Sehgal Raghav, Markov Yaroslav, Qin Chenxi, Meer Margarita, Hadley Courtney, Shadyab Aladdin H, Casanova Ramon, Manson JoAnn E, Bhatti Parveen, Crimmins Eileen M, Hägg Sara, Assimes Themistocles L, Whitsel Eric A, Higgins-Chen Albert Tzongyang, Levine Morgan
bioRxiv. 2024 May 28:2023.07.13.548904. doi: 10.1101/2023.07.13.548904.
Individuals, organs, tissues, and cells age in diverse ways throughout the lifespan. Epigenetic clocks attempt to quantify differential aging between individuals, but they typically summarize aging as a single measure, ignoring within-person heterogeneity. Our aim was to develop novel systems-based methylation clocks that, when assessed in blood, capture aging in distinct physiological systems. We combined supervised and unsupervised machine learning methods to link DNA methylation, system-specific clinical chemistry and functional measures, and mortality risk. This yielded a panel of 11 system-specific scores- Heart, Lung, Kidney, Liver, Brain, Immune, Inflammatory, Blood, Musculoskeletal, Hormone, and Metabolic. Each system score predicted a wide variety of outcomes, aging phenotypes, and conditions specific to the respective system. We also combined the system scores into a composite Systems Age clock that is predictive of aging across physiological systems in an unbiased manner. Finally, we showed that the system scores clustered individuals into unique aging subtypes that had different patterns of age-related disease and decline. Overall, our biological systems based epigenetic framework captures aging in multiple physiological systems using a single blood draw and assay and may inform the development of more personalized clinical approaches for improving age-related quality of life.
在整个生命周期中，个体、器官、组织和细胞以不同方式衰老。表观遗传时钟试图量化个体间的差异衰老，但它们通常将衰老总结为单一指标，忽略了个体内部的异质性。我们的目标是开发基于系统的新型甲基化时钟，当在血液中进行评估时，能够捕捉不同生理系统中的衰老情况。我们结合了监督式和非监督式机器学习方法，将DNA甲基化、特定系统的临床化学和功能指标以及死亡风险联系起来。这产生了一组11个特定系统的分数——心脏、肺、肾脏、肝脏、大脑、免疫、炎症、血液、肌肉骨骼、激素和代谢。每个系统分数都能预测各种结果、衰老表型以及各自系统特有的状况。我们还将系统分数组合成一个综合的系统年龄时钟，它能够以无偏的方式预测跨生理系统的衰老情况。最后，我们表明系统分数将个体聚类为独特的衰老亚型，这些亚型具有不同的与年龄相关疾病和衰退模式。总体而言，我们基于生物系统的表观遗传框架通过一次抽血和检测就能捕捉多个生理系统中的衰老情况，并可能为开发更个性化的临床方法以改善与年龄相关的生活质量提供信息。
