Ma Ling-Zhi, Liu Wei-Shi, He Yu, Zhang Yi, You Jia, Feng Jian-Feng, Tan Lan, Cheng Wei, Yu Jin-Tai
Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China; Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China.
Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China.
J Adv Res. 2025 May 4. doi: 10.1016/j.jare.2025.05.004.
Plasma proteomics examines levels of thousands of proteins and has the potential to identify clinical biomarkers for healthy aging.
This large proteomics study aims to identify clinical biomarkers for healthy aging and further explore potential mechanisms involved in aging.
This study analyzed data from 51,904 UK Biobank participants to explore the association between 2,923 plasma proteins and nine aging-related phenotypes, including PhenoAge, KDM-Biological Age, healthspan, parental lifespan, frailty, and longevity. Protein levels were measured using proteomics, and associations were assessed with a significance threshold of P < 1.90E-06. We utilized the DE-SWAN method to detect and measure the nonlinear alterations in plasma proteome during the process of biological aging. Mendelian randomization was applied to assess causal relationships, and a PheWAS explored the broader health impacts of these proteins.
We identified 227 proteins significantly associated with aging (P < 1.90E-06), with the pathway of inflammation and regeneration being notably implicated. Our findings revealed fluctuating patterns in the plasma proteome during biological aging in middle-aged adults, pinpointing specific peaks of biological age-related changes at 41, 60, and 67 years, alongside distinct age-related protein change patterns across various organs. Furthermore, mendelian randomization further supported the causal association between plasma levels of CXCL13, DPY30, FURIN, IGFBP4, SHISA5, and aging, underscoring the significance of these drug targets. These five proteins have broad-ranging effects. The PheWAS analysis of proteins associated with aging highlighted their crucial roles in vital biological processes, particularly in overall mortality, health maintenance, and cardiovascular health. Moreover, proteins can serve as mediators in healthy lifestyle and aging processes.
These significant discoveries underscore the importance of monitoring and intervening in the aging process at critical periods, alongside identifying potential biomarkers and therapeutic targets for age-related disorders within the plasma proteomic landscape, thus offering valuable insights into healthy aging.
血浆蛋白质组学可检测数千种蛋白质的水平，有潜力识别健康衰老的临床生物标志物。
这项大型蛋白质组学研究旨在识别健康衰老的临床生物标志物，并进一步探索衰老过程中涉及的潜在机制。
本研究分析了来自51904名英国生物银行参与者的数据，以探索2923种血浆蛋白与9种衰老相关表型之间的关联，这些表型包括PhenoAge、KDM-生物年龄、健康寿命、父母寿命、衰弱和长寿。使用蛋白质组学测量蛋白质水平，并以P < 1.90E-06的显著性阈值评估关联。我们利用DE-SWAN方法检测和测量生物衰老过程中血浆蛋白质组的非线性变化。应用孟德尔随机化评估因果关系，并通过全表型关联研究探索这些蛋白质对更广泛健康状况的影响。
我们鉴定出227种与衰老显著相关的蛋白质（P < 1.90E-06），其中炎症和再生途径尤为显著。我们的研究结果揭示了中年成年人在生物衰老过程中血浆蛋白质组的波动模式，确定了在41岁、60岁和67岁时与生物年龄相关变化的特定峰值，以及不同器官中与年龄相关的独特蛋白质变化模式。此外，孟德尔随机化进一步支持了血浆中CXCL13、DPY30、弗林蛋白酶、胰岛素样生长因子结合蛋白4、SHISA5水平与衰老之间的因果关联，强调了这些药物靶点的重要性。这五种蛋白质具有广泛的影响。对与衰老相关蛋白质的全表型关联研究分析突出了它们在重要生物学过程中的关键作用，特别是在总体死亡率、健康维持和心血管健康方面。此外，蛋白质可作为健康生活方式和衰老过程中的调节因子。
这些重大发现强调了在关键时期监测和干预衰老过程的重要性，同时在血浆蛋白质组学领域识别与年龄相关疾病的潜在生物标志物和治疗靶点，从而为健康衰老提供有价值的见解。
