Assessment of body composition in the advanced stage of castration-resistant prostate cancer: special focus on sarcopenia.

PURPOSE: To assess the prevalence of sarcopenia and whether body composition parameters are associated with disease progression and overall survival (OS) in castration-resistant prostate cancer (CRPC) patients. MATERIALS AND METHODS: This single-centre retrospective study evaluated data of 186 consecutive patients who underwent chemohormonal therapy between 2005 and 2016 as first-line systemic treatment for CRPC. Skeletal muscle and fat indices were determined using computerized tomography data before initiation of chemotherapy. Sarcopenia was defined as SMI of <55 cm2/m2. Visceral-to-subcutaneous fat ratio and skeletal muscle volume were calculated with body composition specific areas. Harrell's concordance index was used for predictive accuracy. RESULTS: A total of 154 (82.8%) patients met the criteria for sarcopenia; 139 (74.7%) individuals completed at least six cycles of docetaxel. Within a median follow-up of 24.1 months, age (HR 1.03, 95% CI 1.01-1.06, p = 0.02), high PSA (1.55, 95% CI 1.07-2.25, p = 0.02) and low skeletal muscle volume (HR 1.61, 95% CI 1.10-2.35, p = 0.02) were the only independent prognostic factor for tumor progression. Overall, 93 (50%) patients died during the follow-up period. The established prognosticator, the prechemotherapy presence of liver metastases (HR 1.32, 95% CI 1.08-1.61, p < 0.01) was associated with shorter OS. Moreover, we noted that patients with an elevated visceral-to-subcutaneous fat ratio tended to have a shorter OS (p = 0.06). CONCLUSION: The large majority of men with CRPC suffers from sarcopenia. In our cohort, low skeletal muscle volume was an independent adverse prognosticator for progression of disease. We could not detect a statistically significant body composition parameter for OS, although patients with a high proportion of visceral fat had a trend for shorter OS. However, we suggest that body composition parameters determined by CT data can provide useful objective prognostic factors that may support tailored treatment decision-making.