Development and Cross-Validation of a Simple Model to Estimate Percent Body Fat in Persons with Multiple Sclerosis

Area of Science:

• Biomedical research
• Human physiology
• Clinical assessment

Background:

• Individuals with multiple sclerosis (MS) exhibit greater variability in body composition than the general population.
• Accurate methods for estimating body fat percentage (%BF) are crucial for monitoring health in MS patients.
• Existing methods may not fully capture the unique body composition changes in MS.

Purpose of the Study:

• To develop and cross-validate a novel equation for estimating %BF in persons with MS.
• To determine if body mass index (BMI) and sex are sufficient predictors of %BF in this population.
• To provide a practical tool for healthcare providers to assess adiposity in MS.

Main Methods:

• Developed a %BF estimation equation using data from 77 adults with MS.
• Utilized dual-energy x-ray absorptiometry (DXA) as the criterion standard for %BF measurement.
• Cross-validated the equation in a separate sample of 33 adults with MS.

Main Results:

• The developed equation, incorporating BMI and sex, demonstrated high predictive ability for %BF (R = 0.77).
• Factors such as age, MS type, and disease duration did not significantly improve the model's predictive power.
• Cross-validation confirmed the equation's accuracy, showing strong association (r = 0.89) and minimal bias compared to DXA.

Conclusions:

• A validated equation using BMI and sex can accurately estimate body fat percentage in individuals with MS.
• This equation serves as a valuable, accessible tool for healthcare providers when DXA is not feasible.
• Facilitates better monitoring of body composition and adiposity in the MS population.