Objective: The purpose of this study was to present the results of our investigation of the prognostic value of adipopenia and sarcopenia in patients with amyotrophic lateral sclerosis (ALS).
Methods: Consecutive patients with ALS with abdominal computed tomography (CT) were retrospectively identified at a single tertiary hospital between January 2010 and July 2021. Deep learning-based volumetric CT body composition analysis software was used to obtain abdominal waist fat volume, fat attenuation, and skeletal muscle area at the L3 level, then normalized to the fat volume index (FVI) and skeletal muscle index (SMI). Adipopenia and sarcopenia were defined as the sex-specific lowest quartile and SMI reference values, respectively. The associations of CT-derived body composition parameters with clinical variables, such as body mass index (BMI) and creatinine, were evaluated by Pearson correlation analyses, and associations with survival were assessed using the multivariable Cox regression analysis.
Results: Eighty subjects (40 men, 65.5 ± 9.4 years of age) were investigated (median interval between disease onset and CT examination = 25 months). The mean BMI at the CT examination was 20.3 ± 4.3 kg/m2 . The BMI showed a positive correlation with both FVI (R = 0.70, p < 0.001) and SMI (R = 0.63, p < 0.001), and the serum creatinine level was associated with SMI (R = 0.68, p < 0.001). After adjusting for sex, age, King's stage, BMI, creatinine, progression rate, and sarcopenia, adipopenia was associated with shorter survival (hazard ratio [HR] = 5.94, 95% confidence interval [CI] = 1.01, 35.0, p = 0.049). In a subgroup analysis for subjects with nutritional failure (stage 4a), the HR of adipopenia was 15.1 (95% CI = 2.45, 93.4, p = 0.003).
Interpretation: Deep learning-based CT-derived adipopenia in patients with ALS is an independent poor prognostic factor for survival. ANN NEUROL 2023;94:1116-1125.
© 2023 American Neurological Association.