David R Gater1,2, Gary J Farkas1, David R Dolbow3, Arthur Berg4, Ashraf S Gorgey5. 1. Department of Physical Medicine and Rehabilitation, University of Miami Miller School of Medicine, Miami, Florida. 2. The Miami Project to Cure Paralysis, University of Miami Miller School of Medicine, Miami, Florida. 3. Physical Therapy, William Carey University, Hattiesburg, Mississippi. 4. Department of Public Health Sciences, Penn State College of Medicine, Hershey, Pennsylvania. 5. Spinal Cord Injury and Disorders Center, Hunter Holmes McGuire VA Medical Center, Richmond, Virginia.
Abstract
Background: Obesity is at epidemic proportions in the population with spinal cord injury (SCI), and adipose tissue (AT) is the mediator of the metabolic syndrome. Obesity, however, has been poorly appreciated in SCI because of the lack of sensitivity that body mass index (BMI) conveys for obesity risk in SCI without measuring AT. Objectives: The specific objectives were to compare measures of body composition assessment for body fat with the criterion standard 4-compartment (4C) model in persons with SCI, to develop a regression equation that can be utilized in the clinical setting to estimate fat mass (FM), and to determine cardiometabolic risk using surrogates of obesity in a current model of metabolic syndrome. Methods: Seventy-two individuals with chronic (>1 year) motor complete (AIS A and B) C5-L2 SCI were recruited over 3 years. Subjects underwent assessment with 4C using hydrostatic (underwater) weighing (UWW), dual-energy x-ray absorptiometry (DXA), and total body water (TBW) assessment to determine percent body fat (%BF); fasting glucose and lipid profiles, and resting blood pressure were also obtained. BMI, DXA, bioelectrical impedance analyses (BIA), BodPod, circumferences, diameters, lengths, and nine-site skinfold (SF) were assessed and validated against 4C. A multiple linear regression model was used to fit %BF (dependent variable) using anthropometric and demographic data that had the greatest correlations with variables, followed by a combined forward/backward stepwise regression with Akaike information criterion (AIC) to identify the variables most predictive of the 4C %BF. To allow for a more practical model for use in the clinical setting, we further reduced the AIC model with minimal loss of predictability. Surrogate markers of obesity were employed with metabolic biomarkers of metabolic syndrome to determine prevalence in persons with SCI. Results: Subject characteristics included age 44.4 ± 11.3 years, time since injury (TSI) 14.4 ± 11.0 years, BMI 27.3 ± 5.9 kg/m2; 59 were men and 13 were women. Sitting waist circumference (WCSit ) was 95.5 ± 13.1 cm, supine waist circumference (WCSup) was 93.4 ± 12.7 cm, and abdominal skinfold (ABDSF) was 53.1 ± 19.6 mm. Findings showed 4C %BF 42.4 ± 8.6%, UWW %BF 37.3 ± 9.7%, DXA %BF 39.1 ± 9.4%, BodPod %BF 33.7 ± 11.4%, nine-site SF %BF 37.8 ± 9.3%, and BIA %BF 27.6 ± 8.6%. A regression equation using age, sex, weight, and ABDSF provided R2 correlation of 0.57 with 4C %BF (p < .0001). Metabolic syndrome was identified in 59.4% of the sample. Conclusion: Body composition techniques to determine body fat are labor intensive and expensive for persons with SCI, and the regression equation developed against the criterion standard 4C model may allow clinicians to quickly estimate %BF and more accurately demonstrate obesity-induced cardiometabolic syndrome in this population.
Background: Obesity is at epidemic proportions in the population with spinal cord injury (SCI), and adipose tissue (AT) is the mediator of the metabolic syndrome. Obesity, however, has been poorly appreciated in SCI because of the lack of sensitivity that body mass index (BMI) conveys for obesity risk in SCI without measuring AT. Objectives: The specific objectives were to compare measures of body composition assessment for body fat with the criterion standard 4-compartment (4C) model in persons with SCI, to develop a regression equation that can be utilized in the clinical setting to estimate fat mass (FM), and to determine cardiometabolic risk using surrogates of obesity in a current model of metabolic syndrome. Methods: Seventy-two individuals with chronic (>1 year) motor complete (AIS A and B) C5-L2 SCI were recruited over 3 years. Subjects underwent assessment with 4C using hydrostatic (underwater) weighing (UWW), dual-energy x-ray absorptiometry (DXA), and total body water (TBW) assessment to determine percent body fat (%BF); fasting glucose and lipid profiles, and resting blood pressure were also obtained. BMI, DXA, bioelectrical impedance analyses (BIA), BodPod, circumferences, diameters, lengths, and nine-site skinfold (SF) were assessed and validated against 4C. A multiple linear regression model was used to fit %BF (dependent variable) using anthropometric and demographic data that had the greatest correlations with variables, followed by a combined forward/backward stepwise regression with Akaike information criterion (AIC) to identify the variables most predictive of the 4C %BF. To allow for a more practical model for use in the clinical setting, we further reduced the AIC model with minimal loss of predictability. Surrogate markers of obesity were employed with metabolic biomarkers of metabolic syndrome to determine prevalence in persons with SCI. Results: Subject characteristics included age 44.4 ± 11.3 years, time since injury (TSI) 14.4 ± 11.0 years, BMI 27.3 ± 5.9 kg/m2; 59 were men and 13 were women. Sitting waist circumference (WCSit ) was 95.5 ± 13.1 cm, supine waist circumference (WCSup) was 93.4 ± 12.7 cm, and abdominal skinfold (ABDSF) was 53.1 ± 19.6 mm. Findings showed 4C %BF 42.4 ± 8.6%, UWW %BF 37.3 ± 9.7%, DXA %BF 39.1 ± 9.4%, BodPod %BF 33.7 ± 11.4%, nine-site SF %BF 37.8 ± 9.3%, and BIA %BF 27.6 ± 8.6%. A regression equation using age, sex, weight, and ABDSF provided R2 correlation of 0.57 with 4C %BF (p < .0001). Metabolic syndrome was identified in 59.4% of the sample. Conclusion: Body composition techniques to determine body fat are labor intensive and expensive for persons with SCI, and the regression equation developed against the criterion standard 4C model may allow clinicians to quickly estimate %BF and more accurately demonstrate obesity-induced cardiometabolic syndrome in this population.
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