Capillary blood protein markers of posttraumatic headache in children after concussion

Feiven Fan1,2, Franz E Babl1,3,4,5, Ella E K Swaney1,3

  • 11Murdoch Children's Research Institute, Melbourne, Victoria.

Insights

No blood protein markers predict persistent posttraumatic headache (PTH) in children after concussion. This study found no specific signature in capillary blood to identify children at risk of PTH at 2 weeks postinjury.

Area of Science:

  • Pediatric neurology
  • Traumatic brain injury research
  • Proteomics and biomarker discovery

Background:

  • Posttraumatic headache (PTH) is a common symptom following concussion in children.
  • Current methods lack objective biomarkers to predict persistent PTH, hindering early intervention.
  • Identifying reliable blood protein signatures is crucial for risk stratification.

Purpose of the Study:

  • To identify capillary blood protein markers for predicting persistent PTH in children within 48 hours of concussion.
  • To stratify risk for early intervention in pediatric concussion management.
  • To discover novel blood biomarkers for PTH prediction.

Main Methods:

  • Collected capillary blood from children (8-17 years) presenting to the ED within 48 hours of concussion.
  • Utilized untargeted proteomics with data-independent acquisition (DIA) on 907 identified proteins.
  • Followed up participants at 2 weeks to assess PTH status.

Main Results:

  • No specific blood protein signature was found to predict PTH at 2 weeks postinjury.
  • Five proteins (HBZ, CSTB, CNDP1, HBG1, ZYX) showed weak associations with PTH, but lacked statistical significance.
  • Eighty percent of participants had acute PTH, and one-third experienced PTH at 2-week follow-up.

Conclusions:

  • Capillary blood protein analysis at ED presentation cannot predict persistent PTH in children post-concussion.
  • Further research is urgently needed to discover reliable blood biomarkers for PTH risk stratification.
  • Improved clinical management of pediatric concussion relies on identifying predictive biomarkers.
Abstract