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首页> 外文期刊>The journals of gerontology.Series A. Biological sciences and medical sciences >Tracking the Epigenetic Clock Across the Human Life Course: A Meta-analysis of Longitudinal Cohort Data
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Tracking the Epigenetic Clock Across the Human Life Course: A Meta-analysis of Longitudinal Cohort Data

机译：跟踪跨人类生命课程的表观遗传时钟：纵向队列数据的荟萃分析

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Background: Epigenetic clocks based on DNA methylation yield high correlations with chronological age in cross- sectional data. Due to a paucity of longitudinal data, it is not known how. age (epigenetic age - chronological age) changes over time or if it remains constant from childhood to old age. Here, we investigate this using longitudinal DNA methylation data from five datasets, covering most of the human life course. Methods: Two measures of the epigenetic clock (Hannum and Horvath) are used to calculate. age in the following cohorts: Avon Longitudinal Study of Parents and Children (ALSPAC) offspring (n = 986, total age- range 7- 19 years, 2 waves), ALSPAC mothers (n = 982, 16- 60 years, 2 waves), InCHIANTI (n = 460, 21- 100 years, 2 waves), SATSA (n = 373, 48- 99 years, 5 waves), Lothian Birth Cohort 1936 (n = 1,054, 70- 76 years, 3 waves), and Lothian Birth Cohort 1921 (n = 476, 79- 90 years, 3 waves). Linear mixed models were used to track longitudinal change in. age within each cohort. Results: For both epigenetic age measures,. age showed a declining trend in almost all of the cohorts. The correlation between. age across waves ranged from 0.22 to 0.82 for Horvath and 0.25 to 0.71 for Hannum, with stronger associations in samples collected closer in time. Conclusions: Epigenetic age increases at a slower rate than chronological age across the life course, especially in the oldest population. Some of the effect is likely driven by survival bias, where healthy individuals are those maintained within a longitudinal study, although other factors like the age distribution of the underlying training population may also have influenced this trend.
机译：背景：基于DNA甲基化的表观遗传时钟与横截面数据中的时间年龄增长高相关。由于纵向数据的缺乏，它尚不清楚如何。年龄（表观遗传年龄 - 年代时代）随着时间的推移而变化，或者如果它从小到老年仍然不断变化。在这里，我们使用来自五个数据集的纵向DNA甲基化数据来研究这一点，涵盖了大部分人类寿命课程。方法：使用两种措施表观牙齿（Hannum和Horvath）来计算。以下队列的年龄：艾薇儿童纵向研究父母和儿童（alspac）后代（n = 986，总龄 - 范围7-19岁，2波），羊肉母亲（n = 982,16-60岁，2波） ，inchianti（n = 460,21-100岁，2波），satsa（n = 373,48-99岁，5浪），洛锡尼的出生队列1936（n = 1,054,70-76岁，3波），和Lothian出生队队1921（n = 476,79-90岁，3张浪潮）。线性混合模型用于跟踪纵向变化。每个队列内的年龄。结果：对于表观遗传年龄措施，。 AGE在几乎所有的队列中都表现出趋势下降。之间的相关性。 Horvath的跨越波浪的年龄范围为0.22〜0.82，对于Hannum，0.25至0.71，采样中收集的样本中的较强的关联。结论：表观遗传年龄的增加速度比生命过程中的年龄年龄较慢，特别是在最古老的人群中。一些效果可能由生存偏差驱动，其中健康的个体是那些维持在纵向研究中的人，尽管其他因素，如潜在的培训人口的年龄分布也可能影响了这一趋势。

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