Exploring the optimal indicator of short-term peridiagnosis weight dynamics to predict cancer survival: A multicentre cohort study

Abstract

Background: Body weight and its changes have been associated with cancer outcomes. However, the associations of short-term peridiagnosis weight dynamics in standardized, clinically operational time frames with cancer survival remain largely unknown. This study aimed to screen for and evaluate the optimal indicator of short-term peridiagnosis weight dynamics to predict overall survival (OS) in patients with cancer.

Methods: This multicentre cohort study prospectively collected data from 7460 patients pathologically diagnosed with cancer between 2013 and 2019. Body weight data were recorded 1 month before, at the time of and 1 month following diagnosis. By permuting different types (point value in kg, point height-adjusted value in kg/m², absolute change in kg or relative change in percentage) and time frames (prediagnosis, postdiagnosis or peridiagnosis), we generated 12 different weight-related indicators and compared their prognostic performance using Harrell's C-index, integrated discrimination improvement, continuous net reclassification improvement and time-dependent C-index. We analysed associations of peridiagnosis relative weight change (RWC) with OS using restricted cubic spine (RCS), Kaplan-Meier analysis and multivariable-adjusted Cox regression models.

Results: The study enrolled 5012 males and 2448 females, with a median age of 59 years. During a median follow-up of 37 months, 1026 deaths occurred. Peridiagnosis (1 month before diagnosis to 1 month following diagnosis) RWC showed higher prognostic performance (Harrell's C-index = 0.601, 95% confidence interval [CI] = [0.583, 0.619]) than other types of indicators including body mass index (BMI), absolute weight change, absolute BMI change, prediagnosis RWC and postdiagnosis RWC in the study population (all P < 0.05). Time-dependent C-index analysis also indicated that peridiagnosis RWC was optimal for predicting OS. The multivariable-adjusted RCS analysis revealed an N-shaped non-linear association between peridiagnosis RWC and OS (P_RWC < 0.001, P_non-linear < 0.001). Univariate survival analysis showed that the peridiagnosis RWC groups could represent distinct mortality risk stratifications (P < 0.001). Multivariable survival analysis showed that, compared with the maintenance group (weight change < 5%), the significant (gain >10%, hazard ratio [HR] = 0.530, 95% CI = [0.413, 0.680]) and moderate (gain 5-10%, HR = 0.588, 95% CI = [0.422, 0.819]) weight gain groups were both associated with improved OS. In contrast, the moderate (loss 5-10%, HR = 1.219, 95% CI = [1.029, 1.443]) and significant (loss >10%, HR = 1.280, 95% CI = [1.095, 1.497]) weight loss groups were both associated with poorer OS.

Conclusions: The prognostic performance of peridiagnosis RWC is superior to other weight-related indicators in patients with cancer. The findings underscore the importance of expanding the surveillance of body weight from at diagnosis to both past and future, and conducting it within clinically operational time frames, in order to identify and intervene with patients who are at risk of weight change-related premature deaths.

Keywords: cancer; peridiagnosis; survival; weight change; weight gain; weight loss.

Figure 1

Weight‐related indicators and overall survival. 1 mon−, 1 month before cancer diagnosis; 1 mon+, 1 month after cancer diagnosis; ABC, absolute BMI change; AWC, absolute weight change; baseline, at diagnosis; BMI, body mass index; M, median; RWC, relative weight change. (A) Distribution of BMI at the 1 mon−, baseline and 1 mon+ time points. (B) Percentage, number and flow of the BMI categories at different time points. (C) Comparison of the time‐dependent C‐indices for different weight‐related indicators. All curves were corrected with 100 repetitions of 1000‐sample bootstrap cross‐validation.

Figure 2

Peridiagnosis weight change and survival. (A) Dose–response association between peridiagnosis relative weight change and overall survival. Associations were examined by multivariable Cox regression models based on restricted cubic splines. The blue solid line represents estimates of hazard ratios (HRs), and the blue ribbon represents 95% confidence intervals (CIs). Risk estimates were adjusted for baseline age, sex, smoking, drinking, residency, cancer type, clinical stage, surgery, adjuvant chemotherapy, curative chemotherapy, nutritional intervention, food intake and the Eastern Cooperative Oncology Group physical performance score. (B) Kaplan–Meier analysis on the association between categories of peridiagnosis relative weight change and overall survival.