ABSTRACT

Mortality assessments are conducted for both civil and commercial purposes. Recent advances in epigenetics have resulted in DNA methylation tools to assess risk and aid in this task. However, widely available array-based algorithms are not readily translatable into clinical tools and do not provide a good foundation for clinical recommendations. Further, recent work shows evidence of heritability and possible racial bias in these indices. Using a publicly available array data set, the Framingham Heart Study (FHS), we develop and test a five-locus mortality-risk algorithm using only previously validated methylation biomarkers that have been shown to be free of racial bias, and that provide specific assessments of smoking, alcohol consumption, diabetes and heart disease. We show that a model using age, sex and methylation measurements at these five loci outperforms the 513 probe Levine index and approximates the predictive power of the 1030 probe GrimAge index. We then show each of the five loci in our algorithm can be assessed using a more powerful, reference-free digital PCR approach, further demonstrating that it is readily clinically translatable. Finally, we show the loci do not reflect ethnically specific variation. We conclude that this algorithm is a simple, yet powerful tool for assessing mortality risk. We further suggest that the output from this or similarly derived algorithms using either array or digital PCR can be used to provide powerful feedback to patients, guide recommendations for additional medical assessments, and help monitor the effect of public health prevention interventions.

摘要

为民用和商业目的进行死亡率评估。表观遗传学的最新进展导致了 DNA 甲基化工具来评估风险并帮助完成这项任务。然而，广泛使用的基于阵列的算法不容易转化为临床工具，也不能为临床推荐提供良好的基础。此外，最近的工作显示了这些指数中遗传性和可能的​​种族偏见的证据。使用公开可用的阵列数据集，弗雷明汉心脏研究 (FHS)，我们开发并测试了一个五位点死亡风险算法，仅使用先前验证的甲基化生物标志物，这些生物标志物已被证明没有种族偏见，并提供特定的评估吸烟、饮酒、糖尿病和心脏病。我们展示了一个使用年龄的模型，这五个位点的性别和甲基化测量优于 513 探针 Levine 指数，并接近 1030 探针 GrimAge 指数的预测能力。然后，我们展示了我们算法中的五个基因座中的每一个都可以使用更强大、无参考的数字 PCR 方法进行评估，进一步证明它很容易在临床上翻译。最后，我们显示基因座不反映种族特定的变异。我们得出结论，该算法是评估死亡风险的简单而强大的工具。我们进一步建议，使用阵列或数字 PCR 的这种或类似衍生算法的输出可用于为患者提供强有力的反馈，指导额外医疗评估的建议，并帮助监测公共卫生预防干预措施的效果。