Aims: The association between obesity and triple-negative breast cancer (TNBC) prognosis has been equivocal, with considerable heterogeneity between and within studies. Recent meta-analyses report adverse associations with overall survival (OS) and disease-free survival (DFS) in TNBC. This meta-analysis aimed to update the previous meta-analyses and provide a broader examination of potential sources of heterogeneity in the association between excess body weight and survival in TNBC, including disease and study-specific characteristics.
Methods: A systematic search through PubMed, Embase, CINAHL, and Web of Science databases was conducted until January 14, 2022. Random-effects meta-analyses were used to pool hazard ratios (HR) for OS, DFS, and breast cancer-specific mortality (BCSM). Subgroup analyses examined the impact of sources of study heterogeneity. Methodological quality and risk of bias of included studies were evaluated using the Newcastle-Ottawa Scale.
Results: In meta-analyses of included studies (n=33), significant associations were observed between excess body weight and worse OS (n=24; HR=1.17 [95%CI: 1.05 to 1.31]) and DFS (n=26; HR=1.12 [1.02 to 1.24]) but not BCSM (n=10; HR=1.09 [0.94 to 1.27]). In subgroup meta-analyses, significant inter-study survival differences were observed for exposure/comparison body mass index cut-points (OS, p=0.040), menopausal status (OS, p=0.026), and study location (OS, p<0.001; DFS, p=0.001). Asian and European studies reported significant associations with OS (HR=1.31 [1.11 to 1.54] and HR=1.46 [1.04 to 2.04], respectively) and DFS (HR=1.28 [1.07 to 1.54] and HR=1.44 [1.13 to 1.84], respectively), however no association was observed between obesity and TNBC prognosis in North American studies (OS: HR=1.02 [0.89 to 1.17]; DFS: HR=1.01 [0.91 to 1.12]). These subgroup differences remained robust after excluding poor quality studies.
Conclusions: Ethnic differences in the association between excess body weight and TNBC appear to exist. Further exploration of study heterogeneity is needed to properly understand populations most at risk.