Prevalence of Sarcopenic Obesity and Factors Influencing Body Composition in Persons with Spinal Cord Injury in Japan.
Ryu IshimotoHirotaka MutsuzakiYukiyo ShimizuHiroshi KishimotoRyoko TakeuchiYasushi HadaPublished in: Nutrients (2023)
This study aims to investigate the prevalence of sarcopenic obesity and factors influencing body composition in persons with spinal cord injury (SCI) in Japan. Adults with SCI aged ≥ 20 years who underwent whole-body dual-energy X-ray absorptiometry between 2016 and 2022 were retrospectively analyzed. Data from 97 patients were examined. The primary outcome was appendicular skeletal muscle mass (ASM). Multiple linear regression analysis was conducted to assess factors influencing the lean and adipose indices in persons with SCI. Sarcopenia, obesity, and sarcopenic obesity were prevalent in 76%, 85%, and 64% of patients, respectively. Multivariate linear regression analysis revealed that sex (β = 0.34, p < 0.001), lesion level (β = 0.25, p = 0.007), severity (β = 0.20, p = 0.043), and ability to walk (β = 0.29, p = 0.006) were independently associated with ASM. Sex (β = -0.63, p < 0.001) was independently associated with percent body fat. In conclusion, sarcopenia, obesity, and sarcopenic obesity were prevalent among patients with SCI in Japan. Female sex, tetraplegia, motor-complete injury, and inability to walk were risk factors for sarcopenia, whereas female sex was a risk factor for obesity in persons with SCI. A routine monitoring of body composition is necessary, especially among those with multiple risk factors, to identify individuals in need of preventive and therapeutic interventions.
Keyphrases
- body composition
- insulin resistance
- metabolic syndrome
- weight loss
- bone mineral density
- resistance training
- type diabetes
- high fat diet induced
- weight gain
- spinal cord injury
- risk factors
- dual energy
- skeletal muscle
- end stage renal disease
- ejection fraction
- newly diagnosed
- computed tomography
- adipose tissue
- magnetic resonance imaging
- machine learning
- patient reported outcomes
- big data
- artificial intelligence
- image quality