Liu Mingxin, Legault Véronique, Fülöp Tamàs, Côté Anne-Marie, Gravel Dominique, Blanchet F Guillaume, Leung Diana L, Lee Sylvia Juhong, Nakazato Yuichi, Cohen Alan A
PRIMUS Research Group, Department of Family Medicine, University of Sherbrooke, Sherbrooke, QC, Canada.
Research Center on Aging, Sherbrooke, QC, Canada.
Front Physiol. 2021 Mar 11;12:612494. doi: 10.3389/fphys.2021.612494. eCollection 2021.
There is an increasingly widespread use of biomarkers in network physiology to evaluate an organism's physiological state. A recent study showed that albumin variability increases before death in chronic hemodialysis patients. We hypothesized that a multivariate statistical approach would better allow us to capture signals of impending physiological collapse/death. We proposed a Moving Multivariate Distance (MMD), based on the Mahalanobis distance, to quantify the variability of the multivariate biomarker profile as a whole from one visit to the next. Biomarker profiles from a visit were used as the reference to calculate MMD at the subsequent visit. We selected 16 biomarkers (of which 11 are measured every 2 weeks) from blood samples of 763 chronic kidney disease patients hemodialyzed at the CHUS hospital in Quebec, who visited the hospital regularly (∼every 2 weeks) to perform routine blood tests. MMD tended to increase markedly preceding death, indicating an increasing intraindividual multivariate variability presaging a critical transition. In survival analysis, the hazard ratio between the 97.5th percentile and the 2.5th percentile of MMD reached as high as 21.1 [95% CI: 14.3, 31.2], showing that higher variability indicates substantially higher mortality risk. Multivariate approaches to early warning signs of critical transitions hold substantial clinical promise to identify early signs of critical transitions, such as risk of death in hemodialysis patients; future work should also explore whether the MMD approach works in other complex systems (i.e., ecosystems, economies), and should compare it to other multivariate approaches to quantify system variability.
生物标志物在网络生理学中越来越广泛地用于评估生物体的生理状态。最近一项研究表明，慢性血液透析患者在死亡前白蛋白变异性增加。我们假设多变量统计方法能更好地让我们捕捉即将发生的生理崩溃/死亡信号。我们基于马氏距离提出了移动多变量距离（MMD），以量化多变量生物标志物谱从一次就诊到下一次就诊的整体变异性。将一次就诊的生物标志物谱用作参考，以计算后续就诊时的MMD。我们从魁北克CHUS医院接受血液透析的763例慢性肾脏病患者的血样中选取了16种生物标志物（其中11种每2周测量一次），这些患者定期（约每2周）到医院进行常规血液检查。MMD在死亡前往往会显著增加，表明个体内多变量变异性增加预示着关键转变。在生存分析中，MMD第97.5百分位数与第2.5百分位数之间的风险比高达21.1 [95%置信区间：14.3, 31.2]，表明变异性越高，死亡风险越高。多变量方法用于关键转变的早期预警信号在识别关键转变的早期迹象方面具有重大临床前景，例如血液透析患者的死亡风险；未来的工作还应探索MMD方法是否适用于其他复杂系统（即生态系统、经济体），并应将其与其他多变量方法进行比较以量化系统变异性。
