Pretreatment body mass index and clinical outcomes in cancer patients following immune checkpoint inhibitors: a systematic review and meta-analysis

Abstract
Background This systematic review and meta-analysis aimed to evaluate the association between pretreatment body mass index (BMI) and clinical outcomes in cancer patients treated with immune checkpoint inhibitors (ICIs). Methods Systematical searches of PubMed, Embase, and the Cochrane Library databases were carried out. Studies reporting on the association between BMI and outcomes of ICIs were included. The intended outcomes included overall survival (OS), progression-free survival (PFS), objective response rate (ORR) and immune-related adverse events (irAEs). Quantitative analyses and dose-response meta-analyses were performed under random effect models. Results Twenty-two eligible studies involving 5686 cancer patients treated with ICIs were identified. Compared to those with lower BMI, patients with higher BMI obtained a significant benefit on OS (HR = 0.698, 95% CI 0.614-0.794,P < 0.001;I-2 = 45.9%) and PFS (HR = 0.760, 95% CI 0.672-0.861,P < 0.001;I-2 = 37.9%). Most stratified analyses for OS and PFS also showed similar pooled risk estimates. For an increment of every 5 kg/m(2)in BMI, the risk for death reduced by approximately 15.6% (HR = 0.844, 95% CI 0.752-0.945,P = 0.003). Moreover, patients with higher BMI had a remarkably better ORR (OR = 0.468, 95% CI 0.263-0.833,P = 0.010;I-2 = 73.6%) than that of those with lower BMI. However, no statistically significant differences were found in the incidence of any grade irAEs (P = 0.073) and >= 3 grade irAEs (P = 0.105) between higher and lower BMI. Conclusion Higher BMI is significantly associated with improved outcomes in patients treated with ICIs. Further large-scale prospective research is warranted to better illuminate the association between BMI and outcomes from ICIs.
Funding Information
  • Chinese National Major Project for New Drug Innovation (2017ZX09304015)
  • Chinese Academy of Medical Sciences (CAMS) Innovation Fund for Medical Sciences (2016-I2M-1-001)