Epigenetic clocks and research implications of the lack of data on whom they have been developed: a review of reported and missing sociodemographic characteristics.
Epigenetic clocks and research implications of the lack of data on whom they have been developed: a review of reported and missing sociodemographic characteristics.
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DOI：
10.1093/eep/dvad005
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发表时间：
2023
影响因子：
3.8
通讯作者：
中科院分区：
文献类型：
--
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表观遗传时钟越来越多地被用作评估各种表型和暴露对健康老龄化的影响的工具，最近重点关注健康的社会决定因素。然而，很少有人注意到这些时钟所依据的参与者的社会人口特征。参与者的特征很重要，因为社会人口和社会经济因素已知与DNA甲基化变异和健康老龄化相关。众所周知，机器学习算法有可能通过使用不具代表性的样本来加剧健康不公平-预测模型可能在用于构建模型的训练数据中代表性较差的社会群体中表现不佳。为了解决文献中的这一差距，我们对参与者的社会人口学特征进行了回顾，这些参与者的数据被用来构建13个常用的表观遗传时钟。我们发现，虽然一些表观遗传时钟是利用来自不同年龄，性别/性别和种族群体的个人提供的数据创建的，但社会人口统计学特征通常报道不多。由于对社会层面以及性别和种族不平等所涉影响的认识不足，报告的信息有限，而且很少报告社会经济数据。今后的工作必须确保明确报告研究所有参与者的社会人口和社会经济特征的具体数据，以确保其他研究人员能够就模型对其研究人群的适当性作出知情的判断。
Epigenetic clocks are increasingly being used as a tool to assess the impact of a wide variety of phenotypes and exposures on healthy ageing, with a recent focus on social determinants of health. However, little attention has been paid to the sociodemographic characteristics of participants on whom these clocks have been based. Participant characteristics are important because sociodemographic and socioeconomic factors are known to be associated with both DNA methylation variation and healthy ageing. It is also well known that machine learning algorithms have the potential to exacerbate health inequities through the use of unrepresentative samples – prediction models may underperform in social groups that were poorly represented in the training data used to construct the model. To address this gap in the literature, we conducted a review of the sociodemographic characteristics of the participants whose data were used to construct 13 commonly used epigenetic clocks. We found that although some of the epigenetic clocks were created utilizing data provided by individuals from different ages, sexes/genders, and racialized groups, sociodemographic characteristics are generally poorly reported. Reported information is limited by inadequate conceptualization of the social dimensions and exposure implications of gender and racialized inequality, and socioeconomic data are infrequently reported. It is important for future work to ensure clear reporting of tangible data on the sociodemographic and socioeconomic characteristics of all the participants in the study to ensure that other researchers can make informed judgements about the appropriateness of the model for their study population.