Social & Cognitive Computing Department, Institute of High Performance Computing, Agency for Science, Technology and Research (A*STAR), Fusionopolis, Singapore.
Singapore Immunology Network (SIgN), Agency for Science Technology and Research (A*STAR), Biopolis.
J Gerontol A Biol Sci Med Sci. 2020 Sep 25;75(10):1913-1920. doi: 10.1093/gerona/glz146.
Biological age (BA) is a more accurate measure of the rate of human aging than chronological age (CA). However, there is limited consensus regarding measures of BA in life span and healthspan.
This study investigated measurement sets of 68 physiological biomarkers using data from 2,844 Chinese Singaporeans in two age subgroups (55-70 and 71-94 years) in the Singapore Longitudinal Aging Study (SLAS-2) with 8-year follow-up frailty and mortality data. We computed BA estimate using three commonly used algorithms: Principal Component Analysis (PCA), Multiple Linear Regression (MLR), and Klemera and Doubal (KD) method, and additionally, explored the use of machine learning methods for prediction of mortality and frailty. The most optimal algorithmic estimate of BA compared to CA was evaluated for their associations with risk factors and health outcome.
Stepwise selection procedures resulted in the final selection of 8 biomarkers in males and 10 biomarkers in females. The highest-ranking biomarkers were estimated glomerular filtration rate for both genders, and the forced expiratory volume in 1 second in males and females. The BA estimates robustly predicted frailty and mortality and outperformed CA. The best performing KD measure of BA was notably predictive in the younger group (aged 55-70 years). BA estimates obtained using a machine learning train-test method were not more accurate than conventional BA estimates in predicting mortality and frailty in most situations. Biologically older people with the same CA as biologically younger individuals had higher prevalence of frailty and 8-year mortality, and worse health, behavioral, and functional characteristics.
BA is better than CA for measuring life span (mortality) and healthspan (frailty). This measurement set of physiological markers of biological aging among Chinese robustly differentiate biologically old from younger individuals with the same CA.
生物年龄 (BA) 比实际年龄 (CA) 更能准确衡量人类衰老的速度。然而，关于寿命和健康跨度的 BA 衡量标准尚未达成共识。
本研究使用来自新加坡纵向老龄化研究（SLAS-2）的 2844 名中国新加坡人 55-70 岁和 71-94 岁两个年龄组的数据，调查了 68 种生理生物标志物的测量集，并进行了 8 年的随访虚弱和死亡率数据。我们使用三种常用算法（主成分分析（PCA）、多元线性回归（MLR）和 Klemera 和 Doubal（KD）方法）计算 BA 估计值，并探索了机器学习方法在预测死亡率和虚弱方面的应用。与 CA 相比，BA 的最佳算法估计值与危险因素和健康结果的相关性进行了评估。
逐步选择程序最终在男性中选择了 8 种生物标志物，在女性中选择了 10 种生物标志物。排名最高的生物标志物是男女的估计肾小球滤过率，以及男性和女性的 1 秒用力呼气量。BA 估计值能够可靠地预测虚弱和死亡率，并且优于 CA。KD 方法对 BA 的最佳估计值在年轻组（55-70 岁）中具有显著的预测性。在大多数情况下，使用机器学习训练-测试方法获得的 BA 估计值并不比传统的 BA 估计值更能准确预测死亡率和虚弱。具有相同 CA 的生物学上较老的人与生物学上较年轻的人具有更高的虚弱和 8 年死亡率，以及较差的健康、行为和功能特征。
BA 比 CA 更能衡量寿命（死亡率）和健康跨度（虚弱）。这套中国人群生物老化的生理标志物测量集可以可靠地区分具有相同 CA 的生物学上较老的人与较年轻的人。
