Predicting low bone density in children and young adults with quadriplegic cerebral palsy

Richard C Henderson1, John Kairalla, Almas Abbas

  • 1University of North Carolina, Campus Box #7055, Chapel Hill, NC 27599, USA. rchh@med.unc.edu

Insights

Children and young adults with cerebral palsy (CP) often have low bone mineral density (BMD). Weight z-score is the best predictor of low BMD in CP patients, with other factors like age and severity also playing a role.

Area of Science:

  • Pediatrics
  • Neurology
  • Orthopedics

Background:

  • Children and young adults with cerebral palsy (CP) frequently exhibit reduced bone mineral density (BMD).
  • This diminished BMD increases their susceptibility to fractures from minor trauma.
  • Identifying individuals at high risk for low BMD is crucial for proactive management.

Purpose of the Study:

  • To identify routinely assessed clinical variables that predict low bone mineral density (BMD) in children and young adults with CP.
  • To establish correlations between clinical factors and BMD z-scores in this population.

Main Methods:

  • Detailed assessment of 107 participants (ages 2-21) with moderate to severe spastic CP.
  • Collected clinical data, anthropometric measures (growth, nutrition), and dual-energy X-ray absorptiometry (DXA) for BMD.
  • Classified participants by ambulatory status using the Gross Motor Function Classification System (GMFCS levels III, IV, V).

Main Results:

  • Weight z-score was the strongest predictor of BMD z-score.
  • Declining BMD z-scores correlated with increasing age and greater CP severity.
  • A predictive model indicated a 10-year-old non-ambulatory child with quadriplegic CP and a typical weight z-score (-2) would have a BMD z-score of -2.

Conclusions:

  • Weight z-score is a key indicator for identifying children and young adults with CP at risk of low BMD.
  • Factors such as age, disease severity, prior fractures, anticonvulsant use, and feeding difficulties further impact BMD.
  • Routine clinical assessment of these variables can help identify at-risk individuals for targeted interventions.