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探索预测癌症生存的短期围诊断体重动态最佳指标:一项多中心队列研究。

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

机构信息

Department of Clinical Nutrition, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing, China.

Department of Nephrology, the Key Laboratory for the Prevention and Treatment of Chronic Kidney Disease of Chongqing, Chongqing Clinical Research Center of Kidney and Urology Diseases, Xinqiao Hospital, Army Medical University (Third Military Medical University), Chongqing, China.

出版信息

J Cachexia Sarcopenia Muscle. 2024 Jun;15(3):1177-1186. doi: 10.1002/jcsm.13467. Epub 2024 Apr 21.

DOI:10.1002/jcsm.13467
PMID:38644549
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11154758/
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/m, 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 < 0.001, P < 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.

摘要

背景

体重及其变化与癌症结局相关。然而,在标准化的临床操作时间范围内,短期诊断前体重动态与癌症生存之间的关联在很大程度上仍不清楚。本研究旨在筛选并评估短期诊断前体重动态的最佳指标,以预测癌症患者的总生存(OS)。

方法

这项多中心队列研究前瞻性收集了 2013 年至 2019 年间病理诊断为癌症的 7460 例患者的数据。体重数据在诊断前 1 个月、诊断时和诊断后 1 个月记录。通过排列不同类型(kg 点值、kg/m 点高度调整值、kg 的绝对变化或百分比的相对变化)和时间框架(诊断前、诊断后或诊断前),我们生成了 12 个不同的体重相关指标,并使用 Harrell 的 C 指数、综合判别改善、连续净重新分类改善和时间依赖性 C 指数比较它们的预后性能。我们使用限制性立方样条(RCS)、Kaplan-Meier 分析和多变量调整 Cox 回归模型分析诊断前相对体重变化(RWC)与 OS 的关系。

结果

本研究纳入了 5012 名男性和 2448 名女性,中位年龄为 59 岁。在中位随访 37 个月期间,有 1026 例死亡。诊断前(诊断前 1 个月至诊断后 1 个月)RWC 的预后性能(Harrell 的 C 指数=0.601,95%置信区间[CI]:0.583-0.619)高于其他类型的指标,包括体重指数(BMI)、绝对体重变化、绝对 BMI 变化、诊断前 RWC 和诊断后 RWC(均 P<0.05)。时间依赖性 C 指数分析也表明,诊断前 RWC 是预测 OS 的最佳指标。多变量调整后的 RCS 分析显示,诊断前 RWC 与 OS 之间存在 N 形非线性关系(P<0.001,P<0.001)。单变量生存分析显示,诊断前 RWC 组可代表不同的死亡风险分层(P<0.001)。多变量生存分析显示,与维持组(体重变化<5%)相比,显著(增加>10%,风险比[HR] =0.530,95%CI:0.413-0.680)和中度(增加 5-10%,HR=0.588,95%CI:0.422-0.819)体重增加组均与 OS 改善相关。相比之下,中度(减少 5-10%,HR=1.219,95%CI:1.029-1.443)和显著(减少>10%,HR=1.280,95%CI:1.095-1.497)体重减轻组均与 OS 较差相关。

结论

诊断前 RWC 的预后性能优于癌症患者其他体重相关指标。这些发现强调了在标准化的临床操作时间范围内扩大体重监测范围的重要性,从诊断前到过去和未来,以便识别和干预有体重变化相关过早死亡风险的患者。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1ed/11154758/4e49d7b6be93/JCSM-15-1177-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1ed/11154758/526053cfa593/JCSM-15-1177-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1ed/11154758/4e49d7b6be93/JCSM-15-1177-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1ed/11154758/526053cfa593/JCSM-15-1177-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1ed/11154758/4e49d7b6be93/JCSM-15-1177-g001.jpg

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