表观遗传学
DNA甲基化
脑老化
德纳姆
生物钟
健康衰老
认知
生物
生物信息学
计算生物学
神经科学
医学
昼夜节律
老年学
遗传学
基因
基因表达
作者
Raghav Sehgal,Albert Higgins‐Chen,Margarita Meer,Morgan E. Levine
出处
期刊:Innovation in Aging
[University of Oxford]
日期:2022-11-01
卷期号:6 (Supplement_1): 20-21
被引量:1
标识
DOI:10.1093/geroni/igac059.076
摘要
Abstract Aging is a highly heterogeneous process at multiple levels. Different individuals, organs, tissues, and cell types are innately diverse and age in quantitatively different manners. Epigenetic clocks have been developed to capture overall degree of aging and typically report a single biological age value. However, single measures fail to provide insight into differential aging across organ systems. Our aim was to develop novel systems-specific methylation clocks, that when assessed in blood, capture distinct aging subtypes. We utilized three large human cohort studies and employed both supervised and unsupervised machine learning models by linking DNA methylation to lower dimensional vectors composed of system specific clinical chemistry and functional assays. In doing so, we were able to develop 11 unique system-specific scores–heart, lung, kidney, liver, brain, immune, inflammatory, hematopoietic, musculoskeletal, hormone, and metabolic. We observe that in independent data, the specific systems relate to meaningful outcomes–for instance the brain score is strongly associated with cognitive functioning; musculoskeletal score is strongly associated with physical functioning; and the lung score is strongly associated with lung cancer. Additionally, system scores and the composite systems clock outperforms presently available clocks in terms of associations with a wide variety of aging phenotypes and conditions. Overall, our biological systems based epigenetic clock outperforms presently available epigenetic aging clocks and provides meaningful insights into heterogeneity in aging.
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