Sarcobesity Index Predicts Poor Disease-Specific Survival After Resection for Colorectal Cancer

Introduction: Sarcobesity (SO) is traditionally defined as the association between low muscle mass and obesity and has been reported to worsen prognosis after curative resection for colorectal cancer (CRC). This study aimed to propose a new definition of SO based on computed tomography measurements of the skeletal muscle area (SMA) and visceral ad-ipose tissue (VAT) and to assess its implications on long-term survival after curative resection for stage I-III CRC. Methods: We retrospectively analyzed 506 patients with stage I-III CRC who underwent surgery between January 2010 and December 2019. Preoperative computed tomography images were analyzed and the sarcobesity index (SI) was calculated for each patient as the VAT/SMA ratio. The optimal cutoff value for predicting survival was determined using time-dependent receiver operating characteristic analysis. Overall survival and disease -specific survival (DSS) were compared between SO (SI > 1.25) and non-SO (SI < 1.25) pa-tients. The rates and modes of recurrence were also compared between the two groups. Results: Three hundred (59.3%) patients were identified to be sarcobese. No differences in short-term outcomes and administration of adjuvant chemotherapy were found, except for a longer length of stay in patients with SO. In a univariable analysis, SO was associated with a worse 5-y overall survival and DSS, considering the whole population and stages II and III separately. A multivariable analysis confirmed SO to be an independent risk factor for DSS (hazard ratio 2.29; 95% confidence interval 1.13-4.62, P = 0.02). Although the overall recurrence rate did not differ between the groups, a significantly higher rate of recurrence at multiple sites was observed in patients with SO (P = 0.01). Conclusions: The SI, defined as per the VAT/SMA ratio, seems to be a reliable tool for identifying patients with worse DSS after potentially curative surgery for stage I-III CRC. (c) 2022 Elsevier Inc. All rights reserved.