表观遗传年龄模型在新一代甲基化芯片中的适用性

表观遗传时钟是一类用以基于特定CpG位点DNA甲基化水平估算表观遗传年龄的数学模型。随着新型甲基化微阵列的开发及旧模型的停用，现有的表观遗传时钟可能逐渐过时。在此，我们探讨了新开发的EPICv2 DNA甲基化芯片引入的变更对现有表观遗传时钟的影响。我们在10,835个样本的数据集上测试了四个表观遗传时钟在EPICv2芯片探针集上的性能。我们开发了一个兼容450k、EPICv1和EPICv2微阵列的新型表观遗传年龄预测模型，并在2095个样本上进行了验证。通过两组重复采样的数据集估算了技术噪声和个体内部变异。我们使用（i）接受不同治疗的癌症幸存者数据，（ii）乳腺癌患者及对照组，以及（iii）基于运动的干预研究的数据，测试了模型检测反应于理论抗衰老干预的表观遗传年龄加速变化的能力。测试的四个epiclocks的结果显示，EPICv2探针集显著扭曲了其预测，平均偏差高达25年。我们新模型提供了高度准确的年龄预测，媲美最先进的epiclock。该模型在正常人群中检测到最低的表观遗传年龄加速以及在技术重复和相同个体多次采样中的最低变异性。最后，我们的模型重现了癌症患者及接受放疗的幸存者表观遗传年龄加速增加的先前研究结果，运动干预未引起变化。现有的表观遗传时钟需更新以实现对EPICv2的完全兼容。我们的新模型将最先进的表观遗传时钟的能力拓展到EPICv2平台，并实现了与旧型微阵列的交叉兼容。对表观遗传年龄预测变异的特征分析提供了有价值的指标，有助于理解表观遗传年龄变化的相关性。对受到放疗、癌症和运动干预影响的个体数据分析显示，尽管这些模型在预测年龄方面表现良好，乳腺癌等病理状态、放射线等有害环境因素或运动（有益干预）均未引起由这些第一代模型测定的“表观遗传年龄”值的显著变化。

Epigenetic clocks are mathematical models used to estimate epigenetic age based on DNA methylation at specific CpG sites. As new methylation microarrays are developed and older models discontinued, existing epigenetic clocks might become obsolete. Here, we explored the effects of the changes introduced in the new EPICv2 DNA methylation array on existing epigenetic clocks.We tested the performance of four epigenetic clocks on the probeset of the EPICv2 array using a dataset of 10,835 samples. We developed a new epigenetic age prediction model compatible across the 450 k, EPICv1, and EPICv2 microarrays and validated it on 2095 samples. We estimated technical noise and intra-subject variation using two datasets with repeated sampling. We used data from (i) cancer survivors who had undergone different therapies, (ii) breast cancer patients and controls, and (iii) an exercise-based interventional study, to test the ability of our model to detect alterations in epigenetic age acceleration in response to theoretically antiaging interventions.The results of the four epiclocks tested are significantly distorted by the EPICv2 probeset, causing an average difference of up to 25 years. Our new model produced highly accurate chronological age predictions, comparable to a state-of-the-art epiclock. The model reported the lowest epigenetic age acceleration in normal populations, as well as the lowest variation across technical replicates and repeated samples from the same subjects. Finally, our model reproduced previous results of increased epigenetic age acceleration in cancer patients and in survivors treated with radiation therapy, and no changes from exercise-based interventions.Existing epigenetic clocks require updates for full EPICv2 compatibility. Our new model translates the capabilities of state-of-the-art epigenetic clocks to the EPICv2 platform and is cross-compatible with older microarrays. The characterization of epigenetic age prediction variation provides useful metrics to contextualize the relevance of epigenetic age alterations. The analysis of data from subjects influenced by radiation, cancer, and exercise-based interventions shows that despite being good predictors of chronological age, neither a pathological state like breast cancer, a hazardous environmental factor (radiation), nor exercise (a beneficial intervention) caused significant changes in the values of the "epigenetic age" determined by these first-generation models.