Obesity is associated with biochemical recurrence after radical prostatectomy: A multi-institutional extended validation study.
Academic Article
Overview
abstract
BACKGROUND: There are no clear data regarding the association between body mass index (BMI) and outcomes after radical prostatectomy (RP). This study aimed to investigate the association between BMI and biochemical recurrence (BCR) after RP in a large international contemporary cohort of patients with prostate cancer. METHODS: We retrospectively analyzed data from 6,519 patients who underwent RP at 5 institutions. BMI was analyzed as both a continuous and categorical variable (<25kg/m2, 25-29.9kg/m2 [overweight], and≥30kg/m2 [obese]). The associations of continuous and categorical BMI with BCR were evaluated using univariable and multivariable Cox models, and prognostic accuracy was assessed using Harrell׳s C-index. RESULTS: The median BMI was 28kg/m2 (interquartile range: 24-32kg/m2); 2,155 patients (33.1%) had a BMI = 25 to 29.9kg/m2 and 2,462 patients (37.7%) had a BMI≥30kg/m². Overweight and obese status were associated with extracapsular extension (P = 0.001) and seminal vesicle invasion (P = 0.005). The median follow-up was 28 months, and the estimated 5-year BCR-free survival rates for patients with a BMI<25kg/m2, 25 to 29.9kg/m2, and≥30kg/m² were 92%, 86%, and 79%, respectively (P<0.001). Multivariable analyses (adjusted for preoperative prostate-specific antigen levels, biopsy Gleason score, and clinical stage) revealed that obesity was associated with the risk of extracapsular extension (P<0.001), seminal vesicle invasion (P<0.001), and BCR (hazard ratio: 1.37, P<0.001). BMI and obesity remained associated with BCR after adjusting for postoperative characteristics. Addition of BMI slightly increased the discrimination of the multivariable clinical prognostic model (from 79.9%-80.9%). CONCLUSIONS: Overweight and obese status was associated with adverse pathological features and BCR after RP. However, the addition of BMI did not significantly improve the prognostic accuracy of a model that was based on established predictors.
