Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, Minnesota, United States of America.
Division of Biostatistics and Health Data Science, School of Public Health, University of Minnesota, Minneapolis, Minnesota, United States of America.
PLoS Med. 2024 Sep 24;21(9):e1004464. doi: 10.1371/journal.pmed.1004464. eCollection 2024 Sep.
Biological age may be estimated by proteomic aging clocks (PACs). Previous published PACs were constructed either in smaller studies or mainly in white individuals, and they used proteomic measures from only one-time point. In this study, we created de novo PACs and compared their performance to published PACs at 2 different time points in the Atherosclerosis Risk in Communities (ARIC) study of white and black participants (around 75% white and 25% black).
A total of 4,712 plasma proteins were measured using SomaScan in blood samples collected in 1990 to 1992 from 11,761 midlife participants (aged 46 to 70 years) and in 2011 to 2013 from 5,183 late-life participants (aged 66 to 90 years). The de novo ARIC PACs were constructed by training them against chronological age using elastic net regression in two-thirds of healthy participants in midlife and late life and validated in the remaining one-third of healthy participants at the corresponding time point. We also computed 3 published PACs. We estimated age acceleration for each PAC as residuals after regressing each PAC on chronological age. We also calculated the change in age acceleration from midlife to late life. We examined the associations of age acceleration and change in age acceleration with mortality through 2019 from all-cause, cardiovascular disease (CVD), cancer, and lower respiratory disease (LRD) using Cox proportional hazards regression in participants (irrespective of health) after excluding the training set. The model was adjusted for chronological age, smoking, body mass index (BMI), and other confounders. We externally validated the midlife PAC using the Multi-Ethnic Study of Atherosclerosis (MESA) Exam 1 data. The ARIC PACs had a slightly stronger correlation with chronological age than published PACs in healthy participants at each time point. Associations with mortality were similar for the ARIC PACs and published PACs. For late-life and midlife age acceleration for the ARIC PACs, respectively, hazard ratios (HRs) per 1 standard deviation were 1.65 and 1.38 (both p < 0.001) for all-cause mortality, 1.37 and 1.20 (both p < 0.001) for CVD mortality, 1.21 (p = 0.028) and 1.04 (p = 0.280) for cancer mortality, and 1.68 and 1.36 (both p < 0.001) for LRD mortality. For the change in age acceleration, HRs for all-cause, CVD, and LRD mortality were comparable to the HRs for late-life age acceleration. The association between the change in age acceleration and cancer mortality was not significant. The external validation of the midlife PAC in MESA showed significant associations with mortality, as observed for midlife participants in ARIC. The main limitation is that our PACs were constructed in midlife and late-life participants. It is unknown whether these PACs could be applied to young individuals.
In this longitudinal study, we found that the ARIC PACs and published PACs were similarly associated with an increased risk of mortality. These findings suggested that PACs show promise as biomarkers of biological age. PACs may be serve as tools to predict mortality and evaluate the effect of anti-aging lifestyle and therapeutic interventions.
生物年龄可以通过蛋白质组学衰老时钟(PAC)来估计。之前发表的 PAC 要么是在较小的研究中构建的,要么主要是在白种人群体中构建的,并且它们使用的是单次测量的蛋白质组学指标。在这项研究中,我们创建了新的 PAC,并在 ARIC 研究的白人和黑人参与者(约 75%为白人,25%为黑人)的 2 个不同时间点上,将其与已发表的 PAC 进行了比较。
在 1990 年至 1992 年间采集的来自 11761 名中年参与者(年龄在 46 岁至 70 岁之间)的血液样本中,以及在 2011 年至 2013 年间采集的来自 5183 名老年参与者(年龄在 66 岁至 90 岁之间)的血液样本中,使用 SomaScan 共测量了 4712 种血浆蛋白。新的 ARIC PAC 是通过在中年和老年的健康参与者的三分之二的数据中使用弹性网络回归来训练他们的,然后在相应的时间点在剩余的三分之一的健康参与者中进行验证。我们还计算了 3 个已发表的 PAC。我们通过将每个 PAC 回归到年龄来估计每个 PAC 的年龄加速,然后将其作为残差。我们还计算了从中年到老年的年龄加速变化。我们使用 Cox 比例风险回归,在参与者(不论健康状况如何)中,通过 2019 年的全因、心血管疾病(CVD)、癌症和下呼吸道疾病(LRD)死亡率来检查年龄加速和年龄加速变化与死亡率之间的关联,该模型调整了年龄、吸烟、体重指数(BMI)和其他混杂因素。我们使用 Multi-Ethnic Study of Atherosclerosis(MESA)Exam 1 数据对中年 PAC 进行了外部验证。在每个时间点,与健康参与者相比,ARIC PAC 与年龄的相关性略强于已发表的 PAC。与死亡率相关的结果在 ARIC PAC 和已发表的 PAC 中相似。对于 ARIC PAC 的老年和中年年龄加速,每标准偏差的风险比(HR)分别为全因死亡率的 1.65 和 1.38(均 p < 0.001)、CVD 死亡率的 1.37 和 1.20(均 p < 0.001)、癌症死亡率的 1.21(p = 0.028)和 1.04(p = 0.280)、LRD 死亡率的 1.68 和 1.36(均 p < 0.001)。对于年龄加速的变化,全因、CVD 和 LRD 死亡率的 HR 与老年年龄加速的 HR 相当。年龄加速变化与癌症死亡率之间的关联不显著。MESA 中对中年 PAC 的外部验证显示与死亡率存在显著关联,与 ARIC 中的中年参与者观察到的结果相似。主要限制是我们的 PAC 是在中年和老年参与者中构建的。目前尚不清楚这些 PAC 是否可以应用于年轻人。
在这项纵向研究中,我们发现 ARIC PAC 和已发表的 PAC 与死亡率升高的风险具有相似的相关性。这些发现表明,PAC 作为生物年龄的生物标志物具有潜力。PAC 可能是预测死亡率和评估抗衰老生活方式和治疗干预效果的有用工具。