, and inconsistent outcome measurements. Secondary medical complications further complicate genetic studies. Future opportunities involve pooling resources, establishing biobanks, employing adaptive trial designs, and identifying surrogate biomarkers to advance genetics research in SCI and improve patient outcomes.

Practical Implications

Data Pooling and Collaboration

Encourages the establishment of national and international patient registries and biobanks to collect and share data and biosamples.

Standardized Outcome Measures

Highlights the need for uniform endpoints and standardized outcome recordings to improve the quality and comparability of genetic studies in SCI.

Biomarker Identification

Promotes the identification and validation of surrogate biomarkers to accelerate research and facilitate the interpretation of complex interactions in SCI recovery.

Study Limitations

  • 1
    Heterogeneity of SCI
  • 2
    Small sample sizes
  • 3
    Non-standardized outcome recordings

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