Kuo Chia-Ling, Chen Zhiduo, Liu Peiran, Pilling Luke C, Atkins Janice L, Fortinsky Richard H, Kuchel George A, Diniz Breno S
Department of Public Health Sciences, University of Connecticut Health Center, Farmington CT, USA.
The Cato T. Laurencin Institute for Regenerative Engineering, University of Connecticut Health Center, Farmington, CT, USA.
medRxiv. 2024 Apr 21:2023.12.19.23300228. doi: 10.1101/2023.12.19.23300228.
Beyond mere prognostication, optimal biomarkers of aging provide insights into qualitative and quantitative features of biological aging and might, therefore, offer useful information for the testing and, ultimately, clinical use of gerotherapeutics. We aimed to develop a proteomic aging clock (PAC) for all-cause mortality risk as a proxy of biological age. Data were from the UK Biobank Pharma Proteomics Project, including 53,021 participants aged between 39 and 70 years and 2,923 plasma proteins assessed using the Olink Explore 3072 assay. The Spearman correlation between PAC proteomic age and chronological age was 0.77. A total of 10.9% of the participants died during a mean follow-up of 13.3 years, with the mean age at death 70.1 years. We developed a proteomic aging clock (PAC) for all-cause mortality risk as a surrogate of BA using a combination of least absolute shrinkage and selection operator (LASSO) penalized Cox regression and Gompertz proportional hazards models. PAC showed robust age-adjusted associations and predictions for all-cause mortality and the onset of various diseases in general and disease-free participants. The proteins associated with PAC were enriched in several processes related to the hallmarks of biological aging. Our results expand previous findings by showing that age acceleration, based on PAC, strongly predicts all-cause mortality and several incident disease outcomes. Particularly, it facilitates the evaluation of risk for multiple conditions in a disease-free population, thereby, contributing to the prevention of initial diseases, which vary among individuals and may subsequently lead to additional comorbidities.
除了单纯的预后判断外，理想的衰老生物标志物还能深入了解生物衰老的定性和定量特征，因此可能为老年治疗药物的测试乃至临床应用提供有用信息。我们旨在开发一种蛋白质组衰老时钟（PAC），以全因死亡风险作为生物年龄的替代指标。数据来自英国生物银行药物蛋白质组学项目，包括53021名年龄在39至70岁之间的参与者，以及使用Olink Explore 3072检测法评估的2923种血浆蛋白。PAC蛋白质组年龄与实际年龄之间的斯皮尔曼相关性为0.77。在平均13.3年的随访期间，共有10.9%的参与者死亡，平均死亡年龄为70.1岁。我们使用最小绝对收缩和选择算子（LASSO）惩罚Cox回归和Gompertz比例风险模型相结合的方法，开发了一种蛋白质组衰老时钟（PAC），以全因死亡风险作为生物年龄的替代指标。PAC显示出与年龄调整后的全因死亡率以及一般和无病参与者中各种疾病的发病存在稳健的关联和预测。与PAC相关的蛋白质在与生物衰老特征相关的几个过程中富集。我们的结果扩展了先前的发现，表明基于PAC的年龄加速强烈预测全因死亡率和几种疾病的发病结果。特别是，它有助于评估无病人群中多种疾病的风险，从而有助于预防个体间不同的初始疾病，这些疾病随后可能导致更多的合并症。
