Low Fat-Free Mass Index Measured by Bioelectrical Impedance Analysis Correlates With Hematologic Adverse Events in Early-Stage Breast Cancer Patients Receiving Chemotherapy: A Prospective Observational Cohort Study

Abstract

Background: Low muscle mass is associated with worse cancer treatment outcomes. Although dual-energy X-ray absorptiometry or computerized tomography-based analysis have both been widely studied in this clinical setting, studies in the use of bioelectrical impedance analysis (BIA) remain limited. The aim of this prospective study was to investigate for association between body composition estimated by BIA and hematologic adverse events in early-stage breast cancer patients receiving chemotherapy. Methods: A total of 144 female patients were enrolled. Before the first cycle of chemotherapy, body weight and fat-free mass were measured by a BIA device and then those values were converted into body mass index and fat-free mass index. Association between fat-free mass index and composite adverse events (CAEs), including grade 4 neutropenia, febrile neutropenia, or relative dose intensity <85%, was explored. Results: CAEs occurred in 85 patients (59%), and point biserial correlation showed an inverse correlation between the fat-free mass index and CAE. No included patients were sarcopenic (fat-free mass index <11.4 kg/m 2 ). Receiver operating characteristic curve analysis revealed <14.85 kg/m 2 as the cutoff value indicating a low fat-free mass index. Using this cutoff, 85 patients were classified as having a low fat-free mass index, and 62 of those patients (72.9%) had CAE (relative risk: 1.86, P < .001). After adjusting for other factors, a low fat-free mass index was found to be independently associated with a high CAE (adjusted odds ratio: 4.562, 95% CI: 2.162-9.627, P < .001). Conclusion: Low fat-free mass index is an independent predictor of increased risk of hematologic adverse events in early-stage breast cancer patients receiving chemotherapy. Estimation of fat-free mass index by BIA may identify at-risk patients so that interventions can be considered to improve treatment outcomes.