Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Hum Genomics. 2023 Feb 11;17(1):8. doi: 10.1186/s40246-023-00453-z.
Aging affects the incidence of diseases such as cancer and dementia, so the development of biomarkers for aging is an important research topic in medical science. While such biomarkers have been mainly identified based on the assumption of a linear relationship between phenotypic parameters, including molecular markers, and chronological age, numerous nonlinear changes between markers and aging have been identified. However, the overall landscape of the patterns in nonlinear changes that exist in aging is unknown.
We propose a novel computational method, Data-driven Identification and Classification of Nonlinear Aging Patterns (DICNAP), that is based on functional data analysis to identify biomarkers for aging and potential patterns of change during aging in a data-driven manner. We applied the proposed method to large-scale, public DNA methylation data to explore the potential patterns of age-related changes in methylation intensity. The results showed that not only linear, but also nonlinear changes in DNA methylation patterns exist. A monotonous demethylation pattern during aging, with its rate decreasing at around age 60, was identified as the candidate stable nonlinear pattern. We also analyzed the age-related changes in methylation variability. The results showed that the variability of methylation intensity tends to increase with age at age-associated sites. The representative variability pattern is a monotonically increasing pattern that accelerates after middle age.
DICNAP was able to identify the potential patterns of the changes in the landscape of DNA methylation during aging. It contributes to an improvement in our theoretical understanding of the aging process.
衰老会影响癌症和痴呆等疾病的发病率，因此衰老生物标志物的开发是医学科学的一个重要研究课题。虽然这些生物标志物主要是基于表型参数（包括分子标记物）与实际年龄之间的线性关系假设而确定的，但已经发现了许多标记物与衰老之间的非线性变化。然而，衰老过程中存在的非线性变化模式的整体情况尚不清楚。
我们提出了一种新的计算方法，即基于功能数据分析的衰老的非线性模式数据驱动识别与分类（DICNAP），该方法可以基于数据驱动的方式识别衰老的生物标志物和衰老过程中的潜在变化模式。我们将所提出的方法应用于大规模的公共 DNA 甲基化数据，以探索甲基化强度与年龄相关的潜在变化模式。结果表明，不仅存在线性变化，而且还存在 DNA 甲基化模式的非线性变化。随着年龄的增长，DNA 甲基化模式呈现出单调的去甲基化模式，其速率在 60 岁左右下降，这被认为是候选的稳定非线性模式。我们还分析了甲基化变异性与年龄的关系。结果表明，在年龄相关位点，甲基化强度的变异性随着年龄的增长而增加。代表性的变异性模式是一种单调递增的模式，在中年后加速。
DICNAP 能够识别衰老过程中 DNA 甲基化变化景观的潜在模式。这有助于提高我们对衰老过程的理论认识。
