文献类型： 外文期刊

作者： Feng, Li ¹ ; Yang, Dengcheng ¹ ; Wu, Sinan ⁴ ; Xue, Chengwen ⁵ ; Sang, Mengmeng ⁶ ; Liu, Xiang ¹ ; Che, Jincan ¹ ; Wu, Jie ¹ ; Gragnoli, Claudia ⁸ ; Griffin, Christopher ¹¹ ; Wang, Chen ⁴ ; Yau, Shing-Tung ¹ ; Wu, Rongling ¹ ;

作者机构： 1.Beijing Inst Math Sci & Applicat, Beijing 101408, Peoples R China

2.Chinese Acad Fishery Sci, Fisheries Engn Inst, Beijing 100141, Peoples R China

3.Henan Agr Univ, Coll Vet Med, Zhengzhou 450046, Henan, Peoples R China

4.Chinese Acad Med Sci, Natl Ctr Resp Med, Natl Clin Res Ctr Resp Dis, State Key Lab Resp Hlth & Multimorbid,Ctr Resp Med, Beijing, Peoples R China

5.Tsinghua Univ, Qiuzhen Coll, Beijing 100084, Peoples R China

6.Nantong Univ, Med Coll, Dept Immunol, Nantong 226001, Jiangsu, Peoples R China

7.Nankai Univ, Chern Inst Math, Tianjin 300071, Peoples R China

8.Penn State Coll Med, Dept Publ Hlth Sci, Hershey, PA 17033 USA

9.Creighton Univ, Sch Med, Dept Med, Omaha, NE 68124 USA

10.Bios Biotech Multidiagnost Hlth Ctr, Mol Biol Lab, I-00197 Rome, Italy

11.Penn State Univ, Appl Res Lab, University Pk, PA 16802 USA

12.Chinese Acad Med Sci, Peking Union Med Coll, Beijing 100730, Peoples R China

13.Tsinghua Univ, Yau Math Sci Ctr, Beijing 100084, Peoples R China

关键词： Aging; Evolutionary game theory; idopNetwork; Topology; GLMY homology; Cross-sectional cohort

期刊名称：PHYSICS REPORTS-REVIEW SECTION OF PHYSICS LETTERS （ 影响因子：29.5； 五年影响因子：29.3 ）

ISSN： 0370-1573

年卷期： 2025 年 1101 卷

页码：

收录情况： SCI

摘要： Aging is a universal process of age-dependent physiological and functional declines that are strongly associated with human diseases. Despite extensive studies of the molecular causes of aging, little is known about the overall landscape of how aging proceeds and how it is related with intrinsic and extrinsic agents. Aging is a complex trait involving a large number of interdependent factors that change over spatiotemporal scales like a complex system. We develop an interdisciplinary form of statistical mechanics to reconstruct aging-related informative, dynamic, omnidirectional, and personalized networks (idopNetworks) from experimental or clinical data. The idopNetwork model can reveal how a specific biological entity, such as genes, proteins, or metabolites, mediates the antedependence of aging (i.e., the dependence of current trait values on their previous expression), identify how spatiotemporal crosstalk across different organs accelerate or decelerate the rate of aging, and predict how an individual's chronological age differs from his biological age. We implement GLMY homology theory to dissect the topological architecture and function of aging networks, identifying key subnetworks, surface holes and cubic voids that shape the rate of aging. Aging studies can be ideally conducted by monitoring molecular, physiological, and clinical traits over the full lifecycle. However, it is both impossible and ethically impermissible to collect the kind of data from which idopNetworks are reconstructed. To overcome this limitation, we integrate an allometric scaling law into the model to extract dynamics from snapshots of static data from a population-based cross-sectional study, expanding the utility of the model to a broader domain of cohort data. We show how this model can be used to unravel and predict the biological mechanisms underlying aging by analyzing an experimental metabolic data set of multiple brain regions in the aging mouse and a cross-sectional physiological data set of the lung for smoking and nonsmoking males aged from 20 years to nearly centenarians from the China Pulmonary Health Study. The model opens up a new horizon for studying how aging occurs through intrinsic and extrinsic interactions and could be used as a generic tool to disentangle human aging using various types of molecular, phenotypic or clinical data.