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Body Composition and Metabolic Assessment After Motor Complete Spinal Cord Injury: Development of a Clinically Relevant Equation to Estimate Body Fat

Top Spinal Cord Inj Rehabil, 2021 · DOI: 10.46292/sci20-00079 · Published: January 1, 2021

Spinal Cord InjuryEndocrinologyPhysiology

Simple Explanation

Obesity is a major health concern for people with spinal cord injuries (SCI), and excess body fat can lead to metabolic problems. Traditional methods like BMI aren't accurate for assessing obesity risk in SCI patients, so better ways to measure body fat are needed. This study compares different methods of measuring body fat in SCI patients and develops a simple equation to estimate body fat in a clinical setting.

Study Duration
3 years
Participants
72 individuals with chronic motor complete (AIS A and B) C5-L2 SCI
Evidence Level
Not specified

Key Findings

  • 1
    A regression equation using age, sex, weight, and abdominal skinfold thickness can estimate body fat with reasonable accuracy in SCI patients.
  • 2
    Standard body composition techniques are often impractical, but the developed regression equation provides a quick estimate of body fat.
  • 3
    Metabolic syndrome was identified in a significant portion of the SCI sample, highlighting the importance of addressing obesity-related cardiometabolic risks.

Research Summary

The study compares body composition assessment techniques in individuals with SCI to the 4-compartment (4C) model, the criterion standard for measuring body fat. A regression equation incorporating age, sex, weight, and abdominal skinfold thickness was developed to estimate %BF, showing a correlation of 0.57 with the 4C model. The research concludes that the developed regression equation can assist clinicians in quickly estimating %BF and identifying obesity-induced cardiometabolic syndrome in SCI patients.

Practical Implications

Clinical Assessment

The developed equation can be used to quickly estimate body fat in SCI patients, aiding in the assessment of obesity-related health risks.

Risk Stratification

The equation can help stratify SCI patients for cardiometabolic risk, enabling targeted interventions and management strategies.

Future Research

Further validation of the equation across different SCI populations is needed to ensure its widespread applicability.

Study Limitations

  • 1
    The sample population was from a single site.
  • 2
    The regression equation developed should be validated across centers where persons with SCI are routinely assessed.
  • 3
    The sample may have been biased to include those individuals most motivated and interested in learning about their body composition and health.

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