Tally Health, New York, NY, USA.
Geroscience. 2024 Jun;46(3):3429-3443. doi: 10.1007/s11357-024-01094-3. Epub 2024 Mar 5.
Epigenetic aging clocks are computational models that predict age using DNA methylation information. Initially, first-generation clocks were developed to make predictions using CpGs that change with age. Over time, next-generation clocks were created using CpGs that relate to both age and health. Since existing next-generation clocks were constructed in blood, we sought to develop a next-generation clock optimized for prediction in cheek swabs, which are non-invasive and easy to collect. To do this, we collected MethylationEPIC data as well as lifestyle and health information from 8045 diverse adults. Using a novel simulated annealing approach that allowed us to incorporate lifestyle and health factors into training as well as a combination of CpG filtering, CpG clustering, and clock ensembling, we constructed CheekAge, an epigenetic aging clock that has a strong correlation with age, displays high test-retest reproducibility across replicates, and significantly associates with a plethora of lifestyle and health factors, such as BMI, smoking status, and alcohol intake. We validated CheekAge in an internal dataset and multiple publicly available datasets, including samples from patients with progeria or meningioma. In addition to exploring the underlying biology of the data and clock, we provide a free online tool that allows users to mine our methylomic data and predict epigenetic age.
表观遗传衰老时钟是一种使用 DNA 甲基化信息来预测年龄的计算模型。最初，第一代时钟是使用随年龄变化的 CpG 开发的，用于进行预测。随着时间的推移，使用与年龄和健康相关的 CpG 开发了第二代时钟。由于现有的第二代时钟是在血液中构建的，我们试图开发一种针对颊拭子预测优化的下一代时钟，因为颊拭子非侵入性且易于采集。为此，我们从 8045 名不同的成年人中收集了 MethylationEPIC 数据以及生活方式和健康信息。我们使用一种新颖的模拟退火方法，允许我们将生活方式和健康因素纳入训练中，以及结合 CpG 过滤、CpG 聚类和时钟集成，构建了 CheekAge，这是一种表观遗传衰老时钟，与年龄具有很强的相关性，在复制之间表现出高测试-重测可重复性，并且与大量生活方式和健康因素显著相关，如 BMI、吸烟状况和饮酒量。我们在内部数据集和多个公开可用的数据集（包括来自早衰症或脑膜瘤患者的样本）中验证了 CheekAge。除了探索数据和时钟的潜在生物学外，我们还提供了一个免费的在线工具，允许用户挖掘我们的甲基组学数据并预测表观遗传年龄。
