Großbach Anna, Suderman Matthew J, Hüls Anke, Lussier Alexandre A, Smith Andrew D A C, Walton Esther, Dunn Erin C, Simpkin Andrew J
School of Mathematical and Statistical Sciences, University of Galway, Galway, Ireland.
The SFI Centre for Research Training in Genomics Data Science, Galway, Ireland.
Clin Epigenetics. 2024 Dec 20;16(1):187. doi: 10.1186/s13148-024-01784-x.
Epigenetic age (EA) is an age estimate, developed using DNA methylation (DNAm) states of selected CpG sites across the genome. Although EA and chronological age are highly correlated, EA may not increase uniformly with time. Departures, known as epigenetic age acceleration (EAA), are common and have been linked to various traits and future disease risk. Limited by available data, most studies investigating these relationships have been cross-sectional, using a single EA measurement. However, the recent growth in longitudinal DNAm studies has led to analyses of associations with EA over time. These studies differ in (1) their choice of model; (2) the primary outcome (EA vs. EAA); and (3) in their use of chronological age or age-independent time variables to account for the temporal dynamic. We evaluated the robustness of each approach using simulations and tested our results in two real-world examples, using biological sex and birthweight as predictors of longitudinal EA.
Our simulations showed most accurate effect sizes in a linear mixed model or generalized estimating equation, using chronological age as the time variable. The use of EA versus EAA as an outcome did not strongly impact estimates. Applying the optimal model in real-world data uncovered advanced GrimAge in individuals assigned male at birth that decelerates over time.
Our results can serve as a guide for forthcoming longitudinal EA studies, aiding in methodological decisions that may determine whether an association is accurately estimated, overestimated, or potentially overlooked.
表观遗传年龄（EA）是一种年龄估计值，通过全基因组中选定CpG位点的DNA甲基化（DNAm）状态得出。尽管EA与实际年龄高度相关，但EA可能不会随时间均匀增加。这种差异被称为表观遗传年龄加速（EAA），很常见且与各种特征和未来疾病风险相关。受现有数据限制，大多数研究这些关系的研究都是横断面研究，使用单一的EA测量值。然而，最近纵向DNAm研究的增加导致了对随时间与EA关联的分析。这些研究在以下方面存在差异：（1）模型选择；（2）主要结果（EA与EAA）；（3）使用实际年龄或与年龄无关的时间变量来解释时间动态。我们使用模拟评估了每种方法的稳健性，并在两个实际例子中测试了我们的结果，使用生物学性别和出生体重作为纵向EA的预测因素。
我们的模拟显示，在线性混合模型或广义估计方程中，使用实际年龄作为时间变量时，效应大小最为准确。将EA与EAA用作结果对估计的影响不大。在实际数据中应用最优模型发现，出生时被指定为男性的个体中，GrimAge提前，且随时间推移而减速。
我们的结果可为即将开展的纵向EA研究提供指导，有助于做出可能决定关联是被准确估计、高估还是可能被忽视的方法学决策。
