Moguilner Sebastian, Knight Silvin P, Davis James R C, O'Halloran Aisling M, Kenny Rose Anne, Romero-Ortuno Roman
Nuclear Medicine School Foundation (FUESMEN), National Commission of Atomic Energy, Mendoza M5500CJI, Argentina.
The Global Brain Health Institute (GBHI), Trinity College Dublin, D02 PN40 Dublin, Ireland.
Geriatrics (Basel). 2021 Aug 27;6(3):84. doi: 10.3390/geriatrics6030084.
The quantification of biological age in humans is an important scientific endeavor in the face of ageing populations. The frailty index (FI) methodology is based on the accumulation of health deficits and captures variations in health status within individuals of the same age. The aims of this study were to assess whether the addition of age to an FI improves its mortality prediction and whether the associations of the individual FI items differ in strength. We utilized data from The Irish Longitudinal Study on Ageing to conduct, by sex, machine learning analyses of the ability of a 32-item FI to predict 8-year mortality in 8174 wave 1 participants aged 50 or more years. By wave 5, 559 men and 492 women had died. In the absence of age, the FI was an acceptable predictor of mortality with AUCs of 0.7. When age was included, AUCs improved to 0.8 in men and 0.9 in women. After age, deficits related to physical function and self-rated health tended to have higher importance scores. Not all FI variables seemed equally relevant to predict mortality, and age was by far the most relevant feature. Chronological age should remain an important consideration when interpreting the prognostic significance of an FI.
面对人口老龄化，对人类生物年龄的量化是一项重要的科学工作。衰弱指数（FI）方法基于健康缺陷的累积，并反映了同一年龄段个体健康状况的差异。本研究的目的是评估在FI中加入年龄是否能改善其对死亡率的预测，以及各个FI项目的关联强度是否不同。我们利用爱尔兰纵向老龄化研究的数据，按性别对8174名年龄在50岁及以上的第1波参与者进行了机器学习分析，以研究32项FI预测8年死亡率的能力。到第5波时，有559名男性和492名女性死亡。在不考虑年龄的情况下，FI是死亡率的一个可接受的预测指标，曲线下面积（AUC）为0.7。当纳入年龄因素时，男性的AUC提高到0.8，女性提高到0.9。在考虑年龄因素后，与身体功能和自评健康相关的缺陷往往具有更高的重要性得分。并非所有的FI变量在预测死亡率方面似乎都同样相关，年龄是迄今为止最相关的特征。在解释FI的预后意义时，实足年龄应仍然是一个重要的考虑因素。
