Bradley Olinger, Reema Banarjee, Amit Dey, Dimitrios Tsitsipatis, Toshiko Tanaka, Anjana Ram, Thedoe Nyunt, Gulzar Daya, Zhongsheng Peng, Linna Cui, Julian Candia, Eleanor M Simonsick, Myriam Gorospe, Keenan A Walker, Luigi Ferrucci, Nathan Basisty
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引用次数: 0
摘要
细胞衰老会随着年龄的增长而加剧,并导致与年龄相关的衰退和病变。我们确定了与人类不同临床特征相关的衰老循环生物标志物,以促进未来对个体衰老负担的无创评估和新型衰老治疗药物的疗效测试。利用基于纳米粒子的新型蛋白质组学工作流程,我们分析了单核细胞中的衰老相关分泌表型(SASP),并检测了巴尔的摩老龄化纵向研究(Baltimore Longitudinal Study of Aging,BLSA)血浆样本(N = 1060)中的这些蛋白质。根据单核细胞SASP训练的机器学习模型与测试队列中的几种年龄相关表型有关,包括体脂组成、血脂、炎症和运动相关特征等。值得注意的是,基于 SASP 的预测结果子集(包括预测年龄和肥胖相关临床特征的高影响 SASP 面板)在独立的老龄化队列 InCHIANTI 中得到了验证。这些结果证明了循环 SASP 的临床相关性,并确定了衰老的相关生物标志物,可为未来的临床研究提供参考。
A plasma proteomic signature links secretome of senescent monocytes to aging- and obesity-related clinical outcomes in humans
Cellular senescence increases with age and contributes to age-related declines and pathologies. We identified circulating biomarkers of senescence associated with diverse clinical traits in humans to facilitate future non-invasive assessment of individual senescence burden and efficacy testing of novel senotherapeutics. Using a novel nanoparticle-based proteomic workflow, we profiled the senescence-associated secretory phenotype (SASP) in monocytes and examined these proteins in plasma samples (N = 1060) from the Baltimore Longitudinal Study of Aging (BLSA). Machine learning models trained on monocyte SASP associated with several age-related phenotypes in a test cohort, including body fat composition, blood lipids, inflammation, and mobility-related traits, among others. Notably, a subset of SASP-based predictions, including a high impact SASP panel that predicts age- and obesity-related clinical traits, were validated in InCHIANTI, an independent aging cohort. These results demonstrate the clinical relevance of the circulating SASP and identify relevant biomarkers of senescence that could inform future clinical studies.