Restricted cubic spline analysis shows strong links between BMI and mortality outcomes in prostate cancer patients, suggesting implications for treatment strategies.
Restricted cubic spline analysis for BMI assessed close to diagnosis and all-cause, prostate cancer–specific, and non–prostate cancer mortality. HRs from the Cox proportional hazards model with restricted cubic spline curves describing the association between BMI (kg/m²) collected before or after diagnosis combined and (A) all-cause mortality (deaths = 680), (B) prostate cancer–specific mortality (deaths = 331), and (C) non–prostate cancer mortality (deaths = 347); pre-diagnosis BMI and (D) all-cause mortality (deaths = 262), (E) prostate cancer–specific mortality (deaths = 124), and (F) non–prostate cancer mortality (deaths = 137); and post-diagnosis BMI and (G) all-cause mortality (deaths = 418; H), prostate cancer–specific mortality (deaths = 207), and (I) non–prostate cancer mortality (deaths = 210). HRs are based on the main model adjusted for age of diagnosis, year of diagnosis, smoking status, physical activity, sedentary activities, Townsend deprivation index, and alcohol intake frequency; knots at the 10th, 50th, and 90th percentiles of BMI. The median BMI of the individuals included in analyses was used as referent: 27.3 kg/m² in the pre- or post-diagnosis analysis, 27.1 kg/m² in the pre-diagnosis analysis, and 27.4 kg/m² in the post-diagnosis analysis. The smooth density plot represents the density of the population across the spline variable.
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Cariolou et al. (2025) studied this question.
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