Stubbings Garrett, Farrell Spencer, Mitnitski Arnold, Rockwood Kenneth, Rutenberg Andrew
Department of Physics and Atmospheric Science, Dalhousie University, Halifax, Canada.
Department of Medicine, Dalhousie University, Halifax, Canada.
Biogerontology. 2020 Jun;21(3):345-355. doi: 10.1007/s10522-020-09863-1. Epub 2020 Mar 10.
Frailty indices (FIs) based on continuous valued health data, such as obtained from blood and urine tests, have been shown to be predictive of adverse health outcomes. However, creating FIs from such biomarker data requires a binarization treatment that is difficult to standardize across studies. In this work, we explore a "quantile" methodology for the generic treatment of biomarker data that allows us to construct an FI without preexisting medical knowledge (i.e. risk thresholds) of the included biomarkers. We show that our quantile approach performs as well as, or even slightly better than, established methods for the National Health and Nutrition Examination Survey and the Canadian Study of Health and Aging data sets. Furthermore, we show that our approach is robust to cohort effects within studies as compared to other data-based methods. The success of our binarization approaches provides insight into the robustness of the FI as a health measure, and the upper limits of the FI observed in various data sets, and also highlights general difficulties in obtaining absolute scales for comparing FIs between studies.
基于连续值健康数据（如通过血液和尿液检测获得的数据）构建的衰弱指数（FI）已被证明可预测不良健康结局。然而，从这类生物标志物数据创建FI需要进行二值化处理，而这种处理在不同研究中难以标准化。在这项工作中，我们探索了一种用于生物标志物数据通用处理的“分位数”方法，该方法使我们能够在无需对所纳入生物标志物具备先验医学知识（即风险阈值）的情况下构建FI。我们表明，对于美国国家健康与营养检查调查以及加拿大健康与老龄化研究数据集，我们的分位数方法与既定方法表现相当，甚至略胜一筹。此外，与其他基于数据的方法相比，我们的方法对研究中的队列效应具有更强的稳健性。我们二值化方法的成功为理解FI作为一种健康度量的稳健性、在各种数据集中观察到的FI上限提供了见解，同时也凸显了在获得用于比较不同研究中FI的绝对尺度方面存在的普遍困难。
