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人类衰老过程中多组学特征的非线性动力学。

Nonlinear dynamics of multi-omics profiles during human aging.

机构信息

Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.

Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.

出版信息

Nat Aging. 2024 Nov;4(11):1619-1634. doi: 10.1038/s43587-024-00692-2. Epub 2024 Aug 14.


DOI:10.1038/s43587-024-00692-2
PMID:39143318
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11564093/
Abstract

Aging is a complex process associated with nearly all diseases. Understanding the molecular changes underlying aging and identifying therapeutic targets for aging-related diseases are crucial for increasing healthspan. Although many studies have explored linear changes during aging, the prevalence of aging-related diseases and mortality risk accelerates after specific time points, indicating the importance of studying nonlinear molecular changes. In this study, we performed comprehensive multi-omics profiling on a longitudinal human cohort of 108 participants, aged between 25 years and 75 years. The participants resided in California, United States, and were tracked for a median period of 1.7 years, with a maximum follow-up duration of 6.8 years. The analysis revealed consistent nonlinear patterns in molecular markers of aging, with substantial dysregulation occurring at two major periods occurring at approximately 44 years and 60 years of chronological age. Distinct molecules and functional pathways associated with these periods were also identified, such as immune regulation and carbohydrate metabolism that shifted during the 60-year transition and cardiovascular disease, lipid and alcohol metabolism changes at the 40-year transition. Overall, this research demonstrates that functions and risks of aging-related diseases change nonlinearly across the human lifespan and provides insights into the molecular and biological pathways involved in these changes.

摘要

衰老是与几乎所有疾病都相关的复杂过程。了解衰老的分子变化并确定与衰老相关疾病的治疗靶点,对于延长健康寿命至关重要。尽管许多研究已经探索了衰老过程中的线性变化,但在特定时间点之后,与衰老相关的疾病的流行和死亡风险会加速,这表明研究非线性分子变化的重要性。在这项研究中,我们对来自加利福尼亚州的 108 名年龄在 25 岁至 75 岁之间的纵向人类队列进行了全面的多组学分析。参与者的中位随访时间为 1.7 年,最长随访时间为 6.8 年。分析揭示了衰老分子标志物中一致的非线性模式,在大约 44 岁和 60 岁的两个主要时期发生了大量失调。还确定了与这些时期相关的独特分子和功能途径,例如免疫调节和碳水化合物代谢在 60 年过渡期发生变化,心血管疾病、脂质和酒精代谢变化发生在 40 年过渡期。总的来说,这项研究表明,与衰老相关的疾病的功能和风险在人类寿命中呈非线性变化,并提供了有关这些变化所涉及的分子和生物学途径的见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e45/11564093/c7cbfb2c0dba/43587_2024_692_Fig11_ESM.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e45/11564093/39bd4aef6ac9/43587_2024_692_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e45/11564093/ed291b27086c/43587_2024_692_Fig6_ESM.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e45/11564093/ee57d19cc314/43587_2024_692_Fig7_ESM.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e45/11564093/555a01d6695a/43587_2024_692_Fig8_ESM.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e45/11564093/72830e12c258/43587_2024_692_Fig9_ESM.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e45/11564093/d0925c4e4f38/43587_2024_692_Fig10_ESM.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e45/11564093/c7cbfb2c0dba/43587_2024_692_Fig11_ESM.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e45/11564093/fac820240ab0/43587_2024_692_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e45/11564093/81e717dbe9bf/43587_2024_692_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e45/11564093/061ef5574f8e/43587_2024_692_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e45/11564093/2f3b6d5c4c72/43587_2024_692_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e45/11564093/39bd4aef6ac9/43587_2024_692_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e45/11564093/ed291b27086c/43587_2024_692_Fig6_ESM.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e45/11564093/ee57d19cc314/43587_2024_692_Fig7_ESM.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e45/11564093/555a01d6695a/43587_2024_692_Fig8_ESM.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e45/11564093/72830e12c258/43587_2024_692_Fig9_ESM.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e45/11564093/d0925c4e4f38/43587_2024_692_Fig10_ESM.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e45/11564093/c7cbfb2c0dba/43587_2024_692_Fig11_ESM.jpg

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